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| # 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 | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torchvision.models as models | |
| import torch.utils.model_zoo as model_zoo | |
| from torchvision.models.resnet import ResNet18_Weights, ResNet50_Weights | |
| 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, num_input_images=1): | |
| super(ResNetMultiImageInput, self).__init__(block, layers) | |
| self.inplanes = 64 | |
| self.conv1 = nn.Conv2d( | |
| num_input_images * 3, 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, num_input_images=1): | |
| """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 | |
| num_input_images (int): Number of frames stacked as input | |
| """ | |
| 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, num_input_images=num_input_images) | |
| if pretrained: | |
| # loaded = torch.utils.model_zoo.load_url(ResNet50_Weights.IMAGENET1K_V1.url) | |
| loaded = torch.utils.model_zoo.load_url(ResNet18_Weights.IMAGENET1K_V1.url) | |
| # loaded = model_zoo.load_url(models.resnet.model_urls['resnet{}'.format(num_layers)]) | |
| loaded['conv1.weight'] = torch.cat( | |
| [loaded['conv1.weight']] * num_input_images, 1) / num_input_images | |
| model.load_state_dict(loaded) | |
| return model | |
| class ResnetEncoder(nn.Module): | |
| """Pytorch module for a resnet encoder | |
| """ | |
| def __init__(self, num_layers, pretrained, num_input_images=1, batch_norm_apply = False): | |
| 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 num_input_images > 1: | |
| self.encoder = resnet_multiimage_input(num_layers, pretrained, num_input_images) | |
| else: | |
| self.encoder = resnets[num_layers](pretrained) | |
| if num_layers > 34: | |
| self.num_ch_enc[1:] *= 4 | |
| self.drop = True | |
| self.dropout = torch.nn.Dropout(p=0.2) | |
| def forward(self, input_image): | |
| self.features = [] | |
| # x = (input_image - 0.45) / 0.225 # ? | |
| x = input_image | |
| x = self.encoder.conv1(x) | |
| x = self.encoder.bn1(x) | |
| self.features.append(self.encoder.relu(x)) | |
| if self.drop: | |
| self.features.append(self.encoder.layer1(self.encoder.maxpool(self.features[-1]))) | |
| self.features.append(self.encoder.layer2(self.dropout(self.features[-1]))) | |
| self.features.append(self.encoder.layer3(self.dropout(self.features[-1]))) | |
| self.features.append(self.encoder.layer4(self.dropout(self.features[-1]))) | |
| else: | |
| 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 | |