# --------------------------------------------------------------- # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # This file has been modified from a file in the pytorch_fid library # which was released under the Apache License v2.0 License. # # Source: # https://github.com/mseitzer/pytorch-fid/blob/master/pytorch_fid/inception.py # # The license for the original version of this file can be # found in this directory (LICENSE_pytorch_fid). The modifications # to this file are subject to the same Apache License. # --------------------------------------------------------------- import torch import torch.nn as nn import torch.nn.functional as F import torchvision 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, model_dir=None): """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. model_dir: is used for storing pretrained checkpoints """ 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(model_dir) else: inception = _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 _inception_v3(*args, **kwargs): """Wraps `torchvision.models.inception_v3` Skips default weight inititialization if supported by torchvision version. See https://github.com/mseitzer/pytorch-fid/issues/28. """ try: version = tuple(map(int, torchvision.__version__.split('.')[:2])) except ValueError: # Just a caution against weird version strings version = (0,) if version >= (0, 6): kwargs['init_weights'] = False #if kwargs['pretrained']: #kwargs['weights'] = torchvision.models.Inception_V3_Weights.DEFAULT #kwargs.pop('pretrained') return torchvision.models.inception_v3(*args, **kwargs) def fid_inception_v3(model_dir=None): """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, 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, model_dir=model_dir, 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)