""" This file contains a modified version of the FCN-8s code available in https://github.com/wkentaro/pytorch-fcn The original copyright notice from that repository is included below: Copyright (c) 2017 - 2019 Kentaro Wada. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import numpy as np import torch import torch.nn as nn def _upsampling_weights(in_channels, out_channels, kernel_size): factor = (kernel_size + 1) // 2 if kernel_size % 2 == 1: center = factor - 1 else: center = factor - 0.5 og = np.ogrid[:kernel_size, :kernel_size] filt = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor) weight = np.zeros( (in_channels, out_channels, kernel_size, kernel_size), dtype=np.float64 ) weight[range(in_channels), range(out_channels), :, :] = filt return torch.from_numpy(weight).float() class Fcn8s(nn.Module): def __init__(self, n_class=21): """ Create the FCN-8s network the the given number of classes. Args: n_class: The number of semantic classes. """ super(Fcn8s, self).__init__() # conv1 self.conv1_1 = nn.Conv2d(3, 64, 3, padding=100) self.relu1_1 = nn.ReLU(inplace=True) self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1) self.relu1_2 = nn.ReLU(inplace=True) self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # conv2 self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1) self.relu2_1 = nn.ReLU(inplace=True) self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1) self.relu2_2 = nn.ReLU(inplace=True) self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # conv3 self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1) self.relu3_1 = nn.ReLU(inplace=True) self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1) self.relu3_2 = nn.ReLU(inplace=True) self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1) self.relu3_3 = nn.ReLU(inplace=True) self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # conv4 self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1) self.relu4_1 = nn.ReLU(inplace=True) self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1) self.relu4_2 = nn.ReLU(inplace=True) self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1) self.relu4_3 = nn.ReLU(inplace=True) self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # conv5 self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1) self.relu5_1 = nn.ReLU(inplace=True) self.conv5_2 = nn.Conv2d(512, 512, 3, padding=1) self.relu5_2 = nn.ReLU(inplace=True) self.conv5_3 = nn.Conv2d(512, 512, 3, padding=1) self.relu5_3 = nn.ReLU(inplace=True) self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # fc6 self.fc6 = nn.Conv2d(512, 4096, 7) self.relu6 = nn.ReLU(inplace=True) self.drop6 = nn.Dropout2d() # fc7 self.fc7 = nn.Conv2d(4096, 4096, 1) self.relu7 = nn.ReLU(inplace=True) self.drop7 = nn.Dropout2d() self.score_fr = nn.Conv2d(4096, n_class, 1) self.score_pool3 = nn.Conv2d(256, n_class, 1) self.score_pool4 = nn.Conv2d(512, n_class, 1) self.upscore2 = nn.ConvTranspose2d(n_class, n_class, 4, stride=2, bias=True) self.upscore8 = nn.ConvTranspose2d(n_class, n_class, 16, stride=8, bias=False) self.upscore_pool4 = nn.ConvTranspose2d( n_class, n_class, 4, stride=2, bias=False ) self._initialize_weights() def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): m.weight.data.zero_() if m.bias is not None: m.bias.data.zero_() if isinstance(m, nn.ConvTranspose2d): assert m.kernel_size[0] == m.kernel_size[1] initial_weight = _upsampling_weights( m.in_channels, m.out_channels, m.kernel_size[0] ) m.weight.data.copy_(initial_weight) def forward(self, image): h = self.relu1_1(self.conv1_1(image)) h = self.relu1_2(self.conv1_2(h)) h = self.pool1(h) h = self.relu2_1(self.conv2_1(h)) h = self.relu2_2(self.conv2_2(h)) h = self.pool2(h) h = self.relu3_1(self.conv3_1(h)) h = self.relu3_2(self.conv3_2(h)) h = self.relu3_3(self.conv3_3(h)) h = self.pool3(h) pool3 = h # 1/8 h = self.relu4_1(self.conv4_1(h)) h = self.relu4_2(self.conv4_2(h)) h = self.relu4_3(self.conv4_3(h)) h = self.pool4(h) pool4 = h # 1/16 h = self.relu5_1(self.conv5_1(h)) h = self.relu5_2(self.conv5_2(h)) h = self.relu5_3(self.conv5_3(h)) h = self.pool5(h) h = self.relu6(self.fc6(h)) h = self.drop6(h) h = self.relu7(self.fc7(h)) h = self.drop7(h) h = self.score_fr(h) h = self.upscore2(h) upscore2 = h # 1/16 h = self.score_pool4(pool4) h = h[:, :, 5:5 + upscore2.size()[2], 5:5 + upscore2.size()[3]] score_pool4c = h # 1/16 h = upscore2 + score_pool4c # 1/16 h = self.upscore_pool4(h) upscore_pool4 = h # 1/8 h = self.score_pool3(pool3) h = h[:, :, 9:9 + upscore_pool4.size()[2], 9:9 + upscore_pool4.size()[3]] score_pool3c = h # 1/8 h = upscore_pool4 + score_pool3c # 1/8 h = self.upscore8(h) h = h[:, :, 31:31 + image.size()[2], 31:31 + image.size()[3]].contiguous() return h