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Running on Zero
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41ff959 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, in_planes: int, planes: int, stride: int = 1) -> None:
super().__init__()
self.conv1 = nn.Conv2d(
in_planes,
planes,
kernel_size=3,
padding=1,
stride=stride,
padding_mode="zeros",
)
self.conv2 = nn.Conv2d(
planes,
planes,
kernel_size=3,
padding=1,
padding_mode="zeros",
)
self.relu = nn.ReLU(inplace=True)
self.norm1 = nn.InstanceNorm2d(planes)
self.norm2 = nn.InstanceNorm2d(planes)
if stride != 1:
self.norm3 = nn.InstanceNorm2d(planes)
if stride == 1:
self.downsample = None
else:
self.downsample = nn.Sequential(
nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride),
self.norm3,
)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
residual = inputs
outputs = self.relu(self.norm1(self.conv1(inputs)))
outputs = self.relu(self.norm2(self.conv2(outputs)))
if self.downsample is not None:
residual = self.downsample(residual)
return self.relu(residual + outputs)
class BasicEncoder(nn.Module):
def __init__(self, input_dim=3, output_dim=128, stride=4):
super().__init__()
self.stride = stride
self.in_planes = output_dim // 2
self.norm1 = nn.InstanceNorm2d(self.in_planes)
self.norm2 = nn.InstanceNorm2d(output_dim * 2)
self.conv1 = nn.Conv2d(
input_dim,
self.in_planes,
kernel_size=7,
stride=2,
padding=3,
padding_mode="zeros",
)
self.relu1 = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(output_dim // 2, stride=1)
self.layer2 = self._make_layer(output_dim // 4 * 3, stride=2)
self.layer3 = self._make_layer(output_dim, stride=2)
self.layer4 = self._make_layer(output_dim, stride=2)
self.conv2 = nn.Conv2d(
output_dim * 3 + output_dim // 4,
output_dim * 2,
kernel_size=3,
padding=1,
padding_mode="zeros",
)
self.relu2 = nn.ReLU(inplace=True)
self.conv3 = nn.Conv2d(output_dim * 2, output_dim, kernel_size=1)
for module in self.modules():
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(module, nn.InstanceNorm2d):
if module.weight is not None:
nn.init.constant_(module.weight, 1)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
def _make_layer(self, dim, stride=1):
layer1 = ResidualBlock(self.in_planes, dim, stride=stride)
layer2 = ResidualBlock(dim, dim, stride=1)
self.in_planes = dim
return nn.Sequential(layer1, layer2)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
height, width = inputs.shape[-2:]
features = self.relu1(self.norm1(self.conv1(inputs)))
level1 = self.layer1(features)
level2 = self.layer2(level1)
level3 = self.layer3(level2)
level4 = self.layer4(level3)
output_shape = (height // self.stride, width // self.stride)
levels = [
F.interpolate(
level,
output_shape,
mode="bilinear",
align_corners=True,
)
for level in (level1, level2, level3, level4)
]
features = self.relu2(self.norm2(self.conv2(torch.cat(levels, dim=1))))
return self.conv3(features)
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