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from __future__ import absolute_import, division, print_function |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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import torchvision.models as models |
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import torch.utils.model_zoo as model_zoo |
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class ResNetMultiImageInput(models.ResNet): |
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"""Constructs a resnet model with varying number of input images. |
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Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py |
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""" |
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def __init__(self, block, layers, num_classes=1000, num_input_images=1): |
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super(ResNetMultiImageInput, self).__init__(block, layers) |
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self.inplanes = 64 |
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self.conv1 = nn.Conv2d( |
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num_input_images * 3, 64, kernel_size=7, stride=2, padding=3, bias=False) |
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self.bn1 = nn.BatchNorm2d(64) |
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self.relu = nn.ReLU(inplace=True) |
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
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self.layer1 = self._make_layer(block, 64, layers[0]) |
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') |
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elif isinstance(m, nn.BatchNorm2d): |
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nn.init.constant_(m.weight, 1) |
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nn.init.constant_(m.bias, 0) |
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def resnet_multiimage_input(num_layers, pretrained=False, num_input_images=1): |
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"""Constructs a ResNet model. |
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Args: |
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num_layers (int): Number of resnet layers. Must be 18 or 50 |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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num_input_images (int): Number of frames stacked as input |
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""" |
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assert num_layers in [18, 50], "Can only run with 18 or 50 layer resnet" |
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blocks = {18: [2, 2, 2, 2], 50: [3, 4, 6, 3]}[num_layers] |
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block_type = {18: models.resnet.BasicBlock, 50: models.resnet.Bottleneck}[num_layers] |
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model = ResNetMultiImageInput(block_type, blocks, num_input_images=num_input_images) |
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if pretrained: |
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loaded = model_zoo.load_url(models.resnet.model_urls['resnet{}'.format(num_layers)]) |
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loaded['conv1.weight'] = torch.cat( |
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[loaded['conv1.weight']] * num_input_images, 1) / num_input_images |
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model.load_state_dict(loaded) |
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return model |
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class ResnetEncoder(nn.Module): |
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"""Pytorch module for a resnet encoder |
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""" |
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def __init__(self, num_layers, pretrained, num_input_images=1): |
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super(ResnetEncoder, self).__init__() |
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self.num_ch_enc = np.array([64, 64, 128, 256, 512]) |
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resnets = {18: models.resnet18, |
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34: models.resnet34, |
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50: models.resnet50, |
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101: models.resnet101, |
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152: models.resnet152} |
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if num_layers not in resnets: |
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raise ValueError("{} is not a valid number of resnet layers".format(num_layers)) |
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if num_input_images > 1: |
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self.encoder = resnet_multiimage_input(num_layers, pretrained, num_input_images) |
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else: |
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self.encoder = resnets[num_layers](pretrained) |
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if num_layers > 34: |
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self.num_ch_enc[1:] *= 4 |
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def forward(self, input_image): |
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self.features = [] |
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x = (input_image - 0.45) / 0.225 |
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x = self.encoder.conv1(x) |
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x = self.encoder.bn1(x) |
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self.features.append(self.encoder.relu(x)) |
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self.features.append(self.encoder.layer1(self.encoder.maxpool(self.features[-1]))) |
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self.features.append(self.encoder.layer2(self.features[-1])) |
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self.features.append(self.encoder.layer3(self.features[-1])) |
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self.features.append(self.encoder.layer4(self.features[-1])) |
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return self.features |
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