diff --git a/CLIP b/CLIP deleted file mode 160000 index a9b1bf5920416aaeaec965c25dd9e8f98c864f16..0000000000000000000000000000000000000000 --- a/CLIP +++ /dev/null @@ -1 +0,0 @@ -Subproject commit a9b1bf5920416aaeaec965c25dd9e8f98c864f16 diff --git a/CLIP/CLIP.png b/CLIP/CLIP.png new file mode 100755 index 0000000000000000000000000000000000000000..a1b5ec9171fd7a51e36e845a02304eb837142ba1 Binary files /dev/null and b/CLIP/CLIP.png differ diff --git a/CLIP/LICENSE b/CLIP/LICENSE new file mode 100755 index 0000000000000000000000000000000000000000..4e97f0b45803b7c04ae89548934af4f257a97501 --- /dev/null +++ b/CLIP/LICENSE @@ -0,0 +1,22 @@ +MIT License + +Copyright (c) 2021 OpenAI + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + diff --git a/CLIP/MANIFEST.in b/CLIP/MANIFEST.in new file mode 100755 index 0000000000000000000000000000000000000000..effd8d995ff1842a48c69d2a0f7c8dce4423d7a2 --- /dev/null +++ b/CLIP/MANIFEST.in @@ -0,0 +1 @@ +include clip/bpe_simple_vocab_16e6.txt.gz diff --git a/CLIP/README.md b/CLIP/README.md new file mode 100755 index 0000000000000000000000000000000000000000..db56b56e29258cc871fe0e6297bde00436d9180b --- /dev/null +++ b/CLIP/README.md @@ -0,0 +1,199 @@ +# CLIP + +[[Blog]](https://openai.com/blog/clip/) [[Paper]](https://arxiv.org/abs/2103.00020) [[Model Card]](model-card.md) [[Colab]](https://colab.research.google.com/github/openai/clip/blob/master/notebooks/Interacting_with_CLIP.ipynb) + +CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities of GPT-2 and 3. We found CLIP matches the performance of the original ResNet50 on ImageNet “zero-shot” without using any of the original 1.28M labeled examples, overcoming several major challenges in computer vision. + + + +## Approach + +![CLIP](CLIP.png) + + + +## Usage + +First, [install PyTorch 1.7.1](https://pytorch.org/get-started/locally/) (or later) and torchvision, as well as small additional dependencies, and then install this repo as a Python package. On a CUDA GPU machine, the following will do the trick: + +```bash +$ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0 +$ pip install ftfy regex tqdm +$ pip install git+https://github.com/openai/CLIP.git +``` + +Replace `cudatoolkit=11.0` above with the appropriate CUDA version on your machine or `cpuonly` when installing on a machine without a GPU. + +```python +import torch +import clip +from PIL import Image + +device = "cuda" if torch.cuda.is_available() else "cpu" +model, preprocess = clip.load("ViT-B/32", device=device) + +image = preprocess(Image.open("CLIP.png")).unsqueeze(0).to(device) +text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device) + +with torch.no_grad(): + image_features = model.encode_image(image) + text_features = model.encode_text(text) + + logits_per_image, logits_per_text = model(image, text) + probs = logits_per_image.softmax(dim=-1).cpu().numpy() + +print("Label probs:", probs) # prints: [[0.9927937 0.00421068 0.00299572]] +``` + + +## API + +The CLIP module `clip` provides the following methods: + +#### `clip.available_models()` + +Returns the names of the available CLIP models. + +#### `clip.load(name, device=..., jit=False)` + +Returns the model and the TorchVision transform needed by the model, specified by the model name returned by `clip.available_models()`. It will download the model as necessary. The `name` argument can also be a path to a local checkpoint. + +The device to run the model can be optionally specified, and the default is to use the first CUDA device if there is any, otherwise the CPU. When `jit` is `False`, a non-JIT version of the model will be loaded. + +#### `clip.tokenize(text: Union[str, List[str]], context_length=77)` + +Returns a LongTensor containing tokenized sequences of given text input(s). This can be used as the input to the model + +--- + +The model returned by `clip.load()` supports the following methods: + +#### `model.encode_image(image: Tensor)` + +Given a batch of images, returns the image features encoded by the vision portion of the CLIP model. + +#### `model.encode_text(text: Tensor)` + +Given a batch of text tokens, returns the text features encoded by the language portion of the CLIP model. + +#### `model(image: Tensor, text: Tensor)` + +Given a batch of images and a batch of text tokens, returns two Tensors, containing the logit scores corresponding to each image and text input. The values are cosine similarities between the corresponding image and text features, times 100. + + + +## More Examples + +### Zero-Shot Prediction + +The code below performs zero-shot prediction using CLIP, as shown in Appendix B in the paper. This example takes an image from the [CIFAR-100 dataset](https://www.cs.toronto.edu/~kriz/cifar.html), and predicts the most likely labels among the 100 textual labels from the dataset. + +```python +import os +import clip +import torch +from torchvision.datasets import CIFAR100 + +# Load the model +device = "cuda" if torch.cuda.is_available() else "cpu" +model, preprocess = clip.load('ViT-B/32', device) + +# Download the dataset +cifar100 = CIFAR100(root=os.path.expanduser("~/.cache"), download=True, train=False) + +# Prepare the inputs +image, class_id = cifar100[3637] +image_input = preprocess(image).unsqueeze(0).to(device) +text_inputs = torch.cat([clip.tokenize(f"a photo of a {c}") for c in cifar100.classes]).to(device) + +# Calculate features +with torch.no_grad(): + image_features = model.encode_image(image_input) + text_features = model.encode_text(text_inputs) + +# Pick the top 5 most similar labels for the image +image_features /= image_features.norm(dim=-1, keepdim=True) +text_features /= text_features.norm(dim=-1, keepdim=True) +similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1) +values, indices = similarity[0].topk(5) + +# Print the result +print("\nTop predictions:\n") +for value, index in zip(values, indices): + print(f"{cifar100.classes[index]:>16s}: {100 * value.item():.2f}%") +``` + +The output will look like the following (the exact numbers may be slightly different depending on the compute device): + +``` +Top predictions: + + snake: 65.31% + turtle: 12.29% + sweet_pepper: 3.83% + lizard: 1.88% + crocodile: 1.75% +``` + +Note that this example uses the `encode_image()` and `encode_text()` methods that return the encoded features of given inputs. + + +### Linear-probe evaluation + +The example below uses [scikit-learn](https://scikit-learn.org/) to perform logistic regression on image features. + +```python +import os +import clip +import torch + +import numpy as np +from sklearn.linear_model import LogisticRegression +from torch.utils.data import DataLoader +from torchvision.datasets import CIFAR100 +from tqdm import tqdm + +# Load the model +device = "cuda" if torch.cuda.is_available() else "cpu" +model, preprocess = clip.load('ViT-B/32', device) + +# Load the dataset +root = os.path.expanduser("~/.cache") +train = CIFAR100(root, download=True, train=True, transform=preprocess) +test = CIFAR100(root, download=True, train=False, transform=preprocess) + + +def get_features(dataset): + all_features = [] + all_labels = [] + + with torch.no_grad(): + for images, labels in tqdm(DataLoader(dataset, batch_size=100)): + features = model.encode_image(images.to(device)) + + all_features.append(features) + all_labels.append(labels) + + return torch.cat(all_features).cpu().numpy(), torch.cat(all_labels).cpu().numpy() + +# Calculate the image features +train_features, train_labels = get_features(train) +test_features, test_labels = get_features(test) + +# Perform logistic regression +classifier = LogisticRegression(random_state=0, C=0.316, max_iter=1000, verbose=1) +classifier.fit(train_features, train_labels) + +# Evaluate using the logistic regression classifier +predictions = classifier.predict(test_features) +accuracy = np.mean((test_labels == predictions).astype(float)) * 100. +print(f"Accuracy = {accuracy:.3f}") +``` + +Note that the `C` value should be determined via a hyperparameter sweep using a validation split. + + +## See Also + +* [OpenCLIP](https://github.com/mlfoundations/open_clip): includes larger and independently trained CLIP models up to ViT-G/14 +* [Hugging Face implementation of CLIP](https://huggingface.co/docs/transformers/model_doc/clip): for easier integration with the HF ecosystem diff --git a/CLIP/clip.egg-info/PKG-INFO b/CLIP/clip.egg-info/PKG-INFO new file mode 100755 index 0000000000000000000000000000000000000000..1f4bc3c7c9da8c7d9862c0fdaa5b8119097e64c3 --- /dev/null +++ b/CLIP/clip.egg-info/PKG-INFO @@ -0,0 +1,6 @@ +Metadata-Version: 2.1 +Name: clip +Version: 1.0 +Author: OpenAI +Provides-Extra: dev +License-File: LICENSE diff --git a/CLIP/clip.egg-info/SOURCES.txt b/CLIP/clip.egg-info/SOURCES.txt new file mode 100755 index 0000000000000000000000000000000000000000..42fa96609f43642caf5e26a6079a125ac055b534 --- /dev/null +++ b/CLIP/clip.egg-info/SOURCES.txt @@ -0,0 +1,14 @@ +LICENSE +MANIFEST.in +README.md +setup.py +clip/__init__.py +clip/bpe_simple_vocab_16e6.txt.gz +clip/clip.py +clip/model.py +clip/simple_tokenizer.py +clip.egg-info/PKG-INFO +clip.egg-info/SOURCES.txt +clip.egg-info/dependency_links.txt +clip.egg-info/requires.txt +clip.egg-info/top_level.txt \ No newline at end of file diff --git a/CLIP/clip.egg-info/dependency_links.txt b/CLIP/clip.egg-info/dependency_links.txt new file mode 100755 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/CLIP/clip.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/CLIP/clip.egg-info/requires.txt b/CLIP/clip.egg-info/requires.txt new file mode 100755 index 0000000000000000000000000000000000000000..7a9b1374ccb3dddf9b31acf839705deb0853a10c --- /dev/null +++ b/CLIP/clip.egg-info/requires.txt @@ -0,0 +1,8 @@ +ftfy +regex +tqdm +torch +torchvision + +[dev] +pytest diff --git a/CLIP/clip.egg-info/top_level.txt b/CLIP/clip.egg-info/top_level.txt new file mode 100755 index 0000000000000000000000000000000000000000..361bfc628bf2db07d1190783778ed02699e3ee77 --- /dev/null +++ b/CLIP/clip.egg-info/top_level.txt @@ -0,0 +1 @@ +clip diff --git a/CLIP/clip/__init__.py b/CLIP/clip/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..dcc5619538c0f7c782508bdbd9587259d805e0d9 --- /dev/null +++ b/CLIP/clip/__init__.py @@ -0,0 +1 @@ +from .clip import * diff --git a/CLIP/clip/__pycache__/__init__.cpython-39.pyc b/CLIP/clip/__pycache__/__init__.cpython-39.pyc new file mode 100755 index 0000000000000000000000000000000000000000..f17b9eff0d15e6dc8130e27798112d7bdbabcd5a 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a/CLIP/clip/bpe_simple_vocab_16e6.txt.gz b/CLIP/clip/bpe_simple_vocab_16e6.txt.gz new file mode 100755 index 0000000000000000000000000000000000000000..36a15856e00a06a9fbed8cdd34d2393fea4a3113 --- /dev/null +++ b/CLIP/clip/bpe_simple_vocab_16e6.txt.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a +size 1356917 diff --git a/CLIP/clip/clip.py b/CLIP/clip/clip.py new file mode 100755 index 0000000000000000000000000000000000000000..257511e1d40c120e0d64a0f1562d44b2b8a40a17 --- /dev/null +++ b/CLIP/clip/clip.py @@ -0,0 +1,237 @@ +import hashlib +import os +import urllib +import warnings +from typing import Any, Union, List +from pkg_resources import packaging + +import torch +from PIL import Image +from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize +from tqdm import tqdm + +from .model import build_model +from .simple_tokenizer import SimpleTokenizer as _Tokenizer + +try: + from torchvision.transforms import InterpolationMode + BICUBIC = InterpolationMode.BICUBIC +except ImportError: + BICUBIC = Image.BICUBIC + + +if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"): + warnings.warn("PyTorch version 1.7.1 or higher is recommended") + + +__all__ = ["available_models", "load", "tokenize"] +_tokenizer = _Tokenizer() + +_MODELS = { + "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", + "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", + "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt", + "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt", + "RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt", + "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", + "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt", + "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt", + "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt", +} + + +def _download(url: str, root: str): + os.makedirs(root, exist_ok=True) + filename = os.path.basename(url) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256: + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256: + raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match") + + return download_target + + +def _convert_image_to_rgb(image): + return image.convert("RGB") + + +def _transform(n_px): + return Compose([ + Resize(n_px, interpolation=BICUBIC), + CenterCrop(n_px), + _convert_image_to_rgb, + ToTensor(), + Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), + ]) + + +def available_models() -> List[str]: + """Returns the names of available CLIP models""" + return list(_MODELS.keys()) + + +def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None): + """Load a CLIP model + + Parameters + ---------- + name : str + A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict + + device : Union[str, torch.device] + The device to put the loaded model + + jit : bool + Whether to load the optimized JIT model or more hackable non-JIT model (default). + + download_root: str + path to download the model files; by default, it uses "~/.cache/clip" + + Returns + ------- + model : torch.nn.Module + The CLIP model + + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + if name in _MODELS: + model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip")) + elif os.path.isfile(name): + model_path = name + else: + raise RuntimeError(f"Model {name} not found; available models = {available_models()}") + + with open(model_path, 'rb') as opened_file: + try: + # loading JIT archive + model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval() + state_dict = None + except RuntimeError: + # loading saved state dict + if jit: + warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") + jit = False + state_dict = torch.load(opened_file, map_location="cpu") + + if not jit: + model = build_model(state_dict or model.state_dict()).to(device) + if str(device) == "cpu": + model.float() + return model, _transform(model.visual.input_resolution) + + # patch the device names + device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) + device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] + + def patch_device(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("prim::Constant"): + if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): + node.copyAttributes(device_node) + + model.apply(patch_device) + patch_device(model.encode_image) + patch_device(model.encode_text) + + # patch dtype to float32 on CPU + if str(device) == "cpu": + float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) + float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] + float_node = float_input.node() + + def patch_float(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("aten::to"): + inputs = list(node.inputs()) + for i in [1, 2]: # dtype can be the second or third argument to aten::to() + if inputs[i].node()["value"] == 5: + inputs[i].node().copyAttributes(float_node) + + model.apply(patch_float) + patch_float(model.encode_image) + patch_float(model.encode_text) + + model.float() + + return model, _transform(model.input_resolution.item()) + + +def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> Union[torch.IntTensor, torch.LongTensor]: + """ + Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + + context_length : int + The context length to use; all CLIP models use 77 as the context length + + truncate: bool + Whether to truncate the text in case its encoding is longer than the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]. + We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long. + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = _tokenizer.encoder["<|startoftext|>"] + eot_token = _tokenizer.encoder["<|endoftext|>"] + all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] + if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"): + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + else: + result = torch.zeros(len(all_tokens), context_length, dtype=torch.int) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + if truncate: + tokens = tokens[:context_length] + tokens[-1] = eot_token + else: + raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}") + result[i, :len(tokens)] = torch.tensor(tokens) + + return result diff --git a/CLIP/clip/model.py b/CLIP/clip/model.py new file mode 100755 index 0000000000000000000000000000000000000000..232b7792eb97440642547bd462cf128df9243933 --- /dev/null +++ b/CLIP/clip/model.py @@ -0,0 +1,436 @@ +from collections import OrderedDict +from typing import Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.relu1 = nn.ReLU(inplace=True) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.relu2 = nn.ReLU(inplace=True) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.relu3 = nn.ReLU(inplace=True) + + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential(OrderedDict([ + ("-1", nn.AvgPool2d(stride)), + ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), + ("1", nn.BatchNorm2d(planes * self.expansion)) + ])) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.relu1(self.bn1(self.conv1(x))) + out = self.relu2(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu3(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.flatten(start_dim=2).permute(2, 0, 1) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x[:1], key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0, + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + return x.squeeze(0) + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, input_resolution=224, width=64): + super().__init__() + self.output_dim = output_dim + self.input_resolution = input_resolution + + # the 3-layer stem + self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(width // 2) + self.relu1 = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(width // 2) + self.relu2 = nn.ReLU(inplace=True) + self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(width) + self.relu3 = nn.ReLU(inplace=True) + self.avgpool = nn.AvgPool2d(2) + + # residual layers + self._inplanes = width # this is a *mutable* variable used during construction + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim) + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + def stem(x): + x = self.relu1(self.bn1(self.conv1(x))) + x = self.relu2(self.bn2(self.conv2(x))) + x = self.relu3(self.bn3(self.conv3(x))) + x = self.avgpool(x) + return x + + x = x.type(self.conv1.weight.dtype) + x = stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.attnpool(x) + + return x + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +class QuickGELU(nn.Module): + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model)) + ])) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class Transformer(nn.Module): + def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None): + super().__init__() + self.width = width + self.layers = layers + self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)]) + + def forward(self, x: torch.Tensor): + return self.resblocks(x) + + +class VisionTransformer(nn.Module): + def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int): + super().__init__() + self.input_resolution = input_resolution + self.output_dim = output_dim + self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) + + scale = width ** -0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width)) + self.ln_pre = LayerNorm(width) + + self.transformer = Transformer(width, layers, heads) + + self.ln_post = LayerNorm(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + def forward(self, x: torch.Tensor): + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + x = self.ln_post(x[:, 0, :]) + + if self.proj is not None: + x = x @ self.proj + + return x + + +class CLIP(nn.Module): + def __init__(self, + embed_dim: int, + # vision + image_resolution: int, + vision_layers: Union[Tuple[int, int, int, int], int], + vision_width: int, + vision_patch_size: int, + # text + context_length: int, + vocab_size: int, + transformer_width: int, + transformer_heads: int, + transformer_layers: int + ): + super().__init__() + + self.context_length = context_length + + if isinstance(vision_layers, (tuple, list)): + vision_heads = vision_width * 32 // 64 + self.visual = ModifiedResNet( + layers=vision_layers, + output_dim=embed_dim, + heads=vision_heads, + input_resolution=image_resolution, + width=vision_width + ) + else: + vision_heads = vision_width // 64 + self.visual = VisionTransformer( + input_resolution=image_resolution, + patch_size=vision_patch_size, + width=vision_width, + layers=vision_layers, + heads=vision_heads, + output_dim=embed_dim + ) + + self.transformer = Transformer( + width=transformer_width, + layers=transformer_layers, + heads=transformer_heads, + attn_mask=self.build_attention_mask() + ) + + self.vocab_size = vocab_size + self.token_embedding = nn.Embedding(vocab_size, transformer_width) + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width)) + self.ln_final = LayerNorm(transformer_width) + + self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim)) + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + self.initialize_parameters() + + def initialize_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + + if isinstance(self.visual, ModifiedResNet): + if self.visual.attnpool is not None: + std = self.visual.attnpool.c_proj.in_features ** -0.5 + nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std) + + for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + @property + def dtype(self): + return self.visual.conv1.weight.dtype + + def encode_image(self, image): + return self.visual(image.type(self.dtype)) + + def encode_text(self, text): + x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.type(self.dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x).type(self.dtype) + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + + return x + + def forward(self, image, text): + image_features = self.encode_image(image) + text_features = self.encode_text(text) + + # normalized features + image_features = image_features / image_features.norm(dim=1, keepdim=True) + text_features = text_features / text_features.norm(dim=1, keepdim=True) + + # cosine similarity as logits + logit_scale = self.logit_scale.exp() + logits_per_image = logit_scale * image_features @ text_features.t() + logits_per_text = logits_per_image.t() + + # shape = [global_batch_size, global_batch_size] + return logits_per_image, logits_per_text + + +def convert_weights(model: nn.Module): + """Convert applicable model parameters to fp16""" + + def _convert_weights_to_fp16(l): + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.half() + if l.bias is not None: + l.bias.data = l.bias.data.half() + + if isinstance(l, nn.MultiheadAttention): + for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: + tensor = getattr(l, attr) + if tensor is not None: + tensor.data = tensor.data.half() + + for name in ["text_projection", "proj"]: + if hasattr(l, name): + attr = getattr(l, name) + if attr is not None: + attr.data = attr.data.half() + + model.apply(_convert_weights_to_fp16) + + +def build_model(state_dict: dict): + vit = "visual.proj" in state_dict + + if vit: + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) + image_resolution = vision_patch_size * grid_size + else: + counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] + vision_layers = tuple(counts) + vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] + output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) + vision_patch_size = None + assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] + image_resolution = output_width * 32 + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks"))) + + model = CLIP( + embed_dim, + image_resolution, vision_layers, vision_width, vision_patch_size, + context_length, vocab_size, transformer_width, transformer_heads, transformer_layers + ) + + for key in ["input_resolution", "context_length", "vocab_size"]: + if key in state_dict: + del state_dict[key] + + convert_weights(model) + model.load_state_dict(state_dict) + return model.eval() diff --git a/CLIP/clip/simple_tokenizer.py b/CLIP/clip/simple_tokenizer.py new file mode 100755 index 0000000000000000000000000000000000000000..0a66286b7d5019c6e221932a813768038f839c91 --- /dev/null +++ b/CLIP/clip/simple_tokenizer.py @@ -0,0 +1,132 @@ +import gzip +import html +import os +from functools import lru_cache + +import ftfy +import regex as re + + +@lru_cache() +def default_bpe(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a signficant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe()): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152-256-2+1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v+'' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + vocab.extend(['<|startoftext|>', '<|endoftext|>']) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'} + self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + ( token[-1] + '',) + pairs = get_pairs(word) + + if not pairs: + return token+'' + + while True: + bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word)-1 and word[i+1] == second: + new_word.append(first+second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') + return text diff --git a/CLIP/data/country211.md b/CLIP/data/country211.md new file mode 100755 index 0000000000000000000000000000000000000000..4cd096005c8e5777e0706d97d182c3bd87b651a9 --- /dev/null +++ b/CLIP/data/country211.md @@ -0,0 +1,12 @@ +# The Country211 Dataset + +In the paper, we used an image classification dataset called Country211, to evaluate the model's capability on geolocation. To do so, we filtered the YFCC100m dataset that have GPS coordinate corresponding to a [ISO-3166 country code](https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes) and created a balanced dataset by sampling 150 train images, 50 validation images, and 100 test images images for each country. + +The following command will download an 11GB archive countaining the images and extract into a subdirectory `country211`: + +```bash +wget https://openaipublic.azureedge.net/clip/data/country211.tgz +tar zxvf country211.tgz +``` + +These images are a subset of the YFCC100m dataset. Use of the underlying media files is subject to the Creative Commons licenses chosen by their creators/uploaders. For more information about the YFCC100M dataset, visit [the official website](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/). \ No newline at end of file diff --git a/CLIP/data/prompts.md b/CLIP/data/prompts.md new file mode 100755 index 0000000000000000000000000000000000000000..6d8aaf7b13f04031e7ea00d58a1c131b98bdfe20 --- /dev/null +++ b/CLIP/data/prompts.md @@ -0,0 +1,3401 @@ +# Prompts for Image Classification + +Below are the class names and templates that are used for collecting the zero-shot classification scores in the paper. Each dataset has two lists `classes` and `templates`, where the string `{}` in the template is to be replaced with the corresponding class names. For the Facial Emotion Recognition 2013 dataset specifically, we used multiple class names for certain classes. + +This file contains prompt data for 26 of the 27 datasets shown in Table 9 of the paper; the text prompts for ImageNet (as well as other [ImageNet Testbed](https://modestyachts.github.io/imagenet-testbed/) datasets in Figure 13) can be found in [this notebook](https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb), as well as how to ensemble predictions from multiple prompts using these templates. + +If you are viewing this document on GitHub, use the table of contents icon at the upper left to browse the datasets. + + +## Birdsnap + +```bash +classes = [ + 'Acadian Flycatcher', + 'Acorn Woodpecker', + 'Alder Flycatcher', + 'Allens Hummingbird', + 'Altamira Oriole', + 'American Avocet', + 'American Bittern', + 'American Black Duck', + 'American Coot', + 'American Crow', + 'American Dipper', + 'American Golden Plover', + 'American Goldfinch', + 'American Kestrel', + 'American Oystercatcher', + 'American Pipit', + 'American Redstart', + 'American Robin', + 'American Three toed Woodpecker', + 'American Tree Sparrow', + 'American White Pelican', + 'American Wigeon', + 'American Woodcock', + 'Anhinga', + 'Annas Hummingbird', + 'Arctic Tern', + 'Ash throated Flycatcher', + 'Audubons Oriole', + 'Bairds Sandpiper', + 'Bald Eagle', + 'Baltimore Oriole', + 'Band tailed Pigeon', + 'Barn Swallow', + 'Barred Owl', + 'Barrows Goldeneye', + 'Bay breasted Warbler', + 'Bells Vireo', + 'Belted Kingfisher', + 'Bewicks Wren', + 'Black Guillemot', + 'Black Oystercatcher', + 'Black Phoebe', + 'Black Rosy Finch', + 'Black Scoter', + 'Black Skimmer', + 'Black Tern', + 'Black Turnstone', + 'Black Vulture', + 'Black and white Warbler', + 'Black backed Woodpecker', + 'Black bellied Plover', + 'Black billed Cuckoo', + 'Black billed Magpie', + 'Black capped Chickadee', + 'Black chinned Hummingbird', + 'Black chinned Sparrow', + 'Black crested Titmouse', + 'Black crowned Night Heron', + 'Black headed Grosbeak', + 'Black legged Kittiwake', + 'Black necked Stilt', + 'Black throated Blue Warbler', + 'Black throated Gray Warbler', + 'Black throated Green Warbler', + 'Black throated Sparrow', + 'Blackburnian Warbler', + 'Blackpoll Warbler', + 'Blue Grosbeak', + 'Blue Jay', + 'Blue gray Gnatcatcher', + 'Blue headed Vireo', + 'Blue winged Teal', + 'Blue winged Warbler', + 'Boat tailed Grackle', + 'Bobolink', + 'Bohemian Waxwing', + 'Bonapartes Gull', + 'Boreal Chickadee', + 'Brandts Cormorant', + 'Brant', + 'Brewers Blackbird', + 'Brewers Sparrow', + 'Bridled Titmouse', + 'Broad billed Hummingbird', + 'Broad tailed Hummingbird', + 'Broad winged Hawk', + 'Bronzed Cowbird', + 'Brown Creeper', + 'Brown Pelican', + 'Brown Thrasher', + 'Brown capped Rosy Finch', + 'Brown crested Flycatcher', + 'Brown headed Cowbird', + 'Brown headed Nuthatch', + 'Bufflehead', + 'Bullocks Oriole', + 'Burrowing Owl', + 'Bushtit', + 'Cackling Goose', + 'Cactus Wren', + 'California Gull', + 'California Quail', + 'California Thrasher', + 'California Towhee', + 'Calliope Hummingbird', + 'Canada Goose', + 'Canada Warbler', + 'Canvasback', + 'Canyon Towhee', + 'Canyon Wren', + 'Cape May Warbler', + 'Carolina Chickadee', + 'Carolina Wren', + 'Caspian Tern', + 'Cassins Finch', + 'Cassins Kingbird', + 'Cassins Sparrow', + 'Cassins Vireo', + 'Cattle Egret', + 'Cave Swallow', + 'Cedar Waxwing', + 'Cerulean Warbler', + 'Chestnut backed Chickadee', + 'Chestnut collared Longspur', + 'Chestnut sided Warbler', + 'Chihuahuan Raven', + 'Chimney Swift', + 'Chipping Sparrow', + 'Cinnamon Teal', + 'Clapper Rail', + 'Clarks Grebe', + 'Clarks Nutcracker', + 'Clay colored Sparrow', + 'Cliff Swallow', + 'Common Black Hawk', + 'Common Eider', + 'Common Gallinule', + 'Common Goldeneye', + 'Common Grackle', + 'Common Ground Dove', + 'Common Loon', + 'Common Merganser', + 'Common Murre', + 'Common Nighthawk', + 'Common Raven', + 'Common Redpoll', + 'Common Tern', + 'Common Yellowthroat', + 'Connecticut Warbler', + 'Coopers Hawk', + 'Cordilleran Flycatcher', + 'Costas Hummingbird', + 'Couchs Kingbird', + 'Crested Caracara', + 'Curve billed Thrasher', + 'Dark eyed Junco', + 'Dickcissel', + 'Double crested Cormorant', + 'Downy Woodpecker', + 'Dunlin', + 'Dusky Flycatcher', + 'Dusky Grouse', + 'Eared Grebe', + 'Eastern Bluebird', + 'Eastern Kingbird', + 'Eastern Meadowlark', + 'Eastern Phoebe', + 'Eastern Screech Owl', + 'Eastern Towhee', + 'Eastern Wood Pewee', + 'Elegant Trogon', + 'Elf Owl', + 'Eurasian Collared Dove', + 'Eurasian Wigeon', + 'European Starling', + 'Evening Grosbeak', + 'Ferruginous Hawk', + 'Ferruginous Pygmy Owl', + 'Field Sparrow', + 'Fish Crow', + 'Florida Scrub Jay', + 'Forsters Tern', + 'Fox Sparrow', + 'Franklins Gull', + 'Fulvous Whistling Duck', + 'Gadwall', + 'Gambels Quail', + 'Gila Woodpecker', + 'Glaucous Gull', + 'Glaucous winged Gull', + 'Glossy Ibis', + 'Golden Eagle', + 'Golden crowned Kinglet', + 'Golden crowned Sparrow', + 'Golden fronted Woodpecker', + 'Golden winged Warbler', + 'Grasshopper Sparrow', + 'Gray Catbird', + 'Gray Flycatcher', + 'Gray Jay', + 'Gray Kingbird', + 'Gray cheeked Thrush', + 'Gray crowned Rosy Finch', + 'Great Black backed Gull', + 'Great Blue Heron', + 'Great Cormorant', + 'Great Crested Flycatcher', + 'Great Egret', + 'Great Gray Owl', + 'Great Horned Owl', + 'Great Kiskadee', + 'Great tailed Grackle', + 'Greater Prairie Chicken', + 'Greater Roadrunner', + 'Greater Sage Grouse', + 'Greater Scaup', + 'Greater White fronted Goose', + 'Greater Yellowlegs', + 'Green Jay', + 'Green tailed Towhee', + 'Green winged Teal', + 'Groove billed Ani', + 'Gull billed Tern', + 'Hairy Woodpecker', + 'Hammonds Flycatcher', + 'Harlequin Duck', + 'Harriss Hawk', + 'Harriss Sparrow', + 'Heermanns Gull', + 'Henslows Sparrow', + 'Hepatic Tanager', + 'Hermit Thrush', + 'Herring Gull', + 'Hoary Redpoll', + 'Hooded Merganser', + 'Hooded Oriole', + 'Hooded Warbler', + 'Horned Grebe', + 'Horned Lark', + 'House Finch', + 'House Sparrow', + 'House Wren', + 'Huttons Vireo', + 'Iceland Gull', + 'Inca Dove', + 'Indigo Bunting', + 'Killdeer', + 'King Rail', + 'Ladder backed Woodpecker', + 'Lapland Longspur', + 'Lark Bunting', + 'Lark Sparrow', + 'Laughing Gull', + 'Lazuli Bunting', + 'Le Contes Sparrow', + 'Least Bittern', + 'Least Flycatcher', + 'Least Grebe', + 'Least Sandpiper', + 'Least Tern', + 'Lesser Goldfinch', + 'Lesser Nighthawk', + 'Lesser Scaup', + 'Lesser Yellowlegs', + 'Lewiss Woodpecker', + 'Limpkin', + 'Lincolns Sparrow', + 'Little Blue Heron', + 'Loggerhead Shrike', + 'Long billed Curlew', + 'Long billed Dowitcher', + 'Long billed Thrasher', + 'Long eared Owl', + 'Long tailed Duck', + 'Louisiana Waterthrush', + 'Magnificent Frigatebird', + 'Magnolia Warbler', + 'Mallard', + 'Marbled Godwit', + 'Marsh Wren', + 'Merlin', + 'Mew Gull', + 'Mexican Jay', + 'Mississippi Kite', + 'Monk Parakeet', + 'Mottled Duck', + 'Mountain Bluebird', + 'Mountain Chickadee', + 'Mountain Plover', + 'Mourning Dove', + 'Mourning Warbler', + 'Muscovy Duck', + 'Mute Swan', + 'Nashville Warbler', + 'Nelsons Sparrow', + 'Neotropic Cormorant', + 'Northern Bobwhite', + 'Northern Cardinal', + 'Northern Flicker', + 'Northern Gannet', + 'Northern Goshawk', + 'Northern Harrier', + 'Northern Hawk Owl', + 'Northern Mockingbird', + 'Northern Parula', + 'Northern Pintail', + 'Northern Rough winged Swallow', + 'Northern Saw whet Owl', + 'Northern Shrike', + 'Northern Waterthrush', + 'Nuttalls Woodpecker', + 'Oak Titmouse', + 'Olive Sparrow', + 'Olive sided Flycatcher', + 'Orange crowned Warbler', + 'Orchard Oriole', + 'Osprey', + 'Ovenbird', + 'Pacific Golden Plover', + 'Pacific Loon', + 'Pacific Wren', + 'Pacific slope Flycatcher', + 'Painted Bunting', + 'Painted Redstart', + 'Palm Warbler', + 'Pectoral Sandpiper', + 'Peregrine Falcon', + 'Phainopepla', + 'Philadelphia Vireo', + 'Pied billed Grebe', + 'Pigeon Guillemot', + 'Pileated Woodpecker', + 'Pine Grosbeak', + 'Pine Siskin', + 'Pine Warbler', + 'Piping Plover', + 'Plumbeous Vireo', + 'Prairie Falcon', + 'Prairie Warbler', + 'Prothonotary Warbler', + 'Purple Finch', + 'Purple Gallinule', + 'Purple Martin', + 'Purple Sandpiper', + 'Pygmy Nuthatch', + 'Pyrrhuloxia', + 'Red Crossbill', + 'Red Knot', + 'Red Phalarope', + 'Red bellied Woodpecker', + 'Red breasted Merganser', + 'Red breasted Nuthatch', + 'Red breasted Sapsucker', + 'Red cockaded Woodpecker', + 'Red eyed Vireo', + 'Red headed Woodpecker', + 'Red naped Sapsucker', + 'Red necked Grebe', + 'Red necked Phalarope', + 'Red shouldered Hawk', + 'Red tailed Hawk', + 'Red throated Loon', + 'Red winged Blackbird', + 'Reddish Egret', + 'Redhead', + 'Ring billed Gull', + 'Ring necked Duck', + 'Ring necked Pheasant', + 'Rock Pigeon', + 'Rock Ptarmigan', + 'Rock Sandpiper', + 'Rock Wren', + 'Rose breasted Grosbeak', + 'Roseate Tern', + 'Rosss Goose', + 'Rough legged Hawk', + 'Royal Tern', + 'Ruby crowned Kinglet', + 'Ruby throated Hummingbird', + 'Ruddy Duck', + 'Ruddy Turnstone', + 'Ruffed Grouse', + 'Rufous Hummingbird', + 'Rufous crowned Sparrow', + 'Rusty Blackbird', + 'Sage Thrasher', + 'Saltmarsh Sparrow', + 'Sanderling', + 'Sandhill Crane', + 'Sandwich Tern', + 'Says Phoebe', + 'Scaled Quail', + 'Scarlet Tanager', + 'Scissor tailed Flycatcher', + 'Scotts Oriole', + 'Seaside Sparrow', + 'Sedge Wren', + 'Semipalmated Plover', + 'Semipalmated Sandpiper', + 'Sharp shinned Hawk', + 'Sharp tailed Grouse', + 'Short billed Dowitcher', + 'Short eared Owl', + 'Snail Kite', + 'Snow Bunting', + 'Snow Goose', + 'Snowy Egret', + 'Snowy Owl', + 'Snowy Plover', + 'Solitary Sandpiper', + 'Song Sparrow', + 'Sooty Grouse', + 'Sora', + 'Spotted Owl', + 'Spotted Sandpiper', + 'Spotted Towhee', + 'Spruce Grouse', + 'Stellers Jay', + 'Stilt Sandpiper', + 'Summer Tanager', + 'Surf Scoter', + 'Surfbird', + 'Swainsons Hawk', + 'Swainsons Thrush', + 'Swallow tailed Kite', + 'Swamp Sparrow', + 'Tennessee Warbler', + 'Thayers Gull', + 'Townsends Solitaire', + 'Townsends Warbler', + 'Tree Swallow', + 'Tricolored Heron', + 'Tropical Kingbird', + 'Trumpeter Swan', + 'Tufted Titmouse', + 'Tundra Swan', + 'Turkey Vulture', + 'Upland Sandpiper', + 'Varied Thrush', + 'Veery', + 'Verdin', + 'Vermilion Flycatcher', + 'Vesper Sparrow', + 'Violet green Swallow', + 'Virginia Rail', + 'Wandering Tattler', + 'Warbling Vireo', + 'Western Bluebird', + 'Western Grebe', + 'Western Gull', + 'Western Kingbird', + 'Western Meadowlark', + 'Western Sandpiper', + 'Western Screech Owl', + 'Western Scrub Jay', + 'Western Tanager', + 'Western Wood Pewee', + 'Whimbrel', + 'White Ibis', + 'White breasted Nuthatch', + 'White crowned Sparrow', + 'White eyed Vireo', + 'White faced Ibis', + 'White headed Woodpecker', + 'White rumped Sandpiper', + 'White tailed Hawk', + 'White tailed Kite', + 'White tailed Ptarmigan', + 'White throated Sparrow', + 'White throated Swift', + 'White winged Crossbill', + 'White winged Dove', + 'White winged Scoter', + 'Wild Turkey', + 'Willet', + 'Williamsons Sapsucker', + 'Willow Flycatcher', + 'Willow Ptarmigan', + 'Wilsons Phalarope', + 'Wilsons Plover', + 'Wilsons Snipe', + 'Wilsons Warbler', + 'Winter Wren', + 'Wood Stork', + 'Wood Thrush', + 'Worm eating Warbler', + 'Wrentit', + 'Yellow Warbler', + 'Yellow bellied Flycatcher', + 'Yellow bellied Sapsucker', + 'Yellow billed Cuckoo', + 'Yellow billed Magpie', + 'Yellow breasted Chat', + 'Yellow crowned Night Heron', + 'Yellow eyed Junco', + 'Yellow headed Blackbird', + 'Yellow rumped Warbler', + 'Yellow throated Vireo', + 'Yellow throated Warbler', + 'Zone tailed Hawk', +] + +templates = [ + 'a photo of a {}, a type of bird.', +] +``` + + + +## CIFAR10 + +```bash +classes = [ + 'airplane', + 'automobile', + 'bird', + 'cat', + 'deer', + 'dog', + 'frog', + 'horse', + 'ship', + 'truck', +] + +templates = [ + 'a photo of a {}.', + 'a blurry photo of a {}.', + 'a black and white photo of a {}.', + 'a low contrast photo of a {}.', + 'a high contrast photo of a {}.', + 'a bad photo of a {}.', + 'a good photo of a {}.', + 'a photo of a small {}.', + 'a photo of a big {}.', + 'a photo of the {}.', + 'a blurry photo of the {}.', + 'a black and white photo of the {}.', + 'a low contrast photo of the {}.', + 'a high contrast photo of the {}.', + 'a bad photo of the {}.', + 'a good photo of the {}.', + 'a photo of the small {}.', + 'a photo of the big {}.', +] +``` + + + +## CIFAR100 + +```bash +classes = [ + 'apple', + 'aquarium fish', + 'baby', + 'bear', + 'beaver', + 'bed', + 'bee', + 'beetle', + 'bicycle', + 'bottle', + 'bowl', + 'boy', + 'bridge', + 'bus', + 'butterfly', + 'camel', + 'can', + 'castle', + 'caterpillar', + 'cattle', + 'chair', + 'chimpanzee', + 'clock', + 'cloud', + 'cockroach', + 'couch', + 'crab', + 'crocodile', + 'cup', + 'dinosaur', + 'dolphin', + 'elephant', + 'flatfish', + 'forest', + 'fox', + 'girl', + 'hamster', + 'house', + 'kangaroo', + 'keyboard', + 'lamp', + 'lawn mower', + 'leopard', + 'lion', + 'lizard', + 'lobster', + 'man', + 'maple tree', + 'motorcycle', + 'mountain', + 'mouse', + 'mushroom', + 'oak tree', + 'orange', + 'orchid', + 'otter', + 'palm tree', + 'pear', + 'pickup truck', + 'pine tree', + 'plain', + 'plate', + 'poppy', + 'porcupine', + 'possum', + 'rabbit', + 'raccoon', + 'ray', + 'road', + 'rocket', + 'rose', + 'sea', + 'seal', + 'shark', + 'shrew', + 'skunk', + 'skyscraper', + 'snail', + 'snake', + 'spider', + 'squirrel', + 'streetcar', + 'sunflower', + 'sweet pepper', + 'table', + 'tank', + 'telephone', + 'television', + 'tiger', + 'tractor', + 'train', + 'trout', + 'tulip', + 'turtle', + 'wardrobe', + 'whale', + 'willow tree', + 'wolf', + 'woman', + 'worm', +] + +templates = [ + 'a photo of a {}.', + 'a blurry photo of a {}.', + 'a black and white photo of a {}.', + 'a low contrast photo of a {}.', + 'a high contrast photo of a {}.', + 'a bad photo of a {}.', + 'a good photo of a {}.', + 'a photo of a small {}.', + 'a photo of a big {}.', + 'a photo of the {}.', + 'a blurry photo of the {}.', + 'a black and white photo of the {}.', + 'a low contrast photo of the {}.', + 'a high contrast photo of the {}.', + 'a bad photo of the {}.', + 'a good photo of the {}.', + 'a photo of the small {}.', + 'a photo of the big {}.', +] +``` + + + +## CLEVRCounts + +```bash +classes = [ + '10', + '3', + '4', + '5', + '6', + '7', + '8', + '9', +] + +templates = [ + 'a photo of {} objects.', +] +``` + + + +## Caltech101 + +```bash +classes = [ + 'background', + 'off-center face', + 'centered face', + 'leopard', + 'motorbike', + 'accordion', + 'airplane', + 'anchor', + 'ant', + 'barrel', + 'bass', + 'beaver', + 'binocular', + 'bonsai', + 'brain', + 'brontosaurus', + 'buddha', + 'butterfly', + 'camera', + 'cannon', + 'side of a car', + 'ceiling fan', + 'cellphone', + 'chair', + 'chandelier', + 'body of a cougar cat', + 'face of a cougar cat', + 'crab', + 'crayfish', + 'crocodile', + 'head of a crocodile', + 'cup', + 'dalmatian', + 'dollar bill', + 'dolphin', + 'dragonfly', + 'electric guitar', + 'elephant', + 'emu', + 'euphonium', + 'ewer', + 'ferry', + 'flamingo', + 'head of a flamingo', + 'garfield', + 'gerenuk', + 'gramophone', + 'grand piano', + 'hawksbill', + 'headphone', + 'hedgehog', + 'helicopter', + 'ibis', + 'inline skate', + 'joshua tree', + 'kangaroo', + 'ketch', + 'lamp', + 'laptop', + 'llama', + 'lobster', + 'lotus', + 'mandolin', + 'mayfly', + 'menorah', + 'metronome', + 'minaret', + 'nautilus', + 'octopus', + 'okapi', + 'pagoda', + 'panda', + 'pigeon', + 'pizza', + 'platypus', + 'pyramid', + 'revolver', + 'rhino', + 'rooster', + 'saxophone', + 'schooner', + 'scissors', + 'scorpion', + 'sea horse', + 'snoopy (cartoon beagle)', + 'soccer ball', + 'stapler', + 'starfish', + 'stegosaurus', + 'stop sign', + 'strawberry', + 'sunflower', + 'tick', + 'trilobite', + 'umbrella', + 'watch', + 'water lilly', + 'wheelchair', + 'wild cat', + 'windsor chair', + 'wrench', + 'yin and yang symbol', +] + +templates = [ + 'a photo of a {}.', + 'a painting of a {}.', + 'a plastic {}.', + 'a sculpture of a {}.', + 'a sketch of a {}.', + 'a tattoo of a {}.', + 'a toy {}.', + 'a rendition of a {}.', + 'a embroidered {}.', + 'a cartoon {}.', + 'a {} in a video game.', + 'a plushie {}.', + 'a origami {}.', + 'art of a {}.', + 'graffiti of a {}.', + 'a drawing of a {}.', + 'a doodle of a {}.', + 'a photo of the {}.', + 'a painting of the {}.', + 'the plastic {}.', + 'a sculpture of the {}.', + 'a sketch of the {}.', + 'a tattoo of the {}.', + 'the toy {}.', + 'a rendition of the {}.', + 'the embroidered {}.', + 'the cartoon {}.', + 'the {} in a video game.', + 'the plushie {}.', + 'the origami {}.', + 'art of the {}.', + 'graffiti of the {}.', + 'a drawing of the {}.', + 'a doodle of the {}.', +] +``` + + + +## Country211 + +```bash +classes = [ + 'Andorra', + 'United Arab Emirates', + 'Afghanistan', + 'Antigua and Barbuda', + 'Anguilla', + 'Albania', + 'Armenia', + 'Angola', + 'Antarctica', + 'Argentina', + 'Austria', + 'Australia', + 'Aruba', + 'Aland Islands', + 'Azerbaijan', + 'Bosnia and Herzegovina', + 'Barbados', + 'Bangladesh', + 'Belgium', + 'Burkina Faso', + 'Bulgaria', + 'Bahrain', + 'Benin', + 'Bermuda', + 'Brunei Darussalam', + 'Bolivia', + 'Bonaire, Saint Eustatius and Saba', + 'Brazil', + 'Bahamas', + 'Bhutan', + 'Botswana', + 'Belarus', + 'Belize', + 'Canada', + 'DR Congo', + 'Central African Republic', + 'Switzerland', + "Cote d'Ivoire", + 'Cook Islands', + 'Chile', + 'Cameroon', + 'China', + 'Colombia', + 'Costa Rica', + 'Cuba', + 'Cabo Verde', + 'Curacao', + 'Cyprus', + 'Czech Republic', + 'Germany', + 'Denmark', + 'Dominica', + 'Dominican Republic', + 'Algeria', + 'Ecuador', + 'Estonia', + 'Egypt', + 'Spain', + 'Ethiopia', + 'Finland', + 'Fiji', + 'Falkland Islands', + 'Faeroe Islands', + 'France', + 'Gabon', + 'United Kingdom', + 'Grenada', + 'Georgia', + 'French Guiana', + 'Guernsey', + 'Ghana', + 'Gibraltar', + 'Greenland', + 'Gambia', + 'Guadeloupe', + 'Greece', + 'South Georgia and South Sandwich Is.', + 'Guatemala', + 'Guam', + 'Guyana', + 'Hong Kong', + 'Honduras', + 'Croatia', + 'Haiti', + 'Hungary', + 'Indonesia', + 'Ireland', + 'Israel', + 'Isle of Man', + 'India', + 'Iraq', + 'Iran', + 'Iceland', + 'Italy', + 'Jersey', + 'Jamaica', + 'Jordan', + 'Japan', + 'Kenya', + 'Kyrgyz Republic', + 'Cambodia', + 'St. Kitts and Nevis', + 'North Korea', + 'South Korea', + 'Kuwait', + 'Cayman Islands', + 'Kazakhstan', + 'Laos', + 'Lebanon', + 'St. Lucia', + 'Liechtenstein', + 'Sri Lanka', + 'Liberia', + 'Lithuania', + 'Luxembourg', + 'Latvia', + 'Libya', + 'Morocco', + 'Monaco', + 'Moldova', + 'Montenegro', + 'Saint-Martin', + 'Madagascar', + 'Macedonia', + 'Mali', + 'Myanmar', + 'Mongolia', + 'Macau', + 'Martinique', + 'Mauritania', + 'Malta', + 'Mauritius', + 'Maldives', + 'Malawi', + 'Mexico', + 'Malaysia', + 'Mozambique', + 'Namibia', + 'New Caledonia', + 'Nigeria', + 'Nicaragua', + 'Netherlands', + 'Norway', + 'Nepal', + 'New Zealand', + 'Oman', + 'Panama', + 'Peru', + 'French Polynesia', + 'Papua New Guinea', + 'Philippines', + 'Pakistan', + 'Poland', + 'Puerto Rico', + 'Palestine', + 'Portugal', + 'Palau', + 'Paraguay', + 'Qatar', + 'Reunion', + 'Romania', + 'Serbia', + 'Russia', + 'Rwanda', + 'Saudi Arabia', + 'Solomon Islands', + 'Seychelles', + 'Sudan', + 'Sweden', + 'Singapore', + 'St. Helena', + 'Slovenia', + 'Svalbard and Jan Mayen Islands', + 'Slovakia', + 'Sierra Leone', + 'San Marino', + 'Senegal', + 'Somalia', + 'South Sudan', + 'El Salvador', + 'Sint Maarten', + 'Syria', + 'Eswatini', + 'Togo', + 'Thailand', + 'Tajikistan', + 'Timor-Leste', + 'Turkmenistan', + 'Tunisia', + 'Tonga', + 'Turkey', + 'Trinidad and Tobago', + 'Taiwan', + 'Tanzania', + 'Ukraine', + 'Uganda', + 'United States', + 'Uruguay', + 'Uzbekistan', + 'Vatican', + 'Venezuela', + 'British Virgin Islands', + 'United States Virgin Islands', + 'Vietnam', + 'Vanuatu', + 'Samoa', + 'Kosovo', + 'Yemen', + 'South Africa', + 'Zambia', + 'Zimbabwe', +] + +templates = [ + 'a photo i took in {}.', + 'a photo i took while visiting {}.', + 'a photo from my home country of {}.', + 'a photo from my visit to {}.', + 'a photo showing the country of {}.', +] +``` + + + +## DescribableTextures + +```bash +classes = [ + 'banded', + 'blotchy', + 'braided', + 'bubbly', + 'bumpy', + 'chequered', + 'cobwebbed', + 'cracked', + 'crosshatched', + 'crystalline', + 'dotted', + 'fibrous', + 'flecked', + 'freckled', + 'frilly', + 'gauzy', + 'grid', + 'grooved', + 'honeycombed', + 'interlaced', + 'knitted', + 'lacelike', + 'lined', + 'marbled', + 'matted', + 'meshed', + 'paisley', + 'perforated', + 'pitted', + 'pleated', + 'polka-dotted', + 'porous', + 'potholed', + 'scaly', + 'smeared', + 'spiralled', + 'sprinkled', + 'stained', + 'stratified', + 'striped', + 'studded', + 'swirly', + 'veined', + 'waffled', + 'woven', + 'wrinkled', + 'zigzagged', +] + +templates = [ + 'a photo of a {} texture.', + 'a photo of a {} pattern.', + 'a photo of a {} thing.', + 'a photo of a {} object.', + 'a photo of the {} texture.', + 'a photo of the {} pattern.', + 'a photo of the {} thing.', + 'a photo of the {} object.', +] +``` + + + +## EuroSAT + +```bash +classes = [ + 'forest', + 'permanent crop land', + 'residential buildings or homes or apartments', + 'river', + 'pasture land', + 'lake or sea', + 'brushland or shrubland', + 'annual crop land', + 'industrial buildings or commercial buildings', + 'highway or road', +] + +templates = [ + 'a centered satellite photo of {}.', + 'a centered satellite photo of a {}.', + 'a centered satellite photo of the {}.', +] +``` + + + +## FGVCAircraft + +```bash +classes = [ + '707-320', + '727-200', + '737-200', + '737-300', + '737-400', + '737-500', + '737-600', + '737-700', + '737-800', + '737-900', + '747-100', + '747-200', + '747-300', + '747-400', + '757-200', + '757-300', + '767-200', + '767-300', + '767-400', + '777-200', + '777-300', + 'A300B4', + 'A310', + 'A318', + 'A319', + 'A320', + 'A321', + 'A330-200', + 'A330-300', + 'A340-200', + 'A340-300', + 'A340-500', + 'A340-600', + 'A380', + 'ATR-42', + 'ATR-72', + 'An-12', + 'BAE 146-200', + 'BAE 146-300', + 'BAE-125', + 'Beechcraft 1900', + 'Boeing 717', + 'C-130', + 'C-47', + 'CRJ-200', + 'CRJ-700', + 'CRJ-900', + 'Cessna 172', + 'Cessna 208', + 'Cessna 525', + 'Cessna 560', + 'Challenger 600', + 'DC-10', + 'DC-3', + 'DC-6', + 'DC-8', + 'DC-9-30', + 'DH-82', + 'DHC-1', + 'DHC-6', + 'DHC-8-100', + 'DHC-8-300', + 'DR-400', + 'Dornier 328', + 'E-170', + 'E-190', + 'E-195', + 'EMB-120', + 'ERJ 135', + 'ERJ 145', + 'Embraer Legacy 600', + 'Eurofighter Typhoon', + 'F-16A/B', + 'F/A-18', + 'Falcon 2000', + 'Falcon 900', + 'Fokker 100', + 'Fokker 50', + 'Fokker 70', + 'Global Express', + 'Gulfstream IV', + 'Gulfstream V', + 'Hawk T1', + 'Il-76', + 'L-1011', + 'MD-11', + 'MD-80', + 'MD-87', + 'MD-90', + 'Metroliner', + 'Model B200', + 'PA-28', + 'SR-20', + 'Saab 2000', + 'Saab 340', + 'Spitfire', + 'Tornado', + 'Tu-134', + 'Tu-154', + 'Yak-42', +] + +templates = [ + 'a photo of a {}, a type of aircraft.', + 'a photo of the {}, a type of aircraft.', +] +``` + + + +## FacialEmotionRecognition2013 + +```bash +classes = [ + ['angry'], + ['disgusted'], + ['fearful'], + ['happy', 'smiling'], + ['sad', 'depressed'], + ['surprised', 'shocked', 'spooked'], + ['neutral', 'bored'], +] + +templates = [ + 'a photo of a {} looking face.', + 'a photo of a face showing the emotion: {}.', + 'a photo of a face looking {}.', + 'a face that looks {}.', + 'they look {}.', + 'look at how {} they are.', +] +``` + + + +## Flowers102 + +```bash +classes = [ + 'pink primrose', + 'hard-leaved pocket orchid', + 'canterbury bells', + 'sweet pea', + 'english marigold', + 'tiger lily', + 'moon orchid', + 'bird of paradise', + 'monkshood', + 'globe thistle', + 'snapdragon', + "colt's foot", + 'king protea', + 'spear thistle', + 'yellow iris', + 'globe flower', + 'purple coneflower', + 'peruvian lily', + 'balloon flower', + 'giant white arum lily', + 'fire lily', + 'pincushion flower', + 'fritillary', + 'red ginger', + 'grape hyacinth', + 'corn poppy', + 'prince of wales feathers', + 'stemless gentian', + 'artichoke', + 'sweet william', + 'carnation', + 'garden phlox', + 'love in the mist', + 'mexican aster', + 'alpine sea holly', + 'ruby-lipped cattleya', + 'cape flower', + 'great masterwort', + 'siam tulip', + 'lenten rose', + 'barbeton daisy', + 'daffodil', + 'sword lily', + 'poinsettia', + 'bolero deep blue', + 'wallflower', + 'marigold', + 'buttercup', + 'oxeye daisy', + 'common dandelion', + 'petunia', + 'wild pansy', + 'primula', + 'sunflower', + 'pelargonium', + 'bishop of llandaff', + 'gaura', + 'geranium', + 'orange dahlia', + 'pink and yellow dahlia', + 'cautleya spicata', + 'japanese anemone', + 'black-eyed susan', + 'silverbush', + 'californian poppy', + 'osteospermum', + 'spring crocus', + 'bearded iris', + 'windflower', + 'tree poppy', + 'gazania', + 'azalea', + 'water lily', + 'rose', + 'thorn apple', + 'morning glory', + 'passion flower', + 'lotus', + 'toad lily', + 'anthurium', + 'frangipani', + 'clematis', + 'hibiscus', + 'columbine', + 'desert-rose', + 'tree mallow', + 'magnolia', + 'cyclamen', + 'watercress', + 'canna lily', + 'hippeastrum', + 'bee balm', + 'air plant', + 'foxglove', + 'bougainvillea', + 'camellia', + 'mallow', + 'mexican petunia', + 'bromelia', + 'blanket flower', + 'trumpet creeper', + 'blackberry lily', +] + +templates = [ + 'a photo of a {}, a type of flower.', +] +``` + + + +## Food101 + +```bash +classes = [ + 'apple pie', + 'baby back ribs', + 'baklava', + 'beef carpaccio', + 'beef tartare', + 'beet salad', + 'beignets', + 'bibimbap', + 'bread pudding', + 'breakfast burrito', + 'bruschetta', + 'caesar salad', + 'cannoli', + 'caprese salad', + 'carrot cake', + 'ceviche', + 'cheese plate', + 'cheesecake', + 'chicken curry', + 'chicken quesadilla', + 'chicken wings', + 'chocolate cake', + 'chocolate mousse', + 'churros', + 'clam chowder', + 'club sandwich', + 'crab cakes', + 'creme brulee', + 'croque madame', + 'cup cakes', + 'deviled eggs', + 'donuts', + 'dumplings', + 'edamame', + 'eggs benedict', + 'escargots', + 'falafel', + 'filet mignon', + 'fish and chips', + 'foie gras', + 'french fries', + 'french onion soup', + 'french toast', + 'fried calamari', + 'fried rice', + 'frozen yogurt', + 'garlic bread', + 'gnocchi', + 'greek salad', + 'grilled cheese sandwich', + 'grilled salmon', + 'guacamole', + 'gyoza', + 'hamburger', + 'hot and sour soup', + 'hot dog', + 'huevos rancheros', + 'hummus', + 'ice cream', + 'lasagna', + 'lobster bisque', + 'lobster roll sandwich', + 'macaroni and cheese', + 'macarons', + 'miso soup', + 'mussels', + 'nachos', + 'omelette', + 'onion rings', + 'oysters', + 'pad thai', + 'paella', + 'pancakes', + 'panna cotta', + 'peking duck', + 'pho', + 'pizza', + 'pork chop', + 'poutine', + 'prime rib', + 'pulled pork sandwich', + 'ramen', + 'ravioli', + 'red velvet cake', + 'risotto', + 'samosa', + 'sashimi', + 'scallops', + 'seaweed salad', + 'shrimp and grits', + 'spaghetti bolognese', + 'spaghetti carbonara', + 'spring rolls', + 'steak', + 'strawberry shortcake', + 'sushi', + 'tacos', + 'takoyaki', + 'tiramisu', + 'tuna tartare', + 'waffles', +] + +templates = [ + 'a photo of {}, a type of food.', +] +``` + + + +## GTSRB + +```bash +classes = [ + 'red and white circle 20 kph speed limit', + 'red and white circle 30 kph speed limit', + 'red and white circle 50 kph speed limit', + 'red and white circle 60 kph speed limit', + 'red and white circle 70 kph speed limit', + 'red and white circle 80 kph speed limit', + 'end / de-restriction of 80 kph speed limit', + 'red and white circle 100 kph speed limit', + 'red and white circle 120 kph speed limit', + 'red and white circle red car and black car no passing', + 'red and white circle red truck and black car no passing', + 'red and white triangle road intersection warning', + 'white and yellow diamond priority road', + 'red and white upside down triangle yield right-of-way', + 'stop', + 'empty red and white circle', + 'red and white circle no truck entry', + 'red circle with white horizonal stripe no entry', + 'red and white triangle with exclamation mark warning', + 'red and white triangle with black left curve approaching warning', + 'red and white triangle with black right curve approaching warning', + 'red and white triangle with black double curve approaching warning', + 'red and white triangle rough / bumpy road warning', + 'red and white triangle car skidding / slipping warning', + 'red and white triangle with merging / narrow lanes warning', + 'red and white triangle with person digging / construction / road work warning', + 'red and white triangle with traffic light approaching warning', + 'red and white triangle with person walking warning', + 'red and white triangle with child and person walking warning', + 'red and white triangle with bicyle warning', + 'red and white triangle with snowflake / ice warning', + 'red and white triangle with deer warning', + 'white circle with gray strike bar no speed limit', + 'blue circle with white right turn arrow mandatory', + 'blue circle with white left turn arrow mandatory', + 'blue circle with white forward arrow mandatory', + 'blue circle with white forward or right turn arrow mandatory', + 'blue circle with white forward or left turn arrow mandatory', + 'blue circle with white keep right arrow mandatory', + 'blue circle with white keep left arrow mandatory', + 'blue circle with white arrows indicating a traffic circle', + 'white circle with gray strike bar indicating no passing for cars has ended', + 'white circle with gray strike bar indicating no passing for trucks has ended', +] + +templates = [ + 'a zoomed in photo of a "{}" traffic sign.', + 'a centered photo of a "{}" traffic sign.', + 'a close up photo of a "{}" traffic sign.', +] +``` + + + +## HatefulMemes + +```bash +classes = [ + 'meme', + 'hatespeech meme', +] + +templates = [ + 'a {}.', +] +``` + + + +## KITTI + +```bash +classes = [ + 'a photo i took of a car on my left or right side.', + 'a photo i took with a car nearby.', + 'a photo i took with a car in the distance.', + 'a photo i took with no car.', +] + +templates = [ + '{}', +] +``` + + + +## Kinetics700 + +```bash +classes = [ + 'abseiling', + 'acting in play', + 'adjusting glasses', + 'air drumming', + 'alligator wrestling', + 'answering questions', + 'applauding', + 'applying cream', + 'archaeological excavation', + 'archery', + 'arguing', + 'arm wrestling', + 'arranging flowers', + 'arresting', + 'assembling bicycle', + 'assembling computer', + 'attending conference', + 'auctioning', + 'baby waking up', + 'backflip (human)', + 'baking cookies', + 'bandaging', + 'barbequing', + 'bartending', + 'base jumping', + 'bathing dog', + 'battle rope training', + 'beatboxing', + 'bee keeping', + 'being excited', + 'being in zero gravity', + 'belly dancing', + 'bench pressing', + 'bending back', + 'bending metal', + 'biking through snow', + 'blasting sand', + 'blending fruit', + 'blowdrying hair', + 'blowing bubble gum', + 'blowing glass', + 'blowing leaves', + 'blowing nose', + 'blowing out candles', + 'bobsledding', + 'bodysurfing', + 'bookbinding', + 'bottling', + 'bouncing ball (not juggling)', + 'bouncing on bouncy castle', + 'bouncing on trampoline', + 'bowling', + 'braiding hair', + 'breading or breadcrumbing', + 'breakdancing', + 'breaking boards', + 'breaking glass', + 'breathing fire', + 'brush painting', + 'brushing floor', + 'brushing hair', + 'brushing teeth', + 'building cabinet', + 'building lego', + 'building sandcastle', + 'building shed', + 'bulldozing', + 'bungee jumping', + 'burping', + 'busking', + 'calculating', + 'calligraphy', + 'canoeing or kayaking', + 'capoeira', + 'capsizing', + 'card stacking', + 'card throwing', + 'carrying baby', + 'carrying weight', + 'cartwheeling', + 'carving ice', + 'carving marble', + 'carving pumpkin', + 'carving wood with a knife', + 'casting fishing line', + 'catching fish', + 'catching or throwing baseball', + 'catching or throwing frisbee', + 'catching or throwing softball', + 'celebrating', + 'changing gear in car', + 'changing oil', + 'changing wheel (not on bike)', + 'chasing', + 'checking tires', + 'checking watch', + 'cheerleading', + 'chewing gum', + 'chiseling stone', + 'chiseling wood', + 'chopping meat', + 'chopping wood', + 'clam digging', + 'clapping', + 'clay pottery making', + 'clean and jerk', + 'cleaning gutters', + 'cleaning pool', + 'cleaning shoes', + 'cleaning toilet', + 'cleaning windows', + 'climbing a rope', + 'climbing ladder', + 'climbing tree', + 'closing door', + 'coloring in', + 'combing hair', + 'contact juggling', + 'contorting', + 'cooking chicken', + 'cooking egg', + 'cooking on campfire', + 'cooking sausages (not on barbeque)', + 'cooking scallops', + 'cosplaying', + 'coughing', + 'counting money', + 'country line dancing', + 'cracking back', + 'cracking knuckles', + 'cracking neck', + 'crawling baby', + 'crocheting', + 'crossing eyes', + 'crossing river', + 'crying', + 'cumbia', + 'curling (sport)', + 'curling eyelashes', + 'curling hair', + 'cutting apple', + 'cutting cake', + 'cutting nails', + 'cutting orange', + 'cutting pineapple', + 'cutting watermelon', + 'dancing ballet', + 'dancing charleston', + 'dancing gangnam style', + 'dancing macarena', + 'deadlifting', + 'dealing cards', + 'decorating the christmas tree', + 'decoupage', + 'delivering mail', + 'digging', + 'dining', + 'directing traffic', + 'disc golfing', + 'diving cliff', + 'docking boat', + 'dodgeball', + 'doing aerobics', + 'doing jigsaw puzzle', + 'doing laundry', + 'doing nails', + 'doing sudoku', + 'drawing', + 'dribbling basketball', + 'drinking shots', + 'driving car', + 'driving tractor', + 'drooling', + 'drop kicking', + 'drumming fingers', + 'dumpster diving', + 'dunking basketball', + 'dyeing eyebrows', + 'dyeing hair', + 'eating burger', + 'eating cake', + 'eating carrots', + 'eating chips', + 'eating doughnuts', + 'eating hotdog', + 'eating ice cream', + 'eating nachos', + 'eating spaghetti', + 'eating watermelon', + 'egg hunting', + 'embroidering', + 'entering church', + 'exercising arm', + 'exercising with an exercise ball', + 'extinguishing fire', + 'faceplanting', + 'falling off bike', + 'falling off chair', + 'feeding birds', + 'feeding fish', + 'feeding goats', + 'fencing (sport)', + 'fidgeting', + 'filling cake', + 'filling eyebrows', + 'finger snapping', + 'fixing bicycle', + 'fixing hair', + 'flint knapping', + 'flipping bottle', + 'flipping pancake', + 'fly tying', + 'flying kite', + 'folding clothes', + 'folding napkins', + 'folding paper', + 'front raises', + 'frying vegetables', + 'gargling', + 'geocaching', + 'getting a haircut', + 'getting a piercing', + 'getting a tattoo', + 'giving or receiving award', + 'gold panning', + 'golf chipping', + 'golf driving', + 'golf putting', + 'gospel singing in church', + 'grinding meat', + 'grooming cat', + 'grooming dog', + 'grooming horse', + 'gymnastics tumbling', + 'hammer throw', + 'hand washing clothes', + 'head stand', + 'headbanging', + 'headbutting', + 'helmet diving', + 'herding cattle', + 'high fiving', + 'high jump', + 'high kick', + 'historical reenactment', + 'hitting baseball', + 'hockey stop', + 'holding snake', + 'home roasting coffee', + 'hopscotch', + 'hoverboarding', + 'huddling', + 'hugging (not baby)', + 'hugging baby', + 'hula hooping', + 'hurdling', + 'hurling (sport)', + 'ice climbing', + 'ice fishing', + 'ice skating', + 'ice swimming', + 'inflating balloons', + 'installing carpet', + 'ironing', + 'ironing hair', + 'javelin throw', + 'jaywalking', + 'jetskiing', + 'jogging', + 'juggling balls', + 'juggling fire', + 'juggling soccer ball', + 'jumping bicycle', + 'jumping into pool', + 'jumping jacks', + 'jumping sofa', + 'jumpstyle dancing', + 'karaoke', + 'kicking field goal', + 'kicking soccer ball', + 'kissing', + 'kitesurfing', + 'knitting', + 'krumping', + 'land sailing', + 'laughing', + 'lawn mower racing', + 'laying bricks', + 'laying concrete', + 'laying decking', + 'laying stone', + 'laying tiles', + 'leatherworking', + 'letting go of balloon', + 'licking', + 'lifting hat', + 'lighting candle', + 'lighting fire', + 'listening with headphones', + 'lock picking', + 'long jump', + 'longboarding', + 'looking at phone', + 'looking in mirror', + 'luge', + 'lunge', + 'making a cake', + 'making a sandwich', + 'making balloon shapes', + 'making bubbles', + 'making cheese', + 'making horseshoes', + 'making jewelry', + 'making latte art', + 'making paper aeroplanes', + 'making pizza', + 'making slime', + 'making snowman', + 'making sushi', + 'making tea', + 'making the bed', + 'marching', + 'marriage proposal', + 'massaging back', + 'massaging feet', + 'massaging legs', + 'massaging neck', + "massaging person's head", + 'metal detecting', + 'milking cow', + 'milking goat', + 'mixing colours', + 'moon walking', + 'mopping floor', + 'mosh pit dancing', + 'motorcycling', + 'mountain climber (exercise)', + 'moving baby', + 'moving child', + 'moving furniture', + 'mowing lawn', + 'mushroom foraging', + 'needle felting', + 'news anchoring', + 'opening bottle (not wine)', + 'opening coconuts', + 'opening door', + 'opening present', + 'opening refrigerator', + 'opening wine bottle', + 'packing', + 'paragliding', + 'parasailing', + 'parkour', + 'passing American football (in game)', + 'passing American football (not in game)', + 'passing soccer ball', + 'peeling apples', + 'peeling banana', + 'peeling potatoes', + 'person collecting garbage', + 'petting animal (not cat)', + 'petting cat', + 'petting horse', + 'photobombing', + 'photocopying', + 'picking apples', + 'picking blueberries', + 'pillow fight', + 'pinching', + 'pirouetting', + 'planing wood', + 'planting trees', + 'plastering', + 'playing accordion', + 'playing american football', + 'playing badminton', + 'playing bagpipes', + 'playing basketball', + 'playing bass guitar', + 'playing beer pong', + 'playing billiards', + 'playing blackjack', + 'playing cards', + 'playing cello', + 'playing checkers', + 'playing chess', + 'playing clarinet', + 'playing controller', + 'playing cricket', + 'playing cymbals', + 'playing darts', + 'playing didgeridoo', + 'playing dominoes', + 'playing drums', + 'playing field hockey', + 'playing flute', + 'playing gong', + 'playing guitar', + 'playing hand clapping games', + 'playing harmonica', + 'playing harp', + 'playing ice hockey', + 'playing keyboard', + 'playing kickball', + 'playing laser tag', + 'playing lute', + 'playing mahjong', + 'playing maracas', + 'playing marbles', + 'playing monopoly', + 'playing netball', + 'playing nose flute', + 'playing oboe', + 'playing ocarina', + 'playing organ', + 'playing paintball', + 'playing pan pipes', + 'playing piano', + 'playing piccolo', + 'playing pinball', + 'playing ping pong', + 'playing poker', + 'playing polo', + 'playing recorder', + 'playing road hockey', + 'playing rounders', + 'playing rubiks cube', + 'playing saxophone', + 'playing scrabble', + 'playing shuffleboard', + 'playing slot machine', + 'playing squash or racquetball', + 'playing tennis', + 'playing trombone', + 'playing trumpet', + 'playing ukulele', + 'playing violin', + 'playing volleyball', + 'playing with trains', + 'playing xylophone', + 'poaching eggs', + 'poking bellybutton', + 'pole vault', + 'polishing furniture', + 'polishing metal', + 'popping balloons', + 'pouring beer', + 'pouring milk', + 'pouring wine', + 'preparing salad', + 'presenting weather forecast', + 'pretending to be a statue', + 'pull ups', + 'pulling espresso shot', + 'pulling rope (game)', + 'pumping fist', + 'pumping gas', + 'punching bag', + 'punching person (boxing)', + 'push up', + 'pushing car', + 'pushing cart', + 'pushing wheelbarrow', + 'pushing wheelchair', + 'putting in contact lenses', + 'putting on eyeliner', + 'putting on foundation', + 'putting on lipstick', + 'putting on mascara', + 'putting on sari', + 'putting on shoes', + 'putting wallpaper on wall', + 'raising eyebrows', + 'reading book', + 'reading newspaper', + 'recording music', + 'repairing puncture', + 'riding a bike', + 'riding camel', + 'riding elephant', + 'riding mechanical bull', + 'riding mule', + 'riding or walking with horse', + 'riding scooter', + 'riding snow blower', + 'riding unicycle', + 'ripping paper', + 'roasting marshmallows', + 'roasting pig', + 'robot dancing', + 'rock climbing', + 'rock scissors paper', + 'roller skating', + 'rolling eyes', + 'rolling pastry', + 'rope pushdown', + 'running on treadmill', + 'sailing', + 'salsa dancing', + 'saluting', + 'sanding floor', + 'sanding wood', + 'sausage making', + 'sawing wood', + 'scrambling eggs', + 'scrapbooking', + 'scrubbing face', + 'scuba diving', + 'seasoning food', + 'separating eggs', + 'setting table', + 'sewing', + 'shaking hands', + 'shaking head', + 'shaping bread dough', + 'sharpening knives', + 'sharpening pencil', + 'shaving head', + 'shaving legs', + 'shearing sheep', + 'shining flashlight', + 'shining shoes', + 'shoot dance', + 'shooting basketball', + 'shooting goal (soccer)', + 'shooting off fireworks', + 'shopping', + 'shot put', + 'shouting', + 'shoveling snow', + 'shredding paper', + 'shucking oysters', + 'shuffling cards', + 'shuffling feet', + 'side kick', + 'sieving', + 'sign language interpreting', + 'silent disco', + 'singing', + 'sipping cup', + 'situp', + 'skateboarding', + 'ski ballet', + 'ski jumping', + 'skiing crosscountry', + 'skiing mono', + 'skiing slalom', + 'skipping rope', + 'skipping stone', + 'skydiving', + 'slacklining', + 'slapping', + 'sled dog racing', + 'sleeping', + 'slicing onion', + 'smashing', + 'smelling feet', + 'smoking', + 'smoking hookah', + 'smoking pipe', + 'snatch weight lifting', + 'sneezing', + 'snorkeling', + 'snowboarding', + 'snowkiting', + 'snowmobiling', + 'somersaulting', + 'spelunking', + 'spinning plates', + 'spinning poi', + 'splashing water', + 'spray painting', + 'spraying', + 'springboard diving', + 'square dancing', + 'squat', + 'squeezing orange', + 'stacking cups', + 'stacking dice', + 'standing on hands', + 'staring', + 'steer roping', + 'steering car', + 'sticking tongue out', + 'stomping grapes', + 'stretching arm', + 'stretching leg', + 'sucking lolly', + 'surfing crowd', + 'surfing water', + 'surveying', + 'sweeping floor', + 'swimming backstroke', + 'swimming breast stroke', + 'swimming butterfly stroke', + 'swimming front crawl', + 'swimming with dolphins', + 'swimming with sharks', + 'swing dancing', + 'swinging baseball bat', + 'swinging on something', + 'sword fighting', + 'sword swallowing', + 'tackling', + 'tagging graffiti', + 'tai chi', + 'taking photo', + 'talking on cell phone', + 'tango dancing', + 'tap dancing', + 'tapping guitar', + 'tapping pen', + 'tasting beer', + 'tasting food', + 'tasting wine', + 'testifying', + 'texting', + 'threading needle', + 'throwing axe', + 'throwing ball (not baseball or American football)', + 'throwing discus', + 'throwing knife', + 'throwing snowballs', + 'throwing tantrum', + 'throwing water balloon', + 'tickling', + 'tie dying', + 'tightrope walking', + 'tiptoeing', + 'tobogganing', + 'tossing coin', + 'tossing salad', + 'training dog', + 'trapezing', + 'treating wood', + 'trimming or shaving beard', + 'trimming shrubs', + 'trimming trees', + 'triple jump', + 'twiddling fingers', + 'tying bow tie', + 'tying knot (not on a tie)', + 'tying necktie', + 'tying shoe laces', + 'unboxing', + 'uncorking champagne', + 'unloading truck', + 'using a microscope', + 'using a paint roller', + 'using a power drill', + 'using a sledge hammer', + 'using a wrench', + 'using atm', + 'using bagging machine', + 'using circular saw', + 'using inhaler', + 'using megaphone', + 'using puppets', + 'using remote controller (not gaming)', + 'using segway', + 'vacuuming car', + 'vacuuming floor', + 'visiting the zoo', + 'wading through mud', + 'wading through water', + 'waiting in line', + 'waking up', + 'walking on stilts', + 'walking the dog', + 'walking through snow', + 'walking with crutches', + 'washing dishes', + 'washing feet', + 'washing hair', + 'washing hands', + 'watching tv', + 'water skiing', + 'water sliding', + 'watering plants', + 'waving hand', + 'waxing armpits', + 'waxing back', + 'waxing chest', + 'waxing eyebrows', + 'waxing legs', + 'weaving basket', + 'weaving fabric', + 'welding', + 'whistling', + 'windsurfing', + 'winking', + 'wood burning (art)', + 'wrapping present', + 'wrestling', + 'writing', + 'yarn spinning', + 'yawning', + 'yoga', + 'zumba' +] + +templates = [ + 'a photo of {}.', + 'a photo of a person {}.', + 'a photo of a person using {}.', + 'a photo of a person doing {}.', + 'a photo of a person during {}.', + 'a photo of a person performing {}.', + 'a photo of a person practicing {}.', + 'a video of {}.', + 'a video of a person {}.', + 'a video of a person using {}.', + 'a video of a person doing {}.', + 'a video of a person during {}.', + 'a video of a person performing {}.', + 'a video of a person practicing {}.', + 'a example of {}.', + 'a example of a person {}.', + 'a example of a person using {}.', + 'a example of a person doing {}.', + 'a example of a person during {}.', + 'a example of a person performing {}.', + 'a example of a person practicing {}.', + 'a demonstration of {}.', + 'a demonstration of a person {}.', + 'a demonstration of a person using {}.', + 'a demonstration of a person doing {}.', + 'a demonstration of a person during {}.', + 'a demonstration of a person performing {}.', + 'a demonstration of a person practicing {}.', +] +``` + + + +## MNIST + +```bash +classes = [ + '0', + '1', + '2', + '3', + '4', + '5', + '6', + '7', + '8', + '9', +] + +templates = [ + 'a photo of the number: "{}".', +] +``` + + + +## OxfordPets + +```bash +classes = [ + 'Abyssinian', + 'Bengal', + 'Birman', + 'Bombay', + 'British Shorthair', + 'Egyptian Mau', + 'Maine Coon', + 'Persian', + 'Ragdoll', + 'Russian Blue', + 'Siamese', + 'Sphynx', + 'american bulldog', + 'american pit bull terrier', + 'basset hound', + 'beagle', + 'boxer', + 'chihuahua', + 'english cocker spaniel', + 'english setter', + 'german shorthaired', + 'great pyrenees', + 'havanese', + 'japanese chin', + 'keeshond', + 'leonberger', + 'miniature pinscher', + 'newfoundland', + 'pomeranian', + 'pug', + 'saint bernard', + 'samoyed', + 'scottish terrier', + 'shiba inu', + 'staffordshire bull terrier', + 'wheaten terrier', + 'yorkshire terrier', +] + +templates = [ + 'a photo of a {}, a type of pet.', +] +``` + + + +## PascalVOC2007 + +```bash +classes = [ + 'aeroplane', + 'bicycle', + 'bird', + 'boat', + 'bottle', + 'bus', + 'car', + 'cat', + 'chair', + 'cow', + 'dog', + 'horse', + 'motorbike', + 'person', + 'sheep', + 'sofa', + 'diningtable', + 'pottedplant', + 'train', + 'tvmonitor', +] + +templates = [ + 'a photo of a {}.', +] +``` + + + +## PatchCamelyon + +```bash +classes = [ + 'lymph node', + 'lymph node containing metastatic tumor tissue', +] + +templates = [ + 'this is a photo of {}', +] +``` + + + +## RESISC45 + +```bash +classes = [ + 'airplane', + 'airport', + 'baseball diamond', + 'basketball court', + 'beach', + 'bridge', + 'chaparral', + 'church', + 'circular farmland', + 'cloud', + 'commercial area', + 'dense residential', + 'desert', + 'forest', + 'freeway', + 'golf course', + 'ground track field', + 'harbor', + 'industrial area', + 'intersection', + 'island', + 'lake', + 'meadow', + 'medium residential', + 'mobile home park', + 'mountain', + 'overpass', + 'palace', + 'parking lot', + 'railway', + 'railway station', + 'rectangular farmland', + 'river', + 'roundabout', + 'runway', + 'sea ice', + 'ship', + 'snowberg', + 'sparse residential', + 'stadium', + 'storage tank', + 'tennis court', + 'terrace', + 'thermal power station', + 'wetland', +] + +templates = [ + 'satellite imagery of {}.', + 'aerial imagery of {}.', + 'satellite photo of {}.', + 'aerial photo of {}.', + 'satellite view of {}.', + 'aerial view of {}.', + 'satellite imagery of a {}.', + 'aerial imagery of a {}.', + 'satellite photo of a {}.', + 'aerial photo of a {}.', + 'satellite view of a {}.', + 'aerial view of a {}.', + 'satellite imagery of the {}.', + 'aerial imagery of the {}.', + 'satellite photo of the {}.', + 'aerial photo of the {}.', + 'satellite view of the {}.', + 'aerial view of the {}.', +] +``` + + + +## SST2 + +```bash +classes = [ + 'negative', + 'positive', +] + +templates = [ + 'a {} review of a movie.', +] +``` + + + +## STL10 + +```bash +classes = [ + 'airplane', + 'bird', + 'car', + 'cat', + 'deer', + 'dog', + 'horse', + 'monkey', + 'ship', + 'truck', +] + +templates = [ + 'a photo of a {}.', + 'a photo of the {}.', +] +``` + + + +## SUN397 + +```bash +classes = [ + 'abbey', + 'airplane cabin', + 'airport terminal', + 'alley', + 'amphitheater', + 'amusement arcade', + 'amusement park', + 'anechoic chamber', + 'apartment building outdoor', + 'apse indoor', + 'aquarium', + 'aqueduct', + 'arch', + 'archive', + 'arrival gate outdoor', + 'art gallery', + 'art school', + 'art studio', + 'assembly line', + 'athletic field outdoor', + 'atrium public', + 'attic', + 'auditorium', + 'auto factory', + 'badlands', + 'badminton court indoor', + 'baggage claim', + 'bakery shop', + 'balcony exterior', + 'balcony interior', + 'ball pit', + 'ballroom', + 'bamboo forest', + 'banquet hall', + 'bar', + 'barn', + 'barndoor', + 'baseball field', + 'basement', + 'basilica', + 'basketball court outdoor', + 'bathroom', + 'batters box', + 'bayou', + 'bazaar indoor', + 'bazaar outdoor', + 'beach', + 'beauty salon', + 'bedroom', + 'berth', + 'biology laboratory', + 'bistro indoor', + 'boardwalk', + 'boat deck', + 'boathouse', + 'bookstore', + 'booth indoor', + 'botanical garden', + 'bow window indoor', + 'bow window outdoor', + 'bowling alley', + 'boxing ring', + 'brewery indoor', + 'bridge', + 'building facade', + 'bullring', + 'burial chamber', + 'bus interior', + 'butchers shop', + 'butte', + 'cabin outdoor', + 'cafeteria', + 'campsite', + 'campus', + 'canal natural', + 'canal urban', + 'candy store', + 'canyon', + 'car interior backseat', + 'car interior frontseat', + 'carrousel', + 'casino indoor', + 'castle', + 'catacomb', + 'cathedral indoor', + 'cathedral outdoor', + 'cavern indoor', + 'cemetery', + 'chalet', + 'cheese factory', + 'chemistry lab', + 'chicken coop indoor', + 'chicken coop outdoor', + 'childs room', + 'church indoor', + 'church outdoor', + 'classroom', + 'clean room', + 'cliff', + 'cloister indoor', + 'closet', + 'clothing store', + 'coast', + 'cockpit', + 'coffee shop', + 'computer room', + 'conference center', + 'conference room', + 'construction site', + 'control room', + 'control tower outdoor', + 'corn field', + 'corral', + 'corridor', + 'cottage garden', + 'courthouse', + 'courtroom', + 'courtyard', + 'covered bridge exterior', + 'creek', + 'crevasse', + 'crosswalk', + 'cubicle office', + 'dam', + 'delicatessen', + 'dentists office', + 'desert sand', + 'desert vegetation', + 'diner indoor', + 'diner outdoor', + 'dinette home', + 'dinette vehicle', + 'dining car', + 'dining room', + 'discotheque', + 'dock', + 'doorway outdoor', + 'dorm room', + 'driveway', + 'driving range outdoor', + 'drugstore', + 'electrical substation', + 'elevator door', + 'elevator interior', + 'elevator shaft', + 'engine room', + 'escalator indoor', + 'excavation', + 'factory indoor', + 'fairway', + 'fastfood restaurant', + 'field cultivated', + 'field wild', + 'fire escape', + 'fire station', + 'firing range indoor', + 'fishpond', + 'florist shop indoor', + 'food court', + 'forest broadleaf', + 'forest needleleaf', + 'forest path', + 'forest road', + 'formal garden', + 'fountain', + 'galley', + 'game room', + 'garage indoor', + 'garbage dump', + 'gas station', + 'gazebo exterior', + 'general store indoor', + 'general store outdoor', + 'gift shop', + 'golf course', + 'greenhouse indoor', + 'greenhouse outdoor', + 'gymnasium indoor', + 'hangar indoor', + 'hangar outdoor', + 'harbor', + 'hayfield', + 'heliport', + 'herb garden', + 'highway', + 'hill', + 'home office', + 'hospital', + 'hospital room', + 'hot spring', + 'hot tub outdoor', + 'hotel outdoor', + 'hotel room', + 'house', + 'hunting lodge outdoor', + 'ice cream parlor', + 'ice floe', + 'ice shelf', + 'ice skating rink indoor', + 'ice skating rink outdoor', + 'iceberg', + 'igloo', + 'industrial area', + 'inn outdoor', + 'islet', + 'jacuzzi indoor', + 'jail cell', + 'jail indoor', + 'jewelry shop', + 'kasbah', + 'kennel indoor', + 'kennel outdoor', + 'kindergarden classroom', + 'kitchen', + 'kitchenette', + 'labyrinth outdoor', + 'lake natural', + 'landfill', + 'landing deck', + 'laundromat', + 'lecture room', + 'library indoor', + 'library outdoor', + 'lido deck outdoor', + 'lift bridge', + 'lighthouse', + 'limousine interior', + 'living room', + 'lobby', + 'lock chamber', + 'locker room', + 'mansion', + 'manufactured home', + 'market indoor', + 'market outdoor', + 'marsh', + 'martial arts gym', + 'mausoleum', + 'medina', + 'moat water', + 'monastery outdoor', + 'mosque indoor', + 'mosque outdoor', + 'motel', + 'mountain', + 'mountain snowy', + 'movie theater indoor', + 'museum indoor', + 'music store', + 'music studio', + 'nuclear power plant outdoor', + 'nursery', + 'oast house', + 'observatory outdoor', + 'ocean', + 'office', + 'office building', + 'oil refinery outdoor', + 'oilrig', + 'operating room', + 'orchard', + 'outhouse outdoor', + 'pagoda', + 'palace', + 'pantry', + 'park', + 'parking garage indoor', + 'parking garage outdoor', + 'parking lot', + 'parlor', + 'pasture', + 'patio', + 'pavilion', + 'pharmacy', + 'phone booth', + 'physics laboratory', + 'picnic area', + 'pilothouse indoor', + 'planetarium outdoor', + 'playground', + 'playroom', + 'plaza', + 'podium indoor', + 'podium outdoor', + 'pond', + 'poolroom establishment', + 'poolroom home', + 'power plant outdoor', + 'promenade deck', + 'pub indoor', + 'pulpit', + 'putting green', + 'racecourse', + 'raceway', + 'raft', + 'railroad track', + 'rainforest', + 'reception', + 'recreation room', + 'residential neighborhood', + 'restaurant', + 'restaurant kitchen', + 'restaurant patio', + 'rice paddy', + 'riding arena', + 'river', + 'rock arch', + 'rope bridge', + 'ruin', + 'runway', + 'sandbar', + 'sandbox', + 'sauna', + 'schoolhouse', + 'sea cliff', + 'server room', + 'shed', + 'shoe shop', + 'shopfront', + 'shopping mall indoor', + 'shower', + 'skatepark', + 'ski lodge', + 'ski resort', + 'ski slope', + 'sky', + 'skyscraper', + 'slum', + 'snowfield', + 'squash court', + 'stable', + 'stadium baseball', + 'stadium football', + 'stage indoor', + 'staircase', + 'street', + 'subway interior', + 'subway station platform', + 'supermarket', + 'sushi bar', + 'swamp', + 'swimming pool indoor', + 'swimming pool outdoor', + 'synagogue indoor', + 'synagogue outdoor', + 'television studio', + 'temple east asia', + 'temple south asia', + 'tennis court indoor', + 'tennis court outdoor', + 'tent outdoor', + 'theater indoor procenium', + 'theater indoor seats', + 'thriftshop', + 'throne room', + 'ticket booth', + 'toll plaza', + 'topiary garden', + 'tower', + 'toyshop', + 'track outdoor', + 'train railway', + 'train station platform', + 'tree farm', + 'tree house', + 'trench', + 'underwater coral reef', + 'utility room', + 'valley', + 'van interior', + 'vegetable garden', + 'veranda', + 'veterinarians office', + 'viaduct', + 'videostore', + 'village', + 'vineyard', + 'volcano', + 'volleyball court indoor', + 'volleyball court outdoor', + 'waiting room', + 'warehouse indoor', + 'water tower', + 'waterfall block', + 'waterfall fan', + 'waterfall plunge', + 'watering hole', + 'wave', + 'wet bar', + 'wheat field', + 'wind farm', + 'windmill', + 'wine cellar barrel storage', + 'wine cellar bottle storage', + 'wrestling ring indoor', + 'yard', + 'youth hostel', +] + +templates = [ + 'a photo of a {}.', + 'a photo of the {}.', +] +``` + + + +## StanfordCars + +```bash +classes = [ + 'AM General Hummer SUV 2000', + 'Acura RL Sedan 2012', + 'Acura TL Sedan 2012', + 'Acura TL Type-S 2008', + 'Acura TSX Sedan 2012', + 'Acura Integra Type R 2001', + 'Acura ZDX Hatchback 2012', + 'Aston Martin V8 Vantage Convertible 2012', + 'Aston Martin V8 Vantage Coupe 2012', + 'Aston Martin Virage Convertible 2012', + 'Aston Martin Virage Coupe 2012', + 'Audi RS 4 Convertible 2008', + 'Audi A5 Coupe 2012', + 'Audi TTS Coupe 2012', + 'Audi R8 Coupe 2012', + 'Audi V8 Sedan 1994', + 'Audi 100 Sedan 1994', + 'Audi 100 Wagon 1994', + 'Audi TT Hatchback 2011', + 'Audi S6 Sedan 2011', + 'Audi S5 Convertible 2012', + 'Audi S5 Coupe 2012', + 'Audi S4 Sedan 2012', + 'Audi S4 Sedan 2007', + 'Audi TT RS Coupe 2012', + 'BMW ActiveHybrid 5 Sedan 2012', + 'BMW 1 Series Convertible 2012', + 'BMW 1 Series Coupe 2012', + 'BMW 3 Series Sedan 2012', + 'BMW 3 Series Wagon 2012', + 'BMW 6 Series Convertible 2007', + 'BMW X5 SUV 2007', + 'BMW X6 SUV 2012', + 'BMW M3 Coupe 2012', + 'BMW M5 Sedan 2010', + 'BMW M6 Convertible 2010', + 'BMW X3 SUV 2012', + 'BMW Z4 Convertible 2012', + 'Bentley Continental Supersports Conv. Convertible 2012', + 'Bentley Arnage Sedan 2009', + 'Bentley Mulsanne Sedan 2011', + 'Bentley Continental GT Coupe 2012', + 'Bentley Continental GT Coupe 2007', + 'Bentley Continental Flying Spur Sedan 2007', + 'Bugatti Veyron 16.4 Convertible 2009', + 'Bugatti Veyron 16.4 Coupe 2009', + 'Buick Regal GS 2012', + 'Buick Rainier SUV 2007', + 'Buick Verano Sedan 2012', + 'Buick Enclave SUV 2012', + 'Cadillac CTS-V Sedan 2012', + 'Cadillac SRX SUV 2012', + 'Cadillac Escalade EXT Crew Cab 2007', + 'Chevrolet Silverado 1500 Hybrid Crew Cab 2012', + 'Chevrolet Corvette Convertible 2012', + 'Chevrolet Corvette ZR1 2012', + 'Chevrolet Corvette Ron Fellows Edition Z06 2007', + 'Chevrolet Traverse SUV 2012', + 'Chevrolet Camaro Convertible 2012', + 'Chevrolet HHR SS 2010', + 'Chevrolet Impala Sedan 2007', + 'Chevrolet Tahoe Hybrid SUV 2012', + 'Chevrolet Sonic Sedan 2012', + 'Chevrolet Express Cargo Van 2007', + 'Chevrolet Avalanche Crew Cab 2012', + 'Chevrolet Cobalt SS 2010', + 'Chevrolet Malibu Hybrid Sedan 2010', + 'Chevrolet TrailBlazer SS 2009', + 'Chevrolet Silverado 2500HD Regular Cab 2012', + 'Chevrolet Silverado 1500 Classic Extended Cab 2007', + 'Chevrolet Express Van 2007', + 'Chevrolet Monte Carlo Coupe 2007', + 'Chevrolet Malibu Sedan 2007', + 'Chevrolet Silverado 1500 Extended Cab 2012', + 'Chevrolet Silverado 1500 Regular Cab 2012', + 'Chrysler Aspen SUV 2009', + 'Chrysler Sebring Convertible 2010', + 'Chrysler Town and Country Minivan 2012', + 'Chrysler 300 SRT-8 2010', + 'Chrysler Crossfire Convertible 2008', + 'Chrysler PT Cruiser Convertible 2008', + 'Daewoo Nubira Wagon 2002', + 'Dodge Caliber Wagon 2012', + 'Dodge Caliber Wagon 2007', + 'Dodge Caravan Minivan 1997', + 'Dodge Ram Pickup 3500 Crew Cab 2010', + 'Dodge Ram Pickup 3500 Quad Cab 2009', + 'Dodge Sprinter Cargo Van 2009', + 'Dodge Journey SUV 2012', + 'Dodge Dakota Crew Cab 2010', + 'Dodge Dakota Club Cab 2007', + 'Dodge Magnum Wagon 2008', + 'Dodge Challenger SRT8 2011', + 'Dodge Durango SUV 2012', + 'Dodge Durango SUV 2007', + 'Dodge Charger Sedan 2012', + 'Dodge Charger SRT-8 2009', + 'Eagle Talon Hatchback 1998', + 'FIAT 500 Abarth 2012', + 'FIAT 500 Convertible 2012', + 'Ferrari FF Coupe 2012', + 'Ferrari California Convertible 2012', + 'Ferrari 458 Italia Convertible 2012', + 'Ferrari 458 Italia Coupe 2012', + 'Fisker Karma Sedan 2012', + 'Ford F-450 Super Duty Crew Cab 2012', + 'Ford Mustang Convertible 2007', + 'Ford Freestar Minivan 2007', + 'Ford Expedition EL SUV 2009', + 'Ford Edge SUV 2012', + 'Ford Ranger SuperCab 2011', + 'Ford GT Coupe 2006', + 'Ford F-150 Regular Cab 2012', + 'Ford F-150 Regular Cab 2007', + 'Ford Focus Sedan 2007', + 'Ford E-Series Wagon Van 2012', + 'Ford Fiesta Sedan 2012', + 'GMC Terrain SUV 2012', + 'GMC Savana Van 2012', + 'GMC Yukon Hybrid SUV 2012', + 'GMC Acadia SUV 2012', + 'GMC Canyon Extended Cab 2012', + 'Geo Metro Convertible 1993', + 'HUMMER H3T Crew Cab 2010', + 'HUMMER H2 SUT Crew Cab 2009', + 'Honda Odyssey Minivan 2012', + 'Honda Odyssey Minivan 2007', + 'Honda Accord Coupe 2012', + 'Honda Accord Sedan 2012', + 'Hyundai Veloster Hatchback 2012', + 'Hyundai Santa Fe SUV 2012', + 'Hyundai Tucson SUV 2012', + 'Hyundai Veracruz SUV 2012', + 'Hyundai Sonata Hybrid Sedan 2012', + 'Hyundai Elantra Sedan 2007', + 'Hyundai Accent Sedan 2012', + 'Hyundai Genesis Sedan 2012', + 'Hyundai Sonata Sedan 2012', + 'Hyundai Elantra Touring Hatchback 2012', + 'Hyundai Azera Sedan 2012', + 'Infiniti G Coupe IPL 2012', + 'Infiniti QX56 SUV 2011', + 'Isuzu Ascender SUV 2008', + 'Jaguar XK XKR 2012', + 'Jeep Patriot SUV 2012', + 'Jeep Wrangler SUV 2012', + 'Jeep Liberty SUV 2012', + 'Jeep Grand Cherokee SUV 2012', + 'Jeep Compass SUV 2012', + 'Lamborghini Reventon Coupe 2008', + 'Lamborghini Aventador Coupe 2012', + 'Lamborghini Gallardo LP 570-4 Superleggera 2012', + 'Lamborghini Diablo Coupe 2001', + 'Land Rover Range Rover SUV 2012', + 'Land Rover LR2 SUV 2012', + 'Lincoln Town Car Sedan 2011', + 'MINI Cooper Roadster Convertible 2012', + 'Maybach Landaulet Convertible 2012', + 'Mazda Tribute SUV 2011', + 'McLaren MP4-12C Coupe 2012', + 'Mercedes-Benz 300-Class Convertible 1993', + 'Mercedes-Benz C-Class Sedan 2012', + 'Mercedes-Benz SL-Class Coupe 2009', + 'Mercedes-Benz E-Class Sedan 2012', + 'Mercedes-Benz S-Class Sedan 2012', + 'Mercedes-Benz Sprinter Van 2012', + 'Mitsubishi Lancer Sedan 2012', + 'Nissan Leaf Hatchback 2012', + 'Nissan NV Passenger Van 2012', + 'Nissan Juke Hatchback 2012', + 'Nissan 240SX Coupe 1998', + 'Plymouth Neon Coupe 1999', + 'Porsche Panamera Sedan 2012', + 'Ram C/V Cargo Van Minivan 2012', + 'Rolls-Royce Phantom Drophead Coupe Convertible 2012', + 'Rolls-Royce Ghost Sedan 2012', + 'Rolls-Royce Phantom Sedan 2012', + 'Scion xD Hatchback 2012', + 'Spyker C8 Convertible 2009', + 'Spyker C8 Coupe 2009', + 'Suzuki Aerio Sedan 2007', + 'Suzuki Kizashi Sedan 2012', + 'Suzuki SX4 Hatchback 2012', + 'Suzuki SX4 Sedan 2012', + 'Tesla Model S Sedan 2012', + 'Toyota Sequoia SUV 2012', + 'Toyota Camry Sedan 2012', + 'Toyota Corolla Sedan 2012', + 'Toyota 4Runner SUV 2012', + 'Volkswagen Golf Hatchback 2012', + 'Volkswagen Golf Hatchback 1991', + 'Volkswagen Beetle Hatchback 2012', + 'Volvo C30 Hatchback 2012', + 'Volvo 240 Sedan 1993', + 'Volvo XC90 SUV 2007', + 'smart fortwo Convertible 2012', +] + +templates = [ + 'a photo of a {}.', + 'a photo of the {}.', + 'a photo of my {}.', + 'i love my {}!', + 'a photo of my dirty {}.', + 'a photo of my clean {}.', + 'a photo of my new {}.', + 'a photo of my old {}.', +] +``` + + + +## UCF101 + +```bash +classes = [ + 'Apply Eye Makeup', + 'Apply Lipstick', + 'Archery', + 'Baby Crawling', + 'Balance Beam', + 'Band Marching', + 'Baseball Pitch', + 'Basketball', + 'Basketball Dunk', + 'Bench Press', + 'Biking', + 'Billiards', + 'Blow Dry Hair', + 'Blowing Candles', + 'Body Weight Squats', + 'Bowling', + 'Boxing Punching Bag', + 'Boxing Speed Bag', + 'Breast Stroke', + 'Brushing Teeth', + 'Clean And Jerk', + 'Cliff Diving', + 'Cricket Bowling', + 'Cricket Shot', + 'Cutting In Kitchen', + 'Diving', + 'Drumming', + 'Fencing', + 'Field Hockey Penalty', + 'Floor Gymnastics', + 'Frisbee Catch', + 'Front Crawl', + 'Golf Swing', + 'Haircut', + 'Hammer Throw', + 'Hammering', + 'Hand Stand Pushups', + 'Handstand Walking', + 'Head Massage', + 'High Jump', + 'Horse Race', + 'Horse Riding', + 'Hula Hoop', + 'Ice Dancing', + 'Javelin Throw', + 'Juggling Balls', + 'Jump Rope', + 'Jumping Jack', + 'Kayaking', + 'Knitting', + 'Long Jump', + 'Lunges', + 'Military Parade', + 'Mixing', + 'Mopping Floor', + 'Nunchucks', + 'Parallel Bars', + 'Pizza Tossing', + 'Playing Cello', + 'Playing Daf', + 'Playing Dhol', + 'Playing Flute', + 'Playing Guitar', + 'Playing Piano', + 'Playing Sitar', + 'Playing Tabla', + 'Playing Violin', + 'Pole Vault', + 'Pommel Horse', + 'Pull Ups', + 'Punch', + 'Push Ups', + 'Rafting', + 'Rock Climbing Indoor', + 'Rope Climbing', + 'Rowing', + 'Salsa Spin', + 'Shaving Beard', + 'Shotput', + 'Skate Boarding', + 'Skiing', + 'Skijet', + 'Sky Diving', + 'Soccer Juggling', + 'Soccer Penalty', + 'Still Rings', + 'Sumo Wrestling', + 'Surfing', + 'Swing', + 'Table Tennis Shot', + 'Tai Chi', + 'Tennis Swing', + 'Throw Discus', + 'Trampoline Jumping', + 'Typing', + 'Uneven Bars', + 'Volleyball Spiking', + 'Walking With Dog', + 'Wall Pushups', + 'Writing On Board', + 'Yo Yo', +] + +templates = [ + 'a photo of a person {}.', + 'a video of a person {}.', + 'a example of a person {}.', + 'a demonstration of a person {}.', + 'a photo of the person {}.', + 'a video of the person {}.', + 'a example of the person {}.', + 'a demonstration of the person {}.', + 'a photo of a person using {}.', + 'a video of a person using {}.', + 'a example of a person using {}.', + 'a demonstration of a person using {}.', + 'a photo of the person using {}.', + 'a video of the person using {}.', + 'a example of the person using {}.', + 'a demonstration of the person using {}.', + 'a photo of a person doing {}.', + 'a video of a person doing {}.', + 'a example of a person doing {}.', + 'a demonstration of a person doing {}.', + 'a photo of the person doing {}.', + 'a video of the person doing {}.', + 'a example of the person doing {}.', + 'a demonstration of the person doing {}.', + 'a photo of a person during {}.', + 'a video of a person during {}.', + 'a example of a person during {}.', + 'a demonstration of a person during {}.', + 'a photo of the person during {}.', + 'a video of the person during {}.', + 'a example of the person during {}.', + 'a demonstration of the person during {}.', + 'a photo of a person performing {}.', + 'a video of a person performing {}.', + 'a example of a person performing {}.', + 'a demonstration of a person performing {}.', + 'a photo of the person performing {}.', + 'a video of the person performing {}.', + 'a example of the person performing {}.', + 'a demonstration of the person performing {}.', + 'a photo of a person practicing {}.', + 'a video of a person practicing {}.', + 'a example of a person practicing {}.', + 'a demonstration of a person practicing {}.', + 'a photo of the person practicing {}.', + 'a video of the person practicing {}.', + 'a example of the person practicing {}.', + 'a demonstration of the person practicing {}.', +] +``` + + diff --git a/CLIP/data/rendered-sst2.md b/CLIP/data/rendered-sst2.md new file mode 100755 index 0000000000000000000000000000000000000000..d27454caf046d8d699f87b8cb8404a05e488dbef --- /dev/null +++ b/CLIP/data/rendered-sst2.md @@ -0,0 +1,11 @@ +# The Rendered SST2 Dataset + +In the paper, we used an image classification dataset called Rendered SST2, to evaluate the model's capability on optical character recognition. To do so, we rendered the sentences in the [Standford Sentiment Treebank v2](https://nlp.stanford.edu/sentiment/treebank.html) dataset and used those as the input to the CLIP image encoder. + +The following command will download a 131MB archive countaining the images and extract into a subdirectory `rendered-sst2`: + +```bash +wget https://openaipublic.azureedge.net/clip/data/rendered-sst2.tgz +tar zxvf rendered-sst2.tgz +``` + diff --git a/CLIP/data/yfcc100m.md b/CLIP/data/yfcc100m.md new file mode 100755 index 0000000000000000000000000000000000000000..06083ef9a613b5d360e87c3f395c2a16c6e9208e --- /dev/null +++ b/CLIP/data/yfcc100m.md @@ -0,0 +1,14 @@ +# The YFCC100M Subset + +In the paper, we performed a dataset ablation using a subset of the YFCC100M dataset and showed that the performance remained largely similar. + +The subset contains 14,829,396 images, about 15% of the full dataset, which have been filtered to only keep those with natural languag titles and/or descriptions in English. + +We provide the list of (line number, photo identifier, photo hash) of each image contained in this subset. These correspond to the first three columns in the dataset's metadata TSV file. + +```bash +wget https://openaipublic.azureedge.net/clip/data/yfcc100m_subset_data.tsv.bz2 +bunzip2 yfcc100m_subset_data.tsv.bz2 +``` + +Use of the underlying media files is subject to the Creative Commons licenses chosen by their creators/uploaders. For more information about the YFCC100M dataset, visit [the official website](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/). \ No newline at end of file diff --git a/CLIP/hubconf.py b/CLIP/hubconf.py new file mode 100755 index 0000000000000000000000000000000000000000..520b354b62ab4d199d49462e9c65890d924c69e6 --- /dev/null +++ b/CLIP/hubconf.py @@ -0,0 +1,42 @@ +from clip.clip import tokenize as _tokenize, load as _load, available_models as _available_models +import re +import string + +dependencies = ["torch", "torchvision", "ftfy", "regex", "tqdm"] + +# For compatibility (cannot include special characters in function name) +model_functions = { model: re.sub(f'[{string.punctuation}]', '_', model) for model in _available_models()} + +def _create_hub_entrypoint(model): + def entrypoint(**kwargs): + return _load(model, **kwargs) + + entrypoint.__doc__ = f"""Loads the {model} CLIP model + + Parameters + ---------- + device : Union[str, torch.device] + The device to put the loaded model + + jit : bool + Whether to load the optimized JIT model or more hackable non-JIT model (default). + + download_root: str + path to download the model files; by default, it uses "~/.cache/clip" + + Returns + ------- + model : torch.nn.Module + The {model} CLIP model + + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + return entrypoint + +def tokenize(): + return _tokenize + +_entrypoints = {model_functions[model]: _create_hub_entrypoint(model) for model in _available_models()} + +globals().update(_entrypoints) \ No newline at end of file diff --git a/CLIP/model-card.md b/CLIP/model-card.md new file mode 100755 index 0000000000000000000000000000000000000000..6db1ca46f0706d2276e0c95578f4aa4dc0136e58 --- /dev/null +++ b/CLIP/model-card.md @@ -0,0 +1,120 @@ +# Model Card: CLIP + +Inspired by [Model Cards for Model Reporting (Mitchell et al.)](https://arxiv.org/abs/1810.03993) and [Lessons from Archives (Jo & Gebru)](https://arxiv.org/pdf/1912.10389.pdf), we’re providing some accompanying information about the multimodal model. + +## Model Details + +The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within. + +### Model Date + +January 2021 + +### Model Type + +The base model uses a ResNet50 with several modifications as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. There is also a variant of the model where the ResNet image encoder is replaced with a Vision Transformer. + +### Model Versions + +Initially, we’ve released one CLIP model based on the Vision Transformer architecture equivalent to ViT-B/32, along with the RN50 model, using the architecture equivalent to ResNet-50. + +As part of the staged release process, we have also released the RN101 model, as well as RN50x4, a RN50 scaled up 4x according to the [EfficientNet](https://arxiv.org/abs/1905.11946) scaling rule. In July 2021, we additionally released the RN50x16 and ViT-B/16 models, and in January 2022, the RN50x64 and ViT-L/14 models were released. Lastly, the ViT-L/14@336px model was released in April 2022. + +Please see the paper linked below for further details about their specification. + +### Documents + +- [Blog Post](https://openai.com/blog/clip/) +- [CLIP Paper](https://arxiv.org/abs/2103.00020) + + + +## Model Use + +### Intended Use + +The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. + +#### Primary intended uses + +The primary intended users of these models are AI researchers. + +We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models. + +### Out-of-Scope Use Cases + +**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. + +Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use. + +Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. + + + +## Data + +The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users. + +### Data Mission Statement + +Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset. + + + +## Performance and Limitations + +### Performance + +We have evaluated the performance of CLIP on a wide range of benchmarks across a variety of computer vision datasets such as OCR to texture recognition to fine-grained classification. The paper describes model performance on the following datasets: + +- Food101 +- CIFAR10 +- CIFAR100 +- Birdsnap +- SUN397 +- Stanford Cars +- FGVC Aircraft +- VOC2007 +- DTD +- Oxford-IIIT Pet dataset +- Caltech101 +- Flowers102 +- MNIST +- SVHN +- IIIT5K +- Hateful Memes +- SST-2 +- UCF101 +- Kinetics700 +- Country211 +- CLEVR Counting +- KITTI Distance +- STL-10 +- RareAct +- Flickr30 +- MSCOCO +- ImageNet +- ImageNet-A +- ImageNet-R +- ImageNet Sketch +- ObjectNet (ImageNet Overlap) +- Youtube-BB +- ImageNet-Vid + +## Limitations + +CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance. + +### Bias and Fairness + +We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper). + +We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks. + + + +## Feedback + +### Where to send questions or comments about the model + +Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9) diff --git a/CLIP/requirements.txt b/CLIP/requirements.txt new file mode 100755 index 0000000000000000000000000000000000000000..6b98c33f3a0e09ddf982606430472de3061c6e9f --- /dev/null +++ b/CLIP/requirements.txt @@ -0,0 +1,5 @@ +ftfy +regex +tqdm +torch +torchvision diff --git a/CLIP/setup.py b/CLIP/setup.py new file mode 100755 index 0000000000000000000000000000000000000000..c9ea7d0d2f3d2fcf66d6f6e2aa0eb1a97a524bb6 --- /dev/null +++ b/CLIP/setup.py @@ -0,0 +1,21 @@ +import os + +import pkg_resources +from setuptools import setup, find_packages + +setup( + name="clip", + py_modules=["clip"], + version="1.0", + description="", + author="OpenAI", + packages=find_packages(exclude=["tests*"]), + install_requires=[ + str(r) + for r in pkg_resources.parse_requirements( + open(os.path.join(os.path.dirname(__file__), "requirements.txt")) + ) + ], + include_package_data=True, + extras_require={'dev': ['pytest']}, +) diff --git a/CLIP/tests/test_consistency.py b/CLIP/tests/test_consistency.py new file mode 100755 index 0000000000000000000000000000000000000000..f2c6fd4fe9074143803e0eb6c99fa02a47632094 --- /dev/null +++ b/CLIP/tests/test_consistency.py @@ -0,0 +1,25 @@ +import numpy as np +import pytest +import torch +from PIL import Image + +import clip + + +@pytest.mark.parametrize('model_name', clip.available_models()) +def test_consistency(model_name): + device = "cpu" + jit_model, transform = clip.load(model_name, device=device, jit=True) + py_model, _ = clip.load(model_name, device=device, jit=False) + + image = transform(Image.open("CLIP.png")).unsqueeze(0).to(device) + text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device) + + with torch.no_grad(): + logits_per_image, _ = jit_model(image, text) + jit_probs = logits_per_image.softmax(dim=-1).cpu().numpy() + + logits_per_image, _ = py_model(image, text) + py_probs = logits_per_image.softmax(dim=-1).cpu().numpy() + + assert np.allclose(jit_probs, py_probs, atol=0.01, rtol=0.1) diff --git a/taming-transformers b/taming-transformers deleted file mode 160000 index 3ba01b241669f5ade541ce990f7650a3b8f65318..0000000000000000000000000000000000000000 --- a/taming-transformers +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 3ba01b241669f5ade541ce990f7650a3b8f65318 diff --git a/taming-transformers/.gitignore b/taming-transformers/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..fa656082978224b2162758e2a3778dc9729d1501 --- /dev/null +++ b/taming-transformers/.gitignore @@ -0,0 +1 @@ +*.ipynb diff --git a/taming-transformers/License.txt b/taming-transformers/License.txt new file mode 100755 index 0000000000000000000000000000000000000000..57fb4153bafcd64b60377ba0ba2c79b7530efc1e --- /dev/null +++ b/taming-transformers/License.txt @@ -0,0 +1,19 @@ +Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE +OR OTHER DEALINGS IN THE SOFTWARE./ diff --git a/taming-transformers/configs/coco_cond_stage.yaml b/taming-transformers/configs/coco_cond_stage.yaml new file mode 100755 index 0000000000000000000000000000000000000000..18a3dde147455c281a3687b1a0b42bbbc3fb2725 --- /dev/null +++ b/taming-transformers/configs/coco_cond_stage.yaml @@ -0,0 +1,49 @@ +model: + base_learning_rate: 4.5e-06 + target: taming.models.vqgan.VQSegmentationModel + params: + embed_dim: 256 + n_embed: 1024 + image_key: "segmentation" + n_labels: 183 + ddconfig: + double_z: false + z_channels: 256 + resolution: 256 + in_channels: 183 + out_ch: 183 + ch: 128 + ch_mult: + - 1 + - 1 + - 2 + - 2 + - 4 + num_res_blocks: 2 + attn_resolutions: + - 16 + dropout: 0.0 + + lossconfig: + target: taming.modules.losses.segmentation.BCELossWithQuant + params: + codebook_weight: 1.0 + +data: + target: main.DataModuleFromConfig + params: + batch_size: 12 + train: + target: taming.data.coco.CocoImagesAndCaptionsTrain + params: + size: 296 + crop_size: 256 + onehot_segmentation: true + use_stuffthing: true + validation: + target: taming.data.coco.CocoImagesAndCaptionsValidation + params: + size: 256 + crop_size: 256 + onehot_segmentation: true + use_stuffthing: true diff --git a/taming-transformers/configs/coco_scene_images_transformer.yaml b/taming-transformers/configs/coco_scene_images_transformer.yaml new file mode 100755 index 0000000000000000000000000000000000000000..a03078de708182cc175f139078f8455ca3ec8a09 --- /dev/null +++ b/taming-transformers/configs/coco_scene_images_transformer.yaml @@ -0,0 +1,80 @@ +model: + base_learning_rate: 4.5e-06 + target: taming.models.cond_transformer.Net2NetTransformer + params: + cond_stage_key: objects_bbox + transformer_config: + target: taming.modules.transformer.mingpt.GPT + params: + vocab_size: 8192 + block_size: 348 # = 256 + 92 = dim(vqgan_latent_space,16x16) + dim(conditional_builder.embedding_dim) + n_layer: 40 + n_head: 16 + n_embd: 1408 + embd_pdrop: 0.1 + resid_pdrop: 0.1 + attn_pdrop: 0.1 + first_stage_config: + target: taming.models.vqgan.VQModel + params: + ckpt_path: /path/to/coco_epoch117.ckpt # https://heibox.uni-heidelberg.de/f/78dea9589974474c97c1/ + embed_dim: 256 + n_embed: 8192 + ddconfig: + double_z: false + z_channels: 256 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 1 + - 2 + - 2 + - 4 + num_res_blocks: 2 + attn_resolutions: + - 16 + dropout: 0.0 + lossconfig: + target: taming.modules.losses.DummyLoss + cond_stage_config: + target: taming.models.dummy_cond_stage.DummyCondStage + params: + conditional_key: objects_bbox + +data: + target: main.DataModuleFromConfig + params: + batch_size: 6 + train: + target: taming.data.annotated_objects_coco.AnnotatedObjectsCoco + params: + data_path: data/coco_annotations_100 # substitute with path to full dataset + split: train + keys: [image, objects_bbox, file_name, annotations] + no_tokens: 8192 + target_image_size: 256 + min_object_area: 0.00001 + min_objects_per_image: 2 + max_objects_per_image: 30 + crop_method: random-1d + random_flip: true + use_group_parameter: true + encode_crop: true + validation: + target: taming.data.annotated_objects_coco.AnnotatedObjectsCoco + params: + data_path: data/coco_annotations_100 # substitute with path to full dataset + split: validation + keys: [image, objects_bbox, file_name, annotations] + no_tokens: 8192 + target_image_size: 256 + min_object_area: 0.00001 + min_objects_per_image: 2 + max_objects_per_image: 30 + crop_method: center + random_flip: false + use_group_parameter: true + encode_crop: true diff --git a/taming-transformers/configs/custom_vqgan.yaml b/taming-transformers/configs/custom_vqgan.yaml new file mode 100755 index 0000000000000000000000000000000000000000..908687f38325dfe49430692d668733a8e1598375 --- /dev/null +++ b/taming-transformers/configs/custom_vqgan.yaml @@ -0,0 +1,43 @@ +model: + base_learning_rate: 4.5e-6 + target: taming.models.vqgan.VQModel + params: + embed_dim: 256 + n_embed: 1024 + ddconfig: + double_z: False + z_channels: 256 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [ 1,1,2,2,4] # num_down = len(ch_mult)-1 + num_res_blocks: 2 + attn_resolutions: [16] + dropout: 0.0 + + lossconfig: + target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator + params: + disc_conditional: False + disc_in_channels: 3 + disc_start: 10000 + disc_weight: 0.8 + codebook_weight: 1.0 + +data: + target: main.DataModuleFromConfig + params: + batch_size: 5 + num_workers: 8 + train: + target: taming.data.custom.CustomTrain + params: + training_images_list_file: some/training.txt + size: 256 + validation: + target: taming.data.custom.CustomTest + params: + test_images_list_file: some/test.txt + size: 256 + diff --git a/taming-transformers/configs/drin_transformer.yaml b/taming-transformers/configs/drin_transformer.yaml new file mode 100755 index 0000000000000000000000000000000000000000..bead4567d2dcc3d0f1a7b8eec823df4b427cab07 --- /dev/null +++ b/taming-transformers/configs/drin_transformer.yaml @@ -0,0 +1,77 @@ +model: + base_learning_rate: 4.5e-06 + target: taming.models.cond_transformer.Net2NetTransformer + params: + cond_stage_key: depth + transformer_config: + target: taming.modules.transformer.mingpt.GPT + params: + vocab_size: 1024 + block_size: 512 + n_layer: 24 + n_head: 16 + n_embd: 1024 + first_stage_config: + target: taming.models.vqgan.VQModel + params: + ckpt_path: logs/2020-09-23T17-56-33_imagenet_vqgan/checkpoints/last.ckpt + embed_dim: 256 + n_embed: 1024 + ddconfig: + double_z: false + z_channels: 256 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 1 + - 2 + - 2 + - 4 + num_res_blocks: 2 + attn_resolutions: + - 16 + dropout: 0.0 + lossconfig: + target: taming.modules.losses.DummyLoss + cond_stage_config: + target: taming.models.vqgan.VQModel + params: + ckpt_path: logs/2020-11-03T15-34-24_imagenetdepth_vqgan/checkpoints/last.ckpt + embed_dim: 256 + n_embed: 1024 + ddconfig: + double_z: false + z_channels: 256 + resolution: 256 + in_channels: 1 + out_ch: 1 + ch: 128 + ch_mult: + - 1 + - 1 + - 2 + - 2 + - 4 + num_res_blocks: 2 + attn_resolutions: + - 16 + dropout: 0.0 + lossconfig: + target: taming.modules.losses.DummyLoss + +data: + target: main.DataModuleFromConfig + params: + batch_size: 2 + num_workers: 8 + train: + target: taming.data.imagenet.RINTrainWithDepth + params: + size: 256 + validation: + target: taming.data.imagenet.RINValidationWithDepth + params: + size: 256 diff --git a/taming-transformers/configs/faceshq_transformer.yaml b/taming-transformers/configs/faceshq_transformer.yaml new file mode 100755 index 0000000000000000000000000000000000000000..b93391f9c9c41d63d28dd38dbf83615552642db3 --- /dev/null +++ b/taming-transformers/configs/faceshq_transformer.yaml @@ -0,0 +1,61 @@ +model: + base_learning_rate: 4.5e-06 + target: taming.models.cond_transformer.Net2NetTransformer + params: + cond_stage_key: coord + transformer_config: + target: taming.modules.transformer.mingpt.GPT + params: + vocab_size: 1024 + block_size: 512 + n_layer: 24 + n_head: 16 + n_embd: 1024 + first_stage_config: + target: taming.models.vqgan.VQModel + params: + ckpt_path: logs/2020-11-09T13-33-36_faceshq_vqgan/checkpoints/last.ckpt + embed_dim: 256 + n_embed: 1024 + ddconfig: + double_z: false + z_channels: 256 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 1 + - 2 + - 2 + - 4 + num_res_blocks: 2 + attn_resolutions: + - 16 + dropout: 0.0 + lossconfig: + target: taming.modules.losses.DummyLoss + cond_stage_config: + target: taming.modules.misc.coord.CoordStage + params: + n_embed: 1024 + down_factor: 16 + +data: + target: main.DataModuleFromConfig + params: + batch_size: 2 + num_workers: 8 + train: + target: taming.data.faceshq.FacesHQTrain + params: + size: 256 + crop_size: 256 + coord: True + validation: + target: taming.data.faceshq.FacesHQValidation + params: + size: 256 + crop_size: 256 + coord: True diff --git a/taming-transformers/configs/faceshq_vqgan.yaml b/taming-transformers/configs/faceshq_vqgan.yaml new file mode 100755 index 0000000000000000000000000000000000000000..3960f784551bfd9caddb5b084fc592c6eed6483b --- /dev/null +++ b/taming-transformers/configs/faceshq_vqgan.yaml @@ -0,0 +1,42 @@ +model: + base_learning_rate: 4.5e-6 + target: taming.models.vqgan.VQModel + params: + embed_dim: 256 + n_embed: 1024 + ddconfig: + double_z: False + z_channels: 256 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [ 1,1,2,2,4] # num_down = len(ch_mult)-1 + num_res_blocks: 2 + attn_resolutions: [16] + dropout: 0.0 + + lossconfig: + target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator + params: + disc_conditional: False + disc_in_channels: 3 + disc_start: 30001 + disc_weight: 0.8 + codebook_weight: 1.0 + +data: + target: main.DataModuleFromConfig + params: + batch_size: 3 + num_workers: 8 + train: + target: taming.data.faceshq.FacesHQTrain + params: + size: 256 + crop_size: 256 + validation: + target: taming.data.faceshq.FacesHQValidation + params: + size: 256 + crop_size: 256 diff --git a/taming-transformers/configs/imagenet_vqgan.yaml b/taming-transformers/configs/imagenet_vqgan.yaml new file mode 100755 index 0000000000000000000000000000000000000000..f6dc21ff6de9a26474fa18a0d496a4a0b9bb0837 --- /dev/null +++ b/taming-transformers/configs/imagenet_vqgan.yaml @@ -0,0 +1,42 @@ +model: + base_learning_rate: 4.5e-6 + target: taming.models.vqgan.VQModel + params: + embed_dim: 256 + n_embed: 1024 + ddconfig: + double_z: False + z_channels: 256 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [ 1,1,2,2,4] # num_down = len(ch_mult)-1 + num_res_blocks: 2 + attn_resolutions: [16] + dropout: 0.0 + + lossconfig: + target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator + params: + disc_conditional: False + disc_in_channels: 3 + disc_start: 250001 + disc_weight: 0.8 + codebook_weight: 1.0 + +data: + target: main.DataModuleFromConfig + params: + batch_size: 12 + num_workers: 24 + train: + target: taming.data.imagenet.ImageNetTrain + params: + config: + size: 256 + validation: + target: taming.data.imagenet.ImageNetValidation + params: + config: + size: 256 diff --git a/taming-transformers/configs/imagenetdepth_vqgan.yaml b/taming-transformers/configs/imagenetdepth_vqgan.yaml new file mode 100755 index 0000000000000000000000000000000000000000..88d2f34f1c0661e350899cf4229cdf60697baf0d --- /dev/null +++ b/taming-transformers/configs/imagenetdepth_vqgan.yaml @@ -0,0 +1,41 @@ +model: + base_learning_rate: 4.5e-6 + target: taming.models.vqgan.VQModel + params: + embed_dim: 256 + n_embed: 1024 + image_key: depth + ddconfig: + double_z: False + z_channels: 256 + resolution: 256 + in_channels: 1 + out_ch: 1 + ch: 128 + ch_mult: [ 1,1,2,2,4] # num_down = len(ch_mult)-1 + num_res_blocks: 2 + attn_resolutions: [16] + dropout: 0.0 + + lossconfig: + target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator + params: + disc_conditional: False + disc_in_channels: 1 + disc_start: 50001 + disc_weight: 0.75 + codebook_weight: 1.0 + +data: + target: main.DataModuleFromConfig + params: + batch_size: 3 + num_workers: 8 + train: + target: taming.data.imagenet.ImageNetTrainWithDepth + params: + size: 256 + validation: + target: taming.data.imagenet.ImageNetValidationWithDepth + params: + size: 256 diff --git a/taming-transformers/configs/open_images_scene_images_transformer.yaml b/taming-transformers/configs/open_images_scene_images_transformer.yaml new file mode 100755 index 0000000000000000000000000000000000000000..f4e41e0d67d3e3eb17509862e063e4d626b06d4b --- /dev/null +++ b/taming-transformers/configs/open_images_scene_images_transformer.yaml @@ -0,0 +1,86 @@ +model: + base_learning_rate: 4.5e-06 + target: taming.models.cond_transformer.Net2NetTransformer + params: + cond_stage_key: objects_bbox + transformer_config: + target: taming.modules.transformer.mingpt.GPT + params: + vocab_size: 8192 + block_size: 348 # = 256 + 92 = dim(vqgan_latent_space,16x16) + dim(conditional_builder.embedding_dim) + n_layer: 36 + n_head: 16 + n_embd: 1536 + embd_pdrop: 0.1 + resid_pdrop: 0.1 + attn_pdrop: 0.1 + first_stage_config: + target: taming.models.vqgan.VQModel + params: + ckpt_path: /path/to/coco_oi_epoch12.ckpt # https://heibox.uni-heidelberg.de/f/461d9a9f4fcf48ab84f4/ + embed_dim: 256 + n_embed: 8192 + ddconfig: + double_z: false + z_channels: 256 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 1 + - 2 + - 2 + - 4 + num_res_blocks: 2 + attn_resolutions: + - 16 + dropout: 0.0 + lossconfig: + target: taming.modules.losses.DummyLoss + cond_stage_config: + target: taming.models.dummy_cond_stage.DummyCondStage + params: + conditional_key: objects_bbox + +data: + target: main.DataModuleFromConfig + params: + batch_size: 6 + train: + target: taming.data.annotated_objects_open_images.AnnotatedObjectsOpenImages + params: + data_path: data/open_images_annotations_100 # substitute with path to full dataset + split: train + keys: [image, objects_bbox, file_name, annotations] + no_tokens: 8192 + target_image_size: 256 + category_allow_list_target: taming.data.open_images_helper.top_300_classes_plus_coco_compatibility + category_mapping_target: taming.data.open_images_helper.open_images_unify_categories_for_coco + min_object_area: 0.0001 + min_objects_per_image: 2 + max_objects_per_image: 30 + crop_method: random-2d + random_flip: true + use_group_parameter: true + use_additional_parameters: true + encode_crop: true + validation: + target: taming.data.annotated_objects_open_images.AnnotatedObjectsOpenImages + params: + data_path: data/open_images_annotations_100 # substitute with path to full dataset + split: validation + keys: [image, objects_bbox, file_name, annotations] + no_tokens: 8192 + target_image_size: 256 + category_allow_list_target: taming.data.open_images_helper.top_300_classes_plus_coco_compatibility + category_mapping_target: taming.data.open_images_helper.open_images_unify_categories_for_coco + min_object_area: 0.0001 + min_objects_per_image: 2 + max_objects_per_image: 30 + crop_method: center + random_flip: false + use_group_parameter: true + use_additional_parameters: true + encode_crop: true diff --git a/taming-transformers/configs/sflckr_cond_stage.yaml b/taming-transformers/configs/sflckr_cond_stage.yaml new file mode 100755 index 0000000000000000000000000000000000000000..d48b50a700c4b44098ce4f3c752d5a4d7158f8a9 --- /dev/null +++ b/taming-transformers/configs/sflckr_cond_stage.yaml @@ -0,0 +1,43 @@ +model: + base_learning_rate: 4.5e-06 + target: taming.models.vqgan.VQSegmentationModel + params: + embed_dim: 256 + n_embed: 1024 + image_key: "segmentation" + n_labels: 182 + ddconfig: + double_z: false + z_channels: 256 + resolution: 256 + in_channels: 182 + out_ch: 182 + ch: 128 + ch_mult: + - 1 + - 1 + - 2 + - 2 + - 4 + num_res_blocks: 2 + attn_resolutions: + - 16 + dropout: 0.0 + + lossconfig: + target: taming.modules.losses.segmentation.BCELossWithQuant + params: + codebook_weight: 1.0 + +data: + target: cutlit.DataModuleFromConfig + params: + batch_size: 12 + train: + target: taming.data.sflckr.Examples # adjust + params: + size: 256 + validation: + target: taming.data.sflckr.Examples # adjust + params: + size: 256 diff --git a/taming-transformers/data/ade20k_examples.txt b/taming-transformers/data/ade20k_examples.txt new file mode 100755 index 0000000000000000000000000000000000000000..0b5a07823cb09fea08ac676eb6da6e09fc222b83 --- /dev/null +++ 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b/taming-transformers/environment.yaml new file mode 100755 index 0000000000000000000000000000000000000000..3fbba586e55dbe64184d006319fad969805ef16f --- /dev/null +++ b/taming-transformers/environment.yaml @@ -0,0 +1,25 @@ +name: taming +channels: + - pytorch + - defaults +dependencies: + - python=3.8.5 + - pip=20.3 + - cudatoolkit=10.2 + - pytorch=1.7.0 + - torchvision=0.8.1 + - numpy=1.19.2 + - pip: + - albumentations==0.4.3 + - opencv-python==4.1.2.30 + - pudb==2019.2 + - imageio==2.9.0 + - imageio-ffmpeg==0.4.2 + - pytorch-lightning==1.0.8 + - omegaconf==2.0.0 + - test-tube>=0.7.5 + - streamlit>=0.73.1 + - einops==0.3.0 + - more-itertools>=8.0.0 + - transformers==4.3.1 + - -e . diff --git a/taming-transformers/main.py b/taming-transformers/main.py new file mode 100755 index 0000000000000000000000000000000000000000..5a992a65d1465457f2685ec9f5f63f316d9e3164 --- /dev/null +++ b/taming-transformers/main.py @@ -0,0 +1,585 @@ +import argparse, os, sys, datetime, glob, importlib +from omegaconf import OmegaConf +import numpy as np +from PIL import Image +import torch +import torchvision +from torch.utils.data import random_split, DataLoader, Dataset +import pytorch_lightning as pl +from pytorch_lightning import seed_everything +from pytorch_lightning.trainer import Trainer +from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor +from pytorch_lightning.utilities import rank_zero_only + +from taming.data.utils import custom_collate + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def get_parser(**parser_kwargs): + def str2bool(v): + if isinstance(v, bool): + return v + if v.lower() in ("yes", "true", "t", "y", "1"): + return True + elif v.lower() in ("no", "false", "f", "n", "0"): + return False + else: + raise argparse.ArgumentTypeError("Boolean value expected.") + + parser = argparse.ArgumentParser(**parser_kwargs) + parser.add_argument( + "-n", + "--name", + type=str, + const=True, + default="", + nargs="?", + help="postfix for logdir", + ) + parser.add_argument( + "-r", + "--resume", + type=str, + const=True, + default="", + nargs="?", + help="resume from logdir or checkpoint in logdir", + ) + parser.add_argument( + "-b", + "--base", + nargs="*", + metavar="base_config.yaml", + help="paths to base configs. Loaded from left-to-right. " + "Parameters can be overwritten or added with command-line options of the form `--key value`.", + default=list(), + ) + parser.add_argument( + "-t", + "--train", + type=str2bool, + const=True, + default=False, + nargs="?", + help="train", + ) + parser.add_argument( + "--no-test", + type=str2bool, + const=True, + default=False, + nargs="?", + help="disable test", + ) + parser.add_argument("-p", "--project", help="name of new or path to existing project") + parser.add_argument( + "-d", + "--debug", + type=str2bool, + nargs="?", + const=True, + default=False, + help="enable post-mortem debugging", + ) + parser.add_argument( + "-s", + "--seed", + type=int, + default=23, + help="seed for seed_everything", + ) + parser.add_argument( + "-f", + "--postfix", + type=str, + default="", + help="post-postfix for default name", + ) + + return parser + + +def nondefault_trainer_args(opt): + parser = argparse.ArgumentParser() + parser = Trainer.add_argparse_args(parser) + args = parser.parse_args([]) + return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k)) + + +def instantiate_from_config(config): + if not "target" in config: + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +class WrappedDataset(Dataset): + """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset""" + def __init__(self, dataset): + self.data = dataset + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return self.data[idx] + + +class DataModuleFromConfig(pl.LightningDataModule): + def __init__(self, batch_size, train=None, validation=None, test=None, + wrap=False, num_workers=None): + super().__init__() + self.batch_size = batch_size + self.dataset_configs = dict() + self.num_workers = num_workers if num_workers is not None else batch_size*2 + if train is not None: + self.dataset_configs["train"] = train + self.train_dataloader = self._train_dataloader + if validation is not None: + self.dataset_configs["validation"] = validation + self.val_dataloader = self._val_dataloader + if test is not None: + self.dataset_configs["test"] = test + self.test_dataloader = self._test_dataloader + self.wrap = wrap + + def prepare_data(self): + for data_cfg in self.dataset_configs.values(): + instantiate_from_config(data_cfg) + + def setup(self, stage=None): + self.datasets = dict( + (k, instantiate_from_config(self.dataset_configs[k])) + for k in self.dataset_configs) + if self.wrap: + for k in self.datasets: + self.datasets[k] = WrappedDataset(self.datasets[k]) + + def _train_dataloader(self): + return DataLoader(self.datasets["train"], batch_size=self.batch_size, + num_workers=self.num_workers, shuffle=True, collate_fn=custom_collate) + + def _val_dataloader(self): + return DataLoader(self.datasets["validation"], + batch_size=self.batch_size, + num_workers=self.num_workers, collate_fn=custom_collate) + + def _test_dataloader(self): + return DataLoader(self.datasets["test"], batch_size=self.batch_size, + num_workers=self.num_workers, collate_fn=custom_collate) + + +class SetupCallback(Callback): + def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config): + super().__init__() + self.resume = resume + self.now = now + self.logdir = logdir + self.ckptdir = ckptdir + self.cfgdir = cfgdir + self.config = config + self.lightning_config = lightning_config + + def on_pretrain_routine_start(self, trainer, pl_module): + if trainer.global_rank == 0: + # Create logdirs and save configs + os.makedirs(self.logdir, exist_ok=True) + os.makedirs(self.ckptdir, exist_ok=True) + os.makedirs(self.cfgdir, exist_ok=True) + + print("Project config") + print(self.config.pretty()) + OmegaConf.save(self.config, + os.path.join(self.cfgdir, "{}-project.yaml".format(self.now))) + + print("Lightning config") + print(self.lightning_config.pretty()) + OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}), + os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now))) + + else: + # ModelCheckpoint callback created log directory --- remove it + if not self.resume and os.path.exists(self.logdir): + dst, name = os.path.split(self.logdir) + dst = os.path.join(dst, "child_runs", name) + os.makedirs(os.path.split(dst)[0], exist_ok=True) + try: + os.rename(self.logdir, dst) + except FileNotFoundError: + pass + + +class ImageLogger(Callback): + def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True): + super().__init__() + self.batch_freq = batch_frequency + self.max_images = max_images + self.logger_log_images = { + pl.loggers.WandbLogger: self._wandb, + pl.loggers.TestTubeLogger: self._testtube, + } + self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)] + if not increase_log_steps: + self.log_steps = [self.batch_freq] + self.clamp = clamp + + @rank_zero_only + def _wandb(self, pl_module, images, batch_idx, split): + raise ValueError("No way wandb") + grids = dict() + for k in images: + grid = torchvision.utils.make_grid(images[k]) + grids[f"{split}/{k}"] = wandb.Image(grid) + pl_module.logger.experiment.log(grids) + + @rank_zero_only + def _testtube(self, pl_module, images, batch_idx, split): + for k in images: + grid = torchvision.utils.make_grid(images[k]) + grid = (grid+1.0)/2.0 # -1,1 -> 0,1; c,h,w + + tag = f"{split}/{k}" + pl_module.logger.experiment.add_image( + tag, grid, + global_step=pl_module.global_step) + + @rank_zero_only + def log_local(self, save_dir, split, images, + global_step, current_epoch, batch_idx): + root = os.path.join(save_dir, "images", split) + for k in images: + grid = torchvision.utils.make_grid(images[k], nrow=4) + + grid = (grid+1.0)/2.0 # -1,1 -> 0,1; c,h,w + grid = grid.transpose(0,1).transpose(1,2).squeeze(-1) + grid = grid.numpy() + grid = (grid*255).astype(np.uint8) + filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format( + k, + global_step, + current_epoch, + batch_idx) + path = os.path.join(root, filename) + os.makedirs(os.path.split(path)[0], exist_ok=True) + Image.fromarray(grid).save(path) + + def log_img(self, pl_module, batch, batch_idx, split="train"): + if (self.check_frequency(batch_idx) and # batch_idx % self.batch_freq == 0 + hasattr(pl_module, "log_images") and + callable(pl_module.log_images) and + self.max_images > 0): + logger = type(pl_module.logger) + + is_train = pl_module.training + if is_train: + pl_module.eval() + + with torch.no_grad(): + images = pl_module.log_images(batch, split=split, pl_module=pl_module) + + for k in images: + N = min(images[k].shape[0], self.max_images) + images[k] = images[k][:N] + if isinstance(images[k], torch.Tensor): + images[k] = images[k].detach().cpu() + if self.clamp: + images[k] = torch.clamp(images[k], -1., 1.) + + self.log_local(pl_module.logger.save_dir, split, images, + pl_module.global_step, pl_module.current_epoch, batch_idx) + + logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None) + logger_log_images(pl_module, images, pl_module.global_step, split) + + if is_train: + pl_module.train() + + def check_frequency(self, batch_idx): + if (batch_idx % self.batch_freq) == 0 or (batch_idx in self.log_steps): + try: + self.log_steps.pop(0) + except IndexError: + pass + return True + return False + + def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx): + self.log_img(pl_module, batch, batch_idx, split="train") + + def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx): + self.log_img(pl_module, batch, batch_idx, split="val") + + + +if __name__ == "__main__": + # custom parser to specify config files, train, test and debug mode, + # postfix, resume. + # `--key value` arguments are interpreted as arguments to the trainer. + # `nested.key=value` arguments are interpreted as config parameters. + # configs are merged from left-to-right followed by command line parameters. + + # model: + # base_learning_rate: float + # target: path to lightning module + # params: + # key: value + # data: + # target: main.DataModuleFromConfig + # params: + # batch_size: int + # wrap: bool + # train: + # target: path to train dataset + # params: + # key: value + # validation: + # target: path to validation dataset + # params: + # key: value + # test: + # target: path to test dataset + # params: + # key: value + # lightning: (optional, has sane defaults and can be specified on cmdline) + # trainer: + # additional arguments to trainer + # logger: + # logger to instantiate + # modelcheckpoint: + # modelcheckpoint to instantiate + # callbacks: + # callback1: + # target: importpath + # params: + # key: value + + now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") + + # add cwd for convenience and to make classes in this file available when + # running as `python main.py` + # (in particular `main.DataModuleFromConfig`) + sys.path.append(os.getcwd()) + + parser = get_parser() + parser = Trainer.add_argparse_args(parser) + + opt, unknown = parser.parse_known_args() + if opt.name and opt.resume: + raise ValueError( + "-n/--name and -r/--resume cannot be specified both." + "If you want to resume training in a new log folder, " + "use -n/--name in combination with --resume_from_checkpoint" + ) + if opt.resume: + if not os.path.exists(opt.resume): + raise ValueError("Cannot find {}".format(opt.resume)) + if os.path.isfile(opt.resume): + paths = opt.resume.split("/") + idx = len(paths)-paths[::-1].index("logs")+1 + logdir = "/".join(paths[:idx]) + ckpt = opt.resume + else: + assert os.path.isdir(opt.resume), opt.resume + logdir = opt.resume.rstrip("/") + ckpt = os.path.join(logdir, "checkpoints", "last.ckpt") + + opt.resume_from_checkpoint = ckpt + base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml"))) + opt.base = base_configs+opt.base + _tmp = logdir.split("/") + nowname = _tmp[_tmp.index("logs")+1] + else: + if opt.name: + name = "_"+opt.name + elif opt.base: + cfg_fname = os.path.split(opt.base[0])[-1] + cfg_name = os.path.splitext(cfg_fname)[0] + name = "_"+cfg_name + else: + name = "" + nowname = now+name+opt.postfix + logdir = os.path.join("logs", nowname) + + ckptdir = os.path.join(logdir, "checkpoints") + cfgdir = os.path.join(logdir, "configs") + seed_everything(opt.seed) + + try: + # init and save configs + configs = [OmegaConf.load(cfg) for cfg in opt.base] + cli = OmegaConf.from_dotlist(unknown) + config = OmegaConf.merge(*configs, cli) + lightning_config = config.pop("lightning", OmegaConf.create()) + # merge trainer cli with config + trainer_config = lightning_config.get("trainer", OmegaConf.create()) + # default to ddp + trainer_config["distributed_backend"] = "ddp" + for k in nondefault_trainer_args(opt): + trainer_config[k] = getattr(opt, k) + if not "gpus" in trainer_config: + del trainer_config["distributed_backend"] + cpu = True + else: + gpuinfo = trainer_config["gpus"] + print(f"Running on GPUs {gpuinfo}") + cpu = False + trainer_opt = argparse.Namespace(**trainer_config) + lightning_config.trainer = trainer_config + + # model + model = instantiate_from_config(config.model) + + # trainer and callbacks + trainer_kwargs = dict() + + # default logger configs + # NOTE wandb < 0.10.0 interferes with shutdown + # wandb >= 0.10.0 seems to fix it but still interferes with pudb + # debugging (wrongly sized pudb ui) + # thus prefer testtube for now + default_logger_cfgs = { + "wandb": { + "target": "pytorch_lightning.loggers.WandbLogger", + "params": { + "name": nowname, + "save_dir": logdir, + "offline": opt.debug, + "id": nowname, + } + }, + "testtube": { + "target": "pytorch_lightning.loggers.TestTubeLogger", + "params": { + "name": "testtube", + "save_dir": logdir, + } + }, + } + default_logger_cfg = default_logger_cfgs["testtube"] + logger_cfg = lightning_config.logger or OmegaConf.create() + logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg) + trainer_kwargs["logger"] = instantiate_from_config(logger_cfg) + + # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to + # specify which metric is used to determine best models + default_modelckpt_cfg = { + "target": "pytorch_lightning.callbacks.ModelCheckpoint", + "params": { + "dirpath": ckptdir, + "filename": "{epoch:06}", + "verbose": True, + "save_last": True, + } + } + if hasattr(model, "monitor"): + print(f"Monitoring {model.monitor} as checkpoint metric.") + default_modelckpt_cfg["params"]["monitor"] = model.monitor + default_modelckpt_cfg["params"]["save_top_k"] = 3 + + modelckpt_cfg = lightning_config.modelcheckpoint or OmegaConf.create() + modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg) + trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg) + + # add callback which sets up log directory + default_callbacks_cfg = { + "setup_callback": { + "target": "main.SetupCallback", + "params": { + "resume": opt.resume, + "now": now, + "logdir": logdir, + "ckptdir": ckptdir, + "cfgdir": cfgdir, + "config": config, + "lightning_config": lightning_config, + } + }, + "image_logger": { + "target": "main.ImageLogger", + "params": { + "batch_frequency": 750, + "max_images": 4, + "clamp": True + } + }, + "learning_rate_logger": { + "target": "main.LearningRateMonitor", + "params": { + "logging_interval": "step", + #"log_momentum": True + } + }, + } + callbacks_cfg = lightning_config.callbacks or OmegaConf.create() + callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg) + trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg] + + trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs) + + # data + data = instantiate_from_config(config.data) + # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html + # calling these ourselves should not be necessary but it is. + # lightning still takes care of proper multiprocessing though + data.prepare_data() + data.setup() + + # configure learning rate + bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate + if not cpu: + ngpu = len(lightning_config.trainer.gpus.strip(",").split(',')) + else: + ngpu = 1 + accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches or 1 + print(f"accumulate_grad_batches = {accumulate_grad_batches}") + lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches + model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr + print("Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format( + model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr)) + + # allow checkpointing via USR1 + def melk(*args, **kwargs): + # run all checkpoint hooks + if trainer.global_rank == 0: + print("Summoning checkpoint.") + ckpt_path = os.path.join(ckptdir, "last.ckpt") + trainer.save_checkpoint(ckpt_path) + + def divein(*args, **kwargs): + if trainer.global_rank == 0: + import pudb; pudb.set_trace() + + import signal + signal.signal(signal.SIGUSR1, melk) + signal.signal(signal.SIGUSR2, divein) + + # run + if opt.train: + try: + trainer.fit(model, data) + except Exception: + melk() + raise + if not opt.no_test and not trainer.interrupted: + trainer.test(model, data) + except Exception: + if opt.debug and trainer.global_rank==0: + try: + import pudb as debugger + except ImportError: + import pdb as debugger + debugger.post_mortem() + raise + finally: + # move newly created debug project to debug_runs + if opt.debug and not opt.resume and trainer.global_rank==0: + dst, name = os.path.split(logdir) + dst = os.path.join(dst, "debug_runs", name) + os.makedirs(os.path.split(dst)[0], exist_ok=True) + os.rename(logdir, dst) diff --git a/taming-transformers/scripts/extract_depth.py b/taming-transformers/scripts/extract_depth.py new file mode 100755 index 0000000000000000000000000000000000000000..d6aa0d80c63a3e580fa28e0f2c7af4e9ae003b64 --- /dev/null +++ b/taming-transformers/scripts/extract_depth.py @@ -0,0 +1,112 @@ +import os +import torch +import numpy as np +from tqdm import trange +from PIL import Image + + +def get_state(gpu): + import torch + midas = torch.hub.load("intel-isl/MiDaS", "MiDaS") + if gpu: + midas.cuda() + midas.eval() + + midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms") + transform = midas_transforms.default_transform + + state = {"model": midas, + "transform": transform} + return state + + +def depth_to_rgba(x): + assert x.dtype == np.float32 + assert len(x.shape) == 2 + y = x.copy() + y.dtype = np.uint8 + y = y.reshape(x.shape+(4,)) + return np.ascontiguousarray(y) + + +def rgba_to_depth(x): + assert x.dtype == np.uint8 + assert len(x.shape) == 3 and x.shape[2] == 4 + y = x.copy() + y.dtype = np.float32 + y = y.reshape(x.shape[:2]) + return np.ascontiguousarray(y) + + +def run(x, state): + model = state["model"] + transform = state["transform"] + hw = x.shape[:2] + with torch.no_grad(): + prediction = model(transform((x + 1.0) * 127.5).cuda()) + prediction = torch.nn.functional.interpolate( + prediction.unsqueeze(1), + size=hw, + mode="bicubic", + align_corners=False, + ).squeeze() + output = prediction.cpu().numpy() + return output + + +def get_filename(relpath, level=-2): + # save class folder structure and filename: + fn = relpath.split(os.sep)[level:] + folder = fn[-2] + file = fn[-1].split('.')[0] + return folder, file + + +def save_depth(dataset, path, debug=False): + os.makedirs(path) + N = len(dset) + if debug: + N = 10 + state = get_state(gpu=True) + for idx in trange(N, desc="Data"): + ex = dataset[idx] + image, relpath = ex["image"], ex["relpath"] + folder, filename = get_filename(relpath) + # prepare + folderabspath = os.path.join(path, folder) + os.makedirs(folderabspath, exist_ok=True) + savepath = os.path.join(folderabspath, filename) + # run model + xout = run(image, state) + I = depth_to_rgba(xout) + Image.fromarray(I).save("{}.png".format(savepath)) + + +if __name__ == "__main__": + from taming.data.imagenet import ImageNetTrain, ImageNetValidation + out = "data/imagenet_depth" + if not os.path.exists(out): + print("Please create a folder or symlink '{}' to extract depth data ".format(out) + + "(be prepared that the output size will be larger than ImageNet itself).") + exit(1) + + # go + dset = ImageNetValidation() + abspath = os.path.join(out, "val") + if os.path.exists(abspath): + print("{} exists - not doing anything.".format(abspath)) + else: + print("preparing {}".format(abspath)) + save_depth(dset, abspath) + print("done with validation split") + + dset = ImageNetTrain() + abspath = os.path.join(out, "train") + if os.path.exists(abspath): + print("{} exists - not doing anything.".format(abspath)) + else: + print("preparing {}".format(abspath)) + save_depth(dset, abspath) + print("done with train split") + + print("done done.") diff --git a/taming-transformers/scripts/extract_segmentation.py b/taming-transformers/scripts/extract_segmentation.py new file mode 100755 index 0000000000000000000000000000000000000000..235b3c4b4575981b7533ce18bceaff97e05b55f9 --- /dev/null +++ b/taming-transformers/scripts/extract_segmentation.py @@ -0,0 +1,130 @@ +import sys, os +import numpy as np +import scipy +import torch +import torch.nn as nn +from scipy import ndimage +from tqdm import tqdm, trange +from PIL import Image +import torch.hub +import torchvision +import torch.nn.functional as F + +# download deeplabv2_resnet101_msc-cocostuff164k-100000.pth from +# https://github.com/kazuto1011/deeplab-pytorch/releases/download/v1.0/deeplabv2_resnet101_msc-cocostuff164k-100000.pth +# and put the path here +CKPT_PATH = "TODO" + +rescale = lambda x: (x + 1.) / 2. + +def rescale_bgr(x): + x = (x+1)*127.5 + x = torch.flip(x, dims=[0]) + return x + + +class COCOStuffSegmenter(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.n_labels = 182 + model = torch.hub.load("kazuto1011/deeplab-pytorch", "deeplabv2_resnet101", n_classes=self.n_labels) + ckpt_path = CKPT_PATH + model.load_state_dict(torch.load(ckpt_path)) + self.model = model + + normalize = torchvision.transforms.Normalize(mean=self.mean, std=self.std) + self.image_transform = torchvision.transforms.Compose([ + torchvision.transforms.Lambda(lambda image: torch.stack( + [normalize(rescale_bgr(x)) for x in image])) + ]) + + def forward(self, x, upsample=None): + x = self._pre_process(x) + x = self.model(x) + if upsample is not None: + x = torch.nn.functional.upsample_bilinear(x, size=upsample) + return x + + def _pre_process(self, x): + x = self.image_transform(x) + return x + + @property + def mean(self): + # bgr + return [104.008, 116.669, 122.675] + + @property + def std(self): + return [1.0, 1.0, 1.0] + + @property + def input_size(self): + return [3, 224, 224] + + +def run_model(img, model): + model = model.eval() + with torch.no_grad(): + segmentation = model(img, upsample=(img.shape[2], img.shape[3])) + segmentation = torch.argmax(segmentation, dim=1, keepdim=True) + return segmentation.detach().cpu() + + +def get_input(batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format) + return x.float() + + +def save_segmentation(segmentation, path): + # --> class label to uint8, save as png + os.makedirs(os.path.dirname(path), exist_ok=True) + assert len(segmentation.shape)==4 + assert segmentation.shape[0]==1 + for seg in segmentation: + seg = seg.permute(1,2,0).numpy().squeeze().astype(np.uint8) + seg = Image.fromarray(seg) + seg.save(path) + + +def iterate_dataset(dataloader, destpath, model): + os.makedirs(destpath, exist_ok=True) + num_processed = 0 + for i, batch in tqdm(enumerate(dataloader), desc="Data"): + try: + img = get_input(batch, "image") + img = img.cuda() + seg = run_model(img, model) + + path = batch["relative_file_path_"][0] + path = os.path.splitext(path)[0] + + path = os.path.join(destpath, path + ".png") + save_segmentation(seg, path) + num_processed += 1 + except Exception as e: + print(e) + print("but anyhow..") + + print("Processed {} files. Bye.".format(num_processed)) + + +from taming.data.sflckr import Examples +from torch.utils.data import DataLoader + +if __name__ == "__main__": + dest = sys.argv[1] + batchsize = 1 + print("Running with batch-size {}, saving to {}...".format(batchsize, dest)) + + model = COCOStuffSegmenter({}).cuda() + print("Instantiated model.") + + dataset = Examples() + dloader = DataLoader(dataset, batch_size=batchsize) + iterate_dataset(dataloader=dloader, destpath=dest, model=model) + print("done.") diff --git a/taming-transformers/scripts/extract_submodel.py b/taming-transformers/scripts/extract_submodel.py new file mode 100755 index 0000000000000000000000000000000000000000..559bc5e04281a7cf833a82e3cd48627b20f1a76d --- /dev/null +++ b/taming-transformers/scripts/extract_submodel.py @@ -0,0 +1,17 @@ +import torch +import sys + +if __name__ == "__main__": + inpath = sys.argv[1] + outpath = sys.argv[2] + submodel = "cond_stage_model" + if len(sys.argv) > 3: + submodel = sys.argv[3] + + print("Extracting {} from {} to {}.".format(submodel, inpath, outpath)) + + sd = torch.load(inpath, map_location="cpu") + new_sd = {"state_dict": dict((k.split(".", 1)[-1],v) + for k,v in sd["state_dict"].items() + if k.startswith("cond_stage_model"))} + torch.save(new_sd, outpath) diff --git a/taming-transformers/scripts/make_samples.py b/taming-transformers/scripts/make_samples.py new file mode 100755 index 0000000000000000000000000000000000000000..5e4d6995cd41cc07b4e8861cb941c6052b0f5517 --- /dev/null +++ b/taming-transformers/scripts/make_samples.py @@ -0,0 +1,292 @@ +import argparse, os, sys, glob, math, time +import torch +import numpy as np +from omegaconf import OmegaConf +from PIL import Image +from main import instantiate_from_config, DataModuleFromConfig +from torch.utils.data import DataLoader +from torch.utils.data.dataloader import default_collate +from tqdm import trange + + +def save_image(x, path): + c,h,w = x.shape + assert c==3 + x = ((x.detach().cpu().numpy().transpose(1,2,0)+1.0)*127.5).clip(0,255).astype(np.uint8) + Image.fromarray(x).save(path) + + +@torch.no_grad() +def run_conditional(model, dsets, outdir, top_k, temperature, batch_size=1): + if len(dsets.datasets) > 1: + split = sorted(dsets.datasets.keys())[0] + dset = dsets.datasets[split] + else: + dset = next(iter(dsets.datasets.values())) + print("Dataset: ", dset.__class__.__name__) + for start_idx in trange(0,len(dset)-batch_size+1,batch_size): + indices = list(range(start_idx, start_idx+batch_size)) + example = default_collate([dset[i] for i in indices]) + + x = model.get_input("image", example).to(model.device) + for i in range(x.shape[0]): + save_image(x[i], os.path.join(outdir, "originals", + "{:06}.png".format(indices[i]))) + + cond_key = model.cond_stage_key + c = model.get_input(cond_key, example).to(model.device) + + scale_factor = 1.0 + quant_z, z_indices = model.encode_to_z(x) + quant_c, c_indices = model.encode_to_c(c) + + cshape = quant_z.shape + + xrec = model.first_stage_model.decode(quant_z) + for i in range(xrec.shape[0]): + save_image(xrec[i], os.path.join(outdir, "reconstructions", + "{:06}.png".format(indices[i]))) + + if cond_key == "segmentation": + # get image from segmentation mask + num_classes = c.shape[1] + c = torch.argmax(c, dim=1, keepdim=True) + c = torch.nn.functional.one_hot(c, num_classes=num_classes) + c = c.squeeze(1).permute(0, 3, 1, 2).float() + c = model.cond_stage_model.to_rgb(c) + + idx = z_indices + + half_sample = False + if half_sample: + start = idx.shape[1]//2 + else: + start = 0 + + idx[:,start:] = 0 + idx = idx.reshape(cshape[0],cshape[2],cshape[3]) + start_i = start//cshape[3] + start_j = start %cshape[3] + + cidx = c_indices + cidx = cidx.reshape(quant_c.shape[0],quant_c.shape[2],quant_c.shape[3]) + + sample = True + + for i in range(start_i,cshape[2]-0): + if i <= 8: + local_i = i + elif cshape[2]-i < 8: + local_i = 16-(cshape[2]-i) + else: + local_i = 8 + for j in range(start_j,cshape[3]-0): + if j <= 8: + local_j = j + elif cshape[3]-j < 8: + local_j = 16-(cshape[3]-j) + else: + local_j = 8 + + i_start = i-local_i + i_end = i_start+16 + j_start = j-local_j + j_end = j_start+16 + patch = idx[:,i_start:i_end,j_start:j_end] + patch = patch.reshape(patch.shape[0],-1) + cpatch = cidx[:, i_start:i_end, j_start:j_end] + cpatch = cpatch.reshape(cpatch.shape[0], -1) + patch = torch.cat((cpatch, patch), dim=1) + logits,_ = model.transformer(patch[:,:-1]) + logits = logits[:, -256:, :] + logits = logits.reshape(cshape[0],16,16,-1) + logits = logits[:,local_i,local_j,:] + + logits = logits/temperature + + if top_k is not None: + logits = model.top_k_logits(logits, top_k) + # apply softmax to convert to probabilities + probs = torch.nn.functional.softmax(logits, dim=-1) + # sample from the distribution or take the most likely + if sample: + ix = torch.multinomial(probs, num_samples=1) + else: + _, ix = torch.topk(probs, k=1, dim=-1) + idx[:,i,j] = ix + + xsample = model.decode_to_img(idx[:,:cshape[2],:cshape[3]], cshape) + for i in range(xsample.shape[0]): + save_image(xsample[i], os.path.join(outdir, "samples", + "{:06}.png".format(indices[i]))) + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument( + "-r", + "--resume", + type=str, + nargs="?", + help="load from logdir or checkpoint in logdir", + ) + parser.add_argument( + "-b", + "--base", + nargs="*", + metavar="base_config.yaml", + help="paths to base configs. Loaded from left-to-right. " + "Parameters can be overwritten or added with command-line options of the form `--key value`.", + default=list(), + ) + parser.add_argument( + "-c", + "--config", + nargs="?", + metavar="single_config.yaml", + help="path to single config. If specified, base configs will be ignored " + "(except for the last one if left unspecified).", + const=True, + default="", + ) + parser.add_argument( + "--ignore_base_data", + action="store_true", + help="Ignore data specification from base configs. Useful if you want " + "to specify a custom datasets on the command line.", + ) + parser.add_argument( + "--outdir", + required=True, + type=str, + help="Where to write outputs to.", + ) + parser.add_argument( + "--top_k", + type=int, + default=100, + help="Sample from among top-k predictions.", + ) + parser.add_argument( + "--temperature", + type=float, + default=1.0, + help="Sampling temperature.", + ) + return parser + + +def load_model_from_config(config, sd, gpu=True, eval_mode=True): + if "ckpt_path" in config.params: + print("Deleting the restore-ckpt path from the config...") + config.params.ckpt_path = None + if "downsample_cond_size" in config.params: + print("Deleting downsample-cond-size from the config and setting factor=0.5 instead...") + config.params.downsample_cond_size = -1 + config.params["downsample_cond_factor"] = 0.5 + try: + if "ckpt_path" in config.params.first_stage_config.params: + config.params.first_stage_config.params.ckpt_path = None + print("Deleting the first-stage restore-ckpt path from the config...") + if "ckpt_path" in config.params.cond_stage_config.params: + config.params.cond_stage_config.params.ckpt_path = None + print("Deleting the cond-stage restore-ckpt path from the config...") + except: + pass + + model = instantiate_from_config(config) + if sd is not None: + missing, unexpected = model.load_state_dict(sd, strict=False) + print(f"Missing Keys in State Dict: {missing}") + print(f"Unexpected Keys in State Dict: {unexpected}") + if gpu: + model.cuda() + if eval_mode: + model.eval() + return {"model": model} + + +def get_data(config): + # get data + data = instantiate_from_config(config.data) + data.prepare_data() + data.setup() + return data + + +def load_model_and_dset(config, ckpt, gpu, eval_mode): + # get data + dsets = get_data(config) # calls data.config ... + + # now load the specified checkpoint + if ckpt: + pl_sd = torch.load(ckpt, map_location="cpu") + global_step = pl_sd["global_step"] + else: + pl_sd = {"state_dict": None} + global_step = None + model = load_model_from_config(config.model, + pl_sd["state_dict"], + gpu=gpu, + eval_mode=eval_mode)["model"] + return dsets, model, global_step + + +if __name__ == "__main__": + sys.path.append(os.getcwd()) + + parser = get_parser() + + opt, unknown = parser.parse_known_args() + + ckpt = None + if opt.resume: + if not os.path.exists(opt.resume): + raise ValueError("Cannot find {}".format(opt.resume)) + if os.path.isfile(opt.resume): + paths = opt.resume.split("/") + try: + idx = len(paths)-paths[::-1].index("logs")+1 + except ValueError: + idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt + logdir = "/".join(paths[:idx]) + ckpt = opt.resume + else: + assert os.path.isdir(opt.resume), opt.resume + logdir = opt.resume.rstrip("/") + ckpt = os.path.join(logdir, "checkpoints", "last.ckpt") + print(f"logdir:{logdir}") + base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml"))) + opt.base = base_configs+opt.base + + if opt.config: + if type(opt.config) == str: + opt.base = [opt.config] + else: + opt.base = [opt.base[-1]] + + configs = [OmegaConf.load(cfg) for cfg in opt.base] + cli = OmegaConf.from_dotlist(unknown) + if opt.ignore_base_data: + for config in configs: + if hasattr(config, "data"): del config["data"] + config = OmegaConf.merge(*configs, cli) + + print(ckpt) + gpu = True + eval_mode = True + show_config = False + if show_config: + print(OmegaConf.to_container(config)) + + dsets, model, global_step = load_model_and_dset(config, ckpt, gpu, eval_mode) + print(f"Global step: {global_step}") + + outdir = os.path.join(opt.outdir, "{:06}_{}_{}".format(global_step, + opt.top_k, + opt.temperature)) + os.makedirs(outdir, exist_ok=True) + print("Writing samples to ", outdir) + for k in ["originals", "reconstructions", "samples"]: + os.makedirs(os.path.join(outdir, k), exist_ok=True) + run_conditional(model, dsets, outdir, opt.top_k, opt.temperature) diff --git a/taming-transformers/scripts/make_scene_samples.py b/taming-transformers/scripts/make_scene_samples.py new file mode 100755 index 0000000000000000000000000000000000000000..c096b98460874be0acbe5b85464593fbad4bedd0 --- /dev/null +++ b/taming-transformers/scripts/make_scene_samples.py @@ -0,0 +1,198 @@ +import glob +import os +import sys +from itertools import product +from pathlib import Path +from typing import Literal, List, Optional, Tuple + +import numpy as np +import torch +from omegaconf import OmegaConf +from pytorch_lightning import seed_everything +from torch import Tensor +from torchvision.utils import save_image +from tqdm import tqdm + +from scripts.make_samples import get_parser, load_model_and_dset +from taming.data.conditional_builder.objects_center_points import ObjectsCenterPointsConditionalBuilder +from taming.data.helper_types import BoundingBox, Annotation +from taming.data.annotated_objects_dataset import AnnotatedObjectsDataset +from taming.models.cond_transformer import Net2NetTransformer + +seed_everything(42424242) +device: Literal['cuda', 'cpu'] = 'cuda' +first_stage_factor = 16 +trained_on_res = 256 + + +def _helper(coord: int, coord_max: int, coord_window: int) -> (int, int): + assert 0 <= coord < coord_max + coord_desired_center = (coord_window - 1) // 2 + return np.clip(coord - coord_desired_center, 0, coord_max - coord_window) + + +def get_crop_coordinates(x: int, y: int) -> BoundingBox: + WIDTH, HEIGHT = desired_z_shape[1], desired_z_shape[0] + x0 = _helper(x, WIDTH, first_stage_factor) / WIDTH + y0 = _helper(y, HEIGHT, first_stage_factor) / HEIGHT + w = first_stage_factor / WIDTH + h = first_stage_factor / HEIGHT + return x0, y0, w, h + + +def get_z_indices_crop_out(z_indices: Tensor, predict_x: int, predict_y: int) -> Tensor: + WIDTH, HEIGHT = desired_z_shape[1], desired_z_shape[0] + x0 = _helper(predict_x, WIDTH, first_stage_factor) + y0 = _helper(predict_y, HEIGHT, first_stage_factor) + no_images = z_indices.shape[0] + cut_out_1 = z_indices[:, y0:predict_y, x0:x0+first_stage_factor].reshape((no_images, -1)) + cut_out_2 = z_indices[:, predict_y, x0:predict_x] + return torch.cat((cut_out_1, cut_out_2), dim=1) + + +@torch.no_grad() +def sample(model: Net2NetTransformer, annotations: List[Annotation], dataset: AnnotatedObjectsDataset, + conditional_builder: ObjectsCenterPointsConditionalBuilder, no_samples: int, + temperature: float, top_k: int) -> Tensor: + x_max, y_max = desired_z_shape[1], desired_z_shape[0] + + annotations = [a._replace(category_no=dataset.get_category_number(a.category_id)) for a in annotations] + + recompute_conditional = any((desired_resolution[0] > trained_on_res, desired_resolution[1] > trained_on_res)) + if not recompute_conditional: + crop_coordinates = get_crop_coordinates(0, 0) + conditional_indices = conditional_builder.build(annotations, crop_coordinates) + c_indices = conditional_indices.to(device).repeat(no_samples, 1) + z_indices = torch.zeros((no_samples, 0), device=device).long() + output_indices = model.sample(z_indices, c_indices, steps=x_max*y_max, temperature=temperature, + sample=True, top_k=top_k) + else: + output_indices = torch.zeros((no_samples, y_max, x_max), device=device).long() + for predict_y, predict_x in tqdm(product(range(y_max), range(x_max)), desc='sampling_image', total=x_max*y_max): + crop_coordinates = get_crop_coordinates(predict_x, predict_y) + z_indices = get_z_indices_crop_out(output_indices, predict_x, predict_y) + conditional_indices = conditional_builder.build(annotations, crop_coordinates) + c_indices = conditional_indices.to(device).repeat(no_samples, 1) + new_index = model.sample(z_indices, c_indices, steps=1, temperature=temperature, sample=True, top_k=top_k) + output_indices[:, predict_y, predict_x] = new_index[:, -1] + z_shape = ( + no_samples, + model.first_stage_model.quantize.e_dim, # codebook embed_dim + desired_z_shape[0], # z_height + desired_z_shape[1] # z_width + ) + x_sample = model.decode_to_img(output_indices, z_shape) * 0.5 + 0.5 + x_sample = x_sample.to('cpu') + + plotter = conditional_builder.plot + figure_size = (x_sample.shape[2], x_sample.shape[3]) + scene_graph = conditional_builder.build(annotations, (0., 0., 1., 1.)) + plot = plotter(scene_graph, dataset.get_textual_label_for_category_no, figure_size) + return torch.cat((x_sample, plot.unsqueeze(0))) + + +def get_resolution(resolution_str: str) -> (Tuple[int, int], Tuple[int, int]): + if not resolution_str.count(',') == 1: + raise ValueError("Give resolution as in 'height,width'") + res_h, res_w = resolution_str.split(',') + res_h = max(int(res_h), trained_on_res) + res_w = max(int(res_w), trained_on_res) + z_h = int(round(res_h/first_stage_factor)) + z_w = int(round(res_w/first_stage_factor)) + return (z_h, z_w), (z_h*first_stage_factor, z_w*first_stage_factor) + + +def add_arg_to_parser(parser): + parser.add_argument( + "-R", + "--resolution", + type=str, + default='256,256', + help=f"give resolution in multiples of {first_stage_factor}, default is '256,256'", + ) + parser.add_argument( + "-C", + "--conditional", + type=str, + default='objects_bbox', + help=f"objects_bbox or objects_center_points", + ) + parser.add_argument( + "-N", + "--n_samples_per_layout", + type=int, + default=4, + help=f"how many samples to generate per layout", + ) + return parser + + +if __name__ == "__main__": + sys.path.append(os.getcwd()) + + parser = get_parser() + parser = add_arg_to_parser(parser) + + opt, unknown = parser.parse_known_args() + + ckpt = None + if opt.resume: + if not os.path.exists(opt.resume): + raise ValueError("Cannot find {}".format(opt.resume)) + if os.path.isfile(opt.resume): + paths = opt.resume.split("/") + try: + idx = len(paths)-paths[::-1].index("logs")+1 + except ValueError: + idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt + logdir = "/".join(paths[:idx]) + ckpt = opt.resume + else: + assert os.path.isdir(opt.resume), opt.resume + logdir = opt.resume.rstrip("/") + ckpt = os.path.join(logdir, "checkpoints", "last.ckpt") + print(f"logdir:{logdir}") + base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml"))) + opt.base = base_configs+opt.base + + if opt.config: + if type(opt.config) == str: + opt.base = [opt.config] + else: + opt.base = [opt.base[-1]] + + configs = [OmegaConf.load(cfg) for cfg in opt.base] + cli = OmegaConf.from_dotlist(unknown) + if opt.ignore_base_data: + for config in configs: + if hasattr(config, "data"): + del config["data"] + config = OmegaConf.merge(*configs, cli) + desired_z_shape, desired_resolution = get_resolution(opt.resolution) + conditional = opt.conditional + + print(ckpt) + gpu = True + eval_mode = True + show_config = False + if show_config: + print(OmegaConf.to_container(config)) + + dsets, model, global_step = load_model_and_dset(config, ckpt, gpu, eval_mode) + print(f"Global step: {global_step}") + + data_loader = dsets.val_dataloader() + print(dsets.datasets["validation"].conditional_builders) + conditional_builder = dsets.datasets["validation"].conditional_builders[conditional] + + outdir = Path(opt.outdir).joinpath(f"{global_step:06}_{opt.top_k}_{opt.temperature}") + outdir.mkdir(exist_ok=True, parents=True) + print("Writing samples to ", outdir) + + p_bar_1 = tqdm(enumerate(iter(data_loader)), desc='batch', total=len(data_loader)) + for batch_no, batch in p_bar_1: + save_img: Optional[Tensor] = None + for i, annotations in tqdm(enumerate(batch['annotations']), desc='within_batch', total=data_loader.batch_size): + imgs = sample(model, annotations, dsets.datasets["validation"], conditional_builder, + opt.n_samples_per_layout, opt.temperature, opt.top_k) + save_image(imgs, outdir.joinpath(f'{batch_no:04}_{i:02}.png'), n_row=opt.n_samples_per_layout+1) diff --git a/taming-transformers/scripts/sample_conditional.py b/taming-transformers/scripts/sample_conditional.py new file mode 100755 index 0000000000000000000000000000000000000000..174cf2af07c1a1ca4e6c35fc0e4f8d6e53591b56 --- /dev/null +++ b/taming-transformers/scripts/sample_conditional.py @@ -0,0 +1,355 @@ +import argparse, os, sys, glob, math, time +import torch +import numpy as np +from omegaconf import OmegaConf +import streamlit as st +from streamlit import caching +from PIL import Image +from main import instantiate_from_config, DataModuleFromConfig +from torch.utils.data import DataLoader +from torch.utils.data.dataloader import default_collate + + +rescale = lambda x: (x + 1.) / 2. + + +def bchw_to_st(x): + return rescale(x.detach().cpu().numpy().transpose(0,2,3,1)) + +def save_img(xstart, fname): + I = (xstart.clip(0,1)[0]*255).astype(np.uint8) + Image.fromarray(I).save(fname) + + + +def get_interactive_image(resize=False): + image = st.file_uploader("Input", type=["jpg", "JPEG", "png"]) + if image is not None: + image = Image.open(image) + if not image.mode == "RGB": + image = image.convert("RGB") + image = np.array(image).astype(np.uint8) + print("upload image shape: {}".format(image.shape)) + img = Image.fromarray(image) + if resize: + img = img.resize((256, 256)) + image = np.array(img) + return image + + +def single_image_to_torch(x, permute=True): + assert x is not None, "Please provide an image through the upload function" + x = np.array(x) + x = torch.FloatTensor(x/255.*2. - 1.)[None,...] + if permute: + x = x.permute(0, 3, 1, 2) + return x + + +def pad_to_M(x, M): + hp = math.ceil(x.shape[2]/M)*M-x.shape[2] + wp = math.ceil(x.shape[3]/M)*M-x.shape[3] + x = torch.nn.functional.pad(x, (0,wp,0,hp,0,0,0,0)) + return x + +@torch.no_grad() +def run_conditional(model, dsets): + if len(dsets.datasets) > 1: + split = st.sidebar.radio("Split", sorted(dsets.datasets.keys())) + dset = dsets.datasets[split] + else: + dset = next(iter(dsets.datasets.values())) + batch_size = 1 + start_index = st.sidebar.number_input("Example Index (Size: {})".format(len(dset)), value=0, + min_value=0, + max_value=len(dset)-batch_size) + indices = list(range(start_index, start_index+batch_size)) + + example = default_collate([dset[i] for i in indices]) + + x = model.get_input("image", example).to(model.device) + + cond_key = model.cond_stage_key + c = model.get_input(cond_key, example).to(model.device) + + scale_factor = st.sidebar.slider("Scale Factor", min_value=0.5, max_value=4.0, step=0.25, value=1.00) + if scale_factor != 1.0: + x = torch.nn.functional.interpolate(x, scale_factor=scale_factor, mode="bicubic") + c = torch.nn.functional.interpolate(c, scale_factor=scale_factor, mode="bicubic") + + quant_z, z_indices = model.encode_to_z(x) + quant_c, c_indices = model.encode_to_c(c) + + cshape = quant_z.shape + + xrec = model.first_stage_model.decode(quant_z) + st.write("image: {}".format(x.shape)) + st.image(bchw_to_st(x), clamp=True, output_format="PNG") + st.write("image reconstruction: {}".format(xrec.shape)) + st.image(bchw_to_st(xrec), clamp=True, output_format="PNG") + + if cond_key == "segmentation": + # get image from segmentation mask + num_classes = c.shape[1] + c = torch.argmax(c, dim=1, keepdim=True) + c = torch.nn.functional.one_hot(c, num_classes=num_classes) + c = c.squeeze(1).permute(0, 3, 1, 2).float() + c = model.cond_stage_model.to_rgb(c) + + st.write(f"{cond_key}: {tuple(c.shape)}") + st.image(bchw_to_st(c), clamp=True, output_format="PNG") + + idx = z_indices + + half_sample = st.sidebar.checkbox("Image Completion", value=False) + if half_sample: + start = idx.shape[1]//2 + else: + start = 0 + + idx[:,start:] = 0 + idx = idx.reshape(cshape[0],cshape[2],cshape[3]) + start_i = start//cshape[3] + start_j = start %cshape[3] + + if not half_sample and quant_z.shape == quant_c.shape: + st.info("Setting idx to c_indices") + idx = c_indices.clone().reshape(cshape[0],cshape[2],cshape[3]) + + cidx = c_indices + cidx = cidx.reshape(quant_c.shape[0],quant_c.shape[2],quant_c.shape[3]) + + xstart = model.decode_to_img(idx[:,:cshape[2],:cshape[3]], cshape) + st.image(bchw_to_st(xstart), clamp=True, output_format="PNG") + + temperature = st.number_input("Temperature", value=1.0) + top_k = st.number_input("Top k", value=100) + sample = st.checkbox("Sample", value=True) + update_every = st.number_input("Update every", value=75) + + st.text(f"Sampling shape ({cshape[2]},{cshape[3]})") + + animate = st.checkbox("animate") + if animate: + import imageio + outvid = "sampling.mp4" + writer = imageio.get_writer(outvid, fps=25) + elapsed_t = st.empty() + info = st.empty() + st.text("Sampled") + if st.button("Sample"): + output = st.empty() + start_t = time.time() + for i in range(start_i,cshape[2]-0): + if i <= 8: + local_i = i + elif cshape[2]-i < 8: + local_i = 16-(cshape[2]-i) + else: + local_i = 8 + for j in range(start_j,cshape[3]-0): + if j <= 8: + local_j = j + elif cshape[3]-j < 8: + local_j = 16-(cshape[3]-j) + else: + local_j = 8 + + i_start = i-local_i + i_end = i_start+16 + j_start = j-local_j + j_end = j_start+16 + elapsed_t.text(f"Time: {time.time() - start_t} seconds") + info.text(f"Step: ({i},{j}) | Local: ({local_i},{local_j}) | Crop: ({i_start}:{i_end},{j_start}:{j_end})") + patch = idx[:,i_start:i_end,j_start:j_end] + patch = patch.reshape(patch.shape[0],-1) + cpatch = cidx[:, i_start:i_end, j_start:j_end] + cpatch = cpatch.reshape(cpatch.shape[0], -1) + patch = torch.cat((cpatch, patch), dim=1) + logits,_ = model.transformer(patch[:,:-1]) + logits = logits[:, -256:, :] + logits = logits.reshape(cshape[0],16,16,-1) + logits = logits[:,local_i,local_j,:] + + logits = logits/temperature + + if top_k is not None: + logits = model.top_k_logits(logits, top_k) + # apply softmax to convert to probabilities + probs = torch.nn.functional.softmax(logits, dim=-1) + # sample from the distribution or take the most likely + if sample: + ix = torch.multinomial(probs, num_samples=1) + else: + _, ix = torch.topk(probs, k=1, dim=-1) + idx[:,i,j] = ix + + if (i*cshape[3]+j)%update_every==0: + xstart = model.decode_to_img(idx[:, :cshape[2], :cshape[3]], cshape,) + + xstart = bchw_to_st(xstart) + output.image(xstart, clamp=True, output_format="PNG") + + if animate: + writer.append_data((xstart[0]*255).clip(0, 255).astype(np.uint8)) + + xstart = model.decode_to_img(idx[:,:cshape[2],:cshape[3]], cshape) + xstart = bchw_to_st(xstart) + output.image(xstart, clamp=True, output_format="PNG") + #save_img(xstart, "full_res_sample.png") + if animate: + writer.close() + st.video(outvid) + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument( + "-r", + "--resume", + type=str, + nargs="?", + help="load from logdir or checkpoint in logdir", + ) + parser.add_argument( + "-b", + "--base", + nargs="*", + metavar="base_config.yaml", + help="paths to base configs. Loaded from left-to-right. " + "Parameters can be overwritten or added with command-line options of the form `--key value`.", + default=list(), + ) + parser.add_argument( + "-c", + "--config", + nargs="?", + metavar="single_config.yaml", + help="path to single config. If specified, base configs will be ignored " + "(except for the last one if left unspecified).", + const=True, + default="", + ) + parser.add_argument( + "--ignore_base_data", + action="store_true", + help="Ignore data specification from base configs. Useful if you want " + "to specify a custom datasets on the command line.", + ) + return parser + + +def load_model_from_config(config, sd, gpu=True, eval_mode=True): + if "ckpt_path" in config.params: + st.warning("Deleting the restore-ckpt path from the config...") + config.params.ckpt_path = None + if "downsample_cond_size" in config.params: + st.warning("Deleting downsample-cond-size from the config and setting factor=0.5 instead...") + config.params.downsample_cond_size = -1 + config.params["downsample_cond_factor"] = 0.5 + try: + if "ckpt_path" in config.params.first_stage_config.params: + config.params.first_stage_config.params.ckpt_path = None + st.warning("Deleting the first-stage restore-ckpt path from the config...") + if "ckpt_path" in config.params.cond_stage_config.params: + config.params.cond_stage_config.params.ckpt_path = None + st.warning("Deleting the cond-stage restore-ckpt path from the config...") + except: + pass + + model = instantiate_from_config(config) + if sd is not None: + missing, unexpected = model.load_state_dict(sd, strict=False) + st.info(f"Missing Keys in State Dict: {missing}") + st.info(f"Unexpected Keys in State Dict: {unexpected}") + if gpu: + model.cuda() + if eval_mode: + model.eval() + return {"model": model} + + +def get_data(config): + # get data + data = instantiate_from_config(config.data) + data.prepare_data() + data.setup() + return data + + +@st.cache(allow_output_mutation=True, suppress_st_warning=True) +def load_model_and_dset(config, ckpt, gpu, eval_mode): + # get data + dsets = get_data(config) # calls data.config ... + + # now load the specified checkpoint + if ckpt: + pl_sd = torch.load(ckpt, map_location="cpu") + global_step = pl_sd["global_step"] + else: + pl_sd = {"state_dict": None} + global_step = None + model = load_model_from_config(config.model, + pl_sd["state_dict"], + gpu=gpu, + eval_mode=eval_mode)["model"] + return dsets, model, global_step + + +if __name__ == "__main__": + sys.path.append(os.getcwd()) + + parser = get_parser() + + opt, unknown = parser.parse_known_args() + + ckpt = None + if opt.resume: + if not os.path.exists(opt.resume): + raise ValueError("Cannot find {}".format(opt.resume)) + if os.path.isfile(opt.resume): + paths = opt.resume.split("/") + try: + idx = len(paths)-paths[::-1].index("logs")+1 + except ValueError: + idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt + logdir = "/".join(paths[:idx]) + ckpt = opt.resume + else: + assert os.path.isdir(opt.resume), opt.resume + logdir = opt.resume.rstrip("/") + ckpt = os.path.join(logdir, "checkpoints", "last.ckpt") + print(f"logdir:{logdir}") + base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml"))) + opt.base = base_configs+opt.base + + if opt.config: + if type(opt.config) == str: + opt.base = [opt.config] + else: + opt.base = [opt.base[-1]] + + configs = [OmegaConf.load(cfg) for cfg in opt.base] + cli = OmegaConf.from_dotlist(unknown) + if opt.ignore_base_data: + for config in configs: + if hasattr(config, "data"): del config["data"] + config = OmegaConf.merge(*configs, cli) + + st.sidebar.text(ckpt) + gs = st.sidebar.empty() + gs.text(f"Global step: ?") + st.sidebar.text("Options") + #gpu = st.sidebar.checkbox("GPU", value=True) + gpu = True + #eval_mode = st.sidebar.checkbox("Eval Mode", value=True) + eval_mode = True + #show_config = st.sidebar.checkbox("Show Config", value=False) + show_config = False + if show_config: + st.info("Checkpoint: {}".format(ckpt)) + st.json(OmegaConf.to_container(config)) + + dsets, model, global_step = load_model_and_dset(config, ckpt, gpu, eval_mode) + gs.text(f"Global step: {global_step}") + run_conditional(model, dsets) diff --git a/taming-transformers/scripts/sample_fast.py b/taming-transformers/scripts/sample_fast.py new file mode 100755 index 0000000000000000000000000000000000000000..ff546c7dcbe459807ac3b70f834ccc1082fe8b4e --- /dev/null +++ b/taming-transformers/scripts/sample_fast.py @@ -0,0 +1,260 @@ +import argparse, os, sys, glob +import torch +import time +import numpy as np +from omegaconf import OmegaConf +from PIL import Image +from tqdm import tqdm, trange +from einops import repeat + +from main import instantiate_from_config +from taming.modules.transformer.mingpt import sample_with_past + + +rescale = lambda x: (x + 1.) / 2. + + +def chw_to_pillow(x): + return Image.fromarray((255*rescale(x.detach().cpu().numpy().transpose(1,2,0))).clip(0,255).astype(np.uint8)) + + +@torch.no_grad() +def sample_classconditional(model, batch_size, class_label, steps=256, temperature=None, top_k=None, callback=None, + dim_z=256, h=16, w=16, verbose_time=False, top_p=None): + log = dict() + assert type(class_label) == int, f'expecting type int but type is {type(class_label)}' + qzshape = [batch_size, dim_z, h, w] + assert not model.be_unconditional, 'Expecting a class-conditional Net2NetTransformer.' + c_indices = repeat(torch.tensor([class_label]), '1 -> b 1', b=batch_size).to(model.device) # class token + t1 = time.time() + index_sample = sample_with_past(c_indices, model.transformer, steps=steps, + sample_logits=True, top_k=top_k, callback=callback, + temperature=temperature, top_p=top_p) + if verbose_time: + sampling_time = time.time() - t1 + print(f"Full sampling takes about {sampling_time:.2f} seconds.") + x_sample = model.decode_to_img(index_sample, qzshape) + log["samples"] = x_sample + log["class_label"] = c_indices + return log + + +@torch.no_grad() +def sample_unconditional(model, batch_size, steps=256, temperature=None, top_k=None, top_p=None, callback=None, + dim_z=256, h=16, w=16, verbose_time=False): + log = dict() + qzshape = [batch_size, dim_z, h, w] + assert model.be_unconditional, 'Expecting an unconditional model.' + c_indices = repeat(torch.tensor([model.sos_token]), '1 -> b 1', b=batch_size).to(model.device) # sos token + t1 = time.time() + index_sample = sample_with_past(c_indices, model.transformer, steps=steps, + sample_logits=True, top_k=top_k, callback=callback, + temperature=temperature, top_p=top_p) + if verbose_time: + sampling_time = time.time() - t1 + print(f"Full sampling takes about {sampling_time:.2f} seconds.") + x_sample = model.decode_to_img(index_sample, qzshape) + log["samples"] = x_sample + return log + + +@torch.no_grad() +def run(logdir, model, batch_size, temperature, top_k, unconditional=True, num_samples=50000, + given_classes=None, top_p=None): + batches = [batch_size for _ in range(num_samples//batch_size)] + [num_samples % batch_size] + if not unconditional: + assert given_classes is not None + print("Running in pure class-conditional sampling mode. I will produce " + f"{num_samples} samples for each of the {len(given_classes)} classes, " + f"i.e. {num_samples*len(given_classes)} in total.") + for class_label in tqdm(given_classes, desc="Classes"): + for n, bs in tqdm(enumerate(batches), desc="Sampling Class"): + if bs == 0: break + logs = sample_classconditional(model, batch_size=bs, class_label=class_label, + temperature=temperature, top_k=top_k, top_p=top_p) + save_from_logs(logs, logdir, base_count=n * batch_size, cond_key=logs["class_label"]) + else: + print(f"Running in unconditional sampling mode, producing {num_samples} samples.") + for n, bs in tqdm(enumerate(batches), desc="Sampling"): + if bs == 0: break + logs = sample_unconditional(model, batch_size=bs, temperature=temperature, top_k=top_k, top_p=top_p) + save_from_logs(logs, logdir, base_count=n * batch_size) + + +def save_from_logs(logs, logdir, base_count, key="samples", cond_key=None): + xx = logs[key] + for i, x in enumerate(xx): + x = chw_to_pillow(x) + count = base_count + i + if cond_key is None: + x.save(os.path.join(logdir, f"{count:06}.png")) + else: + condlabel = cond_key[i] + if type(condlabel) == torch.Tensor: condlabel = condlabel.item() + os.makedirs(os.path.join(logdir, str(condlabel)), exist_ok=True) + x.save(os.path.join(logdir, str(condlabel), f"{count:06}.png")) + + +def get_parser(): + def str2bool(v): + if isinstance(v, bool): + return v + if v.lower() in ("yes", "true", "t", "y", "1"): + return True + elif v.lower() in ("no", "false", "f", "n", "0"): + return False + else: + raise argparse.ArgumentTypeError("Boolean value expected.") + + parser = argparse.ArgumentParser() + parser.add_argument( + "-r", + "--resume", + type=str, + nargs="?", + help="load from logdir or checkpoint in logdir", + ) + parser.add_argument( + "-o", + "--outdir", + type=str, + nargs="?", + help="path where the samples will be logged to.", + default="" + ) + parser.add_argument( + "-b", + "--base", + nargs="*", + metavar="base_config.yaml", + help="paths to base configs. Loaded from left-to-right. " + "Parameters can be overwritten or added with command-line options of the form `--key value`.", + default=list(), + ) + parser.add_argument( + "-n", + "--num_samples", + type=int, + nargs="?", + help="num_samples to draw", + default=50000 + ) + parser.add_argument( + "--batch_size", + type=int, + nargs="?", + help="the batch size", + default=25 + ) + parser.add_argument( + "-k", + "--top_k", + type=int, + nargs="?", + help="top-k value to sample with", + default=250, + ) + parser.add_argument( + "-t", + "--temperature", + type=float, + nargs="?", + help="temperature value to sample with", + default=1.0 + ) + parser.add_argument( + "-p", + "--top_p", + type=float, + nargs="?", + help="top-p value to sample with", + default=1.0 + ) + parser.add_argument( + "--classes", + type=str, + nargs="?", + help="specify comma-separated classes to sample from. Uses 1000 classes per default.", + default="imagenet" + ) + return parser + + +def load_model_from_config(config, sd, gpu=True, eval_mode=True): + model = instantiate_from_config(config) + if sd is not None: + model.load_state_dict(sd) + if gpu: + model.cuda() + if eval_mode: + model.eval() + return {"model": model} + + +def load_model(config, ckpt, gpu, eval_mode): + # load the specified checkpoint + if ckpt: + pl_sd = torch.load(ckpt, map_location="cpu") + global_step = pl_sd["global_step"] + print(f"loaded model from global step {global_step}.") + else: + pl_sd = {"state_dict": None} + global_step = None + model = load_model_from_config(config.model, pl_sd["state_dict"], gpu=gpu, eval_mode=eval_mode)["model"] + return model, global_step + + +if __name__ == "__main__": + sys.path.append(os.getcwd()) + parser = get_parser() + + opt, unknown = parser.parse_known_args() + assert opt.resume + + ckpt = None + + if not os.path.exists(opt.resume): + raise ValueError("Cannot find {}".format(opt.resume)) + if os.path.isfile(opt.resume): + paths = opt.resume.split("/") + try: + idx = len(paths)-paths[::-1].index("logs")+1 + except ValueError: + idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt + logdir = "/".join(paths[:idx]) + ckpt = opt.resume + else: + assert os.path.isdir(opt.resume), opt.resume + logdir = opt.resume.rstrip("/") + ckpt = os.path.join(logdir, "checkpoints", "last.ckpt") + + base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml"))) + opt.base = base_configs+opt.base + + configs = [OmegaConf.load(cfg) for cfg in opt.base] + cli = OmegaConf.from_dotlist(unknown) + config = OmegaConf.merge(*configs, cli) + + model, global_step = load_model(config, ckpt, gpu=True, eval_mode=True) + + if opt.outdir: + print(f"Switching logdir from '{logdir}' to '{opt.outdir}'") + logdir = opt.outdir + + if opt.classes == "imagenet": + given_classes = [i for i in range(1000)] + else: + cls_str = opt.classes + assert not cls_str.endswith(","), 'class string should not end with a ","' + given_classes = [int(c) for c in cls_str.split(",")] + + logdir = os.path.join(logdir, "samples", f"top_k_{opt.top_k}_temp_{opt.temperature:.2f}_top_p_{opt.top_p}", + f"{global_step}") + + print(f"Logging to {logdir}") + os.makedirs(logdir, exist_ok=True) + + run(logdir, model, opt.batch_size, opt.temperature, opt.top_k, unconditional=model.be_unconditional, + given_classes=given_classes, num_samples=opt.num_samples, top_p=opt.top_p) + + print("done.") diff --git a/taming-transformers/setup.py b/taming-transformers/setup.py new file mode 100755 index 0000000000000000000000000000000000000000..a220d12b21d96c5093a218c406cf47f1e7c8761a --- /dev/null +++ b/taming-transformers/setup.py @@ -0,0 +1,13 @@ +from setuptools import setup, find_packages + +setup( + name='taming-transformers', + version='0.0.1', + description='Taming Transformers for High-Resolution Image Synthesis', + packages=find_packages(), + install_requires=[ + 'torch', + 'numpy', + 'tqdm', + ], +) diff --git a/taming-transformers/taming/data/ade20k.py b/taming-transformers/taming/data/ade20k.py new file mode 100755 index 0000000000000000000000000000000000000000..366dae97207dbb8356598d636e14ad084d45bc76 --- /dev/null +++ b/taming-transformers/taming/data/ade20k.py @@ -0,0 +1,124 @@ +import os +import numpy as np +import cv2 +import albumentations +from PIL import Image +from torch.utils.data import Dataset + +from taming.data.sflckr import SegmentationBase # for examples included in repo + + +class Examples(SegmentationBase): + def __init__(self, size=256, random_crop=False, interpolation="bicubic"): + super().__init__(data_csv="data/ade20k_examples.txt", + data_root="data/ade20k_images", + segmentation_root="data/ade20k_segmentations", + size=size, random_crop=random_crop, + interpolation=interpolation, + n_labels=151, shift_segmentation=False) + + +# With semantic map and scene label +class ADE20kBase(Dataset): + def __init__(self, config=None, size=None, random_crop=False, interpolation="bicubic", crop_size=None): + self.split = self.get_split() + self.n_labels = 151 # unknown + 150 + self.data_csv = {"train": "data/ade20k_train.txt", + "validation": "data/ade20k_test.txt"}[self.split] + self.data_root = "data/ade20k_root" + with open(os.path.join(self.data_root, "sceneCategories.txt"), "r") as f: + self.scene_categories = f.read().splitlines() + self.scene_categories = dict(line.split() for line in self.scene_categories) + with open(self.data_csv, "r") as f: + self.image_paths = f.read().splitlines() + self._length = len(self.image_paths) + self.labels = { + "relative_file_path_": [l for l in self.image_paths], + "file_path_": [os.path.join(self.data_root, "images", l) + for l in self.image_paths], + "relative_segmentation_path_": [l.replace(".jpg", ".png") + for l in self.image_paths], + "segmentation_path_": [os.path.join(self.data_root, "annotations", + l.replace(".jpg", ".png")) + for l in self.image_paths], + "scene_category": [self.scene_categories[l.split("/")[1].replace(".jpg", "")] + for l in self.image_paths], + } + + size = None if size is not None and size<=0 else size + self.size = size + if crop_size is None: + self.crop_size = size if size is not None else None + else: + self.crop_size = crop_size + if self.size is not None: + self.interpolation = interpolation + self.interpolation = { + "nearest": cv2.INTER_NEAREST, + "bilinear": cv2.INTER_LINEAR, + "bicubic": cv2.INTER_CUBIC, + "area": cv2.INTER_AREA, + "lanczos": cv2.INTER_LANCZOS4}[self.interpolation] + self.image_rescaler = albumentations.SmallestMaxSize(max_size=self.size, + interpolation=self.interpolation) + self.segmentation_rescaler = albumentations.SmallestMaxSize(max_size=self.size, + interpolation=cv2.INTER_NEAREST) + + if crop_size is not None: + self.center_crop = not random_crop + if self.center_crop: + self.cropper = albumentations.CenterCrop(height=self.crop_size, width=self.crop_size) + else: + self.cropper = albumentations.RandomCrop(height=self.crop_size, width=self.crop_size) + self.preprocessor = self.cropper + + def __len__(self): + return self._length + + def __getitem__(self, i): + example = dict((k, self.labels[k][i]) for k in self.labels) + image = Image.open(example["file_path_"]) + if not image.mode == "RGB": + image = image.convert("RGB") + image = np.array(image).astype(np.uint8) + if self.size is not None: + image = self.image_rescaler(image=image)["image"] + segmentation = Image.open(example["segmentation_path_"]) + segmentation = np.array(segmentation).astype(np.uint8) + if self.size is not None: + segmentation = self.segmentation_rescaler(image=segmentation)["image"] + if self.size is not None: + processed = self.preprocessor(image=image, mask=segmentation) + else: + processed = {"image": image, "mask": segmentation} + example["image"] = (processed["image"]/127.5 - 1.0).astype(np.float32) + segmentation = processed["mask"] + onehot = np.eye(self.n_labels)[segmentation] + example["segmentation"] = onehot + return example + + +class ADE20kTrain(ADE20kBase): + # default to random_crop=True + def __init__(self, config=None, size=None, random_crop=True, interpolation="bicubic", crop_size=None): + super().__init__(config=config, size=size, random_crop=random_crop, + interpolation=interpolation, crop_size=crop_size) + + def get_split(self): + return "train" + + +class ADE20kValidation(ADE20kBase): + def get_split(self): + return "validation" + + +if __name__ == "__main__": + dset = ADE20kValidation() + ex = dset[0] + for k in ["image", "scene_category", "segmentation"]: + print(type(ex[k])) + try: + print(ex[k].shape) + except: + print(ex[k]) diff --git a/taming-transformers/taming/data/annotated_objects_coco.py b/taming-transformers/taming/data/annotated_objects_coco.py new file mode 100755 index 0000000000000000000000000000000000000000..af000ecd943d7b8a85d7eb70195c9ecd10ab5edc --- /dev/null +++ b/taming-transformers/taming/data/annotated_objects_coco.py @@ -0,0 +1,139 @@ +import json +from itertools import chain +from pathlib import Path +from typing import Iterable, Dict, List, Callable, Any +from collections import defaultdict + +from tqdm import tqdm + +from taming.data.annotated_objects_dataset import AnnotatedObjectsDataset +from taming.data.helper_types import Annotation, ImageDescription, Category + +COCO_PATH_STRUCTURE = { + 'train': { + 'top_level': '', + 'instances_annotations': 'annotations/instances_train2017.json', + 'stuff_annotations': 'annotations/stuff_train2017.json', + 'files': 'train2017' + }, + 'validation': { + 'top_level': '', + 'instances_annotations': 'annotations/instances_val2017.json', + 'stuff_annotations': 'annotations/stuff_val2017.json', + 'files': 'val2017' + } +} + + +def load_image_descriptions(description_json: List[Dict]) -> Dict[str, ImageDescription]: + return { + str(img['id']): ImageDescription( + id=img['id'], + license=img.get('license'), + file_name=img['file_name'], + coco_url=img['coco_url'], + original_size=(img['width'], img['height']), + date_captured=img.get('date_captured'), + flickr_url=img.get('flickr_url') + ) + for img in description_json + } + + +def load_categories(category_json: Iterable) -> Dict[str, Category]: + return {str(cat['id']): Category(id=str(cat['id']), super_category=cat['supercategory'], name=cat['name']) + for cat in category_json if cat['name'] != 'other'} + + +def load_annotations(annotations_json: List[Dict], image_descriptions: Dict[str, ImageDescription], + category_no_for_id: Callable[[str], int], split: str) -> Dict[str, List[Annotation]]: + annotations = defaultdict(list) + total = sum(len(a) for a in annotations_json) + for ann in tqdm(chain(*annotations_json), f'Loading {split} annotations', total=total): + image_id = str(ann['image_id']) + if image_id not in image_descriptions: + raise ValueError(f'image_id [{image_id}] has no image description.') + category_id = ann['category_id'] + try: + category_no = category_no_for_id(str(category_id)) + except KeyError: + continue + + width, height = image_descriptions[image_id].original_size + bbox = (ann['bbox'][0] / width, ann['bbox'][1] / height, ann['bbox'][2] / width, ann['bbox'][3] / height) + + annotations[image_id].append( + Annotation( + id=ann['id'], + area=bbox[2]*bbox[3], # use bbox area + is_group_of=ann['iscrowd'], + image_id=ann['image_id'], + bbox=bbox, + category_id=str(category_id), + category_no=category_no + ) + ) + return dict(annotations) + + +class AnnotatedObjectsCoco(AnnotatedObjectsDataset): + def __init__(self, use_things: bool = True, use_stuff: bool = True, **kwargs): + """ + @param data_path: is the path to the following folder structure: + coco/ + ├── annotations + │ ├── instances_train2017.json + │ ├── instances_val2017.json + │ ├── stuff_train2017.json + │ └── stuff_val2017.json + ├── train2017 + │ ├── 000000000009.jpg + │ ├── 000000000025.jpg + │ └── ... + ├── val2017 + │ ├── 000000000139.jpg + │ ├── 000000000285.jpg + │ └── ... + @param: split: one of 'train' or 'validation' + @param: desired image size (give square images) + """ + super().__init__(**kwargs) + self.use_things = use_things + self.use_stuff = use_stuff + + with open(self.paths['instances_annotations']) as f: + inst_data_json = json.load(f) + with open(self.paths['stuff_annotations']) as f: + stuff_data_json = json.load(f) + + category_jsons = [] + annotation_jsons = [] + if self.use_things: + category_jsons.append(inst_data_json['categories']) + annotation_jsons.append(inst_data_json['annotations']) + if self.use_stuff: + category_jsons.append(stuff_data_json['categories']) + annotation_jsons.append(stuff_data_json['annotations']) + + self.categories = load_categories(chain(*category_jsons)) + self.filter_categories() + self.setup_category_id_and_number() + + self.image_descriptions = load_image_descriptions(inst_data_json['images']) + annotations = load_annotations(annotation_jsons, self.image_descriptions, self.get_category_number, self.split) + self.annotations = self.filter_object_number(annotations, self.min_object_area, + self.min_objects_per_image, self.max_objects_per_image) + self.image_ids = list(self.annotations.keys()) + self.clean_up_annotations_and_image_descriptions() + + def get_path_structure(self) -> Dict[str, str]: + if self.split not in COCO_PATH_STRUCTURE: + raise ValueError(f'Split [{self.split} does not exist for COCO data.]') + return COCO_PATH_STRUCTURE[self.split] + + def get_image_path(self, image_id: str) -> Path: + return self.paths['files'].joinpath(self.image_descriptions[str(image_id)].file_name) + + def get_image_description(self, image_id: str) -> Dict[str, Any]: + # noinspection PyProtectedMember + return self.image_descriptions[image_id]._asdict() diff --git a/taming-transformers/taming/data/annotated_objects_dataset.py b/taming-transformers/taming/data/annotated_objects_dataset.py new file mode 100755 index 0000000000000000000000000000000000000000..53cc346a1c76289a4964d7dc8a29582172f33dc0 --- /dev/null +++ b/taming-transformers/taming/data/annotated_objects_dataset.py @@ -0,0 +1,218 @@ +from pathlib import Path +from typing import Optional, List, Callable, Dict, Any, Union +import warnings + +import PIL.Image as pil_image +from torch import Tensor +from torch.utils.data import Dataset +from torchvision import transforms + +from taming.data.conditional_builder.objects_bbox import ObjectsBoundingBoxConditionalBuilder +from taming.data.conditional_builder.objects_center_points import ObjectsCenterPointsConditionalBuilder +from taming.data.conditional_builder.utils import load_object_from_string +from taming.data.helper_types import BoundingBox, CropMethodType, Image, Annotation, SplitType +from taming.data.image_transforms import CenterCropReturnCoordinates, RandomCrop1dReturnCoordinates, \ + Random2dCropReturnCoordinates, RandomHorizontalFlipReturn, convert_pil_to_tensor + + +class AnnotatedObjectsDataset(Dataset): + def __init__(self, data_path: Union[str, Path], split: SplitType, keys: List[str], target_image_size: int, + min_object_area: float, min_objects_per_image: int, max_objects_per_image: int, + crop_method: CropMethodType, random_flip: bool, no_tokens: int, use_group_parameter: bool, + encode_crop: bool, category_allow_list_target: str = "", category_mapping_target: str = "", + no_object_classes: Optional[int] = None): + self.data_path = data_path + self.split = split + self.keys = keys + self.target_image_size = target_image_size + self.min_object_area = min_object_area + self.min_objects_per_image = min_objects_per_image + self.max_objects_per_image = max_objects_per_image + self.crop_method = crop_method + self.random_flip = random_flip + self.no_tokens = no_tokens + self.use_group_parameter = use_group_parameter + self.encode_crop = encode_crop + + self.annotations = None + self.image_descriptions = None + self.categories = None + self.category_ids = None + self.category_number = None + self.image_ids = None + self.transform_functions: List[Callable] = self.setup_transform(target_image_size, crop_method, random_flip) + self.paths = self.build_paths(self.data_path) + self._conditional_builders = None + self.category_allow_list = None + if category_allow_list_target: + allow_list = load_object_from_string(category_allow_list_target) + self.category_allow_list = {name for name, _ in allow_list} + self.category_mapping = {} + if category_mapping_target: + self.category_mapping = load_object_from_string(category_mapping_target) + self.no_object_classes = no_object_classes + + def build_paths(self, top_level: Union[str, Path]) -> Dict[str, Path]: + top_level = Path(top_level) + sub_paths = {name: top_level.joinpath(sub_path) for name, sub_path in self.get_path_structure().items()} + for path in sub_paths.values(): + if not path.exists(): + raise FileNotFoundError(f'{type(self).__name__} data structure error: [{path}] does not exist.') + return sub_paths + + @staticmethod + def load_image_from_disk(path: Path) -> Image: + return pil_image.open(path).convert('RGB') + + @staticmethod + def setup_transform(target_image_size: int, crop_method: CropMethodType, random_flip: bool): + transform_functions = [] + if crop_method == 'none': + transform_functions.append(transforms.Resize((target_image_size, target_image_size))) + elif crop_method == 'center': + transform_functions.extend([ + transforms.Resize(target_image_size), + CenterCropReturnCoordinates(target_image_size) + ]) + elif crop_method == 'random-1d': + transform_functions.extend([ + transforms.Resize(target_image_size), + RandomCrop1dReturnCoordinates(target_image_size) + ]) + elif crop_method == 'random-2d': + transform_functions.extend([ + Random2dCropReturnCoordinates(target_image_size), + transforms.Resize(target_image_size) + ]) + elif crop_method is None: + return None + else: + raise ValueError(f'Received invalid crop method [{crop_method}].') + if random_flip: + transform_functions.append(RandomHorizontalFlipReturn()) + transform_functions.append(transforms.Lambda(lambda x: x / 127.5 - 1.)) + return transform_functions + + def image_transform(self, x: Tensor) -> (Optional[BoundingBox], Optional[bool], Tensor): + crop_bbox = None + flipped = None + for t in self.transform_functions: + if isinstance(t, (RandomCrop1dReturnCoordinates, CenterCropReturnCoordinates, Random2dCropReturnCoordinates)): + crop_bbox, x = t(x) + elif isinstance(t, RandomHorizontalFlipReturn): + flipped, x = t(x) + else: + x = t(x) + return crop_bbox, flipped, x + + @property + def no_classes(self) -> int: + return self.no_object_classes if self.no_object_classes else len(self.categories) + + @property + def conditional_builders(self) -> ObjectsCenterPointsConditionalBuilder: + # cannot set this up in init because no_classes is only known after loading data in init of superclass + if self._conditional_builders is None: + self._conditional_builders = { + 'objects_center_points': ObjectsCenterPointsConditionalBuilder( + self.no_classes, + self.max_objects_per_image, + self.no_tokens, + self.encode_crop, + self.use_group_parameter, + getattr(self, 'use_additional_parameters', False) + ), + 'objects_bbox': ObjectsBoundingBoxConditionalBuilder( + self.no_classes, + self.max_objects_per_image, + self.no_tokens, + self.encode_crop, + self.use_group_parameter, + getattr(self, 'use_additional_parameters', False) + ) + } + return self._conditional_builders + + def filter_categories(self) -> None: + if self.category_allow_list: + self.categories = {id_: cat for id_, cat in self.categories.items() if cat.name in self.category_allow_list} + if self.category_mapping: + self.categories = {id_: cat for id_, cat in self.categories.items() if cat.id not in self.category_mapping} + + def setup_category_id_and_number(self) -> None: + self.category_ids = list(self.categories.keys()) + self.category_ids.sort() + if '/m/01s55n' in self.category_ids: + self.category_ids.remove('/m/01s55n') + self.category_ids.append('/m/01s55n') + self.category_number = {category_id: i for i, category_id in enumerate(self.category_ids)} + if self.category_allow_list is not None and self.category_mapping is None \ + and len(self.category_ids) != len(self.category_allow_list): + warnings.warn('Unexpected number of categories: Mismatch with category_allow_list. ' + 'Make sure all names in category_allow_list exist.') + + def clean_up_annotations_and_image_descriptions(self) -> None: + image_id_set = set(self.image_ids) + self.annotations = {k: v for k, v in self.annotations.items() if k in image_id_set} + self.image_descriptions = {k: v for k, v in self.image_descriptions.items() if k in image_id_set} + + @staticmethod + def filter_object_number(all_annotations: Dict[str, List[Annotation]], min_object_area: float, + min_objects_per_image: int, max_objects_per_image: int) -> Dict[str, List[Annotation]]: + filtered = {} + for image_id, annotations in all_annotations.items(): + annotations_with_min_area = [a for a in annotations if a.area > min_object_area] + if min_objects_per_image <= len(annotations_with_min_area) <= max_objects_per_image: + filtered[image_id] = annotations_with_min_area + return filtered + + def __len__(self): + return len(self.image_ids) + + def __getitem__(self, n: int) -> Dict[str, Any]: + image_id = self.get_image_id(n) + sample = self.get_image_description(image_id) + sample['annotations'] = self.get_annotation(image_id) + + if 'image' in self.keys: + sample['image_path'] = str(self.get_image_path(image_id)) + sample['image'] = self.load_image_from_disk(sample['image_path']) + sample['image'] = convert_pil_to_tensor(sample['image']) + sample['crop_bbox'], sample['flipped'], sample['image'] = self.image_transform(sample['image']) + sample['image'] = sample['image'].permute(1, 2, 0) + + for conditional, builder in self.conditional_builders.items(): + if conditional in self.keys: + sample[conditional] = builder.build(sample['annotations'], sample['crop_bbox'], sample['flipped']) + + if self.keys: + # only return specified keys + sample = {key: sample[key] for key in self.keys} + return sample + + def get_image_id(self, no: int) -> str: + return self.image_ids[no] + + def get_annotation(self, image_id: str) -> str: + return self.annotations[image_id] + + def get_textual_label_for_category_id(self, category_id: str) -> str: + return self.categories[category_id].name + + def get_textual_label_for_category_no(self, category_no: int) -> str: + return self.categories[self.get_category_id(category_no)].name + + def get_category_number(self, category_id: str) -> int: + return self.category_number[category_id] + + def get_category_id(self, category_no: int) -> str: + return self.category_ids[category_no] + + def get_image_description(self, image_id: str) -> Dict[str, Any]: + raise NotImplementedError() + + def get_path_structure(self): + raise NotImplementedError + + def get_image_path(self, image_id: str) -> Path: + raise NotImplementedError diff --git a/taming-transformers/taming/data/annotated_objects_open_images.py b/taming-transformers/taming/data/annotated_objects_open_images.py new file mode 100755 index 0000000000000000000000000000000000000000..aede6803d2cef7a74ca784e7907d35fba6c71239 --- /dev/null +++ b/taming-transformers/taming/data/annotated_objects_open_images.py @@ -0,0 +1,137 @@ +from collections import defaultdict +from csv import DictReader, reader as TupleReader +from pathlib import Path +from typing import Dict, List, Any +import warnings + +from taming.data.annotated_objects_dataset import AnnotatedObjectsDataset +from taming.data.helper_types import Annotation, Category +from tqdm import tqdm + +OPEN_IMAGES_STRUCTURE = { + 'train': { + 'top_level': '', + 'class_descriptions': 'class-descriptions-boxable.csv', + 'annotations': 'oidv6-train-annotations-bbox.csv', + 'file_list': 'train-images-boxable.csv', + 'files': 'train' + }, + 'validation': { + 'top_level': '', + 'class_descriptions': 'class-descriptions-boxable.csv', + 'annotations': 'validation-annotations-bbox.csv', + 'file_list': 'validation-images.csv', + 'files': 'validation' + }, + 'test': { + 'top_level': '', + 'class_descriptions': 'class-descriptions-boxable.csv', + 'annotations': 'test-annotations-bbox.csv', + 'file_list': 'test-images.csv', + 'files': 'test' + } +} + + +def load_annotations(descriptor_path: Path, min_object_area: float, category_mapping: Dict[str, str], + category_no_for_id: Dict[str, int]) -> Dict[str, List[Annotation]]: + annotations: Dict[str, List[Annotation]] = defaultdict(list) + with open(descriptor_path) as file: + reader = DictReader(file) + for i, row in tqdm(enumerate(reader), total=14620000, desc='Loading OpenImages annotations'): + width = float(row['XMax']) - float(row['XMin']) + height = float(row['YMax']) - float(row['YMin']) + area = width * height + category_id = row['LabelName'] + if category_id in category_mapping: + category_id = category_mapping[category_id] + if area >= min_object_area and category_id in category_no_for_id: + annotations[row['ImageID']].append( + Annotation( + id=i, + image_id=row['ImageID'], + source=row['Source'], + category_id=category_id, + category_no=category_no_for_id[category_id], + confidence=float(row['Confidence']), + bbox=(float(row['XMin']), float(row['YMin']), width, height), + area=area, + is_occluded=bool(int(row['IsOccluded'])), + is_truncated=bool(int(row['IsTruncated'])), + is_group_of=bool(int(row['IsGroupOf'])), + is_depiction=bool(int(row['IsDepiction'])), + is_inside=bool(int(row['IsInside'])) + ) + ) + if 'train' in str(descriptor_path) and i < 14000000: + warnings.warn(f'Running with subset of Open Images. Train dataset has length [{len(annotations)}].') + return dict(annotations) + + +def load_image_ids(csv_path: Path) -> List[str]: + with open(csv_path) as file: + reader = DictReader(file) + return [row['image_name'] for row in reader] + + +def load_categories(csv_path: Path) -> Dict[str, Category]: + with open(csv_path) as file: + reader = TupleReader(file) + return {row[0]: Category(id=row[0], name=row[1], super_category=None) for row in reader} + + +class AnnotatedObjectsOpenImages(AnnotatedObjectsDataset): + def __init__(self, use_additional_parameters: bool, **kwargs): + """ + @param data_path: is the path to the following folder structure: + open_images/ + │ oidv6-train-annotations-bbox.csv + ├── class-descriptions-boxable.csv + ├── oidv6-train-annotations-bbox.csv + ├── test + │ ├── 000026e7ee790996.jpg + │ ├── 000062a39995e348.jpg + │ └── ... + ├── test-annotations-bbox.csv + ├── test-images.csv + ├── train + │ ├── 000002b66c9c498e.jpg + │ ├── 000002b97e5471a0.jpg + │ └── ... + ├── train-images-boxable.csv + ├── validation + │ ├── 0001eeaf4aed83f9.jpg + │ ├── 0004886b7d043cfd.jpg + │ └── ... + ├── validation-annotations-bbox.csv + └── validation-images.csv + @param: split: one of 'train', 'validation' or 'test' + @param: desired image size (returns square images) + """ + + super().__init__(**kwargs) + self.use_additional_parameters = use_additional_parameters + + self.categories = load_categories(self.paths['class_descriptions']) + self.filter_categories() + self.setup_category_id_and_number() + + self.image_descriptions = {} + annotations = load_annotations(self.paths['annotations'], self.min_object_area, self.category_mapping, + self.category_number) + self.annotations = self.filter_object_number(annotations, self.min_object_area, self.min_objects_per_image, + self.max_objects_per_image) + self.image_ids = list(self.annotations.keys()) + self.clean_up_annotations_and_image_descriptions() + + def get_path_structure(self) -> Dict[str, str]: + if self.split not in OPEN_IMAGES_STRUCTURE: + raise ValueError(f'Split [{self.split} does not exist for Open Images data.]') + return OPEN_IMAGES_STRUCTURE[self.split] + + def get_image_path(self, image_id: str) -> Path: + return self.paths['files'].joinpath(f'{image_id:0>16}.jpg') + + def get_image_description(self, image_id: str) -> Dict[str, Any]: + image_path = self.get_image_path(image_id) + return {'file_path': str(image_path), 'file_name': image_path.name} diff --git a/taming-transformers/taming/data/base.py b/taming-transformers/taming/data/base.py new file mode 100755 index 0000000000000000000000000000000000000000..e21667df4ce4baa6bb6aad9f8679bd756e2ffdb7 --- /dev/null +++ b/taming-transformers/taming/data/base.py @@ -0,0 +1,70 @@ +import bisect +import numpy as np +import albumentations +from PIL import Image +from torch.utils.data import Dataset, ConcatDataset + + +class ConcatDatasetWithIndex(ConcatDataset): + """Modified from original pytorch code to return dataset idx""" + def __getitem__(self, idx): + if idx < 0: + if -idx > len(self): + raise ValueError("absolute value of index should not exceed dataset length") + idx = len(self) + idx + dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx) + if dataset_idx == 0: + sample_idx = idx + else: + sample_idx = idx - self.cumulative_sizes[dataset_idx - 1] + return self.datasets[dataset_idx][sample_idx], dataset_idx + + +class ImagePaths(Dataset): + def __init__(self, paths, size=None, random_crop=False, labels=None): + self.size = size + self.random_crop = random_crop + + self.labels = dict() if labels is None else labels + self.labels["file_path_"] = paths + self._length = len(paths) + + if self.size is not None and self.size > 0: + self.rescaler = albumentations.SmallestMaxSize(max_size = self.size) + if not self.random_crop: + self.cropper = albumentations.CenterCrop(height=self.size,width=self.size) + else: + self.cropper = albumentations.RandomCrop(height=self.size,width=self.size) + self.preprocessor = albumentations.Compose([self.rescaler, self.cropper]) + else: + self.preprocessor = lambda **kwargs: kwargs + + def __len__(self): + return self._length + + def preprocess_image(self, image_path): + image = Image.open(image_path) + if not image.mode == "RGB": + image = image.convert("RGB") + image = np.array(image).astype(np.uint8) + image = self.preprocessor(image=image)["image"] + image = (image/127.5 - 1.0).astype(np.float32) + return image + + def __getitem__(self, i): + example = dict() + example["image"] = self.preprocess_image(self.labels["file_path_"][i]) + for k in self.labels: + example[k] = self.labels[k][i] + return example + + +class NumpyPaths(ImagePaths): + def preprocess_image(self, image_path): + image = np.load(image_path).squeeze(0) # 3 x 1024 x 1024 + image = np.transpose(image, (1,2,0)) + image = Image.fromarray(image, mode="RGB") + image = np.array(image).astype(np.uint8) + image = self.preprocessor(image=image)["image"] + image = (image/127.5 - 1.0).astype(np.float32) + return image diff --git a/taming-transformers/taming/data/coco.py b/taming-transformers/taming/data/coco.py new file mode 100755 index 0000000000000000000000000000000000000000..2b2f7838448cb63dcf96daffe9470d58566d975a --- /dev/null +++ b/taming-transformers/taming/data/coco.py @@ -0,0 +1,176 @@ +import os +import json +import albumentations +import numpy as np +from PIL import Image +from tqdm import tqdm +from torch.utils.data import Dataset + +from taming.data.sflckr import SegmentationBase # for examples included in repo + + +class Examples(SegmentationBase): + def __init__(self, size=256, random_crop=False, interpolation="bicubic"): + super().__init__(data_csv="data/coco_examples.txt", + data_root="data/coco_images", + segmentation_root="data/coco_segmentations", + size=size, random_crop=random_crop, + interpolation=interpolation, + n_labels=183, shift_segmentation=True) + + +class CocoBase(Dataset): + """needed for (image, caption, segmentation) pairs""" + def __init__(self, size=None, dataroot="", datajson="", onehot_segmentation=False, use_stuffthing=False, + crop_size=None, force_no_crop=False, given_files=None): + self.split = self.get_split() + self.size = size + if crop_size is None: + self.crop_size = size + else: + self.crop_size = crop_size + + self.onehot = onehot_segmentation # return segmentation as rgb or one hot + self.stuffthing = use_stuffthing # include thing in segmentation + if self.onehot and not self.stuffthing: + raise NotImplemented("One hot mode is only supported for the " + "stuffthings version because labels are stored " + "a bit different.") + + data_json = datajson + with open(data_json) as json_file: + self.json_data = json.load(json_file) + self.img_id_to_captions = dict() + self.img_id_to_filepath = dict() + self.img_id_to_segmentation_filepath = dict() + + assert data_json.split("/")[-1] in ["captions_train2017.json", + "captions_val2017.json"] + if self.stuffthing: + self.segmentation_prefix = ( + "data/cocostuffthings/val2017" if + data_json.endswith("captions_val2017.json") else + "data/cocostuffthings/train2017") + else: + self.segmentation_prefix = ( + "data/coco/annotations/stuff_val2017_pixelmaps" if + data_json.endswith("captions_val2017.json") else + "data/coco/annotations/stuff_train2017_pixelmaps") + + imagedirs = self.json_data["images"] + self.labels = {"image_ids": list()} + for imgdir in tqdm(imagedirs, desc="ImgToPath"): + self.img_id_to_filepath[imgdir["id"]] = os.path.join(dataroot, imgdir["file_name"]) + self.img_id_to_captions[imgdir["id"]] = list() + pngfilename = imgdir["file_name"].replace("jpg", "png") + self.img_id_to_segmentation_filepath[imgdir["id"]] = os.path.join( + self.segmentation_prefix, pngfilename) + if given_files is not None: + if pngfilename in given_files: + self.labels["image_ids"].append(imgdir["id"]) + else: + self.labels["image_ids"].append(imgdir["id"]) + + capdirs = self.json_data["annotations"] + for capdir in tqdm(capdirs, desc="ImgToCaptions"): + # there are in average 5 captions per image + self.img_id_to_captions[capdir["image_id"]].append(np.array([capdir["caption"]])) + + self.rescaler = albumentations.SmallestMaxSize(max_size=self.size) + if self.split=="validation": + self.cropper = albumentations.CenterCrop(height=self.crop_size, width=self.crop_size) + else: + self.cropper = albumentations.RandomCrop(height=self.crop_size, width=self.crop_size) + self.preprocessor = albumentations.Compose( + [self.rescaler, self.cropper], + additional_targets={"segmentation": "image"}) + if force_no_crop: + self.rescaler = albumentations.Resize(height=self.size, width=self.size) + self.preprocessor = albumentations.Compose( + [self.rescaler], + additional_targets={"segmentation": "image"}) + + def __len__(self): + return len(self.labels["image_ids"]) + + def preprocess_image(self, image_path, segmentation_path): + image = Image.open(image_path) + if not image.mode == "RGB": + image = image.convert("RGB") + image = np.array(image).astype(np.uint8) + + segmentation = Image.open(segmentation_path) + if not self.onehot and not segmentation.mode == "RGB": + segmentation = segmentation.convert("RGB") + segmentation = np.array(segmentation).astype(np.uint8) + if self.onehot: + assert self.stuffthing + # stored in caffe format: unlabeled==255. stuff and thing from + # 0-181. to be compatible with the labels in + # https://github.com/nightrome/cocostuff/blob/master/labels.txt + # we shift stuffthing one to the right and put unlabeled in zero + # as long as segmentation is uint8 shifting to right handles the + # latter too + assert segmentation.dtype == np.uint8 + segmentation = segmentation + 1 + + processed = self.preprocessor(image=image, segmentation=segmentation) + image, segmentation = processed["image"], processed["segmentation"] + image = (image / 127.5 - 1.0).astype(np.float32) + + if self.onehot: + assert segmentation.dtype == np.uint8 + # make it one hot + n_labels = 183 + flatseg = np.ravel(segmentation) + onehot = np.zeros((flatseg.size, n_labels), dtype=np.bool) + onehot[np.arange(flatseg.size), flatseg] = True + onehot = onehot.reshape(segmentation.shape + (n_labels,)).astype(int) + segmentation = onehot + else: + segmentation = (segmentation / 127.5 - 1.0).astype(np.float32) + return image, segmentation + + def __getitem__(self, i): + img_path = self.img_id_to_filepath[self.labels["image_ids"][i]] + seg_path = self.img_id_to_segmentation_filepath[self.labels["image_ids"][i]] + image, segmentation = self.preprocess_image(img_path, seg_path) + captions = self.img_id_to_captions[self.labels["image_ids"][i]] + # randomly draw one of all available captions per image + caption = captions[np.random.randint(0, len(captions))] + example = {"image": image, + "caption": [str(caption[0])], + "segmentation": segmentation, + "img_path": img_path, + "seg_path": seg_path, + "filename_": img_path.split(os.sep)[-1] + } + return example + + +class CocoImagesAndCaptionsTrain(CocoBase): + """returns a pair of (image, caption)""" + def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False): + super().__init__(size=size, + dataroot="data/coco/train2017", + datajson="data/coco/annotations/captions_train2017.json", + onehot_segmentation=onehot_segmentation, + use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop) + + def get_split(self): + return "train" + + +class CocoImagesAndCaptionsValidation(CocoBase): + """returns a pair of (image, caption)""" + def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False, + given_files=None): + super().__init__(size=size, + dataroot="data/coco/val2017", + datajson="data/coco/annotations/captions_val2017.json", + onehot_segmentation=onehot_segmentation, + use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop, + given_files=given_files) + + def get_split(self): + return "validation" diff --git a/taming-transformers/taming/data/conditional_builder/objects_bbox.py b/taming-transformers/taming/data/conditional_builder/objects_bbox.py new file mode 100755 index 0000000000000000000000000000000000000000..15881e76b7ab2a914df8f2dfe08ae4f0c6c511b5 --- /dev/null +++ b/taming-transformers/taming/data/conditional_builder/objects_bbox.py @@ -0,0 +1,60 @@ +from itertools import cycle +from typing import List, Tuple, Callable, Optional + +from PIL import Image as pil_image, ImageDraw as pil_img_draw, ImageFont +from more_itertools.recipes import grouper +from taming.data.image_transforms import convert_pil_to_tensor +from torch import LongTensor, Tensor + +from taming.data.helper_types import BoundingBox, Annotation +from taming.data.conditional_builder.objects_center_points import ObjectsCenterPointsConditionalBuilder +from taming.data.conditional_builder.utils import COLOR_PALETTE, WHITE, GRAY_75, BLACK, additional_parameters_string, \ + pad_list, get_plot_font_size, absolute_bbox + + +class ObjectsBoundingBoxConditionalBuilder(ObjectsCenterPointsConditionalBuilder): + @property + def object_descriptor_length(self) -> int: + return 3 + + def _make_object_descriptors(self, annotations: List[Annotation]) -> List[Tuple[int, ...]]: + object_triples = [ + (self.object_representation(ann), *self.token_pair_from_bbox(ann.bbox)) + for ann in annotations + ] + empty_triple = (self.none, self.none, self.none) + object_triples = pad_list(object_triples, empty_triple, self.no_max_objects) + return object_triples + + def inverse_build(self, conditional: LongTensor) -> Tuple[List[Tuple[int, BoundingBox]], Optional[BoundingBox]]: + conditional_list = conditional.tolist() + crop_coordinates = None + if self.encode_crop: + crop_coordinates = self.bbox_from_token_pair(conditional_list[-2], conditional_list[-1]) + conditional_list = conditional_list[:-2] + object_triples = grouper(conditional_list, 3) + assert conditional.shape[0] == self.embedding_dim + return [ + (object_triple[0], self.bbox_from_token_pair(object_triple[1], object_triple[2])) + for object_triple in object_triples if object_triple[0] != self.none + ], crop_coordinates + + def plot(self, conditional: LongTensor, label_for_category_no: Callable[[int], str], figure_size: Tuple[int, int], + line_width: int = 3, font_size: Optional[int] = None) -> Tensor: + plot = pil_image.new('RGB', figure_size, WHITE) + draw = pil_img_draw.Draw(plot) + font = ImageFont.truetype( + "/usr/share/fonts/truetype/lato/Lato-Regular.ttf", + size=get_plot_font_size(font_size, figure_size) + ) + width, height = plot.size + description, crop_coordinates = self.inverse_build(conditional) + for (representation, bbox), color in zip(description, cycle(COLOR_PALETTE)): + annotation = self.representation_to_annotation(representation) + class_label = label_for_category_no(annotation.category_no) + ' ' + additional_parameters_string(annotation) + bbox = absolute_bbox(bbox, width, height) + draw.rectangle(bbox, outline=color, width=line_width) + draw.text((bbox[0] + line_width, bbox[1] + line_width), class_label, anchor='la', fill=BLACK, font=font) + if crop_coordinates is not None: + draw.rectangle(absolute_bbox(crop_coordinates, width, height), outline=GRAY_75, width=line_width) + return convert_pil_to_tensor(plot) / 127.5 - 1. diff --git a/taming-transformers/taming/data/conditional_builder/objects_center_points.py b/taming-transformers/taming/data/conditional_builder/objects_center_points.py new file mode 100755 index 0000000000000000000000000000000000000000..9a480329cc47fb38a7b8729d424e092b77d40749 --- /dev/null +++ b/taming-transformers/taming/data/conditional_builder/objects_center_points.py @@ -0,0 +1,168 @@ +import math +import random +import warnings +from itertools import cycle +from typing import List, Optional, Tuple, Callable + +from PIL import Image as pil_image, ImageDraw as pil_img_draw, ImageFont +from more_itertools.recipes import grouper +from taming.data.conditional_builder.utils import COLOR_PALETTE, WHITE, GRAY_75, BLACK, FULL_CROP, filter_annotations, \ + additional_parameters_string, horizontally_flip_bbox, pad_list, get_circle_size, get_plot_font_size, \ + absolute_bbox, rescale_annotations +from taming.data.helper_types import BoundingBox, Annotation +from taming.data.image_transforms import convert_pil_to_tensor +from torch import LongTensor, Tensor + + +class ObjectsCenterPointsConditionalBuilder: + def __init__(self, no_object_classes: int, no_max_objects: int, no_tokens: int, encode_crop: bool, + use_group_parameter: bool, use_additional_parameters: bool): + self.no_object_classes = no_object_classes + self.no_max_objects = no_max_objects + self.no_tokens = no_tokens + self.encode_crop = encode_crop + self.no_sections = int(math.sqrt(self.no_tokens)) + self.use_group_parameter = use_group_parameter + self.use_additional_parameters = use_additional_parameters + + @property + def none(self) -> int: + return self.no_tokens - 1 + + @property + def object_descriptor_length(self) -> int: + return 2 + + @property + def embedding_dim(self) -> int: + extra_length = 2 if self.encode_crop else 0 + return self.no_max_objects * self.object_descriptor_length + extra_length + + def tokenize_coordinates(self, x: float, y: float) -> int: + """ + Express 2d coordinates with one number. + Example: assume self.no_tokens = 16, then no_sections = 4: + 0 0 0 0 + 0 0 # 0 + 0 0 0 0 + 0 0 0 x + Then the # position corresponds to token 6, the x position to token 15. + @param x: float in [0, 1] + @param y: float in [0, 1] + @return: discrete tokenized coordinate + """ + x_discrete = int(round(x * (self.no_sections - 1))) + y_discrete = int(round(y * (self.no_sections - 1))) + return y_discrete * self.no_sections + x_discrete + + def coordinates_from_token(self, token: int) -> (float, float): + x = token % self.no_sections + y = token // self.no_sections + return x / (self.no_sections - 1), y / (self.no_sections - 1) + + def bbox_from_token_pair(self, token1: int, token2: int) -> BoundingBox: + x0, y0 = self.coordinates_from_token(token1) + x1, y1 = self.coordinates_from_token(token2) + return x0, y0, x1 - x0, y1 - y0 + + def token_pair_from_bbox(self, bbox: BoundingBox) -> Tuple[int, int]: + return self.tokenize_coordinates(bbox[0], bbox[1]), \ + self.tokenize_coordinates(bbox[0] + bbox[2], bbox[1] + bbox[3]) + + def inverse_build(self, conditional: LongTensor) \ + -> Tuple[List[Tuple[int, Tuple[float, float]]], Optional[BoundingBox]]: + conditional_list = conditional.tolist() + crop_coordinates = None + if self.encode_crop: + crop_coordinates = self.bbox_from_token_pair(conditional_list[-2], conditional_list[-1]) + conditional_list = conditional_list[:-2] + table_of_content = grouper(conditional_list, self.object_descriptor_length) + assert conditional.shape[0] == self.embedding_dim + return [ + (object_tuple[0], self.coordinates_from_token(object_tuple[1])) + for object_tuple in table_of_content if object_tuple[0] != self.none + ], crop_coordinates + + def plot(self, conditional: LongTensor, label_for_category_no: Callable[[int], str], figure_size: Tuple[int, int], + line_width: int = 3, font_size: Optional[int] = None) -> Tensor: + plot = pil_image.new('RGB', figure_size, WHITE) + draw = pil_img_draw.Draw(plot) + circle_size = get_circle_size(figure_size) + font = ImageFont.truetype('/usr/share/fonts/truetype/lato/Lato-Regular.ttf', + size=get_plot_font_size(font_size, figure_size)) + width, height = plot.size + description, crop_coordinates = self.inverse_build(conditional) + for (representation, (x, y)), color in zip(description, cycle(COLOR_PALETTE)): + x_abs, y_abs = x * width, y * height + ann = self.representation_to_annotation(representation) + label = label_for_category_no(ann.category_no) + ' ' + additional_parameters_string(ann) + ellipse_bbox = [x_abs - circle_size, y_abs - circle_size, x_abs + circle_size, y_abs + circle_size] + draw.ellipse(ellipse_bbox, fill=color, width=0) + draw.text((x_abs, y_abs), label, anchor='md', fill=BLACK, font=font) + if crop_coordinates is not None: + draw.rectangle(absolute_bbox(crop_coordinates, width, height), outline=GRAY_75, width=line_width) + return convert_pil_to_tensor(plot) / 127.5 - 1. + + def object_representation(self, annotation: Annotation) -> int: + modifier = 0 + if self.use_group_parameter: + modifier |= 1 * (annotation.is_group_of is True) + if self.use_additional_parameters: + modifier |= 2 * (annotation.is_occluded is True) + modifier |= 4 * (annotation.is_depiction is True) + modifier |= 8 * (annotation.is_inside is True) + return annotation.category_no + self.no_object_classes * modifier + + def representation_to_annotation(self, representation: int) -> Annotation: + category_no = representation % self.no_object_classes + modifier = representation // self.no_object_classes + # noinspection PyTypeChecker + return Annotation( + area=None, image_id=None, bbox=None, category_id=None, id=None, source=None, confidence=None, + category_no=category_no, + is_group_of=bool((modifier & 1) * self.use_group_parameter), + is_occluded=bool((modifier & 2) * self.use_additional_parameters), + is_depiction=bool((modifier & 4) * self.use_additional_parameters), + is_inside=bool((modifier & 8) * self.use_additional_parameters) + ) + + def _crop_encoder(self, crop_coordinates: BoundingBox) -> List[int]: + return list(self.token_pair_from_bbox(crop_coordinates)) + + def _make_object_descriptors(self, annotations: List[Annotation]) -> List[Tuple[int, ...]]: + object_tuples = [ + (self.object_representation(a), + self.tokenize_coordinates(a.bbox[0] + a.bbox[2] / 2, a.bbox[1] + a.bbox[3] / 2)) + for a in annotations + ] + empty_tuple = (self.none, self.none) + object_tuples = pad_list(object_tuples, empty_tuple, self.no_max_objects) + return object_tuples + + def build(self, annotations: List, crop_coordinates: Optional[BoundingBox] = None, horizontal_flip: bool = False) \ + -> LongTensor: + if len(annotations) == 0: + warnings.warn('Did not receive any annotations.') + if len(annotations) > self.no_max_objects: + warnings.warn('Received more annotations than allowed.') + annotations = annotations[:self.no_max_objects] + + if not crop_coordinates: + crop_coordinates = FULL_CROP + + random.shuffle(annotations) + annotations = filter_annotations(annotations, crop_coordinates) + if self.encode_crop: + annotations = rescale_annotations(annotations, FULL_CROP, horizontal_flip) + if horizontal_flip: + crop_coordinates = horizontally_flip_bbox(crop_coordinates) + extra = self._crop_encoder(crop_coordinates) + else: + annotations = rescale_annotations(annotations, crop_coordinates, horizontal_flip) + extra = [] + + object_tuples = self._make_object_descriptors(annotations) + flattened = [token for tuple_ in object_tuples for token in tuple_] + extra + assert len(flattened) == self.embedding_dim + assert all(0 <= value < self.no_tokens for value in flattened) + return LongTensor(flattened) diff --git a/taming-transformers/taming/data/conditional_builder/utils.py b/taming-transformers/taming/data/conditional_builder/utils.py new file mode 100755 index 0000000000000000000000000000000000000000..d0ee175f2e05a80dbc71c22acbecb22dddadbb42 --- /dev/null +++ b/taming-transformers/taming/data/conditional_builder/utils.py @@ -0,0 +1,105 @@ +import importlib +from typing import List, Any, Tuple, Optional + +from taming.data.helper_types import BoundingBox, Annotation + +# source: seaborn, color palette tab10 +COLOR_PALETTE = [(30, 118, 179), (255, 126, 13), (43, 159, 43), (213, 38, 39), (147, 102, 188), + (139, 85, 74), (226, 118, 193), (126, 126, 126), (187, 188, 33), (22, 189, 206)] +BLACK = (0, 0, 0) +GRAY_75 = (63, 63, 63) +GRAY_50 = (127, 127, 127) +GRAY_25 = (191, 191, 191) +WHITE = (255, 255, 255) +FULL_CROP = (0., 0., 1., 1.) + + +def intersection_area(rectangle1: BoundingBox, rectangle2: BoundingBox) -> float: + """ + Give intersection area of two rectangles. + @param rectangle1: (x0, y0, w, h) of first rectangle + @param rectangle2: (x0, y0, w, h) of second rectangle + """ + rectangle1 = rectangle1[0], rectangle1[1], rectangle1[0] + rectangle1[2], rectangle1[1] + rectangle1[3] + rectangle2 = rectangle2[0], rectangle2[1], rectangle2[0] + rectangle2[2], rectangle2[1] + rectangle2[3] + x_overlap = max(0., min(rectangle1[2], rectangle2[2]) - max(rectangle1[0], rectangle2[0])) + y_overlap = max(0., min(rectangle1[3], rectangle2[3]) - max(rectangle1[1], rectangle2[1])) + return x_overlap * y_overlap + + +def horizontally_flip_bbox(bbox: BoundingBox) -> BoundingBox: + return 1 - (bbox[0] + bbox[2]), bbox[1], bbox[2], bbox[3] + + +def absolute_bbox(relative_bbox: BoundingBox, width: int, height: int) -> Tuple[int, int, int, int]: + bbox = relative_bbox + bbox = bbox[0] * width, bbox[1] * height, (bbox[0] + bbox[2]) * width, (bbox[1] + bbox[3]) * height + return int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3]) + + +def pad_list(list_: List, pad_element: Any, pad_to_length: int) -> List: + return list_ + [pad_element for _ in range(pad_to_length - len(list_))] + + +def rescale_annotations(annotations: List[Annotation], crop_coordinates: BoundingBox, flip: bool) -> \ + List[Annotation]: + def clamp(x: float): + return max(min(x, 1.), 0.) + + def rescale_bbox(bbox: BoundingBox) -> BoundingBox: + x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) + y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) + w = min(bbox[2] / crop_coordinates[2], 1 - x0) + h = min(bbox[3] / crop_coordinates[3], 1 - y0) + if flip: + x0 = 1 - (x0 + w) + return x0, y0, w, h + + return [a._replace(bbox=rescale_bbox(a.bbox)) for a in annotations] + + +def filter_annotations(annotations: List[Annotation], crop_coordinates: BoundingBox) -> List: + return [a for a in annotations if intersection_area(a.bbox, crop_coordinates) > 0.0] + + +def additional_parameters_string(annotation: Annotation, short: bool = True) -> str: + sl = slice(1) if short else slice(None) + string = '' + if not (annotation.is_group_of or annotation.is_occluded or annotation.is_depiction or annotation.is_inside): + return string + if annotation.is_group_of: + string += 'group'[sl] + ',' + if annotation.is_occluded: + string += 'occluded'[sl] + ',' + if annotation.is_depiction: + string += 'depiction'[sl] + ',' + if annotation.is_inside: + string += 'inside'[sl] + return '(' + string.strip(",") + ')' + + +def get_plot_font_size(font_size: Optional[int], figure_size: Tuple[int, int]) -> int: + if font_size is None: + font_size = 10 + if max(figure_size) >= 256: + font_size = 12 + if max(figure_size) >= 512: + font_size = 15 + return font_size + + +def get_circle_size(figure_size: Tuple[int, int]) -> int: + circle_size = 2 + if max(figure_size) >= 256: + circle_size = 3 + if max(figure_size) >= 512: + circle_size = 4 + return circle_size + + +def load_object_from_string(object_string: str) -> Any: + """ + Source: https://stackoverflow.com/a/10773699 + """ + module_name, class_name = object_string.rsplit(".", 1) + return getattr(importlib.import_module(module_name), class_name) diff --git a/taming-transformers/taming/data/custom.py b/taming-transformers/taming/data/custom.py new file mode 100755 index 0000000000000000000000000000000000000000..33f302a4b55ba1e8ec282ec3292b6263c06dfb91 --- /dev/null +++ b/taming-transformers/taming/data/custom.py @@ -0,0 +1,38 @@ +import os +import numpy as np +import albumentations +from torch.utils.data import Dataset + +from taming.data.base import ImagePaths, NumpyPaths, ConcatDatasetWithIndex + + +class CustomBase(Dataset): + def __init__(self, *args, **kwargs): + super().__init__() + self.data = None + + def __len__(self): + return len(self.data) + + def __getitem__(self, i): + example = self.data[i] + return example + + + +class CustomTrain(CustomBase): + def __init__(self, size, training_images_list_file): + super().__init__() + with open(training_images_list_file, "r") as f: + paths = f.read().splitlines() + self.data = ImagePaths(paths=paths, size=size, random_crop=False) + + +class CustomTest(CustomBase): + def __init__(self, size, test_images_list_file): + super().__init__() + with open(test_images_list_file, "r") as f: + paths = f.read().splitlines() + self.data = ImagePaths(paths=paths, size=size, random_crop=False) + + diff --git a/taming-transformers/taming/data/faceshq.py b/taming-transformers/taming/data/faceshq.py new file mode 100755 index 0000000000000000000000000000000000000000..6912d04b66a6d464c1078e4b51d5da290f5e767e --- /dev/null +++ b/taming-transformers/taming/data/faceshq.py @@ -0,0 +1,134 @@ +import os +import numpy as np +import albumentations +from torch.utils.data import Dataset + +from taming.data.base import ImagePaths, NumpyPaths, ConcatDatasetWithIndex + + +class FacesBase(Dataset): + def __init__(self, *args, **kwargs): + super().__init__() + self.data = None + self.keys = None + + def __len__(self): + return len(self.data) + + def __getitem__(self, i): + example = self.data[i] + ex = {} + if self.keys is not None: + for k in self.keys: + ex[k] = example[k] + else: + ex = example + return ex + + +class CelebAHQTrain(FacesBase): + def __init__(self, size, keys=None): + super().__init__() + root = "data/celebahq" + with open("data/celebahqtrain.txt", "r") as f: + relpaths = f.read().splitlines() + paths = [os.path.join(root, relpath) for relpath in relpaths] + self.data = NumpyPaths(paths=paths, size=size, random_crop=False) + self.keys = keys + + +class CelebAHQValidation(FacesBase): + def __init__(self, size, keys=None): + super().__init__() + root = "data/celebahq" + with open("data/celebahqvalidation.txt", "r") as f: + relpaths = f.read().splitlines() + paths = [os.path.join(root, relpath) for relpath in relpaths] + self.data = NumpyPaths(paths=paths, size=size, random_crop=False) + self.keys = keys + + +class FFHQTrain(FacesBase): + def __init__(self, size, keys=None): + super().__init__() + root = "data/ffhq" + with open("data/ffhqtrain.txt", "r") as f: + relpaths = f.read().splitlines() + paths = [os.path.join(root, relpath) for relpath in relpaths] + self.data = ImagePaths(paths=paths, size=size, random_crop=False) + self.keys = keys + + +class FFHQValidation(FacesBase): + def __init__(self, size, keys=None): + super().__init__() + root = "data/ffhq" + with open("data/ffhqvalidation.txt", "r") as f: + relpaths = f.read().splitlines() + paths = [os.path.join(root, relpath) for relpath in relpaths] + self.data = ImagePaths(paths=paths, size=size, random_crop=False) + self.keys = keys + + +class FacesHQTrain(Dataset): + # CelebAHQ [0] + FFHQ [1] + def __init__(self, size, keys=None, crop_size=None, coord=False): + d1 = CelebAHQTrain(size=size, keys=keys) + d2 = FFHQTrain(size=size, keys=keys) + self.data = ConcatDatasetWithIndex([d1, d2]) + self.coord = coord + if crop_size is not None: + self.cropper = albumentations.RandomCrop(height=crop_size,width=crop_size) + if self.coord: + self.cropper = albumentations.Compose([self.cropper], + additional_targets={"coord": "image"}) + + def __len__(self): + return len(self.data) + + def __getitem__(self, i): + ex, y = self.data[i] + if hasattr(self, "cropper"): + if not self.coord: + out = self.cropper(image=ex["image"]) + ex["image"] = out["image"] + else: + h,w,_ = ex["image"].shape + coord = np.arange(h*w).reshape(h,w,1)/(h*w) + out = self.cropper(image=ex["image"], coord=coord) + ex["image"] = out["image"] + ex["coord"] = out["coord"] + ex["class"] = y + return ex + + +class FacesHQValidation(Dataset): + # CelebAHQ [0] + FFHQ [1] + def __init__(self, size, keys=None, crop_size=None, coord=False): + d1 = CelebAHQValidation(size=size, keys=keys) + d2 = FFHQValidation(size=size, keys=keys) + self.data = ConcatDatasetWithIndex([d1, d2]) + self.coord = coord + if crop_size is not None: + self.cropper = albumentations.CenterCrop(height=crop_size,width=crop_size) + if self.coord: + self.cropper = albumentations.Compose([self.cropper], + additional_targets={"coord": "image"}) + + def __len__(self): + return len(self.data) + + def __getitem__(self, i): + ex, y = self.data[i] + if hasattr(self, "cropper"): + if not self.coord: + out = self.cropper(image=ex["image"]) + ex["image"] = out["image"] + else: + h,w,_ = ex["image"].shape + coord = np.arange(h*w).reshape(h,w,1)/(h*w) + out = self.cropper(image=ex["image"], coord=coord) + ex["image"] = out["image"] + ex["coord"] = out["coord"] + ex["class"] = y + return ex diff --git a/taming-transformers/taming/data/helper_types.py b/taming-transformers/taming/data/helper_types.py new file mode 100755 index 0000000000000000000000000000000000000000..fb51e301da08602cfead5961c4f7e1d89f6aba79 --- /dev/null +++ b/taming-transformers/taming/data/helper_types.py @@ -0,0 +1,49 @@ +from typing import Dict, Tuple, Optional, NamedTuple, Union +from PIL.Image import Image as pil_image +from torch import Tensor + +try: + from typing import Literal +except ImportError: + from typing_extensions import Literal + +Image = Union[Tensor, pil_image] +BoundingBox = Tuple[float, float, float, float] # x0, y0, w, h +CropMethodType = Literal['none', 'random', 'center', 'random-2d'] +SplitType = Literal['train', 'validation', 'test'] + + +class ImageDescription(NamedTuple): + id: int + file_name: str + original_size: Tuple[int, int] # w, h + url: Optional[str] = None + license: Optional[int] = None + coco_url: Optional[str] = None + date_captured: Optional[str] = None + flickr_url: Optional[str] = None + flickr_id: Optional[str] = None + coco_id: Optional[str] = None + + +class Category(NamedTuple): + id: str + super_category: Optional[str] + name: str + + +class Annotation(NamedTuple): + area: float + image_id: str + bbox: BoundingBox + category_no: int + category_id: str + id: Optional[int] = None + source: Optional[str] = None + confidence: Optional[float] = None + is_group_of: Optional[bool] = None + is_truncated: Optional[bool] = None + is_occluded: Optional[bool] = None + is_depiction: Optional[bool] = None + is_inside: Optional[bool] = None + segmentation: Optional[Dict] = None diff --git a/taming-transformers/taming/data/image_transforms.py b/taming-transformers/taming/data/image_transforms.py new file mode 100755 index 0000000000000000000000000000000000000000..657ac332174e0ac72f68315271ffbd757b771a0f --- /dev/null +++ b/taming-transformers/taming/data/image_transforms.py @@ -0,0 +1,132 @@ +import random +import warnings +from typing import Union + +import torch +from torch import Tensor +from torchvision.transforms import RandomCrop, functional as F, CenterCrop, RandomHorizontalFlip, PILToTensor +from torchvision.transforms.functional import _get_image_size as get_image_size + +from taming.data.helper_types import BoundingBox, Image + +pil_to_tensor = PILToTensor() + + +def convert_pil_to_tensor(image: Image) -> Tensor: + with warnings.catch_warnings(): + # to filter PyTorch UserWarning as described here: https://github.com/pytorch/vision/issues/2194 + warnings.simplefilter("ignore") + return pil_to_tensor(image) + + +class RandomCrop1dReturnCoordinates(RandomCrop): + def forward(self, img: Image) -> (BoundingBox, Image): + """ + Additionally to cropping, returns the relative coordinates of the crop bounding box. + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + Bounding box: x0, y0, w, h + PIL Image or Tensor: Cropped image. + + Based on: + torchvision.transforms.RandomCrop, torchvision 1.7.0 + """ + if self.padding is not None: + img = F.pad(img, self.padding, self.fill, self.padding_mode) + + width, height = get_image_size(img) + # pad the width if needed + if self.pad_if_needed and width < self.size[1]: + padding = [self.size[1] - width, 0] + img = F.pad(img, padding, self.fill, self.padding_mode) + # pad the height if needed + if self.pad_if_needed and height < self.size[0]: + padding = [0, self.size[0] - height] + img = F.pad(img, padding, self.fill, self.padding_mode) + + i, j, h, w = self.get_params(img, self.size) + bbox = (j / width, i / height, w / width, h / height) # x0, y0, w, h + return bbox, F.crop(img, i, j, h, w) + + +class Random2dCropReturnCoordinates(torch.nn.Module): + """ + Additionally to cropping, returns the relative coordinates of the crop bounding box. + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + Bounding box: x0, y0, w, h + PIL Image or Tensor: Cropped image. + + Based on: + torchvision.transforms.RandomCrop, torchvision 1.7.0 + """ + + def __init__(self, min_size: int): + super().__init__() + self.min_size = min_size + + def forward(self, img: Image) -> (BoundingBox, Image): + width, height = get_image_size(img) + max_size = min(width, height) + if max_size <= self.min_size: + size = max_size + else: + size = random.randint(self.min_size, max_size) + top = random.randint(0, height - size) + left = random.randint(0, width - size) + bbox = left / width, top / height, size / width, size / height + return bbox, F.crop(img, top, left, size, size) + + +class CenterCropReturnCoordinates(CenterCrop): + @staticmethod + def get_bbox_of_center_crop(width: int, height: int) -> BoundingBox: + if width > height: + w = height / width + h = 1.0 + x0 = 0.5 - w / 2 + y0 = 0. + else: + w = 1.0 + h = width / height + x0 = 0. + y0 = 0.5 - h / 2 + return x0, y0, w, h + + def forward(self, img: Union[Image, Tensor]) -> (BoundingBox, Union[Image, Tensor]): + """ + Additionally to cropping, returns the relative coordinates of the crop bounding box. + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + Bounding box: x0, y0, w, h + PIL Image or Tensor: Cropped image. + Based on: + torchvision.transforms.RandomHorizontalFlip (version 1.7.0) + """ + width, height = get_image_size(img) + return self.get_bbox_of_center_crop(width, height), F.center_crop(img, self.size) + + +class RandomHorizontalFlipReturn(RandomHorizontalFlip): + def forward(self, img: Image) -> (bool, Image): + """ + Additionally to flipping, returns a boolean whether it was flipped or not. + Args: + img (PIL Image or Tensor): Image to be flipped. + + Returns: + flipped: whether the image was flipped or not + PIL Image or Tensor: Randomly flipped image. + + Based on: + torchvision.transforms.RandomHorizontalFlip (version 1.7.0) + """ + if torch.rand(1) < self.p: + return True, F.hflip(img) + return False, img diff --git a/taming-transformers/taming/data/imagenet.py b/taming-transformers/taming/data/imagenet.py new file mode 100755 index 0000000000000000000000000000000000000000..9a02ec44ba4af9e993f58c91fa43482a4ecbe54c --- /dev/null +++ b/taming-transformers/taming/data/imagenet.py @@ -0,0 +1,558 @@ +import os, tarfile, glob, shutil +import yaml +import numpy as np +from tqdm import tqdm +from PIL import Image +import albumentations +from omegaconf import OmegaConf +from torch.utils.data import Dataset + +from taming.data.base import ImagePaths +from taming.util import download, retrieve +import taming.data.utils as bdu + + +def give_synsets_from_indices(indices, path_to_yaml="data/imagenet_idx_to_synset.yaml"): + synsets = [] + with open(path_to_yaml) as f: + di2s = yaml.load(f) + for idx in indices: + synsets.append(str(di2s[idx])) + print("Using {} different synsets for construction of Restriced Imagenet.".format(len(synsets))) + return synsets + + +def str_to_indices(string): + """Expects a string in the format '32-123, 256, 280-321'""" + assert not string.endswith(","), "provided string '{}' ends with a comma, pls remove it".format(string) + subs = string.split(",") + indices = [] + for sub in subs: + subsubs = sub.split("-") + assert len(subsubs) > 0 + if len(subsubs) == 1: + indices.append(int(subsubs[0])) + else: + rang = [j for j in range(int(subsubs[0]), int(subsubs[1]))] + indices.extend(rang) + return sorted(indices) + + +class ImageNetBase(Dataset): + def __init__(self, config=None): + self.config = config or OmegaConf.create() + if not type(self.config)==dict: + self.config = OmegaConf.to_container(self.config) + self._prepare() + self._prepare_synset_to_human() + self._prepare_idx_to_synset() + self._load() + + def __len__(self): + return len(self.data) + + def __getitem__(self, i): + return self.data[i] + + def _prepare(self): + raise NotImplementedError() + + def _filter_relpaths(self, relpaths): + ignore = set([ + "n06596364_9591.JPEG", + ]) + relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore] + if "sub_indices" in self.config: + indices = str_to_indices(self.config["sub_indices"]) + synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn) # returns a list of strings + files = [] + for rpath in relpaths: + syn = rpath.split("/")[0] + if syn in synsets: + files.append(rpath) + return files + else: + return relpaths + + def _prepare_synset_to_human(self): + SIZE = 2655750 + URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1" + self.human_dict = os.path.join(self.root, "synset_human.txt") + if (not os.path.exists(self.human_dict) or + not os.path.getsize(self.human_dict)==SIZE): + download(URL, self.human_dict) + + def _prepare_idx_to_synset(self): + URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1" + self.idx2syn = os.path.join(self.root, "index_synset.yaml") + if (not os.path.exists(self.idx2syn)): + download(URL, self.idx2syn) + + def _load(self): + with open(self.txt_filelist, "r") as f: + self.relpaths = f.read().splitlines() + l1 = len(self.relpaths) + self.relpaths = self._filter_relpaths(self.relpaths) + print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths))) + + self.synsets = [p.split("/")[0] for p in self.relpaths] + self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths] + + unique_synsets = np.unique(self.synsets) + class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets)) + self.class_labels = [class_dict[s] for s in self.synsets] + + with open(self.human_dict, "r") as f: + human_dict = f.read().splitlines() + human_dict = dict(line.split(maxsplit=1) for line in human_dict) + + self.human_labels = [human_dict[s] for s in self.synsets] + + labels = { + "relpath": np.array(self.relpaths), + "synsets": np.array(self.synsets), + "class_label": np.array(self.class_labels), + "human_label": np.array(self.human_labels), + } + self.data = ImagePaths(self.abspaths, + labels=labels, + size=retrieve(self.config, "size", default=0), + random_crop=self.random_crop) + + +class ImageNetTrain(ImageNetBase): + NAME = "ILSVRC2012_train" + URL = "http://www.image-net.org/challenges/LSVRC/2012/" + AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2" + FILES = [ + "ILSVRC2012_img_train.tar", + ] + SIZES = [ + 147897477120, + ] + + def _prepare(self): + self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop", + default=True) + cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache")) + self.root = os.path.join(cachedir, "autoencoders/data", self.NAME) + self.datadir = os.path.join(self.root, "data") + self.txt_filelist = os.path.join(self.root, "filelist.txt") + self.expected_length = 1281167 + if not bdu.is_prepared(self.root): + # prep + print("Preparing dataset {} in {}".format(self.NAME, self.root)) + + datadir = self.datadir + if not os.path.exists(datadir): + path = os.path.join(self.root, self.FILES[0]) + if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]: + import academictorrents as at + atpath = at.get(self.AT_HASH, datastore=self.root) + assert atpath == path + + print("Extracting {} to {}".format(path, datadir)) + os.makedirs(datadir, exist_ok=True) + with tarfile.open(path, "r:") as tar: + tar.extractall(path=datadir) + + print("Extracting sub-tars.") + subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar"))) + for subpath in tqdm(subpaths): + subdir = subpath[:-len(".tar")] + os.makedirs(subdir, exist_ok=True) + with tarfile.open(subpath, "r:") as tar: + tar.extractall(path=subdir) + + + filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG")) + filelist = [os.path.relpath(p, start=datadir) for p in filelist] + filelist = sorted(filelist) + filelist = "\n".join(filelist)+"\n" + with open(self.txt_filelist, "w") as f: + f.write(filelist) + + bdu.mark_prepared(self.root) + + +class ImageNetValidation(ImageNetBase): + NAME = "ILSVRC2012_validation" + URL = "http://www.image-net.org/challenges/LSVRC/2012/" + AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5" + VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1" + FILES = [ + "ILSVRC2012_img_val.tar", + "validation_synset.txt", + ] + SIZES = [ + 6744924160, + 1950000, + ] + + def _prepare(self): + self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop", + default=False) + cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache")) + self.root = os.path.join(cachedir, "autoencoders/data", self.NAME) + self.datadir = os.path.join(self.root, "data") + self.txt_filelist = os.path.join(self.root, "filelist.txt") + self.expected_length = 50000 + if not bdu.is_prepared(self.root): + # prep + print("Preparing dataset {} in {}".format(self.NAME, self.root)) + + datadir = self.datadir + if not os.path.exists(datadir): + path = os.path.join(self.root, self.FILES[0]) + if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]: + import academictorrents as at + atpath = at.get(self.AT_HASH, datastore=self.root) + assert atpath == path + + print("Extracting {} to {}".format(path, datadir)) + os.makedirs(datadir, exist_ok=True) + with tarfile.open(path, "r:") as tar: + tar.extractall(path=datadir) + + vspath = os.path.join(self.root, self.FILES[1]) + if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]: + download(self.VS_URL, vspath) + + with open(vspath, "r") as f: + synset_dict = f.read().splitlines() + synset_dict = dict(line.split() for line in synset_dict) + + print("Reorganizing into synset folders") + synsets = np.unique(list(synset_dict.values())) + for s in synsets: + os.makedirs(os.path.join(datadir, s), exist_ok=True) + for k, v in synset_dict.items(): + src = os.path.join(datadir, k) + dst = os.path.join(datadir, v) + shutil.move(src, dst) + + filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG")) + filelist = [os.path.relpath(p, start=datadir) for p in filelist] + filelist = sorted(filelist) + filelist = "\n".join(filelist)+"\n" + with open(self.txt_filelist, "w") as f: + f.write(filelist) + + bdu.mark_prepared(self.root) + + +def get_preprocessor(size=None, random_crop=False, additional_targets=None, + crop_size=None): + if size is not None and size > 0: + transforms = list() + rescaler = albumentations.SmallestMaxSize(max_size = size) + transforms.append(rescaler) + if not random_crop: + cropper = albumentations.CenterCrop(height=size,width=size) + transforms.append(cropper) + else: + cropper = albumentations.RandomCrop(height=size,width=size) + transforms.append(cropper) + flipper = albumentations.HorizontalFlip() + transforms.append(flipper) + preprocessor = albumentations.Compose(transforms, + additional_targets=additional_targets) + elif crop_size is not None and crop_size > 0: + if not random_crop: + cropper = albumentations.CenterCrop(height=crop_size,width=crop_size) + else: + cropper = albumentations.RandomCrop(height=crop_size,width=crop_size) + transforms = [cropper] + preprocessor = albumentations.Compose(transforms, + additional_targets=additional_targets) + else: + preprocessor = lambda **kwargs: kwargs + return preprocessor + + +def rgba_to_depth(x): + assert x.dtype == np.uint8 + assert len(x.shape) == 3 and x.shape[2] == 4 + y = x.copy() + y.dtype = np.float32 + y = y.reshape(x.shape[:2]) + return np.ascontiguousarray(y) + + +class BaseWithDepth(Dataset): + DEFAULT_DEPTH_ROOT="data/imagenet_depth" + + def __init__(self, config=None, size=None, random_crop=False, + crop_size=None, root=None): + self.config = config + self.base_dset = self.get_base_dset() + self.preprocessor = get_preprocessor( + size=size, + crop_size=crop_size, + random_crop=random_crop, + additional_targets={"depth": "image"}) + self.crop_size = crop_size + if self.crop_size is not None: + self.rescaler = albumentations.Compose( + [albumentations.SmallestMaxSize(max_size = self.crop_size)], + additional_targets={"depth": "image"}) + if root is not None: + self.DEFAULT_DEPTH_ROOT = root + + def __len__(self): + return len(self.base_dset) + + def preprocess_depth(self, path): + rgba = np.array(Image.open(path)) + depth = rgba_to_depth(rgba) + depth = (depth - depth.min())/max(1e-8, depth.max()-depth.min()) + depth = 2.0*depth-1.0 + return depth + + def __getitem__(self, i): + e = self.base_dset[i] + e["depth"] = self.preprocess_depth(self.get_depth_path(e)) + # up if necessary + h,w,c = e["image"].shape + if self.crop_size and min(h,w) < self.crop_size: + # have to upscale to be able to crop - this just uses bilinear + out = self.rescaler(image=e["image"], depth=e["depth"]) + e["image"] = out["image"] + e["depth"] = out["depth"] + transformed = self.preprocessor(image=e["image"], depth=e["depth"]) + e["image"] = transformed["image"] + e["depth"] = transformed["depth"] + return e + + +class ImageNetTrainWithDepth(BaseWithDepth): + # default to random_crop=True + def __init__(self, random_crop=True, sub_indices=None, **kwargs): + self.sub_indices = sub_indices + super().__init__(random_crop=random_crop, **kwargs) + + def get_base_dset(self): + if self.sub_indices is None: + return ImageNetTrain() + else: + return ImageNetTrain({"sub_indices": self.sub_indices}) + + def get_depth_path(self, e): + fid = os.path.splitext(e["relpath"])[0]+".png" + fid = os.path.join(self.DEFAULT_DEPTH_ROOT, "train", fid) + return fid + + +class ImageNetValidationWithDepth(BaseWithDepth): + def __init__(self, sub_indices=None, **kwargs): + self.sub_indices = sub_indices + super().__init__(**kwargs) + + def get_base_dset(self): + if self.sub_indices is None: + return ImageNetValidation() + else: + return ImageNetValidation({"sub_indices": self.sub_indices}) + + def get_depth_path(self, e): + fid = os.path.splitext(e["relpath"])[0]+".png" + fid = os.path.join(self.DEFAULT_DEPTH_ROOT, "val", fid) + return fid + + +class RINTrainWithDepth(ImageNetTrainWithDepth): + def __init__(self, config=None, size=None, random_crop=True, crop_size=None): + sub_indices = "30-32, 33-37, 151-268, 281-285, 80-100, 365-382, 389-397, 118-121, 300-319" + super().__init__(config=config, size=size, random_crop=random_crop, + sub_indices=sub_indices, crop_size=crop_size) + + +class RINValidationWithDepth(ImageNetValidationWithDepth): + def __init__(self, config=None, size=None, random_crop=False, crop_size=None): + sub_indices = "30-32, 33-37, 151-268, 281-285, 80-100, 365-382, 389-397, 118-121, 300-319" + super().__init__(config=config, size=size, random_crop=random_crop, + sub_indices=sub_indices, crop_size=crop_size) + + +class DRINExamples(Dataset): + def __init__(self): + self.preprocessor = get_preprocessor(size=256, additional_targets={"depth": "image"}) + with open("data/drin_examples.txt", "r") as f: + relpaths = f.read().splitlines() + self.image_paths = [os.path.join("data/drin_images", + relpath) for relpath in relpaths] + self.depth_paths = [os.path.join("data/drin_depth", + relpath.replace(".JPEG", ".png")) for relpath in relpaths] + + def __len__(self): + return len(self.image_paths) + + def preprocess_image(self, image_path): + image = Image.open(image_path) + if not image.mode == "RGB": + image = image.convert("RGB") + image = np.array(image).astype(np.uint8) + image = self.preprocessor(image=image)["image"] + image = (image/127.5 - 1.0).astype(np.float32) + return image + + def preprocess_depth(self, path): + rgba = np.array(Image.open(path)) + depth = rgba_to_depth(rgba) + depth = (depth - depth.min())/max(1e-8, depth.max()-depth.min()) + depth = 2.0*depth-1.0 + return depth + + def __getitem__(self, i): + e = dict() + e["image"] = self.preprocess_image(self.image_paths[i]) + e["depth"] = self.preprocess_depth(self.depth_paths[i]) + transformed = self.preprocessor(image=e["image"], depth=e["depth"]) + e["image"] = transformed["image"] + e["depth"] = transformed["depth"] + return e + + +def imscale(x, factor, keepshapes=False, keepmode="bicubic"): + if factor is None or factor==1: + return x + + dtype = x.dtype + assert dtype in [np.float32, np.float64] + assert x.min() >= -1 + assert x.max() <= 1 + + keepmode = {"nearest": Image.NEAREST, "bilinear": Image.BILINEAR, + "bicubic": Image.BICUBIC}[keepmode] + + lr = (x+1.0)*127.5 + lr = lr.clip(0,255).astype(np.uint8) + lr = Image.fromarray(lr) + + h, w, _ = x.shape + nh = h//factor + nw = w//factor + assert nh > 0 and nw > 0, (nh, nw) + + lr = lr.resize((nw,nh), Image.BICUBIC) + if keepshapes: + lr = lr.resize((w,h), keepmode) + lr = np.array(lr)/127.5-1.0 + lr = lr.astype(dtype) + + return lr + + +class ImageNetScale(Dataset): + def __init__(self, size=None, crop_size=None, random_crop=False, + up_factor=None, hr_factor=None, keep_mode="bicubic"): + self.base = self.get_base() + + self.size = size + self.crop_size = crop_size if crop_size is not None else self.size + self.random_crop = random_crop + self.up_factor = up_factor + self.hr_factor = hr_factor + self.keep_mode = keep_mode + + transforms = list() + + if self.size is not None and self.size > 0: + rescaler = albumentations.SmallestMaxSize(max_size = self.size) + self.rescaler = rescaler + transforms.append(rescaler) + + if self.crop_size is not None and self.crop_size > 0: + if len(transforms) == 0: + self.rescaler = albumentations.SmallestMaxSize(max_size = self.crop_size) + + if not self.random_crop: + cropper = albumentations.CenterCrop(height=self.crop_size,width=self.crop_size) + else: + cropper = albumentations.RandomCrop(height=self.crop_size,width=self.crop_size) + transforms.append(cropper) + + if len(transforms) > 0: + if self.up_factor is not None: + additional_targets = {"lr": "image"} + else: + additional_targets = None + self.preprocessor = albumentations.Compose(transforms, + additional_targets=additional_targets) + else: + self.preprocessor = lambda **kwargs: kwargs + + def __len__(self): + return len(self.base) + + def __getitem__(self, i): + example = self.base[i] + image = example["image"] + # adjust resolution + image = imscale(image, self.hr_factor, keepshapes=False) + h,w,c = image.shape + if self.crop_size and min(h,w) < self.crop_size: + # have to upscale to be able to crop - this just uses bilinear + image = self.rescaler(image=image)["image"] + if self.up_factor is None: + image = self.preprocessor(image=image)["image"] + example["image"] = image + else: + lr = imscale(image, self.up_factor, keepshapes=True, + keepmode=self.keep_mode) + + out = self.preprocessor(image=image, lr=lr) + example["image"] = out["image"] + example["lr"] = out["lr"] + + return example + +class ImageNetScaleTrain(ImageNetScale): + def __init__(self, random_crop=True, **kwargs): + super().__init__(random_crop=random_crop, **kwargs) + + def get_base(self): + return ImageNetTrain() + +class ImageNetScaleValidation(ImageNetScale): + def get_base(self): + return ImageNetValidation() + + +from skimage.feature import canny +from skimage.color import rgb2gray + + +class ImageNetEdges(ImageNetScale): + def __init__(self, up_factor=1, **kwargs): + super().__init__(up_factor=1, **kwargs) + + def __getitem__(self, i): + example = self.base[i] + image = example["image"] + h,w,c = image.shape + if self.crop_size and min(h,w) < self.crop_size: + # have to upscale to be able to crop - this just uses bilinear + image = self.rescaler(image=image)["image"] + + lr = canny(rgb2gray(image), sigma=2) + lr = lr.astype(np.float32) + lr = lr[:,:,None][:,:,[0,0,0]] + + out = self.preprocessor(image=image, lr=lr) + example["image"] = out["image"] + example["lr"] = out["lr"] + + return example + + +class ImageNetEdgesTrain(ImageNetEdges): + def __init__(self, random_crop=True, **kwargs): + super().__init__(random_crop=random_crop, **kwargs) + + def get_base(self): + return ImageNetTrain() + +class ImageNetEdgesValidation(ImageNetEdges): + def get_base(self): + return ImageNetValidation() diff --git a/taming-transformers/taming/data/open_images_helper.py b/taming-transformers/taming/data/open_images_helper.py new file mode 100755 index 0000000000000000000000000000000000000000..8feb7c6e705fc165d2983303192aaa88f579b243 --- /dev/null +++ b/taming-transformers/taming/data/open_images_helper.py @@ -0,0 +1,379 @@ +open_images_unify_categories_for_coco = { + '/m/03bt1vf': '/m/01g317', + '/m/04yx4': '/m/01g317', + '/m/05r655': '/m/01g317', + '/m/01bl7v': '/m/01g317', + '/m/0cnyhnx': '/m/01xq0k1', + '/m/01226z': '/m/018xm', + '/m/05ctyq': '/m/018xm', + '/m/058qzx': '/m/04ctx', + '/m/06pcq': '/m/0l515', + '/m/03m3pdh': '/m/02crq1', + '/m/046dlr': '/m/01x3z', + '/m/0h8mzrc': '/m/01x3z', +} + + +top_300_classes_plus_coco_compatibility = [ + ('Man', 1060962), + ('Clothing', 986610), + ('Tree', 748162), + ('Woman', 611896), + ('Person', 610294), + ('Human face', 442948), + ('Girl', 175399), + ('Building', 162147), + ('Car', 159135), + ('Plant', 155704), + ('Human body', 137073), + ('Flower', 133128), + ('Window', 127485), + ('Human arm', 118380), + ('House', 114365), + ('Wheel', 111684), + ('Suit', 99054), + ('Human hair', 98089), + ('Human head', 92763), + ('Chair', 88624), + ('Boy', 79849), + ('Table', 73699), + ('Jeans', 57200), + ('Tire', 55725), + ('Skyscraper', 53321), + ('Food', 52400), + ('Footwear', 50335), + ('Dress', 50236), + ('Human leg', 47124), + ('Toy', 46636), + ('Tower', 45605), + ('Boat', 43486), + ('Land vehicle', 40541), + ('Bicycle wheel', 34646), + ('Palm tree', 33729), + ('Fashion accessory', 32914), + ('Glasses', 31940), + ('Bicycle', 31409), + ('Furniture', 30656), + ('Sculpture', 29643), + ('Bottle', 27558), + ('Dog', 26980), + ('Snack', 26796), + ('Human hand', 26664), + ('Bird', 25791), + ('Book', 25415), + ('Guitar', 24386), + ('Jacket', 23998), + ('Poster', 22192), + ('Dessert', 21284), + ('Baked goods', 20657), + ('Drink', 19754), + ('Flag', 18588), + ('Houseplant', 18205), + ('Tableware', 17613), + ('Airplane', 17218), + ('Door', 17195), + ('Sports uniform', 17068), + ('Shelf', 16865), + ('Drum', 16612), + ('Vehicle', 16542), + ('Microphone', 15269), + ('Street light', 14957), + ('Cat', 14879), + ('Fruit', 13684), + ('Fast food', 13536), + ('Animal', 12932), + ('Vegetable', 12534), + ('Train', 12358), + ('Horse', 11948), + ('Flowerpot', 11728), + ('Motorcycle', 11621), + ('Fish', 11517), + ('Desk', 11405), + ('Helmet', 10996), + ('Truck', 10915), + ('Bus', 10695), + ('Hat', 10532), + ('Auto part', 10488), + ('Musical instrument', 10303), + ('Sunglasses', 10207), + ('Picture frame', 10096), + ('Sports equipment', 10015), + ('Shorts', 9999), + ('Wine glass', 9632), + ('Duck', 9242), + ('Wine', 9032), + ('Rose', 8781), + ('Tie', 8693), + ('Butterfly', 8436), + ('Beer', 7978), + ('Cabinetry', 7956), + ('Laptop', 7907), + ('Insect', 7497), + ('Goggles', 7363), + ('Shirt', 7098), + ('Dairy Product', 7021), + ('Marine invertebrates', 7014), + ('Cattle', 7006), + ('Trousers', 6903), + ('Van', 6843), + ('Billboard', 6777), + ('Balloon', 6367), + ('Human nose', 6103), + ('Tent', 6073), + ('Camera', 6014), + ('Doll', 6002), + ('Coat', 5951), + ('Mobile phone', 5758), + ('Swimwear', 5729), + ('Strawberry', 5691), + ('Stairs', 5643), + ('Goose', 5599), + ('Umbrella', 5536), + ('Cake', 5508), + ('Sun hat', 5475), + ('Bench', 5310), + ('Bookcase', 5163), + ('Bee', 5140), + ('Computer monitor', 5078), + ('Hiking equipment', 4983), + ('Office building', 4981), + ('Coffee cup', 4748), + ('Curtain', 4685), + ('Plate', 4651), + ('Box', 4621), + ('Tomato', 4595), + ('Coffee table', 4529), + ('Office supplies', 4473), + ('Maple', 4416), + ('Muffin', 4365), + ('Cocktail', 4234), + ('Castle', 4197), + ('Couch', 4134), + ('Pumpkin', 3983), + ('Computer keyboard', 3960), + ('Human mouth', 3926), + ('Christmas tree', 3893), + ('Mushroom', 3883), + ('Swimming pool', 3809), + ('Pastry', 3799), + ('Lavender (Plant)', 3769), + ('Football helmet', 3732), + ('Bread', 3648), + ('Traffic sign', 3628), + ('Common sunflower', 3597), + ('Television', 3550), + ('Bed', 3525), + ('Cookie', 3485), + ('Fountain', 3484), + ('Paddle', 3447), + ('Bicycle helmet', 3429), + ('Porch', 3420), + ('Deer', 3387), + ('Fedora', 3339), + ('Canoe', 3338), + ('Carnivore', 3266), + ('Bowl', 3202), + ('Human eye', 3166), + ('Ball', 3118), + ('Pillow', 3077), + ('Salad', 3061), + ('Beetle', 3060), + ('Orange', 3050), + ('Drawer', 2958), + ('Platter', 2937), + ('Elephant', 2921), + ('Seafood', 2921), + ('Monkey', 2915), + ('Countertop', 2879), + ('Watercraft', 2831), + ('Helicopter', 2805), + ('Kitchen appliance', 2797), + ('Personal flotation device', 2781), + ('Swan', 2739), + ('Lamp', 2711), + ('Boot', 2695), + ('Bronze sculpture', 2693), + ('Chicken', 2677), + ('Taxi', 2643), + ('Juice', 2615), + ('Cowboy hat', 2604), + ('Apple', 2600), + ('Tin can', 2590), + ('Necklace', 2564), + ('Ice cream', 2560), + ('Human beard', 2539), + ('Coin', 2536), + ('Candle', 2515), + ('Cart', 2512), + ('High heels', 2441), + ('Weapon', 2433), + ('Handbag', 2406), + ('Penguin', 2396), + ('Rifle', 2352), + ('Violin', 2336), + ('Skull', 2304), + ('Lantern', 2285), + ('Scarf', 2269), + ('Saucer', 2225), + ('Sheep', 2215), + ('Vase', 2189), + ('Lily', 2180), + ('Mug', 2154), + ('Parrot', 2140), + ('Human ear', 2137), + ('Sandal', 2115), + ('Lizard', 2100), + ('Kitchen & dining room table', 2063), + ('Spider', 1977), + ('Coffee', 1974), + ('Goat', 1926), + ('Squirrel', 1922), + ('Cello', 1913), + ('Sushi', 1881), + ('Tortoise', 1876), + ('Pizza', 1870), + ('Studio couch', 1864), + ('Barrel', 1862), + ('Cosmetics', 1841), + ('Moths and butterflies', 1841), + ('Convenience store', 1817), + ('Watch', 1792), + ('Home appliance', 1786), + ('Harbor seal', 1780), + ('Luggage and bags', 1756), + ('Vehicle registration plate', 1754), + ('Shrimp', 1751), + ('Jellyfish', 1730), + ('French fries', 1723), + ('Egg (Food)', 1698), + ('Football', 1697), + ('Musical keyboard', 1683), + ('Falcon', 1674), + ('Candy', 1660), + ('Medical equipment', 1654), + ('Eagle', 1651), + ('Dinosaur', 1634), + ('Surfboard', 1630), + ('Tank', 1628), + ('Grape', 1624), + ('Lion', 1624), + ('Owl', 1622), + ('Ski', 1613), + ('Waste container', 1606), + ('Frog', 1591), + ('Sparrow', 1585), + ('Rabbit', 1581), + ('Pen', 1546), + ('Sea lion', 1537), + ('Spoon', 1521), + ('Sink', 1512), + ('Teddy bear', 1507), + ('Bull', 1495), + ('Sofa bed', 1490), + ('Dragonfly', 1479), + ('Brassiere', 1478), + ('Chest of drawers', 1472), + ('Aircraft', 1466), + ('Human foot', 1463), + ('Pig', 1455), + ('Fork', 1454), + ('Antelope', 1438), + ('Tripod', 1427), + ('Tool', 1424), + ('Cheese', 1422), + ('Lemon', 1397), + ('Hamburger', 1393), + ('Dolphin', 1390), + ('Mirror', 1390), + ('Marine mammal', 1387), + ('Giraffe', 1385), + ('Snake', 1368), + ('Gondola', 1364), + ('Wheelchair', 1360), + ('Piano', 1358), + ('Cupboard', 1348), + ('Banana', 1345), + ('Trumpet', 1335), + ('Lighthouse', 1333), + ('Invertebrate', 1317), + ('Carrot', 1268), + ('Sock', 1260), + ('Tiger', 1241), + ('Camel', 1224), + ('Parachute', 1224), + ('Bathroom accessory', 1223), + ('Earrings', 1221), + ('Headphones', 1218), + ('Skirt', 1198), + ('Skateboard', 1190), + ('Sandwich', 1148), + ('Saxophone', 1141), + ('Goldfish', 1136), + ('Stool', 1104), + ('Traffic light', 1097), + ('Shellfish', 1081), + ('Backpack', 1079), + ('Sea turtle', 1078), + ('Cucumber', 1075), + ('Tea', 1051), + ('Toilet', 1047), + ('Roller skates', 1040), + ('Mule', 1039), + ('Bust', 1031), + ('Broccoli', 1030), + ('Crab', 1020), + ('Oyster', 1019), + ('Cannon', 1012), + ('Zebra', 1012), + ('French horn', 1008), + ('Grapefruit', 998), + ('Whiteboard', 997), + ('Zucchini', 997), + ('Crocodile', 992), + + ('Clock', 960), + ('Wall clock', 958), + + ('Doughnut', 869), + ('Snail', 868), + + ('Baseball glove', 859), + + ('Panda', 830), + ('Tennis racket', 830), + + ('Pear', 652), + + ('Bagel', 617), + ('Oven', 616), + ('Ladybug', 615), + ('Shark', 615), + ('Polar bear', 614), + ('Ostrich', 609), + + ('Hot dog', 473), + ('Microwave oven', 467), + ('Fire hydrant', 20), + ('Stop sign', 20), + ('Parking meter', 20), + ('Bear', 20), + ('Flying disc', 20), + ('Snowboard', 20), + ('Tennis ball', 20), + ('Kite', 20), + ('Baseball bat', 20), + ('Kitchen knife', 20), + ('Knife', 20), + ('Submarine sandwich', 20), + ('Computer mouse', 20), + ('Remote control', 20), + ('Toaster', 20), + ('Sink', 20), + ('Refrigerator', 20), + ('Alarm clock', 20), + ('Wall clock', 20), + ('Scissors', 20), + ('Hair dryer', 20), + ('Toothbrush', 20), + ('Suitcase', 20) +] diff --git a/taming-transformers/taming/data/sflckr.py b/taming-transformers/taming/data/sflckr.py new file mode 100755 index 0000000000000000000000000000000000000000..91101be5953b113f1e58376af637e43f366b3dee --- /dev/null +++ b/taming-transformers/taming/data/sflckr.py @@ -0,0 +1,91 @@ +import os +import numpy as np +import cv2 +import albumentations +from PIL import Image +from torch.utils.data import Dataset + + +class SegmentationBase(Dataset): + def __init__(self, + data_csv, data_root, segmentation_root, + size=None, random_crop=False, interpolation="bicubic", + n_labels=182, shift_segmentation=False, + ): + self.n_labels = n_labels + self.shift_segmentation = shift_segmentation + self.data_csv = data_csv + self.data_root = data_root + self.segmentation_root = segmentation_root + with open(self.data_csv, "r") as f: + self.image_paths = f.read().splitlines() + self._length = len(self.image_paths) + self.labels = { + "relative_file_path_": [l for l in self.image_paths], + "file_path_": [os.path.join(self.data_root, l) + for l in self.image_paths], + "segmentation_path_": [os.path.join(self.segmentation_root, l.replace(".jpg", ".png")) + for l in self.image_paths] + } + + size = None if size is not None and size<=0 else size + self.size = size + if self.size is not None: + self.interpolation = interpolation + self.interpolation = { + "nearest": cv2.INTER_NEAREST, + "bilinear": cv2.INTER_LINEAR, + "bicubic": cv2.INTER_CUBIC, + "area": cv2.INTER_AREA, + "lanczos": cv2.INTER_LANCZOS4}[self.interpolation] + self.image_rescaler = albumentations.SmallestMaxSize(max_size=self.size, + interpolation=self.interpolation) + self.segmentation_rescaler = albumentations.SmallestMaxSize(max_size=self.size, + interpolation=cv2.INTER_NEAREST) + self.center_crop = not random_crop + if self.center_crop: + self.cropper = albumentations.CenterCrop(height=self.size, width=self.size) + else: + self.cropper = albumentations.RandomCrop(height=self.size, width=self.size) + self.preprocessor = self.cropper + + def __len__(self): + return self._length + + def __getitem__(self, i): + example = dict((k, self.labels[k][i]) for k in self.labels) + image = Image.open(example["file_path_"]) + if not image.mode == "RGB": + image = image.convert("RGB") + image = np.array(image).astype(np.uint8) + if self.size is not None: + image = self.image_rescaler(image=image)["image"] + segmentation = Image.open(example["segmentation_path_"]) + assert segmentation.mode == "L", segmentation.mode + segmentation = np.array(segmentation).astype(np.uint8) + if self.shift_segmentation: + # used to support segmentations containing unlabeled==255 label + segmentation = segmentation+1 + if self.size is not None: + segmentation = self.segmentation_rescaler(image=segmentation)["image"] + if self.size is not None: + processed = self.preprocessor(image=image, + mask=segmentation + ) + else: + processed = {"image": image, + "mask": segmentation + } + example["image"] = (processed["image"]/127.5 - 1.0).astype(np.float32) + segmentation = processed["mask"] + onehot = np.eye(self.n_labels)[segmentation] + example["segmentation"] = onehot + return example + + +class Examples(SegmentationBase): + def __init__(self, size=None, random_crop=False, interpolation="bicubic"): + super().__init__(data_csv="data/sflckr_examples.txt", + data_root="data/sflckr_images", + segmentation_root="data/sflckr_segmentations", + size=size, random_crop=random_crop, interpolation=interpolation) diff --git a/taming-transformers/taming/data/utils.py b/taming-transformers/taming/data/utils.py new file mode 100755 index 0000000000000000000000000000000000000000..2b3c3d53cd2b6c72b481b59834cf809d3735b394 --- /dev/null +++ b/taming-transformers/taming/data/utils.py @@ -0,0 +1,169 @@ +import collections +import os +import tarfile +import urllib +import zipfile +from pathlib import Path + +import numpy as np +import torch +from taming.data.helper_types import Annotation +from torch._six import string_classes +from torch.utils.data._utils.collate import np_str_obj_array_pattern, default_collate_err_msg_format +from tqdm import tqdm + + +def unpack(path): + if path.endswith("tar.gz"): + with tarfile.open(path, "r:gz") as tar: + tar.extractall(path=os.path.split(path)[0]) + elif path.endswith("tar"): + with tarfile.open(path, "r:") as tar: + tar.extractall(path=os.path.split(path)[0]) + elif path.endswith("zip"): + with zipfile.ZipFile(path, "r") as f: + f.extractall(path=os.path.split(path)[0]) + else: + raise NotImplementedError( + "Unknown file extension: {}".format(os.path.splitext(path)[1]) + ) + + +def reporthook(bar): + """tqdm progress bar for downloads.""" + + def hook(b=1, bsize=1, tsize=None): + if tsize is not None: + bar.total = tsize + bar.update(b * bsize - bar.n) + + return hook + + +def get_root(name): + base = "data/" + root = os.path.join(base, name) + os.makedirs(root, exist_ok=True) + return root + + +def is_prepared(root): + return Path(root).joinpath(".ready").exists() + + +def mark_prepared(root): + Path(root).joinpath(".ready").touch() + + +def prompt_download(file_, source, target_dir, content_dir=None): + targetpath = os.path.join(target_dir, file_) + while not os.path.exists(targetpath): + if content_dir is not None and os.path.exists( + os.path.join(target_dir, content_dir) + ): + break + print( + "Please download '{}' from '{}' to '{}'.".format(file_, source, targetpath) + ) + if content_dir is not None: + print( + "Or place its content into '{}'.".format( + os.path.join(target_dir, content_dir) + ) + ) + input("Press Enter when done...") + return targetpath + + +def download_url(file_, url, target_dir): + targetpath = os.path.join(target_dir, file_) + os.makedirs(target_dir, exist_ok=True) + with tqdm( + unit="B", unit_scale=True, unit_divisor=1024, miniters=1, desc=file_ + ) as bar: + urllib.request.urlretrieve(url, targetpath, reporthook=reporthook(bar)) + return targetpath + + +def download_urls(urls, target_dir): + paths = dict() + for fname, url in urls.items(): + outpath = download_url(fname, url, target_dir) + paths[fname] = outpath + return paths + + +def quadratic_crop(x, bbox, alpha=1.0): + """bbox is xmin, ymin, xmax, ymax""" + im_h, im_w = x.shape[:2] + bbox = np.array(bbox, dtype=np.float32) + bbox = np.clip(bbox, 0, max(im_h, im_w)) + center = 0.5 * (bbox[0] + bbox[2]), 0.5 * (bbox[1] + bbox[3]) + w = bbox[2] - bbox[0] + h = bbox[3] - bbox[1] + l = int(alpha * max(w, h)) + l = max(l, 2) + + required_padding = -1 * min( + center[0] - l, center[1] - l, im_w - (center[0] + l), im_h - (center[1] + l) + ) + required_padding = int(np.ceil(required_padding)) + if required_padding > 0: + padding = [ + [required_padding, required_padding], + [required_padding, required_padding], + ] + padding += [[0, 0]] * (len(x.shape) - 2) + x = np.pad(x, padding, "reflect") + center = center[0] + required_padding, center[1] + required_padding + xmin = int(center[0] - l / 2) + ymin = int(center[1] - l / 2) + return np.array(x[ymin : ymin + l, xmin : xmin + l, ...]) + + +def custom_collate(batch): + r"""source: pytorch 1.9.0, only one modification to original code """ + + elem = batch[0] + elem_type = type(elem) + if isinstance(elem, torch.Tensor): + out = None + if torch.utils.data.get_worker_info() is not None: + # If we're in a background process, concatenate directly into a + # shared memory tensor to avoid an extra copy + numel = sum([x.numel() for x in batch]) + storage = elem.storage()._new_shared(numel) + out = elem.new(storage) + return torch.stack(batch, 0, out=out) + elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ + and elem_type.__name__ != 'string_': + if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap': + # array of string classes and object + if np_str_obj_array_pattern.search(elem.dtype.str) is not None: + raise TypeError(default_collate_err_msg_format.format(elem.dtype)) + + return custom_collate([torch.as_tensor(b) for b in batch]) + elif elem.shape == (): # scalars + return torch.as_tensor(batch) + elif isinstance(elem, float): + return torch.tensor(batch, dtype=torch.float64) + elif isinstance(elem, int): + return torch.tensor(batch) + elif isinstance(elem, string_classes): + return batch + elif isinstance(elem, collections.abc.Mapping): + return {key: custom_collate([d[key] for d in batch]) for key in elem} + elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple + return elem_type(*(custom_collate(samples) for samples in zip(*batch))) + if isinstance(elem, collections.abc.Sequence) and isinstance(elem[0], Annotation): # added + return batch # added + elif isinstance(elem, collections.abc.Sequence): + # check to make sure that the elements in batch have consistent size + it = iter(batch) + elem_size = len(next(it)) + if not all(len(elem) == elem_size for elem in it): + raise RuntimeError('each element in list of batch should be of equal size') + transposed = zip(*batch) + return [custom_collate(samples) for samples in transposed] + + raise TypeError(default_collate_err_msg_format.format(elem_type)) diff --git a/taming-transformers/taming/lr_scheduler.py b/taming-transformers/taming/lr_scheduler.py new file mode 100755 index 0000000000000000000000000000000000000000..e598ed120159c53da6820a55ad86b89f5c70c82d --- /dev/null +++ b/taming-transformers/taming/lr_scheduler.py @@ -0,0 +1,34 @@ +import numpy as np + + +class LambdaWarmUpCosineScheduler: + """ + note: use with a base_lr of 1.0 + """ + def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): + self.lr_warm_up_steps = warm_up_steps + self.lr_start = lr_start + self.lr_min = lr_min + self.lr_max = lr_max + self.lr_max_decay_steps = max_decay_steps + self.last_lr = 0. + self.verbosity_interval = verbosity_interval + + def schedule(self, n): + if self.verbosity_interval > 0: + if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") + if n < self.lr_warm_up_steps: + lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start + self.last_lr = lr + return lr + else: + t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps) + t = min(t, 1.0) + lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * ( + 1 + np.cos(t * np.pi)) + self.last_lr = lr + return lr + + def __call__(self, n): + return self.schedule(n) + diff --git a/taming-transformers/taming/models/cond_transformer.py b/taming-transformers/taming/models/cond_transformer.py new file mode 100755 index 0000000000000000000000000000000000000000..e4c63730fa86ac1b92b37af14c14fb696595b1ab --- /dev/null +++ b/taming-transformers/taming/models/cond_transformer.py @@ -0,0 +1,352 @@ +import os, math +import torch +import torch.nn.functional as F +import pytorch_lightning as pl + +from main import instantiate_from_config +from taming.modules.util import SOSProvider + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +class Net2NetTransformer(pl.LightningModule): + def __init__(self, + transformer_config, + first_stage_config, + cond_stage_config, + permuter_config=None, + ckpt_path=None, + ignore_keys=[], + first_stage_key="image", + cond_stage_key="depth", + downsample_cond_size=-1, + pkeep=1.0, + sos_token=0, + unconditional=False, + ): + super().__init__() + self.be_unconditional = unconditional + self.sos_token = sos_token + self.first_stage_key = first_stage_key + self.cond_stage_key = cond_stage_key + self.init_first_stage_from_ckpt(first_stage_config) + self.init_cond_stage_from_ckpt(cond_stage_config) + if permuter_config is None: + permuter_config = {"target": "taming.modules.transformer.permuter.Identity"} + self.permuter = instantiate_from_config(config=permuter_config) + self.transformer = instantiate_from_config(config=transformer_config) + + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + self.downsample_cond_size = downsample_cond_size + self.pkeep = pkeep + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["state_dict"] + for k in sd.keys(): + for ik in ignore_keys: + if k.startswith(ik): + self.print("Deleting key {} from state_dict.".format(k)) + del sd[k] + self.load_state_dict(sd, strict=False) + print(f"Restored from {path}") + + def init_first_stage_from_ckpt(self, config): + model = instantiate_from_config(config) + model = model.eval() + model.train = disabled_train + self.first_stage_model = model + + def init_cond_stage_from_ckpt(self, config): + if config == "__is_first_stage__": + print("Using first stage also as cond stage.") + self.cond_stage_model = self.first_stage_model + elif config == "__is_unconditional__" or self.be_unconditional: + print(f"Using no cond stage. Assuming the training is intended to be unconditional. " + f"Prepending {self.sos_token} as a sos token.") + self.be_unconditional = True + self.cond_stage_key = self.first_stage_key + self.cond_stage_model = SOSProvider(self.sos_token) + else: + model = instantiate_from_config(config) + model = model.eval() + model.train = disabled_train + self.cond_stage_model = model + + def forward(self, x, c): + # one step to produce the logits + _, z_indices = self.encode_to_z(x) + _, c_indices = self.encode_to_c(c) + + if self.training and self.pkeep < 1.0: + mask = torch.bernoulli(self.pkeep*torch.ones(z_indices.shape, + device=z_indices.device)) + mask = mask.round().to(dtype=torch.int64) + r_indices = torch.randint_like(z_indices, self.transformer.config.vocab_size) + a_indices = mask*z_indices+(1-mask)*r_indices + else: + a_indices = z_indices + + cz_indices = torch.cat((c_indices, a_indices), dim=1) + + # target includes all sequence elements (no need to handle first one + # differently because we are conditioning) + target = z_indices + # make the prediction + logits, _ = self.transformer(cz_indices[:, :-1]) + # cut off conditioning outputs - output i corresponds to p(z_i | z_{ -1: + c = F.interpolate(c, size=(self.downsample_cond_size, self.downsample_cond_size)) + quant_c, _, [_,_,indices] = self.cond_stage_model.encode(c) + if len(indices.shape) > 2: + indices = indices.view(c.shape[0], -1) + return quant_c, indices + + @torch.no_grad() + def decode_to_img(self, index, zshape): + index = self.permuter(index, reverse=True) + bhwc = (zshape[0],zshape[2],zshape[3],zshape[1]) + quant_z = self.first_stage_model.quantize.get_codebook_entry( + index.reshape(-1), shape=bhwc) + x = self.first_stage_model.decode(quant_z) + return x + + @torch.no_grad() + def log_images(self, batch, temperature=None, top_k=None, callback=None, lr_interface=False, **kwargs): + log = dict() + + N = 4 + if lr_interface: + x, c = self.get_xc(batch, N, diffuse=False, upsample_factor=8) + else: + x, c = self.get_xc(batch, N) + x = x.to(device=self.device) + c = c.to(device=self.device) + + quant_z, z_indices = self.encode_to_z(x) + quant_c, c_indices = self.encode_to_c(c) + + # create a "half"" sample + z_start_indices = z_indices[:,:z_indices.shape[1]//2] + index_sample = self.sample(z_start_indices, c_indices, + steps=z_indices.shape[1]-z_start_indices.shape[1], + temperature=temperature if temperature is not None else 1.0, + sample=True, + top_k=top_k if top_k is not None else 100, + callback=callback if callback is not None else lambda k: None) + x_sample = self.decode_to_img(index_sample, quant_z.shape) + + # sample + z_start_indices = z_indices[:, :0] + index_sample = self.sample(z_start_indices, c_indices, + steps=z_indices.shape[1], + temperature=temperature if temperature is not None else 1.0, + sample=True, + top_k=top_k if top_k is not None else 100, + callback=callback if callback is not None else lambda k: None) + x_sample_nopix = self.decode_to_img(index_sample, quant_z.shape) + + # det sample + z_start_indices = z_indices[:, :0] + index_sample = self.sample(z_start_indices, c_indices, + steps=z_indices.shape[1], + sample=False, + callback=callback if callback is not None else lambda k: None) + x_sample_det = self.decode_to_img(index_sample, quant_z.shape) + + # reconstruction + x_rec = self.decode_to_img(z_indices, quant_z.shape) + + log["inputs"] = x + log["reconstructions"] = x_rec + + if self.cond_stage_key in ["objects_bbox", "objects_center_points"]: + figure_size = (x_rec.shape[2], x_rec.shape[3]) + dataset = kwargs["pl_module"].trainer.datamodule.datasets["validation"] + label_for_category_no = dataset.get_textual_label_for_category_no + plotter = dataset.conditional_builders[self.cond_stage_key].plot + log["conditioning"] = torch.zeros_like(log["reconstructions"]) + for i in range(quant_c.shape[0]): + log["conditioning"][i] = plotter(quant_c[i], label_for_category_no, figure_size) + log["conditioning_rec"] = log["conditioning"] + elif self.cond_stage_key != "image": + cond_rec = self.cond_stage_model.decode(quant_c) + if self.cond_stage_key == "segmentation": + # get image from segmentation mask + num_classes = cond_rec.shape[1] + + c = torch.argmax(c, dim=1, keepdim=True) + c = F.one_hot(c, num_classes=num_classes) + c = c.squeeze(1).permute(0, 3, 1, 2).float() + c = self.cond_stage_model.to_rgb(c) + + cond_rec = torch.argmax(cond_rec, dim=1, keepdim=True) + cond_rec = F.one_hot(cond_rec, num_classes=num_classes) + cond_rec = cond_rec.squeeze(1).permute(0, 3, 1, 2).float() + cond_rec = self.cond_stage_model.to_rgb(cond_rec) + log["conditioning_rec"] = cond_rec + log["conditioning"] = c + + log["samples_half"] = x_sample + log["samples_nopix"] = x_sample_nopix + log["samples_det"] = x_sample_det + return log + + def get_input(self, key, batch): + x = batch[key] + if len(x.shape) == 3: + x = x[..., None] + if len(x.shape) == 4: + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format) + if x.dtype == torch.double: + x = x.float() + return x + + def get_xc(self, batch, N=None): + x = self.get_input(self.first_stage_key, batch) + c = self.get_input(self.cond_stage_key, batch) + if N is not None: + x = x[:N] + c = c[:N] + return x, c + + def shared_step(self, batch, batch_idx): + x, c = self.get_xc(batch) + logits, target = self(x, c) + loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), target.reshape(-1)) + return loss + + def training_step(self, batch, batch_idx): + loss = self.shared_step(batch, batch_idx) + self.log("train/loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + return loss + + def validation_step(self, batch, batch_idx): + loss = self.shared_step(batch, batch_idx) + self.log("val/loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + return loss + + def configure_optimizers(self): + """ + Following minGPT: + This long function is unfortunately doing something very simple and is being very defensive: + We are separating out all parameters of the model into two buckets: those that will experience + weight decay for regularization and those that won't (biases, and layernorm/embedding weights). + We are then returning the PyTorch optimizer object. + """ + # separate out all parameters to those that will and won't experience regularizing weight decay + decay = set() + no_decay = set() + whitelist_weight_modules = (torch.nn.Linear, ) + blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding) + for mn, m in self.transformer.named_modules(): + for pn, p in m.named_parameters(): + fpn = '%s.%s' % (mn, pn) if mn else pn # full param name + + if pn.endswith('bias'): + # all biases will not be decayed + no_decay.add(fpn) + elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules): + # weights of whitelist modules will be weight decayed + decay.add(fpn) + elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules): + # weights of blacklist modules will NOT be weight decayed + no_decay.add(fpn) + + # special case the position embedding parameter in the root GPT module as not decayed + no_decay.add('pos_emb') + + # validate that we considered every parameter + param_dict = {pn: p for pn, p in self.transformer.named_parameters()} + inter_params = decay & no_decay + union_params = decay | no_decay + assert len(inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (str(inter_params), ) + assert len(param_dict.keys() - union_params) == 0, "parameters %s were not separated into either decay/no_decay set!" \ + % (str(param_dict.keys() - union_params), ) + + # create the pytorch optimizer object + optim_groups = [ + {"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": 0.01}, + {"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0}, + ] + optimizer = torch.optim.AdamW(optim_groups, lr=self.learning_rate, betas=(0.9, 0.95)) + return optimizer diff --git a/taming-transformers/taming/models/dummy_cond_stage.py b/taming-transformers/taming/models/dummy_cond_stage.py new file mode 100755 index 0000000000000000000000000000000000000000..6e19938078752e09b926a3e749907ee99a258ca0 --- /dev/null +++ b/taming-transformers/taming/models/dummy_cond_stage.py @@ -0,0 +1,22 @@ +from torch import Tensor + + +class DummyCondStage: + def __init__(self, conditional_key): + self.conditional_key = conditional_key + self.train = None + + def eval(self): + return self + + @staticmethod + def encode(c: Tensor): + return c, None, (None, None, c) + + @staticmethod + def decode(c: Tensor): + return c + + @staticmethod + def to_rgb(c: Tensor): + return c diff --git a/taming-transformers/taming/models/vqgan.py b/taming-transformers/taming/models/vqgan.py new file mode 100755 index 0000000000000000000000000000000000000000..a6950baa5f739111cd64c17235dca8be3a5f8037 --- /dev/null +++ b/taming-transformers/taming/models/vqgan.py @@ -0,0 +1,404 @@ +import torch +import torch.nn.functional as F +import pytorch_lightning as pl + +from main import instantiate_from_config + +from taming.modules.diffusionmodules.model import Encoder, Decoder +from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer +from taming.modules.vqvae.quantize import GumbelQuantize +from taming.modules.vqvae.quantize import EMAVectorQuantizer + +class VQModel(pl.LightningModule): + def __init__(self, + ddconfig, + lossconfig, + n_embed, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + remap=None, + sane_index_shape=False, # tell vector quantizer to return indices as bhw + ): + super().__init__() + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, + remap=remap, sane_index_shape=sane_index_shape) + self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + self.image_key = image_key + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + self.load_state_dict(sd, strict=False) + print(f"Restored from {path}") + + def encode(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + quant, emb_loss, info = self.quantize(h) + return quant, emb_loss, info + + def decode(self, quant): + quant = self.post_quant_conv(quant) + dec = self.decoder(quant) + return dec + + def decode_code(self, code_b): + quant_b = self.quantize.embed_code(code_b) + dec = self.decode(quant_b) + return dec + + def forward(self, input): + quant, diff, _ = self.encode(input) + dec = self.decode(quant) + return dec, diff + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format) + return x.float() + + def training_step(self, batch, batch_idx, optimizer_idx): + x = self.get_input(batch, self.image_key) + xrec, qloss = self(x) + + if optimizer_idx == 0: + # autoencode + aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + + self.log("train/aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return aeloss + + if optimizer_idx == 1: + # discriminator + discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + self.log("train/discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return discloss + + def validation_step(self, batch, batch_idx): + x = self.get_input(batch, self.image_key) + xrec, qloss = self(x) + aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, self.global_step, + last_layer=self.get_last_layer(), split="val") + + discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, self.global_step, + last_layer=self.get_last_layer(), split="val") + rec_loss = log_dict_ae["val/rec_loss"] + self.log("val/rec_loss", rec_loss, + prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True) + self.log("val/aeloss", aeloss, + prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True) + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr = self.learning_rate + opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ + list(self.decoder.parameters())+ + list(self.quantize.parameters())+ + list(self.quant_conv.parameters())+ + list(self.post_quant_conv.parameters()), + lr=lr, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), + lr=lr, betas=(0.5, 0.9)) + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + def log_images(self, batch, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + xrec, _ = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["inputs"] = x + log["reconstructions"] = xrec + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + + +class VQSegmentationModel(VQModel): + def __init__(self, n_labels, *args, **kwargs): + super().__init__(*args, **kwargs) + self.register_buffer("colorize", torch.randn(3, n_labels, 1, 1)) + + def configure_optimizers(self): + lr = self.learning_rate + opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ + list(self.decoder.parameters())+ + list(self.quantize.parameters())+ + list(self.quant_conv.parameters())+ + list(self.post_quant_conv.parameters()), + lr=lr, betas=(0.5, 0.9)) + return opt_ae + + def training_step(self, batch, batch_idx): + x = self.get_input(batch, self.image_key) + xrec, qloss = self(x) + aeloss, log_dict_ae = self.loss(qloss, x, xrec, split="train") + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return aeloss + + def validation_step(self, batch, batch_idx): + x = self.get_input(batch, self.image_key) + xrec, qloss = self(x) + aeloss, log_dict_ae = self.loss(qloss, x, xrec, split="val") + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) + total_loss = log_dict_ae["val/total_loss"] + self.log("val/total_loss", total_loss, + prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True) + return aeloss + + @torch.no_grad() + def log_images(self, batch, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + xrec, _ = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + # convert logits to indices + xrec = torch.argmax(xrec, dim=1, keepdim=True) + xrec = F.one_hot(xrec, num_classes=x.shape[1]) + xrec = xrec.squeeze(1).permute(0, 3, 1, 2).float() + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["inputs"] = x + log["reconstructions"] = xrec + return log + + +class VQNoDiscModel(VQModel): + def __init__(self, + ddconfig, + lossconfig, + n_embed, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None + ): + super().__init__(ddconfig=ddconfig, lossconfig=lossconfig, n_embed=n_embed, embed_dim=embed_dim, + ckpt_path=ckpt_path, ignore_keys=ignore_keys, image_key=image_key, + colorize_nlabels=colorize_nlabels) + + def training_step(self, batch, batch_idx): + x = self.get_input(batch, self.image_key) + xrec, qloss = self(x) + # autoencode + aeloss, log_dict_ae = self.loss(qloss, x, xrec, self.global_step, split="train") + output = pl.TrainResult(minimize=aeloss) + output.log("train/aeloss", aeloss, + prog_bar=True, logger=True, on_step=True, on_epoch=True) + output.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return output + + def validation_step(self, batch, batch_idx): + x = self.get_input(batch, self.image_key) + xrec, qloss = self(x) + aeloss, log_dict_ae = self.loss(qloss, x, xrec, self.global_step, split="val") + rec_loss = log_dict_ae["val/rec_loss"] + output = pl.EvalResult(checkpoint_on=rec_loss) + output.log("val/rec_loss", rec_loss, + prog_bar=True, logger=True, on_step=True, on_epoch=True) + output.log("val/aeloss", aeloss, + prog_bar=True, logger=True, on_step=True, on_epoch=True) + output.log_dict(log_dict_ae) + + return output + + def configure_optimizers(self): + optimizer = torch.optim.Adam(list(self.encoder.parameters())+ + list(self.decoder.parameters())+ + list(self.quantize.parameters())+ + list(self.quant_conv.parameters())+ + list(self.post_quant_conv.parameters()), + lr=self.learning_rate, betas=(0.5, 0.9)) + return optimizer + + +class GumbelVQ(VQModel): + def __init__(self, + ddconfig, + lossconfig, + n_embed, + embed_dim, + temperature_scheduler_config, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + kl_weight=1e-8, + remap=None, + ): + + z_channels = ddconfig["z_channels"] + super().__init__(ddconfig, + lossconfig, + n_embed, + embed_dim, + ckpt_path=None, + ignore_keys=ignore_keys, + image_key=image_key, + colorize_nlabels=colorize_nlabels, + monitor=monitor, + ) + + self.loss.n_classes = n_embed + self.vocab_size = n_embed + + self.quantize = GumbelQuantize(z_channels, embed_dim, + n_embed=n_embed, + kl_weight=kl_weight, temp_init=1.0, + remap=remap) + + self.temperature_scheduler = instantiate_from_config(temperature_scheduler_config) # annealing of temp + + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + + def temperature_scheduling(self): + self.quantize.temperature = self.temperature_scheduler(self.global_step) + + def encode_to_prequant(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + return h + + def decode_code(self, code_b): + raise NotImplementedError + + def training_step(self, batch, batch_idx, optimizer_idx): + self.temperature_scheduling() + x = self.get_input(batch, self.image_key) + xrec, qloss = self(x) + + if optimizer_idx == 0: + # autoencode + aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) + self.log("temperature", self.quantize.temperature, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return aeloss + + if optimizer_idx == 1: + # discriminator + discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return discloss + + def validation_step(self, batch, batch_idx): + x = self.get_input(batch, self.image_key) + xrec, qloss = self(x, return_pred_indices=True) + aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, self.global_step, + last_layer=self.get_last_layer(), split="val") + + discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, self.global_step, + last_layer=self.get_last_layer(), split="val") + rec_loss = log_dict_ae["val/rec_loss"] + self.log("val/rec_loss", rec_loss, + prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) + self.log("val/aeloss", aeloss, + prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def log_images(self, batch, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + # encode + h = self.encoder(x) + h = self.quant_conv(h) + quant, _, _ = self.quantize(h) + # decode + x_rec = self.decode(quant) + log["inputs"] = x + log["reconstructions"] = x_rec + return log + + +class EMAVQ(VQModel): + def __init__(self, + ddconfig, + lossconfig, + n_embed, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + remap=None, + sane_index_shape=False, # tell vector quantizer to return indices as bhw + ): + super().__init__(ddconfig, + lossconfig, + n_embed, + embed_dim, + ckpt_path=None, + ignore_keys=ignore_keys, + image_key=image_key, + colorize_nlabels=colorize_nlabels, + monitor=monitor, + ) + self.quantize = EMAVectorQuantizer(n_embed=n_embed, + embedding_dim=embed_dim, + beta=0.25, + remap=remap) + def configure_optimizers(self): + lr = self.learning_rate + #Remove self.quantize from parameter list since it is updated via EMA + opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ + list(self.decoder.parameters())+ + list(self.quant_conv.parameters())+ + list(self.post_quant_conv.parameters()), + lr=lr, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), + lr=lr, betas=(0.5, 0.9)) + return [opt_ae, opt_disc], [] \ No newline at end of file diff --git a/taming-transformers/taming/modules/diffusionmodules/model.py b/taming-transformers/taming/modules/diffusionmodules/model.py new file mode 100755 index 0000000000000000000000000000000000000000..d3a5db6aa2ef915e270f1ae135e4a9918fdd884c --- /dev/null +++ b/taming-transformers/taming/modules/diffusionmodules/model.py @@ -0,0 +1,776 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import torch.nn as nn +import numpy as np + + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + else: + self.nin_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x+h + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = q.reshape(b,c,h*w) + q = q.permute(0,2,1) # b,hw,c + k = k.reshape(b,c,h*w) # b,c,hw + w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b,c,h*w) + w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b,c,h,w) + + h_ = self.proj_out(h_) + + return x+h_ + + +class Model(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, use_timestep=True): + super().__init__() + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + torch.nn.Linear(self.ch, + self.temb_ch), + torch.nn.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + + def forward(self, x, t=None): + #assert x.shape[2] == x.shape[3] == self.resolution + + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, double_z=True, **ignore_kwargs): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1) + + + def forward(self, x): + #assert x.shape[2] == x.shape[3] == self.resolution, "{}, {}, {}".format(x.shape[2], x.shape[3], self.resolution) + + # timestep embedding + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, **ignorekwargs): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) + block_in = ch*ch_mult[self.num_resolutions-1] + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + print("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class VUNet(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, + in_channels, c_channels, + resolution, z_channels, use_timestep=False, **ignore_kwargs): + super().__init__() + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + torch.nn.Linear(self.ch, + self.temb_ch), + torch.nn.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = torch.nn.Conv2d(c_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + self.z_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=1, + stride=1, + padding=0) + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=2*block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + + def forward(self, x, z): + #assert x.shape[2] == x.shape[3] == self.resolution + + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + z = self.z_in(z) + h = torch.cat((h,z),dim=1) + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class SimpleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, *args, **kwargs): + super().__init__() + self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), + ResnetBlock(in_channels=in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=2 * in_channels, + out_channels=4 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=4 * in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + nn.Conv2d(2*in_channels, in_channels, 1), + Upsample(in_channels, with_conv=True)]) + # end + self.norm_out = Normalize(in_channels) + self.conv_out = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + for i, layer in enumerate(self.model): + if i in [1,2,3]: + x = layer(x, None) + else: + x = layer(x) + + h = self.norm_out(x) + h = nonlinearity(h) + x = self.conv_out(h) + return x + + +class UpsampleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, + ch_mult=(2,2), dropout=0.0): + super().__init__() + # upsampling + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = in_channels + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.res_blocks = nn.ModuleList() + self.upsample_blocks = nn.ModuleList() + for i_level in range(self.num_resolutions): + res_block = [] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + res_block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + self.res_blocks.append(nn.ModuleList(res_block)) + if i_level != self.num_resolutions - 1: + self.upsample_blocks.append(Upsample(block_in, True)) + curr_res = curr_res * 2 + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # upsampling + h = x + for k, i_level in enumerate(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.res_blocks[i_level][i_block](h, None) + if i_level != self.num_resolutions - 1: + h = self.upsample_blocks[k](h) + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + diff --git a/taming-transformers/taming/modules/discriminator/model.py b/taming-transformers/taming/modules/discriminator/model.py new file mode 100755 index 0000000000000000000000000000000000000000..2aaa3110d0a7bcd05de7eca1e45101589ca5af05 --- /dev/null +++ b/taming-transformers/taming/modules/discriminator/model.py @@ -0,0 +1,67 @@ +import functools +import torch.nn as nn + + +from taming.modules.util import ActNorm + + +def weights_init(m): + classname = m.__class__.__name__ + if classname.find('Conv') != -1: + nn.init.normal_(m.weight.data, 0.0, 0.02) + elif classname.find('BatchNorm') != -1: + nn.init.normal_(m.weight.data, 1.0, 0.02) + nn.init.constant_(m.bias.data, 0) + + +class NLayerDiscriminator(nn.Module): + """Defines a PatchGAN discriminator as in Pix2Pix + --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py + """ + def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): + """Construct a PatchGAN discriminator + Parameters: + input_nc (int) -- the number of channels in input images + ndf (int) -- the number of filters in the last conv layer + n_layers (int) -- the number of conv layers in the discriminator + norm_layer -- normalization layer + """ + super(NLayerDiscriminator, self).__init__() + if not use_actnorm: + norm_layer = nn.BatchNorm2d + else: + norm_layer = ActNorm + if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters + use_bias = norm_layer.func != nn.BatchNorm2d + else: + use_bias = norm_layer != nn.BatchNorm2d + + kw = 4 + padw = 1 + sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] + nf_mult = 1 + nf_mult_prev = 1 + for n in range(1, n_layers): # gradually increase the number of filters + nf_mult_prev = nf_mult + nf_mult = min(2 ** n, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + nf_mult_prev = nf_mult + nf_mult = min(2 ** n_layers, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + sequence += [ + nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map + self.main = nn.Sequential(*sequence) + + def forward(self, input): + """Standard forward.""" + return self.main(input) diff --git a/taming-transformers/taming/modules/losses/__init__.py b/taming-transformers/taming/modules/losses/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..d09caf9eb805f849a517f1b23503e1a4d6ea1ec5 --- /dev/null +++ b/taming-transformers/taming/modules/losses/__init__.py @@ -0,0 +1,2 @@ +from taming.modules.losses.vqperceptual import DummyLoss + diff --git a/taming-transformers/taming/modules/losses/lpips.py b/taming-transformers/taming/modules/losses/lpips.py new file mode 100755 index 0000000000000000000000000000000000000000..a7280447694ffc302a7636e7e4d6183408e0aa95 --- /dev/null +++ b/taming-transformers/taming/modules/losses/lpips.py @@ -0,0 +1,123 @@ +"""Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models""" + +import torch +import torch.nn as nn +from torchvision import models +from collections import namedtuple + +from taming.util import get_ckpt_path + + +class LPIPS(nn.Module): + # Learned perceptual metric + def __init__(self, use_dropout=True): + super().__init__() + self.scaling_layer = ScalingLayer() + self.chns = [64, 128, 256, 512, 512] # vg16 features + self.net = vgg16(pretrained=True, requires_grad=False) + self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout) + self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout) + self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout) + self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout) + self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout) + self.load_from_pretrained() + for param in self.parameters(): + param.requires_grad = False + + def load_from_pretrained(self, name="vgg_lpips"): + ckpt = get_ckpt_path(name, "taming/modules/autoencoder/lpips") + self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False) + print("loaded pretrained LPIPS loss from {}".format(ckpt)) + + @classmethod + def from_pretrained(cls, name="vgg_lpips"): + if name != "vgg_lpips": + raise NotImplementedError + model = cls() + ckpt = get_ckpt_path(name) + model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False) + return model + + def forward(self, input, target): + in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target)) + outs0, outs1 = self.net(in0_input), self.net(in1_input) + feats0, feats1, diffs = {}, {}, {} + lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4] + for kk in range(len(self.chns)): + feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk]) + diffs[kk] = (feats0[kk] - feats1[kk]) ** 2 + + res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))] + val = res[0] + for l in range(1, len(self.chns)): + val += res[l] + return val + + +class ScalingLayer(nn.Module): + def __init__(self): + super(ScalingLayer, self).__init__() + self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None]) + self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None]) + + def forward(self, inp): + return (inp - self.shift) / self.scale + + +class NetLinLayer(nn.Module): + """ A single linear layer which does a 1x1 conv """ + def __init__(self, chn_in, chn_out=1, use_dropout=False): + super(NetLinLayer, self).__init__() + layers = [nn.Dropout(), ] if (use_dropout) else [] + layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ] + self.model = nn.Sequential(*layers) + + +class vgg16(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(vgg16, self).__init__() + vgg_pretrained_features = models.vgg16(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(4): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(4, 9): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(9, 16): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(16, 23): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(23, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1_2 = h + h = self.slice2(h) + h_relu2_2 = h + h = self.slice3(h) + h_relu3_3 = h + h = self.slice4(h) + h_relu4_3 = h + h = self.slice5(h) + h_relu5_3 = h + vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3']) + out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) + return out + + +def normalize_tensor(x,eps=1e-10): + norm_factor = torch.sqrt(torch.sum(x**2,dim=1,keepdim=True)) + return x/(norm_factor+eps) + + +def spatial_average(x, keepdim=True): + return x.mean([2,3],keepdim=keepdim) + diff --git a/taming-transformers/taming/modules/losses/segmentation.py b/taming-transformers/taming/modules/losses/segmentation.py new file mode 100755 index 0000000000000000000000000000000000000000..4ba77deb5159a6307ed2acba9945e4764a4ff0a5 --- /dev/null +++ b/taming-transformers/taming/modules/losses/segmentation.py @@ -0,0 +1,22 @@ +import torch.nn as nn +import torch.nn.functional as F + + +class BCELoss(nn.Module): + def forward(self, prediction, target): + loss = F.binary_cross_entropy_with_logits(prediction,target) + return loss, {} + + +class BCELossWithQuant(nn.Module): + def __init__(self, codebook_weight=1.): + super().__init__() + self.codebook_weight = codebook_weight + + def forward(self, qloss, target, prediction, split): + bce_loss = F.binary_cross_entropy_with_logits(prediction,target) + loss = bce_loss + self.codebook_weight*qloss + return loss, {"{}/total_loss".format(split): loss.clone().detach().mean(), + "{}/bce_loss".format(split): bce_loss.detach().mean(), + "{}/quant_loss".format(split): qloss.detach().mean() + } diff --git a/taming-transformers/taming/modules/losses/vqperceptual.py b/taming-transformers/taming/modules/losses/vqperceptual.py new file mode 100755 index 0000000000000000000000000000000000000000..c2febd445728479d4cd9aacdb2572cb1f1af04db --- /dev/null +++ b/taming-transformers/taming/modules/losses/vqperceptual.py @@ -0,0 +1,136 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from taming.modules.losses.lpips import LPIPS +from taming.modules.discriminator.model import NLayerDiscriminator, weights_init + + +class DummyLoss(nn.Module): + def __init__(self): + super().__init__() + + +def adopt_weight(weight, global_step, threshold=0, value=0.): + if global_step < threshold: + weight = value + return weight + + +def hinge_d_loss(logits_real, logits_fake): + loss_real = torch.mean(F.relu(1. - logits_real)) + loss_fake = torch.mean(F.relu(1. + logits_fake)) + d_loss = 0.5 * (loss_real + loss_fake) + return d_loss + + +def vanilla_d_loss(logits_real, logits_fake): + d_loss = 0.5 * ( + torch.mean(torch.nn.functional.softplus(-logits_real)) + + torch.mean(torch.nn.functional.softplus(logits_fake))) + return d_loss + + +class VQLPIPSWithDiscriminator(nn.Module): + def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, + disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, + perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, + disc_ndf=64, disc_loss="hinge"): + super().__init__() + assert disc_loss in ["hinge", "vanilla"] + self.codebook_weight = codebook_weight + self.pixel_weight = pixelloss_weight + self.perceptual_loss = LPIPS().eval() + self.perceptual_weight = perceptual_weight + + self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, + n_layers=disc_num_layers, + use_actnorm=use_actnorm, + ndf=disc_ndf + ).apply(weights_init) + self.discriminator_iter_start = disc_start + if disc_loss == "hinge": + self.disc_loss = hinge_d_loss + elif disc_loss == "vanilla": + self.disc_loss = vanilla_d_loss + else: + raise ValueError(f"Unknown GAN loss '{disc_loss}'.") + print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.") + self.disc_factor = disc_factor + self.discriminator_weight = disc_weight + self.disc_conditional = disc_conditional + + def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): + if last_layer is not None: + nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] + else: + nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] + + d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) + d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() + d_weight = d_weight * self.discriminator_weight + return d_weight + + def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx, + global_step, last_layer=None, cond=None, split="train"): + rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) + if self.perceptual_weight > 0: + p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) + rec_loss = rec_loss + self.perceptual_weight * p_loss + else: + p_loss = torch.tensor([0.0]) + + nll_loss = rec_loss + #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] + nll_loss = torch.mean(nll_loss) + + # now the GAN part + if optimizer_idx == 0: + # generator update + if cond is None: + assert not self.disc_conditional + logits_fake = self.discriminator(reconstructions.contiguous()) + else: + assert self.disc_conditional + logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) + g_loss = -torch.mean(logits_fake) + + try: + d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) + except RuntimeError: + assert not self.training + d_weight = torch.tensor(0.0) + + disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) + loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean() + + log = {"{}/total_loss".format(split): loss.clone().detach().mean(), + "{}/quant_loss".format(split): codebook_loss.detach().mean(), + "{}/nll_loss".format(split): nll_loss.detach().mean(), + "{}/rec_loss".format(split): rec_loss.detach().mean(), + "{}/p_loss".format(split): p_loss.detach().mean(), + "{}/d_weight".format(split): d_weight.detach(), + "{}/disc_factor".format(split): torch.tensor(disc_factor), + "{}/g_loss".format(split): g_loss.detach().mean(), + } + return loss, log + + if optimizer_idx == 1: + # second pass for discriminator update + if cond is None: + logits_real = self.discriminator(inputs.contiguous().detach()) + logits_fake = self.discriminator(reconstructions.contiguous().detach()) + else: + logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) + logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) + + disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) + d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) + + log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), + "{}/logits_real".format(split): logits_real.detach().mean(), + "{}/logits_fake".format(split): logits_fake.detach().mean() + } + return d_loss, log diff --git a/taming-transformers/taming/modules/misc/coord.py b/taming-transformers/taming/modules/misc/coord.py new file mode 100755 index 0000000000000000000000000000000000000000..ee69b0c897b6b382ae673622e420f55e494f5b09 --- /dev/null +++ b/taming-transformers/taming/modules/misc/coord.py @@ -0,0 +1,31 @@ +import torch + +class CoordStage(object): + def __init__(self, n_embed, down_factor): + self.n_embed = n_embed + self.down_factor = down_factor + + def eval(self): + return self + + def encode(self, c): + """fake vqmodel interface""" + assert 0.0 <= c.min() and c.max() <= 1.0 + b,ch,h,w = c.shape + assert ch == 1 + + c = torch.nn.functional.interpolate(c, scale_factor=1/self.down_factor, + mode="area") + c = c.clamp(0.0, 1.0) + c = self.n_embed*c + c_quant = c.round() + c_ind = c_quant.to(dtype=torch.long) + + info = None, None, c_ind + return c_quant, None, info + + def decode(self, c): + c = c/self.n_embed + c = torch.nn.functional.interpolate(c, scale_factor=self.down_factor, + mode="nearest") + return c diff --git a/taming-transformers/taming/modules/transformer/mingpt.py b/taming-transformers/taming/modules/transformer/mingpt.py new file mode 100755 index 0000000000000000000000000000000000000000..d14b7b68117f4b9f297b2929397cd4f55089334c --- /dev/null +++ b/taming-transformers/taming/modules/transformer/mingpt.py @@ -0,0 +1,415 @@ +""" +taken from: https://github.com/karpathy/minGPT/ +GPT model: +- the initial stem consists of a combination of token encoding and a positional encoding +- the meat of it is a uniform sequence of Transformer blocks + - each Transformer is a sequential combination of a 1-hidden-layer MLP block and a self-attention block + - all blocks feed into a central residual pathway similar to resnets +- the final decoder is a linear projection into a vanilla Softmax classifier +""" + +import math +import logging + +import torch +import torch.nn as nn +from torch.nn import functional as F +from transformers import top_k_top_p_filtering + +logger = logging.getLogger(__name__) + + +class GPTConfig: + """ base GPT config, params common to all GPT versions """ + embd_pdrop = 0.1 + resid_pdrop = 0.1 + attn_pdrop = 0.1 + + def __init__(self, vocab_size, block_size, **kwargs): + self.vocab_size = vocab_size + self.block_size = block_size + for k,v in kwargs.items(): + setattr(self, k, v) + + +class GPT1Config(GPTConfig): + """ GPT-1 like network roughly 125M params """ + n_layer = 12 + n_head = 12 + n_embd = 768 + + +class CausalSelfAttention(nn.Module): + """ + A vanilla multi-head masked self-attention layer with a projection at the end. + It is possible to use torch.nn.MultiheadAttention here but I am including an + explicit implementation here to show that there is nothing too scary here. + """ + + def __init__(self, config): + super().__init__() + assert config.n_embd % config.n_head == 0 + # key, query, value projections for all heads + self.key = nn.Linear(config.n_embd, config.n_embd) + self.query = nn.Linear(config.n_embd, config.n_embd) + self.value = nn.Linear(config.n_embd, config.n_embd) + # regularization + self.attn_drop = nn.Dropout(config.attn_pdrop) + self.resid_drop = nn.Dropout(config.resid_pdrop) + # output projection + self.proj = nn.Linear(config.n_embd, config.n_embd) + # causal mask to ensure that attention is only applied to the left in the input sequence + mask = torch.tril(torch.ones(config.block_size, + config.block_size)) + if hasattr(config, "n_unmasked"): + mask[:config.n_unmasked, :config.n_unmasked] = 1 + self.register_buffer("mask", mask.view(1, 1, config.block_size, config.block_size)) + self.n_head = config.n_head + + def forward(self, x, layer_past=None): + B, T, C = x.size() + + # calculate query, key, values for all heads in batch and move head forward to be the batch dim + k = self.key(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) + q = self.query(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) + v = self.value(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) + + present = torch.stack((k, v)) + if layer_past is not None: + past_key, past_value = layer_past + k = torch.cat((past_key, k), dim=-2) + v = torch.cat((past_value, v), dim=-2) + + # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T) + att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) + if layer_past is None: + att = att.masked_fill(self.mask[:,:,:T,:T] == 0, float('-inf')) + + att = F.softmax(att, dim=-1) + att = self.attn_drop(att) + y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs) + y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side + + # output projection + y = self.resid_drop(self.proj(y)) + return y, present # TODO: check that this does not break anything + + +class Block(nn.Module): + """ an unassuming Transformer block """ + def __init__(self, config): + super().__init__() + self.ln1 = nn.LayerNorm(config.n_embd) + self.ln2 = nn.LayerNorm(config.n_embd) + self.attn = CausalSelfAttention(config) + self.mlp = nn.Sequential( + nn.Linear(config.n_embd, 4 * config.n_embd), + nn.GELU(), # nice + nn.Linear(4 * config.n_embd, config.n_embd), + nn.Dropout(config.resid_pdrop), + ) + + def forward(self, x, layer_past=None, return_present=False): + # TODO: check that training still works + if return_present: assert not self.training + # layer past: tuple of length two with B, nh, T, hs + attn, present = self.attn(self.ln1(x), layer_past=layer_past) + + x = x + attn + x = x + self.mlp(self.ln2(x)) + if layer_past is not None or return_present: + return x, present + return x + + +class GPT(nn.Module): + """ the full GPT language model, with a context size of block_size """ + def __init__(self, vocab_size, block_size, n_layer=12, n_head=8, n_embd=256, + embd_pdrop=0., resid_pdrop=0., attn_pdrop=0., n_unmasked=0): + super().__init__() + config = GPTConfig(vocab_size=vocab_size, block_size=block_size, + embd_pdrop=embd_pdrop, resid_pdrop=resid_pdrop, attn_pdrop=attn_pdrop, + n_layer=n_layer, n_head=n_head, n_embd=n_embd, + n_unmasked=n_unmasked) + # input embedding stem + self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd) + self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd)) + self.drop = nn.Dropout(config.embd_pdrop) + # transformer + self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)]) + # decoder head + self.ln_f = nn.LayerNorm(config.n_embd) + self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False) + self.block_size = config.block_size + self.apply(self._init_weights) + self.config = config + logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters())) + + def get_block_size(self): + return self.block_size + + def _init_weights(self, module): + if isinstance(module, (nn.Linear, nn.Embedding)): + module.weight.data.normal_(mean=0.0, std=0.02) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def forward(self, idx, embeddings=None, targets=None): + # forward the GPT model + token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector + + if embeddings is not None: # prepend explicit embeddings + token_embeddings = torch.cat((embeddings, token_embeddings), dim=1) + + t = token_embeddings.shape[1] + assert t <= self.block_size, "Cannot forward, model block size is exhausted." + position_embeddings = self.pos_emb[:, :t, :] # each position maps to a (learnable) vector + x = self.drop(token_embeddings + position_embeddings) + x = self.blocks(x) + x = self.ln_f(x) + logits = self.head(x) + + # if we are given some desired targets also calculate the loss + loss = None + if targets is not None: + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1)) + + return logits, loss + + def forward_with_past(self, idx, embeddings=None, targets=None, past=None, past_length=None): + # inference only + assert not self.training + token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector + if embeddings is not None: # prepend explicit embeddings + token_embeddings = torch.cat((embeddings, token_embeddings), dim=1) + + if past is not None: + assert past_length is not None + past = torch.cat(past, dim=-2) # n_layer, 2, b, nh, len_past, dim_head + past_shape = list(past.shape) + expected_shape = [self.config.n_layer, 2, idx.shape[0], self.config.n_head, past_length, self.config.n_embd//self.config.n_head] + assert past_shape == expected_shape, f"{past_shape} =/= {expected_shape}" + position_embeddings = self.pos_emb[:, past_length, :] # each position maps to a (learnable) vector + else: + position_embeddings = self.pos_emb[:, :token_embeddings.shape[1], :] + + x = self.drop(token_embeddings + position_embeddings) + presents = [] # accumulate over layers + for i, block in enumerate(self.blocks): + x, present = block(x, layer_past=past[i, ...] if past is not None else None, return_present=True) + presents.append(present) + + x = self.ln_f(x) + logits = self.head(x) + # if we are given some desired targets also calculate the loss + loss = None + if targets is not None: + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1)) + + return logits, loss, torch.stack(presents) # _, _, n_layer, 2, b, nh, 1, dim_head + + +class DummyGPT(nn.Module): + # for debugging + def __init__(self, add_value=1): + super().__init__() + self.add_value = add_value + + def forward(self, idx): + return idx + self.add_value, None + + +class CodeGPT(nn.Module): + """Takes in semi-embeddings""" + def __init__(self, vocab_size, block_size, in_channels, n_layer=12, n_head=8, n_embd=256, + embd_pdrop=0., resid_pdrop=0., attn_pdrop=0., n_unmasked=0): + super().__init__() + config = GPTConfig(vocab_size=vocab_size, block_size=block_size, + embd_pdrop=embd_pdrop, resid_pdrop=resid_pdrop, attn_pdrop=attn_pdrop, + n_layer=n_layer, n_head=n_head, n_embd=n_embd, + n_unmasked=n_unmasked) + # input embedding stem + self.tok_emb = nn.Linear(in_channels, config.n_embd) + self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd)) + self.drop = nn.Dropout(config.embd_pdrop) + # transformer + self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)]) + # decoder head + self.ln_f = nn.LayerNorm(config.n_embd) + self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False) + self.block_size = config.block_size + self.apply(self._init_weights) + self.config = config + logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters())) + + def get_block_size(self): + return self.block_size + + def _init_weights(self, module): + if isinstance(module, (nn.Linear, nn.Embedding)): + module.weight.data.normal_(mean=0.0, std=0.02) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def forward(self, idx, embeddings=None, targets=None): + # forward the GPT model + token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector + + if embeddings is not None: # prepend explicit embeddings + token_embeddings = torch.cat((embeddings, token_embeddings), dim=1) + + t = token_embeddings.shape[1] + assert t <= self.block_size, "Cannot forward, model block size is exhausted." + position_embeddings = self.pos_emb[:, :t, :] # each position maps to a (learnable) vector + x = self.drop(token_embeddings + position_embeddings) + x = self.blocks(x) + x = self.taming_cinln_f(x) + logits = self.head(x) + + # if we are given some desired targets also calculate the loss + loss = None + if targets is not None: + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1)) + + return logits, loss + + + +#### sampling utils + +def top_k_logits(logits, k): + v, ix = torch.topk(logits, k) + out = logits.clone() + out[out < v[:, [-1]]] = -float('Inf') + return out + +@torch.no_grad() +def sample(model, x, steps, temperature=1.0, sample=False, top_k=None): + """ + take a conditioning sequence of indices in x (of shape (b,t)) and predict the next token in + the sequence, feeding the predictions back into the model each time. Clearly the sampling + has quadratic complexity unlike an RNN that is only linear, and has a finite context window + of block_size, unlike an RNN that has an infinite context window. + """ + block_size = model.get_block_size() + model.eval() + for k in range(steps): + x_cond = x if x.size(1) <= block_size else x[:, -block_size:] # crop context if needed + logits, _ = model(x_cond) + # pluck the logits at the final step and scale by temperature + logits = logits[:, -1, :] / temperature + # optionally crop probabilities to only the top k options + if top_k is not None: + logits = top_k_logits(logits, top_k) + # apply softmax to convert to probabilities + probs = F.softmax(logits, dim=-1) + # sample from the distribution or take the most likely + if sample: + ix = torch.multinomial(probs, num_samples=1) + else: + _, ix = torch.topk(probs, k=1, dim=-1) + # append to the sequence and continue + x = torch.cat((x, ix), dim=1) + + return x + + +@torch.no_grad() +def sample_with_past(x, model, steps, temperature=1., sample_logits=True, + top_k=None, top_p=None, callback=None): + # x is conditioning + sample = x + cond_len = x.shape[1] + past = None + for n in range(steps): + if callback is not None: + callback(n) + logits, _, present = model.forward_with_past(x, past=past, past_length=(n+cond_len-1)) + if past is None: + past = [present] + else: + past.append(present) + logits = logits[:, -1, :] / temperature + if top_k is not None: + logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p) + + probs = F.softmax(logits, dim=-1) + if not sample_logits: + _, x = torch.topk(probs, k=1, dim=-1) + else: + x = torch.multinomial(probs, num_samples=1) + # append to the sequence and continue + sample = torch.cat((sample, x), dim=1) + del past + sample = sample[:, cond_len:] # cut conditioning off + return sample + + +#### clustering utils + +class KMeans(nn.Module): + def __init__(self, ncluster=512, nc=3, niter=10): + super().__init__() + self.ncluster = ncluster + self.nc = nc + self.niter = niter + self.shape = (3,32,32) + self.register_buffer("C", torch.zeros(self.ncluster,nc)) + self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8)) + + def is_initialized(self): + return self.initialized.item() == 1 + + @torch.no_grad() + def initialize(self, x): + N, D = x.shape + assert D == self.nc, D + c = x[torch.randperm(N)[:self.ncluster]] # init clusters at random + for i in range(self.niter): + # assign all pixels to the closest codebook element + a = ((x[:, None, :] - c[None, :, :])**2).sum(-1).argmin(1) + # move each codebook element to be the mean of the pixels that assigned to it + c = torch.stack([x[a==k].mean(0) for k in range(self.ncluster)]) + # re-assign any poorly positioned codebook elements + nanix = torch.any(torch.isnan(c), dim=1) + ndead = nanix.sum().item() + print('done step %d/%d, re-initialized %d dead clusters' % (i+1, self.niter, ndead)) + c[nanix] = x[torch.randperm(N)[:ndead]] # re-init dead clusters + + self.C.copy_(c) + self.initialized.fill_(1) + + + def forward(self, x, reverse=False, shape=None): + if not reverse: + # flatten + bs,c,h,w = x.shape + assert c == self.nc + x = x.reshape(bs,c,h*w,1) + C = self.C.permute(1,0) + C = C.reshape(1,c,1,self.ncluster) + a = ((x-C)**2).sum(1).argmin(-1) # bs, h*w indices + return a + else: + # flatten + bs, HW = x.shape + """ + c = self.C.reshape( 1, self.nc, 1, self.ncluster) + c = c[bs*[0],:,:,:] + c = c[:,:,HW*[0],:] + x = x.reshape(bs, 1, HW, 1) + x = x[:,3*[0],:,:] + x = torch.gather(c, dim=3, index=x) + """ + x = self.C[x] + x = x.permute(0,2,1) + shape = shape if shape is not None else self.shape + x = x.reshape(bs, *shape) + + return x diff --git a/taming-transformers/taming/modules/transformer/permuter.py b/taming-transformers/taming/modules/transformer/permuter.py new file mode 100755 index 0000000000000000000000000000000000000000..0d43bb135adde38d94bf18a7e5edaa4523cd95cf --- /dev/null +++ b/taming-transformers/taming/modules/transformer/permuter.py @@ -0,0 +1,248 @@ +import torch +import torch.nn as nn +import numpy as np + + +class AbstractPermuter(nn.Module): + def __init__(self, *args, **kwargs): + super().__init__() + def forward(self, x, reverse=False): + raise NotImplementedError + + +class Identity(AbstractPermuter): + def __init__(self): + super().__init__() + + def forward(self, x, reverse=False): + return x + + +class Subsample(AbstractPermuter): + def __init__(self, H, W): + super().__init__() + C = 1 + indices = np.arange(H*W).reshape(C,H,W) + while min(H, W) > 1: + indices = indices.reshape(C,H//2,2,W//2,2) + indices = indices.transpose(0,2,4,1,3) + indices = indices.reshape(C*4,H//2, W//2) + H = H//2 + W = W//2 + C = C*4 + assert H == W == 1 + idx = torch.tensor(indices.ravel()) + self.register_buffer('forward_shuffle_idx', + nn.Parameter(idx, requires_grad=False)) + self.register_buffer('backward_shuffle_idx', + nn.Parameter(torch.argsort(idx), requires_grad=False)) + + def forward(self, x, reverse=False): + if not reverse: + return x[:, self.forward_shuffle_idx] + else: + return x[:, self.backward_shuffle_idx] + + +def mortonify(i, j): + """(i,j) index to linear morton code""" + i = np.uint64(i) + j = np.uint64(j) + + z = np.uint(0) + + for pos in range(32): + z = (z | + ((j & (np.uint64(1) << np.uint64(pos))) << np.uint64(pos)) | + ((i & (np.uint64(1) << np.uint64(pos))) << np.uint64(pos+1)) + ) + return z + + +class ZCurve(AbstractPermuter): + def __init__(self, H, W): + super().__init__() + reverseidx = [np.int64(mortonify(i,j)) for i in range(H) for j in range(W)] + idx = np.argsort(reverseidx) + idx = torch.tensor(idx) + reverseidx = torch.tensor(reverseidx) + self.register_buffer('forward_shuffle_idx', + idx) + self.register_buffer('backward_shuffle_idx', + reverseidx) + + def forward(self, x, reverse=False): + if not reverse: + return x[:, self.forward_shuffle_idx] + else: + return x[:, self.backward_shuffle_idx] + + +class SpiralOut(AbstractPermuter): + def __init__(self, H, W): + super().__init__() + assert H == W + size = W + indices = np.arange(size*size).reshape(size,size) + + i0 = size//2 + j0 = size//2-1 + + i = i0 + j = j0 + + idx = [indices[i0, j0]] + step_mult = 0 + for c in range(1, size//2+1): + step_mult += 1 + # steps left + for k in range(step_mult): + i = i - 1 + j = j + idx.append(indices[i, j]) + + # step down + for k in range(step_mult): + i = i + j = j + 1 + idx.append(indices[i, j]) + + step_mult += 1 + if c < size//2: + # step right + for k in range(step_mult): + i = i + 1 + j = j + idx.append(indices[i, j]) + + # step up + for k in range(step_mult): + i = i + j = j - 1 + idx.append(indices[i, j]) + else: + # end reached + for k in range(step_mult-1): + i = i + 1 + idx.append(indices[i, j]) + + assert len(idx) == size*size + idx = torch.tensor(idx) + self.register_buffer('forward_shuffle_idx', idx) + self.register_buffer('backward_shuffle_idx', torch.argsort(idx)) + + def forward(self, x, reverse=False): + if not reverse: + return x[:, self.forward_shuffle_idx] + else: + return x[:, self.backward_shuffle_idx] + + +class SpiralIn(AbstractPermuter): + def __init__(self, H, W): + super().__init__() + assert H == W + size = W + indices = np.arange(size*size).reshape(size,size) + + i0 = size//2 + j0 = size//2-1 + + i = i0 + j = j0 + + idx = [indices[i0, j0]] + step_mult = 0 + for c in range(1, size//2+1): + step_mult += 1 + # steps left + for k in range(step_mult): + i = i - 1 + j = j + idx.append(indices[i, j]) + + # step down + for k in range(step_mult): + i = i + j = j + 1 + idx.append(indices[i, j]) + + step_mult += 1 + if c < size//2: + # step right + for k in range(step_mult): + i = i + 1 + j = j + idx.append(indices[i, j]) + + # step up + for k in range(step_mult): + i = i + j = j - 1 + idx.append(indices[i, j]) + else: + # end reached + for k in range(step_mult-1): + i = i + 1 + idx.append(indices[i, j]) + + assert len(idx) == size*size + idx = idx[::-1] + idx = torch.tensor(idx) + self.register_buffer('forward_shuffle_idx', idx) + self.register_buffer('backward_shuffle_idx', torch.argsort(idx)) + + def forward(self, x, reverse=False): + if not reverse: + return x[:, self.forward_shuffle_idx] + else: + return x[:, self.backward_shuffle_idx] + + +class Random(nn.Module): + def __init__(self, H, W): + super().__init__() + indices = np.random.RandomState(1).permutation(H*W) + idx = torch.tensor(indices.ravel()) + self.register_buffer('forward_shuffle_idx', idx) + self.register_buffer('backward_shuffle_idx', torch.argsort(idx)) + + def forward(self, x, reverse=False): + if not reverse: + return x[:, self.forward_shuffle_idx] + else: + return x[:, self.backward_shuffle_idx] + + +class AlternateParsing(AbstractPermuter): + def __init__(self, H, W): + super().__init__() + indices = np.arange(W*H).reshape(H,W) + for i in range(1, H, 2): + indices[i, :] = indices[i, ::-1] + idx = indices.flatten() + assert len(idx) == H*W + idx = torch.tensor(idx) + self.register_buffer('forward_shuffle_idx', idx) + self.register_buffer('backward_shuffle_idx', torch.argsort(idx)) + + def forward(self, x, reverse=False): + if not reverse: + return x[:, self.forward_shuffle_idx] + else: + return x[:, self.backward_shuffle_idx] + + +if __name__ == "__main__": + p0 = AlternateParsing(16, 16) + print(p0.forward_shuffle_idx) + print(p0.backward_shuffle_idx) + + x = torch.randint(0, 768, size=(11, 256)) + y = p0(x) + xre = p0(y, reverse=True) + assert torch.equal(x, xre) + + p1 = SpiralOut(2, 2) + print(p1.forward_shuffle_idx) + print(p1.backward_shuffle_idx) diff --git a/taming-transformers/taming/modules/util.py b/taming-transformers/taming/modules/util.py new file mode 100755 index 0000000000000000000000000000000000000000..9ee16385d8b1342a2d60a5f1aa5cadcfbe934bd8 --- /dev/null +++ b/taming-transformers/taming/modules/util.py @@ -0,0 +1,130 @@ +import torch +import torch.nn as nn + + +def count_params(model): + total_params = sum(p.numel() for p in model.parameters()) + return total_params + + +class ActNorm(nn.Module): + def __init__(self, num_features, logdet=False, affine=True, + allow_reverse_init=False): + assert affine + super().__init__() + self.logdet = logdet + self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1)) + self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1)) + self.allow_reverse_init = allow_reverse_init + + self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8)) + + def initialize(self, input): + with torch.no_grad(): + flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) + mean = ( + flatten.mean(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + std = ( + flatten.std(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + + self.loc.data.copy_(-mean) + self.scale.data.copy_(1 / (std + 1e-6)) + + def forward(self, input, reverse=False): + if reverse: + return self.reverse(input) + if len(input.shape) == 2: + input = input[:,:,None,None] + squeeze = True + else: + squeeze = False + + _, _, height, width = input.shape + + if self.training and self.initialized.item() == 0: + self.initialize(input) + self.initialized.fill_(1) + + h = self.scale * (input + self.loc) + + if squeeze: + h = h.squeeze(-1).squeeze(-1) + + if self.logdet: + log_abs = torch.log(torch.abs(self.scale)) + logdet = height*width*torch.sum(log_abs) + logdet = logdet * torch.ones(input.shape[0]).to(input) + return h, logdet + + return h + + def reverse(self, output): + if self.training and self.initialized.item() == 0: + if not self.allow_reverse_init: + raise RuntimeError( + "Initializing ActNorm in reverse direction is " + "disabled by default. Use allow_reverse_init=True to enable." + ) + else: + self.initialize(output) + self.initialized.fill_(1) + + if len(output.shape) == 2: + output = output[:,:,None,None] + squeeze = True + else: + squeeze = False + + h = output / self.scale - self.loc + + if squeeze: + h = h.squeeze(-1).squeeze(-1) + return h + + +class AbstractEncoder(nn.Module): + def __init__(self): + super().__init__() + + def encode(self, *args, **kwargs): + raise NotImplementedError + + +class Labelator(AbstractEncoder): + """Net2Net Interface for Class-Conditional Model""" + def __init__(self, n_classes, quantize_interface=True): + super().__init__() + self.n_classes = n_classes + self.quantize_interface = quantize_interface + + def encode(self, c): + c = c[:,None] + if self.quantize_interface: + return c, None, [None, None, c.long()] + return c + + +class SOSProvider(AbstractEncoder): + # for unconditional training + def __init__(self, sos_token, quantize_interface=True): + super().__init__() + self.sos_token = sos_token + self.quantize_interface = quantize_interface + + def encode(self, x): + # get batch size from data and replicate sos_token + c = torch.ones(x.shape[0], 1)*self.sos_token + c = c.long().to(x.device) + if self.quantize_interface: + return c, None, [None, None, c] + return c diff --git a/taming-transformers/taming/modules/vqvae/__pycache__/quantize.cpython-39.pyc b/taming-transformers/taming/modules/vqvae/__pycache__/quantize.cpython-39.pyc new file mode 100755 index 0000000000000000000000000000000000000000..46100656b5c46a1d3b17072812fa04136529ccdc Binary files /dev/null and b/taming-transformers/taming/modules/vqvae/__pycache__/quantize.cpython-39.pyc differ diff --git a/taming-transformers/taming/modules/vqvae/quantize.py b/taming-transformers/taming/modules/vqvae/quantize.py new file mode 100755 index 0000000000000000000000000000000000000000..d75544e41fa01bce49dd822b1037963d62f79b51 --- /dev/null +++ b/taming-transformers/taming/modules/vqvae/quantize.py @@ -0,0 +1,445 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from torch import einsum +from einops import rearrange + + +class VectorQuantizer(nn.Module): + """ + see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py + ____________________________________________ + Discretization bottleneck part of the VQ-VAE. + Inputs: + - n_e : number of embeddings + - e_dim : dimension of embedding + - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 + _____________________________________________ + """ + + # NOTE: this class contains a bug regarding beta; see VectorQuantizer2 for + # a fix and use legacy=False to apply that fix. VectorQuantizer2 can be + # used wherever VectorQuantizer has been used before and is additionally + # more efficient. + def __init__(self, n_e, e_dim, beta): + super(VectorQuantizer, self).__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + def forward(self, z): + """ + Inputs the output of the encoder network z and maps it to a discrete + one-hot vector that is the index of the closest embedding vector e_j + z (continuous) -> z_q (discrete) + z.shape = (batch, channel, height, width) + quantization pipeline: + 1. get encoder input (B,C,H,W) + 2. flatten input to (B*H*W,C) + """ + # reshape z -> (batch, height, width, channel) and flatten + z = z.permute(0, 2, 3, 1).contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ + torch.sum(self.embedding.weight**2, dim=1) - 2 * \ + torch.matmul(z_flattened, self.embedding.weight.t()) + + ## could possible replace this here + # #\start... + # find closest encodings + min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) + + min_encodings = torch.zeros( + min_encoding_indices.shape[0], self.n_e).to(z) + min_encodings.scatter_(1, min_encoding_indices, 1) + + # dtype min encodings: torch.float32 + # min_encodings shape: torch.Size([2048, 512]) + # min_encoding_indices.shape: torch.Size([2048, 1]) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) + #.........\end + + # with: + # .........\start + #min_encoding_indices = torch.argmin(d, dim=1) + #z_q = self.embedding(min_encoding_indices) + # ......\end......... (TODO) + + # compute loss for embedding + loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ + torch.mean((z_q - z.detach()) ** 2) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # perplexity + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + # TODO: check for more easy handling with nn.Embedding + min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices) + min_encodings.scatter_(1, indices[:,None], 1) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings.float(), self.embedding.weight) + + if shape is not None: + z_q = z_q.view(shape) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q + + +class GumbelQuantize(nn.Module): + """ + credit to @karpathy: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!) + Gumbel Softmax trick quantizer + Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016 + https://arxiv.org/abs/1611.01144 + """ + def __init__(self, num_hiddens, embedding_dim, n_embed, straight_through=True, + kl_weight=5e-4, temp_init=1.0, use_vqinterface=True, + remap=None, unknown_index="random"): + super().__init__() + + self.embedding_dim = embedding_dim + self.n_embed = n_embed + + self.straight_through = straight_through + self.temperature = temp_init + self.kl_weight = kl_weight + + self.proj = nn.Conv2d(num_hiddens, n_embed, 1) + self.embed = nn.Embedding(n_embed, embedding_dim) + + self.use_vqinterface = use_vqinterface + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed+1 + print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_embed + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + match = (inds[:,:,None]==used[None,None,...]).long() + new = match.argmax(-1) + unknown = match.sum(2)<1 + if self.unknown_index == "random": + new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds>=self.used.shape[0]] = 0 # simply set to zero + back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) + return back.reshape(ishape) + + def forward(self, z, temp=None, return_logits=False): + # force hard = True when we are in eval mode, as we must quantize. actually, always true seems to work + hard = self.straight_through if self.training else True + temp = self.temperature if temp is None else temp + + logits = self.proj(z) + if self.remap is not None: + # continue only with used logits + full_zeros = torch.zeros_like(logits) + logits = logits[:,self.used,...] + + soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard) + if self.remap is not None: + # go back to all entries but unused set to zero + full_zeros[:,self.used,...] = soft_one_hot + soft_one_hot = full_zeros + z_q = einsum('b n h w, n d -> b d h w', soft_one_hot, self.embed.weight) + + # + kl divergence to the prior loss + qy = F.softmax(logits, dim=1) + diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean() + + ind = soft_one_hot.argmax(dim=1) + if self.remap is not None: + ind = self.remap_to_used(ind) + if self.use_vqinterface: + if return_logits: + return z_q, diff, (None, None, ind), logits + return z_q, diff, (None, None, ind) + return z_q, diff, ind + + def get_codebook_entry(self, indices, shape): + b, h, w, c = shape + assert b*h*w == indices.shape[0] + indices = rearrange(indices, '(b h w) -> b h w', b=b, h=h, w=w) + if self.remap is not None: + indices = self.unmap_to_all(indices) + one_hot = F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float() + z_q = einsum('b n h w, n d -> b d h w', one_hot, self.embed.weight) + return z_q + + +class VectorQuantizer2(nn.Module): + """ + Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly + avoids costly matrix multiplications and allows for post-hoc remapping of indices. + """ + # NOTE: due to a bug the beta term was applied to the wrong term. for + # backwards compatibility we use the buggy version by default, but you can + # specify legacy=False to fix it. + def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random", + sane_index_shape=False, legacy=True): + super().__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + self.legacy = legacy + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed+1 + print(f"Remapping {self.n_e} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_e + + self.sane_index_shape = sane_index_shape + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + match = (inds[:,:,None]==used[None,None,...]).long() + new = match.argmax(-1) + unknown = match.sum(2)<1 + if self.unknown_index == "random": + new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds>=self.used.shape[0]] = 0 # simply set to zero + back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) + return back.reshape(ishape) + + def forward(self, z, temp=None, rescale_logits=False, return_logits=False): + assert temp is None or temp==1.0, "Only for interface compatible with Gumbel" + assert rescale_logits==False, "Only for interface compatible with Gumbel" + assert return_logits==False, "Only for interface compatible with Gumbel" + # reshape z -> (batch, height, width, channel) and flatten + z = rearrange(z, 'b c h w -> b h w c').contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ + torch.sum(self.embedding.weight**2, dim=1) - 2 * \ + torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n')) + + min_encoding_indices = torch.argmin(d, dim=1) + z_q = self.embedding(min_encoding_indices).view(z.shape) + perplexity = None + min_encodings = None + + # compute loss for embedding + if not self.legacy: + loss = self.beta * torch.mean((z_q.detach()-z)**2) + \ + torch.mean((z_q - z.detach()) ** 2) + else: + loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ + torch.mean((z_q - z.detach()) ** 2) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # reshape back to match original input shape + z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous() + + if self.remap is not None: + min_encoding_indices = min_encoding_indices.reshape(z.shape[0],-1) # add batch axis + min_encoding_indices = self.remap_to_used(min_encoding_indices) + min_encoding_indices = min_encoding_indices.reshape(-1,1) # flatten + + if self.sane_index_shape: + min_encoding_indices = min_encoding_indices.reshape( + z_q.shape[0], z_q.shape[2], z_q.shape[3]) + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + if self.remap is not None: + indices = indices.reshape(shape[0],-1) # add batch axis + indices = self.unmap_to_all(indices) + indices = indices.reshape(-1) # flatten again + + # get quantized latent vectors + z_q = self.embedding(indices) + + if shape is not None: + z_q = z_q.view(shape) + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q + +class EmbeddingEMA(nn.Module): + def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5): + super().__init__() + self.decay = decay + self.eps = eps + weight = torch.randn(num_tokens, codebook_dim) + self.weight = nn.Parameter(weight, requires_grad = False) + self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad = False) + self.embed_avg = nn.Parameter(weight.clone(), requires_grad = False) + self.update = True + + def forward(self, embed_id): + return F.embedding(embed_id, self.weight) + + def cluster_size_ema_update(self, new_cluster_size): + self.cluster_size.data.mul_(self.decay).add_(new_cluster_size, alpha=1 - self.decay) + + def embed_avg_ema_update(self, new_embed_avg): + self.embed_avg.data.mul_(self.decay).add_(new_embed_avg, alpha=1 - self.decay) + + def weight_update(self, num_tokens): + n = self.cluster_size.sum() + smoothed_cluster_size = ( + (self.cluster_size + self.eps) / (n + num_tokens * self.eps) * n + ) + #normalize embedding average with smoothed cluster size + embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1) + self.weight.data.copy_(embed_normalized) + + +class EMAVectorQuantizer(nn.Module): + def __init__(self, n_embed, embedding_dim, beta, decay=0.99, eps=1e-5, + remap=None, unknown_index="random"): + super().__init__() + self.codebook_dim = codebook_dim + self.num_tokens = num_tokens + self.beta = beta + self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps) + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed+1 + print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_embed + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + match = (inds[:,:,None]==used[None,None,...]).long() + new = match.argmax(-1) + unknown = match.sum(2)<1 + if self.unknown_index == "random": + new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds>=self.used.shape[0]] = 0 # simply set to zero + back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) + return back.reshape(ishape) + + def forward(self, z): + # reshape z -> (batch, height, width, channel) and flatten + #z, 'b c h w -> b h w c' + z = rearrange(z, 'b c h w -> b h w c') + z_flattened = z.reshape(-1, self.codebook_dim) + + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + d = z_flattened.pow(2).sum(dim=1, keepdim=True) + \ + self.embedding.weight.pow(2).sum(dim=1) - 2 * \ + torch.einsum('bd,nd->bn', z_flattened, self.embedding.weight) # 'n d -> d n' + + + encoding_indices = torch.argmin(d, dim=1) + + z_q = self.embedding(encoding_indices).view(z.shape) + encodings = F.one_hot(encoding_indices, self.num_tokens).type(z.dtype) + avg_probs = torch.mean(encodings, dim=0) + perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10))) + + if self.training and self.embedding.update: + #EMA cluster size + encodings_sum = encodings.sum(0) + self.embedding.cluster_size_ema_update(encodings_sum) + #EMA embedding average + embed_sum = encodings.transpose(0,1) @ z_flattened + self.embedding.embed_avg_ema_update(embed_sum) + #normalize embed_avg and update weight + self.embedding.weight_update(self.num_tokens) + + # compute loss for embedding + loss = self.beta * F.mse_loss(z_q.detach(), z) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # reshape back to match original input shape + #z_q, 'b h w c -> b c h w' + z_q = rearrange(z_q, 'b h w c -> b c h w') + return z_q, loss, (perplexity, encodings, encoding_indices) diff --git a/taming-transformers/taming/util.py b/taming-transformers/taming/util.py new file mode 100755 index 0000000000000000000000000000000000000000..06053e5defb87977f9ab07e69bf4da12201de9b7 --- /dev/null +++ b/taming-transformers/taming/util.py @@ -0,0 +1,157 @@ +import os, hashlib +import requests +from tqdm import tqdm + +URL_MAP = { + "vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1" +} + +CKPT_MAP = { + "vgg_lpips": "vgg.pth" +} + +MD5_MAP = { + "vgg_lpips": "d507d7349b931f0638a25a48a722f98a" +} + + +def download(url, local_path, chunk_size=1024): + os.makedirs(os.path.split(local_path)[0], exist_ok=True) + with requests.get(url, stream=True) as r: + total_size = int(r.headers.get("content-length", 0)) + with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: + with open(local_path, "wb") as f: + for data in r.iter_content(chunk_size=chunk_size): + if data: + f.write(data) + pbar.update(chunk_size) + + +def md5_hash(path): + with open(path, "rb") as f: + content = f.read() + return hashlib.md5(content).hexdigest() + + +def get_ckpt_path(name, root, check=False): + assert name in URL_MAP + path = os.path.join(root, CKPT_MAP[name]) + if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]): + print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path)) + download(URL_MAP[name], path) + md5 = md5_hash(path) + assert md5 == MD5_MAP[name], md5 + return path + + +class KeyNotFoundError(Exception): + def __init__(self, cause, keys=None, visited=None): + self.cause = cause + self.keys = keys + self.visited = visited + messages = list() + if keys is not None: + messages.append("Key not found: {}".format(keys)) + if visited is not None: + messages.append("Visited: {}".format(visited)) + messages.append("Cause:\n{}".format(cause)) + message = "\n".join(messages) + super().__init__(message) + + +def retrieve( + list_or_dict, key, splitval="/", default=None, expand=True, pass_success=False +): + """Given a nested list or dict return the desired value at key expanding + callable nodes if necessary and :attr:`expand` is ``True``. The expansion + is done in-place. + + Parameters + ---------- + list_or_dict : list or dict + Possibly nested list or dictionary. + key : str + key/to/value, path like string describing all keys necessary to + consider to get to the desired value. List indices can also be + passed here. + splitval : str + String that defines the delimiter between keys of the + different depth levels in `key`. + default : obj + Value returned if :attr:`key` is not found. + expand : bool + Whether to expand callable nodes on the path or not. + + Returns + ------- + The desired value or if :attr:`default` is not ``None`` and the + :attr:`key` is not found returns ``default``. + + Raises + ------ + Exception if ``key`` not in ``list_or_dict`` and :attr:`default` is + ``None``. + """ + + keys = key.split(splitval) + + success = True + try: + visited = [] + parent = None + last_key = None + for key in keys: + if callable(list_or_dict): + if not expand: + raise KeyNotFoundError( + ValueError( + "Trying to get past callable node with expand=False." + ), + keys=keys, + visited=visited, + ) + list_or_dict = list_or_dict() + parent[last_key] = list_or_dict + + last_key = key + parent = list_or_dict + + try: + if isinstance(list_or_dict, dict): + list_or_dict = list_or_dict[key] + else: + list_or_dict = list_or_dict[int(key)] + except (KeyError, IndexError, ValueError) as e: + raise KeyNotFoundError(e, keys=keys, visited=visited) + + visited += [key] + # final expansion of retrieved value + if expand and callable(list_or_dict): + list_or_dict = list_or_dict() + parent[last_key] = list_or_dict + except KeyNotFoundError as e: + if default is None: + raise e + else: + list_or_dict = default + success = False + + if not pass_success: + return list_or_dict + else: + return list_or_dict, success + + +if __name__ == "__main__": + config = {"keya": "a", + "keyb": "b", + "keyc": + {"cc1": 1, + "cc2": 2, + } + } + from omegaconf import OmegaConf + config = OmegaConf.create(config) + print(config) + retrieve(config, "keya") + diff --git a/taming-transformers/taming_transformers.egg-info/PKG-INFO b/taming-transformers/taming_transformers.egg-info/PKG-INFO new file mode 100755 index 0000000000000000000000000000000000000000..a9f90162ef22c6128a5a09bb05241a588626e554 --- /dev/null +++ b/taming-transformers/taming_transformers.egg-info/PKG-INFO @@ -0,0 +1,4 @@ +Metadata-Version: 2.1 +Name: taming-transformers +Version: 0.0.1 +Summary: Taming Transformers for High-Resolution Image Synthesis diff --git a/taming-transformers/taming_transformers.egg-info/SOURCES.txt b/taming-transformers/taming_transformers.egg-info/SOURCES.txt new file mode 100755 index 0000000000000000000000000000000000000000..30a640334f1a156d739794180a154e5b5ce6406d --- /dev/null +++ b/taming-transformers/taming_transformers.egg-info/SOURCES.txt @@ -0,0 +1,7 @@ +README.md +setup.py +taming_transformers.egg-info/PKG-INFO +taming_transformers.egg-info/SOURCES.txt +taming_transformers.egg-info/dependency_links.txt +taming_transformers.egg-info/requires.txt +taming_transformers.egg-info/top_level.txt \ No newline at end of file diff --git a/taming-transformers/taming_transformers.egg-info/dependency_links.txt b/taming-transformers/taming_transformers.egg-info/dependency_links.txt new file mode 100755 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/taming-transformers/taming_transformers.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/taming-transformers/taming_transformers.egg-info/requires.txt b/taming-transformers/taming_transformers.egg-info/requires.txt new file mode 100755 index 0000000000000000000000000000000000000000..8cb0fc949dc18643ff84a6b0d62232e4fb1ebc65 --- /dev/null +++ b/taming-transformers/taming_transformers.egg-info/requires.txt @@ -0,0 +1,3 @@ +torch +numpy +tqdm diff --git a/taming-transformers/taming_transformers.egg-info/top_level.txt b/taming-transformers/taming_transformers.egg-info/top_level.txt new file mode 100755 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/taming-transformers/taming_transformers.egg-info/top_level.txt @@ -0,0 +1 @@ +