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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.
import numpy as np
import torch
import torch.nn as nn
import torchvision.models as models
RESNETS = {18: (models.resnet18, models.ResNet18_Weights.IMAGENET1K_V1),
50: (models.resnet50, models.ResNet50_Weights.IMAGENET1K_V2)}
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)
model, weigths = RESNETS[num_layers]
if pretrained:
loaded = torch.hub.load_state_dict_from_url(weigths.url)
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, bn_order, num_input_images=1):
super(ResnetEncoder, self).__init__()
self.num_ch_enc = np.array([64, 64, 128, 256, 512])
self.bn_order = bn_order
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:
model, weights = RESNETS[num_layers]
self.encoder = model(weights=weights)
if num_layers > 34:
self.num_ch_enc[1:] *= 4
def forward(self, input_image):
encoder = self.encoder
features = []
x = (input_image - 0.45) / 0.225
x = encoder.conv1(x)
if self.bn_order == "pre_bn":
# Concatenating pre-norm features allows us to
# keep the scale and shift of RGB colours
# and recover them at output
features.append(x)
x = encoder.bn1(x)
x = encoder.relu(x)
features.append(encoder.layer1(encoder.maxpool(x)))
elif self.bn_order == "monodepth":
# Batchnorm gets rid of constants due to colour shift
# will make the network not able to recover absolute colour shift
# of the input image
# used in old models
x = encoder.bn1(x)
x = encoder.relu(x)
features.append(x)
features.append(encoder.layer1(encoder.maxpool(x)))
else:
assert False
features.append(encoder.layer2(features[-1]))
features.append(encoder.layer3(features[-1]))
features.append(encoder.layer4(features[-1]))
return features
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