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Running on Zero
Running on Zero
| # 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 | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| def bilinear_sampler(input, coords, align_corners=True, padding_mode="border"): | |
| r"""Sample a tensor using bilinear interpolation | |
| `bilinear_sampler(input, coords)` samples a tensor :attr:`input` at | |
| coordinates :attr:`coords` using bilinear interpolation. It is the same | |
| as `torch.nn.functional.grid_sample()` but with a different coordinate | |
| convention. | |
| The input tensor is assumed to be of shape :math:`(B, C, H, W)`, where | |
| :math:`B` is the batch size, :math:`C` is the number of channels, | |
| :math:`H` is the height of the image, and :math:`W` is the width of the | |
| image. The tensor :attr:`coords` of shape :math:`(B, H_o, W_o, 2)` is | |
| interpreted as an array of 2D point coordinates :math:`(x_i,y_i)`. | |
| Alternatively, the input tensor can be of size :math:`(B, C, T, H, W)`, | |
| in which case sample points are triplets :math:`(t_i,x_i,y_i)`. Note | |
| that in this case the order of the components is slightly different | |
| from `grid_sample()`, which would expect :math:`(x_i,y_i,t_i)`. | |
| If `align_corners` is `True`, the coordinate :math:`x` is assumed to be | |
| in the range :math:`[0,W-1]`, with 0 corresponding to the center of the | |
| left-most image pixel :math:`W-1` to the center of the right-most | |
| pixel. | |
| If `align_corners` is `False`, the coordinate :math:`x` is assumed to | |
| be in the range :math:`[0,W]`, with 0 corresponding to the left edge of | |
| the left-most pixel :math:`W` to the right edge of the right-most | |
| pixel. | |
| Similar conventions apply to the :math:`y` for the range | |
| :math:`[0,H-1]` and :math:`[0,H]` and to :math:`t` for the range | |
| :math:`[0,T-1]` and :math:`[0,T]`. | |
| Args: | |
| input (Tensor): batch of input images. | |
| coords (Tensor): batch of coordinates. | |
| align_corners (bool, optional): Coordinate convention. Defaults to `True`. | |
| padding_mode (str, optional): Padding mode. Defaults to `"border"`. | |
| Returns: | |
| Tensor: sampled points. | |
| """ | |
| sizes = input.shape[2:] | |
| assert len(sizes) in [2, 3] | |
| if len(sizes) == 3: | |
| # t x y -> x y t to match dimensions T H W in grid_sample | |
| coords = coords[..., [1, 2, 0]] | |
| if align_corners: | |
| coords = coords * torch.tensor( | |
| [2 / max(size - 1, 1) for size in reversed(sizes)], device=coords.device | |
| ) | |
| else: | |
| coords = coords * torch.tensor( | |
| [2 / size for size in reversed(sizes)], device=coords.device | |
| ) | |
| coords -= 1 | |
| return F.grid_sample( | |
| input, coords, align_corners=align_corners, padding_mode=padding_mode | |
| ) | |
| class ResidualBlock(nn.Module): | |
| def __init__(self, in_planes, planes, norm_fn="group", stride=1): | |
| super(ResidualBlock, self).__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) | |
| num_groups = planes // 8 | |
| if norm_fn == "group": | |
| self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) | |
| self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) | |
| if not stride == 1: | |
| self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) | |
| elif norm_fn == "batch": | |
| self.norm1 = nn.BatchNorm2d(planes) | |
| self.norm2 = nn.BatchNorm2d(planes) | |
| if not stride == 1: | |
| self.norm3 = nn.BatchNorm2d(planes) | |
| elif norm_fn == "instance": | |
| self.norm1 = nn.InstanceNorm2d(planes) | |
| self.norm2 = nn.InstanceNorm2d(planes) | |
| if not stride == 1: | |
| self.norm3 = nn.InstanceNorm2d(planes) | |
| elif norm_fn == "none": | |
| self.norm1 = nn.Sequential() | |
| self.norm2 = nn.Sequential() | |
| if not stride == 1: | |
| self.norm3 = nn.Sequential() | |
| 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, x): | |
| y = x | |
| y = self.relu(self.norm1(self.conv1(y))) | |
| y = self.relu(self.norm2(self.conv2(y))) | |
| if self.downsample is not None: | |
| x = self.downsample(x) | |
| return self.relu(x + y) | |
| class BasicEncoder(nn.Module): | |
| def __init__(self, input_dim=3, output_dim=128, stride=4): | |
| super(BasicEncoder, self).__init__() | |
| self.stride = stride | |
| self.norm_fn = "instance" | |
| 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 m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") | |
| elif isinstance(m, (nn.InstanceNorm2d)): | |
| if m.weight is not None: | |
| nn.init.constant_(m.weight, 1) | |
| if m.bias is not None: | |
| nn.init.constant_(m.bias, 0) | |
| def _make_layer(self, dim, stride=1): | |
| layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride) | |
| layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1) | |
| layers = (layer1, layer2) | |
| self.in_planes = dim | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| _, _, H, W = x.shape | |
| x = self.conv1(x) | |
| x = self.norm1(x) | |
| x = self.relu1(x) | |
| a = self.layer1(x) | |
| b = self.layer2(a) | |
| c = self.layer3(b) | |
| d = self.layer4(c) | |
| def _bilinear_intepolate(x): | |
| return F.interpolate( | |
| x, | |
| (H // self.stride, W // self.stride), | |
| mode="bilinear", | |
| align_corners=True, | |
| ) | |
| a = _bilinear_intepolate(a) | |
| b = _bilinear_intepolate(b) | |
| c = _bilinear_intepolate(c) | |
| d = _bilinear_intepolate(d) | |
| x = self.conv2(torch.cat([a, b, c, d], dim=1)) | |
| x = self.norm2(x) | |
| x = self.relu2(x) | |
| x = self.conv3(x) | |
| return x | |