"""Calculates the Frechet Inception Distance (FID) to evalulate GANs The FID metric calculates the distance between two distributions of images. Typically, we have summary statistics (mean & covariance matrix) of one of these distributions, while the 2nd distribution is given by a GAN. When run as a stand-alone program, it compares the distribution of images that are stored as PNG/JPEG at a specified location with a distribution given by summary statistics (in pickle format). The FID is calculated by assuming that X_1 and X_2 are the activations of the pool_3 layer of the inception net for generated samples and real world samples respectively. See --help to see further details. Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead of Tensorflow Copyright 2018 Institute of Bioinformatics, JKU Linz Licensed 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 CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import os import pathlib from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser import numpy as np import torch import torchvision.transforms as TF from PIL import Image from scipy import linalg from torch.nn.functional import adaptive_avg_pool2d import torch.nn as nn import torch.nn.functional as F import torchvision try: from tqdm import tqdm except ImportError: # If tqdm is not available, provide a mock version of it def tqdm(x): return x IMAGE_EXTENSIONS = {'bmp', 'jpg', 'jpeg', 'pgm', 'png', 'ppm', 'tif', 'tiff', 'webp'} 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' # noqa: E501 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 = _inception_v3(weights='DEFAULT') # 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 _inception_v3(*args, **kwargs): """Wraps `torchvision.models.inception_v3`""" try: version = tuple(map(int, torchvision.__version__.split('.')[:2])) except ValueError: # Just a caution against weird version strings version = (0,) # Skips default weight inititialization if supported by torchvision # version. See https://github.com/mseitzer/pytorch-fid/issues/28. if version >= (0, 6): kwargs['init_weights'] = False # Backwards compatibility: `weights` argument was handled by `pretrained` # argument prior to version 0.13. if version < (0, 13) and 'weights' in kwargs: if kwargs['weights'] == 'DEFAULT': kwargs['pretrained'] = True elif kwargs['weights'] is None: kwargs['pretrained'] = False else: raise ValueError( 'weights=={} not supported in torchvision {}'.format( kwargs['weights'], torchvision.__version__ ) ) del kwargs['weights'] return torchvision.models.inception_v3(*args, **kwargs) 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 = _inception_v3(num_classes=1008, aux_logits=False, weights=None) 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(torchvision.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(torchvision.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(torchvision.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(torchvision.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 ImagePathDataset(torch.utils.data.Dataset): def __init__(self, files, transforms=None): self.files = files self.transforms = transforms def __len__(self): return len(self.files) def __getitem__(self, i): path = self.files[i] img = Image.open(path).convert('RGB') if self.transforms is not None: img = self.transforms(img) return img def get_activations(files, model, batch_size=50, dims=2048, device='cpu', num_workers=1, resize=0): """Calculates the activations of the pool_3 layer for all images. Params: -- files : List of image files paths -- model : Instance of inception model -- batch_size : Batch size of images for the model to process at once. Make sure that the number of samples is a multiple of the batch size, otherwise some samples are ignored. This behavior is retained to match the original FID score implementation. -- dims : Dimensionality of features returned by Inception -- device : Device to run calculations -- num_workers : Number of parallel dataloader workers Returns: -- A numpy array of dimension (num images, dims) that contains the activations of the given tensor when feeding inception with the query tensor. """ model.eval() if batch_size > len(files): print(('Warning: batch size is bigger than the data size. ' 'Setting batch size to data size')) batch_size = len(files) if resize > 0: tform = TF.Compose([TF.Resize((resize, resize)), TF.ToTensor()]) else: tform = TF.ToTensor() dataset = ImagePathDataset(files, transforms=tform) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=num_workers) pred_arr = np.empty((len(files), dims)) start_idx = 0 for batch in tqdm(dataloader): batch = batch.to(device) with torch.no_grad(): pred = model(batch)[0] # If model output is not scalar, apply global spatial average pooling. # This happens if you choose a dimensionality not equal 2048. if pred.size(2) != 1 or pred.size(3) != 1: pred = adaptive_avg_pool2d(pred, output_size=(1, 1)) pred = pred.squeeze(3).squeeze(2).cpu().numpy() pred_arr[start_idx:start_idx + pred.shape[0]] = pred start_idx = start_idx + pred.shape[0] return pred_arr def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): """Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). Stable version by Dougal J. Sutherland. Params: -- mu1 : Numpy array containing the activations of a layer of the inception net (like returned by the function 'get_predictions') for generated samples. -- mu2 : The sample mean over activations, precalculated on an representative data set. -- sigma1: The covariance matrix over activations for generated samples. -- sigma2: The covariance matrix over activations, precalculated on an representative data set. Returns: -- : The Frechet Distance. """ mu1 = np.atleast_1d(mu1) mu2 = np.atleast_1d(mu2) sigma1 = np.atleast_2d(sigma1) sigma2 = np.atleast_2d(sigma2) assert mu1.shape == mu2.shape, \ 'Training and test mean vectors have different lengths' assert sigma1.shape == sigma2.shape, \ 'Training and test covariances have different dimensions' diff = mu1 - mu2 # Product might be almost singular covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) if not np.isfinite(covmean).all(): msg = ('fid calculation produces singular product; ' 'adding %s to diagonal of cov estimates') % eps print(msg) offset = np.eye(sigma1.shape[0]) * eps covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) # Numerical error might give slight imaginary component if np.iscomplexobj(covmean): if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): m = np.max(np.abs(covmean.imag)) raise ValueError('Imaginary component {}'.format(m)) covmean = covmean.real tr_covmean = np.trace(covmean) return (diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean) def calculate_activation_statistics(files, model, batch_size=50, dims=2048, device='cpu', num_workers=1, resize=0): """Calculation of the statistics used by the FID. Params: -- files : List of image files paths -- model : Instance of inception model -- batch_size : The images numpy array is split into batches with batch size batch_size. A reasonable batch size depends on the hardware. -- dims : Dimensionality of features returned by Inception -- device : Device to run calculations -- num_workers : Number of parallel dataloader workers Returns: -- mu : The mean over samples of the activations of the pool_3 layer of the inception model. -- sigma : The covariance matrix of the activations of the pool_3 layer of the inception model. """ act = get_activations(files, model, batch_size, dims, device, num_workers, resize) mu = np.mean(act, axis=0) sigma = np.cov(act, rowvar=False) return mu, sigma def compute_statistics_of_path(path, model, batch_size, dims, device, num_workers=1, nimages=None, resize=0): if path.endswith('.npz'): with np.load(path) as f: m, s = f['mu'][:], f['sigma'][:] else: path = pathlib.Path(path) files = sorted([file for ext in IMAGE_EXTENSIONS for file in path.glob('**/*.{}'.format(ext))]) nfiles = len(files) n = nfiles if nimages is None else min(nimages, nfiles) print(f'Found {nfiles} images. Computing FID with {n} images.') files = files[:n] m, s = calculate_activation_statistics(files, model, batch_size, dims, device, num_workers, resize) return m, s def calculate_fid_given_paths(paths, batch_size, device, dims, num_workers=1, nimages=None, resize=0): """Calculates the FID of two paths""" for p in paths: if not os.path.exists(p): raise RuntimeError('Invalid path: %s' % p) block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] model = InceptionV3([block_idx]).to(device) m1, s1 = compute_statistics_of_path(paths[0], model, batch_size, dims, device, num_workers, nimages, resize) m2, s2 = compute_statistics_of_path(paths[1], model, batch_size, dims, device, num_workers, nimages, resize) fid_value = calculate_frechet_distance(m1, s1, m2, s2) return fid_value def save_fid_stats(paths, batch_size, device, dims, num_workers=1, nimages=None, resize=0): """Calculates the FID of two paths""" if not os.path.exists(paths[0]): raise RuntimeError('Invalid path: %s' % paths[0]) if os.path.exists(paths[1]): raise RuntimeError('Existing output file: %s' % paths[1]) block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] model = InceptionV3([block_idx]).to(device) print(f"Saving statistics for {paths[0]}") m1, s1 = compute_statistics_of_path(paths[0], model, batch_size, dims, device, num_workers, nimages, resize=0) np.savez_compressed(paths[1], mu=m1, sigma=s1) def main(): parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) parser.add_argument('--batch-size', type=int, default=20, help='Batch size to use') parser.add_argument('--num-workers', type=int, help=('Number of processes to use for data loading. ' 'Defaults to `min(8, num_cpus)`')) parser.add_argument('--device', type=str, default='cuda:0', help='Device to use. Like cuda, cuda:0 or cpu') parser.add_argument('--dims', type=int, default=2048, choices=list(InceptionV3.BLOCK_INDEX_BY_DIM), help=('Dimensionality of Inception features to use. ' 'By default, uses pool3 features')) parser.add_argument('--nimages', type=int, default=50000, help='max number of images to use') parser.add_argument('--resize', type=int, default=0, help='resize images to this size, 0 mean keep original size') parser.add_argument('--save-stats', action='store_true', help=('Generate an npz archive from a directory of samples. ' 'The first path is used as input and the second as output.')) parser.add_argument('path', type=str, nargs=2, help=('Paths to the generated images or ' 'to .npz statistic files')) args = parser.parse_args() if args.device is None: device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu') else: device = torch.device(args.device) if args.num_workers is None: try: num_cpus = len(os.sched_getaffinity(0)) except AttributeError: # os.sched_getaffinity is not available under Windows, use # os.cpu_count instead (which may not return the *available* number # of CPUs). num_cpus = os.cpu_count() num_workers = min(num_cpus, 8) if num_cpus is not None else 0 else: num_workers = args.num_workers if args.save_stats: save_fid_stats(args.path, args.batch_size, device, args.dims, num_workers, args.nimages, args.resize) return fid_value = calculate_fid_given_paths(args.path, args.batch_size, device, args.dims, num_workers, args.nimages, args.resize) print('FID: ', fid_value) if __name__ == '__main__': main()