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+dataset1/ibqlid61o5.jpg filter=lfs diff=lfs merge=lfs -text
+output/train_results/test1/images/0.jpg filter=lfs diff=lfs merge=lfs -text
+output/train_results/test1/images/rec_0.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,140 @@
+# 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/
+share/python-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/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# 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/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+storage/*
+train_results/*

LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
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+
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+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -0,0 +1,63 @@
+# A Fast and Stable GAN for Small and High Resolution Imagesets - pytorch
+The official pytorch implementation of the paper "Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis", the paper can be found [here](https://arxiv.org/abs/2101.04775).
+
+## 0. Data
+The datasets used in the paper can be found at [link](https://drive.google.com/file/d/1aAJCZbXNHyraJ6Mi13dSbe7pTyfPXha0/view?usp=sharing). 
+
+After testing on over 20 datasets with each has less than 100 images, this GAN converges on 80% of them.
+I still cannot summarize an obvious pattern of the "good properties" for a dataset which this GAN can converge on, please feel free to try with your own datasets.
+ 
+
+## 1. Description
+The code is structured as follows:
+* models.py: all the models' structure definition.
+
+* operation.py: the helper functions and data loading methods during training.
+
+* train.py: the main entry of the code, execute this file to train the model, the intermediate results and checkpoints will be automatically saved periodically into a folder "train_results".
+
+* eval.py: generates images from a trained generator into a folder, which can be used to calculate FID score.
+
+* benchmarking: the functions we used to compute FID are located here, it automatically downloads the pytorch official inception model. 
+
+* lpips: this folder contains the code to compute the LPIPS score, the inception model is also automatically download from official location.
+
+* scripts: this folder contains many scripts you can use to play around the trained model. Including: 
+    1. style_mix.py: style-mixing as introduced in the paper;
+    2. generate_video.py: generating a continuous video from the interpolation of generated images;
+    3. find_nearest_neighbor.py: given a generated image, find the closest real-image from the training set;
+    4. train_backtracking_one.py: given a real-image, find the latent vector of this image from a trained Generator.
+
+## 2. How to run
+Place all your training images in a folder, and simply call
+```
+python train.py --path /path/to/RGB-image-folder --output_path /path/to/the/output
+```
+You can also see all the training options by:
+```
+python train.py --help
+```
+The code will automatically create a new folder (you have to specify the name of the folder using --name option) to store the trained checkpoints and intermediate synthesis results.
+
+Once finish training, you can generate 100 images (or as many as you want) by:
+```
+cd ./train_results/name_of_your_training/
+python eval.py --n_sample 100 
+```
+
+## 3. Pre-trained models
+The pre-trained models and the respective code of each model are shared [here](https://drive.google.com/drive/folders/1nCpr84nKkrs9-aVMET5h8gqFbUYJRPLR?usp=sharing).
+
+You can also use FastGAN to generate images with a pre-packaged Docker image, hosted on the Replicate registry: https://beta.replicate.ai/odegeasslbc/FastGAN
+
+## 4. Important notes
+1. The provided code is for research use only.
+2. Different model and training configurations are needed on different datasets. You may have to tune the hyper-parameters to get the best results on your own datasets. 
+
+    2.1. The hyper-parameters includes: the augmentation options, the model depth (how many layers), the model width (channel numbers of each layer). To change these, you have to change the code in models.py and train.py directly. 
+    
+    2.2. Please check the code in the shared pre-trained models on how each of them are configured differently on different datasets. Especially, compare the models.py for ffhq and art datasets, you will get an idea on what chages could be made on different datasets.
+
+## 5. Other notes
+1. The provided scripts are not well organized, contributions are welcomed to clean them.
+2.  An third-party implementation of this paper can be found [here](https://github.com/lucidrains/lightweight-gan), where some other techniques are included. I suggest you try both implementation if you find one of them does not work. 

benchmarking/benchmark.py

@@ -0,0 +1,579 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import models
+from torchvision.models import inception_v3, Inception3
+from torchvision.utils import save_image
+
+try:
+    from torchvision.models.utils import load_state_dict_from_url
+except ImportError:
+    from torch.utils.model_zoo import load_url as load_state_dict_from_url
+
+import numpy as np
+from scipy import linalg
+from tqdm import tqdm
+import pickle
+import os
+
+# Inception weights ported to Pytorch from
+# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
+FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'
+
+
+class InceptionV3(nn.Module):
+    """Pretrained InceptionV3 network returning feature maps"""
+
+    # Index of default block of inception to return,
+    # corresponds to output of final average pooling
+    DEFAULT_BLOCK_INDEX = 3
+
+    # Maps feature dimensionality to their output blocks indices
+    BLOCK_INDEX_BY_DIM = {
+        64: 0,   # First max pooling features
+        192: 1,  # Second max pooling featurs
+        768: 2,  # Pre-aux classifier features
+        2048: 3  # Final average pooling features
+    }
+
+    def __init__(self,
+                 output_blocks=[DEFAULT_BLOCK_INDEX],
+                 resize_input=True,
+                 normalize_input=True,
+                 requires_grad=False,
+                 use_fid_inception=True):
+        """Build pretrained InceptionV3
+        Parameters
+        ----------
+        output_blocks : list of int
+            Indices of blocks to return features of. Possible values are:
+                - 0: corresponds to output of first max pooling
+                - 1: corresponds to output of second max pooling
+                - 2: corresponds to output which is fed to aux classifier
+                - 3: corresponds to output of final average pooling
+        resize_input : bool
+            If true, bilinearly resizes input to width and height 299 before
+            feeding input to model. As the network without fully connected
+            layers is fully convolutional, it should be able to handle inputs
+            of arbitrary size, so resizing might not be strictly needed
+        normalize_input : bool
+            If true, scales the input from range (0, 1) to the range the
+            pretrained Inception network expects, namely (-1, 1)
+        requires_grad : bool
+            If true, parameters of the model require gradients. Possibly useful
+            for finetuning the network
+        use_fid_inception : bool
+            If true, uses the pretrained Inception model used in Tensorflow's
+            FID implementation. If false, uses the pretrained Inception model
+            available in torchvision. The FID Inception model has different
+            weights and a slightly different structure from torchvision's
+            Inception model. If you want to compute FID scores, you are
+            strongly advised to set this parameter to true to get comparable
+            results.
+        """
+        super(InceptionV3, self).__init__()
+
+        self.resize_input = resize_input
+        self.normalize_input = normalize_input
+        self.output_blocks = sorted(output_blocks)
+        self.last_needed_block = max(output_blocks)
+
+        assert self.last_needed_block <= 3, \
+            'Last possible output block index is 3'
+
+        self.blocks = nn.ModuleList()
+
+        if use_fid_inception:
+            inception = fid_inception_v3()
+        else:
+            inception = models.inception_v3(pretrained=True)
+
+        # Block 0: input to maxpool1
+        block0 = [
+            inception.Conv2d_1a_3x3,
+            inception.Conv2d_2a_3x3,
+            inception.Conv2d_2b_3x3,
+            nn.MaxPool2d(kernel_size=3, stride=2)
+        ]
+        self.blocks.append(nn.Sequential(*block0))
+
+        # Block 1: maxpool1 to maxpool2
+        if self.last_needed_block >= 1:
+            block1 = [
+                inception.Conv2d_3b_1x1,
+                inception.Conv2d_4a_3x3,
+                nn.MaxPool2d(kernel_size=3, stride=2)
+            ]
+            self.blocks.append(nn.Sequential(*block1))
+
+        # Block 2: maxpool2 to aux classifier
+        if self.last_needed_block >= 2:
+            block2 = [
+                inception.Mixed_5b,
+                inception.Mixed_5c,
+                inception.Mixed_5d,
+                inception.Mixed_6a,
+                inception.Mixed_6b,
+                inception.Mixed_6c,
+                inception.Mixed_6d,
+                inception.Mixed_6e,
+            ]
+            self.blocks.append(nn.Sequential(*block2))
+
+        # Block 3: aux classifier to final avgpool
+        if self.last_needed_block >= 3:
+            block3 = [
+                inception.Mixed_7a,
+                inception.Mixed_7b,
+                inception.Mixed_7c,
+                nn.AdaptiveAvgPool2d(output_size=(1, 1))
+            ]
+            self.blocks.append(nn.Sequential(*block3))
+
+        for param in self.parameters():
+            param.requires_grad = requires_grad
+
+    def forward(self, inp):
+        """Get Inception feature maps
+        Parameters
+        ----------
+        inp : torch.autograd.Variable
+            Input tensor of shape Bx3xHxW. Values are expected to be in
+            range (0, 1)
+        Returns
+        -------
+        List of torch.autograd.Variable, corresponding to the selected output
+        block, sorted ascending by index
+        """
+        outp = []
+        x = inp
+
+        if self.resize_input:
+            x = F.interpolate(x,
+                              size=(299, 299),
+                              mode='bilinear',
+                              align_corners=False)
+
+        if self.normalize_input:
+            x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)
+
+        for idx, block in enumerate(self.blocks):
+            x = block(x)
+            if idx in self.output_blocks:
+                outp.append(x)
+
+            if idx == self.last_needed_block:
+                break
+
+        return outp
+
+
+def fid_inception_v3():
+    """Build pretrained Inception model for FID computation
+    The Inception model for FID computation uses a different set of weights
+    and has a slightly different structure than torchvision's Inception.
+    This method first constructs torchvision's Inception and then patches the
+    necessary parts that are different in the FID Inception model.
+    """
+    inception = models.inception_v3(num_classes=1008,
+                                    aux_logits=False,
+                                    pretrained=False)
+    inception.Mixed_5b = FIDInceptionA(192, pool_features=32)
+    inception.Mixed_5c = FIDInceptionA(256, pool_features=64)
+    inception.Mixed_5d = FIDInceptionA(288, pool_features=64)
+    inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)
+    inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)
+    inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)
+    inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)
+    inception.Mixed_7b = FIDInceptionE_1(1280)
+    inception.Mixed_7c = FIDInceptionE_2(2048)
+
+    state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)
+    inception.load_state_dict(state_dict)
+    return inception
+
+
+class FIDInceptionA(models.inception.InceptionA):
+    """InceptionA block patched for FID computation"""
+    def __init__(self, in_channels, pool_features):
+        super(FIDInceptionA, self).__init__(in_channels, pool_features)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch5x5 = self.branch5x5_1(x)
+        branch5x5 = self.branch5x5_2(branch5x5)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
+                                   count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionC(models.inception.InceptionC):
+    """InceptionC block patched for FID computation"""
+    def __init__(self, in_channels, channels_7x7):
+        super(FIDInceptionC, self).__init__(in_channels, channels_7x7)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch7x7 = self.branch7x7_1(x)
+        branch7x7 = self.branch7x7_2(branch7x7)
+        branch7x7 = self.branch7x7_3(branch7x7)
+
+        branch7x7dbl = self.branch7x7dbl_1(x)
+        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
+                                   count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionE_1(models.inception.InceptionE):
+    """First InceptionE block patched for FID computation"""
+    def __init__(self, in_channels):
+        super(FIDInceptionE_1, self).__init__(in_channels)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = [
+            self.branch3x3_2a(branch3x3),
+            self.branch3x3_2b(branch3x3),
+        ]
+        branch3x3 = torch.cat(branch3x3, 1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = [
+            self.branch3x3dbl_3a(branch3x3dbl),
+            self.branch3x3dbl_3b(branch3x3dbl),
+        ]
+        branch3x3dbl = torch.cat(branch3x3dbl, 1)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
+                                   count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionE_2(models.inception.InceptionE):
+    """Second InceptionE block patched for FID computation"""
+    def __init__(self, in_channels):
+        super(FIDInceptionE_2, self).__init__(in_channels)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = [
+            self.branch3x3_2a(branch3x3),
+            self.branch3x3_2b(branch3x3),
+        ]
+        branch3x3 = torch.cat(branch3x3, 1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = [
+            self.branch3x3dbl_3a(branch3x3dbl),
+            self.branch3x3dbl_3b(branch3x3dbl),
+        ]
+        branch3x3dbl = torch.cat(branch3x3dbl, 1)
+
+        # Patch: The FID Inception model uses max pooling instead of average
+        # pooling. This is likely an error in this specific Inception
+        # implementation, as other Inception models use average pooling here
+        # (which matches the description in the paper).
+        branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class Inception3Feature(Inception3):
+    def forward(self, x):
+        if x.shape[2] != 299 or x.shape[3] != 299:
+            x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=True)
+
+        x = self.Conv2d_1a_3x3(x)  # 299 x 299 x 3
+        x = self.Conv2d_2a_3x3(x)  # 149 x 149 x 32
+        x = self.Conv2d_2b_3x3(x)  # 147 x 147 x 32
+        x = F.max_pool2d(x, kernel_size=3, stride=2)  # 147 x 147 x 64
+
+        x = self.Conv2d_3b_1x1(x)  # 73 x 73 x 64
+        x = self.Conv2d_4a_3x3(x)  # 73 x 73 x 80
+        x = F.max_pool2d(x, kernel_size=3, stride=2)  # 71 x 71 x 192
+
+        x = self.Mixed_5b(x)  # 35 x 35 x 192
+        x = self.Mixed_5c(x)  # 35 x 35 x 256
+        x = self.Mixed_5d(x)  # 35 x 35 x 288
+
+        x = self.Mixed_6a(x)  # 35 x 35 x 288
+        x = self.Mixed_6b(x)  # 17 x 17 x 768
+        x = self.Mixed_6c(x)  # 17 x 17 x 768
+        x = self.Mixed_6d(x)  # 17 x 17 x 768
+        x = self.Mixed_6e(x)  # 17 x 17 x 768
+
+        x = self.Mixed_7a(x)  # 17 x 17 x 768
+        x = self.Mixed_7b(x)  # 8 x 8 x 1280
+        x = self.Mixed_7c(x)  # 8 x 8 x 2048
+
+        x = F.avg_pool2d(x, kernel_size=8)  # 8 x 8 x 2048
+
+        return x.view(x.shape[0], x.shape[1])  # 1 x 1 x 2048
+
+
+def load_patched_inception_v3():
+    # inception = inception_v3(pretrained=True)
+    # inception_feat = Inception3Feature()
+    # inception_feat.load_state_dict(inception.state_dict())
+    inception_feat = InceptionV3([3], normalize_input=False)
+
+    return inception_feat
+
+
+@torch.no_grad()
+def extract_features(loader, inception, device):
+    pbar = tqdm(loader)
+
+    feature_list = []
+
+    for img in pbar:
+        img = img.to(device)
+        feature = inception(img)[0].view(img.shape[0], -1)
+        feature_list.append(feature.to('cpu'))
+
+    features = torch.cat(feature_list, 0)
+
+    return features
+
+
+
+@torch.no_grad()
+def extract_feature_from_samples(generator, inception, device='cuda'):
+    n_batch = n_sample // batch_size
+    resid = n_sample - (n_batch * batch_size)
+    batch_sizes = [batch_size] * n_batch + [resid]
+    features = []
+
+    for batch in tqdm(batch_sizes):
+        latent = torch.randn(batch, 512, device=device)
+        img, _ = g([latent], truncation=truncation, truncation_latent=truncation_latent)
+        feat = inception(img)[0].view(img.shape[0], -1)
+        features.append(feat.to('cpu'))
+
+    features = torch.cat(features, 0)
+
+    return features
+
+
+@torch.no_grad()
+def extract_feature_from_generator_fn(generator_fn, inception, device='cuda', total=1000):
+    features = []
+    for batch in tqdm(generator_fn, total=total):
+        feat = inception(batch)[0].view(batch.shape[0], -1)
+        features.append(feat.to('cpu'))
+
+    features = torch.cat(features, 0).detach()
+    return features.numpy()
+
+
+def calc_fid(sample_features, real_features=None, real_mean=None, real_cov=None, eps=1e-6):
+    sample_mean = np.mean(sample_features, 0)
+    sample_cov = np.cov(sample_features, rowvar=False)
+
+    if real_features is not None:
+        real_mean = np.mean(real_features, 0)
+        real_cov = np.cov(real_features, rowvar=False)
+
+    cov_sqrt, _ = linalg.sqrtm(sample_cov @ real_cov, disp=False)
+
+    if not np.isfinite(cov_sqrt).all():
+        print('product of cov matrices is singular')
+        offset = np.eye(sample_cov.shape[0]) * eps
+        cov_sqrt = linalg.sqrtm((sample_cov + offset) @ (real_cov + offset))
+
+    if np.iscomplexobj(cov_sqrt):
+        if not np.allclose(np.diagonal(cov_sqrt).imag, 0, atol=1e-3):
+            m = np.max(np.abs(cov_sqrt.imag))
+
+            raise ValueError(f'Imaginary component {m}')
+
+        cov_sqrt = cov_sqrt.real
+
+    mean_diff = sample_mean - real_mean
+    mean_norm = mean_diff @ mean_diff
+
+    trace = np.trace(sample_cov) + np.trace(real_cov) - 2 * np.trace(cov_sqrt)
+
+    fid = mean_norm + trace
+
+    return fid
+
+
+if __name__ == "__main__":
+    #from utils import PairedMultiDataset, InfiniteSamplerWrapper, make_folders, AverageMeter
+    from torch.utils.data import DataLoader
+    from torchvision import utils as vutils
+    
+    IM_SIZE = 1024
+    BATCH_SIZE = 16
+    DATALOADER_WORKERS = 8
+    NBR_CLS = 2000
+    TRIAL_NAME = 'trial_vae_512_1'
+    SAVE_FOLDER = './'
+
+    from torchvision.datasets import ImageFolder
+
+    '''
+    data_root_colorful = '../images/celebA/CelebA_512/img'
+    data_root_sketch_1 = './sketch_simplification/vggadin_iter_700'
+    data_root_sketch_2 = './sketch_simplification/vggadin_iter_1900'
+    data_root_sketch_3 = './sketch_simplification/vggadin_iter_2300'
+
+    dataset = PairedMultiDataset(data_root_colorful, data_root_sketch_1, data_root_sketch_2, data_root_sketch_3, im_size=IM_SIZE, rand_crop=False)
+    dataloader = iter(DataLoader(dataset, BATCH_SIZE, shuffle=False, num_workers=DATALOADER_WORKERS, pin_memory=True))
+
+
+    from pretrain_ae import StyleEncoder, ContentEncoder, Decoder
+    import pickle
+    from refine_ae_as_gan import AE, RefineGenerator
+    from utils import load_params
+
+    net_ig = RefineGenerator().cuda()
+    net_ig = nn.DataParallel(net_ig)
+
+    ckpt = './train_results/trial_refine_ae_as_gan_1024_2/models/4.pth'
+    if ckpt is not None:
+        ckpt = torch.load(ckpt)
+        #net_ig.load_state_dict(ckpt['ig'])
+        #net_id.load_state_dict(ckpt['id'])
+        net_ig_ema = ckpt['ig_ema']
+        load_params(net_ig, net_ig_ema)
+    net_ig = net_ig.module
+    #net_ig.eval()
+
+    net_ae = AE()
+    net_ae.load_state_dicts('./train_results/trial_vae_512_1/models/176000.pth')
+    net_ae.cuda()
+    net_ae.eval()
+
+    #style_encoder = StyleEncoder(nbr_cls=NBR_CLS).cuda()
+    #content_encoder = ContentEncoder().cuda()
+    #decoder = Decoder().cuda()
+    '''
+
+    def real_image_loader(dataloader, n_batches=10):
+        counter = 0
+        while counter < n_batches:
+            counter += 1
+            rgb_img, _ = next(dataloader)
+            if counter == 1:
+                vutils.save_image(0.5*(rgb_img+1), 'tmp_real.jpg')  
+            yield rgb_img.cuda()
+
+    '''
+    @torch.no_grad()
+    def image_generator_1(dataloader, n_batches=10):
+        counter = 0
+        while counter < n_batches:
+            counter += 1
+            rgb_img, _, _, skt_img = next(dataloader)
+            rgb_img = rgb_img.cuda()
+            skt_img = skt_img.cuda()
+
+            style_feat, _ = style_encoder(rgb_img)
+            content_feats = content_encoder( F.interpolate( skt_img , size=512 ) )
+            gimg = decoder(content_feats, style_feat)
+
+            vutils.save_image(0.5*(gimg+1), 'tmp.jpg')        
+            yield gimg
+
+    from utils import true_randperm
+    @torch.no_grad()
+    def image_generator(dataset, net_ae, net_ig, n_batches=500):
+        counter = 0
+        dataloader = iter(DataLoader(dataset, BATCH_SIZE, shuffle=False, num_workers=DATALOADER_WORKERS, pin_memory=False))
+
+        while counter < n_batches:
+            counter += 1
+            rgb_img, _, _, skt_img = next(dataloader)
+            rgb_img = F.interpolate( rgb_img, size=512 ).cuda()
+            skt_img = F.interpolate( skt_img, size=512 ).cuda()
+
+            #perm = true_randperm(rgb_img.shape[0], device=rgb_img.device)
+
+            gimg_ae, style_feat = net_ae(skt_img, rgb_img)
+            g_image = net_ig(gimg_ae, style_feat, skt_img)
+            if counter == 1:
+                vutils.save_image(0.5*(g_image+1), 'tmp.jpg')        
+            yield g_image
+    '''
+    inception = load_patched_inception_v3().cuda()
+    inception.eval()
+    
+    path_a = '../../../database/images/celebaMask/CelebA_1024'
+    path_b = '../../stylegan/celebahq_samples'
+
+    from torchvision import transforms
+
+    transform = transforms.Compose(
+        [
+            transforms.Resize( (299, 299) ),
+            #transforms.RandomHorizontalFlip(p=0.5 if args.flip else 0),
+            transforms.ToTensor(),
+            transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
+        ]
+    )
+
+    dset_a = ImageFolder(path_a, transform)
+    loader_a = iter(DataLoader(dset_a, batch_size=16, num_workers=4))
+
+    real_features = extract_feature_from_generator_fn( 
+        real_image_loader(loader_a, n_batches=900), inception )
+    real_mean = np.mean(real_features, 0)
+    real_cov = np.cov(real_features, rowvar=False)
+    
+    #pickle.dump({'feats': real_features, 'mean': real_mean, 'cov': real_cov}, open('celeba_fid_feats.npy','wb') ) 
+
+    #real_features = pickle.load( open('celeba_fid_feats.npy', 'rb') )
+    #real_mean = real_features['mean']
+    #real_cov = real_features['cov']
+    #sample_features = extract_feature_from_generator_fn( real_image_loader(dataloader, n_batches=100), inception )
+    
+    dset_b = ImageFolder(path_b, transform)
+    loader_b = iter(DataLoader(dset_b, batch_size=16, num_workers=4))
+
+    sample_features = extract_feature_from_generator_fn( 
+        real_image_loader(loader_b, n_batches=900), inception )
+    #sample_features = extract_feature_from_generator_fn( 
+    #        image_generator(dataset, net_ae, net_ig, n_batches=1800), inception,
+    #         total=1800 )
+
+        #fid = calc_fid(sample_features, real_mean=real_features['mean'], real_cov=real_features['cov'])
+    fid = calc_fid(sample_features, real_mean=real_mean, real_cov=real_cov)
+        
+    print(fid)

benchmarking/calc_inception.py

@@ -0,0 +1,116 @@
+import argparse
+import pickle
+import os
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torchvision import transforms
+from torchvision.models import inception_v3, Inception3
+import numpy as np
+from tqdm import tqdm
+
+from inception import InceptionV3
+from torchvision.datasets import ImageFolder
+
+class Inception3Feature(Inception3):
+    def forward(self, x):
+        if x.shape[2] != 299 or x.shape[3] != 299:
+            x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=True)
+
+        x = self.Conv2d_1a_3x3(x)  # 299 x 299 x 3
+        x = self.Conv2d_2a_3x3(x)  # 149 x 149 x 32
+        x = self.Conv2d_2b_3x3(x)  # 147 x 147 x 32
+        x = F.max_pool2d(x, kernel_size=3, stride=2)  # 147 x 147 x 64
+
+        x = self.Conv2d_3b_1x1(x)  # 73 x 73 x 64
+        x = self.Conv2d_4a_3x3(x)  # 73 x 73 x 80
+        x = F.max_pool2d(x, kernel_size=3, stride=2)  # 71 x 71 x 192
+
+        x = self.Mixed_5b(x)  # 35 x 35 x 192
+        x = self.Mixed_5c(x)  # 35 x 35 x 256
+        x = self.Mixed_5d(x)  # 35 x 35 x 288
+
+        x = self.Mixed_6a(x)  # 35 x 35 x 288
+        x = self.Mixed_6b(x)  # 17 x 17 x 768
+        x = self.Mixed_6c(x)  # 17 x 17 x 768
+        x = self.Mixed_6d(x)  # 17 x 17 x 768
+        x = self.Mixed_6e(x)  # 17 x 17 x 768
+
+        x = self.Mixed_7a(x)  # 17 x 17 x 768
+        x = self.Mixed_7b(x)  # 8 x 8 x 1280
+        x = self.Mixed_7c(x)  # 8 x 8 x 2048
+
+        x = F.avg_pool2d(x, kernel_size=8)  # 8 x 8 x 2048
+
+        return x.view(x.shape[0], x.shape[1])  # 1 x 1 x 2048
+
+
+def load_patched_inception_v3():
+    # inception = inception_v3(pretrained=True)
+    # inception_feat = Inception3Feature()
+    # inception_feat.load_state_dict(inception.state_dict())
+    inception_feat = InceptionV3([3], normalize_input=False)
+
+    return inception_feat
+
+
+@torch.no_grad()
+def extract_features(loader, inception, device):
+    pbar = tqdm(loader)
+
+    feature_list = []
+
+    for img,_ in pbar:
+        img = img.to(device)
+        feature = inception(img)[0].view(img.shape[0], -1)
+        feature_list.append(feature.to('cpu'))
+
+    features = torch.cat(feature_list, 0)
+
+    return features
+
+
+if __name__ == '__main__':
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    parser = argparse.ArgumentParser(
+        description='Calculate Inception v3 features for datasets'
+    )
+    parser.add_argument('--size', type=int, default=256)
+    parser.add_argument('--batch', default=64, type=int, help='batch size')
+    parser.add_argument('--n_sample', type=int, default=50000)
+    parser.add_argument('--flip', action='store_true')
+    parser.add_argument('path', metavar='PATH', help='path to datset lmdb file')
+
+    args = parser.parse_args()
+
+    inception = load_patched_inception_v3().eval().to(device)
+
+    transform = transforms.Compose(
+        [
+            transforms.Resize( (args.size, args.size) ),
+            transforms.RandomHorizontalFlip(p=0.5 if args.flip else 0),
+            transforms.ToTensor(),
+            transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
+        ]
+    )
+
+    dset = ImageFolder(args.path, transform)
+    loader = DataLoader(dset, batch_size=args.batch, num_workers=4)
+
+    features = extract_features(loader, inception, device).numpy()
+
+    features = features[: args.n_sample]
+
+    print(f'extracted {features.shape[0]} features')
+
+    mean = np.mean(features, 0)
+    cov = np.cov(features, rowvar=False)
+
+    name = os.path.splitext(os.path.basename(args.path))[0]
+
+    print({'mean': mean.mean(), 'cov': cov.mean()})
+    with open(f'inception_{name}.pkl', 'wb') as f:
+        pickle.dump({'mean': mean, 'cov': cov, 'size': args.size, 'path': args.path}, f)

benchmarking/fid.py

@@ -0,0 +1,109 @@
+import argparse
+import pickle
+
+import torch
+from torch import nn
+import numpy as np
+from scipy import linalg
+from tqdm import tqdm
+
+from torchvision import transforms
+from torchvision.datasets import ImageFolder
+from torch.utils.data import DataLoader
+
+from calc_inception import load_patched_inception_v3
+import os
+
+@torch.no_grad()
+def extract_features(loader, inception, device):
+    pbar = tqdm(loader)
+
+    feature_list = []
+
+    for img,_ in pbar:
+        img = img.to(device)
+        feature = inception(img)[0].view(img.shape[0], -1)
+        feature_list.append(feature.to('cpu'))
+
+    features = torch.cat(feature_list, 0)
+
+    return features
+
+
+def calc_fid(sample_mean, sample_cov, real_mean, real_cov, eps=1e-6):
+    cov_sqrt, _ = linalg.sqrtm(sample_cov @ real_cov, disp=False)
+
+    if not np.isfinite(cov_sqrt).all():
+        print('product of cov matrices is singular')
+        offset = np.eye(sample_cov.shape[0]) * eps
+        cov_sqrt = linalg.sqrtm((sample_cov + offset) @ (real_cov + offset))
+
+    if np.iscomplexobj(cov_sqrt):
+        if not np.allclose(np.diagonal(cov_sqrt).imag, 0, atol=1e-3):
+            m = np.max(np.abs(cov_sqrt.imag))
+
+            raise ValueError(f'Imaginary component {m}')
+
+        cov_sqrt = cov_sqrt.real
+
+    mean_diff = sample_mean - real_mean
+    mean_norm = mean_diff @ mean_diff
+
+    trace = np.trace(sample_cov) + np.trace(real_cov) - 2 * np.trace(cov_sqrt)
+
+    fid = mean_norm + trace
+
+    return fid
+
+
+if __name__ == '__main__':
+    device = 'cuda'
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--batch', type=int, default=64)
+    parser.add_argument('--size', type=int, default=256)
+    parser.add_argument('--path_a', type=str)
+    parser.add_argument('--path_b', type=str)
+    parser.add_argument('--iter', type=int, default=3)
+    parser.add_argument('--end', type=int, default=13)
+
+    args = parser.parse_args()
+
+    inception = load_patched_inception_v3().eval().to(device)
+
+    transform = transforms.Compose(
+        [
+            transforms.Resize( (args.size, args.size) ),
+            #transforms.RandomHorizontalFlip(p=0.5 if args.flip else 0),
+            transforms.ToTensor(),
+            transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
+        ]
+    )
+
+    dset_a = ImageFolder(args.path_a, transform)
+    loader_a = DataLoader(dset_a, batch_size=args.batch, num_workers=4)
+
+    features_a = extract_features(loader_a, inception, device).numpy()
+    print(f'extracted {features_a.shape[0]} features')
+
+    real_mean = np.mean(features_a, 0)
+    real_cov = np.cov(features_a, rowvar=False)
+    
+    #for folder in os.listdir(args.path_b):
+    for folder in range(args.iter,args.end+1):
+        folder = 'eval_%d'%(folder*10000)
+        if os.path.exists(os.path.join( args.path_b, folder )):
+            print(folder)
+            dset_b = ImageFolder( os.path.join( args.path_b, folder ), transform)
+            loader_b = DataLoader(dset_b, batch_size=args.batch, num_workers=4)
+
+            features_b = extract_features(loader_b, inception, device).numpy()
+            print(f'extracted {features_b.shape[0]} features')
+
+            sample_mean = np.mean(features_b, 0)
+            sample_cov = np.cov(features_b, rowvar=False)
+
+            fid = calc_fid(sample_mean, sample_cov, real_mean, real_cov)
+
+            print(folder, ' fid:', fid)

benchmarking/inception.py

@@ -0,0 +1,310 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import models
+
+try:
+    from torchvision.models.utils import load_state_dict_from_url
+except ImportError:
+    from torch.utils.model_zoo import load_url as load_state_dict_from_url
+
+# Inception weights ported to Pytorch from
+# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
+FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'
+
+
+class InceptionV3(nn.Module):
+    """Pretrained InceptionV3 network returning feature maps"""
+
+    # Index of default block of inception to return,
+    # corresponds to output of final average pooling
+    DEFAULT_BLOCK_INDEX = 3
+
+    # Maps feature dimensionality to their output blocks indices
+    BLOCK_INDEX_BY_DIM = {
+        64: 0,   # First max pooling features
+        192: 1,  # Second max pooling featurs
+        768: 2,  # Pre-aux classifier features
+        2048: 3  # Final average pooling features
+    }
+
+    def __init__(self,
+                 output_blocks=[DEFAULT_BLOCK_INDEX],
+                 resize_input=True,
+                 normalize_input=True,
+                 requires_grad=False,
+                 use_fid_inception=True):
+        """Build pretrained InceptionV3
+
+        Parameters
+        ----------
+        output_blocks : list of int
+            Indices of blocks to return features of. Possible values are:
+                - 0: corresponds to output of first max pooling
+                - 1: corresponds to output of second max pooling
+                - 2: corresponds to output which is fed to aux classifier
+                - 3: corresponds to output of final average pooling
+        resize_input : bool
+            If true, bilinearly resizes input to width and height 299 before
+            feeding input to model. As the network without fully connected
+            layers is fully convolutional, it should be able to handle inputs
+            of arbitrary size, so resizing might not be strictly needed
+        normalize_input : bool
+            If true, scales the input from range (0, 1) to the range the
+            pretrained Inception network expects, namely (-1, 1)
+        requires_grad : bool
+            If true, parameters of the model require gradients. Possibly useful
+            for finetuning the network
+        use_fid_inception : bool
+            If true, uses the pretrained Inception model used in Tensorflow's
+            FID implementation. If false, uses the pretrained Inception model
+            available in torchvision. The FID Inception model has different
+            weights and a slightly different structure from torchvision's
+            Inception model. If you want to compute FID scores, you are
+            strongly advised to set this parameter to true to get comparable
+            results.
+        """
+        super(InceptionV3, self).__init__()
+
+        self.resize_input = resize_input
+        self.normalize_input = normalize_input
+        self.output_blocks = sorted(output_blocks)
+        self.last_needed_block = max(output_blocks)
+
+        assert self.last_needed_block <= 3, \
+            'Last possible output block index is 3'
+
+        self.blocks = nn.ModuleList()
+
+        if use_fid_inception:
+            inception = fid_inception_v3()
+        else:
+            inception = models.inception_v3(pretrained=True)
+
+        # Block 0: input to maxpool1
+        block0 = [
+            inception.Conv2d_1a_3x3,
+            inception.Conv2d_2a_3x3,
+            inception.Conv2d_2b_3x3,
+            nn.MaxPool2d(kernel_size=3, stride=2)
+        ]
+        self.blocks.append(nn.Sequential(*block0))
+
+        # Block 1: maxpool1 to maxpool2
+        if self.last_needed_block >= 1:
+            block1 = [
+                inception.Conv2d_3b_1x1,
+                inception.Conv2d_4a_3x3,
+                nn.MaxPool2d(kernel_size=3, stride=2)
+            ]
+            self.blocks.append(nn.Sequential(*block1))
+
+        # Block 2: maxpool2 to aux classifier
+        if self.last_needed_block >= 2:
+            block2 = [
+                inception.Mixed_5b,
+                inception.Mixed_5c,
+                inception.Mixed_5d,
+                inception.Mixed_6a,
+                inception.Mixed_6b,
+                inception.Mixed_6c,
+                inception.Mixed_6d,
+                inception.Mixed_6e,
+            ]
+            self.blocks.append(nn.Sequential(*block2))
+
+        # Block 3: aux classifier to final avgpool
+        if self.last_needed_block >= 3:
+            block3 = [
+                inception.Mixed_7a,
+                inception.Mixed_7b,
+                inception.Mixed_7c,
+                nn.AdaptiveAvgPool2d(output_size=(1, 1))
+            ]
+            self.blocks.append(nn.Sequential(*block3))
+
+        for param in self.parameters():
+            param.requires_grad = requires_grad
+
+    def forward(self, inp):
+        """Get Inception feature maps
+
+        Parameters
+        ----------
+        inp : torch.autograd.Variable
+            Input tensor of shape Bx3xHxW. Values are expected to be in
+            range (0, 1)
+
+        Returns
+        -------
+        List of torch.autograd.Variable, corresponding to the selected output
+        block, sorted ascending by index
+        """
+        outp = []
+        x = inp
+
+        if self.resize_input:
+            x = F.interpolate(x,
+                              size=(299, 299),
+                              mode='bilinear',
+                              align_corners=False)
+
+        if self.normalize_input:
+            x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)
+
+        for idx, block in enumerate(self.blocks):
+            x = block(x)
+            if idx in self.output_blocks:
+                outp.append(x)
+
+            if idx == self.last_needed_block:
+                break
+
+        return outp
+
+
+def fid_inception_v3():
+    """Build pretrained Inception model for FID computation
+
+    The Inception model for FID computation uses a different set of weights
+    and has a slightly different structure than torchvision's Inception.
+
+    This method first constructs torchvision's Inception and then patches the
+    necessary parts that are different in the FID Inception model.
+    """
+    inception = models.inception_v3(num_classes=1008,
+                                    aux_logits=False,
+                                    pretrained=False)
+    inception.Mixed_5b = FIDInceptionA(192, pool_features=32)
+    inception.Mixed_5c = FIDInceptionA(256, pool_features=64)
+    inception.Mixed_5d = FIDInceptionA(288, pool_features=64)
+    inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)
+    inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)
+    inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)
+    inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)
+    inception.Mixed_7b = FIDInceptionE_1(1280)
+    inception.Mixed_7c = FIDInceptionE_2(2048)
+
+    state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)
+    inception.load_state_dict(state_dict)
+    return inception
+
+
+class FIDInceptionA(models.inception.InceptionA):
+    """InceptionA block patched for FID computation"""
+    def __init__(self, in_channels, pool_features):
+        super(FIDInceptionA, self).__init__(in_channels, pool_features)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch5x5 = self.branch5x5_1(x)
+        branch5x5 = self.branch5x5_2(branch5x5)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
+                                   count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionC(models.inception.InceptionC):
+    """InceptionC block patched for FID computation"""
+    def __init__(self, in_channels, channels_7x7):
+        super(FIDInceptionC, self).__init__(in_channels, channels_7x7)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch7x7 = self.branch7x7_1(x)
+        branch7x7 = self.branch7x7_2(branch7x7)
+        branch7x7 = self.branch7x7_3(branch7x7)
+
+        branch7x7dbl = self.branch7x7dbl_1(x)
+        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
+                                   count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionE_1(models.inception.InceptionE):
+    """First InceptionE block patched for FID computation"""
+    def __init__(self, in_channels):
+        super(FIDInceptionE_1, self).__init__(in_channels)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = [
+            self.branch3x3_2a(branch3x3),
+            self.branch3x3_2b(branch3x3),
+        ]
+        branch3x3 = torch.cat(branch3x3, 1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = [
+            self.branch3x3dbl_3a(branch3x3dbl),
+            self.branch3x3dbl_3b(branch3x3dbl),
+        ]
+        branch3x3dbl = torch.cat(branch3x3dbl, 1)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
+                                   count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionE_2(models.inception.InceptionE):
+    """Second InceptionE block patched for FID computation"""
+    def __init__(self, in_channels):
+        super(FIDInceptionE_2, self).__init__(in_channels)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = [
+            self.branch3x3_2a(branch3x3),
+            self.branch3x3_2b(branch3x3),
+        ]
+        branch3x3 = torch.cat(branch3x3, 1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = [
+            self.branch3x3dbl_3a(branch3x3dbl),
+            self.branch3x3dbl_3b(branch3x3dbl),
+        ]
+        branch3x3dbl = torch.cat(branch3x3dbl, 1)
+
+        # Patch: The FID Inception model uses max pooling instead of average
+        # pooling. This is likely an error in this specific Inception
+        # implementation, as other Inception models use average pooling here
+        # (which matches the description in the paper).
+        branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)