text: # 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
  
  
  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 = 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):
          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
  
      def forward(self, input_image):
          self.features = []
          x = (input_image - 0.45) / 0.225
          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
  