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import random
import gradio as gr
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torchvision.models import mobilenet_v2, resnet18
from torchvision.transforms.functional import InterpolationMode
datasets_n_classes = {
"Imagenette": 10,
"Imagewoof": 10,
"Stanford_dogs": 120,
}
datasets_model_types = {
"Imagenette": [
"base_200",
"base_200+100",
"synthetic_200",
"augment_noisy_200",
"augment_noisy_200+100",
"augment_clean_200",
],
"Imagewoof": [
"base_200",
"base_200+100",
"synthetic_200",
"augment_noisy_200",
"augment_noisy_200+100",
"augment_clean_200",
],
"Stanford_dogs": [
"base_200",
"base_200+100",
"synthetic_200",
"augment_noisy_200",
"augment_noisy_200+100",
],
}
model_arch = ["resnet18", "mobilenet_v2"]
list_200 = [
"Original",
"Synthetic",
"Original + Synthetic (Noisy)",
"Original + Synthetic (Clean)",
]
list_200_100 = ["Base+100", "AugmentNoisy+100"]
methods_map = {
"200 Epochs": list_200,
"200 Epochs on Original + 100": list_200_100,
}
label_map = dict()
label_map["Imagenette (10 classes)"] = "Imagenette"
label_map["Imagewoof (10 classes)"] = "Imagewoof"
label_map["Stanford Dogs (120 classes)"] = "Stanford_dogs"
label_map["ResNet-18"] = "resnet18"
label_map["MobileNetV2"] = "mobilenet_v2"
label_map["200 Epochs"] = "200"
label_map["200 Epochs on Original + 100"] = "200+100"
label_map["Original"] = "base"
label_map["Synthetic"] = "synthetic"
label_map["Original + Synthetic (Noisy)"] = "augment_noisy"
label_map["Original + Synthetic (Clean)"] = "augment_clean"
label_map["Base+100"] = "base"
label_map["AugmentNoisy+100"] = "augment_noisy"
dataset_models = dict()
for dataset, n_classes in datasets_n_classes.items():
models = dict()
for model_type in datasets_model_types[dataset]:
for arch in model_arch:
if arch == "resnet18":
model = resnet18(weights=None, num_classes=n_classes)
models[f"{arch}_{model_type}"] = (
model,
f"./models/{arch}/{dataset}/{dataset}_{model_type}.pth",
)
elif arch == "mobilenet_v2":
model = mobilenet_v2(weights=None, num_classes=n_classes)
models[f"{arch}_{model_type}"] = (
model,
f"./models/{arch}/{dataset}/{dataset}_{model_type}.pth",
)
else:
raise ValueError(f"Model architecture unavailable: {arch}")
dataset_models[dataset] = models
def get_random_image(dataset, label_map=label_map) -> Image:
dataset_root = f"./data/{label_map[dataset]}/val"
dataset_img = torchvision.datasets.ImageFolder(
dataset_root,
transforms.Compose([transforms.PILToTensor()]),
)
random_idx = random.randint(0, len(dataset_img) - 1)
image, _ = dataset_img[random_idx]
image = transforms.ToPILImage()(image)
image = image.resize(
(256, 256),
)
return image
def load_model(model_dict, model_name: str) -> nn.Module:
model_name_lower = model_name.lower()
if model_name_lower in model_dict:
model = model_dict[model_name_lower][0]
model_path = model_dict[model_name_lower][1]
if torch.cuda.is_available():
checkpoint = torch.load(model_path)
else:
checkpoint = torch.load(model_path, map_location="cpu")
if "setup" in checkpoint:
if checkpoint["setup"]["distributed"]:
torch.nn.modules.utils.consume_prefix_in_state_dict_if_present(
checkpoint["model"], "module."
)
model.load_state_dict(checkpoint["model"])
else:
model.load_state_dict(checkpoint)
return model
else:
raise ValueError(
f"Model {model_name} is not available for image prediction. Please choose from {[name.capitalize() for name in model_dict.keys()]}."
)
def postprocess_default(labels, output) -> dict:
probabilities = nn.functional.softmax(output[0], dim=0)
top_prob, top_catid = torch.topk(probabilities, 5)
confidences = {
labels[top_catid.tolist()[i]]: top_prob.tolist()[i]
for i in range(top_prob.shape[0])
}
return confidences
def classify(
input_image: Image,
dataset_type: str,
arch_type: str,
methods: str,
training_ds: str,
dataset_models=dataset_models,
label_map=label_map,
) -> dict:
for i in [dataset_type, arch_type, methods, training_ds]:
if i is None:
raise ValueError("Please select all options.")
dataset_type = label_map[dataset_type]
arch_type = label_map[arch_type]
methods = label_map[methods]
training_ds = label_map[training_ds]
preprocess_input = transforms.Compose(
[
transforms.Resize(
256,
interpolation=InterpolationMode.BILINEAR,
antialias=True,
),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
)
if input_image is None:
raise ValueError("No image was provided.")
input_tensor: torch.Tensor = preprocess_input(input_image)
input_batch = input_tensor.unsqueeze(0)
model = load_model(
dataset_models[dataset_type], f"{arch_type}_{training_ds}_{methods}"
)
if torch.cuda.is_available():
input_batch = input_batch.to("cuda")
model.to("cuda")
model.eval()
with torch.inference_mode():
output: torch.Tensor = model(input_batch)
with open(f"./data/{dataset_type}.txt", "r") as f:
labels = {i: line.strip() for i, line in enumerate(f.readlines())}
return postprocess_default(labels, output)
def update_methods(method, ds_type):
if ds_type == "Stanford Dogs (120 classes)" and method == "200 Epochs":
methods = list_200[:-1]
else:
methods = methods_map[method]
return gr.update(choices=methods, value=None)
def downloadModel(
dataset_type, arch_type, methods, training_ds, dataset_models=dataset_models
):
for i in [dataset_type, arch_type, methods, training_ds]:
if i is None:
return gr.update(label="Select Model", value=None)
dataset_type = label_map[dataset_type]
arch_type = label_map[arch_type]
methods = label_map[methods]
training_ds = label_map[training_ds]
if f"{arch_type}_{training_ds}_{methods}" not in dataset_models[dataset_type]:
return gr.update(label="Select Model", value=None)
model_path = dataset_models[dataset_type][f"{arch_type}_{training_ds}_{methods}"][1]
return gr.update(
label=f"Download Model: '{dataset_type}_{arch_type}_{training_ds}_{methods}'",
value=model_path,
)
if __name__ == "__main__":
with gr.Blocks(title="Generative Augmented Image Classifiers") as demo:
gr.Markdown(
"""
# Generative Augmented Image Classifiers
Main GitHub Repo: [Generative Data Augmentation](https://github.com/zhulinchng/generative-data-augmentation) | Generative Data Augmentation Demo: [Generative Data Augmented](https://huggingface.co/spaces/czl/generative-data-augmentation-demo).
"""
)
with gr.Row():
with gr.Column():
dataset_type = gr.Radio(
choices=[
"Imagenette (10 classes)",
"Imagewoof (10 classes)",
"Stanford Dogs (120 classes)",
],
label="Dataset",
value="Imagenette (10 classes)",
)
arch_type = gr.Radio(
choices=["ResNet-18", "MobileNetV2"],
label="Model Architecture",
value="ResNet-18",
interactive=True,
)
methods = gr.Radio(
label="Methods",
choices=["200 Epochs", "200 Epochs on Original + 100"],
interactive=True,
value="200 Epochs",
)
training_ds = gr.Radio(
label="Training Dataset",
choices=methods_map["200 Epochs"],
interactive=True,
value="Original",
)
dataset_type.change(
fn=update_methods,
inputs=[methods, dataset_type],
outputs=[training_ds],
)
methods.change(
fn=update_methods,
inputs=[methods, dataset_type],
outputs=[training_ds],
)
random_image_output = gr.Image(type="pil", label="Image to Classify")
with gr.Row():
generate_button = gr.Button("Sample Random Image")
classify_button_random = gr.Button("Classify")
with gr.Column():
output_label_random = gr.Label(num_top_classes=5)
download_model = gr.DownloadButton(
label=f"Download Model: '{label_map[dataset_type.value]}_{label_map[arch_type.value]}_{label_map[training_ds.value]}_{label_map[methods.value]}'",
value=dataset_models[label_map[dataset_type.value]][
f"{label_map[arch_type.value]}_{label_map[training_ds.value]}_{label_map[methods.value]}"
][1],
)
dataset_type.change(
fn=downloadModel,
inputs=[dataset_type, arch_type, methods, training_ds],
outputs=[download_model],
)
arch_type.change(
fn=downloadModel,
inputs=[dataset_type, arch_type, methods, training_ds],
outputs=[download_model],
)
methods.change(
fn=downloadModel,
inputs=[dataset_type, arch_type, methods, training_ds],
outputs=[download_model],
)
training_ds.change(
fn=downloadModel,
inputs=[dataset_type, arch_type, methods, training_ds],
outputs=[download_model],
)
gr.Markdown(
"""
This demo showcases the performance of image classifiers trained on various datasets as part of the project 'Improving Fine-Grained Image Classification Using Diffusion-Based Generated Synthetic Images' dissertation.
View the models and files used in this demo [here](https://huggingface.co/spaces/czl/generative-augmented-classifiers/tree/main).
Usage Instructions & Documentation [here](https://huggingface.co/spaces/czl/generative-augmented-classifiers/blob/main/README.md).
"""
)
generate_button.click(
get_random_image,
inputs=[dataset_type],
outputs=random_image_output,
)
classify_button_random.click(
classify,
inputs=[random_image_output, dataset_type, arch_type, methods, training_ds],
outputs=output_label_random,
)
demo.launch(show_error=True)
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