Added app

.gitignore

@@ -0,0 +1,7 @@
+**/__pycache__
+**/.vscode
+**/.DS_Store
+cache/
+out/
+checkpoints/
+**/.ipynb_checkpoints/

.gitmodules

@@ -0,0 +1,8 @@
+[submodule "stylegan/stylegan_tf"]
+	path = models/stylegan/stylegan_tf
+	url = https://github.com/NVlabs/stylegan.git
+	ignore = untracked
+[submodule "stylegan2/stylegan2-pytorch"]
+	path = models/stylegan2/stylegan2-pytorch
+	url = https://github.com/harskish/stylegan2-pytorch.git
+	ignore = untracked

LICENSE

@@ -0,0 +1,201 @@
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app.py

@@ -0,0 +1,188 @@
+from ipywidgets import fixed
+import gradio as gr
+from skimage import img_as_ubyte
+from config import Config
+from decomposition import get_or_compute
+from models import get_instrumented_model
+import imageio
+from PIL import Image
+import ipywidgets as widgets
+import numpy as np
+import PIL
+import torch
+from IPython.utils import io
+import nltk
+nltk.download('wordnet')
+
+# @title Load Model
+selected_model = 'lookbook'
+
+# Load model
+
+# Speed up computation
+torch.autograd.set_grad_enabled(False)
+torch.backends.cudnn.benchmark = True
+
+# Specify model to use
+config = Config(
+    model='StyleGAN2',
+    layer='style',
+    output_class=selected_model,
+    components=80,
+    use_w=True,
+    batch_size=5_000,  # style layer quite small
+)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+inst = get_instrumented_model(config.model, config.output_class,
+                              config.layer, torch.device(device), use_w=config.use_w)
+
+path_to_components = get_or_compute(config, inst)
+
+model = inst.model
+
+comps = np.load(path_to_components)
+lst = comps.files
+latent_dirs = []
+latent_stdevs = []
+
+load_activations = False
+
+for item in lst:
+    if load_activations:
+        if item == 'act_comp':
+            for i in range(comps[item].shape[0]):
+                latent_dirs.append(comps[item][i])
+        if item == 'act_stdev':
+            for i in range(comps[item].shape[0]):
+                latent_stdevs.append(comps[item][i])
+    else:
+        if item == 'lat_comp':
+            for i in range(comps[item].shape[0]):
+                latent_dirs.append(comps[item][i])
+        if item == 'lat_stdev':
+            for i in range(comps[item].shape[0]):
+                latent_stdevs.append(comps[item][i])
+
+
+def mix_w(w1, w2, content, style):
+    for i in range(0, 5):
+        w2[i] = w1[i] * (1 - content) + w2[i] * content
+
+    for i in range(5, 16):
+        w2[i] = w1[i] * (1 - style) + w2[i] * style
+
+    return w2
+
+
+def display_sample_pytorch(seed, truncation, directions, distances, scale, start, end, w=None, disp=True, save=None, noise_spec=None):
+    # blockPrint()
+    model.truncation = truncation
+    if w is None:
+        w = model.sample_latent(1, seed=seed).detach().cpu().numpy()
+        w = [w]*model.get_max_latents()  # one per layer
+    else:
+        w = [np.expand_dims(x, 0) for x in w]
+
+    for l in range(start, end):
+        for i in range(len(directions)):
+            w[l] = w[l] + directions[i] * distances[i] * scale
+
+    torch.cuda.empty_cache()
+    # save image and display
+    out = model.sample_np(w)
+    final_im = Image.fromarray(
+        (out * 255).astype(np.uint8)).resize((500, 500), Image.LANCZOS)
+
+    if save is not None:
+        if disp == False:
+            print(save)
+        final_im.save(f'out/{seed}_{save:05}.png')
+    if disp:
+        display(final_im)
+
+    return final_im
+
+
+# @title Demo UI
+
+
+def generate_image(seed1, seed2, content, style, truncation, c0, c1, c2, c3, c4, c5, c6, start_layer, end_layer):
+    seed1 = int(seed1)
+    seed2 = int(seed2)
+
+    scale = 1
+    params = {'c0': c0,
+              'c1': c1,
+              'c2': c2,
+              'c3': c3,
+              'c4': c4,
+              'c5': c5,
+              'c6': c6}
+
+    param_indexes = {'c0': 0,
+                     'c1': 1,
+                     'c2': 2,
+                     'c3': 3,
+                     'c4': 4,
+                     'c5': 5,
+                     'c6': 6}
+
+    directions = []
+    distances = []
+    for k, v in params.items():
+        directions.append(latent_dirs[param_indexes[k]])
+        distances.append(v)
+
+    w1 = model.sample_latent(1, seed=seed1).detach().cpu().numpy()
+    w1 = [w1]*model.get_max_latents()  # one per layer
+    im1 = model.sample_np(w1)
+
+    w2 = model.sample_latent(1, seed=seed2).detach().cpu().numpy()
+    w2 = [w2]*model.get_max_latents()  # one per layer
+    im2 = model.sample_np(w2)
+    combined_im = np.concatenate([im1, im2], axis=1)
+    input_im = Image.fromarray((combined_im * 255).astype(np.uint8))
+
+    mixed_w = mix_w(w1, w2, content, style)
+    return input_im, display_sample_pytorch(seed1, truncation, directions, distances, scale, int(start_layer), int(end_layer), w=mixed_w, disp=False)
+
+
+truncation = gr.inputs.Slider(
+    minimum=0, maximum=1, default=0.5, label="Truncation")
+start_layer = gr.inputs.Number(default=3, label="Start Layer")
+end_layer = gr.inputs.Number(default=14, label="End Layer")
+seed1 = gr.inputs.Number(default=0, label="Seed 1")
+seed2 = gr.inputs.Number(default=0, label="Seed 2")
+content = gr.inputs.Slider(
+    label="Structure", minimum=0, maximum=1, default=0.5)
+style = gr.inputs.Slider(label="Style", minimum=0, maximum=1, default=0.5)
+
+slider_max_val = 20
+slider_min_val = -20
+slider_step = 1
+
+c0 = gr.inputs.Slider(label="Sleeve & Size",
+                      minimum=slider_min_val, maximum=slider_max_val, default=0)
+c1 = gr.inputs.Slider(label="Dress - Jacket",
+                      minimum=slider_min_val, maximum=slider_max_val, default=0)
+c2 = gr.inputs.Slider(
+    label="Female Coat", minimum=slider_min_val, maximum=slider_max_val, default=0)
+c3 = gr.inputs.Slider(label="Coat", minimum=slider_min_val,
+                      maximum=slider_max_val, default=0)
+c4 = gr.inputs.Slider(label="Graphics", minimum=slider_min_val,
+                      maximum=slider_max_val, default=0)
+c5 = gr.inputs.Slider(label="Dark", minimum=slider_min_val,
+                      maximum=slider_max_val, default=0)
+c6 = gr.inputs.Slider(label="Less Cleavage",
+                      minimum=slider_min_val, maximum=slider_max_val, default=0)
+
+
+scale = 1
+
+inputs = [seed1, seed2, content, style, truncation,
+          c0, c1, c2, c3, c4, c5, c6, start_layer, end_layer]
+description = "Change the seed number to generate different parent design."
+
+gr.Interface(generate_image, inputs, [
+             "image", "image"], description=description, live=True, title="Fashion Eye").launch()

components/stylegan2-lookbook_style_ipca_c80_n300000_w.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc8dd611f9eba549338aaac546bc11dfda01ced79456ee0bb63387adf997bde1
+size 312337

config.py

@@ -0,0 +1,72 @@
+# Copyright 2020 Erik Härkönen. All rights reserved.
+# This file is licensed to you under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License. You may obtain a copy
+# of the License at http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software distributed under
+# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
+# OF ANY KIND, either express or implied. See the License for the specific language
+# governing permissions and limitations under the License.
+
+import sys
+import argparse
+import json
+from copy import deepcopy
+
+class Config:
+    def __init__(self, **kwargs):
+        self.from_args([]) # set all defaults
+        self.default_args = deepcopy(self.__dict__)
+        self.from_dict(kwargs) # override
+
+    def __str__(self):
+        custom = {}
+        default = {}
+
+        # Find non-default arguments
+        for k, v in self.__dict__.items():
+            if k == 'default_args':
+                continue
+            
+            in_default = k in self.default_args
+            same_value = self.default_args.get(k) == v
+            
+            if in_default and same_value:
+                default[k] = v
+            else:
+                custom[k] = v
+
+        config = {
+            'custom': custom,
+            'default': default
+        }
+
+        return json.dumps(config, indent=4)
+    
+    def __repr__(self):
+        return self.__str__()
+    
+    def from_dict(self, dictionary):
+        for k, v in dictionary.items():
+            setattr(self, k, v)
+        return self
+    
+    def from_args(self, args=sys.argv[1:]):
+        parser = argparse.ArgumentParser(description='GAN component analysis config')
+        parser.add_argument('--model', dest='model', type=str, default='StyleGAN', help='The network to analyze') # StyleGAN, DCGAN, ProGAN, BigGAN-XYZ
+        parser.add_argument('--layer', dest='layer', type=str, default='g_mapping', help='The layer to analyze')
+        parser.add_argument('--class', dest='output_class', type=str, default=None, help='Output class to generate (BigGAN: Imagenet, ProGAN: LSUN)')
+        parser.add_argument('--est', dest='estimator', type=str, default='ipca', help='The algorithm to use [pca, fbpca, cupca, spca, ica]')
+        parser.add_argument('--sparsity', type=float, default=1.0, help='Sparsity parameter of SPCA')
+        parser.add_argument('--video', dest='make_video', action='store_true', help='Generate output videos (MP4s)')
+        parser.add_argument('--batch', dest='batch_mode', action='store_true', help="Don't open windows, instead save results to file")
+        parser.add_argument('-b', dest='batch_size', type=int, default=None, help='Minibatch size, leave empty for automatic detection')
+        parser.add_argument('-c', dest='components', type=int, default=80, help='Number of components to keep')
+        parser.add_argument('-n', type=int, default=300_000, help='Number of examples to use in decomposition')
+        parser.add_argument('--use_w', action='store_true', help='Use W latent space (StyleGAN(2))')
+        parser.add_argument('--sigma', type=float, default=2.0, help='Number of stdevs to walk in visualize.py')
+        parser.add_argument('--inputs', type=str, default=None, help='Path to directory with named components')
+        parser.add_argument('--seed', type=int, default=None, help='Seed used in decomposition')
+        args = parser.parse_args(args)
+
+        return self.from_dict(args.__dict__)

decomposition.py

@@ -0,0 +1,402 @@
+# Copyright 2020 Erik Härkönen. All rights reserved.
+# This file is licensed to you under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License. You may obtain a copy
+# of the License at http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software distributed under
+# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
+# OF ANY KIND, either express or implied. See the License for the specific language
+# governing permissions and limitations under the License.
+
+# Patch for broken CTRL+C handler
+# https://github.com/ContinuumIO/anaconda-issues/issues/905
+import os
+os.environ['FOR_DISABLE_CONSOLE_CTRL_HANDLER'] = '1'
+
+import numpy as np
+import os
+from pathlib import Path
+import re
+import sys
+import datetime
+import argparse
+import torch
+import json
+from types import SimpleNamespace
+import scipy
+from scipy.cluster.vq import kmeans
+from tqdm import trange
+from netdissect.nethook import InstrumentedModel
+from config import Config
+from estimators import get_estimator
+from models import get_instrumented_model
+
+SEED_SAMPLING = 1
+SEED_RANDOM_DIRS = 2
+SEED_LINREG = 3
+SEED_VISUALIZATION = 5
+
+B = 20
+n_clusters = 500
+
+def get_random_dirs(components, dimensions):
+    gen = np.random.RandomState(seed=SEED_RANDOM_DIRS)
+    dirs = gen.normal(size=(components, dimensions))
+    dirs /= np.sqrt(np.sum(dirs**2, axis=1, keepdims=True))
+    return dirs.astype(np.float32)
+
+# Compute maximum batch size for given VRAM and network
+def get_max_batch_size(inst, device, layer_name=None):
+    inst.remove_edits()
+
+    # Reset statistics
+    torch.cuda.reset_max_memory_cached(device)
+    torch.cuda.reset_max_memory_allocated(device)
+    total_mem = torch.cuda.get_device_properties(device).total_memory
+
+    B_max = 20
+
+    # Measure actual usage
+    for i in range(2, B_max, 2):
+        z = inst.model.sample_latent(n_samples=i)
+        if layer_name:
+            inst.model.partial_forward(z, layer_name)
+        else:
+            inst.model.forward(z)
+
+        maxmem = torch.cuda.max_memory_allocated(device)
+        del z
+
+        if maxmem > 0.5*total_mem:
+            print('Batch size {:d}: memory usage {:.0f}MB'.format(i, maxmem / 1e6))
+            return i
+
+    return B_max
+
+# Solve for directions in latent space that match PCs in activaiton space
+def linreg_lstsq(comp_np, mean_np, stdev_np, inst, config):
+    print('Performing least squares regression', flush=True)
+
+    torch.manual_seed(SEED_LINREG)
+    np.random.seed(SEED_LINREG)
+
+    comp = torch.from_numpy(comp_np).float().to(inst.model.device)
+    mean = torch.from_numpy(mean_np).float().to(inst.model.device)
+    stdev = torch.from_numpy(stdev_np).float().to(inst.model.device)
+
+    n_samp = max(10_000, config.n) // B * B # make divisible
+    n_comp = comp.shape[0]
+    latent_dims = inst.model.get_latent_dims()
+    
+    # We're looking for M s.t. M*P*G'(Z) = Z => M*A = Z
+    #   Z = batch of latent vectors (n_samples x latent_dims)
+    #   G'(Z) = batch of activations at intermediate layer
+    #   A = P*G'(Z) = projected activations (n_samples x pca_coords)
+    #   M = linear mapping (pca_coords x latent_dims)
+
+    # Minimization min_M ||MA - Z||_l2 rewritten as min_M.T ||A.T*M.T - Z.T||_l2
+    # to match format expected by pytorch.lstsq
+
+    # TODO: regression on pixel-space outputs? (using nonlinear optimizer)
+    # min_M lpips(G_full(MA), G_full(Z))
+
+    # Tensors to fill with data
+    # Dimensions other way around, so these are actually the transposes
+    A = np.zeros((n_samp, n_comp), dtype=np.float32)
+    Z = np.zeros((n_samp, latent_dims), dtype=np.float32)
+    
+    # Project tensor X onto PCs, return coordinates
+    def project(X, comp):
+        N = X.shape[0]
+        K = comp.shape[0]
+        coords = torch.bmm(comp.expand([N]+[-1]*comp.ndim), X.view(N, -1, 1))
+        return coords.reshape(N, K)
+
+    for i in trange(n_samp // B, desc='Collecting samples', ascii=True):
+        z = inst.model.sample_latent(B)
+        inst.model.partial_forward(z, config.layer)
+        act = inst.retained_features()[config.layer].reshape(B, -1)
+
+        # Project onto basis
+        act = act - mean
+        coords = project(act, comp)
+        coords_scaled = coords / stdev
+
+        A[i*B:(i+1)*B] = coords_scaled.detach().cpu().numpy()
+        Z[i*B:(i+1)*B] = z.detach().cpu().numpy().reshape(B, -1)
+
+    # Solve least squares fit
+
+    # gelsd = divide-and-conquer SVD; good default
+    # gelsy = complete orthogonal factorization; sometimes faster
+    # gelss = SVD; slow but less memory hungry
+    M_t = scipy.linalg.lstsq(A, Z, lapack_driver='gelsd')[0] # torch.lstsq(Z, A)[0][:n_comp, :]
+    
+    # Solution given by rows of M_t
+    Z_comp = M_t[:n_comp, :]
+    Z_mean = np.mean(Z, axis=0, keepdims=True)
+
+    return Z_comp, Z_mean
+
+def regression(comp, mean, stdev, inst, config):
+    # Sanity check: verify orthonormality
+    M = np.dot(comp, comp.T)
+    if not np.allclose(M, np.identity(M.shape[0])):
+        det = np.linalg.det(M)
+        print(f'WARNING: Computed basis is not orthonormal (determinant={det})')
+
+    return linreg_lstsq(comp, mean, stdev, inst, config)
+
+def compute(config, dump_name, instrumented_model):
+    global B
+
+    timestamp = lambda : datetime.datetime.now().strftime("%d.%m %H:%M")
+    print(f'[{timestamp()}] Computing', dump_name.name)
+
+    # Ensure reproducibility
+    torch.manual_seed(0) # also sets cuda seeds
+    np.random.seed(0)
+
+    # Speed up backend
+    torch.backends.cudnn.benchmark = True
+
+    has_gpu = torch.cuda.is_available()
+    device = torch.device('cuda' if has_gpu else 'cpu')
+    layer_key = config.layer
+
+    if instrumented_model is None:
+        inst = get_instrumented_model(config.model, config.output_class, layer_key, device)
+        model = inst.model
+    else:
+        print('Reusing InstrumentedModel instance')
+        inst = instrumented_model
+        model = inst.model
+        inst.remove_edits()
+        model.set_output_class(config.output_class)
+
+    # Regress back to w space
+    if config.use_w:
+        print('Using W latent space')
+        model.use_w()
+
+    inst.retain_layer(layer_key)
+    model.partial_forward(model.sample_latent(1), layer_key)
+    sample_shape = inst.retained_features()[layer_key].shape
+    sample_dims = np.prod(sample_shape)
+    print('Feature shape:', sample_shape)
+
+    input_shape = inst.model.get_latent_shape()
+    input_dims = inst.model.get_latent_dims()
+
+    config.components = min(config.components, sample_dims)
+    transformer = get_estimator(config.estimator, config.components, config.sparsity)
+
+    X = None
+    X_global_mean = None
+
+    # Figure out batch size if not provided
+    B = config.batch_size or get_max_batch_size(inst, device, layer_key)
+
+    # Divisible by B (ignored in output name)
+    N = config.n // B * B
+
+    # Compute maximum batch size based on RAM + pagefile budget
+    target_bytes = 20 * 1_000_000_000 # GB
+    feat_size_bytes = sample_dims * np.dtype('float64').itemsize
+    N_limit_RAM = np.floor_divide(target_bytes, feat_size_bytes)
+    if not transformer.batch_support and N > N_limit_RAM:
+        print('WARNING: estimator does not support batching, ' \
+            'given config will use {:.1f} GB memory.'.format(feat_size_bytes / 1_000_000_000 * N))
+
+    # 32-bit LAPACK gets very unhappy about huge matrices (in linalg.svd)
+    if config.estimator == 'ica':
+        lapack_max_N = np.floor_divide(np.iinfo(np.int32).max // 4, sample_dims) # 4x extra buffer
+        if N > lapack_max_N:
+            raise RuntimeError(f'Matrices too large for ICA, please use N <= {lapack_max_N}')
+
+    print('B={}, N={}, dims={}, N/dims={:.1f}'.format(B, N, sample_dims, N/sample_dims), flush=True)
+
+    # Must not depend on chosen batch size (reproducibility)
+    NB = max(B, max(2_000, 3*config.components)) # ipca: as large as possible!
+    
+    samples = None
+    if not transformer.batch_support:
+        samples = np.zeros((N + NB, sample_dims), dtype=np.float32)
+
+    torch.manual_seed(config.seed or SEED_SAMPLING)
+    np.random.seed(config.seed or SEED_SAMPLING)
+
+    # Use exactly the same latents regardless of batch size
+    # Store in main memory, since N might be huge (1M+)
+    # Run in batches, since sample_latent() might perform Z -> W mapping
+    n_lat = ((N + NB - 1) // B + 1) * B
+    latents = np.zeros((n_lat, *input_shape[1:]), dtype=np.float32)
+    with torch.no_grad():
+        for i in trange(n_lat // B, desc='Sampling latents'):
+            latents[i*B:(i+1)*B] = model.sample_latent(n_samples=B).cpu().numpy()
+
+    # Decomposition on non-Gaussian latent space
+    samples_are_latents = layer_key in ['g_mapping', 'style'] and inst.model.latent_space_name() == 'W'
+
+    canceled = False
+    try:
+        X = np.ones((NB, sample_dims), dtype=np.float32)
+        action = 'Fitting' if transformer.batch_support else 'Collecting'
+        for gi in trange(0, N, NB, desc=f'{action} batches (NB={NB})', ascii=True):
+            for mb in range(0, NB, B):
+                z = torch.from_numpy(latents[gi+mb:gi+mb+B]).to(device)
+                
+                if samples_are_latents:
+                    # Decomposition on latents directly (e.g. StyleGAN W)
+                    batch = z.reshape((B, -1))
+                else:
+                    # Decomposition on intermediate layer
+                    with torch.no_grad():
+                        model.partial_forward(z, layer_key)
+                    
+                    # Permuted to place PCA dimensions last
+                    batch = inst.retained_features()[layer_key].reshape((B, -1))
+
+                space_left = min(B, NB - mb)
+                X[mb:mb+space_left] = batch.cpu().numpy()[:space_left]
+
+            if transformer.batch_support:
+                if not transformer.fit_partial(X.reshape(-1, sample_dims)):
+                    break
+            else:
+                samples[gi:gi+NB, :] = X.copy()
+    except KeyboardInterrupt:
+        if not transformer.batch_support:
+            sys.exit(1) # no progress yet
+        
+        dump_name = dump_name.parent / dump_name.name.replace(f'n{N}', f'n{gi}')
+        print(f'Saving current state to "{dump_name.name}" before exiting')
+        canceled = True
+        
+    if not transformer.batch_support:
+        X = samples # Use all samples
+        X_global_mean = X.mean(axis=0, keepdims=True, dtype=np.float32) # TODO: activations surely multi-modal...!
+        X -= X_global_mean
+        
+        print(f'[{timestamp()}] Fitting whole batch')
+        t_start_fit = datetime.datetime.now()
+
+        transformer.fit(X)
+        
+        print(f'[{timestamp()}] Done in {datetime.datetime.now() - t_start_fit}')
+        assert np.all(transformer.transformer.mean_ < 1e-3), 'Mean of normalized data should be zero'
+    else:
+        X_global_mean = transformer.transformer.mean_.reshape((1, sample_dims))
+        X = X.reshape(-1, sample_dims)
+        X -= X_global_mean
+
+    X_comp, X_stdev, X_var_ratio = transformer.get_components()
+    
+    assert X_comp.shape[1] == sample_dims \
+        and X_comp.shape[0] == config.components \
+        and X_global_mean.shape[1] == sample_dims \
+        and X_stdev.shape[0] == config.components, 'Invalid shape'
+
+    # 'Activations' are really latents in a secondary latent space
+    if samples_are_latents:
+        Z_comp = X_comp
+        Z_global_mean = X_global_mean
+    else:
+        Z_comp, Z_global_mean = regression(X_comp, X_global_mean, X_stdev, inst, config)
+
+    # Normalize
+    Z_comp /= np.linalg.norm(Z_comp, axis=-1, keepdims=True)
+
+    # Random projections
+    # We expect these to explain much less of the variance
+    random_dirs = get_random_dirs(config.components, np.prod(sample_shape))
+    n_rand_samples = min(5000, X.shape[0])
+    X_view = X[:n_rand_samples, :].T
+    assert np.shares_memory(X_view, X), "Error: slice produced copy"
+    X_stdev_random = np.dot(random_dirs, X_view).std(axis=1)
+
+    # Inflate back to proper shapes (for easier broadcasting)
+    X_comp = X_comp.reshape(-1, *sample_shape)
+    X_global_mean = X_global_mean.reshape(sample_shape)
+    Z_comp = Z_comp.reshape(-1, *input_shape)
+    Z_global_mean = Z_global_mean.reshape(input_shape)
+
+    # Compute stdev in latent space if non-Gaussian
+    lat_stdev = np.ones_like(X_stdev)
+    if config.use_w:
+        samples = model.sample_latent(5000).reshape(5000, input_dims).detach().cpu().numpy()
+        coords = np.dot(Z_comp.reshape(-1, input_dims), samples.T)
+        lat_stdev = coords.std(axis=1)
+
+    os.makedirs(dump_name.parent, exist_ok=True)
+    np.savez_compressed(dump_name, **{
+        'act_comp': X_comp.astype(np.float32),
+        'act_mean': X_global_mean.astype(np.float32),
+        'act_stdev': X_stdev.astype(np.float32),
+        'lat_comp': Z_comp.astype(np.float32),
+        'lat_mean': Z_global_mean.astype(np.float32),
+        'lat_stdev': lat_stdev.astype(np.float32),
+        'var_ratio': X_var_ratio.astype(np.float32),
+        'random_stdevs': X_stdev_random.astype(np.float32),
+    })
+
+    if canceled:
+        sys.exit(1)
+
+    # Don't shutdown if passed as param
+    if instrumented_model is None:
+        inst.close()
+        del inst
+        del model
+
+    del X
+    del X_comp
+    del random_dirs
+    del batch
+    del samples
+    del latents
+    torch.cuda.empty_cache()
+
+# Return cached results or commpute if needed
+# Pass existing InstrumentedModel instance to reuse it
+def get_or_compute(config, model=None, submit_config=None, force_recompute=False):
+    if submit_config is None:
+        wrkdir = str(Path(__file__).parent.resolve())
+        submit_config = SimpleNamespace(run_dir_root = wrkdir, run_dir = wrkdir)
+    
+    # Called directly by run.py
+    return _compute(submit_config, config, model, force_recompute)
+
+def _compute(submit_config, config, model=None, force_recompute=False):
+    basedir = Path(submit_config.run_dir)
+    outdir = basedir / 'out'
+    
+    if config.n is None:
+        raise RuntimeError('Must specify number of samples with -n=XXX')
+
+    if model and not isinstance(model, InstrumentedModel):
+        raise RuntimeError('Passed model has to be wrapped in "InstrumentedModel"')
+    
+    if config.use_w and not 'StyleGAN' in config.model:
+        raise RuntimeError(f'Cannot change latent space of non-StyleGAN model {config.model}')
+
+    transformer = get_estimator(config.estimator, config.components, config.sparsity)
+    dump_name = "{}-{}_{}_{}_n{}{}{}.npz".format(
+        config.model.lower(),
+        config.output_class.replace(' ', '_'),
+        config.layer.lower(),
+        transformer.get_param_str(),
+        config.n,
+        '_w' if config.use_w else '',
+        f'_seed{config.seed}' if config.seed else ''
+    )
+
+    dump_path = basedir / 'cache' / 'components' / dump_name
+
+    if not dump_path.is_file() or force_recompute:
+        print('Not cached')
+        t_start = datetime.datetime.now()
+        compute(config, dump_path, model)
+        print('Total time:', datetime.datetime.now() - t_start)
+    
+    return dump_path

environment.yml

@@ -0,0 +1,25 @@
+name: ganspace
+channels:
+  - defaults
+  - conda-forge
+  - pytorch
+dependencies:
+  - python=3.7
+  - pytorch::pytorch=1.3
+  - pytorch::torchvision
+  - cudatoolkit=10.1
+  - pillow=6.2
+  - ffmpeg
+  - tqdm
+  - scipy
+  - scikit-learn
+  - scikit-image
+  - boto3
+  - requests
+  - nltk
+  - pip
+  - pip:
+    - fbpca
+    - pyopengltk
+
+# conda env update -f environment.yml --prune

estimators.py

@@ -0,0 +1,218 @@
+# Copyright 2020 Erik Härkönen. All rights reserved.
+# This file is licensed to you under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License. You may obtain a copy
+# of the License at http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software distributed under
+# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
+# OF ANY KIND, either express or implied. See the License for the specific language
+# governing permissions and limitations under the License.
+
+from sklearn.decomposition import FastICA, PCA, IncrementalPCA, MiniBatchSparsePCA, SparsePCA, KernelPCA
+import fbpca
+import numpy as np
+import itertools
+from types import SimpleNamespace
+
+# ICA
+class ICAEstimator():
+    def __init__(self, n_components):
+        self.n_components = n_components
+        self.maxiter = 10000
+        self.whiten = True # ICA: whitening is essential, should not be skipped
+        self.transformer = FastICA(n_components, random_state=0, whiten=self.whiten, max_iter=self.maxiter)
+        self.batch_support = False
+        self.stdev = np.zeros((n_components,))
+        self.total_var = 0.0
+
+    def get_param_str(self):
+        return "ica_c{}{}".format(self.n_components, '_w' if self.whiten else '')
+    
+    def fit(self, X):
+        self.transformer.fit(X)
+        if self.transformer.n_iter_ >= self.maxiter:
+            raise RuntimeError(f'FastICA did not converge (N={X.shape[0]}, it={self.maxiter})')
+
+        # Normalize components
+        self.transformer.components_ /= np.sqrt(np.sum(self.transformer.components_**2, axis=-1, keepdims=True))
+
+        # Save variance for later
+        self.total_var = X.var(axis=0).sum()
+
+        # Compute projected standard deviations
+        self.stdev = np.dot(self.transformer.components_, X.T).std(axis=1)
+
+        # Sort components based on explained variance
+        idx = np.argsort(self.stdev)[::-1]
+        self.stdev = self.stdev[idx]
+        self.transformer.components_[:] = self.transformer.components_[idx]
+
+    def get_components(self):
+        var_ratio = self.stdev**2 / self.total_var
+        return self.transformer.components_, self.stdev, var_ratio # ICA outputs are not normalized
+
+# Incremental PCA
+class IPCAEstimator():
+    def __init__(self, n_components):
+        self.n_components = n_components
+        self.whiten = False
+        self.transformer = IncrementalPCA(n_components, whiten=self.whiten, batch_size=max(100, 2*n_components))
+        self.batch_support = True
+
+    def get_param_str(self):
+        return "ipca_c{}{}".format(self.n_components, '_w' if self.whiten else '')
+
+    def fit(self, X):
+        self.transformer.fit(X)
+
+    def fit_partial(self, X):
+        try:
+            self.transformer.partial_fit(X)
+            self.transformer.n_samples_seen_ = \
+                self.transformer.n_samples_seen_.astype(np.int64) # avoid overflow
+            return True
+        except ValueError as e:
+            print(f'\nIPCA error:', e)
+            return False
+
+    def get_components(self):
+        stdev = np.sqrt(self.transformer.explained_variance_) # already sorted
+        var_ratio = self.transformer.explained_variance_ratio_
+        return self.transformer.components_, stdev, var_ratio # PCA outputs are normalized
+
+# Standard PCA
+class PCAEstimator():
+    def __init__(self, n_components):
+        self.n_components = n_components
+        self.solver = 'full'
+        self.transformer = PCA(n_components, svd_solver=self.solver)
+        self.batch_support = False
+
+    def get_param_str(self):
+        return f"pca-{self.solver}_c{self.n_components}"
+
+    def fit(self, X):
+        self.transformer.fit(X)
+
+        # Save variance for later
+        self.total_var = X.var(axis=0).sum()
+
+        # Compute projected standard deviations
+        self.stdev = np.dot(self.transformer.components_, X.T).std(axis=1)
+
+        # Sort components based on explained variance
+        idx = np.argsort(self.stdev)[::-1]
+        self.stdev = self.stdev[idx]
+        self.transformer.components_[:] = self.transformer.components_[idx]
+
+        # Check orthogonality
+        dotps = [np.dot(*self.transformer.components_[[i, j]])
+            for (i, j) in itertools.combinations(range(self.n_components), 2)]
+        if not np.allclose(dotps, 0, atol=1e-4):
+            print('IPCA components not orghogonal, max dot', np.abs(dotps).max())
+
+        self.transformer.mean_ = X.mean(axis=0, keepdims=True)
+
+    def get_components(self):
+        var_ratio = self.stdev**2 / self.total_var
+        return self.transformer.components_, self.stdev, var_ratio
+
+# Facebook's PCA
+# Good default choice: very fast and accurate.
+# Very high sample counts won't fit into RAM,
+# in which case IncrementalPCA must be used.
+class FacebookPCAEstimator():
+    def __init__(self, n_components):
+        self.n_components = n_components
+        self.transformer = SimpleNamespace()
+        self.batch_support = False
+        self.n_iter = 2
+        self.l = 2*self.n_components
+
+    def get_param_str(self):
+        return "fbpca_c{}_it{}_l{}".format(self.n_components, self.n_iter, self.l)
+
+    def fit(self, X):
+        U, s, Va = fbpca.pca(X, k=self.n_components, n_iter=self.n_iter, raw=True, l=self.l)
+        self.transformer.components_ = Va
+        
+        # Save variance for later
+        self.total_var = X.var(axis=0).sum()
+
+        # Compute projected standard deviations
+        self.stdev = np.dot(self.transformer.components_, X.T).std(axis=1)
+
+        # Sort components based on explained variance
+        idx = np.argsort(self.stdev)[::-1]
+        self.stdev = self.stdev[idx]
+        self.transformer.components_[:] = self.transformer.components_[idx]
+
+        # Check orthogonality
+        dotps = [np.dot(*self.transformer.components_[[i, j]])
+            for (i, j) in itertools.combinations(range(self.n_components), 2)]
+        if not np.allclose(dotps, 0, atol=1e-4):
+            print('FBPCA components not orghogonal, max dot', np.abs(dotps).max())
+
+        self.transformer.mean_ = X.mean(axis=0, keepdims=True)
+        
+    def get_components(self):
+        var_ratio = self.stdev**2 / self.total_var
+        return self.transformer.components_, self.stdev, var_ratio
+
+# Sparse PCA
+# The algorithm is online along the features direction, not the samples direction
+#   => no partial_fit
+class SPCAEstimator():
+    def __init__(self, n_components, alpha=10.0):
+        self.n_components = n_components
+        self.whiten = False
+        self.alpha = alpha  # higher alpha => sparser components
+        #self.transformer = MiniBatchSparsePCA(n_components, alpha=alpha, n_iter=100,
+        #    batch_size=max(20, n_components//5), random_state=0, normalize_components=True)
+        self.transformer = SparsePCA(n_components, alpha=alpha, ridge_alpha=0.01,
+            max_iter=100, random_state=0, n_jobs=-1, normalize_components=True) # TODO: warm start using PCA result?
+        self.batch_support = False # maybe through memmap and HDD-stored tensor
+        self.stdev = np.zeros((n_components,))
+        self.total_var = 0.0
+
+    def get_param_str(self):
+        return "spca_c{}_a{}{}".format(self.n_components, self.alpha, '_w' if self.whiten else '')
+        
+    def fit(self, X):
+        self.transformer.fit(X)
+
+        # Save variance for later
+        self.total_var = X.var(axis=0).sum()
+
+        # Compute projected standard deviations
+        # NB: cannot simply project with dot product!
+        self.stdev = self.transformer.transform(X).std(axis=0) # X = (n_samples, n_features)
+
+        # Sort components based on explained variance
+        idx = np.argsort(self.stdev)[::-1]
+        self.stdev = self.stdev[idx]
+        self.transformer.components_[:] = self.transformer.components_[idx]
+
+        # Check orthogonality
+        dotps = [np.dot(*self.transformer.components_[[i, j]])
+            for (i, j) in itertools.combinations(range(self.n_components), 2)]
+        if not np.allclose(dotps, 0, atol=1e-4):
+            print('SPCA components not orghogonal, max dot', np.abs(dotps).max())
+
+    def get_components(self):
+        var_ratio = self.stdev**2 / self.total_var
+        return self.transformer.components_, self.stdev, var_ratio # SPCA outputs are normalized
+
+def get_estimator(name, n_components, alpha):
+    if name == 'pca':
+        return PCAEstimator(n_components)
+    if name == 'ipca':
+        return IPCAEstimator(n_components)
+    elif name == 'fbpca':
+        return FacebookPCAEstimator(n_components)
+    elif name == 'ica':
+        return ICAEstimator(n_components)
+    elif name == 'spca':
+        return SPCAEstimator(n_components, alpha)
+    else:
+        raise RuntimeError('Unknown estimator')

models/__init__.py

@@ -0,0 +1,11 @@
+# Copyright 2020 Erik Härkönen. All rights reserved.
+# This file is licensed to you under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License. You may obtain a copy
+# of the License at http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software distributed under
+# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
+# OF ANY KIND, either express or implied. See the License for the specific language
+# governing permissions and limitations under the License.
+
+from .wrappers import *

models/biggan/__init__.py

@@ -0,0 +1,8 @@
+from pathlib import Path
+import sys
+
+module_path = Path(__file__).parent / 'pytorch_biggan'
+sys.path.append(str(module_path.resolve()))
+from pytorch_pretrained_biggan import *
+from pytorch_pretrained_biggan.model import GenBlock
+from pytorch_pretrained_biggan.file_utils import http_get, s3_get

models/biggan/pytorch_biggan/.gitignore

@@ -0,0 +1,110 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# vscode
+.vscode/
+
+# models
+models/

models/biggan/pytorch_biggan/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Erik Härkönen
+
+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.

models/biggan/pytorch_biggan/MANIFEST.in

@@ -0,0 +1 @@
+include LICENSE

models/biggan/pytorch_biggan/README.md

@@ -0,0 +1,227 @@
+# BigStyleGAN
+This is a copy of HuggingFace's BigGAN implementation, with the addition of layerwise latent inputs.
+
+# PyTorch pretrained BigGAN
+An op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind.
+
+## Introduction
+
+This repository contains an op-for-op PyTorch reimplementation of DeepMind's BigGAN that was released with the paper [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://openreview.net/forum?id=B1xsqj09Fm) by Andrew Brock, Jeff Donahue and Karen Simonyan.
+
+This PyTorch implementation of BigGAN is provided with the [pretrained 128x128, 256x256 and 512x512 models by DeepMind](https://tfhub.dev/deepmind/biggan-deep-128/1). We also provide the scripts used to download and convert these models from the TensorFlow Hub models.
+
+This reimplementation was done from the raw computation graph of the Tensorflow version and behave similarly to the TensorFlow version (variance of the output difference of the order of 1e-5).
+
+This implementation currently only contains the generator as the weights of the discriminator were not released (although the structure of the discriminator is very similar to the generator so it could be added pretty easily. Tell me if you want to do a PR on that, I would be happy to help.)
+
+## Installation
+
+This repo was tested on Python 3.6 and PyTorch 1.0.1
+
+PyTorch pretrained BigGAN can be installed from pip as follows:
+```bash
+pip install pytorch-pretrained-biggan
+```
+
+If you simply want to play with the GAN this should be enough.
+
+If you want to use the conversion scripts and the imagenet utilities, additional requirements are needed, in particular TensorFlow and NLTK. To install all the requirements please use the `full_requirements.txt` file:
+```bash
+git clone https://github.com/huggingface/pytorch-pretrained-BigGAN.git
+cd pytorch-pretrained-BigGAN
+pip install -r full_requirements.txt
+```
+
+## Models
+
+This repository provide direct and simple access to the pretrained "deep" versions of BigGAN for 128, 256 and 512 pixels resolutions as described in the [associated publication](https://openreview.net/forum?id=B1xsqj09Fm).
+Here are some details on the models:
+
+- `BigGAN-deep-128`: a 50.4M parameters model generating 128x128 pixels images, the model dump weights 201 MB,
+- `BigGAN-deep-256`: a 55.9M parameters model generating 256x256 pixels images, the model dump weights 224 MB,
+- `BigGAN-deep-512`: a 56.2M parameters model generating 512x512 pixels images, the model dump weights 225 MB.
+
+Please refer to Appendix B of the paper for details on the architectures.
+
+All models comprise pre-computed batch norm statistics for 51 truncation values between 0 and 1 (see Appendix C.1 in the paper for details).
+
+## Usage
+
+Here is a quick-start example using `BigGAN` with a pre-trained model.
+
+See the [doc section](#doc) below for details on these classes and methods.
+
+```python
+import torch
+from pytorch_pretrained_biggan import (BigGAN, one_hot_from_names, truncated_noise_sample,
+                                       save_as_images, display_in_terminal)
+
+# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
+import logging
+logging.basicConfig(level=logging.INFO)
+
+# Load pre-trained model tokenizer (vocabulary)
+model = BigGAN.from_pretrained('biggan-deep-256')
+
+# Prepare a input
+truncation = 0.4
+class_vector = one_hot_from_names(['soap bubble', 'coffee', 'mushroom'], batch_size=3)
+noise_vector = truncated_noise_sample(truncation=truncation, batch_size=3)
+
+# All in tensors
+noise_vector = torch.from_numpy(noise_vector)
+class_vector = torch.from_numpy(class_vector)
+
+# If you have a GPU, put everything on cuda
+noise_vector = noise_vector.to('cuda')
+class_vector = class_vector.to('cuda')
+model.to('cuda')
+
+# Generate an image
+with torch.no_grad():
+    output = model(noise_vector, class_vector, truncation)
+
+# If you have a GPU put back on CPU
+output = output.to('cpu')
+
+# If you have a sixtel compatible terminal you can display the images in the terminal
+# (see https://github.com/saitoha/libsixel for details)
+display_in_terminal(output)
+
+# Save results as png images
+save_as_images(output)
+```
+
+![output_0](assets/output_0.png)
+![output_1](assets/output_1.png)
+![output_2](assets/output_2.png)
+
+## Doc
+
+### Loading DeepMind's pre-trained weights
+
+To load one of DeepMind's pre-trained models, instantiate a `BigGAN` model with `from_pretrained()` as:
+
+```python
+model = BigGAN.from_pretrained(PRE_TRAINED_MODEL_NAME_OR_PATH, cache_dir=None)
+```
+
+where
+
+- `PRE_TRAINED_MODEL_NAME_OR_PATH` is either:
+
+  - the shortcut name of a Google AI's or OpenAI's pre-trained model selected in the list:
+
+    - `biggan-deep-128`: 12-layer, 768-hidden, 12-heads, 110M parameters
+    - `biggan-deep-256`: 24-layer, 1024-hidden, 16-heads, 340M parameters
+    - `biggan-deep-512`: 12-layer, 768-hidden, 12-heads , 110M parameters
+
+  - a path or url to a pretrained model archive containing:
+
+    - `config.json`: a configuration file for the model, and
+    - `pytorch_model.bin` a PyTorch dump of a pre-trained instance of `BigGAN` (saved with the usual `torch.save()`).
+
+  If `PRE_TRAINED_MODEL_NAME_OR_PATH` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links [here](pytorch_pretrained_biggan/model.py)) and stored in a cache folder to avoid future download (the cache folder can be found at `~/.pytorch_pretrained_biggan/`).
+- `cache_dir` can be an optional path to a specific directory to download and cache the pre-trained model weights.
+
+### Configuration
+
+`BigGANConfig` is a class to store and load BigGAN configurations. It's defined in [`config.py`](./pytorch_pretrained_biggan/config.py).
+
+Here are some details on the attributes:
+
+- `output_dim`: output resolution of the GAN (128, 256 or 512) for the pre-trained models,
+- `z_dim`: size of the noise vector (128 for the pre-trained models).
+- `class_embed_dim`: size of the class embedding vectors (128 for the pre-trained models).
+- `channel_width`: size of each channel (128 for the pre-trained models).
+- `num_classes`: number of classes in the training dataset, like imagenet (1000 for the pre-trained models).
+- `layers`: A list of layers definition. Each definition for a layer is a triple of [up-sample in the layer ? (bool), number of input channels (int), number of output channels (int)]
+- `attention_layer_position`: Position of the self-attention layer in the layer hierarchy (8 for the pre-trained models).
+- `eps`: epsilon value to use for spectral and batch normalization layers (1e-4 for the pre-trained models).
+- `n_stats`: number of pre-computed statistics for the batch normalization layers associated to various truncation values between 0 and 1 (51 for the pre-trained models).
+
+### Model
+
+`BigGAN` is a PyTorch model (`torch.nn.Module`) of BigGAN defined in [`model.py`](./pytorch_pretrained_biggan/model.py). This model comprises the class embeddings (a linear layer) and the generator with a series of convolutions and conditional batch norms. The discriminator is currently not implemented since pre-trained weights have not been released for it.
+
+The inputs and output are **identical to the TensorFlow model inputs and outputs**.
+
+We detail them here.
+
+`BigGAN` takes as *inputs*:
+
+- `z`: a torch.FloatTensor of shape [batch_size, config.z_dim] with noise sampled from a truncated normal distribution, and
+- `class_label`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
+- `truncation`: a float between 0 (not comprised) and 1. The truncation of the truncated normal used for creating the noise vector. This truncation value is used to selecte between a set of pre-computed statistics (means and variances) for the batch norm layers.
+
+`BigGAN` *outputs* an array of shape [batch_size, 3, resolution, resolution] where resolution is 128, 256 or 512 depending of the model:
+
+### Utilities: Images, Noise, Imagenet classes
+
+We provide a few utility method to use the model. They are defined in [`utils.py`](./pytorch_pretrained_biggan/utils.py).
+
+Here are some details on these methods:
+
+- `truncated_noise_sample(batch_size=1, dim_z=128, truncation=1., seed=None)`:
+
+    Create a truncated noise vector.
+    - Params:
+        - batch_size: batch size.
+        - dim_z: dimension of z
+        - truncation: truncation value to use
+        - seed: seed for the random generator
+    - Output:
+        array of shape (batch_size, dim_z)
+
+- `convert_to_images(obj)`:
+
+    Convert an output tensor from BigGAN in a list of images.
+    - Params:
+        - obj: tensor or numpy array of shape (batch_size, channels, height, width)
+    - Output:
+        - list of Pillow Images of size (height, width)
+
+- `save_as_images(obj, file_name='output')`:
+
+    Convert and save an output tensor from BigGAN in a list of saved images.
+    - Params:
+        - obj: tensor or numpy array of shape (batch_size, channels, height, width)
+        - file_name: path and beggingin of filename to save.
+            Images will be saved as `file_name_{image_number}.png`
+
+- `display_in_terminal(obj)`:
+
+    Convert and display an output tensor from BigGAN in the terminal. This function use `libsixel` and will only work in a libsixel-compatible terminal. Please refer to https://github.com/saitoha/libsixel for more details.
+    - Params:
+        - obj: tensor or numpy array of shape (batch_size, channels, height, width)
+        - file_name: path and beggingin of filename to save.
+            Images will be saved as `file_name_{image_number}.png`
+
+- `one_hot_from_int(int_or_list, batch_size=1)`:
+
+    Create a one-hot vector from a class index or a list of class indices.
+    - Params:
+        - int_or_list: int, or list of int, of the imagenet classes (between 0 and 999)
+        - batch_size: batch size.
+            - If int_or_list is an int create a batch of identical classes.
+            - If int_or_list is a list, we should have `len(int_or_list) == batch_size`
+    - Output:
+        - array of shape (batch_size, 1000)
+
+- `one_hot_from_names(class_name, batch_size=1)`:
+
+    Create a one-hot vector from the name of an imagenet class ('tennis ball', 'daisy', ...). We use NLTK's wordnet search to try to find the relevant synset of ImageNet and take the first one. If we can't find it direcly, we look at the hyponyms and hypernyms of the class name.
+    - Params:
+        - class_name: string containing the name of an imagenet object.
+    - Output:
+        - array of shape (batch_size, 1000)
+
+## Download and conversion scripts
+
+Scripts to download and convert the TensorFlow models from TensorFlow Hub are provided in [./scripts](./scripts/).
+
+The scripts can be used directly as:
+```bash
+./scripts/download_tf_hub_models.sh
+./scripts/convert_tf_hub_models.sh
+```

models/biggan/pytorch_biggan/full_requirements.txt

@@ -0,0 +1,5 @@
+tensorflow
+tensorflow-hub
+Pillow
+nltk
+libsixel-python

models/biggan/pytorch_biggan/pytorch_pretrained_biggan/__init__.py

@@ -0,0 +1,6 @@
+from .config import BigGANConfig
+from .model import BigGAN
+from .file_utils import PYTORCH_PRETRAINED_BIGGAN_CACHE, cached_path
+from .utils import (truncated_noise_sample, save_as_images,
+                    convert_to_images, display_in_terminal,
+                    one_hot_from_int, one_hot_from_names)

models/biggan/pytorch_biggan/pytorch_pretrained_biggan/config.py

@@ -0,0 +1,70 @@
+# coding: utf-8
+"""
+BigGAN config.
+"""
+from __future__ import (absolute_import, division, print_function, unicode_literals)
+
+import copy
+import json
+
+class BigGANConfig(object):
+    """ Configuration class to store the configuration of a `BigGAN`. 
+        Defaults are for the 128x128 model.
+        layers tuple are (up-sample in the layer ?, input channels, output channels)
+    """
+    def __init__(self,
+                 output_dim=128,
+                 z_dim=128,
+                 class_embed_dim=128,
+                 channel_width=128,
+                 num_classes=1000,
+                 layers=[(False, 16, 16),
+                         (True, 16, 16),
+                         (False, 16, 16),
+                         (True, 16, 8),
+                         (False, 8, 8),
+                         (True, 8, 4),
+                         (False, 4, 4),
+                         (True, 4, 2),
+                         (False, 2, 2),
+                         (True, 2, 1)],
+                 attention_layer_position=8,
+                 eps=1e-4,
+                 n_stats=51):
+        """Constructs BigGANConfig. """
+        self.output_dim = output_dim
+        self.z_dim = z_dim
+        self.class_embed_dim = class_embed_dim
+        self.channel_width = channel_width
+        self.num_classes = num_classes
+        self.layers = layers
+        self.attention_layer_position = attention_layer_position
+        self.eps = eps
+        self.n_stats = n_stats
+
+    @classmethod
+    def from_dict(cls, json_object):
+        """Constructs a `BigGANConfig` from a Python dictionary of parameters."""
+        config = BigGANConfig()
+        for key, value in json_object.items():
+            config.__dict__[key] = value
+        return config
+
+    @classmethod
+    def from_json_file(cls, json_file):
+        """Constructs a `BigGANConfig` from a json file of parameters."""
+        with open(json_file, "r", encoding='utf-8') as reader:
+            text = reader.read()
+        return cls.from_dict(json.loads(text))
+
+    def __repr__(self):
+        return str(self.to_json_string())
+
+    def to_dict(self):
+        """Serializes this instance to a Python dictionary."""
+        output = copy.deepcopy(self.__dict__)
+        return output
+
+    def to_json_string(self):
+        """Serializes this instance to a JSON string."""
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"

models/biggan/pytorch_biggan/pytorch_pretrained_biggan/convert_tf_to_pytorch.py

@@ -0,0 +1,312 @@
+# coding: utf-8
+"""
+Convert a TF Hub model for BigGAN in a PT one.
+"""
+from __future__ import (absolute_import, division, print_function, unicode_literals)
+
+from itertools import chain
+
+import os
+import argparse
+import logging
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.functional import normalize
+
+from .model import BigGAN, WEIGHTS_NAME, CONFIG_NAME
+from .config import BigGANConfig
+
+logger = logging.getLogger(__name__)
+
+
+def extract_batch_norm_stats(tf_model_path, batch_norm_stats_path=None):
+    try:
+        import numpy as np
+        import tensorflow as tf
+        import tensorflow_hub as hub
+    except ImportError:
+        raise ImportError("Loading a TensorFlow models in PyTorch, requires TensorFlow and TF Hub to be installed. "
+                          "Please see https://www.tensorflow.org/install/ for installation instructions for TensorFlow. "
+                          "And see https://github.com/tensorflow/hub for installing Hub. "
+                          "Probably pip install tensorflow tensorflow-hub")
+    tf.reset_default_graph()
+    logger.info('Loading BigGAN module from: {}'.format(tf_model_path))
+    module = hub.Module(tf_model_path)
+    inputs = {k: tf.placeholder(v.dtype, v.get_shape().as_list(), k)
+              for k, v in module.get_input_info_dict().items()}
+    output = module(inputs)
+
+    initializer = tf.global_variables_initializer()
+    sess = tf.Session()
+    stacks = sum(((i*10 + 1, i*10 + 3, i*10 + 6, i*10 + 8) for i in range(50)), ())
+    numpy_stacks = []
+    for i in stacks:
+        logger.info("Retrieving module_apply_default/stack_{}".format(i))
+        try:
+            stack_var = tf.get_default_graph().get_tensor_by_name("module_apply_default/stack_%d:0" % i)
+        except KeyError:
+            break  # We have all the stats
+        numpy_stacks.append(sess.run(stack_var))
+
+    if batch_norm_stats_path is not None:
+        torch.save(numpy_stacks, batch_norm_stats_path)
+    else:
+        return numpy_stacks
+
+
+def build_tf_to_pytorch_map(model, config):
+    """ Build a map from TF variables to PyTorch modules. """
+    tf_to_pt_map = {}
+
+    # Embeddings and GenZ
+    tf_to_pt_map.update({'linear/w/ema_0.9999': model.embeddings.weight,
+                         'Generator/GenZ/G_linear/b/ema_0.9999': model.generator.gen_z.bias,
+                         'Generator/GenZ/G_linear/w/ema_0.9999': model.generator.gen_z.weight_orig,
+                         'Generator/GenZ/G_linear/u0': model.generator.gen_z.weight_u})
+
+    # GBlock blocks
+    model_layer_idx = 0
+    for i, (up, in_channels, out_channels) in enumerate(config.layers):
+        if i == config.attention_layer_position:
+            model_layer_idx += 1
+        layer_str = "Generator/GBlock_%d/" % i if i > 0 else "Generator/GBlock/"
+        layer_pnt = model.generator.layers[model_layer_idx]
+        for i in range(4):  #  Batchnorms
+            batch_str = layer_str + ("BatchNorm_%d/" % i if i > 0 else "BatchNorm/")
+            batch_pnt = getattr(layer_pnt, 'bn_%d' % i)
+            for name in ('offset', 'scale'):
+                sub_module_str = batch_str + name + "/"
+                sub_module_pnt = getattr(batch_pnt, name)
+                tf_to_pt_map.update({sub_module_str + "w/ema_0.9999": sub_module_pnt.weight_orig,
+                                     sub_module_str + "u0": sub_module_pnt.weight_u})
+        for i in range(4):  # Convolutions
+            conv_str = layer_str + "conv%d/" % i
+            conv_pnt = getattr(layer_pnt, 'conv_%d' % i)
+            tf_to_pt_map.update({conv_str + "b/ema_0.9999": conv_pnt.bias,
+                                 conv_str + "w/ema_0.9999": conv_pnt.weight_orig,
+                                 conv_str + "u0": conv_pnt.weight_u})
+        model_layer_idx += 1
+
+    # Attention block
+    layer_str = "Generator/attention/"
+    layer_pnt = model.generator.layers[config.attention_layer_position]
+    tf_to_pt_map.update({layer_str + "gamma/ema_0.9999": layer_pnt.gamma})
+    for pt_name, tf_name in zip(['snconv1x1_g', 'snconv1x1_o_conv', 'snconv1x1_phi', 'snconv1x1_theta'],
+                                ['g/', 'o_conv/', 'phi/', 'theta/']):
+        sub_module_str = layer_str + tf_name
+        sub_module_pnt = getattr(layer_pnt, pt_name)
+        tf_to_pt_map.update({sub_module_str + "w/ema_0.9999": sub_module_pnt.weight_orig,
+                             sub_module_str + "u0": sub_module_pnt.weight_u})
+
+    # final batch norm and conv to rgb
+    layer_str = "Generator/BatchNorm/"
+    layer_pnt = model.generator.bn
+    tf_to_pt_map.update({layer_str + "offset/ema_0.9999": layer_pnt.bias,
+                         layer_str + "scale/ema_0.9999": layer_pnt.weight})
+    layer_str = "Generator/conv_to_rgb/"
+    layer_pnt = model.generator.conv_to_rgb
+    tf_to_pt_map.update({layer_str + "b/ema_0.9999": layer_pnt.bias,
+                         layer_str + "w/ema_0.9999": layer_pnt.weight_orig,
+                         layer_str + "u0": layer_pnt.weight_u})
+    return tf_to_pt_map
+
+
+def load_tf_weights_in_biggan(model, config, tf_model_path, batch_norm_stats_path=None):
+    """ Load tf checkpoints and standing statistics in a pytorch model
+    """
+    try:
+        import numpy as np
+        import tensorflow as tf
+    except ImportError:
+        raise ImportError("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
+            "https://www.tensorflow.org/install/ for installation instructions.")
+    # Load weights from TF model
+    checkpoint_path = tf_model_path + "/variables/variables"
+    init_vars = tf.train.list_variables(checkpoint_path)
+    from pprint import pprint
+    pprint(init_vars)
+
+    # Extract batch norm statistics from model if needed
+    if batch_norm_stats_path:
+        stats = torch.load(batch_norm_stats_path)
+    else:
+        logger.info("Extracting batch norm stats")
+        stats = extract_batch_norm_stats(tf_model_path)
+
+    # Build TF to PyTorch weights loading map
+    tf_to_pt_map = build_tf_to_pytorch_map(model, config)
+
+    tf_weights = {}
+    for name in tf_to_pt_map.keys():
+        array = tf.train.load_variable(checkpoint_path, name)
+        tf_weights[name] = array
+        # logger.info("Loading TF weight {} with shape {}".format(name, array.shape))
+
+    # Load parameters
+    with torch.no_grad():
+        pt_params_pnt = set()
+        for name, pointer in tf_to_pt_map.items():
+            array = tf_weights[name]
+            if pointer.dim() == 1:
+                if pointer.dim() < array.ndim:
+                    array = np.squeeze(array)
+            elif pointer.dim() == 2:  # Weights
+                array = np.transpose(array)
+            elif pointer.dim() == 4:  # Convolutions
+                array = np.transpose(array, (3, 2, 0, 1))
+            else:
+                raise "Wrong dimensions to adjust: " + str((pointer.shape, array.shape))
+            if pointer.shape != array.shape:
+                raise ValueError("Wrong dimensions: " + str((pointer.shape, array.shape)))
+            logger.info("Initialize PyTorch weight {} with shape {}".format(name, pointer.shape))
+            pointer.data = torch.from_numpy(array) if isinstance(array, np.ndarray) else torch.tensor(array)
+            tf_weights.pop(name, None)
+            pt_params_pnt.add(pointer.data_ptr())
+
+        # Prepare SpectralNorm buffers by running one step of Spectral Norm (no need to train the model):
+        for module in model.modules():
+            for n, buffer in module.named_buffers():
+                if n == 'weight_v':
+                    weight_mat = module.weight_orig
+                    weight_mat = weight_mat.reshape(weight_mat.size(0), -1)
+                    u = module.weight_u
+
+                    v = normalize(torch.mv(weight_mat.t(), u), dim=0, eps=config.eps)
+                    buffer.data = v
+                    pt_params_pnt.add(buffer.data_ptr())
+
+                    u = normalize(torch.mv(weight_mat, v), dim=0, eps=config.eps)
+                    module.weight_u.data = u
+                    pt_params_pnt.add(module.weight_u.data_ptr())
+
+        # Load batch norm statistics
+        index = 0
+        for layer in model.generator.layers:
+            if not hasattr(layer, 'bn_0'):
+                continue
+            for i in range(4):  #  Batchnorms
+                bn_pointer = getattr(layer, 'bn_%d' % i)
+                pointer = bn_pointer.running_means
+                if pointer.shape != stats[index].shape:
+                    raise "Wrong dimensions: " + str((pointer.shape, stats[index].shape))
+                pointer.data = torch.from_numpy(stats[index])
+                pt_params_pnt.add(pointer.data_ptr())
+
+                pointer = bn_pointer.running_vars
+                if pointer.shape != stats[index+1].shape:
+                    raise "Wrong dimensions: " + str((pointer.shape, stats[index].shape))
+                pointer.data = torch.from_numpy(stats[index+1])
+                pt_params_pnt.add(pointer.data_ptr())
+
+                index += 2
+
+        bn_pointer = model.generator.bn
+        pointer = bn_pointer.running_means
+        if pointer.shape != stats[index].shape:
+            raise "Wrong dimensions: " + str((pointer.shape, stats[index].shape))
+        pointer.data = torch.from_numpy(stats[index])
+        pt_params_pnt.add(pointer.data_ptr())
+
+        pointer = bn_pointer.running_vars
+        if pointer.shape != stats[index+1].shape:
+            raise "Wrong dimensions: " + str((pointer.shape, stats[index].shape))
+        pointer.data = torch.from_numpy(stats[index+1])
+        pt_params_pnt.add(pointer.data_ptr())
+
+    remaining_params = list(n for n, t in chain(model.named_parameters(), model.named_buffers()) \
+                            if t.data_ptr() not in pt_params_pnt)
+
+    logger.info("TF Weights not copied to PyTorch model: {} -".format(', '.join(tf_weights.keys())))
+    logger.info("Remanining parameters/buffers from PyTorch model: {} -".format(', '.join(remaining_params)))
+
+    return model
+
+
+BigGAN128 = BigGANConfig(output_dim=128, z_dim=128, class_embed_dim=128, channel_width=128, num_classes=1000,
+                         layers=[(False, 16, 16),
+                                 (True, 16, 16),
+                                 (False, 16, 16),
+                                 (True, 16, 8),
+                                 (False, 8, 8),
+                                 (True, 8, 4),
+                                 (False, 4, 4),
+                                 (True, 4, 2),
+                                 (False, 2, 2),
+                                 (True, 2, 1)],
+                         attention_layer_position=8, eps=1e-4, n_stats=51)
+
+BigGAN256 = BigGANConfig(output_dim=256, z_dim=128, class_embed_dim=128, channel_width=128, num_classes=1000,
+                         layers=[(False, 16, 16),
+                                 (True, 16, 16),
+                                 (False, 16, 16),
+                                 (True, 16, 8),
+                                 (False, 8, 8),
+                                 (True, 8, 8),
+                                 (False, 8, 8),
+                                 (True, 8, 4),
+                                 (False, 4, 4),
+                                 (True, 4, 2),
+                                 (False, 2, 2),
+                                 (True, 2, 1)],
+                         attention_layer_position=8, eps=1e-4, n_stats=51)
+
+BigGAN512 = BigGANConfig(output_dim=512, z_dim=128, class_embed_dim=128, channel_width=128, num_classes=1000,
+                         layers=[(False, 16, 16),
+                                 (True, 16, 16),
+                                 (False, 16, 16),
+                                 (True, 16, 8),
+                                 (False, 8, 8),
+                                 (True, 8, 8),
+                                 (False, 8, 8),
+                                 (True, 8, 4),
+                                 (False, 4, 4),
+                                 (True, 4, 2),
+                                 (False, 2, 2),
+                                 (True, 2, 1),
+                                 (False, 1, 1),
+                                 (True, 1, 1)],
+                         attention_layer_position=8, eps=1e-4, n_stats=51)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Convert a BigGAN TF Hub model in a PyTorch model")
+    parser.add_argument("--model_type", type=str, default="", required=True,
+                        help="BigGAN model type (128, 256, 512)")
+    parser.add_argument("--tf_model_path", type=str, default="", required=True,
+                        help="Path of the downloaded TF Hub model")
+    parser.add_argument("--pt_save_path", type=str, default="",
+                        help="Folder to save the PyTorch model (default: Folder of the TF Hub model)")
+    parser.add_argument("--batch_norm_stats_path", type=str, default="",
+                        help="Path of previously extracted batch norm statistics")
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.INFO)
+
+    if not args.pt_save_path:
+        args.pt_save_path = args.tf_model_path
+
+    if args.model_type == "128":
+        config = BigGAN128
+    elif args.model_type == "256":
+        config = BigGAN256
+    elif args.model_type == "512":
+        config = BigGAN512
+    else:
+        raise ValueError("model_type should be one of 128, 256 or 512")
+
+    model = BigGAN(config)
+    model = load_tf_weights_in_biggan(model, config, args.tf_model_path, args.batch_norm_stats_path)
+
+    model_save_path = os.path.join(args.pt_save_path, WEIGHTS_NAME)
+    config_save_path = os.path.join(args.pt_save_path, CONFIG_NAME)
+
+    logger.info("Save model dump to {}".format(model_save_path))
+    torch.save(model.state_dict(), model_save_path)
+    logger.info("Save configuration file to {}".format(config_save_path))
+    with open(config_save_path, "w", encoding="utf-8") as f:
+        f.write(config.to_json_string())
+
+if __name__ == "__main__":
+    main()

models/biggan/pytorch_biggan/pytorch_pretrained_biggan/file_utils.py

@@ -0,0 +1,249 @@
+"""
+Utilities for working with the local dataset cache.
+This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
+Copyright by the AllenNLP authors.
+"""
+from __future__ import (absolute_import, division, print_function, unicode_literals)
+
+import json
+import logging
+import os
+import shutil
+import tempfile
+from functools import wraps
+from hashlib import sha256
+import sys
+from io import open
+
+import boto3
+import requests
+from botocore.exceptions import ClientError
+from tqdm import tqdm
+
+try:
+    from urllib.parse import urlparse
+except ImportError:
+    from urlparse import urlparse
+
+try:
+    from pathlib import Path
+    PYTORCH_PRETRAINED_BIGGAN_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BIGGAN_CACHE',
+                                                   Path.home() / '.pytorch_pretrained_biggan'))
+except (AttributeError, ImportError):
+    PYTORCH_PRETRAINED_BIGGAN_CACHE = os.getenv('PYTORCH_PRETRAINED_BIGGAN_CACHE',
+                                              os.path.join(os.path.expanduser("~"), '.pytorch_pretrained_biggan'))
+
+logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
+
+
+def url_to_filename(url, etag=None):
+    """
+    Convert `url` into a hashed filename in a repeatable way.
+    If `etag` is specified, append its hash to the url's, delimited
+    by a period.
+    """
+    url_bytes = url.encode('utf-8')
+    url_hash = sha256(url_bytes)
+    filename = url_hash.hexdigest()
+
+    if etag:
+        etag_bytes = etag.encode('utf-8')
+        etag_hash = sha256(etag_bytes)
+        filename += '.' + etag_hash.hexdigest()
+
+    return filename
+
+
+def filename_to_url(filename, cache_dir=None):
+    """
+    Return the url and etag (which may be ``None``) stored for `filename`.
+    Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
+    """
+    if cache_dir is None:
+        cache_dir = PYTORCH_PRETRAINED_BIGGAN_CACHE
+    if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    cache_path = os.path.join(cache_dir, filename)
+    if not os.path.exists(cache_path):
+        raise EnvironmentError("file {} not found".format(cache_path))
+
+    meta_path = cache_path + '.json'
+    if not os.path.exists(meta_path):
+        raise EnvironmentError("file {} not found".format(meta_path))
+
+    with open(meta_path, encoding="utf-8") as meta_file:
+        metadata = json.load(meta_file)
+    url = metadata['url']
+    etag = metadata['etag']
+
+    return url, etag
+
+
+def cached_path(url_or_filename, cache_dir=None):
+    """
+    Given something that might be a URL (or might be a local path),
+    determine which. If it's a URL, download the file and cache it, and
+    return the path to the cached file. If it's already a local path,
+    make sure the file exists and then return the path.
+    """
+    if cache_dir is None:
+        cache_dir = PYTORCH_PRETRAINED_BIGGAN_CACHE
+    if sys.version_info[0] == 3 and isinstance(url_or_filename, Path):
+        url_or_filename = str(url_or_filename)
+    if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    parsed = urlparse(url_or_filename)
+
+    if parsed.scheme in ('http', 'https', 's3'):
+        # URL, so get it from the cache (downloading if necessary)
+        return get_from_cache(url_or_filename, cache_dir)
+    elif os.path.exists(url_or_filename):
+        # File, and it exists.
+        return url_or_filename
+    elif parsed.scheme == '':
+        # File, but it doesn't exist.
+        raise EnvironmentError("file {} not found".format(url_or_filename))
+    else:
+        # Something unknown
+        raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
+
+
+def split_s3_path(url):
+    """Split a full s3 path into the bucket name and path."""
+    parsed = urlparse(url)
+    if not parsed.netloc or not parsed.path:
+        raise ValueError("bad s3 path {}".format(url))
+    bucket_name = parsed.netloc
+    s3_path = parsed.path
+    # Remove '/' at beginning of path.
+    if s3_path.startswith("/"):
+        s3_path = s3_path[1:]
+    return bucket_name, s3_path
+
+
+def s3_request(func):
+    """
+    Wrapper function for s3 requests in order to create more helpful error
+    messages.
+    """
+
+    @wraps(func)
+    def wrapper(url, *args, **kwargs):
+        try:
+            return func(url, *args, **kwargs)
+        except ClientError as exc:
+            if int(exc.response["Error"]["Code"]) == 404:
+                raise EnvironmentError("file {} not found".format(url))
+            else:
+                raise
+
+    return wrapper
+
+
+@s3_request
+def s3_etag(url):
+    """Check ETag on S3 object."""
+    s3_resource = boto3.resource("s3")
+    bucket_name, s3_path = split_s3_path(url)
+    s3_object = s3_resource.Object(bucket_name, s3_path)
+    return s3_object.e_tag
+
+
+@s3_request
+def s3_get(url, temp_file):
+    """Pull a file directly from S3."""
+    s3_resource = boto3.resource("s3")
+    bucket_name, s3_path = split_s3_path(url)
+    s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
+
+
+def http_get(url, temp_file):
+    req = requests.get(url, stream=True)
+    content_length = req.headers.get('Content-Length')
+    total = int(content_length) if content_length is not None else None
+    progress = tqdm(unit="B", total=total)
+    for chunk in req.iter_content(chunk_size=1024):
+        if chunk: # filter out keep-alive new chunks
+            progress.update(len(chunk))
+            temp_file.write(chunk)
+    progress.close()
+
+
+def get_from_cache(url, cache_dir=None):
+    """
+    Given a URL, look for the corresponding dataset in the local cache.
+    If it's not there, download it. Then return the path to the cached file.
+    """
+    if cache_dir is None:
+        cache_dir = PYTORCH_PRETRAINED_BIGGAN_CACHE
+    if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    if not os.path.exists(cache_dir):
+        os.makedirs(cache_dir)
+
+    # Get eTag to add to filename, if it exists.
+    if url.startswith("s3://"):
+        etag = s3_etag(url)
+    else:
+        response = requests.head(url, allow_redirects=True)
+        if response.status_code != 200:
+            raise IOError("HEAD request failed for url {} with status code {}"
+                          .format(url, response.status_code))
+        etag = response.headers.get("ETag")
+
+    filename = url_to_filename(url, etag)
+
+    # get cache path to put the file
+    cache_path = os.path.join(cache_dir, filename)
+
+    if not os.path.exists(cache_path):
+        # Download to temporary file, then copy to cache dir once finished.
+        # Otherwise you get corrupt cache entries if the download gets interrupted.
+        with tempfile.NamedTemporaryFile() as temp_file:
+            logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
+
+            # GET file object
+            if url.startswith("s3://"):
+                s3_get(url, temp_file)
+            else:
+                http_get(url, temp_file)
+
+            # we are copying the file before closing it, so flush to avoid truncation
+            temp_file.flush()
+            # shutil.copyfileobj() starts at the current position, so go to the start
+            temp_file.seek(0)
+
+            logger.info("copying %s to cache at %s", temp_file.name, cache_path)
+            with open(cache_path, 'wb') as cache_file:
+                shutil.copyfileobj(temp_file, cache_file)
+
+            logger.info("creating metadata file for %s", cache_path)
+            meta = {'url': url, 'etag': etag}
+            meta_path = cache_path + '.json'
+            with open(meta_path, 'w', encoding="utf-8") as meta_file:
+                json.dump(meta, meta_file)
+
+            logger.info("removing temp file %s", temp_file.name)
+
+    return cache_path
+
+
+def read_set_from_file(filename):
+    '''
+    Extract a de-duped collection (set) of text from a file.
+    Expected file format is one item per line.
+    '''
+    collection = set()
+    with open(filename, 'r', encoding='utf-8') as file_:
+        for line in file_:
+            collection.add(line.rstrip())
+    return collection
+
+
+def get_file_extension(path, dot=True, lower=True):
+    ext = os.path.splitext(path)[1]
+    ext = ext if dot else ext[1:]
+    return ext.lower() if lower else ext

models/biggan/pytorch_biggan/pytorch_pretrained_biggan/model.py

@@ -0,0 +1,345 @@
+# coding: utf-8
+""" BigGAN PyTorch model.
+    From "Large Scale GAN Training for High Fidelity Natural Image Synthesis"
+    By Andrew Brocky, Jeff Donahuey and Karen Simonyan.
+    https://openreview.net/forum?id=B1xsqj09Fm
+
+    PyTorch version implemented from the computational graph of the TF Hub module for BigGAN.
+    Some part of the code are adapted from https://github.com/brain-research/self-attention-gan
+
+    This version only comprises the generator (since the discriminator's weights are not released).
+    This version only comprises the "deep" version of BigGAN (see publication).
+
+    Modified by Erik Härkönen:
+    * Added support for per-layer latent vectors
+"""
+from __future__ import (absolute_import, division, print_function, unicode_literals)
+
+import os
+import logging
+import math
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .config import BigGANConfig
+from .file_utils import cached_path
+
+logger = logging.getLogger(__name__)
+
+PRETRAINED_MODEL_ARCHIVE_MAP = {
+    'biggan-deep-128': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-128-pytorch_model.bin",
+    'biggan-deep-256': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-256-pytorch_model.bin",
+    'biggan-deep-512': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-pytorch_model.bin",
+}
+
+PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    'biggan-deep-128': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-128-config.json",
+    'biggan-deep-256': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-256-config.json",
+    'biggan-deep-512': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-config.json",
+}
+
+WEIGHTS_NAME = 'pytorch_model.bin'
+CONFIG_NAME = 'config.json'
+
+
+def snconv2d(eps=1e-12, **kwargs):
+    return nn.utils.spectral_norm(nn.Conv2d(**kwargs), eps=eps)
+
+def snlinear(eps=1e-12, **kwargs):
+    return nn.utils.spectral_norm(nn.Linear(**kwargs), eps=eps)
+
+def sn_embedding(eps=1e-12, **kwargs):
+    return nn.utils.spectral_norm(nn.Embedding(**kwargs), eps=eps)
+
+class SelfAttn(nn.Module):
+    """ Self attention Layer"""
+    def __init__(self, in_channels, eps=1e-12):
+        super(SelfAttn, self).__init__()
+        self.in_channels = in_channels
+        self.snconv1x1_theta = snconv2d(in_channels=in_channels, out_channels=in_channels//8,
+                                        kernel_size=1, bias=False, eps=eps)
+        self.snconv1x1_phi = snconv2d(in_channels=in_channels, out_channels=in_channels//8,
+                                      kernel_size=1, bias=False, eps=eps)
+        self.snconv1x1_g = snconv2d(in_channels=in_channels, out_channels=in_channels//2,
+                                    kernel_size=1, bias=False, eps=eps)
+        self.snconv1x1_o_conv = snconv2d(in_channels=in_channels//2, out_channels=in_channels,
+                                         kernel_size=1, bias=False, eps=eps)
+        self.maxpool = nn.MaxPool2d(2, stride=2, padding=0)
+        self.softmax  = nn.Softmax(dim=-1)
+        self.gamma = nn.Parameter(torch.zeros(1))
+
+    def forward(self, x):
+        _, ch, h, w = x.size()
+        # Theta path
+        theta = self.snconv1x1_theta(x)
+        theta = theta.view(-1, ch//8, h*w)
+        # Phi path
+        phi = self.snconv1x1_phi(x)
+        phi = self.maxpool(phi)
+        phi = phi.view(-1, ch//8, h*w//4)
+        # Attn map
+        attn = torch.bmm(theta.permute(0, 2, 1), phi)
+        attn = self.softmax(attn)
+        # g path
+        g = self.snconv1x1_g(x)
+        g = self.maxpool(g)
+        g = g.view(-1, ch//2, h*w//4)
+        # Attn_g - o_conv
+        attn_g = torch.bmm(g, attn.permute(0, 2, 1))
+        attn_g = attn_g.view(-1, ch//2, h, w)
+        attn_g = self.snconv1x1_o_conv(attn_g)
+        # Out
+        out = x + self.gamma*attn_g
+        return out
+
+
+class BigGANBatchNorm(nn.Module):
+    """ This is a batch norm module that can handle conditional input and can be provided with pre-computed
+        activation means and variances for various truncation parameters.
+
+        We cannot just rely on torch.batch_norm since it cannot handle
+        batched weights (pytorch 1.0.1). We computate batch_norm our-self without updating running means and variances.
+        If you want to train this model you should add running means and variance computation logic.
+    """
+    def __init__(self, num_features, condition_vector_dim=None, n_stats=51, eps=1e-4, conditional=True):
+        super(BigGANBatchNorm, self).__init__()
+        self.num_features = num_features
+        self.eps = eps
+        self.conditional = conditional
+
+        # We use pre-computed statistics for n_stats values of truncation between 0 and 1
+        self.register_buffer('running_means', torch.zeros(n_stats, num_features))
+        self.register_buffer('running_vars', torch.ones(n_stats, num_features))
+        self.step_size = 1.0 / (n_stats - 1)
+
+        if conditional:
+            assert condition_vector_dim is not None
+            self.scale = snlinear(in_features=condition_vector_dim, out_features=num_features, bias=False, eps=eps)
+            self.offset = snlinear(in_features=condition_vector_dim, out_features=num_features, bias=False, eps=eps)
+        else:
+            self.weight = torch.nn.Parameter(torch.Tensor(num_features))
+            self.bias = torch.nn.Parameter(torch.Tensor(num_features))
+
+    def forward(self, x, truncation, condition_vector=None):
+        # Retreive pre-computed statistics associated to this truncation
+        coef, start_idx = math.modf(truncation / self.step_size)
+        start_idx = int(start_idx)
+        if coef != 0.0:  # Interpolate
+            running_mean = self.running_means[start_idx] * coef + self.running_means[start_idx + 1] * (1 - coef)
+            running_var = self.running_vars[start_idx] * coef + self.running_vars[start_idx + 1] * (1 - coef)
+        else:
+            running_mean = self.running_means[start_idx]
+            running_var = self.running_vars[start_idx]
+
+        if self.conditional:
+            running_mean = running_mean.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+            running_var = running_var.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+
+            weight = 1 + self.scale(condition_vector).unsqueeze(-1).unsqueeze(-1)
+            bias = self.offset(condition_vector).unsqueeze(-1).unsqueeze(-1)
+
+            out = (x - running_mean) / torch.sqrt(running_var + self.eps) * weight + bias
+        else:
+            out = F.batch_norm(x, running_mean, running_var, self.weight, self.bias,
+                               training=False, momentum=0.0, eps=self.eps)
+
+        return out
+
+
+class GenBlock(nn.Module):
+    def __init__(self, in_size, out_size, condition_vector_dim, reduction_factor=4, up_sample=False,
+                 n_stats=51, eps=1e-12):
+        super(GenBlock, self).__init__()
+        self.up_sample = up_sample
+        self.drop_channels = (in_size != out_size)
+        middle_size = in_size // reduction_factor
+
+        self.bn_0 = BigGANBatchNorm(in_size, condition_vector_dim, n_stats=n_stats, eps=eps, conditional=True)
+        self.conv_0 = snconv2d(in_channels=in_size, out_channels=middle_size, kernel_size=1, eps=eps)
+
+        self.bn_1 = BigGANBatchNorm(middle_size, condition_vector_dim, n_stats=n_stats, eps=eps, conditional=True)
+        self.conv_1 = snconv2d(in_channels=middle_size, out_channels=middle_size, kernel_size=3, padding=1, eps=eps)
+
+        self.bn_2 = BigGANBatchNorm(middle_size, condition_vector_dim, n_stats=n_stats, eps=eps, conditional=True)
+        self.conv_2 = snconv2d(in_channels=middle_size, out_channels=middle_size, kernel_size=3, padding=1, eps=eps)
+
+        self.bn_3 = BigGANBatchNorm(middle_size, condition_vector_dim, n_stats=n_stats, eps=eps, conditional=True)
+        self.conv_3 = snconv2d(in_channels=middle_size, out_channels=out_size, kernel_size=1, eps=eps)
+
+        self.relu = nn.ReLU()
+
+    def forward(self, x, cond_vector, truncation):
+        x0 = x
+
+        x = self.bn_0(x, truncation, cond_vector)
+        x = self.relu(x)
+        x = self.conv_0(x)
+
+        x = self.bn_1(x, truncation, cond_vector)
+        x = self.relu(x)
+        if self.up_sample:
+            x = F.interpolate(x, scale_factor=2, mode='nearest')
+        x = self.conv_1(x)
+
+        x = self.bn_2(x, truncation, cond_vector)
+        x = self.relu(x)
+        x = self.conv_2(x)
+
+        x = self.bn_3(x, truncation, cond_vector)
+        x = self.relu(x)
+        x = self.conv_3(x)
+
+        if self.drop_channels:
+            new_channels = x0.shape[1] // 2
+            x0 = x0[:, :new_channels, ...]
+        if self.up_sample:
+            x0 = F.interpolate(x0, scale_factor=2, mode='nearest')
+
+        out = x + x0
+        return out
+
+class Generator(nn.Module):
+    def __init__(self, config):
+        super(Generator, self).__init__()
+        self.config = config
+        ch = config.channel_width
+        condition_vector_dim = config.z_dim * 2
+
+        self.gen_z = snlinear(in_features=condition_vector_dim,
+                              out_features=4 * 4 * 16 * ch, eps=config.eps)
+
+        layers = []
+        for i, layer in enumerate(config.layers):
+            if i == config.attention_layer_position:
+                layers.append(SelfAttn(ch*layer[1], eps=config.eps))
+            layers.append(GenBlock(ch*layer[1],
+                                   ch*layer[2],
+                                   condition_vector_dim,
+                                   up_sample=layer[0],
+                                   n_stats=config.n_stats,
+                                   eps=config.eps))
+        self.layers = nn.ModuleList(layers)
+
+        self.bn = BigGANBatchNorm(ch, n_stats=config.n_stats, eps=config.eps, conditional=False)
+        self.relu = nn.ReLU()
+        self.conv_to_rgb = snconv2d(in_channels=ch, out_channels=ch, kernel_size=3, padding=1, eps=config.eps)
+        self.tanh = nn.Tanh()
+
+    def forward(self, cond_vector, truncation):
+        z = self.gen_z(cond_vector[0])
+
+        # We use this conversion step to be able to use TF weights:
+        # TF convention on shape is [batch, height, width, channels]
+        # PT convention on shape is [batch, channels, height, width]
+        z = z.view(-1, 4, 4, 16 * self.config.channel_width)
+        z = z.permute(0, 3, 1, 2).contiguous()
+
+        cond_idx = 1
+        for i, layer in enumerate(self.layers):
+            if isinstance(layer, GenBlock):
+                z = layer(z, cond_vector[cond_idx], truncation)
+                cond_idx += 1
+            else:
+                z = layer(z)
+
+        z = self.bn(z, truncation)
+        z = self.relu(z)
+        z = self.conv_to_rgb(z)
+        z = z[:, :3, ...]
+        z = self.tanh(z)
+        return z
+
+class BigGAN(nn.Module):
+    """BigGAN Generator."""
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
+        if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
+            model_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
+            config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
+        else:
+            model_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
+            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
+
+        try:
+            resolved_model_file = cached_path(model_file, cache_dir=cache_dir)
+            resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
+        except EnvironmentError:
+            logger.error("Wrong model name, should be a valid path to a folder containing "
+                         "a {} file and a {} file or a model name in {}".format(
+                         WEIGHTS_NAME, CONFIG_NAME, PRETRAINED_MODEL_ARCHIVE_MAP.keys()))
+            raise
+
+        logger.info("loading model {} from cache at {}".format(pretrained_model_name_or_path, resolved_model_file))
+
+        # Load config
+        config = BigGANConfig.from_json_file(resolved_config_file)
+        logger.info("Model config {}".format(config))
+
+        # Instantiate model.
+        model = cls(config, *inputs, **kwargs)
+        state_dict = torch.load(resolved_model_file, map_location='cpu' if not torch.cuda.is_available() else None)
+        model.load_state_dict(state_dict, strict=False)
+        return model
+
+    def __init__(self, config):
+        super(BigGAN, self).__init__()
+        self.config = config
+        self.embeddings = nn.Linear(config.num_classes, config.z_dim, bias=False)
+        self.generator = Generator(config)
+        self.n_latents = len(config.layers) + 1 # one for gen_z + one per layer
+
+    def forward(self, z, class_label, truncation):
+        assert 0 < truncation <= 1
+
+        if not isinstance(z, list):
+            z = self.n_latents*[z]
+        
+        if isinstance(class_label, list):
+            embed = [self.embeddings(l) for l in class_label]
+        else:
+            embed = self.n_latents*[self.embeddings(class_label)]
+        
+        assert len(z) == self.n_latents, f'Expected {self.n_latents} latents, got {len(z)}'
+        assert len(embed) == self.n_latents, f'Expected {self.n_latents} class vectors, got {len(class_label)}'
+
+        cond_vectors = [torch.cat((z, e), dim=1) for (z, e) in zip(z, embed)]
+        z = self.generator(cond_vectors, truncation)
+        return z
+
+
+if __name__ == "__main__":
+    import PIL
+    from .utils import truncated_noise_sample, save_as_images, one_hot_from_names
+    from .convert_tf_to_pytorch import load_tf_weights_in_biggan
+
+    load_cache = False
+    cache_path = './saved_model.pt'
+    config = BigGANConfig()
+    model = BigGAN(config)
+    if not load_cache:
+        model = load_tf_weights_in_biggan(model, config, './models/model_128/', './models/model_128/batchnorms_stats.bin')
+        torch.save(model.state_dict(), cache_path)
+    else:
+        model.load_state_dict(torch.load(cache_path))
+
+    model.eval()
+
+    truncation = 0.4
+    noise = truncated_noise_sample(batch_size=2, truncation=truncation)
+    label = one_hot_from_names('diver', batch_size=2)
+
+    # Tests
+    # noise = np.zeros((1, 128))
+    # label = [983]
+
+    noise = torch.tensor(noise, dtype=torch.float)
+    label = torch.tensor(label, dtype=torch.float)
+    with torch.no_grad():
+        outputs = model(noise, label, truncation)
+    print(outputs.shape)
+
+    save_as_images(outputs)

models/biggan/pytorch_biggan/pytorch_pretrained_biggan/utils.py

@@ -0,0 +1,216 @@
+# coding: utf-8
+""" BigGAN utilities to prepare truncated noise samples and convert/save/display output images.
+    Also comprise ImageNet utilities to prepare one hot input vectors for ImageNet classes.
+    We use Wordnet so you can just input a name in a string and automatically get a corresponding
+    imagenet class if it exists (or a hypo/hypernym exists in imagenet).
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+from io import BytesIO
+
+import numpy as np
+from scipy.stats import truncnorm
+
+logger = logging.getLogger(__name__)
+
+NUM_CLASSES = 1000
+
+
+def truncated_noise_sample(batch_size=1, dim_z=128, truncation=1., seed=None):
+    """ Create a truncated noise vector.
+        Params:
+            batch_size: batch size.
+            dim_z: dimension of z
+            truncation: truncation value to use
+            seed: seed for the random generator
+        Output:
+            array of shape (batch_size, dim_z)
+    """
+    state = None if seed is None else np.random.RandomState(seed)
+    values = truncnorm.rvs(-2, 2, size=(batch_size, dim_z), random_state=state).astype(np.float32)
+    return truncation * values
+
+
+def convert_to_images(obj):
+    """ Convert an output tensor from BigGAN in a list of images.
+        Params:
+            obj: tensor or numpy array of shape (batch_size, channels, height, width)
+        Output:
+            list of Pillow Images of size (height, width)
+    """
+    try:
+        import PIL
+    except ImportError:
+        raise ImportError("Please install Pillow to use images: pip install Pillow")
+
+    if not isinstance(obj, np.ndarray):
+        obj = obj.detach().numpy()
+
+    obj = obj.transpose((0, 2, 3, 1))
+    obj = np.clip(((obj + 1) / 2.0) * 256, 0, 255)
+
+    img = []
+    for i, out in enumerate(obj):
+        out_array = np.asarray(np.uint8(out), dtype=np.uint8)
+        img.append(PIL.Image.fromarray(out_array))
+    return img
+
+
+def save_as_images(obj, file_name='output'):
+    """ Convert and save an output tensor from BigGAN in a list of saved images.
+        Params:
+            obj: tensor or numpy array of shape (batch_size, channels, height, width)
+            file_name: path and beggingin of filename to save.
+                Images will be saved as `file_name_{image_number}.png`
+    """
+    img = convert_to_images(obj)
+
+    for i, out in enumerate(img):
+        current_file_name = file_name + '_%d.png' % i
+        logger.info("Saving image to {}".format(current_file_name))
+        out.save(current_file_name, 'png')
+
+
+def display_in_terminal(obj):
+    """ Convert and display an output tensor from BigGAN in the terminal.
+        This function use `libsixel` and will only work in a libsixel-compatible terminal.
+        Please refer to https://github.com/saitoha/libsixel for more details.
+
+        Params:
+            obj: tensor or numpy array of shape (batch_size, channels, height, width)
+            file_name: path and beggingin of filename to save.
+                Images will be saved as `file_name_{image_number}.png`
+    """
+    try:
+        import PIL
+        from libsixel import (sixel_output_new, sixel_dither_new, sixel_dither_initialize,
+                              sixel_dither_set_palette, sixel_dither_set_pixelformat,
+                              sixel_dither_get, sixel_encode, sixel_dither_unref,
+                              sixel_output_unref, SIXEL_PIXELFORMAT_RGBA8888,
+                              SIXEL_PIXELFORMAT_RGB888, SIXEL_PIXELFORMAT_PAL8,
+                              SIXEL_PIXELFORMAT_G8, SIXEL_PIXELFORMAT_G1)
+    except ImportError:
+        raise ImportError("Display in Terminal requires Pillow, libsixel "
+                          "and a libsixel compatible terminal. "
+                          "Please read info at https://github.com/saitoha/libsixel "
+                          "and install with pip install Pillow libsixel-python")
+
+    s = BytesIO()
+
+    images = convert_to_images(obj)
+    widths, heights = zip(*(i.size for i in images))
+
+    output_width = sum(widths)
+    output_height = max(heights)
+
+    output_image = PIL.Image.new('RGB', (output_width, output_height))
+
+    x_offset = 0
+    for im in images:
+        output_image.paste(im, (x_offset,0))
+        x_offset += im.size[0]
+
+    try:
+        data = output_image.tobytes()
+    except NotImplementedError:
+        data = output_image.tostring()
+    output = sixel_output_new(lambda data, s: s.write(data), s)
+
+    try:
+        if output_image.mode == 'RGBA':
+            dither = sixel_dither_new(256)
+            sixel_dither_initialize(dither, data, output_width, output_height, SIXEL_PIXELFORMAT_RGBA8888)
+        elif output_image.mode == 'RGB':
+            dither = sixel_dither_new(256)
+            sixel_dither_initialize(dither, data, output_width, output_height, SIXEL_PIXELFORMAT_RGB888)
+        elif output_image.mode == 'P':
+            palette = output_image.getpalette()
+            dither = sixel_dither_new(256)
+            sixel_dither_set_palette(dither, palette)
+            sixel_dither_set_pixelformat(dither, SIXEL_PIXELFORMAT_PAL8)
+        elif output_image.mode == 'L':
+            dither = sixel_dither_get(SIXEL_BUILTIN_G8)
+            sixel_dither_set_pixelformat(dither, SIXEL_PIXELFORMAT_G8)
+        elif output_image.mode == '1':
+            dither = sixel_dither_get(SIXEL_BUILTIN_G1)
+            sixel_dither_set_pixelformat(dither, SIXEL_PIXELFORMAT_G1)
+        else:
+            raise RuntimeError('unexpected output_image mode')
+        try:
+            sixel_encode(data, output_width, output_height, 1, dither, output)
+            print(s.getvalue().decode('ascii'))
+        finally:
+            sixel_dither_unref(dither)
+    finally:
+        sixel_output_unref(output)
+
+
+def one_hot_from_int(int_or_list, batch_size=1):
+    """ Create a one-hot vector from a class index or a list of class indices.
+        Params:
+            int_or_list: int, or list of int, of the imagenet classes (between 0 and 999)
+            batch_size: batch size.
+                If int_or_list is an int create a batch of identical classes.
+                If int_or_list is a list, we should have `len(int_or_list) == batch_size`
+        Output:
+            array of shape (batch_size, 1000)
+    """
+    if isinstance(int_or_list, int):
+        int_or_list = [int_or_list]
+
+    if len(int_or_list) == 1 and batch_size > 1:
+        int_or_list = [int_or_list[0]] * batch_size
+
+    assert batch_size == len(int_or_list)
+
+    array = np.zeros((batch_size, NUM_CLASSES), dtype=np.float32)
+    for i, j in enumerate(int_or_list):
+        array[i, j] = 1.0
+    return array
+
+
+def one_hot_from_names(class_name_or_list, batch_size=1):
+    """ Create a one-hot vector from the name of an imagenet class ('tennis ball', 'daisy', ...).
+        We use NLTK's wordnet search to try to find the relevant synset of ImageNet and take the first one.
+        If we can't find it direcly, we look at the hyponyms and hypernyms of the class name.
+
+        Params:
+            class_name_or_list: string containing the name of an imagenet object or a list of such strings (for a batch).
+        Output:
+            array of shape (batch_size, 1000)
+    """
+    try:
+        from nltk.corpus import wordnet as wn
+    except ImportError:
+        raise ImportError("You need to install nltk to use this function")
+
+    if not isinstance(class_name_or_list, (list, tuple)):
+        class_name_or_list = [class_name_or_list]
+    else:
+        batch_size = max(batch_size, len(class_name_or_list))
+
+    classes = []
+    for class_name in class_name_or_list:
+        class_name = class_name.replace(" ", "_")
+
+        original_synsets = wn.synsets(class_name)
+        original_synsets = list(filter(lambda s: s.pos() == 'n', original_synsets))  # keep only names
+        if not original_synsets:
+            return None
+
+        possible_synsets = list(filter(lambda s: s.offset() in IMAGENET, original_synsets))
+        if possible_synsets:
+            classes.append(IMAGENET[possible_synsets[0].offset()])
+        else:
+            # try hypernyms and hyponyms
+            possible_synsets = sum([s.hypernyms() + s.hyponyms() for s in original_synsets], [])
+            possible_synsets = list(filter(lambda s: s.offset() in IMAGENET, possible_synsets))
+            if possible_synsets:
+                classes.append(IMAGENET[possible_synsets[0].offset()])
+
+    return one_hot_from_int(classes, batch_size=batch_size)
+
+
+IMAGENET = {1440764: 0, 1443537: 1, 1484850: 2, 1491361: 3, 1494475: 4, 1496331: 5, 1498041: 6, 1514668: 7, 1514859: 8, 1518878: 9, 1530575: 10, 1531178: 11, 1532829: 12, 1534433: 13, 1537544: 14, 1558993: 15, 1560419: 16, 1580077: 17, 1582220: 18, 1592084: 19, 1601694: 20, 1608432: 21, 1614925: 22, 1616318: 23, 1622779: 24, 1629819: 25, 1630670: 26, 1631663: 27, 1632458: 28, 1632777: 29, 1641577: 30, 1644373: 31, 1644900: 32, 1664065: 33, 1665541: 34, 1667114: 35, 1667778: 36, 1669191: 37, 1675722: 38, 1677366: 39, 1682714: 40, 1685808: 41, 1687978: 42, 1688243: 43, 1689811: 44, 1692333: 45, 1693334: 46, 1694178: 47, 1695060: 48, 1697457: 49, 1698640: 50, 1704323: 51, 1728572: 52, 1728920: 53, 1729322: 54, 1729977: 55, 1734418: 56, 1735189: 57, 1737021: 58, 1739381: 59, 1740131: 60, 1742172: 61, 1744401: 62, 1748264: 63, 1749939: 64, 1751748: 65, 1753488: 66, 1755581: 67, 1756291: 68, 1768244: 69, 1770081: 70, 1770393: 71, 1773157: 72, 1773549: 73, 1773797: 74, 1774384: 75, 1774750: 76, 1775062: 77, 1776313: 78, 1784675: 79, 1795545: 80, 1796340: 81, 1797886: 82, 1798484: 83, 1806143: 84, 1806567: 85, 1807496: 86, 1817953: 87, 1818515: 88, 1819313: 89, 1820546: 90, 1824575: 91, 1828970: 92, 1829413: 93, 1833805: 94, 1843065: 95, 1843383: 96, 1847000: 97, 1855032: 98, 1855672: 99, 1860187: 100, 1871265: 101, 1872401: 102, 1873310: 103, 1877812: 104, 1882714: 105, 1883070: 106, 1910747: 107, 1914609: 108, 1917289: 109, 1924916: 110, 1930112: 111, 1943899: 112, 1944390: 113, 1945685: 114, 1950731: 115, 1955084: 116, 1968897: 117, 1978287: 118, 1978455: 119, 1980166: 120, 1981276: 121, 1983481: 122, 1984695: 123, 1985128: 124, 1986214: 125, 1990800: 126, 2002556: 127, 2002724: 128, 2006656: 129, 2007558: 130, 2009229: 131, 2009912: 132, 2011460: 133, 2012849: 134, 2013706: 135, 2017213: 136, 2018207: 137, 2018795: 138, 2025239: 139, 2027492: 140, 2028035: 141, 2033041: 142, 2037110: 143, 2051845: 144, 2056570: 145, 2058221: 146, 2066245: 147, 2071294: 148, 2074367: 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2102973: 221, 2104029: 222, 2104365: 223, 2105056: 224, 2105162: 225, 2105251: 226, 2105412: 227, 2105505: 228, 2105641: 229, 2105855: 230, 2106030: 231, 2106166: 232, 2106382: 233, 2106550: 234, 2106662: 235, 2107142: 236, 2107312: 237, 2107574: 238, 2107683: 239, 2107908: 240, 2108000: 241, 2108089: 242, 2108422: 243, 2108551: 244, 2108915: 245, 2109047: 246, 2109525: 247, 2109961: 248, 2110063: 249, 2110185: 250, 2110341: 251, 2110627: 252, 2110806: 253, 2110958: 254, 2111129: 255, 2111277: 256, 2111500: 257, 2111889: 258, 2112018: 259, 2112137: 260, 2112350: 261, 2112706: 262, 2113023: 263, 2113186: 264, 2113624: 265, 2113712: 266, 2113799: 267, 2113978: 268, 2114367: 269, 2114548: 270, 2114712: 271, 2114855: 272, 2115641: 273, 2115913: 274, 2116738: 275, 2117135: 276, 2119022: 277, 2119789: 278, 2120079: 279, 2120505: 280, 2123045: 281, 2123159: 282, 2123394: 283, 2123597: 284, 2124075: 285, 2125311: 286, 2127052: 287, 2128385: 288, 2128757: 289, 2128925: 290, 2129165: 291, 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models/biggan/pytorch_biggan/requirements.txt

@@ -0,0 +1,8 @@
+# PyTorch
+torch>=0.4.1
+# progress bars in model download and training scripts
+tqdm
+# Accessing files from S3 directly.
+boto3
+# Used for downloading models over HTTP
+requests

models/biggan/pytorch_biggan/scripts/convert_tf_hub_models.sh

@@ -0,0 +1,21 @@
+# Copyright (c) 2019-present, Thomas Wolf, Huggingface Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+set -e
+set -x
+
+models="128 256 512"
+
+mkdir -p models/model_128
+mkdir -p models/model_256
+mkdir -p models/model_512
+
+# Convert TF Hub models.
+for model in $models
+do
+    pytorch_pretrained_biggan --model_type $model --tf_model_path models/model_$model --pt_save_path models/model_$model
+done

models/biggan/pytorch_biggan/scripts/download_tf_hub_models.sh

@@ -0,0 +1,21 @@
+# Copyright (c) 2019-present, Thomas Wolf, Huggingface Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+set -e
+set -x
+
+models="128 256 512"
+
+mkdir -p models/model_128
+mkdir -p models/model_256
+mkdir -p models/model_512
+
+# Download TF Hub models.
+for model in $models
+do
+    curl -L "https://tfhub.dev/deepmind/biggan-deep-$model/1?tf-hub-format=compressed" | tar -zxvC models/model_$model
+done

models/biggan/pytorch_biggan/setup.py

@@ -0,0 +1,69 @@
+"""
+Simple check list from AllenNLP repo: https://github.com/allenai/allennlp/blob/master/setup.py
+
+To create the package for pypi.
+
+1. Change the version in __init__.py and setup.py.
+
+2. Commit these changes with the message: "Release: VERSION"
+
+3. Add a tag in git to mark the release: "git tag VERSION -m'Adds tag VERSION for pypi' "
+   Push the tag to git: git push --tags origin master
+
+4. Build both the sources and the wheel. Do not change anything in setup.py between
+   creating the wheel and the source distribution (obviously).
+
+   For the wheel, run: "python setup.py bdist_wheel" in the top level allennlp directory.
+   (this will build a wheel for the python version you use to build it - make sure you use python 3.x).
+
+   For the sources, run: "python setup.py sdist"
+   You should now have a /dist directory with both .whl and .tar.gz source versions of allennlp.
+
+5. Check that everything looks correct by uploading the package to the pypi test server:
+
+   twine upload dist/* -r pypitest
+   (pypi suggest using twine as other methods upload files via plaintext.)
+
+   Check that you can install it in a virtualenv by running:
+   pip install -i https://testpypi.python.org/pypi allennlp
+
+6. Upload the final version to actual pypi:
+   twine upload dist/* -r pypi
+
+7. Copy the release notes from RELEASE.md to the tag in github once everything is looking hunky-dory.
+
+"""
+from io import open
+from setuptools import find_packages, setup
+
+setup(
+    name="pytorch_pretrained_biggan",
+    version="0.1.0",
+    author="Thomas Wolf",
+    author_email="[email protected]",
+    description="PyTorch version of DeepMind's BigGAN model with pre-trained models",
+    long_description=open("README.md", "r", encoding='utf-8').read(),
+    long_description_content_type="text/markdown",
+    keywords='BIGGAN GAN deep learning google deepmind',
+    license='Apache',
+    url="https://github.com/huggingface/pytorch-pretrained-BigGAN",
+    packages=find_packages(exclude=["*.tests", "*.tests.*",
+                                    "tests.*", "tests"]),
+    install_requires=['torch>=0.4.1',
+                      'numpy',
+                      'boto3',
+                      'requests',
+                      'tqdm'],
+    tests_require=['pytest'],
+    entry_points={
+      'console_scripts': [
+        "pytorch_pretrained_biggan=pytorch_pretrained_biggan.convert_tf_to_pytorch:main",
+      ]
+    },
+    classifiers=[
+          'Intended Audience :: Science/Research',
+          'License :: OSI Approved :: Apache Software License',
+          'Programming Language :: Python :: 3',
+          'Topic :: Scientific/Engineering :: Artificial Intelligence',
+    ],
+)

models/stylegan/__init__.py

@@ -0,0 +1,17 @@
+# Copyright 2020 Erik Härkönen. All rights reserved.
+# This file is licensed to you under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License. You may obtain a copy
+# of the License at http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software distributed under
+# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
+# OF ANY KIND, either express or implied. See the License for the specific language
+# governing permissions and limitations under the License.
+
+from pathlib import Path
+import sys
+
+#module_path = Path(__file__).parent / 'pytorch_biggan'
+#sys.path.append(str(module_path.resolve()))
+
+from .model import StyleGAN_G, NoiseLayer

models/stylegan/model.py

@@ -0,0 +1,456 @@
+# Copyright 2020 Erik Härkönen. All rights reserved.
+# This file is licensed to you under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License. You may obtain a copy
+# of the License at http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software distributed under
+# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
+# OF ANY KIND, either express or implied. See the License for the specific language
+# governing permissions and limitations under the License.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from collections import OrderedDict
+from pathlib import Path
+import requests
+import pickle
+import sys
+
+import numpy as np
+
+# Reimplementation of StyleGAN in PyTorch
+# Source: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
+
+class MyLinear(nn.Module):
+    """Linear layer with equalized learning rate and custom learning rate multiplier."""
+    def __init__(self, input_size, output_size, gain=2**(0.5), use_wscale=False, lrmul=1, bias=True):
+        super().__init__()
+        he_std = gain * input_size**(-0.5) # He init
+        # Equalized learning rate and custom learning rate multiplier.
+        if use_wscale:
+            init_std = 1.0 / lrmul
+            self.w_mul = he_std * lrmul
+        else:
+            init_std = he_std / lrmul
+            self.w_mul = lrmul
+        self.weight = torch.nn.Parameter(torch.randn(output_size, input_size) * init_std)
+        if bias:
+            self.bias = torch.nn.Parameter(torch.zeros(output_size))
+            self.b_mul = lrmul
+        else:
+            self.bias = None
+
+    def forward(self, x):
+        bias = self.bias
+        if bias is not None:
+            bias = bias * self.b_mul
+        return F.linear(x, self.weight * self.w_mul, bias)
+
+class MyConv2d(nn.Module):
+    """Conv layer with equalized learning rate and custom learning rate multiplier."""
+    def __init__(self, input_channels, output_channels, kernel_size, gain=2**(0.5), use_wscale=False, lrmul=1, bias=True,
+                intermediate=None, upscale=False):
+        super().__init__()
+        if upscale:
+            self.upscale = Upscale2d()
+        else:
+            self.upscale = None
+        he_std = gain * (input_channels * kernel_size ** 2) ** (-0.5) # He init
+        self.kernel_size = kernel_size
+        if use_wscale:
+            init_std = 1.0 / lrmul
+            self.w_mul = he_std * lrmul
+        else:
+            init_std = he_std / lrmul
+            self.w_mul = lrmul
+        self.weight = torch.nn.Parameter(torch.randn(output_channels, input_channels, kernel_size, kernel_size) * init_std)
+        if bias:
+            self.bias = torch.nn.Parameter(torch.zeros(output_channels))
+            self.b_mul = lrmul
+        else:
+            self.bias = None
+        self.intermediate = intermediate
+
+    def forward(self, x):
+        bias = self.bias
+        if bias is not None:
+            bias = bias * self.b_mul
+        
+        have_convolution = False
+        if self.upscale is not None and min(x.shape[2:]) * 2 >= 128:
+            # this is the fused upscale + conv from StyleGAN, sadly this seems incompatible with the non-fused way
+            # this really needs to be cleaned up and go into the conv...
+            w = self.weight * self.w_mul
+            w = w.permute(1, 0, 2, 3)
+            # probably applying a conv on w would be more efficient. also this quadruples the weight (average)?!
+            w = F.pad(w, (1,1,1,1))
+            w = w[:, :, 1:, 1:]+ w[:, :, :-1, 1:] + w[:, :, 1:, :-1] + w[:, :, :-1, :-1]
+            x = F.conv_transpose2d(x, w, stride=2, padding=(w.size(-1)-1)//2)
+            have_convolution = True
+        elif self.upscale is not None:
+            x = self.upscale(x)
+    
+        if not have_convolution and self.intermediate is None:
+            return F.conv2d(x, self.weight * self.w_mul, bias, padding=self.kernel_size//2)
+        elif not have_convolution:
+            x = F.conv2d(x, self.weight * self.w_mul, None, padding=self.kernel_size//2)
+        
+        if self.intermediate is not None:
+            x = self.intermediate(x)
+        if bias is not None:
+            x = x + bias.view(1, -1, 1, 1)
+        return x
+
+class NoiseLayer(nn.Module):
+    """adds noise. noise is per pixel (constant over channels) with per-channel weight"""
+    def __init__(self, channels):
+        super().__init__()
+        self.weight = nn.Parameter(torch.zeros(channels))
+        self.noise = None
+    
+    def forward(self, x, noise=None):
+        if noise is None and self.noise is None:
+            noise = torch.randn(x.size(0), 1, x.size(2), x.size(3), device=x.device, dtype=x.dtype)
+        elif noise is None:
+            # here is a little trick: if you get all the noiselayers and set each
+            # modules .noise attribute, you can have pre-defined noise.
+            # Very useful for analysis
+            noise = self.noise
+        x = x + self.weight.view(1, -1, 1, 1) * noise
+        return x
+
+class StyleMod(nn.Module):
+    def __init__(self, latent_size, channels, use_wscale):
+        super(StyleMod, self).__init__()
+        self.lin = MyLinear(latent_size,
+                            channels * 2,
+                            gain=1.0, use_wscale=use_wscale)
+        
+    def forward(self, x, latent):
+        style = self.lin(latent) # style => [batch_size, n_channels*2]
+        shape = [-1, 2, x.size(1)] + (x.dim() - 2) * [1]
+        style = style.view(shape)  # [batch_size, 2, n_channels, ...]
+        x = x * (style[:, 0] + 1.) + style[:, 1]
+        return x
+
+class PixelNormLayer(nn.Module):
+    def __init__(self, epsilon=1e-8):
+        super().__init__()
+        self.epsilon = epsilon
+    def forward(self, x):
+        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + self.epsilon)
+
+class BlurLayer(nn.Module):
+    def __init__(self, kernel=[1, 2, 1], normalize=True, flip=False, stride=1):
+        super(BlurLayer, self).__init__()
+        kernel=[1, 2, 1]
+        kernel = torch.tensor(kernel, dtype=torch.float32)
+        kernel = kernel[:, None] * kernel[None, :]
+        kernel = kernel[None, None]
+        if normalize:
+            kernel = kernel / kernel.sum()
+        if flip:
+            kernel = kernel[:, :, ::-1, ::-1]
+        self.register_buffer('kernel', kernel)
+        self.stride = stride
+    
+    def forward(self, x):
+        # expand kernel channels
+        kernel = self.kernel.expand(x.size(1), -1, -1, -1)
+        x = F.conv2d(
+            x,
+            kernel,
+            stride=self.stride,
+            padding=int((self.kernel.size(2)-1)/2),
+            groups=x.size(1)
+        )
+        return x
+
+def upscale2d(x, factor=2, gain=1):
+    assert x.dim() == 4
+    if gain != 1:
+        x = x * gain
+    if factor != 1:
+        shape = x.shape
+        x = x.view(shape[0], shape[1], shape[2], 1, shape[3], 1).expand(-1, -1, -1, factor, -1, factor)
+        x = x.contiguous().view(shape[0], shape[1], factor * shape[2], factor * shape[3])
+    return x
+
+class Upscale2d(nn.Module):
+    def __init__(self, factor=2, gain=1):
+        super().__init__()
+        assert isinstance(factor, int) and factor >= 1
+        self.gain = gain
+        self.factor = factor
+    def forward(self, x):
+        return upscale2d(x, factor=self.factor, gain=self.gain)
+
+class G_mapping(nn.Sequential):
+    def __init__(self, nonlinearity='lrelu', use_wscale=True):
+        act, gain = {'relu': (torch.relu, np.sqrt(2)),
+                     'lrelu': (nn.LeakyReLU(negative_slope=0.2), np.sqrt(2))}[nonlinearity]
+        layers = [
+            ('pixel_norm', PixelNormLayer()),
+            ('dense0', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
+            ('dense0_act', act),
+            ('dense1', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
+            ('dense1_act', act),
+            ('dense2', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
+            ('dense2_act', act),
+            ('dense3', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
+            ('dense3_act', act),
+            ('dense4', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
+            ('dense4_act', act),
+            ('dense5', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
+            ('dense5_act', act),
+            ('dense6', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
+            ('dense6_act', act),
+            ('dense7', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
+            ('dense7_act', act)
+        ]
+        super().__init__(OrderedDict(layers))
+        
+    def forward(self, x):
+        return super().forward(x)
+
+class Truncation(nn.Module):
+    def __init__(self, avg_latent, max_layer=8, threshold=0.7):
+        super().__init__()
+        self.max_layer = max_layer
+        self.threshold = threshold
+        self.register_buffer('avg_latent', avg_latent)
+    def forward(self, x):
+        assert x.dim() == 3
+        interp = torch.lerp(self.avg_latent, x, self.threshold)
+        do_trunc = (torch.arange(x.size(1)) < self.max_layer).view(1, -1, 1)
+        return torch.where(do_trunc, interp, x)
+
+class LayerEpilogue(nn.Module):
+    """Things to do at the end of each layer."""
+    def __init__(self, channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer):
+        super().__init__()
+        layers = []
+        if use_noise:
+            layers.append(('noise', NoiseLayer(channels)))
+        layers.append(('activation', activation_layer))
+        if use_pixel_norm:
+            layers.append(('pixel_norm', PixelNorm()))
+        if use_instance_norm:
+            layers.append(('instance_norm', nn.InstanceNorm2d(channels)))
+        self.top_epi = nn.Sequential(OrderedDict(layers))
+        if use_styles:
+            self.style_mod = StyleMod(dlatent_size, channels, use_wscale=use_wscale)
+        else:
+            self.style_mod = None
+    def forward(self, x, dlatents_in_slice=None):
+        x = self.top_epi(x)
+        if self.style_mod is not None:
+            x = self.style_mod(x, dlatents_in_slice)
+        else:
+            assert dlatents_in_slice is None
+        return x
+
+
+class InputBlock(nn.Module):
+    def __init__(self, nf, dlatent_size, const_input_layer, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer):
+        super().__init__()
+        self.const_input_layer = const_input_layer
+        self.nf = nf
+        if self.const_input_layer:
+            # called 'const' in tf
+            self.const = nn.Parameter(torch.ones(1, nf, 4, 4))
+            self.bias = nn.Parameter(torch.ones(nf))
+        else:
+            self.dense = MyLinear(dlatent_size, nf*16, gain=gain/4, use_wscale=use_wscale) # tweak gain to match the official implementation of Progressing GAN
+        self.epi1 = LayerEpilogue(nf, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
+        self.conv = MyConv2d(nf, nf, 3, gain=gain, use_wscale=use_wscale)
+        self.epi2 = LayerEpilogue(nf, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
+        
+    def forward(self, dlatents_in_range):
+        batch_size = dlatents_in_range.size(0)
+        if self.const_input_layer:
+            x = self.const.expand(batch_size, -1, -1, -1)
+            x = x + self.bias.view(1, -1, 1, 1)
+        else:
+            x = self.dense(dlatents_in_range[:, 0]).view(batch_size, self.nf, 4, 4)
+        x = self.epi1(x, dlatents_in_range[:, 0])
+        x = self.conv(x)
+        x = self.epi2(x, dlatents_in_range[:, 1])
+        return x
+
+
+class GSynthesisBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, blur_filter, dlatent_size, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer):
+        # 2**res x 2**res # res = 3..resolution_log2
+        super().__init__()
+        if blur_filter:
+            blur = BlurLayer(blur_filter)
+        else:
+            blur = None
+        self.conv0_up = MyConv2d(in_channels, out_channels, kernel_size=3, gain=gain, use_wscale=use_wscale,
+                                 intermediate=blur, upscale=True)
+        self.epi1 = LayerEpilogue(out_channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
+        self.conv1 = MyConv2d(out_channels, out_channels, kernel_size=3, gain=gain, use_wscale=use_wscale)
+        self.epi2 = LayerEpilogue(out_channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
+            
+    def forward(self, x, dlatents_in_range):
+        x = self.conv0_up(x)
+        x = self.epi1(x, dlatents_in_range[:, 0])
+        x = self.conv1(x)
+        x = self.epi2(x, dlatents_in_range[:, 1])
+        return x
+
+class G_synthesis(nn.Module):
+    def __init__(self,
+            dlatent_size        = 512,          # Disentangled latent (W) dimensionality.
+            num_channels        = 3,            # Number of output color channels.
+            resolution          = 1024,         # Output resolution.
+            fmap_base           = 8192,         # Overall multiplier for the number of feature maps.
+            fmap_decay          = 1.0,          # log2 feature map reduction when doubling the resolution.
+            fmap_max            = 512,          # Maximum number of feature maps in any layer.
+            use_styles          = True,         # Enable style inputs?
+            const_input_layer   = True,         # First layer is a learned constant?
+            use_noise           = True,         # Enable noise inputs?
+            randomize_noise     = True,         # True = randomize noise inputs every time (non-deterministic), False = read noise inputs from variables.
+            nonlinearity        = 'lrelu',      # Activation function: 'relu', 'lrelu'
+            use_wscale          = True,         # Enable equalized learning rate?
+            use_pixel_norm      = False,        # Enable pixelwise feature vector normalization?
+            use_instance_norm   = True,         # Enable instance normalization?
+            dtype               = torch.float32,  # Data type to use for activations and outputs.
+            blur_filter         = [1,2,1],      # Low-pass filter to apply when resampling activations. None = no filtering.
+        ):
+        
+        super().__init__()
+        def nf(stage):
+            return min(int(fmap_base / (2.0 ** (stage * fmap_decay))), fmap_max)
+        self.dlatent_size = dlatent_size
+        resolution_log2 = int(np.log2(resolution))
+        assert resolution == 2**resolution_log2 and resolution >= 4
+
+        act, gain = {'relu': (torch.relu, np.sqrt(2)),
+                     'lrelu': (nn.LeakyReLU(negative_slope=0.2), np.sqrt(2))}[nonlinearity]
+        num_layers = resolution_log2 * 2 - 2
+        num_styles = num_layers if use_styles else 1
+        torgbs = []
+        blocks = []
+        for res in range(2, resolution_log2 + 1):
+            channels = nf(res-1)
+            name = '{s}x{s}'.format(s=2**res)
+            if res == 2:
+                blocks.append((name,
+                               InputBlock(channels, dlatent_size, const_input_layer, gain, use_wscale,
+                                      use_noise, use_pixel_norm, use_instance_norm, use_styles, act)))
+                
+            else:
+                blocks.append((name,
+                               GSynthesisBlock(last_channels, channels, blur_filter, dlatent_size, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, act)))
+            last_channels = channels
+        self.torgb = MyConv2d(channels, num_channels, 1, gain=1, use_wscale=use_wscale)
+        self.blocks = nn.ModuleDict(OrderedDict(blocks))
+        
+    def forward(self, dlatents_in):
+        # Input: Disentangled latents (W) [minibatch, num_layers, dlatent_size].
+        # lod_in = tf.cast(tf.get_variable('lod', initializer=np.float32(0), trainable=False), dtype)
+        batch_size = dlatents_in.size(0)       
+        for i, m in enumerate(self.blocks.values()):
+            if i == 0:
+                x = m(dlatents_in[:, 2*i:2*i+2])
+            else:
+                x = m(x, dlatents_in[:, 2*i:2*i+2])
+        rgb = self.torgb(x)
+        return rgb
+
+
+class StyleGAN_G(nn.Sequential):
+    def __init__(self, resolution, truncation=1.0):
+        self.resolution = resolution
+        self.layers = OrderedDict([
+            ('g_mapping', G_mapping()),
+            #('truncation', Truncation(avg_latent)),
+            ('g_synthesis', G_synthesis(resolution=resolution)),
+        ])
+        super().__init__(self.layers)
+
+    def forward(self, x, latent_is_w=False):
+        if isinstance(x, list):
+            assert len(x) == 18, 'Must provide 1 or 18 latents'
+            if not latent_is_w:
+                x = [self.layers['g_mapping'].forward(l) for l in x]
+            x = torch.stack(x, dim=1)
+        else:
+            if not latent_is_w:
+                x = self.layers['g_mapping'].forward(x)
+            x = x.unsqueeze(1).expand(-1, 18, -1)
+
+        x = self.layers['g_synthesis'].forward(x)
+
+        return x
+
+    # From: https://github.com/lernapparat/lernapparat/releases/download/v2019-02-01/
+    def load_weights(self, checkpoint):    
+        self.load_state_dict(torch.load(checkpoint))
+
+    def export_from_tf(self, pickle_path):
+        module_path = Path(__file__).parent / 'stylegan_tf'
+        sys.path.append(str(module_path.resolve()))
+
+        import dnnlib, dnnlib.tflib, pickle, torch, collections
+        dnnlib.tflib.init_tf()
+
+        weights = pickle.load(open(pickle_path,'rb'))
+        weights_pt = [collections.OrderedDict([(k, torch.from_numpy(v.value().eval())) for k,v in w.trainables.items()]) for w in weights]
+        #torch.save(weights_pt, pytorch_name)
+
+        # then on the PyTorch side run
+        state_G, state_D, state_Gs = weights_pt #torch.load('./karras2019stylegan-ffhq-1024x1024.pt')
+        def key_translate(k):
+            k = k.lower().split('/')
+            if k[0] == 'g_synthesis':
+                if not k[1].startswith('torgb'):
+                    k.insert(1, 'blocks')
+                k = '.'.join(k)
+                k = (k.replace('const.const','const').replace('const.bias','bias').replace('const.stylemod','epi1.style_mod.lin')
+                    .replace('const.noise.weight','epi1.top_epi.noise.weight')
+                    .replace('conv.noise.weight','epi2.top_epi.noise.weight')
+                    .replace('conv.stylemod','epi2.style_mod.lin')
+                    .replace('conv0_up.noise.weight', 'epi1.top_epi.noise.weight')
+                    .replace('conv0_up.stylemod','epi1.style_mod.lin')
+                    .replace('conv1.noise.weight', 'epi2.top_epi.noise.weight')
+                    .replace('conv1.stylemod','epi2.style_mod.lin')
+                    .replace('torgb_lod0','torgb'))
+            else:
+                k = '.'.join(k)
+            return k
+
+        def weight_translate(k, w):
+            k = key_translate(k)
+            if k.endswith('.weight'):
+                if w.dim() == 2:
+                    w = w.t()
+                elif w.dim() == 1:
+                    pass
+                else:
+                    assert w.dim() == 4
+                    w = w.permute(3, 2, 0, 1)
+            return w
+
+        # we delete the useless torgb filters
+        param_dict = {key_translate(k) : weight_translate(k, v) for k,v in state_Gs.items() if 'torgb_lod' not in key_translate(k)}
+        if 1:
+            sd_shapes = {k : v.shape for k,v in self.state_dict().items()}
+            param_shapes = {k : v.shape for k,v in param_dict.items() }
+
+            for k in list(sd_shapes)+list(param_shapes):
+                pds = param_shapes.get(k)
+                sds = sd_shapes.get(k)
+                if pds is None:
+                    print ("sd only", k, sds)
+                elif sds is None:
+                    print ("pd only", k, pds)
+                elif sds != pds:
+                    print ("mismatch!", k, pds, sds)
+
+        self.load_state_dict(param_dict, strict=False) # needed for the blur kernels
+        torch.save(self.state_dict(), Path(pickle_path).with_suffix('.pt'))

models/stylegan/stylegan_tf/LICENSE.txt

@@ -0,0 +1,410 @@
+Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+
+
+Attribution-NonCommercial 4.0 International
+
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models/stylegan/stylegan_tf/README.md

@@ -0,0 +1,232 @@
+## StyleGAN — Official TensorFlow Implementation
+![Python 3.6](https://img.shields.io/badge/python-3.6-green.svg?style=plastic)
+![TensorFlow 1.10](https://img.shields.io/badge/tensorflow-1.10-green.svg?style=plastic)
+![cuDNN 7.3.1](https://img.shields.io/badge/cudnn-7.3.1-green.svg?style=plastic)
+![License CC BY-NC](https://img.shields.io/badge/license-CC_BY--NC-green.svg?style=plastic)
+
+![Teaser image](./stylegan-teaser.png)
+**Picture:** *These people are not real – they were produced by our generator that allows control over different aspects of the image.*
+
+This repository contains the official TensorFlow implementation of the following paper:
+
+> **A Style-Based Generator Architecture for Generative Adversarial Networks**<br>
+> Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA)<br>
+> https://arxiv.org/abs/1812.04948
+>
+> **Abstract:** *We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.*
+
+For business inquiries, please contact [[email protected]](mailto:[email protected])<br>
+For press and other inquiries, please contact Hector Marinez at [[email protected]](mailto:[email protected])<br>
+
+**&#9733;&#9733;&#9733; NEW: StyleGAN2 is available at [https://github.com/NVlabs/stylegan2](https://github.com/NVlabs/stylegan2) &#9733;&#9733;&#9733;**
+
+## Resources
+
+Material related to our paper is available via the following links:
+
+- Paper: https://arxiv.org/abs/1812.04948
+- Video: https://youtu.be/kSLJriaOumA
+- Code: https://github.com/NVlabs/stylegan
+- FFHQ: https://github.com/NVlabs/ffhq-dataset
+
+Additional material can be found on Google Drive:
+
+| Path | Description
+| :--- | :----------
+| [StyleGAN](https://drive.google.com/open?id=1uka3a1noXHAydRPRbknqwKVGODvnmUBX) | Main folder.
+| &boxvr;&nbsp; [stylegan-paper.pdf](https://drive.google.com/open?id=1v-HkF3Ehrpon7wVIx4r5DLcko_U_V6Lt) | High-quality version of the paper PDF.
+| &boxvr;&nbsp; [stylegan-video.mp4](https://drive.google.com/open?id=1uzwkZHQX_9pYg1i0d1Nbe3D9xPO8-qBf) | High-quality version of the result video.
+| &boxvr;&nbsp; [images](https://drive.google.com/open?id=1-l46akONUWF6LCpDoeq63H53rD7MeiTd) | Example images produced using our generator.
+| &boxv;&nbsp; &boxvr;&nbsp; [representative-images](https://drive.google.com/open?id=1ToY5P4Vvf5_c3TyUizQ8fckFFoFtBvD8) | High-quality images to be used in articles, blog posts, etc.
+| &boxv;&nbsp; &boxur;&nbsp; [100k-generated-images](https://drive.google.com/open?id=100DJ0QXyG89HZzB4w2Cbyf4xjNK54cQ1) | 100,000 generated images for different amounts of truncation.
+| &boxv;&nbsp; &ensp;&ensp; &boxvr;&nbsp; [ffhq-1024x1024](https://drive.google.com/open?id=14lm8VRN1pr4g_KVe6_LvyDX1PObst6d4) | Generated using Flickr-Faces-HQ dataset at 1024&times;1024.
+| &boxv;&nbsp; &ensp;&ensp; &boxvr;&nbsp; [bedrooms-256x256](https://drive.google.com/open?id=1Vxz9fksw4kgjiHrvHkX4Hze4dyThFW6t) | Generated using LSUN Bedroom dataset at 256&times;256.
+| &boxv;&nbsp; &ensp;&ensp; &boxvr;&nbsp; [cars-512x384](https://drive.google.com/open?id=1MFCvOMdLE2_mpeLPTiDw5dxc2CRuKkzS) | Generated using LSUN Car dataset at 512&times;384.
+| &boxv;&nbsp; &ensp;&ensp; &boxur;&nbsp; [cats-256x256](https://drive.google.com/open?id=1gq-Gj3GRFiyghTPKhp8uDMA9HV_0ZFWQ) | Generated using LSUN Cat dataset at 256&times;256.
+| &boxvr;&nbsp; [videos](https://drive.google.com/open?id=1N8pOd_Bf8v89NGUaROdbD8-ayLPgyRRo) | Example videos produced using our generator.
+| &boxv;&nbsp; &boxur;&nbsp; [high-quality-video-clips](https://drive.google.com/open?id=1NFO7_vH0t98J13ckJYFd7kuaTkyeRJ86) | Individual segments of the result video as high-quality MP4.
+| &boxvr;&nbsp; [ffhq-dataset](https://drive.google.com/open?id=1u2xu7bSrWxrbUxk-dT-UvEJq8IjdmNTP) | Raw data for the [Flickr-Faces-HQ dataset](https://github.com/NVlabs/ffhq-dataset).
+| &boxur;&nbsp; [networks](https://drive.google.com/open?id=1MASQyN5m0voPcx7-9K0r5gObhvvPups7) | Pre-trained networks as pickled instances of [dnnlib.tflib.Network](./dnnlib/tflib/network.py).
+| &ensp;&ensp; &boxvr;&nbsp; [stylegan-ffhq-1024x1024.pkl](https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ) | StyleGAN trained with Flickr-Faces-HQ dataset at 1024&times;1024.
+| &ensp;&ensp; &boxvr;&nbsp; [stylegan-celebahq-1024x1024.pkl](https://drive.google.com/uc?id=1MGqJl28pN4t7SAtSrPdSRJSQJqahkzUf) | StyleGAN trained with CelebA-HQ dataset at 1024&times;1024.
+| &ensp;&ensp; &boxvr;&nbsp; [stylegan-bedrooms-256x256.pkl](https://drive.google.com/uc?id=1MOSKeGF0FJcivpBI7s63V9YHloUTORiF) | StyleGAN trained with LSUN Bedroom dataset at 256&times;256.
+| &ensp;&ensp; &boxvr;&nbsp; [stylegan-cars-512x384.pkl](https://drive.google.com/uc?id=1MJ6iCfNtMIRicihwRorsM3b7mmtmK9c3) | StyleGAN trained with LSUN Car dataset at 512&times;384.
+| &ensp;&ensp; &boxvr;&nbsp; [stylegan-cats-256x256.pkl](https://drive.google.com/uc?id=1MQywl0FNt6lHu8E_EUqnRbviagS7fbiJ) | StyleGAN trained with LSUN Cat dataset at 256&times;256.
+| &ensp;&ensp; &boxur;&nbsp; [metrics](https://drive.google.com/open?id=1MvYdWCBuMfnoYGptRH-AgKLbPTsIQLhl) | Auxiliary networks for the quality and disentanglement metrics.
+| &ensp;&ensp; &ensp;&ensp; &boxvr;&nbsp; [inception_v3_features.pkl](https://drive.google.com/uc?id=1MzTY44rLToO5APn8TZmfR7_ENSe5aZUn) | Standard [Inception-v3](https://arxiv.org/abs/1512.00567) classifier that outputs a raw feature vector.
+| &ensp;&ensp; &ensp;&ensp; &boxvr;&nbsp; [vgg16_zhang_perceptual.pkl](https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2) | Standard [LPIPS](https://arxiv.org/abs/1801.03924) metric to estimate perceptual similarity.
+| &ensp;&ensp; &ensp;&ensp; &boxvr;&nbsp; [celebahq-classifier-00-male.pkl](https://drive.google.com/uc?id=1Q5-AI6TwWhCVM7Muu4tBM7rp5nG_gmCX) | Binary classifier trained to detect a single attribute of CelebA-HQ.
+| &ensp;&ensp; &ensp;&ensp; &boxur;&nbsp;&#x22ef; | Please see the file listing for remaining networks.
+
+## Licenses
+
+All material, excluding the Flickr-Faces-HQ dataset, is made available under [Creative Commons BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license by NVIDIA Corporation. You can **use, redistribute, and adapt** the material for **non-commercial purposes**, as long as you give appropriate credit by **citing our paper** and **indicating any changes** that you've made.
+
+For license information regarding the FFHQ dataset, please refer to the [Flickr-Faces-HQ repository](https://github.com/NVlabs/ffhq-dataset).
+
+`inception_v3_features.pkl` and `inception_v3_softmax.pkl` are derived from the pre-trained [Inception-v3](https://arxiv.org/abs/1512.00567) network by Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. The network was originally shared under [Apache 2.0](https://github.com/tensorflow/models/blob/master/LICENSE) license on the [TensorFlow Models](https://github.com/tensorflow/models) repository.
+
+`vgg16.pkl` and `vgg16_zhang_perceptual.pkl` are derived from the pre-trained [VGG-16](https://arxiv.org/abs/1409.1556) network by Karen Simonyan and Andrew Zisserman. The network was originally shared under [Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/) license on the [Very Deep Convolutional Networks for Large-Scale Visual Recognition](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) project page.
+
+`vgg16_zhang_perceptual.pkl` is further derived from the pre-trained [LPIPS](https://arxiv.org/abs/1801.03924) weights by Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The weights were originally shared under [BSD 2-Clause "Simplified" License](https://github.com/richzhang/PerceptualSimilarity/blob/master/LICENSE) on the [PerceptualSimilarity](https://github.com/richzhang/PerceptualSimilarity) repository.
+
+## System requirements
+
+* Both Linux and Windows are supported, but we strongly recommend Linux for performance and compatibility reasons.
+* 64-bit Python 3.6 installation. We recommend Anaconda3 with numpy 1.14.3 or newer.
+* TensorFlow 1.10.0 or newer with GPU support.
+* One or more high-end NVIDIA GPUs with at least 11GB of DRAM. We recommend NVIDIA DGX-1 with 8 Tesla V100 GPUs.
+* NVIDIA driver 391.35 or newer, CUDA toolkit 9.0 or newer, cuDNN 7.3.1 or newer.
+
+## Using pre-trained networks
+
+A minimal example of using a pre-trained StyleGAN generator is given in [pretrained_example.py](./pretrained_example.py). When executed, the script downloads a pre-trained StyleGAN generator from Google Drive and uses it to generate an image:
+
+```
+> python pretrained_example.py
+Downloading https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ .... done
+
+Gs                              Params    OutputShape          WeightShape
+---                             ---       ---                  ---
+latents_in                      -         (?, 512)             -
+...
+images_out                      -         (?, 3, 1024, 1024)   -
+---                             ---       ---                  ---
+Total                           26219627
+
+> ls results
+example.png # https://drive.google.com/uc?id=1UDLT_zb-rof9kKH0GwiJW_bS9MoZi8oP
+```
+
+A more advanced example is given in [generate_figures.py](./generate_figures.py). The script reproduces the figures from our paper in order to illustrate style mixing, noise inputs, and truncation:
+```
+> python generate_figures.py
+results/figure02-uncurated-ffhq.png     # https://drive.google.com/uc?id=1U3r1xgcD7o-Fd0SBRpq8PXYajm7_30cu
+results/figure03-style-mixing.png       # https://drive.google.com/uc?id=1U-nlMDtpnf1RcYkaFQtbh5oxnhA97hy6
+results/figure04-noise-detail.png       # https://drive.google.com/uc?id=1UX3m39u_DTU6eLnEW6MqGzbwPFt2R9cG
+results/figure05-noise-components.png   # https://drive.google.com/uc?id=1UQKPcvYVeWMRccGMbs2pPD9PVv1QDyp_
+results/figure08-truncation-trick.png   # https://drive.google.com/uc?id=1ULea0C12zGlxdDQFNLXOWZCHi3QNfk_v
+results/figure10-uncurated-bedrooms.png # https://drive.google.com/uc?id=1UEBnms1XMfj78OHj3_cx80mUf_m9DUJr
+results/figure11-uncurated-cars.png     # https://drive.google.com/uc?id=1UO-4JtAs64Kun5vIj10UXqAJ1d5Ir1Ke
+results/figure12-uncurated-cats.png     # https://drive.google.com/uc?id=1USnJc14prlu3QAYxstrtlfXC9sDWPA-W
+```
+
+The pre-trained networks are stored as standard pickle files on Google Drive:
+
+```
+# Load pre-trained network.
+url = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ' # karras2019stylegan-ffhq-1024x1024.pkl
+with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
+    _G, _D, Gs = pickle.load(f)
+    # _G = Instantaneous snapshot of the generator. Mainly useful for resuming a previous training run.
+    # _D = Instantaneous snapshot of the discriminator. Mainly useful for resuming a previous training run.
+    # Gs = Long-term average of the generator. Yields higher-quality results than the instantaneous snapshot.
+```
+
+The above code downloads the file and unpickles it to yield 3 instances of [dnnlib.tflib.Network](./dnnlib/tflib/network.py). To generate images, you will typically want to use `Gs` &ndash; the other two networks are provided for completeness. In order for `pickle.load()` to work, you will need to have the `dnnlib` source directory in your PYTHONPATH and a `tf.Session` set as default. The session can initialized by calling `dnnlib.tflib.init_tf()`.
+
+There are three ways to use the pre-trained generator:
+
+1. Use `Gs.run()` for immediate-mode operation where the inputs and outputs are numpy arrays:
+   ```
+   # Pick latent vector.
+   rnd = np.random.RandomState(5)
+   latents = rnd.randn(1, Gs.input_shape[1])
+
+   # Generate image.
+   fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
+   images = Gs.run(latents, None, truncation_psi=0.7, randomize_noise=True, output_transform=fmt)
+   ```
+   The first argument is a batch of latent vectors of shape `[num, 512]`. The second argument is reserved for class labels (not used by StyleGAN). The remaining keyword arguments are optional and can be used to further modify the operation (see below). The output is a batch of images, whose format is dictated by the `output_transform` argument.
+
+2. Use `Gs.get_output_for()` to incorporate the generator as a part of a larger TensorFlow expression:
+   ```
+   latents = tf.random_normal([self.minibatch_per_gpu] + Gs_clone.input_shape[1:])
+   images = Gs_clone.get_output_for(latents, None, is_validation=True, randomize_noise=True)
+   images = tflib.convert_images_to_uint8(images)
+   result_expr.append(inception_clone.get_output_for(images))
+   ```
+   The above code is from [metrics/frechet_inception_distance.py](./metrics/frechet_inception_distance.py). It generates a batch of random images and feeds them directly to the [Inception-v3](https://arxiv.org/abs/1512.00567) network without having to convert the data to numpy arrays in between.
+
+3. Look up `Gs.components.mapping` and `Gs.components.synthesis` to access individual sub-networks of the generator. Similar to `Gs`, the sub-networks are represented as independent instances of [dnnlib.tflib.Network](./dnnlib/tflib/network.py):
+   ```
+   src_latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in src_seeds)
+   src_dlatents = Gs.components.mapping.run(src_latents, None) # [seed, layer, component]
+   src_images = Gs.components.synthesis.run(src_dlatents, randomize_noise=False, **synthesis_kwargs)
+   ```
+   The above code is from [generate_figures.py](./generate_figures.py). It first transforms a batch of latent vectors into the intermediate *W* space using the mapping network and then turns these vectors into a batch of images using the synthesis network. The `dlatents` array stores a separate copy of the same *w* vector for each layer of the synthesis network to facilitate style mixing.
+
+The exact details of the generator are defined in [training/networks_stylegan.py](./training/networks_stylegan.py) (see `G_style`, `G_mapping`, and `G_synthesis`). The following keyword arguments can be specified to modify the behavior when calling `run()` and `get_output_for()`:
+
+* `truncation_psi` and `truncation_cutoff` control the truncation trick that that is performed by default when using `Gs` (&psi;=0.7, cutoff=8). It can be disabled by setting `truncation_psi=1` or `is_validation=True`, and the image quality can be further improved at the cost of variation by setting e.g. `truncation_psi=0.5`. Note that truncation is always disabled when using the sub-networks directly. The average *w* needed to manually perform the truncation trick can be looked up using `Gs.get_var('dlatent_avg')`.
+
+* `randomize_noise` determines whether to use re-randomize the noise inputs for each generated image (`True`, default) or whether to use specific noise values for the entire minibatch (`False`). The specific values can be accessed via the `tf.Variable` instances that are found using `[var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]`.
+
+* When using the mapping network directly, you can specify `dlatent_broadcast=None` to disable the automatic duplication of `dlatents` over the layers of the synthesis network.
+
+* Runtime performance can be fine-tuned via `structure='fixed'` and `dtype='float16'`. The former disables support for progressive growing, which is not needed for a fully-trained generator, and the latter performs all computation using half-precision floating point arithmetic.
+
+## Preparing datasets for training
+
+The training and evaluation scripts operate on datasets stored as multi-resolution TFRecords. Each dataset is represented by a directory containing the same image data in several resolutions to enable efficient streaming. There is a separate *.tfrecords file for each resolution, and if the dataset contains labels, they are stored in a separate file as well. By default, the scripts expect to find the datasets at `datasets/<NAME>/<NAME>-<RESOLUTION>.tfrecords`. The directory can be changed by editing [config.py](./config.py):
+
+```
+result_dir = 'results'
+data_dir = 'datasets'
+cache_dir = 'cache'
+```
+
+To obtain the FFHQ dataset (`datasets/ffhq`), please refer to the [Flickr-Faces-HQ repository](https://github.com/NVlabs/ffhq-dataset).
+
+To obtain the CelebA-HQ dataset (`datasets/celebahq`), please refer to the [Progressive GAN repository](https://github.com/tkarras/progressive_growing_of_gans).
+
+To obtain other datasets, including LSUN, please consult their corresponding project pages. The datasets can be converted to multi-resolution TFRecords using the provided [dataset_tool.py](./dataset_tool.py):
+
+```
+> python dataset_tool.py create_lsun datasets/lsun-bedroom-full ~/lsun/bedroom_lmdb --resolution 256
+> python dataset_tool.py create_lsun_wide datasets/lsun-car-512x384 ~/lsun/car_lmdb --width 512 --height 384
+> python dataset_tool.py create_lsun datasets/lsun-cat-full ~/lsun/cat_lmdb --resolution 256
+> python dataset_tool.py create_cifar10 datasets/cifar10 ~/cifar10
+> python dataset_tool.py create_from_images datasets/custom-dataset ~/custom-images
+```
+
+## Training networks
+
+Once the datasets are set up, you can train your own StyleGAN networks as follows:
+
+1. Edit [train.py](./train.py) to specify the dataset and training configuration by uncommenting or editing specific lines.
+2. Run the training script with `python train.py`.
+3. The results are written to a newly created directory `results/<ID>-<DESCRIPTION>`.
+4. The training may take several days (or weeks) to complete, depending on the configuration.
+
+By default, `train.py` is configured to train the highest-quality StyleGAN (configuration F in Table 1) for the FFHQ dataset at 1024&times;1024 resolution using 8 GPUs. Please note that we have used 8 GPUs in all of our experiments. Training with fewer GPUs may not produce identical results &ndash; if you wish to compare against our technique, we strongly recommend using the same number of GPUs.
+
+Expected training times for the default configuration using Tesla V100 GPUs:
+
+| GPUs | 1024&times;1024  | 512&times;512    | 256&times;256    |
+| :--- | :--------------  | :------------    | :------------    |
+| 1    | 41 days 4 hours  | 24 days 21 hours | 14 days 22 hours |
+| 2    | 21 days 22 hours | 13 days 7 hours  | 9 days 5 hours   |
+| 4    | 11 days 8 hours  | 7 days 0 hours   | 4 days 21 hours  |
+| 8    | 6 days 14 hours  | 4 days 10 hours  | 3 days 8 hours   |
+
+## Evaluating quality and disentanglement
+
+The quality and disentanglement metrics used in our paper can be evaluated using [run_metrics.py](./run_metrics.py). By default, the script will evaluate the Fr&eacute;chet Inception Distance (`fid50k`) for the pre-trained FFHQ generator and write the results into a newly created directory under `results`. The exact behavior can be changed by uncommenting or editing specific lines in [run_metrics.py](./run_metrics.py).
+
+Expected evaluation time and results for the pre-trained FFHQ generator using one Tesla V100 GPU:
+
+| Metric    | Time      | Result   | Description
+| :-----    | :---      | :-----   | :----------
+| fid50k    | 16 min    | 4.4159   | Fr&eacute;chet Inception Distance using 50,000 images.
+| ppl_zfull | 55 min    | 664.8854 | Perceptual Path Length for full paths in *Z*.
+| ppl_wfull | 55 min    | 233.3059 | Perceptual Path Length for full paths in *W*.
+| ppl_zend  | 55 min    | 666.1057 | Perceptual Path Length for path endpoints in *Z*.
+| ppl_wend  | 55 min    | 197.2266 | Perceptual Path Length for path endpoints in *W*.
+| ls        | 10 hours  | z: 165.0106<br>w: 3.7447 | Linear Separability in *Z* and *W*.
+
+Please note that the exact results may vary from run to run due to the non-deterministic nature of TensorFlow.
+
+## Acknowledgements
+
+We thank Jaakko Lehtinen, David Luebke, and Tuomas Kynk&auml;&auml;nniemi for in-depth discussions and helpful comments; Janne Hellsten, Tero Kuosmanen, and Pekka J&auml;nis for compute infrastructure and help with the code release.

models/stylegan/stylegan_tf/config.py

@@ -0,0 +1,18 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Global configuration."""
+
+#----------------------------------------------------------------------------
+# Paths.
+
+result_dir = 'results'
+data_dir = 'datasets'
+cache_dir = 'cache'
+run_dir_ignore = ['results', 'datasets', 'cache']
+
+#----------------------------------------------------------------------------

models/stylegan/stylegan_tf/dataset_tool.py

@@ -0,0 +1,645 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Tool for creating multi-resolution TFRecords datasets for StyleGAN and ProGAN."""
+
+# pylint: disable=too-many-lines
+import os
+import sys
+import glob
+import argparse
+import threading
+import six.moves.queue as Queue # pylint: disable=import-error
+import traceback
+import numpy as np
+import tensorflow as tf
+import PIL.Image
+import dnnlib.tflib as tflib
+
+from training import dataset
+
+#----------------------------------------------------------------------------
+
+def error(msg):
+    print('Error: ' + msg)
+    exit(1)
+
+#----------------------------------------------------------------------------
+
+class TFRecordExporter:
+    def __init__(self, tfrecord_dir, expected_images, print_progress=True, progress_interval=10):
+        self.tfrecord_dir       = tfrecord_dir
+        self.tfr_prefix         = os.path.join(self.tfrecord_dir, os.path.basename(self.tfrecord_dir))
+        self.expected_images    = expected_images
+        self.cur_images         = 0
+        self.shape              = None
+        self.resolution_log2    = None
+        self.tfr_writers        = []
+        self.print_progress     = print_progress
+        self.progress_interval  = progress_interval
+
+        if self.print_progress:
+            print('Creating dataset "%s"' % tfrecord_dir)
+        if not os.path.isdir(self.tfrecord_dir):
+            os.makedirs(self.tfrecord_dir)
+        assert os.path.isdir(self.tfrecord_dir)
+
+    def close(self):
+        if self.print_progress:
+            print('%-40s\r' % 'Flushing data...', end='', flush=True)
+        for tfr_writer in self.tfr_writers:
+            tfr_writer.close()
+        self.tfr_writers = []
+        if self.print_progress:
+            print('%-40s\r' % '', end='', flush=True)
+            print('Added %d images.' % self.cur_images)
+
+    def choose_shuffled_order(self): # Note: Images and labels must be added in shuffled order.
+        order = np.arange(self.expected_images)
+        np.random.RandomState(123).shuffle(order)
+        return order
+
+    def add_image(self, img):
+        if self.print_progress and self.cur_images % self.progress_interval == 0:
+            print('%d / %d\r' % (self.cur_images, self.expected_images), end='', flush=True)
+        if self.shape is None:
+            self.shape = img.shape
+            self.resolution_log2 = int(np.log2(self.shape[1]))
+            assert self.shape[0] in [1, 3]
+            assert self.shape[1] == self.shape[2]
+            assert self.shape[1] == 2**self.resolution_log2
+            tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)
+            for lod in range(self.resolution_log2 - 1):
+                tfr_file = self.tfr_prefix + '-r%02d.tfrecords' % (self.resolution_log2 - lod)
+                self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt))
+        assert img.shape == self.shape
+        for lod, tfr_writer in enumerate(self.tfr_writers):
+            if lod:
+                img = img.astype(np.float32)
+                img = (img[:, 0::2, 0::2] + img[:, 0::2, 1::2] + img[:, 1::2, 0::2] + img[:, 1::2, 1::2]) * 0.25
+            quant = np.rint(img).clip(0, 255).astype(np.uint8)
+            ex = tf.train.Example(features=tf.train.Features(feature={
+                'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=quant.shape)),
+                'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[quant.tostring()]))}))
+            tfr_writer.write(ex.SerializeToString())
+        self.cur_images += 1
+
+    def add_labels(self, labels):
+        if self.print_progress:
+            print('%-40s\r' % 'Saving labels...', end='', flush=True)
+        assert labels.shape[0] == self.cur_images
+        with open(self.tfr_prefix + '-rxx.labels', 'wb') as f:
+            np.save(f, labels.astype(np.float32))
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        self.close()
+
+#----------------------------------------------------------------------------
+
+class ExceptionInfo(object):
+    def __init__(self):
+        self.value = sys.exc_info()[1]
+        self.traceback = traceback.format_exc()
+
+#----------------------------------------------------------------------------
+
+class WorkerThread(threading.Thread):
+    def __init__(self, task_queue):
+        threading.Thread.__init__(self)
+        self.task_queue = task_queue
+
+    def run(self):
+        while True:
+            func, args, result_queue = self.task_queue.get()
+            if func is None:
+                break
+            try:
+                result = func(*args)
+            except:
+                result = ExceptionInfo()
+            result_queue.put((result, args))
+
+#----------------------------------------------------------------------------
+
+class ThreadPool(object):
+    def __init__(self, num_threads):
+        assert num_threads >= 1
+        self.task_queue = Queue.Queue()
+        self.result_queues = dict()
+        self.num_threads = num_threads
+        for _idx in range(self.num_threads):
+            thread = WorkerThread(self.task_queue)
+            thread.daemon = True
+            thread.start()
+
+    def add_task(self, func, args=()):
+        assert hasattr(func, '__call__') # must be a function
+        if func not in self.result_queues:
+            self.result_queues[func] = Queue.Queue()
+        self.task_queue.put((func, args, self.result_queues[func]))
+
+    def get_result(self, func): # returns (result, args)
+        result, args = self.result_queues[func].get()
+        if isinstance(result, ExceptionInfo):
+            print('\n\nWorker thread caught an exception:\n' + result.traceback)
+            raise result.value
+        return result, args
+
+    def finish(self):
+        for _idx in range(self.num_threads):
+            self.task_queue.put((None, (), None))
+
+    def __enter__(self): # for 'with' statement
+        return self
+
+    def __exit__(self, *excinfo):
+        self.finish()
+
+    def process_items_concurrently(self, item_iterator, process_func=lambda x: x, pre_func=lambda x: x, post_func=lambda x: x, max_items_in_flight=None):
+        if max_items_in_flight is None: max_items_in_flight = self.num_threads * 4
+        assert max_items_in_flight >= 1
+        results = []
+        retire_idx = [0]
+
+        def task_func(prepared, _idx):
+            return process_func(prepared)
+
+        def retire_result():
+            processed, (_prepared, idx) = self.get_result(task_func)
+            results[idx] = processed
+            while retire_idx[0] < len(results) and results[retire_idx[0]] is not None:
+                yield post_func(results[retire_idx[0]])
+                results[retire_idx[0]] = None
+                retire_idx[0] += 1
+
+        for idx, item in enumerate(item_iterator):
+            prepared = pre_func(item)
+            results.append(None)
+            self.add_task(func=task_func, args=(prepared, idx))
+            while retire_idx[0] < idx - max_items_in_flight + 2:
+                for res in retire_result(): yield res
+        while retire_idx[0] < len(results):
+            for res in retire_result(): yield res
+
+#----------------------------------------------------------------------------
+
+def display(tfrecord_dir):
+    print('Loading dataset "%s"' % tfrecord_dir)
+    tflib.init_tf({'gpu_options.allow_growth': True})
+    dset = dataset.TFRecordDataset(tfrecord_dir, max_label_size='full', repeat=False, shuffle_mb=0)
+    tflib.init_uninitialized_vars()
+    import cv2  # pip install opencv-python
+
+    idx = 0
+    while True:
+        try:
+            images, labels = dset.get_minibatch_np(1)
+        except tf.errors.OutOfRangeError:
+            break
+        if idx == 0:
+            print('Displaying images')
+            cv2.namedWindow('dataset_tool')
+            print('Press SPACE or ENTER to advance, ESC to exit')
+        print('\nidx = %-8d\nlabel = %s' % (idx, labels[0].tolist()))
+        cv2.imshow('dataset_tool', images[0].transpose(1, 2, 0)[:, :, ::-1]) # CHW => HWC, RGB => BGR
+        idx += 1
+        if cv2.waitKey() == 27:
+            break
+    print('\nDisplayed %d images.' % idx)
+
+#----------------------------------------------------------------------------
+
+def extract(tfrecord_dir, output_dir):
+    print('Loading dataset "%s"' % tfrecord_dir)
+    tflib.init_tf({'gpu_options.allow_growth': True})
+    dset = dataset.TFRecordDataset(tfrecord_dir, max_label_size=0, repeat=False, shuffle_mb=0)
+    tflib.init_uninitialized_vars()
+
+    print('Extracting images to "%s"' % output_dir)
+    if not os.path.isdir(output_dir):
+        os.makedirs(output_dir)
+    idx = 0
+    while True:
+        if idx % 10 == 0:
+            print('%d\r' % idx, end='', flush=True)
+        try:
+            images, _labels = dset.get_minibatch_np(1)
+        except tf.errors.OutOfRangeError:
+            break
+        if images.shape[1] == 1:
+            img = PIL.Image.fromarray(images[0][0], 'L')
+        else:
+            img = PIL.Image.fromarray(images[0].transpose(1, 2, 0), 'RGB')
+        img.save(os.path.join(output_dir, 'img%08d.png' % idx))
+        idx += 1
+    print('Extracted %d images.' % idx)
+
+#----------------------------------------------------------------------------
+
+def compare(tfrecord_dir_a, tfrecord_dir_b, ignore_labels):
+    max_label_size = 0 if ignore_labels else 'full'
+    print('Loading dataset "%s"' % tfrecord_dir_a)
+    tflib.init_tf({'gpu_options.allow_growth': True})
+    dset_a = dataset.TFRecordDataset(tfrecord_dir_a, max_label_size=max_label_size, repeat=False, shuffle_mb=0)
+    print('Loading dataset "%s"' % tfrecord_dir_b)
+    dset_b = dataset.TFRecordDataset(tfrecord_dir_b, max_label_size=max_label_size, repeat=False, shuffle_mb=0)
+    tflib.init_uninitialized_vars()
+
+    print('Comparing datasets')
+    idx = 0
+    identical_images = 0
+    identical_labels = 0
+    while True:
+        if idx % 100 == 0:
+            print('%d\r' % idx, end='', flush=True)
+        try:
+            images_a, labels_a = dset_a.get_minibatch_np(1)
+        except tf.errors.OutOfRangeError:
+            images_a, labels_a = None, None
+        try:
+            images_b, labels_b = dset_b.get_minibatch_np(1)
+        except tf.errors.OutOfRangeError:
+            images_b, labels_b = None, None
+        if images_a is None or images_b is None:
+            if images_a is not None or images_b is not None:
+                print('Datasets contain different number of images')
+            break
+        if images_a.shape == images_b.shape and np.all(images_a == images_b):
+            identical_images += 1
+        else:
+            print('Image %d is different' % idx)
+        if labels_a.shape == labels_b.shape and np.all(labels_a == labels_b):
+            identical_labels += 1
+        else:
+            print('Label %d is different' % idx)
+        idx += 1
+    print('Identical images: %d / %d' % (identical_images, idx))
+    if not ignore_labels:
+        print('Identical labels: %d / %d' % (identical_labels, idx))
+
+#----------------------------------------------------------------------------
+
+def create_mnist(tfrecord_dir, mnist_dir):
+    print('Loading MNIST from "%s"' % mnist_dir)
+    import gzip
+    with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
+        images = np.frombuffer(file.read(), np.uint8, offset=16)
+    with gzip.open(os.path.join(mnist_dir, 'train-labels-idx1-ubyte.gz'), 'rb') as file:
+        labels = np.frombuffer(file.read(), np.uint8, offset=8)
+    images = images.reshape(-1, 1, 28, 28)
+    images = np.pad(images, [(0,0), (0,0), (2,2), (2,2)], 'constant', constant_values=0)
+    assert images.shape == (60000, 1, 32, 32) and images.dtype == np.uint8
+    assert labels.shape == (60000,) and labels.dtype == np.uint8
+    assert np.min(images) == 0 and np.max(images) == 255
+    assert np.min(labels) == 0 and np.max(labels) == 9
+    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
+    onehot[np.arange(labels.size), labels] = 1.0
+
+    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
+        order = tfr.choose_shuffled_order()
+        for idx in range(order.size):
+            tfr.add_image(images[order[idx]])
+        tfr.add_labels(onehot[order])
+
+#----------------------------------------------------------------------------
+
+def create_mnistrgb(tfrecord_dir, mnist_dir, num_images=1000000, random_seed=123):
+    print('Loading MNIST from "%s"' % mnist_dir)
+    import gzip
+    with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
+        images = np.frombuffer(file.read(), np.uint8, offset=16)
+    images = images.reshape(-1, 28, 28)
+    images = np.pad(images, [(0,0), (2,2), (2,2)], 'constant', constant_values=0)
+    assert images.shape == (60000, 32, 32) and images.dtype == np.uint8
+    assert np.min(images) == 0 and np.max(images) == 255
+
+    with TFRecordExporter(tfrecord_dir, num_images) as tfr:
+        rnd = np.random.RandomState(random_seed)
+        for _idx in range(num_images):
+            tfr.add_image(images[rnd.randint(images.shape[0], size=3)])
+
+#----------------------------------------------------------------------------
+
+def create_cifar10(tfrecord_dir, cifar10_dir):
+    print('Loading CIFAR-10 from "%s"' % cifar10_dir)
+    import pickle
+    images = []
+    labels = []
+    for batch in range(1, 6):
+        with open(os.path.join(cifar10_dir, 'data_batch_%d' % batch), 'rb') as file:
+            data = pickle.load(file, encoding='latin1')
+        images.append(data['data'].reshape(-1, 3, 32, 32))
+        labels.append(data['labels'])
+    images = np.concatenate(images)
+    labels = np.concatenate(labels)
+    assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
+    assert labels.shape == (50000,) and labels.dtype == np.int32
+    assert np.min(images) == 0 and np.max(images) == 255
+    assert np.min(labels) == 0 and np.max(labels) == 9
+    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
+    onehot[np.arange(labels.size), labels] = 1.0
+
+    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
+        order = tfr.choose_shuffled_order()
+        for idx in range(order.size):
+            tfr.add_image(images[order[idx]])
+        tfr.add_labels(onehot[order])
+
+#----------------------------------------------------------------------------
+
+def create_cifar100(tfrecord_dir, cifar100_dir):
+    print('Loading CIFAR-100 from "%s"' % cifar100_dir)
+    import pickle
+    with open(os.path.join(cifar100_dir, 'train'), 'rb') as file:
+        data = pickle.load(file, encoding='latin1')
+    images = data['data'].reshape(-1, 3, 32, 32)
+    labels = np.array(data['fine_labels'])
+    assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
+    assert labels.shape == (50000,) and labels.dtype == np.int32
+    assert np.min(images) == 0 and np.max(images) == 255
+    assert np.min(labels) == 0 and np.max(labels) == 99
+    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
+    onehot[np.arange(labels.size), labels] = 1.0
+
+    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
+        order = tfr.choose_shuffled_order()
+        for idx in range(order.size):
+            tfr.add_image(images[order[idx]])
+        tfr.add_labels(onehot[order])
+
+#----------------------------------------------------------------------------
+
+def create_svhn(tfrecord_dir, svhn_dir):
+    print('Loading SVHN from "%s"' % svhn_dir)
+    import pickle
+    images = []
+    labels = []
+    for batch in range(1, 4):
+        with open(os.path.join(svhn_dir, 'train_%d.pkl' % batch), 'rb') as file:
+            data = pickle.load(file, encoding='latin1')
+        images.append(data[0])
+        labels.append(data[1])
+    images = np.concatenate(images)
+    labels = np.concatenate(labels)
+    assert images.shape == (73257, 3, 32, 32) and images.dtype == np.uint8
+    assert labels.shape == (73257,) and labels.dtype == np.uint8
+    assert np.min(images) == 0 and np.max(images) == 255
+    assert np.min(labels) == 0 and np.max(labels) == 9
+    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
+    onehot[np.arange(labels.size), labels] = 1.0
+
+    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
+        order = tfr.choose_shuffled_order()
+        for idx in range(order.size):
+            tfr.add_image(images[order[idx]])
+        tfr.add_labels(onehot[order])
+
+#----------------------------------------------------------------------------
+
+def create_lsun(tfrecord_dir, lmdb_dir, resolution=256, max_images=None):
+    print('Loading LSUN dataset from "%s"' % lmdb_dir)
+    import lmdb # pip install lmdb # pylint: disable=import-error
+    import cv2 # pip install opencv-python
+    import io
+    with lmdb.open(lmdb_dir, readonly=True).begin(write=False) as txn:
+        total_images = txn.stat()['entries'] # pylint: disable=no-value-for-parameter
+        if max_images is None:
+            max_images = total_images
+        with TFRecordExporter(tfrecord_dir, max_images) as tfr:
+            for _idx, (_key, value) in enumerate(txn.cursor()):
+                try:
+                    try:
+                        img = cv2.imdecode(np.fromstring(value, dtype=np.uint8), 1)
+                        if img is None:
+                            raise IOError('cv2.imdecode failed')
+                        img = img[:, :, ::-1] # BGR => RGB
+                    except IOError:
+                        img = np.asarray(PIL.Image.open(io.BytesIO(value)))
+                    crop = np.min(img.shape[:2])
+                    img = img[(img.shape[0] - crop) // 2 : (img.shape[0] + crop) // 2, (img.shape[1] - crop) // 2 : (img.shape[1] + crop) // 2]
+                    img = PIL.Image.fromarray(img, 'RGB')
+                    img = img.resize((resolution, resolution), PIL.Image.ANTIALIAS)
+                    img = np.asarray(img)
+                    img = img.transpose([2, 0, 1]) # HWC => CHW
+                    tfr.add_image(img)
+                except:
+                    print(sys.exc_info()[1])
+                if tfr.cur_images == max_images:
+                    break
+
+#----------------------------------------------------------------------------
+
+def create_lsun_wide(tfrecord_dir, lmdb_dir, width=512, height=384, max_images=None):
+    assert width == 2 ** int(np.round(np.log2(width)))
+    assert height <= width
+    print('Loading LSUN dataset from "%s"' % lmdb_dir)
+    import lmdb # pip install lmdb # pylint: disable=import-error
+    import cv2 # pip install opencv-python
+    import io
+    with lmdb.open(lmdb_dir, readonly=True).begin(write=False) as txn:
+        total_images = txn.stat()['entries'] # pylint: disable=no-value-for-parameter
+        if max_images is None:
+            max_images = total_images
+        with TFRecordExporter(tfrecord_dir, max_images, print_progress=False) as tfr:
+            for idx, (_key, value) in enumerate(txn.cursor()):
+                try:
+                    try:
+                        img = cv2.imdecode(np.fromstring(value, dtype=np.uint8), 1)
+                        if img is None:
+                            raise IOError('cv2.imdecode failed')
+                        img = img[:, :, ::-1] # BGR => RGB
+                    except IOError:
+                        img = np.asarray(PIL.Image.open(io.BytesIO(value)))
+
+                    ch = int(np.round(width * img.shape[0] / img.shape[1]))
+                    if img.shape[1] < width or ch < height:
+                        continue
+
+                    img = img[(img.shape[0] - ch) // 2 : (img.shape[0] + ch) // 2]
+                    img = PIL.Image.fromarray(img, 'RGB')
+                    img = img.resize((width, height), PIL.Image.ANTIALIAS)
+                    img = np.asarray(img)
+                    img = img.transpose([2, 0, 1]) # HWC => CHW
+
+                    canvas = np.zeros([3, width, width], dtype=np.uint8)
+                    canvas[:, (width - height) // 2 : (width + height) // 2] = img
+                    tfr.add_image(canvas)
+                    print('\r%d / %d => %d ' % (idx + 1, total_images, tfr.cur_images), end='')
+
+                except:
+                    print(sys.exc_info()[1])
+                if tfr.cur_images == max_images:
+                    break
+    print()
+
+#----------------------------------------------------------------------------
+
+def create_celeba(tfrecord_dir, celeba_dir, cx=89, cy=121):
+    print('Loading CelebA from "%s"' % celeba_dir)
+    glob_pattern = os.path.join(celeba_dir, 'img_align_celeba_png', '*.png')
+    image_filenames = sorted(glob.glob(glob_pattern))
+    expected_images = 202599
+    if len(image_filenames) != expected_images:
+        error('Expected to find %d images' % expected_images)
+
+    with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
+        order = tfr.choose_shuffled_order()
+        for idx in range(order.size):
+            img = np.asarray(PIL.Image.open(image_filenames[order[idx]]))
+            assert img.shape == (218, 178, 3)
+            img = img[cy - 64 : cy + 64, cx - 64 : cx + 64]
+            img = img.transpose(2, 0, 1) # HWC => CHW
+            tfr.add_image(img)
+
+#----------------------------------------------------------------------------
+
+def create_from_images(tfrecord_dir, image_dir, shuffle):
+    print('Loading images from "%s"' % image_dir)
+    image_filenames = sorted(glob.glob(os.path.join(image_dir, '*')))
+    if len(image_filenames) == 0:
+        error('No input images found')
+
+    img = np.asarray(PIL.Image.open(image_filenames[0]))
+    resolution = img.shape[0]
+    channels = img.shape[2] if img.ndim == 3 else 1
+    if img.shape[1] != resolution:
+        error('Input images must have the same width and height')
+    if resolution != 2 ** int(np.floor(np.log2(resolution))):
+        error('Input image resolution must be a power-of-two')
+    if channels not in [1, 3]:
+        error('Input images must be stored as RGB or grayscale')
+
+    with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
+        order = tfr.choose_shuffled_order() if shuffle else np.arange(len(image_filenames))
+        for idx in range(order.size):
+            img = np.asarray(PIL.Image.open(image_filenames[order[idx]]))
+            if channels == 1:
+                img = img[np.newaxis, :, :] # HW => CHW
+            else:
+                img = img.transpose([2, 0, 1]) # HWC => CHW
+            tfr.add_image(img)
+
+#----------------------------------------------------------------------------
+
+def create_from_hdf5(tfrecord_dir, hdf5_filename, shuffle):
+    print('Loading HDF5 archive from "%s"' % hdf5_filename)
+    import h5py # conda install h5py
+    with h5py.File(hdf5_filename, 'r') as hdf5_file:
+        hdf5_data = max([value for key, value in hdf5_file.items() if key.startswith('data')], key=lambda lod: lod.shape[3])
+        with TFRecordExporter(tfrecord_dir, hdf5_data.shape[0]) as tfr:
+            order = tfr.choose_shuffled_order() if shuffle else np.arange(hdf5_data.shape[0])
+            for idx in range(order.size):
+                tfr.add_image(hdf5_data[order[idx]])
+            npy_filename = os.path.splitext(hdf5_filename)[0] + '-labels.npy'
+            if os.path.isfile(npy_filename):
+                tfr.add_labels(np.load(npy_filename)[order])
+
+#----------------------------------------------------------------------------
+
+def execute_cmdline(argv):
+    prog = argv[0]
+    parser = argparse.ArgumentParser(
+        prog        = prog,
+        description = 'Tool for creating multi-resolution TFRecords datasets for StyleGAN and ProGAN.',
+        epilog      = 'Type "%s <command> -h" for more information.' % prog)
+
+    subparsers = parser.add_subparsers(dest='command')
+    subparsers.required = True
+    def add_command(cmd, desc, example=None):
+        epilog = 'Example: %s %s' % (prog, example) if example is not None else None
+        return subparsers.add_parser(cmd, description=desc, help=desc, epilog=epilog)
+
+    p = add_command(    'display',          'Display images in dataset.',
+                                            'display datasets/mnist')
+    p.add_argument(     'tfrecord_dir',     help='Directory containing dataset')
+
+    p = add_command(    'extract',          'Extract images from dataset.',
+                                            'extract datasets/mnist mnist-images')
+    p.add_argument(     'tfrecord_dir',     help='Directory containing dataset')
+    p.add_argument(     'output_dir',       help='Directory to extract the images into')
+
+    p = add_command(    'compare',          'Compare two datasets.',
+                                            'compare datasets/mydataset datasets/mnist')
+    p.add_argument(     'tfrecord_dir_a',   help='Directory containing first dataset')
+    p.add_argument(     'tfrecord_dir_b',   help='Directory containing second dataset')
+    p.add_argument(     '--ignore_labels',  help='Ignore labels (default: 0)', type=int, default=0)
+
+    p = add_command(    'create_mnist',     'Create dataset for MNIST.',
+                                            'create_mnist datasets/mnist ~/downloads/mnist')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'mnist_dir',        help='Directory containing MNIST')
+
+    p = add_command(    'create_mnistrgb',  'Create dataset for MNIST-RGB.',
+                                            'create_mnistrgb datasets/mnistrgb ~/downloads/mnist')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'mnist_dir',        help='Directory containing MNIST')
+    p.add_argument(     '--num_images',     help='Number of composite images to create (default: 1000000)', type=int, default=1000000)
+    p.add_argument(     '--random_seed',    help='Random seed (default: 123)', type=int, default=123)
+
+    p = add_command(    'create_cifar10',   'Create dataset for CIFAR-10.',
+                                            'create_cifar10 datasets/cifar10 ~/downloads/cifar10')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'cifar10_dir',      help='Directory containing CIFAR-10')
+
+    p = add_command(    'create_cifar100',  'Create dataset for CIFAR-100.',
+                                            'create_cifar100 datasets/cifar100 ~/downloads/cifar100')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'cifar100_dir',     help='Directory containing CIFAR-100')
+
+    p = add_command(    'create_svhn',      'Create dataset for SVHN.',
+                                            'create_svhn datasets/svhn ~/downloads/svhn')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'svhn_dir',         help='Directory containing SVHN')
+
+    p = add_command(    'create_lsun',      'Create dataset for single LSUN category.',
+                                            'create_lsun datasets/lsun-car-100k ~/downloads/lsun/car_lmdb --resolution 256 --max_images 100000')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'lmdb_dir',         help='Directory containing LMDB database')
+    p.add_argument(     '--resolution',     help='Output resolution (default: 256)', type=int, default=256)
+    p.add_argument(     '--max_images',     help='Maximum number of images (default: none)', type=int, default=None)
+
+    p = add_command(    'create_lsun_wide', 'Create LSUN dataset with non-square aspect ratio.',
+                                            'create_lsun_wide datasets/lsun-car-512x384 ~/downloads/lsun/car_lmdb --width 512 --height 384')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'lmdb_dir',         help='Directory containing LMDB database')
+    p.add_argument(     '--width',          help='Output width (default: 512)', type=int, default=512)
+    p.add_argument(     '--height',         help='Output height (default: 384)', type=int, default=384)
+    p.add_argument(     '--max_images',     help='Maximum number of images (default: none)', type=int, default=None)
+
+    p = add_command(    'create_celeba',    'Create dataset for CelebA.',
+                                            'create_celeba datasets/celeba ~/downloads/celeba')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'celeba_dir',       help='Directory containing CelebA')
+    p.add_argument(     '--cx',             help='Center X coordinate (default: 89)', type=int, default=89)
+    p.add_argument(     '--cy',             help='Center Y coordinate (default: 121)', type=int, default=121)
+
+    p = add_command(    'create_from_images', 'Create dataset from a directory full of images.',
+                                            'create_from_images datasets/mydataset myimagedir')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'image_dir',        help='Directory containing the images')
+    p.add_argument(     '--shuffle',        help='Randomize image order (default: 1)', type=int, default=1)
+
+    p = add_command(    'create_from_hdf5', 'Create dataset from legacy HDF5 archive.',
+                                            'create_from_hdf5 datasets/celebahq ~/downloads/celeba-hq-1024x1024.h5')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'hdf5_filename',    help='HDF5 archive containing the images')
+    p.add_argument(     '--shuffle',        help='Randomize image order (default: 1)', type=int, default=1)
+
+    args = parser.parse_args(argv[1:] if len(argv) > 1 else ['-h'])
+    func = globals()[args.command]
+    del args.command
+    func(**vars(args))
+
+#----------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    execute_cmdline(sys.argv)
+
+#----------------------------------------------------------------------------

models/stylegan/stylegan_tf/dnnlib/__init__.py

@@ -0,0 +1,20 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+from . import submission
+
+from .submission.run_context import RunContext
+
+from .submission.submit import SubmitTarget
+from .submission.submit import PathType
+from .submission.submit import SubmitConfig
+from .submission.submit import get_path_from_template
+from .submission.submit import submit_run
+
+from .util import EasyDict
+
+submit_config: SubmitConfig = None # Package level variable for SubmitConfig which is only valid when inside the run function.

models/stylegan/stylegan_tf/dnnlib/submission/__init__.py

@@ -0,0 +1,9 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+from . import run_context
+from . import submit

models/stylegan/stylegan_tf/dnnlib/submission/_internal/run.py

@@ -0,0 +1,45 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Helper for launching run functions in computing clusters.
+
+During the submit process, this file is copied to the appropriate run dir.
+When the job is launched in the cluster, this module is the first thing that
+is run inside the docker container.
+"""
+
+import os
+import pickle
+import sys
+
+# PYTHONPATH should have been set so that the run_dir/src is in it
+import dnnlib
+
+def main():
+    if not len(sys.argv) >= 4:
+        raise RuntimeError("This script needs three arguments: run_dir, task_name and host_name!")
+
+    run_dir = str(sys.argv[1])
+    task_name = str(sys.argv[2])
+    host_name = str(sys.argv[3])
+
+    submit_config_path = os.path.join(run_dir, "submit_config.pkl")
+
+    # SubmitConfig should have been pickled to the run dir
+    if not os.path.exists(submit_config_path):
+        raise RuntimeError("SubmitConfig pickle file does not exist!")
+
+    submit_config: dnnlib.SubmitConfig = pickle.load(open(submit_config_path, "rb"))
+    dnnlib.submission.submit.set_user_name_override(submit_config.user_name)
+
+    submit_config.task_name = task_name
+    submit_config.host_name = host_name
+
+    dnnlib.submission.submit.run_wrapper(submit_config)
+
+if __name__ == "__main__":
+    main()

models/stylegan/stylegan_tf/dnnlib/submission/run_context.py

@@ -0,0 +1,99 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Helpers for managing the run/training loop."""
+
+import datetime
+import json
+import os
+import pprint
+import time
+import types
+
+from typing import Any
+
+from . import submit
+
+
+class RunContext(object):
+    """Helper class for managing the run/training loop.
+
+    The context will hide the implementation details of a basic run/training loop.
+    It will set things up properly, tell if run should be stopped, and then cleans up.
+    User should call update periodically and use should_stop to determine if run should be stopped.
+
+    Args:
+        submit_config: The SubmitConfig that is used for the current run.
+        config_module: The whole config module that is used for the current run.
+        max_epoch: Optional cached value for the max_epoch variable used in update.
+    """
+
+    def __init__(self, submit_config: submit.SubmitConfig, config_module: types.ModuleType = None, max_epoch: Any = None):
+        self.submit_config = submit_config
+        self.should_stop_flag = False
+        self.has_closed = False
+        self.start_time = time.time()
+        self.last_update_time = time.time()
+        self.last_update_interval = 0.0
+        self.max_epoch = max_epoch
+
+        # pretty print the all the relevant content of the config module to a text file
+        if config_module is not None:
+            with open(os.path.join(submit_config.run_dir, "config.txt"), "w") as f:
+                filtered_dict = {k: v for k, v in config_module.__dict__.items() if not k.startswith("_") and not isinstance(v, (types.ModuleType, types.FunctionType, types.LambdaType, submit.SubmitConfig, type))}
+                pprint.pprint(filtered_dict, stream=f, indent=4, width=200, compact=False)
+
+        # write out details about the run to a text file
+        self.run_txt_data = {"task_name": submit_config.task_name, "host_name": submit_config.host_name, "start_time": datetime.datetime.now().isoformat(sep=" ")}
+        with open(os.path.join(submit_config.run_dir, "run.txt"), "w") as f:
+            pprint.pprint(self.run_txt_data, stream=f, indent=4, width=200, compact=False)
+
+    def __enter__(self) -> "RunContext":
+        return self
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        self.close()
+
+    def update(self, loss: Any = 0, cur_epoch: Any = 0, max_epoch: Any = None) -> None:
+        """Do general housekeeping and keep the state of the context up-to-date.
+        Should be called often enough but not in a tight loop."""
+        assert not self.has_closed
+
+        self.last_update_interval = time.time() - self.last_update_time
+        self.last_update_time = time.time()
+
+        if os.path.exists(os.path.join(self.submit_config.run_dir, "abort.txt")):
+            self.should_stop_flag = True
+
+        max_epoch_val = self.max_epoch if max_epoch is None else max_epoch
+
+    def should_stop(self) -> bool:
+        """Tell whether a stopping condition has been triggered one way or another."""
+        return self.should_stop_flag
+
+    def get_time_since_start(self) -> float:
+        """How much time has passed since the creation of the context."""
+        return time.time() - self.start_time
+
+    def get_time_since_last_update(self) -> float:
+        """How much time has passed since the last call to update."""
+        return time.time() - self.last_update_time
+
+    def get_last_update_interval(self) -> float:
+        """How much time passed between the previous two calls to update."""
+        return self.last_update_interval
+
+    def close(self) -> None:
+        """Close the context and clean up.
+        Should only be called once."""
+        if not self.has_closed:
+            # update the run.txt with stopping time
+            self.run_txt_data["stop_time"] = datetime.datetime.now().isoformat(sep=" ")
+            with open(os.path.join(self.submit_config.run_dir, "run.txt"), "w") as f:
+                pprint.pprint(self.run_txt_data, stream=f, indent=4, width=200, compact=False)
+
+            self.has_closed = True

models/stylegan/stylegan_tf/dnnlib/submission/submit.py

@@ -0,0 +1,290 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Submit a function to be run either locally or in a computing cluster."""
+
+import copy
+import io
+import os
+import pathlib
+import pickle
+import platform
+import pprint
+import re
+import shutil
+import time
+import traceback
+
+import zipfile
+
+from enum import Enum
+
+from .. import util
+from ..util import EasyDict
+
+
+class SubmitTarget(Enum):
+    """The target where the function should be run.
+
+    LOCAL: Run it locally.
+    """
+    LOCAL = 1
+
+
+class PathType(Enum):
+    """Determines in which format should a path be formatted.
+
+    WINDOWS: Format with Windows style.
+    LINUX: Format with Linux/Posix style.
+    AUTO: Use current OS type to select either WINDOWS or LINUX.
+    """
+    WINDOWS = 1
+    LINUX = 2
+    AUTO = 3
+
+
+_user_name_override = None
+
+
+class SubmitConfig(util.EasyDict):
+    """Strongly typed config dict needed to submit runs.
+
+    Attributes:
+        run_dir_root: Path to the run dir root. Can be optionally templated with tags. Needs to always be run through get_path_from_template.
+        run_desc: Description of the run. Will be used in the run dir and task name.
+        run_dir_ignore: List of file patterns used to ignore files when copying files to the run dir.
+        run_dir_extra_files: List of (abs_path, rel_path) tuples of file paths. rel_path root will be the src directory inside the run dir.
+        submit_target: Submit target enum value. Used to select where the run is actually launched.
+        num_gpus: Number of GPUs used/requested for the run.
+        print_info: Whether to print debug information when submitting.
+        ask_confirmation: Whether to ask a confirmation before submitting.
+        run_id: Automatically populated value during submit.
+        run_name: Automatically populated value during submit.
+        run_dir: Automatically populated value during submit.
+        run_func_name: Automatically populated value during submit.
+        run_func_kwargs: Automatically populated value during submit.
+        user_name: Automatically populated value during submit. Can be set by the user which will then override the automatic value.
+        task_name: Automatically populated value during submit.
+        host_name: Automatically populated value during submit.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+        # run (set these)
+        self.run_dir_root = ""  # should always be passed through get_path_from_template
+        self.run_desc = ""
+        self.run_dir_ignore = ["__pycache__", "*.pyproj", "*.sln", "*.suo", ".cache", ".idea", ".vs", ".vscode"]
+        self.run_dir_extra_files = None
+
+        # submit (set these)
+        self.submit_target = SubmitTarget.LOCAL
+        self.num_gpus = 1
+        self.print_info = False
+        self.ask_confirmation = False
+
+        # (automatically populated)
+        self.run_id = None
+        self.run_name = None
+        self.run_dir = None
+        self.run_func_name = None
+        self.run_func_kwargs = None
+        self.user_name = None
+        self.task_name = None
+        self.host_name = "localhost"
+
+
+def get_path_from_template(path_template: str, path_type: PathType = PathType.AUTO) -> str:
+    """Replace tags in the given path template and return either Windows or Linux formatted path."""
+    # automatically select path type depending on running OS
+    if path_type == PathType.AUTO:
+        if platform.system() == "Windows":
+            path_type = PathType.WINDOWS
+        elif platform.system() == "Linux":
+            path_type = PathType.LINUX
+        else:
+            raise RuntimeError("Unknown platform")
+
+    path_template = path_template.replace("<USERNAME>", get_user_name())
+
+    # return correctly formatted path
+    if path_type == PathType.WINDOWS:
+        return str(pathlib.PureWindowsPath(path_template))
+    elif path_type == PathType.LINUX:
+        return str(pathlib.PurePosixPath(path_template))
+    else:
+        raise RuntimeError("Unknown platform")
+
+
+def get_template_from_path(path: str) -> str:
+    """Convert a normal path back to its template representation."""
+    # replace all path parts with the template tags
+    path = path.replace("\\", "/")
+    return path
+
+
+def convert_path(path: str, path_type: PathType = PathType.AUTO) -> str:
+    """Convert a normal path to template and the convert it back to a normal path with given path type."""
+    path_template = get_template_from_path(path)
+    path = get_path_from_template(path_template, path_type)
+    return path
+
+
+def set_user_name_override(name: str) -> None:
+    """Set the global username override value."""
+    global _user_name_override
+    _user_name_override = name
+
+
+def get_user_name():
+    """Get the current user name."""
+    if _user_name_override is not None:
+        return _user_name_override
+    elif platform.system() == "Windows":
+        return os.getlogin()
+    elif platform.system() == "Linux":
+        try:
+            import pwd # pylint: disable=import-error
+            return pwd.getpwuid(os.geteuid()).pw_name # pylint: disable=no-member
+        except:
+            return "unknown"
+    else:
+        raise RuntimeError("Unknown platform")
+
+
+def _create_run_dir_local(submit_config: SubmitConfig) -> str:
+    """Create a new run dir with increasing ID number at the start."""
+    run_dir_root = get_path_from_template(submit_config.run_dir_root, PathType.AUTO)
+
+    if not os.path.exists(run_dir_root):
+        print("Creating the run dir root: {}".format(run_dir_root))
+        os.makedirs(run_dir_root)
+
+    submit_config.run_id = _get_next_run_id_local(run_dir_root)
+    submit_config.run_name = "{0:05d}-{1}".format(submit_config.run_id, submit_config.run_desc)
+    run_dir = os.path.join(run_dir_root, submit_config.run_name)
+
+    if os.path.exists(run_dir):
+        raise RuntimeError("The run dir already exists! ({0})".format(run_dir))
+
+    print("Creating the run dir: {}".format(run_dir))
+    os.makedirs(run_dir)
+
+    return run_dir
+
+
+def _get_next_run_id_local(run_dir_root: str) -> int:
+    """Reads all directory names in a given directory (non-recursive) and returns the next (increasing) run id. Assumes IDs are numbers at the start of the directory names."""
+    dir_names = [d for d in os.listdir(run_dir_root) if os.path.isdir(os.path.join(run_dir_root, d))]
+    r = re.compile("^\\d+")  # match one or more digits at the start of the string
+    run_id = 0
+
+    for dir_name in dir_names:
+        m = r.match(dir_name)
+
+        if m is not None:
+            i = int(m.group())
+            run_id = max(run_id, i + 1)
+
+    return run_id
+
+
+def _populate_run_dir(run_dir: str, submit_config: SubmitConfig) -> None:
+    """Copy all necessary files into the run dir. Assumes that the dir exists, is local, and is writable."""
+    print("Copying files to the run dir")
+    files = []
+
+    run_func_module_dir_path = util.get_module_dir_by_obj_name(submit_config.run_func_name)
+    assert '.' in submit_config.run_func_name
+    for _idx in range(submit_config.run_func_name.count('.') - 1):
+        run_func_module_dir_path = os.path.dirname(run_func_module_dir_path)
+    files += util.list_dir_recursively_with_ignore(run_func_module_dir_path, ignores=submit_config.run_dir_ignore, add_base_to_relative=False)
+
+    dnnlib_module_dir_path = util.get_module_dir_by_obj_name("dnnlib")
+    files += util.list_dir_recursively_with_ignore(dnnlib_module_dir_path, ignores=submit_config.run_dir_ignore, add_base_to_relative=True)
+
+    if submit_config.run_dir_extra_files is not None:
+        files += submit_config.run_dir_extra_files
+
+    files = [(f[0], os.path.join(run_dir, "src", f[1])) for f in files]
+    files += [(os.path.join(dnnlib_module_dir_path, "submission", "_internal", "run.py"), os.path.join(run_dir, "run.py"))]
+
+    util.copy_files_and_create_dirs(files)
+
+    pickle.dump(submit_config, open(os.path.join(run_dir, "submit_config.pkl"), "wb"))
+
+    with open(os.path.join(run_dir, "submit_config.txt"), "w") as f:
+        pprint.pprint(submit_config, stream=f, indent=4, width=200, compact=False)
+
+
+def run_wrapper(submit_config: SubmitConfig) -> None:
+    """Wrap the actual run function call for handling logging, exceptions, typing, etc."""
+    is_local = submit_config.submit_target == SubmitTarget.LOCAL
+
+    checker = None
+
+    # when running locally, redirect stderr to stdout, log stdout to a file, and force flushing
+    if is_local:
+        logger = util.Logger(file_name=os.path.join(submit_config.run_dir, "log.txt"), file_mode="w", should_flush=True)
+    else:  # when running in a cluster, redirect stderr to stdout, and just force flushing (log writing is handled by run.sh)
+        logger = util.Logger(file_name=None, should_flush=True)
+
+    import dnnlib
+    dnnlib.submit_config = submit_config
+
+    try:
+        print("dnnlib: Running {0}() on {1}...".format(submit_config.run_func_name, submit_config.host_name))
+        start_time = time.time()
+        util.call_func_by_name(func_name=submit_config.run_func_name, submit_config=submit_config, **submit_config.run_func_kwargs)
+        print("dnnlib: Finished {0}() in {1}.".format(submit_config.run_func_name, util.format_time(time.time() - start_time)))
+    except:
+        if is_local:
+            raise
+        else:
+            traceback.print_exc()
+
+            log_src = os.path.join(submit_config.run_dir, "log.txt")
+            log_dst = os.path.join(get_path_from_template(submit_config.run_dir_root), "{0}-error.txt".format(submit_config.run_name))
+            shutil.copyfile(log_src, log_dst)
+    finally:
+        open(os.path.join(submit_config.run_dir, "_finished.txt"), "w").close()
+
+    dnnlib.submit_config = None
+    logger.close()
+
+    if checker is not None:
+        checker.stop()
+
+
+def submit_run(submit_config: SubmitConfig, run_func_name: str, **run_func_kwargs) -> None:
+    """Create a run dir, gather files related to the run, copy files to the run dir, and launch the run in appropriate place."""
+    submit_config = copy.copy(submit_config)
+
+    if submit_config.user_name is None:
+        submit_config.user_name = get_user_name()
+
+    submit_config.run_func_name = run_func_name
+    submit_config.run_func_kwargs = run_func_kwargs
+
+    assert submit_config.submit_target == SubmitTarget.LOCAL
+    if submit_config.submit_target in {SubmitTarget.LOCAL}:
+        run_dir = _create_run_dir_local(submit_config)
+
+        submit_config.task_name = "{0}-{1:05d}-{2}".format(submit_config.user_name, submit_config.run_id, submit_config.run_desc)
+        submit_config.run_dir = run_dir
+        _populate_run_dir(run_dir, submit_config)
+
+    if submit_config.print_info:
+        print("\nSubmit config:\n")
+        pprint.pprint(submit_config, indent=4, width=200, compact=False)
+        print()
+
+    if submit_config.ask_confirmation:
+        if not util.ask_yes_no("Continue submitting the job?"):
+            return
+
+    run_wrapper(submit_config)

models/stylegan/stylegan_tf/dnnlib/tflib/__init__.py

@@ -0,0 +1,16 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+from . import autosummary
+from . import network
+from . import optimizer
+from . import tfutil
+
+from .tfutil import *
+from .network import Network
+
+from .optimizer import Optimizer

models/stylegan/stylegan_tf/dnnlib/tflib/autosummary.py

@@ -0,0 +1,184 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Helper for adding automatically tracked values to Tensorboard.
+
+Autosummary creates an identity op that internally keeps track of the input
+values and automatically shows up in TensorBoard. The reported value
+represents an average over input components. The average is accumulated
+constantly over time and flushed when save_summaries() is called.
+
+Notes:
+- The output tensor must be used as an input for something else in the
+  graph. Otherwise, the autosummary op will not get executed, and the average
+  value will not get accumulated.
+- It is perfectly fine to include autosummaries with the same name in
+  several places throughout the graph, even if they are executed concurrently.
+- It is ok to also pass in a python scalar or numpy array. In this case, it
+  is added to the average immediately.
+"""
+
+from collections import OrderedDict
+import numpy as np
+import tensorflow as tf
+from tensorboard import summary as summary_lib
+from tensorboard.plugins.custom_scalar import layout_pb2
+
+from . import tfutil
+from .tfutil import TfExpression
+from .tfutil import TfExpressionEx
+
+_dtype = tf.float64
+_vars = OrderedDict()  # name => [var, ...]
+_immediate = OrderedDict()  # name => update_op, update_value
+_finalized = False
+_merge_op = None
+
+
+def _create_var(name: str, value_expr: TfExpression) -> TfExpression:
+    """Internal helper for creating autosummary accumulators."""
+    assert not _finalized
+    name_id = name.replace("/", "_")
+    v = tf.cast(value_expr, _dtype)
+
+    if v.shape.is_fully_defined():
+        size = np.prod(tfutil.shape_to_list(v.shape))
+        size_expr = tf.constant(size, dtype=_dtype)
+    else:
+        size = None
+        size_expr = tf.reduce_prod(tf.cast(tf.shape(v), _dtype))
+
+    if size == 1:
+        if v.shape.ndims != 0:
+            v = tf.reshape(v, [])
+        v = [size_expr, v, tf.square(v)]
+    else:
+        v = [size_expr, tf.reduce_sum(v), tf.reduce_sum(tf.square(v))]
+    v = tf.cond(tf.is_finite(v[1]), lambda: tf.stack(v), lambda: tf.zeros(3, dtype=_dtype))
+
+    with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.control_dependencies(None):
+        var = tf.Variable(tf.zeros(3, dtype=_dtype), trainable=False)  # [sum(1), sum(x), sum(x**2)]
+    update_op = tf.cond(tf.is_variable_initialized(var), lambda: tf.assign_add(var, v), lambda: tf.assign(var, v))
+
+    if name in _vars:
+        _vars[name].append(var)
+    else:
+        _vars[name] = [var]
+    return update_op
+
+
+def autosummary(name: str, value: TfExpressionEx, passthru: TfExpressionEx = None) -> TfExpressionEx:
+    """Create a new autosummary.
+
+    Args:
+        name:     Name to use in TensorBoard
+        value:    TensorFlow expression or python value to track
+        passthru: Optionally return this TF node without modifications but tack an autosummary update side-effect to this node.
+
+    Example use of the passthru mechanism:
+
+    n = autosummary('l2loss', loss, passthru=n)
+
+    This is a shorthand for the following code:
+
+    with tf.control_dependencies([autosummary('l2loss', loss)]):
+        n = tf.identity(n)
+    """
+    tfutil.assert_tf_initialized()
+    name_id = name.replace("/", "_")
+
+    if tfutil.is_tf_expression(value):
+        with tf.name_scope("summary_" + name_id), tf.device(value.device):
+            update_op = _create_var(name, value)
+            with tf.control_dependencies([update_op]):
+                return tf.identity(value if passthru is None else passthru)
+
+    else:  # python scalar or numpy array
+        if name not in _immediate:
+            with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.device(None), tf.control_dependencies(None):
+                update_value = tf.placeholder(_dtype)
+                update_op = _create_var(name, update_value)
+                _immediate[name] = update_op, update_value
+
+        update_op, update_value = _immediate[name]
+        tfutil.run(update_op, {update_value: value})
+        return value if passthru is None else passthru
+
+
+def finalize_autosummaries() -> None:
+    """Create the necessary ops to include autosummaries in TensorBoard report.
+    Note: This should be done only once per graph.
+    """
+    global _finalized
+    tfutil.assert_tf_initialized()
+
+    if _finalized:
+        return None
+
+    _finalized = True
+    tfutil.init_uninitialized_vars([var for vars_list in _vars.values() for var in vars_list])
+
+    # Create summary ops.
+    with tf.device(None), tf.control_dependencies(None):
+        for name, vars_list in _vars.items():
+            name_id = name.replace("/", "_")
+            with tfutil.absolute_name_scope("Autosummary/" + name_id):
+                moments = tf.add_n(vars_list)
+                moments /= moments[0]
+                with tf.control_dependencies([moments]):  # read before resetting
+                    reset_ops = [tf.assign(var, tf.zeros(3, dtype=_dtype)) for var in vars_list]
+                    with tf.name_scope(None), tf.control_dependencies(reset_ops):  # reset before reporting
+                        mean = moments[1]
+                        std = tf.sqrt(moments[2] - tf.square(moments[1]))
+                        tf.summary.scalar(name, mean)
+                        tf.summary.scalar("xCustomScalars/" + name + "/margin_lo", mean - std)
+                        tf.summary.scalar("xCustomScalars/" + name + "/margin_hi", mean + std)
+
+    # Group by category and chart name.
+    cat_dict = OrderedDict()
+    for series_name in sorted(_vars.keys()):
+        p = series_name.split("/")
+        cat = p[0] if len(p) >= 2 else ""
+        chart = "/".join(p[1:-1]) if len(p) >= 3 else p[-1]
+        if cat not in cat_dict:
+            cat_dict[cat] = OrderedDict()
+        if chart not in cat_dict[cat]:
+            cat_dict[cat][chart] = []
+        cat_dict[cat][chart].append(series_name)
+
+    # Setup custom_scalar layout.
+    categories = []
+    for cat_name, chart_dict in cat_dict.items():
+        charts = []
+        for chart_name, series_names in chart_dict.items():
+            series = []
+            for series_name in series_names:
+                series.append(layout_pb2.MarginChartContent.Series(
+                    value=series_name,
+                    lower="xCustomScalars/" + series_name + "/margin_lo",
+                    upper="xCustomScalars/" + series_name + "/margin_hi"))
+            margin = layout_pb2.MarginChartContent(series=series)
+            charts.append(layout_pb2.Chart(title=chart_name, margin=margin))
+        categories.append(layout_pb2.Category(title=cat_name, chart=charts))
+    layout = summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories))
+    return layout
+
+def save_summaries(file_writer, global_step=None):
+    """Call FileWriter.add_summary() with all summaries in the default graph,
+    automatically finalizing and merging them on the first call.
+    """
+    global _merge_op
+    tfutil.assert_tf_initialized()
+
+    if _merge_op is None:
+        layout = finalize_autosummaries()
+        if layout is not None:
+            file_writer.add_summary(layout)
+        with tf.device(None), tf.control_dependencies(None):
+            _merge_op = tf.summary.merge_all()
+
+    file_writer.add_summary(_merge_op.eval(), global_step)

models/stylegan/stylegan_tf/dnnlib/tflib/network.py

@@ -0,0 +1,591 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Helper for managing networks."""
+
+import types
+import inspect
+import re
+import uuid
+import sys
+import numpy as np
+import tensorflow as tf
+
+from collections import OrderedDict
+from typing import Any, List, Tuple, Union
+
+from . import tfutil
+from .. import util
+
+from .tfutil import TfExpression, TfExpressionEx
+
+_import_handlers = []  # Custom import handlers for dealing with legacy data in pickle import.
+_import_module_src = dict()  # Source code for temporary modules created during pickle import.
+
+
+def import_handler(handler_func):
+    """Function decorator for declaring custom import handlers."""
+    _import_handlers.append(handler_func)
+    return handler_func
+
+
+class Network:
+    """Generic network abstraction.
+
+    Acts as a convenience wrapper for a parameterized network construction
+    function, providing several utility methods and convenient access to
+    the inputs/outputs/weights.
+
+    Network objects can be safely pickled and unpickled for long-term
+    archival purposes. The pickling works reliably as long as the underlying
+    network construction function is defined in a standalone Python module
+    that has no side effects or application-specific imports.
+
+    Args:
+        name: Network name. Used to select TensorFlow name and variable scopes.
+        func_name: Fully qualified name of the underlying network construction function, or a top-level function object.
+        static_kwargs: Keyword arguments to be passed in to the network construction function.
+
+    Attributes:
+        name: User-specified name, defaults to build func name if None.
+        scope: Unique TensorFlow scope containing template graph and variables, derived from the user-specified name.
+        static_kwargs: Arguments passed to the user-supplied build func.
+        components: Container for sub-networks. Passed to the build func, and retained between calls.
+        num_inputs: Number of input tensors.
+        num_outputs: Number of output tensors.
+        input_shapes: Input tensor shapes (NC or NCHW), including minibatch dimension.
+        output_shapes: Output tensor shapes (NC or NCHW), including minibatch dimension.
+        input_shape: Short-hand for input_shapes[0].
+        output_shape: Short-hand for output_shapes[0].
+        input_templates: Input placeholders in the template graph.
+        output_templates: Output tensors in the template graph.
+        input_names: Name string for each input.
+        output_names: Name string for each output.
+        own_vars: Variables defined by this network (local_name => var), excluding sub-networks.
+        vars: All variables (local_name => var).
+        trainables: All trainable variables (local_name => var).
+        var_global_to_local: Mapping from variable global names to local names.
+    """
+
+    def __init__(self, name: str = None, func_name: Any = None, **static_kwargs):
+        tfutil.assert_tf_initialized()
+        assert isinstance(name, str) or name is None
+        assert func_name is not None
+        assert isinstance(func_name, str) or util.is_top_level_function(func_name)
+        assert util.is_pickleable(static_kwargs)
+
+        self._init_fields()
+        self.name = name
+        self.static_kwargs = util.EasyDict(static_kwargs)
+
+        # Locate the user-specified network build function.
+        if util.is_top_level_function(func_name):
+            func_name = util.get_top_level_function_name(func_name)
+        module, self._build_func_name = util.get_module_from_obj_name(func_name)
+        self._build_func = util.get_obj_from_module(module, self._build_func_name)
+        assert callable(self._build_func)
+
+        # Dig up source code for the module containing the build function.
+        self._build_module_src = _import_module_src.get(module, None)
+        if self._build_module_src is None:
+            self._build_module_src = inspect.getsource(module)
+
+        # Init TensorFlow graph.
+        self._init_graph()
+        self.reset_own_vars()
+
+    def _init_fields(self) -> None:
+        self.name = None
+        self.scope = None
+        self.static_kwargs = util.EasyDict()
+        self.components = util.EasyDict()
+        self.num_inputs = 0
+        self.num_outputs = 0
+        self.input_shapes = [[]]
+        self.output_shapes = [[]]
+        self.input_shape = []
+        self.output_shape = []
+        self.input_templates = []
+        self.output_templates = []
+        self.input_names = []
+        self.output_names = []
+        self.own_vars = OrderedDict()
+        self.vars = OrderedDict()
+        self.trainables = OrderedDict()
+        self.var_global_to_local = OrderedDict()
+
+        self._build_func = None  # User-supplied build function that constructs the network.
+        self._build_func_name = None  # Name of the build function.
+        self._build_module_src = None  # Full source code of the module containing the build function.
+        self._run_cache = dict()  # Cached graph data for Network.run().
+
+    def _init_graph(self) -> None:
+        # Collect inputs.
+        self.input_names = []
+
+        for param in inspect.signature(self._build_func).parameters.values():
+            if param.kind == param.POSITIONAL_OR_KEYWORD and param.default is param.empty:
+                self.input_names.append(param.name)
+
+        self.num_inputs = len(self.input_names)
+        assert self.num_inputs >= 1
+
+        # Choose name and scope.
+        if self.name is None:
+            self.name = self._build_func_name
+        assert re.match("^[A-Za-z0-9_.\\-]*$", self.name)
+        with tf.name_scope(None):
+            self.scope = tf.get_default_graph().unique_name(self.name, mark_as_used=True)
+
+        # Finalize build func kwargs.
+        build_kwargs = dict(self.static_kwargs)
+        build_kwargs["is_template_graph"] = True
+        build_kwargs["components"] = self.components
+
+        # Build template graph.
+        with tfutil.absolute_variable_scope(self.scope, reuse=tf.AUTO_REUSE), tfutil.absolute_name_scope(self.scope):  # ignore surrounding scopes
+            assert tf.get_variable_scope().name == self.scope
+            assert tf.get_default_graph().get_name_scope() == self.scope
+            with tf.control_dependencies(None):  # ignore surrounding control dependencies
+                self.input_templates = [tf.placeholder(tf.float32, name=name) for name in self.input_names]
+                out_expr = self._build_func(*self.input_templates, **build_kwargs)
+
+        # Collect outputs.
+        assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple)
+        self.output_templates = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr)
+        self.num_outputs = len(self.output_templates)
+        assert self.num_outputs >= 1
+        assert all(tfutil.is_tf_expression(t) for t in self.output_templates)
+
+        # Perform sanity checks.
+        if any(t.shape.ndims is None for t in self.input_templates):
+            raise ValueError("Network input shapes not defined. Please call x.set_shape() for each input.")
+        if any(t.shape.ndims is None for t in self.output_templates):
+            raise ValueError("Network output shapes not defined. Please call x.set_shape() where applicable.")
+        if any(not isinstance(comp, Network) for comp in self.components.values()):
+            raise ValueError("Components of a Network must be Networks themselves.")
+        if len(self.components) != len(set(comp.name for comp in self.components.values())):
+            raise ValueError("Components of a Network must have unique names.")
+
+        # List inputs and outputs.
+        self.input_shapes = [tfutil.shape_to_list(t.shape) for t in self.input_templates]
+        self.output_shapes = [tfutil.shape_to_list(t.shape) for t in self.output_templates]
+        self.input_shape = self.input_shapes[0]
+        self.output_shape = self.output_shapes[0]
+        self.output_names = [t.name.split("/")[-1].split(":")[0] for t in self.output_templates]
+
+        # List variables.
+        self.own_vars = OrderedDict((var.name[len(self.scope) + 1:].split(":")[0], var) for var in tf.global_variables(self.scope + "/"))
+        self.vars = OrderedDict(self.own_vars)
+        self.vars.update((comp.name + "/" + name, var) for comp in self.components.values() for name, var in comp.vars.items())
+        self.trainables = OrderedDict((name, var) for name, var in self.vars.items() if var.trainable)
+        self.var_global_to_local = OrderedDict((var.name.split(":")[0], name) for name, var in self.vars.items())
+
+    def reset_own_vars(self) -> None:
+        """Re-initialize all variables of this network, excluding sub-networks."""
+        tfutil.run([var.initializer for var in self.own_vars.values()])
+
+    def reset_vars(self) -> None:
+        """Re-initialize all variables of this network, including sub-networks."""
+        tfutil.run([var.initializer for var in self.vars.values()])
+
+    def reset_trainables(self) -> None:
+        """Re-initialize all trainable variables of this network, including sub-networks."""
+        tfutil.run([var.initializer for var in self.trainables.values()])
+
+    def get_output_for(self, *in_expr: TfExpression, return_as_list: bool = False, **dynamic_kwargs) -> Union[TfExpression, List[TfExpression]]:
+        """Construct TensorFlow expression(s) for the output(s) of this network, given the input expression(s)."""
+        assert len(in_expr) == self.num_inputs
+        assert not all(expr is None for expr in in_expr)
+
+        # Finalize build func kwargs.
+        build_kwargs = dict(self.static_kwargs)
+        build_kwargs.update(dynamic_kwargs)
+        build_kwargs["is_template_graph"] = False
+        build_kwargs["components"] = self.components
+
+        # Build TensorFlow graph to evaluate the network.
+        with tfutil.absolute_variable_scope(self.scope, reuse=True), tf.name_scope(self.name):
+            assert tf.get_variable_scope().name == self.scope
+            valid_inputs = [expr for expr in in_expr if expr is not None]
+            final_inputs = []
+            for expr, name, shape in zip(in_expr, self.input_names, self.input_shapes):
+                if expr is not None:
+                    expr = tf.identity(expr, name=name)
+                else:
+                    expr = tf.zeros([tf.shape(valid_inputs[0])[0]] + shape[1:], name=name)
+                final_inputs.append(expr)
+            out_expr = self._build_func(*final_inputs, **build_kwargs)
+
+        # Propagate input shapes back to the user-specified expressions.
+        for expr, final in zip(in_expr, final_inputs):
+            if isinstance(expr, tf.Tensor):
+                expr.set_shape(final.shape)
+
+        # Express outputs in the desired format.
+        assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple)
+        if return_as_list:
+            out_expr = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr)
+        return out_expr
+
+    def get_var_local_name(self, var_or_global_name: Union[TfExpression, str]) -> str:
+        """Get the local name of a given variable, without any surrounding name scopes."""
+        assert tfutil.is_tf_expression(var_or_global_name) or isinstance(var_or_global_name, str)
+        global_name = var_or_global_name if isinstance(var_or_global_name, str) else var_or_global_name.name
+        return self.var_global_to_local[global_name]
+
+    def find_var(self, var_or_local_name: Union[TfExpression, str]) -> TfExpression:
+        """Find variable by local or global name."""
+        assert tfutil.is_tf_expression(var_or_local_name) or isinstance(var_or_local_name, str)
+        return self.vars[var_or_local_name] if isinstance(var_or_local_name, str) else var_or_local_name
+
+    def get_var(self, var_or_local_name: Union[TfExpression, str]) -> np.ndarray:
+        """Get the value of a given variable as NumPy array.
+        Note: This method is very inefficient -- prefer to use tflib.run(list_of_vars) whenever possible."""
+        return self.find_var(var_or_local_name).eval()
+
+    def set_var(self, var_or_local_name: Union[TfExpression, str], new_value: Union[int, float, np.ndarray]) -> None:
+        """Set the value of a given variable based on the given NumPy array.
+        Note: This method is very inefficient -- prefer to use tflib.set_vars() whenever possible."""
+        tfutil.set_vars({self.find_var(var_or_local_name): new_value})
+
+    def __getstate__(self) -> dict:
+        """Pickle export."""
+        state = dict()
+        state["version"]            = 3
+        state["name"]               = self.name
+        state["static_kwargs"]      = dict(self.static_kwargs)
+        state["components"]         = dict(self.components)
+        state["build_module_src"]   = self._build_module_src
+        state["build_func_name"]    = self._build_func_name
+        state["variables"]          = list(zip(self.own_vars.keys(), tfutil.run(list(self.own_vars.values()))))
+        return state
+
+    def __setstate__(self, state: dict) -> None:
+        """Pickle import."""
+        # pylint: disable=attribute-defined-outside-init
+        tfutil.assert_tf_initialized()
+        self._init_fields()
+
+        # Execute custom import handlers.
+        for handler in _import_handlers:
+            state = handler(state)
+
+        # Set basic fields.
+        assert state["version"] in [2, 3]
+        self.name = state["name"]
+        self.static_kwargs = util.EasyDict(state["static_kwargs"])
+        self.components = util.EasyDict(state.get("components", {}))
+        self._build_module_src = state["build_module_src"]
+        self._build_func_name = state["build_func_name"]
+
+        # Create temporary module from the imported source code.
+        module_name = "_tflib_network_import_" + uuid.uuid4().hex
+        module = types.ModuleType(module_name)
+        sys.modules[module_name] = module
+        _import_module_src[module] = self._build_module_src
+        exec(self._build_module_src, module.__dict__) # pylint: disable=exec-used
+
+        # Locate network build function in the temporary module.
+        self._build_func = util.get_obj_from_module(module, self._build_func_name)
+        assert callable(self._build_func)
+
+        # Init TensorFlow graph.
+        self._init_graph()
+        self.reset_own_vars()
+        tfutil.set_vars({self.find_var(name): value for name, value in state["variables"]})
+
+    def clone(self, name: str = None, **new_static_kwargs) -> "Network":
+        """Create a clone of this network with its own copy of the variables."""
+        # pylint: disable=protected-access
+        net = object.__new__(Network)
+        net._init_fields()
+        net.name = name if name is not None else self.name
+        net.static_kwargs = util.EasyDict(self.static_kwargs)
+        net.static_kwargs.update(new_static_kwargs)
+        net._build_module_src = self._build_module_src
+        net._build_func_name = self._build_func_name
+        net._build_func = self._build_func
+        net._init_graph()
+        net.copy_vars_from(self)
+        return net
+
+    def copy_own_vars_from(self, src_net: "Network") -> None:
+        """Copy the values of all variables from the given network, excluding sub-networks."""
+        names = [name for name in self.own_vars.keys() if name in src_net.own_vars]
+        tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names}))
+
+    def copy_vars_from(self, src_net: "Network") -> None:
+        """Copy the values of all variables from the given network, including sub-networks."""
+        names = [name for name in self.vars.keys() if name in src_net.vars]
+        tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names}))
+
+    def copy_trainables_from(self, src_net: "Network") -> None:
+        """Copy the values of all trainable variables from the given network, including sub-networks."""
+        names = [name for name in self.trainables.keys() if name in src_net.trainables]
+        tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names}))
+
+    def convert(self, new_func_name: str, new_name: str = None, **new_static_kwargs) -> "Network":
+        """Create new network with the given parameters, and copy all variables from this network."""
+        if new_name is None:
+            new_name = self.name
+        static_kwargs = dict(self.static_kwargs)
+        static_kwargs.update(new_static_kwargs)
+        net = Network(name=new_name, func_name=new_func_name, **static_kwargs)
+        net.copy_vars_from(self)
+        return net
+
+    def setup_as_moving_average_of(self, src_net: "Network", beta: TfExpressionEx = 0.99, beta_nontrainable: TfExpressionEx = 0.0) -> tf.Operation:
+        """Construct a TensorFlow op that updates the variables of this network
+        to be slightly closer to those of the given network."""
+        with tfutil.absolute_name_scope(self.scope + "/_MovingAvg"):
+            ops = []
+            for name, var in self.vars.items():
+                if name in src_net.vars:
+                    cur_beta = beta if name in self.trainables else beta_nontrainable
+                    new_value = tfutil.lerp(src_net.vars[name], var, cur_beta)
+                    ops.append(var.assign(new_value))
+            return tf.group(*ops)
+
+    def run(self,
+            *in_arrays: Tuple[Union[np.ndarray, None], ...],
+            input_transform: dict = None,
+            output_transform: dict = None,
+            return_as_list: bool = False,
+            print_progress: bool = False,
+            minibatch_size: int = None,
+            num_gpus: int = 1,
+            assume_frozen: bool = False,
+            **dynamic_kwargs) -> Union[np.ndarray, Tuple[np.ndarray, ...], List[np.ndarray]]:
+        """Run this network for the given NumPy array(s), and return the output(s) as NumPy array(s).
+
+        Args:
+            input_transform:    A dict specifying a custom transformation to be applied to the input tensor(s) before evaluating the network.
+                                The dict must contain a 'func' field that points to a top-level function. The function is called with the input
+                                TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs.
+            output_transform:   A dict specifying a custom transformation to be applied to the output tensor(s) after evaluating the network.
+                                The dict must contain a 'func' field that points to a top-level function. The function is called with the output
+                                TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs.
+            return_as_list:     True = return a list of NumPy arrays, False = return a single NumPy array, or a tuple if there are multiple outputs.
+            print_progress:     Print progress to the console? Useful for very large input arrays.
+            minibatch_size:     Maximum minibatch size to use, None = disable batching.
+            num_gpus:           Number of GPUs to use.
+            assume_frozen:      Improve multi-GPU performance by assuming that the trainable parameters will remain changed between calls.
+            dynamic_kwargs:     Additional keyword arguments to be passed into the network build function.
+        """
+        assert len(in_arrays) == self.num_inputs
+        assert not all(arr is None for arr in in_arrays)
+        assert input_transform is None or util.is_top_level_function(input_transform["func"])
+        assert output_transform is None or util.is_top_level_function(output_transform["func"])
+        output_transform, dynamic_kwargs = _handle_legacy_output_transforms(output_transform, dynamic_kwargs)
+        num_items = in_arrays[0].shape[0]
+        if minibatch_size is None:
+            minibatch_size = num_items
+
+        # Construct unique hash key from all arguments that affect the TensorFlow graph.
+        key = dict(input_transform=input_transform, output_transform=output_transform, num_gpus=num_gpus, assume_frozen=assume_frozen, dynamic_kwargs=dynamic_kwargs)
+        def unwind_key(obj):
+            if isinstance(obj, dict):
+                return [(key, unwind_key(value)) for key, value in sorted(obj.items())]
+            if callable(obj):
+                return util.get_top_level_function_name(obj)
+            return obj
+        key = repr(unwind_key(key))
+
+        # Build graph.
+        if key not in self._run_cache:
+            with tfutil.absolute_name_scope(self.scope + "/_Run"), tf.control_dependencies(None):
+                with tf.device("/cpu:0"):
+                    in_expr = [tf.placeholder(tf.float32, name=name) for name in self.input_names]
+                    in_split = list(zip(*[tf.split(x, num_gpus) for x in in_expr]))
+
+                out_split = []
+                for gpu in range(num_gpus):
+                    with tf.device("/gpu:%d" % gpu):
+                        net_gpu = self.clone() if assume_frozen else self
+                        in_gpu = in_split[gpu]
+
+                        if input_transform is not None:
+                            in_kwargs = dict(input_transform)
+                            in_gpu = in_kwargs.pop("func")(*in_gpu, **in_kwargs)
+                            in_gpu = [in_gpu] if tfutil.is_tf_expression(in_gpu) else list(in_gpu)
+
+                        assert len(in_gpu) == self.num_inputs
+                        out_gpu = net_gpu.get_output_for(*in_gpu, return_as_list=True, **dynamic_kwargs)
+
+                        if output_transform is not None:
+                            out_kwargs = dict(output_transform)
+                            out_gpu = out_kwargs.pop("func")(*out_gpu, **out_kwargs)
+                            out_gpu = [out_gpu] if tfutil.is_tf_expression(out_gpu) else list(out_gpu)
+
+                        assert len(out_gpu) == self.num_outputs
+                        out_split.append(out_gpu)
+
+                with tf.device("/cpu:0"):
+                    out_expr = [tf.concat(outputs, axis=0) for outputs in zip(*out_split)]
+                    self._run_cache[key] = in_expr, out_expr
+
+        # Run minibatches.
+        in_expr, out_expr = self._run_cache[key]
+        out_arrays = [np.empty([num_items] + tfutil.shape_to_list(expr.shape)[1:], expr.dtype.name) for expr in out_expr]
+
+        for mb_begin in range(0, num_items, minibatch_size):
+            if print_progress:
+                print("\r%d / %d" % (mb_begin, num_items), end="")
+
+            mb_end = min(mb_begin + minibatch_size, num_items)
+            mb_num = mb_end - mb_begin
+            mb_in = [src[mb_begin : mb_end] if src is not None else np.zeros([mb_num] + shape[1:]) for src, shape in zip(in_arrays, self.input_shapes)]
+            mb_out = tf.get_default_session().run(out_expr, dict(zip(in_expr, mb_in)))
+
+            for dst, src in zip(out_arrays, mb_out):
+                dst[mb_begin: mb_end] = src
+
+        # Done.
+        if print_progress:
+            print("\r%d / %d" % (num_items, num_items))
+
+        if not return_as_list:
+            out_arrays = out_arrays[0] if len(out_arrays) == 1 else tuple(out_arrays)
+        return out_arrays
+
+    def list_ops(self) -> List[TfExpression]:
+        include_prefix = self.scope + "/"
+        exclude_prefix = include_prefix + "_"
+        ops = tf.get_default_graph().get_operations()
+        ops = [op for op in ops if op.name.startswith(include_prefix)]
+        ops = [op for op in ops if not op.name.startswith(exclude_prefix)]
+        return ops
+
+    def list_layers(self) -> List[Tuple[str, TfExpression, List[TfExpression]]]:
+        """Returns a list of (layer_name, output_expr, trainable_vars) tuples corresponding to
+        individual layers of the network. Mainly intended to be used for reporting."""
+        layers = []
+
+        def recurse(scope, parent_ops, parent_vars, level):
+            # Ignore specific patterns.
+            if any(p in scope for p in ["/Shape", "/strided_slice", "/Cast", "/concat", "/Assign"]):
+                return
+
+            # Filter ops and vars by scope.
+            global_prefix = scope + "/"
+            local_prefix = global_prefix[len(self.scope) + 1:]
+            cur_ops = [op for op in parent_ops if op.name.startswith(global_prefix) or op.name == global_prefix[:-1]]
+            cur_vars = [(name, var) for name, var in parent_vars if name.startswith(local_prefix) or name == local_prefix[:-1]]
+            if not cur_ops and not cur_vars:
+                return
+
+            # Filter out all ops related to variables.
+            for var in [op for op in cur_ops if op.type.startswith("Variable")]:
+                var_prefix = var.name + "/"
+                cur_ops = [op for op in cur_ops if not op.name.startswith(var_prefix)]
+
+            # Scope does not contain ops as immediate children => recurse deeper.
+            contains_direct_ops = any("/" not in op.name[len(global_prefix):] and op.type != "Identity" for op in cur_ops)
+            if (level == 0 or not contains_direct_ops) and (len(cur_ops) + len(cur_vars)) > 1:
+                visited = set()
+                for rel_name in [op.name[len(global_prefix):] for op in cur_ops] + [name[len(local_prefix):] for name, _var in cur_vars]:
+                    token = rel_name.split("/")[0]
+                    if token not in visited:
+                        recurse(global_prefix + token, cur_ops, cur_vars, level + 1)
+                        visited.add(token)
+                return
+
+            # Report layer.
+            layer_name = scope[len(self.scope) + 1:]
+            layer_output = cur_ops[-1].outputs[0] if cur_ops else cur_vars[-1][1]
+            layer_trainables = [var for _name, var in cur_vars if var.trainable]
+            layers.append((layer_name, layer_output, layer_trainables))
+
+        recurse(self.scope, self.list_ops(), list(self.vars.items()), 0)
+        return layers
+
+    def print_layers(self, title: str = None, hide_layers_with_no_params: bool = False) -> None:
+        """Print a summary table of the network structure."""
+        rows = [[title if title is not None else self.name, "Params", "OutputShape", "WeightShape"]]
+        rows += [["---"] * 4]
+        total_params = 0
+
+        for layer_name, layer_output, layer_trainables in self.list_layers():
+            num_params = sum(np.prod(tfutil.shape_to_list(var.shape)) for var in layer_trainables)
+            weights = [var for var in layer_trainables if var.name.endswith("/weight:0")]
+            weights.sort(key=lambda x: len(x.name))
+            if len(weights) == 0 and len(layer_trainables) == 1:
+                weights = layer_trainables
+            total_params += num_params
+
+            if not hide_layers_with_no_params or num_params != 0:
+                num_params_str = str(num_params) if num_params > 0 else "-"
+                output_shape_str = str(layer_output.shape)
+                weight_shape_str = str(weights[0].shape) if len(weights) >= 1 else "-"
+                rows += [[layer_name, num_params_str, output_shape_str, weight_shape_str]]
+
+        rows += [["---"] * 4]
+        rows += [["Total", str(total_params), "", ""]]
+
+        widths = [max(len(cell) for cell in column) for column in zip(*rows)]
+        print()
+        for row in rows:
+            print("  ".join(cell + " " * (width - len(cell)) for cell, width in zip(row, widths)))
+        print()
+
+    def setup_weight_histograms(self, title: str = None) -> None:
+        """Construct summary ops to include histograms of all trainable parameters in TensorBoard."""
+        if title is None:
+            title = self.name
+
+        with tf.name_scope(None), tf.device(None), tf.control_dependencies(None):
+            for local_name, var in self.trainables.items():
+                if "/" in local_name:
+                    p = local_name.split("/")
+                    name = title + "_" + p[-1] + "/" + "_".join(p[:-1])
+                else:
+                    name = title + "_toplevel/" + local_name
+
+                tf.summary.histogram(name, var)
+
+#----------------------------------------------------------------------------
+# Backwards-compatible emulation of legacy output transformation in Network.run().
+
+_print_legacy_warning = True
+
+def _handle_legacy_output_transforms(output_transform, dynamic_kwargs):
+    global _print_legacy_warning
+    legacy_kwargs = ["out_mul", "out_add", "out_shrink", "out_dtype"]
+    if not any(kwarg in dynamic_kwargs for kwarg in legacy_kwargs):
+        return output_transform, dynamic_kwargs
+
+    if _print_legacy_warning:
+        _print_legacy_warning = False
+        print()
+        print("WARNING: Old-style output transformations in Network.run() are deprecated.")
+        print("Consider using 'output_transform=dict(func=tflib.convert_images_to_uint8)'")
+        print("instead of 'out_mul=127.5, out_add=127.5, out_dtype=np.uint8'.")
+        print()
+    assert output_transform is None
+
+    new_kwargs = dict(dynamic_kwargs)
+    new_transform = {kwarg: new_kwargs.pop(kwarg) for kwarg in legacy_kwargs if kwarg in dynamic_kwargs}
+    new_transform["func"] = _legacy_output_transform_func
+    return new_transform, new_kwargs
+
+def _legacy_output_transform_func(*expr, out_mul=1.0, out_add=0.0, out_shrink=1, out_dtype=None):
+    if out_mul != 1.0:
+        expr = [x * out_mul for x in expr]
+
+    if out_add != 0.0:
+        expr = [x + out_add for x in expr]
+
+    if out_shrink > 1:
+        ksize = [1, 1, out_shrink, out_shrink]
+        expr = [tf.nn.avg_pool(x, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW") for x in expr]
+
+    if out_dtype is not None:
+        if tf.as_dtype(out_dtype).is_integer:
+            expr = [tf.round(x) for x in expr]
+        expr = [tf.saturate_cast(x, out_dtype) for x in expr]
+    return expr

models/stylegan/stylegan_tf/dnnlib/tflib/optimizer.py

@@ -0,0 +1,214 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Helper wrapper for a Tensorflow optimizer."""
+
+import numpy as np
+import tensorflow as tf
+
+from collections import OrderedDict
+from typing import List, Union
+
+from . import autosummary
+from . import tfutil
+from .. import util
+
+from .tfutil import TfExpression, TfExpressionEx
+
+try:
+    # TensorFlow 1.13
+    from tensorflow.python.ops import nccl_ops
+except:
+    # Older TensorFlow versions
+    import tensorflow.contrib.nccl as nccl_ops
+
+class Optimizer:
+    """A Wrapper for tf.train.Optimizer.
+
+    Automatically takes care of:
+    - Gradient averaging for multi-GPU training.
+    - Dynamic loss scaling and typecasts for FP16 training.
+    - Ignoring corrupted gradients that contain NaNs/Infs.
+    - Reporting statistics.
+    - Well-chosen default settings.
+    """
+
+    def __init__(self,
+                 name: str = "Train",
+                 tf_optimizer: str = "tf.train.AdamOptimizer",
+                 learning_rate: TfExpressionEx = 0.001,
+                 use_loss_scaling: bool = False,
+                 loss_scaling_init: float = 64.0,
+                 loss_scaling_inc: float = 0.0005,
+                 loss_scaling_dec: float = 1.0,
+                 **kwargs):
+
+        # Init fields.
+        self.name = name
+        self.learning_rate = tf.convert_to_tensor(learning_rate)
+        self.id = self.name.replace("/", ".")
+        self.scope = tf.get_default_graph().unique_name(self.id)
+        self.optimizer_class = util.get_obj_by_name(tf_optimizer)
+        self.optimizer_kwargs = dict(kwargs)
+        self.use_loss_scaling = use_loss_scaling
+        self.loss_scaling_init = loss_scaling_init
+        self.loss_scaling_inc = loss_scaling_inc
+        self.loss_scaling_dec = loss_scaling_dec
+        self._grad_shapes = None  # [shape, ...]
+        self._dev_opt = OrderedDict()  # device => optimizer
+        self._dev_grads = OrderedDict()  # device => [[(grad, var), ...], ...]
+        self._dev_ls_var = OrderedDict()  # device => variable (log2 of loss scaling factor)
+        self._updates_applied = False
+
+    def register_gradients(self, loss: TfExpression, trainable_vars: Union[List, dict]) -> None:
+        """Register the gradients of the given loss function with respect to the given variables.
+        Intended to be called once per GPU."""
+        assert not self._updates_applied
+
+        # Validate arguments.
+        if isinstance(trainable_vars, dict):
+            trainable_vars = list(trainable_vars.values())  # allow passing in Network.trainables as vars
+
+        assert isinstance(trainable_vars, list) and len(trainable_vars) >= 1
+        assert all(tfutil.is_tf_expression(expr) for expr in trainable_vars + [loss])
+
+        if self._grad_shapes is None:
+            self._grad_shapes = [tfutil.shape_to_list(var.shape) for var in trainable_vars]
+
+        assert len(trainable_vars) == len(self._grad_shapes)
+        assert all(tfutil.shape_to_list(var.shape) == var_shape for var, var_shape in zip(trainable_vars, self._grad_shapes))
+
+        dev = loss.device
+
+        assert all(var.device == dev for var in trainable_vars)
+
+        # Register device and compute gradients.
+        with tf.name_scope(self.id + "_grad"), tf.device(dev):
+            if dev not in self._dev_opt:
+                opt_name = self.scope.replace("/", "_") + "_opt%d" % len(self._dev_opt)
+                assert callable(self.optimizer_class)
+                self._dev_opt[dev] = self.optimizer_class(name=opt_name, learning_rate=self.learning_rate, **self.optimizer_kwargs)
+                self._dev_grads[dev] = []
+
+            loss = self.apply_loss_scaling(tf.cast(loss, tf.float32))
+            grads = self._dev_opt[dev].compute_gradients(loss, trainable_vars, gate_gradients=tf.train.Optimizer.GATE_NONE)  # disable gating to reduce memory usage
+            grads = [(g, v) if g is not None else (tf.zeros_like(v), v) for g, v in grads]  # replace disconnected gradients with zeros
+            self._dev_grads[dev].append(grads)
+
+    def apply_updates(self) -> tf.Operation:
+        """Construct training op to update the registered variables based on their gradients."""
+        tfutil.assert_tf_initialized()
+        assert not self._updates_applied
+        self._updates_applied = True
+        devices = list(self._dev_grads.keys())
+        total_grads = sum(len(grads) for grads in self._dev_grads.values())
+        assert len(devices) >= 1 and total_grads >= 1
+        ops = []
+
+        with tfutil.absolute_name_scope(self.scope):
+            # Cast gradients to FP32 and calculate partial sum within each device.
+            dev_grads = OrderedDict()  # device => [(grad, var), ...]
+
+            for dev_idx, dev in enumerate(devices):
+                with tf.name_scope("ProcessGrads%d" % dev_idx), tf.device(dev):
+                    sums = []
+
+                    for gv in zip(*self._dev_grads[dev]):
+                        assert all(v is gv[0][1] for g, v in gv)
+                        g = [tf.cast(g, tf.float32) for g, v in gv]
+                        g = g[0] if len(g) == 1 else tf.add_n(g)
+                        sums.append((g, gv[0][1]))
+
+                    dev_grads[dev] = sums
+
+            # Sum gradients across devices.
+            if len(devices) > 1:
+                with tf.name_scope("SumAcrossGPUs"), tf.device(None):
+                    for var_idx, grad_shape in enumerate(self._grad_shapes):
+                        g = [dev_grads[dev][var_idx][0] for dev in devices]
+
+                        if np.prod(grad_shape):  # nccl does not support zero-sized tensors
+                            g = nccl_ops.all_sum(g)
+
+                        for dev, gg in zip(devices, g):
+                            dev_grads[dev][var_idx] = (gg, dev_grads[dev][var_idx][1])
+
+            # Apply updates separately on each device.
+            for dev_idx, (dev, grads) in enumerate(dev_grads.items()):
+                with tf.name_scope("ApplyGrads%d" % dev_idx), tf.device(dev):
+                    # Scale gradients as needed.
+                    if self.use_loss_scaling or total_grads > 1:
+                        with tf.name_scope("Scale"):
+                            coef = tf.constant(np.float32(1.0 / total_grads), name="coef")
+                            coef = self.undo_loss_scaling(coef)
+                            grads = [(g * coef, v) for g, v in grads]
+
+                    # Check for overflows.
+                    with tf.name_scope("CheckOverflow"):
+                        grad_ok = tf.reduce_all(tf.stack([tf.reduce_all(tf.is_finite(g)) for g, v in grads]))
+
+                    # Update weights and adjust loss scaling.
+                    with tf.name_scope("UpdateWeights"):
+                        # pylint: disable=cell-var-from-loop
+                        opt = self._dev_opt[dev]
+                        ls_var = self.get_loss_scaling_var(dev)
+
+                        if not self.use_loss_scaling:
+                            ops.append(tf.cond(grad_ok, lambda: opt.apply_gradients(grads), tf.no_op))
+                        else:
+                            ops.append(tf.cond(grad_ok,
+                                               lambda: tf.group(tf.assign_add(ls_var, self.loss_scaling_inc), opt.apply_gradients(grads)),
+                                               lambda: tf.group(tf.assign_sub(ls_var, self.loss_scaling_dec))))
+
+                    # Report statistics on the last device.
+                    if dev == devices[-1]:
+                        with tf.name_scope("Statistics"):
+                            ops.append(autosummary.autosummary(self.id + "/learning_rate", self.learning_rate))
+                            ops.append(autosummary.autosummary(self.id + "/overflow_frequency", tf.where(grad_ok, 0, 1)))
+
+                            if self.use_loss_scaling:
+                                ops.append(autosummary.autosummary(self.id + "/loss_scaling_log2", ls_var))
+
+            # Initialize variables and group everything into a single op.
+            self.reset_optimizer_state()
+            tfutil.init_uninitialized_vars(list(self._dev_ls_var.values()))
+
+            return tf.group(*ops, name="TrainingOp")
+
+    def reset_optimizer_state(self) -> None:
+        """Reset internal state of the underlying optimizer."""
+        tfutil.assert_tf_initialized()
+        tfutil.run([var.initializer for opt in self._dev_opt.values() for var in opt.variables()])
+
+    def get_loss_scaling_var(self, device: str) -> Union[tf.Variable, None]:
+        """Get or create variable representing log2 of the current dynamic loss scaling factor."""
+        if not self.use_loss_scaling:
+            return None
+
+        if device not in self._dev_ls_var:
+            with tfutil.absolute_name_scope(self.scope + "/LossScalingVars"), tf.control_dependencies(None):
+                self._dev_ls_var[device] = tf.Variable(np.float32(self.loss_scaling_init), name="loss_scaling_var")
+
+        return self._dev_ls_var[device]
+
+    def apply_loss_scaling(self, value: TfExpression) -> TfExpression:
+        """Apply dynamic loss scaling for the given expression."""
+        assert tfutil.is_tf_expression(value)
+
+        if not self.use_loss_scaling:
+            return value
+
+        return value * tfutil.exp2(self.get_loss_scaling_var(value.device))
+
+    def undo_loss_scaling(self, value: TfExpression) -> TfExpression:
+        """Undo the effect of dynamic loss scaling for the given expression."""
+        assert tfutil.is_tf_expression(value)
+
+        if not self.use_loss_scaling:
+            return value
+
+        return value * tfutil.exp2(-self.get_loss_scaling_var(value.device)) # pylint: disable=invalid-unary-operand-type

models/stylegan/stylegan_tf/dnnlib/tflib/tfutil.py

@@ -0,0 +1,240 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Miscellaneous helper utils for Tensorflow."""
+
+import os
+import numpy as np
+import tensorflow as tf
+
+from typing import Any, Iterable, List, Union
+
+TfExpression = Union[tf.Tensor, tf.Variable, tf.Operation]
+"""A type that represents a valid Tensorflow expression."""
+
+TfExpressionEx = Union[TfExpression, int, float, np.ndarray]
+"""A type that can be converted to a valid Tensorflow expression."""
+
+
+def run(*args, **kwargs) -> Any:
+    """Run the specified ops in the default session."""
+    assert_tf_initialized()
+    return tf.get_default_session().run(*args, **kwargs)
+
+
+def is_tf_expression(x: Any) -> bool:
+    """Check whether the input is a valid Tensorflow expression, i.e., Tensorflow Tensor, Variable, or Operation."""
+    return isinstance(x, (tf.Tensor, tf.Variable, tf.Operation))
+
+
+def shape_to_list(shape: Iterable[tf.Dimension]) -> List[Union[int, None]]:
+    """Convert a Tensorflow shape to a list of ints."""
+    return [dim.value for dim in shape]
+
+
+def flatten(x: TfExpressionEx) -> TfExpression:
+    """Shortcut function for flattening a tensor."""
+    with tf.name_scope("Flatten"):
+        return tf.reshape(x, [-1])
+
+
+def log2(x: TfExpressionEx) -> TfExpression:
+    """Logarithm in base 2."""
+    with tf.name_scope("Log2"):
+        return tf.log(x) * np.float32(1.0 / np.log(2.0))
+
+
+def exp2(x: TfExpressionEx) -> TfExpression:
+    """Exponent in base 2."""
+    with tf.name_scope("Exp2"):
+        return tf.exp(x * np.float32(np.log(2.0)))
+
+
+def lerp(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfExpressionEx:
+    """Linear interpolation."""
+    with tf.name_scope("Lerp"):
+        return a + (b - a) * t
+
+
+def lerp_clip(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfExpression:
+    """Linear interpolation with clip."""
+    with tf.name_scope("LerpClip"):
+        return a + (b - a) * tf.clip_by_value(t, 0.0, 1.0)
+
+
+def absolute_name_scope(scope: str) -> tf.name_scope:
+    """Forcefully enter the specified name scope, ignoring any surrounding scopes."""
+    return tf.name_scope(scope + "/")
+
+
+def absolute_variable_scope(scope: str, **kwargs) -> tf.variable_scope:
+    """Forcefully enter the specified variable scope, ignoring any surrounding scopes."""
+    return tf.variable_scope(tf.VariableScope(name=scope, **kwargs), auxiliary_name_scope=False)
+
+
+def _sanitize_tf_config(config_dict: dict = None) -> dict:
+    # Defaults.
+    cfg = dict()
+    cfg["rnd.np_random_seed"]               = None      # Random seed for NumPy. None = keep as is.
+    cfg["rnd.tf_random_seed"]               = "auto"    # Random seed for TensorFlow. 'auto' = derive from NumPy random state. None = keep as is.
+    cfg["env.TF_CPP_MIN_LOG_LEVEL"]         = "1"       # 0 = Print all available debug info from TensorFlow. 1 = Print warnings and errors, but disable debug info.
+    cfg["graph_options.place_pruned_graph"] = True      # False = Check that all ops are available on the designated device. True = Skip the check for ops that are not used.
+    cfg["gpu_options.allow_growth"]         = True      # False = Allocate all GPU memory at the beginning. True = Allocate only as much GPU memory as needed.
+
+    # User overrides.
+    if config_dict is not None:
+        cfg.update(config_dict)
+    return cfg
+
+
+def init_tf(config_dict: dict = None) -> None:
+    """Initialize TensorFlow session using good default settings."""
+    # Skip if already initialized.
+    if tf.get_default_session() is not None:
+        return
+
+    # Setup config dict and random seeds.
+    cfg = _sanitize_tf_config(config_dict)
+    np_random_seed = cfg["rnd.np_random_seed"]
+    if np_random_seed is not None:
+        np.random.seed(np_random_seed)
+    tf_random_seed = cfg["rnd.tf_random_seed"]
+    if tf_random_seed == "auto":
+        tf_random_seed = np.random.randint(1 << 31)
+    if tf_random_seed is not None:
+        tf.set_random_seed(tf_random_seed)
+
+    # Setup environment variables.
+    for key, value in list(cfg.items()):
+        fields = key.split(".")
+        if fields[0] == "env":
+            assert len(fields) == 2
+            os.environ[fields[1]] = str(value)
+
+    # Create default TensorFlow session.
+    create_session(cfg, force_as_default=True)
+
+
+def assert_tf_initialized():
+    """Check that TensorFlow session has been initialized."""
+    if tf.get_default_session() is None:
+        raise RuntimeError("No default TensorFlow session found. Please call dnnlib.tflib.init_tf().")
+
+
+def create_session(config_dict: dict = None, force_as_default: bool = False) -> tf.Session:
+    """Create tf.Session based on config dict."""
+    # Setup TensorFlow config proto.
+    cfg = _sanitize_tf_config(config_dict)
+    config_proto = tf.ConfigProto()
+    for key, value in cfg.items():
+        fields = key.split(".")
+        if fields[0] not in ["rnd", "env"]:
+            obj = config_proto
+            for field in fields[:-1]:
+                obj = getattr(obj, field)
+            setattr(obj, fields[-1], value)
+
+    # Create session.
+    session = tf.Session(config=config_proto)
+    if force_as_default:
+        # pylint: disable=protected-access
+        session._default_session = session.as_default()
+        session._default_session.enforce_nesting = False
+        session._default_session.__enter__() # pylint: disable=no-member
+
+    return session
+
+
+def init_uninitialized_vars(target_vars: List[tf.Variable] = None) -> None:
+    """Initialize all tf.Variables that have not already been initialized.
+
+    Equivalent to the following, but more efficient and does not bloat the tf graph:
+    tf.variables_initializer(tf.report_uninitialized_variables()).run()
+    """
+    assert_tf_initialized()
+    if target_vars is None:
+        target_vars = tf.global_variables()
+
+    test_vars = []
+    test_ops = []
+
+    with tf.control_dependencies(None):  # ignore surrounding control_dependencies
+        for var in target_vars:
+            assert is_tf_expression(var)
+
+            try:
+                tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/IsVariableInitialized:0"))
+            except KeyError:
+                # Op does not exist => variable may be uninitialized.
+                test_vars.append(var)
+
+                with absolute_name_scope(var.name.split(":")[0]):
+                    test_ops.append(tf.is_variable_initialized(var))
+
+    init_vars = [var for var, inited in zip(test_vars, run(test_ops)) if not inited]
+    run([var.initializer for var in init_vars])
+
+
+def set_vars(var_to_value_dict: dict) -> None:
+    """Set the values of given tf.Variables.
+
+    Equivalent to the following, but more efficient and does not bloat the tf graph:
+    tflib.run([tf.assign(var, value) for var, value in var_to_value_dict.items()]
+    """
+    assert_tf_initialized()
+    ops = []
+    feed_dict = {}
+
+    for var, value in var_to_value_dict.items():
+        assert is_tf_expression(var)
+
+        try:
+            setter = tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/setter:0"))  # look for existing op
+        except KeyError:
+            with absolute_name_scope(var.name.split(":")[0]):
+                with tf.control_dependencies(None):  # ignore surrounding control_dependencies
+                    setter = tf.assign(var, tf.placeholder(var.dtype, var.shape, "new_value"), name="setter")  # create new setter
+
+        ops.append(setter)
+        feed_dict[setter.op.inputs[1]] = value
+
+    run(ops, feed_dict)
+
+
+def create_var_with_large_initial_value(initial_value: np.ndarray, *args, **kwargs):
+    """Create tf.Variable with large initial value without bloating the tf graph."""
+    assert_tf_initialized()
+    assert isinstance(initial_value, np.ndarray)
+    zeros = tf.zeros(initial_value.shape, initial_value.dtype)
+    var = tf.Variable(zeros, *args, **kwargs)
+    set_vars({var: initial_value})
+    return var
+
+
+def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False):
+    """Convert a minibatch of images from uint8 to float32 with configurable dynamic range.
+    Can be used as an input transformation for Network.run().
+    """
+    images = tf.cast(images, tf.float32)
+    if nhwc_to_nchw:
+        images = tf.transpose(images, [0, 3, 1, 2])
+    return (images - drange[0]) * ((drange[1] - drange[0]) / 255)
+
+
+def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False, shrink=1):
+    """Convert a minibatch of images from float32 to uint8 with configurable dynamic range.
+    Can be used as an output transformation for Network.run().
+    """
+    images = tf.cast(images, tf.float32)
+    if shrink > 1:
+        ksize = [1, 1, shrink, shrink]
+        images = tf.nn.avg_pool(images, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW")
+    if nchw_to_nhwc:
+        images = tf.transpose(images, [0, 2, 3, 1])
+    scale = 255 / (drange[1] - drange[0])
+    images = images * scale + (0.5 - drange[0] * scale)
+    return tf.saturate_cast(images, tf.uint8)

models/stylegan/stylegan_tf/dnnlib/util.py

@@ -0,0 +1,405 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Miscellaneous utility classes and functions."""
+
+import ctypes
+import fnmatch
+import importlib
+import inspect
+import numpy as np
+import os
+import shutil
+import sys
+import types
+import io
+import pickle
+import re
+import requests
+import html
+import hashlib
+import glob
+import uuid
+
+from distutils.util import strtobool
+from typing import Any, List, Tuple, Union
+
+
+# Util classes
+# ------------------------------------------------------------------------------------------
+
+
+class EasyDict(dict):
+    """Convenience class that behaves like a dict but allows access with the attribute syntax."""
+
+    def __getattr__(self, name: str) -> Any:
+        try:
+            return self[name]
+        except KeyError:
+            raise AttributeError(name)
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        self[name] = value
+
+    def __delattr__(self, name: str) -> None:
+        del self[name]
+
+
+class Logger(object):
+    """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""
+
+    def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True):
+        self.file = None
+
+        if file_name is not None:
+            self.file = open(file_name, file_mode)
+
+        self.should_flush = should_flush
+        self.stdout = sys.stdout
+        self.stderr = sys.stderr
+
+        sys.stdout = self
+        sys.stderr = self
+
+    def __enter__(self) -> "Logger":
+        return self
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        self.close()
+
+    def write(self, text: str) -> None:
+        """Write text to stdout (and a file) and optionally flush."""
+        if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
+            return
+
+        if self.file is not None:
+            self.file.write(text)
+
+        self.stdout.write(text)
+
+        if self.should_flush:
+            self.flush()
+
+    def flush(self) -> None:
+        """Flush written text to both stdout and a file, if open."""
+        if self.file is not None:
+            self.file.flush()
+
+        self.stdout.flush()
+
+    def close(self) -> None:
+        """Flush, close possible files, and remove stdout/stderr mirroring."""
+        self.flush()
+
+        # if using multiple loggers, prevent closing in wrong order
+        if sys.stdout is self:
+            sys.stdout = self.stdout
+        if sys.stderr is self:
+            sys.stderr = self.stderr
+
+        if self.file is not None:
+            self.file.close()
+
+
+# Small util functions
+# ------------------------------------------------------------------------------------------
+
+
+def format_time(seconds: Union[int, float]) -> str:
+    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
+    s = int(np.rint(seconds))
+
+    if s < 60:
+        return "{0}s".format(s)
+    elif s < 60 * 60:
+        return "{0}m {1:02}s".format(s // 60, s % 60)
+    elif s < 24 * 60 * 60:
+        return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60)
+    else:
+        return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60)
+
+
+def ask_yes_no(question: str) -> bool:
+    """Ask the user the question until the user inputs a valid answer."""
+    while True:
+        try:
+            print("{0} [y/n]".format(question))
+            return strtobool(input().lower())
+        except ValueError:
+            pass
+
+
+def tuple_product(t: Tuple) -> Any:
+    """Calculate the product of the tuple elements."""
+    result = 1
+
+    for v in t:
+        result *= v
+
+    return result
+
+
+_str_to_ctype = {
+    "uint8": ctypes.c_ubyte,
+    "uint16": ctypes.c_uint16,
+    "uint32": ctypes.c_uint32,
+    "uint64": ctypes.c_uint64,
+    "int8": ctypes.c_byte,
+    "int16": ctypes.c_int16,
+    "int32": ctypes.c_int32,
+    "int64": ctypes.c_int64,
+    "float32": ctypes.c_float,
+    "float64": ctypes.c_double
+}
+
+
+def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
+    """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes."""
+    type_str = None
+
+    if isinstance(type_obj, str):
+        type_str = type_obj
+    elif hasattr(type_obj, "__name__"):
+        type_str = type_obj.__name__
+    elif hasattr(type_obj, "name"):
+        type_str = type_obj.name
+    else:
+        raise RuntimeError("Cannot infer type name from input")
+
+    assert type_str in _str_to_ctype.keys()
+
+    my_dtype = np.dtype(type_str)
+    my_ctype = _str_to_ctype[type_str]
+
+    assert my_dtype.itemsize == ctypes.sizeof(my_ctype)
+
+    return my_dtype, my_ctype
+
+
+def is_pickleable(obj: Any) -> bool:
+    try:
+        with io.BytesIO() as stream:
+            pickle.dump(obj, stream)
+        return True
+    except:
+        return False
+
+
+# Functionality to import modules/objects by name, and call functions by name
+# ------------------------------------------------------------------------------------------
+
+def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]:
+    """Searches for the underlying module behind the name to some python object.
+    Returns the module and the object name (original name with module part removed)."""
+
+    # allow convenience shorthands, substitute them by full names
+    obj_name = re.sub("^np.", "numpy.", obj_name)
+    obj_name = re.sub("^tf.", "tensorflow.", obj_name)
+
+    # list alternatives for (module_name, local_obj_name)
+    parts = obj_name.split(".")
+    name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)]
+
+    # try each alternative in turn
+    for module_name, local_obj_name in name_pairs:
+        try:
+            module = importlib.import_module(module_name) # may raise ImportError
+            get_obj_from_module(module, local_obj_name) # may raise AttributeError
+            return module, local_obj_name
+        except:
+            pass
+
+    # maybe some of the modules themselves contain errors?
+    for module_name, _local_obj_name in name_pairs:
+        try:
+            importlib.import_module(module_name) # may raise ImportError
+        except ImportError:
+            if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"):
+                raise
+
+    # maybe the requested attribute is missing?
+    for module_name, local_obj_name in name_pairs:
+        try:
+            module = importlib.import_module(module_name) # may raise ImportError
+            get_obj_from_module(module, local_obj_name) # may raise AttributeError
+        except ImportError:
+            pass
+
+    # we are out of luck, but we have no idea why
+    raise ImportError(obj_name)
+
+
+def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
+    """Traverses the object name and returns the last (rightmost) python object."""
+    if obj_name == '':
+        return module
+    obj = module
+    for part in obj_name.split("."):
+        obj = getattr(obj, part)
+    return obj
+
+
+def get_obj_by_name(name: str) -> Any:
+    """Finds the python object with the given name."""
+    module, obj_name = get_module_from_obj_name(name)
+    return get_obj_from_module(module, obj_name)
+
+
+def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
+    """Finds the python object with the given name and calls it as a function."""
+    assert func_name is not None
+    func_obj = get_obj_by_name(func_name)
+    assert callable(func_obj)
+    return func_obj(*args, **kwargs)
+
+
+def get_module_dir_by_obj_name(obj_name: str) -> str:
+    """Get the directory path of the module containing the given object name."""
+    module, _ = get_module_from_obj_name(obj_name)
+    return os.path.dirname(inspect.getfile(module))
+
+
+def is_top_level_function(obj: Any) -> bool:
+    """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'."""
+    return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__
+
+
+def get_top_level_function_name(obj: Any) -> str:
+    """Return the fully-qualified name of a top-level function."""
+    assert is_top_level_function(obj)
+    return obj.__module__ + "." + obj.__name__
+
+
+# File system helpers
+# ------------------------------------------------------------------------------------------
+
+def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]:
+    """List all files recursively in a given directory while ignoring given file and directory names.
+    Returns list of tuples containing both absolute and relative paths."""
+    assert os.path.isdir(dir_path)
+    base_name = os.path.basename(os.path.normpath(dir_path))
+
+    if ignores is None:
+        ignores = []
+
+    result = []
+
+    for root, dirs, files in os.walk(dir_path, topdown=True):
+        for ignore_ in ignores:
+            dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)]
+
+            # dirs need to be edited in-place
+            for d in dirs_to_remove:
+                dirs.remove(d)
+
+            files = [f for f in files if not fnmatch.fnmatch(f, ignore_)]
+
+        absolute_paths = [os.path.join(root, f) for f in files]
+        relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths]
+
+        if add_base_to_relative:
+            relative_paths = [os.path.join(base_name, p) for p in relative_paths]
+
+        assert len(absolute_paths) == len(relative_paths)
+        result += zip(absolute_paths, relative_paths)
+
+    return result
+
+
+def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
+    """Takes in a list of tuples of (src, dst) paths and copies files.
+    Will create all necessary directories."""
+    for file in files:
+        target_dir_name = os.path.dirname(file[1])
+
+        # will create all intermediate-level directories
+        if not os.path.exists(target_dir_name):
+            os.makedirs(target_dir_name)
+
+        shutil.copyfile(file[0], file[1])
+
+
+# URL helpers
+# ------------------------------------------------------------------------------------------
+
+def is_url(obj: Any) -> bool:
+    """Determine whether the given object is a valid URL string."""
+    if not isinstance(obj, str) or not "://" in obj:
+        return False
+    try:
+        res = requests.compat.urlparse(obj)
+        if not res.scheme or not res.netloc or not "." in res.netloc:
+            return False
+        res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
+        if not res.scheme or not res.netloc or not "." in res.netloc:
+            return False
+    except:
+        return False
+    return True
+
+
+def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True) -> Any:
+    """Download the given URL and return a binary-mode file object to access the data."""
+    assert is_url(url)
+    assert num_attempts >= 1
+
+    # Lookup from cache.
+    url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
+    if cache_dir is not None:
+        cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
+        if len(cache_files) == 1:
+            return open(cache_files[0], "rb")
+
+    # Download.
+    url_name = None
+    url_data = None
+    with requests.Session() as session:
+        if verbose:
+            print("Downloading %s ..." % url, end="", flush=True)
+        for attempts_left in reversed(range(num_attempts)):
+            try:
+                with session.get(url) as res:
+                    res.raise_for_status()
+                    if len(res.content) == 0:
+                        raise IOError("No data received")
+
+                    if len(res.content) < 8192:
+                        content_str = res.content.decode("utf-8")
+                        if "download_warning" in res.headers.get("Set-Cookie", ""):
+                            links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link]
+                            if len(links) == 1:
+                                url = requests.compat.urljoin(url, links[0])
+                                raise IOError("Google Drive virus checker nag")
+                        if "Google Drive - Quota exceeded" in content_str:
+                            raise IOError("Google Drive quota exceeded")
+
+                    match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", ""))
+                    url_name = match[1] if match else url
+                    url_data = res.content
+                    if verbose:
+                        print(" done")
+                    break
+            except:
+                if not attempts_left:
+                    if verbose:
+                        print(" failed")
+                    raise
+                if verbose:
+                    print(".", end="", flush=True)
+
+    # Save to cache.
+    if cache_dir is not None:
+        safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
+        cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
+        temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
+        os.makedirs(cache_dir, exist_ok=True)
+        with open(temp_file, "wb") as f:
+            f.write(url_data)
+        os.replace(temp_file, cache_file) # atomic
+
+    # Return data as file object.
+    return io.BytesIO(url_data)

models/stylegan/stylegan_tf/generate_figures.py

@@ -0,0 +1,161 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Minimal script for reproducing the figures of the StyleGAN paper using pre-trained generators."""
+
+import os
+import pickle
+import numpy as np
+import PIL.Image
+import dnnlib
+import dnnlib.tflib as tflib
+import config
+
+#----------------------------------------------------------------------------
+# Helpers for loading and using pre-trained generators.
+
+url_ffhq        = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ' # karras2019stylegan-ffhq-1024x1024.pkl
+url_celebahq    = 'https://drive.google.com/uc?id=1MGqJl28pN4t7SAtSrPdSRJSQJqahkzUf' # karras2019stylegan-celebahq-1024x1024.pkl
+url_bedrooms    = 'https://drive.google.com/uc?id=1MOSKeGF0FJcivpBI7s63V9YHloUTORiF' # karras2019stylegan-bedrooms-256x256.pkl
+url_cars        = 'https://drive.google.com/uc?id=1MJ6iCfNtMIRicihwRorsM3b7mmtmK9c3' # karras2019stylegan-cars-512x384.pkl
+url_cats        = 'https://drive.google.com/uc?id=1MQywl0FNt6lHu8E_EUqnRbviagS7fbiJ' # karras2019stylegan-cats-256x256.pkl
+
+synthesis_kwargs = dict(output_transform=dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True), minibatch_size=8)
+
+_Gs_cache = dict()
+
+def load_Gs(url):
+    if url not in _Gs_cache:
+        with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
+            _G, _D, Gs = pickle.load(f)
+        _Gs_cache[url] = Gs
+    return _Gs_cache[url]
+
+#----------------------------------------------------------------------------
+# Figures 2, 3, 10, 11, 12: Multi-resolution grid of uncurated result images.
+
+def draw_uncurated_result_figure(png, Gs, cx, cy, cw, ch, rows, lods, seed):
+    print(png)
+    latents = np.random.RandomState(seed).randn(sum(rows * 2**lod for lod in lods), Gs.input_shape[1])
+    images = Gs.run(latents, None, **synthesis_kwargs) # [seed, y, x, rgb]
+
+    canvas = PIL.Image.new('RGB', (sum(cw // 2**lod for lod in lods), ch * rows), 'white')
+    image_iter = iter(list(images))
+    for col, lod in enumerate(lods):
+        for row in range(rows * 2**lod):
+            image = PIL.Image.fromarray(next(image_iter), 'RGB')
+            image = image.crop((cx, cy, cx + cw, cy + ch))
+            image = image.resize((cw // 2**lod, ch // 2**lod), PIL.Image.ANTIALIAS)
+            canvas.paste(image, (sum(cw // 2**lod for lod in lods[:col]), row * ch // 2**lod))
+    canvas.save(png)
+
+#----------------------------------------------------------------------------
+# Figure 3: Style mixing.
+
+def draw_style_mixing_figure(png, Gs, w, h, src_seeds, dst_seeds, style_ranges):
+    print(png)
+    src_latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in src_seeds)
+    dst_latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in dst_seeds)
+    src_dlatents = Gs.components.mapping.run(src_latents, None) # [seed, layer, component]
+    dst_dlatents = Gs.components.mapping.run(dst_latents, None) # [seed, layer, component]
+    src_images = Gs.components.synthesis.run(src_dlatents, randomize_noise=False, **synthesis_kwargs)
+    dst_images = Gs.components.synthesis.run(dst_dlatents, randomize_noise=False, **synthesis_kwargs)
+
+    canvas = PIL.Image.new('RGB', (w * (len(src_seeds) + 1), h * (len(dst_seeds) + 1)), 'white')
+    for col, src_image in enumerate(list(src_images)):
+        canvas.paste(PIL.Image.fromarray(src_image, 'RGB'), ((col + 1) * w, 0))
+    for row, dst_image in enumerate(list(dst_images)):
+        canvas.paste(PIL.Image.fromarray(dst_image, 'RGB'), (0, (row + 1) * h))
+        row_dlatents = np.stack([dst_dlatents[row]] * len(src_seeds))
+        row_dlatents[:, style_ranges[row]] = src_dlatents[:, style_ranges[row]]
+        row_images = Gs.components.synthesis.run(row_dlatents, randomize_noise=False, **synthesis_kwargs)
+        for col, image in enumerate(list(row_images)):
+            canvas.paste(PIL.Image.fromarray(image, 'RGB'), ((col + 1) * w, (row + 1) * h))
+    canvas.save(png)
+
+#----------------------------------------------------------------------------
+# Figure 4: Noise detail.
+
+def draw_noise_detail_figure(png, Gs, w, h, num_samples, seeds):
+    print(png)
+    canvas = PIL.Image.new('RGB', (w * 3, h * len(seeds)), 'white')
+    for row, seed in enumerate(seeds):
+        latents = np.stack([np.random.RandomState(seed).randn(Gs.input_shape[1])] * num_samples)
+        images = Gs.run(latents, None, truncation_psi=1, **synthesis_kwargs)
+        canvas.paste(PIL.Image.fromarray(images[0], 'RGB'), (0, row * h))
+        for i in range(4):
+            crop = PIL.Image.fromarray(images[i + 1], 'RGB')
+            crop = crop.crop((650, 180, 906, 436))
+            crop = crop.resize((w//2, h//2), PIL.Image.NEAREST)
+            canvas.paste(crop, (w + (i%2) * w//2, row * h + (i//2) * h//2))
+        diff = np.std(np.mean(images, axis=3), axis=0) * 4
+        diff = np.clip(diff + 0.5, 0, 255).astype(np.uint8)
+        canvas.paste(PIL.Image.fromarray(diff, 'L'), (w * 2, row * h))
+    canvas.save(png)
+
+#----------------------------------------------------------------------------
+# Figure 5: Noise components.
+
+def draw_noise_components_figure(png, Gs, w, h, seeds, noise_ranges, flips):
+    print(png)
+    Gsc = Gs.clone()
+    noise_vars = [var for name, var in Gsc.components.synthesis.vars.items() if name.startswith('noise')]
+    noise_pairs = list(zip(noise_vars, tflib.run(noise_vars))) # [(var, val), ...]
+    latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in seeds)
+    all_images = []
+    for noise_range in noise_ranges:
+        tflib.set_vars({var: val * (1 if i in noise_range else 0) for i, (var, val) in enumerate(noise_pairs)})
+        range_images = Gsc.run(latents, None, truncation_psi=1, randomize_noise=False, **synthesis_kwargs)
+        range_images[flips, :, :] = range_images[flips, :, ::-1]
+        all_images.append(list(range_images))
+
+    canvas = PIL.Image.new('RGB', (w * 2, h * 2), 'white')
+    for col, col_images in enumerate(zip(*all_images)):
+        canvas.paste(PIL.Image.fromarray(col_images[0], 'RGB').crop((0, 0, w//2, h)), (col * w, 0))
+        canvas.paste(PIL.Image.fromarray(col_images[1], 'RGB').crop((w//2, 0, w, h)), (col * w + w//2, 0))
+        canvas.paste(PIL.Image.fromarray(col_images[2], 'RGB').crop((0, 0, w//2, h)), (col * w, h))
+        canvas.paste(PIL.Image.fromarray(col_images[3], 'RGB').crop((w//2, 0, w, h)), (col * w + w//2, h))
+    canvas.save(png)
+
+#----------------------------------------------------------------------------
+# Figure 8: Truncation trick.
+
+def draw_truncation_trick_figure(png, Gs, w, h, seeds, psis):
+    print(png)
+    latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in seeds)
+    dlatents = Gs.components.mapping.run(latents, None) # [seed, layer, component]
+    dlatent_avg = Gs.get_var('dlatent_avg') # [component]
+
+    canvas = PIL.Image.new('RGB', (w * len(psis), h * len(seeds)), 'white')
+    for row, dlatent in enumerate(list(dlatents)):
+        row_dlatents = (dlatent[np.newaxis] - dlatent_avg) * np.reshape(psis, [-1, 1, 1]) + dlatent_avg
+        row_images = Gs.components.synthesis.run(row_dlatents, randomize_noise=False, **synthesis_kwargs)
+        for col, image in enumerate(list(row_images)):
+            canvas.paste(PIL.Image.fromarray(image, 'RGB'), (col * w, row * h))
+    canvas.save(png)
+
+#----------------------------------------------------------------------------
+# Main program.
+
+def main():
+    tflib.init_tf()
+    os.makedirs(config.result_dir, exist_ok=True)
+    draw_uncurated_result_figure(os.path.join(config.result_dir, 'figure02-uncurated-ffhq.png'), load_Gs(url_ffhq), cx=0, cy=0, cw=1024, ch=1024, rows=3, lods=[0,1,2,2,3,3], seed=5)
+    draw_style_mixing_figure(os.path.join(config.result_dir, 'figure03-style-mixing.png'), load_Gs(url_ffhq), w=1024, h=1024, src_seeds=[639,701,687,615,2268], dst_seeds=[888,829,1898,1733,1614,845], style_ranges=[range(0,4)]*3+[range(4,8)]*2+[range(8,18)])
+    draw_noise_detail_figure(os.path.join(config.result_dir, 'figure04-noise-detail.png'), load_Gs(url_ffhq), w=1024, h=1024, num_samples=100, seeds=[1157,1012])
+    draw_noise_components_figure(os.path.join(config.result_dir, 'figure05-noise-components.png'), load_Gs(url_ffhq), w=1024, h=1024, seeds=[1967,1555], noise_ranges=[range(0, 18), range(0, 0), range(8, 18), range(0, 8)], flips=[1])
+    draw_truncation_trick_figure(os.path.join(config.result_dir, 'figure08-truncation-trick.png'), load_Gs(url_ffhq), w=1024, h=1024, seeds=[91,388], psis=[1, 0.7, 0.5, 0, -0.5, -1])
+    draw_uncurated_result_figure(os.path.join(config.result_dir, 'figure10-uncurated-bedrooms.png'), load_Gs(url_bedrooms), cx=0, cy=0, cw=256, ch=256, rows=5, lods=[0,0,1,1,2,2,2], seed=0)
+    draw_uncurated_result_figure(os.path.join(config.result_dir, 'figure11-uncurated-cars.png'), load_Gs(url_cars), cx=0, cy=64, cw=512, ch=384, rows=4, lods=[0,1,2,2,3,3], seed=2)
+    draw_uncurated_result_figure(os.path.join(config.result_dir, 'figure12-uncurated-cats.png'), load_Gs(url_cats), cx=0, cy=0, cw=256, ch=256, rows=5, lods=[0,0,1,1,2,2,2], seed=1)
+
+#----------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    main()
+
+#----------------------------------------------------------------------------

models/stylegan/stylegan_tf/metrics/__init__.py

@@ -0,0 +1,8 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+# empty

models/stylegan/stylegan_tf/metrics/frechet_inception_distance.py

@@ -0,0 +1,72 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Frechet Inception Distance (FID)."""
+
+import os
+import numpy as np
+import scipy
+import tensorflow as tf
+import dnnlib.tflib as tflib
+
+from metrics import metric_base
+from training import misc
+
+#----------------------------------------------------------------------------
+
+class FID(metric_base.MetricBase):
+    def __init__(self, num_images, minibatch_per_gpu, **kwargs):
+        super().__init__(**kwargs)
+        self.num_images = num_images
+        self.minibatch_per_gpu = minibatch_per_gpu
+
+    def _evaluate(self, Gs, num_gpus):
+        minibatch_size = num_gpus * self.minibatch_per_gpu
+        inception = misc.load_pkl('https://drive.google.com/uc?id=1MzTY44rLToO5APn8TZmfR7_ENSe5aZUn') # inception_v3_features.pkl
+        activations = np.empty([self.num_images, inception.output_shape[1]], dtype=np.float32)
+
+        # Calculate statistics for reals.
+        cache_file = self._get_cache_file_for_reals(num_images=self.num_images)
+        os.makedirs(os.path.dirname(cache_file), exist_ok=True)
+        if os.path.isfile(cache_file):
+            mu_real, sigma_real = misc.load_pkl(cache_file)
+        else:
+            for idx, images in enumerate(self._iterate_reals(minibatch_size=minibatch_size)):
+                begin = idx * minibatch_size
+                end = min(begin + minibatch_size, self.num_images)
+                activations[begin:end] = inception.run(images[:end-begin], num_gpus=num_gpus, assume_frozen=True)
+                if end == self.num_images:
+                    break
+            mu_real = np.mean(activations, axis=0)
+            sigma_real = np.cov(activations, rowvar=False)
+            misc.save_pkl((mu_real, sigma_real), cache_file)
+
+        # Construct TensorFlow graph.
+        result_expr = []
+        for gpu_idx in range(num_gpus):
+            with tf.device('/gpu:%d' % gpu_idx):
+                Gs_clone = Gs.clone()
+                inception_clone = inception.clone()
+                latents = tf.random_normal([self.minibatch_per_gpu] + Gs_clone.input_shape[1:])
+                images = Gs_clone.get_output_for(latents, None, is_validation=True, randomize_noise=True)
+                images = tflib.convert_images_to_uint8(images)
+                result_expr.append(inception_clone.get_output_for(images))
+
+        # Calculate statistics for fakes.
+        for begin in range(0, self.num_images, minibatch_size):
+            end = min(begin + minibatch_size, self.num_images)
+            activations[begin:end] = np.concatenate(tflib.run(result_expr), axis=0)[:end-begin]
+        mu_fake = np.mean(activations, axis=0)
+        sigma_fake = np.cov(activations, rowvar=False)
+
+        # Calculate FID.
+        m = np.square(mu_fake - mu_real).sum()
+        s, _ = scipy.linalg.sqrtm(np.dot(sigma_fake, sigma_real), disp=False) # pylint: disable=no-member
+        dist = m + np.trace(sigma_fake + sigma_real - 2*s)
+        self._report_result(np.real(dist))
+
+#----------------------------------------------------------------------------

models/stylegan/stylegan_tf/metrics/linear_separability.py

@@ -0,0 +1,177 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Linear Separability (LS)."""
+
+from collections import defaultdict
+import numpy as np
+import sklearn.svm
+import tensorflow as tf
+import dnnlib.tflib as tflib
+
+from metrics import metric_base
+from training import misc
+
+#----------------------------------------------------------------------------
+
+classifier_urls = [
+    'https://drive.google.com/uc?id=1Q5-AI6TwWhCVM7Muu4tBM7rp5nG_gmCX', # celebahq-classifier-00-male.pkl
+    'https://drive.google.com/uc?id=1Q5c6HE__ReW2W8qYAXpao68V1ryuisGo', # celebahq-classifier-01-smiling.pkl
+    'https://drive.google.com/uc?id=1Q7738mgWTljPOJQrZtSMLxzShEhrvVsU', # celebahq-classifier-02-attractive.pkl
+    'https://drive.google.com/uc?id=1QBv2Mxe7ZLvOv1YBTLq-T4DS3HjmXV0o', # celebahq-classifier-03-wavy-hair.pkl
+    'https://drive.google.com/uc?id=1QIvKTrkYpUrdA45nf7pspwAqXDwWOLhV', # celebahq-classifier-04-young.pkl
+    'https://drive.google.com/uc?id=1QJPH5rW7MbIjFUdZT7vRYfyUjNYDl4_L', # celebahq-classifier-05-5-o-clock-shadow.pkl
+    'https://drive.google.com/uc?id=1QPZXSYf6cptQnApWS_T83sqFMun3rULY', # celebahq-classifier-06-arched-eyebrows.pkl
+    'https://drive.google.com/uc?id=1QPgoAZRqINXk_PFoQ6NwMmiJfxc5d2Pg', # celebahq-classifier-07-bags-under-eyes.pkl
+    'https://drive.google.com/uc?id=1QQPQgxgI6wrMWNyxFyTLSgMVZmRr1oO7', # celebahq-classifier-08-bald.pkl
+    'https://drive.google.com/uc?id=1QcSphAmV62UrCIqhMGgcIlZfoe8hfWaF', # celebahq-classifier-09-bangs.pkl
+    'https://drive.google.com/uc?id=1QdWTVwljClTFrrrcZnPuPOR4mEuz7jGh', # celebahq-classifier-10-big-lips.pkl
+    'https://drive.google.com/uc?id=1QgvEWEtr2mS4yj1b_Y3WKe6cLWL3LYmK', # celebahq-classifier-11-big-nose.pkl
+    'https://drive.google.com/uc?id=1QidfMk9FOKgmUUIziTCeo8t-kTGwcT18', # celebahq-classifier-12-black-hair.pkl
+    'https://drive.google.com/uc?id=1QthrJt-wY31GPtV8SbnZQZ0_UEdhasHO', # celebahq-classifier-13-blond-hair.pkl
+    'https://drive.google.com/uc?id=1QvCAkXxdYT4sIwCzYDnCL9Nb5TDYUxGW', # celebahq-classifier-14-blurry.pkl
+    'https://drive.google.com/uc?id=1QvLWuwSuWI9Ln8cpxSGHIciUsnmaw8L0', # celebahq-classifier-15-brown-hair.pkl
+    'https://drive.google.com/uc?id=1QxW6THPI2fqDoiFEMaV6pWWHhKI_OoA7', # celebahq-classifier-16-bushy-eyebrows.pkl
+    'https://drive.google.com/uc?id=1R71xKw8oTW2IHyqmRDChhTBkW9wq4N9v', # celebahq-classifier-17-chubby.pkl
+    'https://drive.google.com/uc?id=1RDn_fiLfEGbTc7JjazRXuAxJpr-4Pl67', # celebahq-classifier-18-double-chin.pkl
+    'https://drive.google.com/uc?id=1RGBuwXbaz5052bM4VFvaSJaqNvVM4_cI', # celebahq-classifier-19-eyeglasses.pkl
+    'https://drive.google.com/uc?id=1RIxOiWxDpUwhB-9HzDkbkLegkd7euRU9', # celebahq-classifier-20-goatee.pkl
+    'https://drive.google.com/uc?id=1RPaNiEnJODdr-fwXhUFdoSQLFFZC7rC-', # celebahq-classifier-21-gray-hair.pkl
+    'https://drive.google.com/uc?id=1RQH8lPSwOI2K_9XQCZ2Ktz7xm46o80ep', # celebahq-classifier-22-heavy-makeup.pkl
+    'https://drive.google.com/uc?id=1RXZM61xCzlwUZKq-X7QhxOg0D2telPow', # celebahq-classifier-23-high-cheekbones.pkl
+    'https://drive.google.com/uc?id=1RgASVHW8EWMyOCiRb5fsUijFu-HfxONM', # celebahq-classifier-24-mouth-slightly-open.pkl
+    'https://drive.google.com/uc?id=1RkC8JLqLosWMaRne3DARRgolhbtg_wnr', # celebahq-classifier-25-mustache.pkl
+    'https://drive.google.com/uc?id=1RqtbtFT2EuwpGTqsTYJDyXdnDsFCPtLO', # celebahq-classifier-26-narrow-eyes.pkl
+    'https://drive.google.com/uc?id=1Rs7hU-re8bBMeRHR-fKgMbjPh-RIbrsh', # celebahq-classifier-27-no-beard.pkl
+    'https://drive.google.com/uc?id=1RynDJQWdGOAGffmkPVCrLJqy_fciPF9E', # celebahq-classifier-28-oval-face.pkl
+    'https://drive.google.com/uc?id=1S0TZ_Hdv5cb06NDaCD8NqVfKy7MuXZsN', # celebahq-classifier-29-pale-skin.pkl
+    'https://drive.google.com/uc?id=1S3JPhZH2B4gVZZYCWkxoRP11q09PjCkA', # celebahq-classifier-30-pointy-nose.pkl
+    'https://drive.google.com/uc?id=1S3pQuUz-Jiywq_euhsfezWfGkfzLZ87W', # celebahq-classifier-31-receding-hairline.pkl
+    'https://drive.google.com/uc?id=1S6nyIl_SEI3M4l748xEdTV2vymB_-lrY', # celebahq-classifier-32-rosy-cheeks.pkl
+    'https://drive.google.com/uc?id=1S9P5WCi3GYIBPVYiPTWygrYIUSIKGxbU', # celebahq-classifier-33-sideburns.pkl
+    'https://drive.google.com/uc?id=1SANviG-pp08n7AFpE9wrARzozPIlbfCH', # celebahq-classifier-34-straight-hair.pkl
+    'https://drive.google.com/uc?id=1SArgyMl6_z7P7coAuArqUC2zbmckecEY', # celebahq-classifier-35-wearing-earrings.pkl
+    'https://drive.google.com/uc?id=1SC5JjS5J-J4zXFO9Vk2ZU2DT82TZUza_', # celebahq-classifier-36-wearing-hat.pkl
+    'https://drive.google.com/uc?id=1SDAQWz03HGiu0MSOKyn7gvrp3wdIGoj-', # celebahq-classifier-37-wearing-lipstick.pkl
+    'https://drive.google.com/uc?id=1SEtrVK-TQUC0XeGkBE9y7L8VXfbchyKX', # celebahq-classifier-38-wearing-necklace.pkl
+    'https://drive.google.com/uc?id=1SF_mJIdyGINXoV-I6IAxHB_k5dxiF6M-', # celebahq-classifier-39-wearing-necktie.pkl
+]
+
+#----------------------------------------------------------------------------
+
+def prob_normalize(p):
+    p = np.asarray(p).astype(np.float32)
+    assert len(p.shape) == 2
+    return p / np.sum(p)
+
+def mutual_information(p):
+    p = prob_normalize(p)
+    px = np.sum(p, axis=1)
+    py = np.sum(p, axis=0)
+    result = 0.0
+    for x in range(p.shape[0]):
+        p_x = px[x]
+        for y in range(p.shape[1]):
+            p_xy = p[x][y]
+            p_y = py[y]
+            if p_xy > 0.0:
+                result += p_xy * np.log2(p_xy / (p_x * p_y)) # get bits as output
+    return result
+
+def entropy(p):
+    p = prob_normalize(p)
+    result = 0.0
+    for x in range(p.shape[0]):
+        for y in range(p.shape[1]):
+            p_xy = p[x][y]
+            if p_xy > 0.0:
+                result -= p_xy * np.log2(p_xy)
+    return result
+
+def conditional_entropy(p):
+    # H(Y|X) where X corresponds to axis 0, Y to axis 1
+    # i.e., How many bits of additional information are needed to where we are on axis 1 if we know where we are on axis 0?
+    p = prob_normalize(p)
+    y = np.sum(p, axis=0, keepdims=True) # marginalize to calculate H(Y)
+    return max(0.0, entropy(y) - mutual_information(p)) # can slip just below 0 due to FP inaccuracies, clean those up.
+
+#----------------------------------------------------------------------------
+
+class LS(metric_base.MetricBase):
+    def __init__(self, num_samples, num_keep, attrib_indices, minibatch_per_gpu, **kwargs):
+        assert num_keep <= num_samples
+        super().__init__(**kwargs)
+        self.num_samples = num_samples
+        self.num_keep = num_keep
+        self.attrib_indices = attrib_indices
+        self.minibatch_per_gpu = minibatch_per_gpu
+
+    def _evaluate(self, Gs, num_gpus):
+        minibatch_size = num_gpus * self.minibatch_per_gpu
+
+        # Construct TensorFlow graph for each GPU.
+        result_expr = []
+        for gpu_idx in range(num_gpus):
+            with tf.device('/gpu:%d' % gpu_idx):
+                Gs_clone = Gs.clone()
+
+                # Generate images.
+                latents = tf.random_normal([self.minibatch_per_gpu] + Gs_clone.input_shape[1:])
+                dlatents = Gs_clone.components.mapping.get_output_for(latents, None, is_validation=True)
+                images = Gs_clone.components.synthesis.get_output_for(dlatents, is_validation=True, randomize_noise=True)
+
+                # Downsample to 256x256. The attribute classifiers were built for 256x256.
+                if images.shape[2] > 256:
+                    factor = images.shape[2] // 256
+                    images = tf.reshape(images, [-1, images.shape[1], images.shape[2] // factor, factor, images.shape[3] // factor, factor])
+                    images = tf.reduce_mean(images, axis=[3, 5])
+
+                # Run classifier for each attribute.
+                result_dict = dict(latents=latents, dlatents=dlatents[:,-1])
+                for attrib_idx in self.attrib_indices:
+                    classifier = misc.load_pkl(classifier_urls[attrib_idx])
+                    logits = classifier.get_output_for(images, None)
+                    predictions = tf.nn.softmax(tf.concat([logits, -logits], axis=1))
+                    result_dict[attrib_idx] = predictions
+                result_expr.append(result_dict)
+
+        # Sampling loop.
+        results = []
+        for _ in range(0, self.num_samples, minibatch_size):
+            results += tflib.run(result_expr)
+        results = {key: np.concatenate([value[key] for value in results], axis=0) for key in results[0].keys()}
+
+        # Calculate conditional entropy for each attribute.
+        conditional_entropies = defaultdict(list)
+        for attrib_idx in self.attrib_indices:
+            # Prune the least confident samples.
+            pruned_indices = list(range(self.num_samples))
+            pruned_indices = sorted(pruned_indices, key=lambda i: -np.max(results[attrib_idx][i]))
+            pruned_indices = pruned_indices[:self.num_keep]
+
+            # Fit SVM to the remaining samples.
+            svm_targets = np.argmax(results[attrib_idx][pruned_indices], axis=1)
+            for space in ['latents', 'dlatents']:
+                svm_inputs = results[space][pruned_indices]
+                try:
+                    svm = sklearn.svm.LinearSVC()
+                    svm.fit(svm_inputs, svm_targets)
+                    svm.score(svm_inputs, svm_targets)
+                    svm_outputs = svm.predict(svm_inputs)
+                except:
+                    svm_outputs = svm_targets # assume perfect prediction
+
+                # Calculate conditional entropy.
+                p = [[np.mean([case == (row, col) for case in zip(svm_outputs, svm_targets)]) for col in (0, 1)] for row in (0, 1)]
+                conditional_entropies[space].append(conditional_entropy(p))
+
+        # Calculate separability scores.
+        scores = {key: 2**np.sum(values) for key, values in conditional_entropies.items()}
+        self._report_result(scores['latents'], suffix='_z')
+        self._report_result(scores['dlatents'], suffix='_w')
+
+#----------------------------------------------------------------------------

models/stylegan/stylegan_tf/metrics/metric_base.py

@@ -0,0 +1,142 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Common definitions for GAN metrics."""
+
+import os
+import time
+import hashlib
+import numpy as np
+import tensorflow as tf
+import dnnlib
+import dnnlib.tflib as tflib
+
+import config
+from training import misc
+from training import dataset
+
+#----------------------------------------------------------------------------
+# Standard metrics.
+
+fid50k = dnnlib.EasyDict(func_name='metrics.frechet_inception_distance.FID', name='fid50k', num_images=50000, minibatch_per_gpu=8)
+ppl_zfull = dnnlib.EasyDict(func_name='metrics.perceptual_path_length.PPL', name='ppl_zfull', num_samples=100000, epsilon=1e-4, space='z', sampling='full', minibatch_per_gpu=16)
+ppl_wfull = dnnlib.EasyDict(func_name='metrics.perceptual_path_length.PPL', name='ppl_wfull', num_samples=100000, epsilon=1e-4, space='w', sampling='full', minibatch_per_gpu=16)
+ppl_zend = dnnlib.EasyDict(func_name='metrics.perceptual_path_length.PPL', name='ppl_zend', num_samples=100000, epsilon=1e-4, space='z', sampling='end', minibatch_per_gpu=16)
+ppl_wend = dnnlib.EasyDict(func_name='metrics.perceptual_path_length.PPL', name='ppl_wend', num_samples=100000, epsilon=1e-4, space='w', sampling='end', minibatch_per_gpu=16)
+ls = dnnlib.EasyDict(func_name='metrics.linear_separability.LS', name='ls', num_samples=200000, num_keep=100000, attrib_indices=range(40), minibatch_per_gpu=4)
+dummy = dnnlib.EasyDict(func_name='metrics.metric_base.DummyMetric', name='dummy') # for debugging
+
+#----------------------------------------------------------------------------
+# Base class for metrics.
+
+class MetricBase:
+    def __init__(self, name):
+        self.name = name
+        self._network_pkl = None
+        self._dataset_args = None
+        self._mirror_augment = None
+        self._results = []
+        self._eval_time = None
+
+    def run(self, network_pkl, run_dir=None, dataset_args=None, mirror_augment=None, num_gpus=1, tf_config=None, log_results=True):
+        self._network_pkl = network_pkl
+        self._dataset_args = dataset_args
+        self._mirror_augment = mirror_augment
+        self._results = []
+
+        if (dataset_args is None or mirror_augment is None) and run_dir is not None:
+            run_config = misc.parse_config_for_previous_run(run_dir)
+            self._dataset_args = dict(run_config['dataset'])
+            self._dataset_args['shuffle_mb'] = 0
+            self._mirror_augment = run_config['train'].get('mirror_augment', False)
+
+        time_begin = time.time()
+        with tf.Graph().as_default(), tflib.create_session(tf_config).as_default(): # pylint: disable=not-context-manager
+            _G, _D, Gs = misc.load_pkl(self._network_pkl)
+            self._evaluate(Gs, num_gpus=num_gpus)
+        self._eval_time = time.time() - time_begin
+
+        if log_results:
+            result_str = self.get_result_str()
+            if run_dir is not None:
+                log = os.path.join(run_dir, 'metric-%s.txt' % self.name)
+                with dnnlib.util.Logger(log, 'a'):
+                    print(result_str)
+            else:
+                print(result_str)
+
+    def get_result_str(self):
+        network_name = os.path.splitext(os.path.basename(self._network_pkl))[0]
+        if len(network_name) > 29:
+            network_name = '...' + network_name[-26:]
+        result_str = '%-30s' % network_name
+        result_str += ' time %-12s' % dnnlib.util.format_time(self._eval_time)
+        for res in self._results:
+            result_str += ' ' + self.name + res.suffix + ' '
+            result_str += res.fmt % res.value
+        return result_str
+
+    def update_autosummaries(self):
+        for res in self._results:
+            tflib.autosummary.autosummary('Metrics/' + self.name + res.suffix, res.value)
+
+    def _evaluate(self, Gs, num_gpus):
+        raise NotImplementedError # to be overridden by subclasses
+
+    def _report_result(self, value, suffix='', fmt='%-10.4f'):
+        self._results += [dnnlib.EasyDict(value=value, suffix=suffix, fmt=fmt)]
+
+    def _get_cache_file_for_reals(self, extension='pkl', **kwargs):
+        all_args = dnnlib.EasyDict(metric_name=self.name, mirror_augment=self._mirror_augment)
+        all_args.update(self._dataset_args)
+        all_args.update(kwargs)
+        md5 = hashlib.md5(repr(sorted(all_args.items())).encode('utf-8'))
+        dataset_name = self._dataset_args['tfrecord_dir'].replace('\\', '/').split('/')[-1]
+        return os.path.join(config.cache_dir, '%s-%s-%s.%s' % (md5.hexdigest(), self.name, dataset_name, extension))
+
+    def _iterate_reals(self, minibatch_size):
+        dataset_obj = dataset.load_dataset(data_dir=config.data_dir, **self._dataset_args)
+        while True:
+            images, _labels = dataset_obj.get_minibatch_np(minibatch_size)
+            if self._mirror_augment:
+                images = misc.apply_mirror_augment(images)
+            yield images
+
+    def _iterate_fakes(self, Gs, minibatch_size, num_gpus):
+        while True:
+            latents = np.random.randn(minibatch_size, *Gs.input_shape[1:])
+            fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
+            images = Gs.run(latents, None, output_transform=fmt, is_validation=True, num_gpus=num_gpus, assume_frozen=True)
+            yield images
+
+#----------------------------------------------------------------------------
+# Group of multiple metrics.
+
+class MetricGroup:
+    def __init__(self, metric_kwarg_list):
+        self.metrics = [dnnlib.util.call_func_by_name(**kwargs) for kwargs in metric_kwarg_list]
+
+    def run(self, *args, **kwargs):
+        for metric in self.metrics:
+            metric.run(*args, **kwargs)
+
+    def get_result_str(self):
+        return ' '.join(metric.get_result_str() for metric in self.metrics)
+
+    def update_autosummaries(self):
+        for metric in self.metrics:
+            metric.update_autosummaries()
+
+#----------------------------------------------------------------------------
+# Dummy metric for debugging purposes.
+
+class DummyMetric(MetricBase):
+    def _evaluate(self, Gs, num_gpus):
+        _ = Gs, num_gpus
+        self._report_result(0.0)
+
+#----------------------------------------------------------------------------

models/stylegan/stylegan_tf/metrics/perceptual_path_length.py

@@ -0,0 +1,108 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Perceptual Path Length (PPL)."""
+
+import numpy as np
+import tensorflow as tf
+import dnnlib.tflib as tflib
+
+from metrics import metric_base
+from training import misc
+
+#----------------------------------------------------------------------------
+
+# Normalize batch of vectors.
+def normalize(v):
+    return v / tf.sqrt(tf.reduce_sum(tf.square(v), axis=-1, keepdims=True))
+
+# Spherical interpolation of a batch of vectors.
+def slerp(a, b, t):
+    a = normalize(a)
+    b = normalize(b)
+    d = tf.reduce_sum(a * b, axis=-1, keepdims=True)
+    p = t * tf.math.acos(d)
+    c = normalize(b - d * a)
+    d = a * tf.math.cos(p) + c * tf.math.sin(p)
+    return normalize(d)
+
+#----------------------------------------------------------------------------
+
+class PPL(metric_base.MetricBase):
+    def __init__(self, num_samples, epsilon, space, sampling, minibatch_per_gpu, **kwargs):
+        assert space in ['z', 'w']
+        assert sampling in ['full', 'end']
+        super().__init__(**kwargs)
+        self.num_samples = num_samples
+        self.epsilon = epsilon
+        self.space = space
+        self.sampling = sampling
+        self.minibatch_per_gpu = minibatch_per_gpu
+
+    def _evaluate(self, Gs, num_gpus):
+        minibatch_size = num_gpus * self.minibatch_per_gpu
+
+        # Construct TensorFlow graph.
+        distance_expr = []
+        for gpu_idx in range(num_gpus):
+            with tf.device('/gpu:%d' % gpu_idx):
+                Gs_clone = Gs.clone()
+                noise_vars = [var for name, var in Gs_clone.components.synthesis.vars.items() if name.startswith('noise')]
+
+                # Generate random latents and interpolation t-values.
+                lat_t01 = tf.random_normal([self.minibatch_per_gpu * 2] + Gs_clone.input_shape[1:])
+                lerp_t = tf.random_uniform([self.minibatch_per_gpu], 0.0, 1.0 if self.sampling == 'full' else 0.0)
+
+                # Interpolate in W or Z.
+                if self.space == 'w':
+                    dlat_t01 = Gs_clone.components.mapping.get_output_for(lat_t01, None, is_validation=True)
+                    dlat_t0, dlat_t1 = dlat_t01[0::2], dlat_t01[1::2]
+                    dlat_e0 = tflib.lerp(dlat_t0, dlat_t1, lerp_t[:, np.newaxis, np.newaxis])
+                    dlat_e1 = tflib.lerp(dlat_t0, dlat_t1, lerp_t[:, np.newaxis, np.newaxis] + self.epsilon)
+                    dlat_e01 = tf.reshape(tf.stack([dlat_e0, dlat_e1], axis=1), dlat_t01.shape)
+                else: # space == 'z'
+                    lat_t0, lat_t1 = lat_t01[0::2], lat_t01[1::2]
+                    lat_e0 = slerp(lat_t0, lat_t1, lerp_t[:, np.newaxis])
+                    lat_e1 = slerp(lat_t0, lat_t1, lerp_t[:, np.newaxis] + self.epsilon)
+                    lat_e01 = tf.reshape(tf.stack([lat_e0, lat_e1], axis=1), lat_t01.shape)
+                    dlat_e01 = Gs_clone.components.mapping.get_output_for(lat_e01, None, is_validation=True)
+
+                # Synthesize images.
+                with tf.control_dependencies([var.initializer for var in noise_vars]): # use same noise inputs for the entire minibatch
+                    images = Gs_clone.components.synthesis.get_output_for(dlat_e01, is_validation=True, randomize_noise=False)
+
+                # Crop only the face region.
+                c = int(images.shape[2] // 8)
+                images = images[:, :, c*3 : c*7, c*2 : c*6]
+
+                # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
+                if images.shape[2] > 256:
+                    factor = images.shape[2] // 256
+                    images = tf.reshape(images, [-1, images.shape[1], images.shape[2] // factor, factor, images.shape[3] // factor, factor])
+                    images = tf.reduce_mean(images, axis=[3,5])
+
+                # Scale dynamic range from [-1,1] to [0,255] for VGG.
+                images = (images + 1) * (255 / 2)
+
+                # Evaluate perceptual distance.
+                img_e0, img_e1 = images[0::2], images[1::2]
+                distance_measure = misc.load_pkl('https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2') # vgg16_zhang_perceptual.pkl
+                distance_expr.append(distance_measure.get_output_for(img_e0, img_e1) * (1 / self.epsilon**2))
+
+        # Sampling loop.
+        all_distances = []
+        for _ in range(0, self.num_samples, minibatch_size):
+            all_distances += tflib.run(distance_expr)
+        all_distances = np.concatenate(all_distances, axis=0)
+
+        # Reject outliers.
+        lo = np.percentile(all_distances, 1, interpolation='lower')
+        hi = np.percentile(all_distances, 99, interpolation='higher')
+        filtered_distances = np.extract(np.logical_and(lo <= all_distances, all_distances <= hi), all_distances)
+        self._report_result(np.mean(filtered_distances))
+
+#----------------------------------------------------------------------------

models/stylegan/stylegan_tf/pretrained_example.py

@@ -0,0 +1,47 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+"""Minimal script for generating an image using pre-trained StyleGAN generator."""
+
+import os
+import pickle
+import numpy as np
+import PIL.Image
+import dnnlib
+import dnnlib.tflib as tflib
+import config
+
+def main():
+    # Initialize TensorFlow.
+    tflib.init_tf()
+
+    # Load pre-trained network.
+    url = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ' # karras2019stylegan-ffhq-1024x1024.pkl
+    with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
+        _G, _D, Gs = pickle.load(f)
+        # _G = Instantaneous snapshot of the generator. Mainly useful for resuming a previous training run.
+        # _D = Instantaneous snapshot of the discriminator. Mainly useful for resuming a previous training run.
+        # Gs = Long-term average of the generator. Yields higher-quality results than the instantaneous snapshot.
+
+    # Print network details.
+    Gs.print_layers()
+
+    # Pick latent vector.
+    rnd = np.random.RandomState(5)
+    latents = rnd.randn(1, Gs.input_shape[1])
+
+    # Generate image.
+    fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
+    images = Gs.run(latents, None, truncation_psi=0.7, randomize_noise=True, output_transform=fmt)
+
+    # Save image.
+    os.makedirs(config.result_dir, exist_ok=True)
+    png_filename = os.path.join(config.result_dir, 'example.png')
+    PIL.Image.fromarray(images[0], 'RGB').save(png_filename)
+
+if __name__ == "__main__":
+    main()