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| """ | |
| Copyright (C) 2018 NVIDIA Corporation. All rights reserved. | |
| Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). | |
| """ | |
| import torch.nn as nn | |
| class VGGEncoder(nn.Module): | |
| def __init__(self, level): | |
| super(VGGEncoder, self).__init__() | |
| self.level = level | |
| # 224 x 224 | |
| self.conv0 = nn.Conv2d(3, 3, 1, 1, 0) | |
| self.pad1_1 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| # 226 x 226 | |
| self.conv1_1 = nn.Conv2d(3, 64, 3, 1, 0) | |
| self.relu1_1 = nn.ReLU(inplace=True) | |
| # 224 x 224 | |
| if level < 2: return | |
| self.pad1_2 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv1_2 = nn.Conv2d(64, 64, 3, 1, 0) | |
| self.relu1_2 = nn.ReLU(inplace=True) | |
| # 224 x 224 | |
| self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2, return_indices=True) | |
| # 112 x 112 | |
| self.pad2_1 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv2_1 = nn.Conv2d(64, 128, 3, 1, 0) | |
| self.relu2_1 = nn.ReLU(inplace=True) | |
| # 112 x 112 | |
| if level < 3: return | |
| self.pad2_2 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv2_2 = nn.Conv2d(128, 128, 3, 1, 0) | |
| self.relu2_2 = nn.ReLU(inplace=True) | |
| # 112 x 112 | |
| self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2, return_indices=True) | |
| # 56 x 56 | |
| self.pad3_1 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv3_1 = nn.Conv2d(128, 256, 3, 1, 0) | |
| self.relu3_1 = nn.ReLU(inplace=True) | |
| # 56 x 56 | |
| if level < 4: return | |
| self.pad3_2 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv3_2 = nn.Conv2d(256, 256, 3, 1, 0) | |
| self.relu3_2 = nn.ReLU(inplace=True) | |
| # 56 x 56 | |
| self.pad3_3 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv3_3 = nn.Conv2d(256, 256, 3, 1, 0) | |
| self.relu3_3 = nn.ReLU(inplace=True) | |
| # 56 x 56 | |
| self.pad3_4 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv3_4 = nn.Conv2d(256, 256, 3, 1, 0) | |
| self.relu3_4 = nn.ReLU(inplace=True) | |
| # 56 x 56 | |
| self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2, return_indices=True) | |
| # 28 x 28 | |
| self.pad4_1 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv4_1 = nn.Conv2d(256, 512, 3, 1, 0) | |
| self.relu4_1 = nn.ReLU(inplace=True) | |
| # 28 x 28 | |
| def forward(self, x): | |
| out = self.conv0(x) | |
| out = self.pad1_1(out) | |
| out = self.conv1_1(out) | |
| out = self.relu1_1(out) | |
| if self.level < 2: | |
| return out | |
| out = self.pad1_2(out) | |
| out = self.conv1_2(out) | |
| pool1 = self.relu1_2(out) | |
| out, pool1_idx = self.maxpool1(pool1) | |
| out = self.pad2_1(out) | |
| out = self.conv2_1(out) | |
| out = self.relu2_1(out) | |
| if self.level < 3: | |
| return out, pool1_idx, pool1.size() | |
| out = self.pad2_2(out) | |
| out = self.conv2_2(out) | |
| pool2 = self.relu2_2(out) | |
| out, pool2_idx = self.maxpool2(pool2) | |
| out = self.pad3_1(out) | |
| out = self.conv3_1(out) | |
| out = self.relu3_1(out) | |
| if self.level < 4: | |
| return out, pool1_idx, pool1.size(), pool2_idx, pool2.size() | |
| out = self.pad3_2(out) | |
| out = self.conv3_2(out) | |
| out = self.relu3_2(out) | |
| out = self.pad3_3(out) | |
| out = self.conv3_3(out) | |
| out = self.relu3_3(out) | |
| out = self.pad3_4(out) | |
| out = self.conv3_4(out) | |
| pool3 = self.relu3_4(out) | |
| out, pool3_idx = self.maxpool3(pool3) | |
| out = self.pad4_1(out) | |
| out = self.conv4_1(out) | |
| out = self.relu4_1(out) | |
| return out, pool1_idx, pool1.size(), pool2_idx, pool2.size(), pool3_idx, pool3.size() | |
| def forward_multiple(self, x): | |
| out = self.conv0(x) | |
| out = self.pad1_1(out) | |
| out = self.conv1_1(out) | |
| out = self.relu1_1(out) | |
| if self.level < 2: return out | |
| out1 = out | |
| out = self.pad1_2(out) | |
| out = self.conv1_2(out) | |
| pool1 = self.relu1_2(out) | |
| out, pool1_idx = self.maxpool1(pool1) | |
| out = self.pad2_1(out) | |
| out = self.conv2_1(out) | |
| out = self.relu2_1(out) | |
| if self.level < 3: return out, out1 | |
| out2 = out | |
| out = self.pad2_2(out) | |
| out = self.conv2_2(out) | |
| pool2 = self.relu2_2(out) | |
| out, pool2_idx = self.maxpool2(pool2) | |
| out = self.pad3_1(out) | |
| out = self.conv3_1(out) | |
| out = self.relu3_1(out) | |
| if self.level < 4: return out, out2, out1 | |
| out3 = out | |
| out = self.pad3_2(out) | |
| out = self.conv3_2(out) | |
| out = self.relu3_2(out) | |
| out = self.pad3_3(out) | |
| out = self.conv3_3(out) | |
| out = self.relu3_3(out) | |
| out = self.pad3_4(out) | |
| out = self.conv3_4(out) | |
| pool3 = self.relu3_4(out) | |
| out, pool3_idx = self.maxpool3(pool3) | |
| out = self.pad4_1(out) | |
| out = self.conv4_1(out) | |
| out = self.relu4_1(out) | |
| return out, out3, out2, out1 | |
| class VGGDecoder(nn.Module): | |
| def __init__(self, level): | |
| super(VGGDecoder, self).__init__() | |
| self.level = level | |
| if level > 3: | |
| self.pad4_1 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv4_1 = nn.Conv2d(512, 256, 3, 1, 0) | |
| self.relu4_1 = nn.ReLU(inplace=True) | |
| # 28 x 28 | |
| self.unpool3 = nn.MaxUnpool2d(kernel_size=2, stride=2) | |
| # 56 x 56 | |
| self.pad3_4 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv3_4 = nn.Conv2d(256, 256, 3, 1, 0) | |
| self.relu3_4 = nn.ReLU(inplace=True) | |
| # 56 x 56 | |
| self.pad3_3 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv3_3 = nn.Conv2d(256, 256, 3, 1, 0) | |
| self.relu3_3 = nn.ReLU(inplace=True) | |
| # 56 x 56 | |
| self.pad3_2 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv3_2 = nn.Conv2d(256, 256, 3, 1, 0) | |
| self.relu3_2 = nn.ReLU(inplace=True) | |
| # 56 x 56 | |
| if level > 2: | |
| self.pad3_1 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv3_1 = nn.Conv2d(256, 128, 3, 1, 0) | |
| self.relu3_1 = nn.ReLU(inplace=True) | |
| # 56 x 56 | |
| self.unpool2 = nn.MaxUnpool2d(kernel_size=2, stride=2) | |
| # 112 x 112 | |
| self.pad2_2 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv2_2 = nn.Conv2d(128, 128, 3, 1, 0) | |
| self.relu2_2 = nn.ReLU(inplace=True) | |
| # 112 x 112 | |
| if level > 1: | |
| self.pad2_1 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv2_1 = nn.Conv2d(128, 64, 3, 1, 0) | |
| self.relu2_1 = nn.ReLU(inplace=True) | |
| # 112 x 112 | |
| self.unpool1 = nn.MaxUnpool2d(kernel_size=2, stride=2) | |
| # 224 x 224 | |
| self.pad1_2 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv1_2 = nn.Conv2d(64, 64, 3, 1, 0) | |
| self.relu1_2 = nn.ReLU(inplace=True) | |
| # 224 x 224 | |
| if level > 0: | |
| self.pad1_1 = nn.ReflectionPad2d((1, 1, 1, 1)) | |
| self.conv1_1 = nn.Conv2d(64, 3, 3, 1, 0) | |
| def forward(self, x, pool1_idx=None, pool1_size=None, pool2_idx=None, pool2_size=None, pool3_idx=None, | |
| pool3_size=None): | |
| out = x | |
| if self.level > 3: | |
| out = self.pad4_1(out) | |
| out = self.conv4_1(out) | |
| out = self.relu4_1(out) | |
| out = self.unpool3(out, pool3_idx, output_size=pool3_size) | |
| out = self.pad3_4(out) | |
| out = self.conv3_4(out) | |
| out = self.relu3_4(out) | |
| out = self.pad3_3(out) | |
| out = self.conv3_3(out) | |
| out = self.relu3_3(out) | |
| out = self.pad3_2(out) | |
| out = self.conv3_2(out) | |
| out = self.relu3_2(out) | |
| if self.level > 2: | |
| out = self.pad3_1(out) | |
| out = self.conv3_1(out) | |
| out = self.relu3_1(out) | |
| out = self.unpool2(out, pool2_idx, output_size=pool2_size) | |
| out = self.pad2_2(out) | |
| out = self.conv2_2(out) | |
| out = self.relu2_2(out) | |
| if self.level > 1: | |
| out = self.pad2_1(out) | |
| out = self.conv2_1(out) | |
| out = self.relu2_1(out) | |
| out = self.unpool1(out, pool1_idx, output_size=pool1_size) | |
| out = self.pad1_2(out) | |
| out = self.conv1_2(out) | |
| out = self.relu1_2(out) | |
| if self.level > 0: | |
| out = self.pad1_1(out) | |
| out = self.conv1_1(out) | |
| return out | |