Spaces:
Build error
Build error
| # Adapted from monodepth2 | |
| # https://github.com/nianticlabs/monodepth2/blob/master/networks/depth_decoder.py | |
| # | |
| # Copyright Niantic 2019. Patent Pending. All rights reserved. | |
| # | |
| # This software is licensed under the terms of the Monodepth2 licence | |
| # which allows for non-commercial use only, the full terms of which are made | |
| # available in the LICENSE file. | |
| from __future__ import absolute_import, division, print_function | |
| from collections import OrderedDict | |
| from easydict import EasyDict | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torchvision.models as models | |
| import torch.utils.model_zoo as model_zoo | |
| class ConvBlock(torch.nn.Module): | |
| """Layer to perform a convolution followed by ELU.""" | |
| def __init__(self, in_channels, out_channels, bn=False, dropout=0.0): | |
| super(ConvBlock, self).__init__() | |
| self.block = nn.Sequential( | |
| Conv3x3(in_channels, out_channels), | |
| nn.BatchNorm2d(out_channels) if bn else nn.Identity(), | |
| nn.ELU(inplace=True), | |
| # Pay attention: 2d version of dropout is used | |
| nn.Dropout2d(dropout) if dropout > 0 else nn.Identity()) | |
| def forward(self, x): | |
| out = self.block(x) | |
| return out | |
| class Conv3x3(nn.Module): | |
| """Layer to pad and convolve input with 3x3 kernels.""" | |
| def __init__(self, in_channels, out_channels, use_refl=True): | |
| super(Conv3x3, self).__init__() | |
| if use_refl: | |
| self.pad = nn.ReflectionPad2d(1) | |
| else: | |
| self.pad = nn.ZeroPad2d(1) | |
| self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3) | |
| def forward(self, x): | |
| out = self.pad(x) | |
| out = self.conv(out) | |
| return out | |
| def upsample(x): | |
| """Upsample input tensor by a factor of 2.""" | |
| return F.interpolate(x, scale_factor=2, mode="nearest") | |
| class ResNetMultiImageInput(models.ResNet): | |
| """Constructs a resnet model with varying number of input images. | |
| Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py | |
| """ | |
| def __init__(self, block, layers, num_classes=1000, in_channels=3): | |
| super(ResNetMultiImageInput, self).__init__(block, layers) | |
| self.inplanes = 64 | |
| self.conv1 = nn.Conv2d( | |
| in_channels, 64, kernel_size=7, stride=2, padding=3, bias=False) | |
| self.bn1 = nn.BatchNorm2d(64) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.layer1 = self._make_layer(block, 64, layers[0]) | |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2) | |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2) | |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') | |
| elif isinstance(m, nn.BatchNorm2d): | |
| nn.init.constant_(m.weight, 1) | |
| nn.init.constant_(m.bias, 0) | |
| def resnet_multiimage_input(num_layers, pretrained=False, in_channels=3): | |
| """Constructs a ResNet model. | |
| Args: | |
| num_layers (int): Number of resnet layers. Must be 18 or 50 | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| in_channels (int): Number of input channels | |
| """ | |
| assert num_layers in [18, 50], "Can only run with 18 or 50 layer resnet" | |
| blocks = {18: [2, 2, 2, 2], 50: [3, 4, 6, 3]}[num_layers] | |
| block_type = {18: models.resnet.BasicBlock, 50: models.resnet.Bottleneck}[num_layers] | |
| model = ResNetMultiImageInput(block_type, blocks, in_channels=in_channels) | |
| if pretrained: | |
| print('loading imagnet weights on resnet...') | |
| loaded = model_zoo.load_url(models.resnet.model_urls['resnet{}'.format(num_layers)]) | |
| # loaded['conv1.weight'] = torch.cat( | |
| # (loaded['conv1.weight'], loaded['conv1.weight']), 1) | |
| # diff = model.load_state_dict(loaded, strict=False) | |
| return model | |
| class ResnetEncoder(nn.Module): | |
| """Pytorch module for a resnet encoder | |
| """ | |
| def __init__(self, num_layers, pretrained, in_channels=3): | |
| super(ResnetEncoder, self).__init__() | |
| self.num_ch_enc = np.array([64, 64, 128, 256, 512]) | |
| resnets = {18: models.resnet18, | |
| 34: models.resnet34, | |
| 50: models.resnet50, | |
| 101: models.resnet101, | |
| 152: models.resnet152} | |
| if num_layers not in resnets: | |
| raise ValueError("{} is not a valid number of resnet layers".format(num_layers)) | |
| if in_channels > 3: | |
| self.encoder = resnet_multiimage_input(num_layers, pretrained, in_channels) | |
| else: | |
| weights = models.ResNet101_Weights.IMAGENET1K_V1 if pretrained else None | |
| self.encoder = resnets[num_layers](weights=weights) | |
| if num_layers > 34: | |
| self.num_ch_enc[1:] *= 4 | |
| def forward(self, x): | |
| self.features = [] | |
| # input_image, normals = xx | |
| # x = (input_image - 0.45) / 0.225 | |
| # x = torch.cat((input_image, normals),1) | |
| x = self.encoder.conv1(x) | |
| x = self.encoder.bn1(x) | |
| self.features.append(self.encoder.relu(x)) | |
| self.features.append(self.encoder.layer1(self.encoder.maxpool(self.features[-1]))) | |
| self.features.append(self.encoder.layer2(self.features[-1])) | |
| self.features.append(self.encoder.layer3(self.features[-1])) | |
| self.features.append(self.encoder.layer4(self.features[-1])) | |
| return self.features | |
| class Decoder(nn.Module): | |
| def __init__(self, num_ch_enc, scales=range(4), num_output_channels=1, use_skips=True, | |
| kaiming_init=False, return_feats=False): | |
| super().__init__() | |
| self.num_output_channels = num_output_channels | |
| self.use_skips = use_skips | |
| self.upsample_mode = 'nearest' | |
| self.scales = scales | |
| self.return_feats = return_feats | |
| self.num_ch_enc = num_ch_enc | |
| self.num_ch_dec = np.array([16, 32, 64, 128, 256]) | |
| # decoder | |
| self.convs = OrderedDict() | |
| for i in range(4, -1, -1): | |
| # upconv_0 | |
| num_ch_in = self.num_ch_enc[-1] if i == 4 else self.num_ch_dec[i + 1] | |
| num_ch_out = self.num_ch_dec[i] | |
| self.convs[("upconv", i, 0)] = ConvBlock(num_ch_in, num_ch_out) | |
| # upconv_1 | |
| num_ch_in = self.num_ch_dec[i] | |
| if self.use_skips and i > 0: | |
| num_ch_in += self.num_ch_enc[i - 1] | |
| num_ch_out = self.num_ch_dec[i] | |
| self.convs[("upconv", i, 1)] = ConvBlock(num_ch_in, num_ch_out) | |
| # for s in self.scales: | |
| self.convs[("dispconv", 0)] = Conv3x3(self.num_ch_dec[0], self.num_output_channels) | |
| self.decoder = nn.ModuleList(list(self.convs.values())) | |
| # self.sigmoid = nn.Sigmoid() | |
| if kaiming_init: | |
| print('init weights of decoder') | |
| for m in self.children(): | |
| if isinstance(m, nn.Conv2d): | |
| nn.init.kaiming_normal_(m.weight) | |
| if m.bias is not None: | |
| m.bias.data.fill_(0.01) | |
| def forward(self, input_features): | |
| x = input_features[-1] | |
| for i in range(4, -1, -1): | |
| x = self.convs[("upconv", i, 0)](x) | |
| x = [upsample(x)] | |
| if self.use_skips and i > 0: | |
| x += [input_features[i - 1]] | |
| x = torch.cat(x, 1) | |
| x = self.convs[("upconv", i, 1)](x) | |
| # assert self.scales[0] == 0 | |
| final_conv = self.convs[("dispconv", 0)] | |
| out = final_conv(x) | |
| if self.return_feats: | |
| return out, input_features[-1] | |
| return out | |
| class MultiHeadDecoder(nn.Module): | |
| def __init__(self, num_ch_enc, tasks, return_feats, use_skips): | |
| super().__init__() | |
| self.decoders = nn.ModuleDict({k: | |
| Decoder(num_ch_enc=num_ch_enc, | |
| num_output_channels=num_ch, | |
| scales=[0], | |
| kaiming_init=False, | |
| use_skips=use_skips, | |
| return_feats=return_feats) | |
| for k, num_ch in tasks.items()}) | |
| def forward(self, x): | |
| y = EasyDict({k: v(x) for k, v in self.decoders.items()}) | |
| return y | |
| class DenseMTL(nn.Module): | |
| def __init__(self, encoder, decoder): | |
| super().__init__() | |
| self.encoder = encoder | |
| self.decoder = decoder | |
| def forward(self, x): | |
| return self.decoder(self.encoder(x)) |