05963157c1
- Fixes to 'after' and 'before' defs for steps (turns out they werent working) - Feature nets take in a list of layers to extract. Not fully implemented yet. - Fixes bugs with RAGAN - Allows real input into generator gan to not be detached by param
96 lines
4.1 KiB
Python
96 lines
4.1 KiB
Python
import torch
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import torch.nn as nn
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import torchvision
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import torch.nn.functional as F
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# Utilizes pretrained torchvision modules for feature extraction
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class VGGFeatureExtractor(nn.Module):
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def __init__(self, feature_layers=[34], use_bn=False, use_input_norm=True,
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device=torch.device('cpu')):
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super(VGGFeatureExtractor, self).__init__()
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self.use_input_norm = use_input_norm
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if use_bn:
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model = torchvision.models.vgg19_bn(pretrained=True)
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else:
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model = torchvision.models.vgg19(pretrained=True)
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if self.use_input_norm:
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mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
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# [0.485 - 1, 0.456 - 1, 0.406 - 1] if input in range [-1, 1]
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std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
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# [0.229 * 2, 0.224 * 2, 0.225 * 2] if input in range [-1, 1]
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self.register_buffer('mean', mean)
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self.register_buffer('std', std)
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self.feature_layers = feature_layers
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self.features = nn.Sequential(*list(model.features.children())[:(max(feature_layers) + 1)])
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# No need to BP to variable
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for k, v in self.features.named_parameters():
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v.requires_grad = False
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def forward(self, x, interpolate_factor=1):
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if interpolate_factor > 1:
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x = F.interpolate(x, scale_factor=interpolate_factor, mode='bicubic')
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if self.use_input_norm:
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x = (x - self.mean) / self.std
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output = self.features(x)
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return output
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class TrainableVGGFeatureExtractor(nn.Module):
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def __init__(self, feature_layer=34, use_bn=False, use_input_norm=True,
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device=torch.device('cpu')):
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super(TrainableVGGFeatureExtractor, self).__init__()
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self.use_input_norm = use_input_norm
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if use_bn:
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model = torchvision.models.vgg19_bn(pretrained=False)
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else:
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model = torchvision.models.vgg19(pretrained=False)
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if self.use_input_norm:
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mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
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# [0.485 - 1, 0.456 - 1, 0.406 - 1] if input in range [-1, 1]
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std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
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# [0.229 * 2, 0.224 * 2, 0.225 * 2] if input in range [-1, 1]
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self.register_buffer('mean', mean)
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self.register_buffer('std', std)
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self.features = nn.Sequential(*list(model.features.children())[:(feature_layer + 1)])
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def forward(self, x, interpolate_factor=1):
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if interpolate_factor > 1:
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x = F.interpolate(x, scale_factor=interpolate_factor, mode='bicubic')
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# Assume input range is [0, 1]
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if self.use_input_norm:
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x = (x - self.mean) / self.std
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output = self.features(x)
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return output
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class WideResnetFeatureExtractor(nn.Module):
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def __init__(self, use_input_norm=True, device=torch.device('cpu')):
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print("Using wide resnet extractor.")
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super(WideResnetFeatureExtractor, self).__init__()
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self.use_input_norm = use_input_norm
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self.model = torchvision.models.wide_resnet50_2(pretrained=True)
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if self.use_input_norm:
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mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
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# [0.485 - 1, 0.456 - 1, 0.406 - 1] if input in range [-1, 1]
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std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
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# [0.229 * 2, 0.224 * 2, 0.225 * 2] if input in range [-1, 1]
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self.register_buffer('mean', mean)
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self.register_buffer('std', std)
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# No need to BP to variable
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for p in self.model.parameters():
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p.requires_grad = False
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def forward(self, x):
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# Assume input range is [0, 1]
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if self.use_input_norm:
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x = (x - self.mean) / self.std
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x = self.model.conv1(x)
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x = self.model.bn1(x)
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x = self.model.relu(x)
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x = self.model.maxpool(x)
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x = self.model.layer1(x)
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x = self.model.layer2(x)
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x = self.model.layer3(x)
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return x |