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| # #!/usr/bin/env python3 | |
| # # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. | |
| # import torch | |
| # import torch.nn.functional as F | |
| # from torch import nn | |
| # class FPN(nn.Module): | |
| # """ | |
| # Module that adds FPN on top of a list of feature maps. | |
| # The feature maps are currently supposed to be in increasing depth | |
| # order, and must be consecutive | |
| # """ | |
| # def __init__(self, in_channels_list, out_channels, top_blocks=None): | |
| # """ | |
| # Arguments: | |
| # in_channels_list (list[int]): number of channels for each feature map that | |
| # will be fed | |
| # out_channels (int): number of channels of the FPN representation | |
| # top_blocks (nn.Module or None): if provided, an extra operation will | |
| # be performed on the output of the last (smallest resolution) | |
| # FPN output, and the result will extend the result list | |
| # """ | |
| # super(FPN, self).__init__() | |
| # self.inner_blocks = [] | |
| # self.layer_blocks = [] | |
| # for idx, in_channels in enumerate(in_channels_list, 1): | |
| # inner_block = "fpn_inner{}".format(idx) | |
| # layer_block = "fpn_layer{}".format(idx) | |
| # inner_block_module = nn.Conv2d(in_channels, out_channels, 1) | |
| # layer_block_module = nn.Conv2d(out_channels, out_channels, 3, 1, 1) | |
| # for module in [inner_block_module, layer_block_module]: | |
| # # Caffe2 implementation uses XavierFill, which in fact | |
| # # corresponds to kaiming_uniform_ in PyTorch | |
| # nn.init.kaiming_uniform_(module.weight, a=1) | |
| # nn.init.constant_(module.bias, 0) | |
| # self.add_module(inner_block, inner_block_module) | |
| # self.add_module(layer_block, layer_block_module) | |
| # self.inner_blocks.append(inner_block) | |
| # self.layer_blocks.append(layer_block) | |
| # self.top_blocks = top_blocks | |
| # def forward(self, x): | |
| # """ | |
| # Arguments: | |
| # x (list[Tensor]): feature maps for each feature level. | |
| # Returns: | |
| # results (tuple[Tensor]): feature maps after FPN layers. | |
| # They are ordered from highest resolution first. | |
| # """ | |
| # last_inner = getattr(self, self.inner_blocks[-1])(x[-1]) | |
| # results = [] | |
| # results.append(getattr(self, self.layer_blocks[-1])(last_inner)) | |
| # for feature, inner_block, layer_block in zip( | |
| # x[:-1][::-1], self.inner_blocks[:-1][::-1], self.layer_blocks[:-1][::-1] | |
| # ): | |
| # inner_top_down = F.interpolate(last_inner, scale_factor=2, mode="nearest") | |
| # inner_lateral = getattr(self, inner_block)(feature) | |
| # # TODO use size instead of scale to make it robust to different sizes | |
| # # inner_top_down = F.upsample(last_inner, size=inner_lateral.shape[-2:], | |
| # # mode='bilinear', align_corners=False) | |
| # last_inner = inner_lateral + inner_top_down | |
| # results.insert(0, getattr(self, layer_block)(last_inner)) | |
| # if self.top_blocks is not None: | |
| # last_results = self.top_blocks(results[-1]) | |
| # results.extend(last_results) | |
| # return tuple(results) | |
| # class LastLevelMaxPool(nn.Module): | |
| # def forward(self, x): | |
| # return [F.max_pool2d(x, 1, 2, 0)] | |
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. | |
| import torch | |
| import torch.nn.functional as F | |
| from torch import nn | |
| class FPN(nn.Module): | |
| """ | |
| Module that adds FPN on top of a list of feature maps. | |
| The feature maps are currently supposed to be in increasing depth | |
| order, and must be consecutive | |
| """ | |
| def __init__( | |
| self, in_channels_list, out_channels, conv_block, top_blocks=None | |
| ): | |
| """ | |
| Arguments: | |
| in_channels_list (list[int]): number of channels for each feature map that | |
| will be fed | |
| out_channels (int): number of channels of the FPN representation | |
| top_blocks (nn.Module or None): if provided, an extra operation will | |
| be performed on the output of the last (smallest resolution) | |
| FPN output, and the result will extend the result list | |
| """ | |
| super(FPN, self).__init__() | |
| self.inner_blocks = [] | |
| self.layer_blocks = [] | |
| for idx, in_channels in enumerate(in_channels_list, 1): | |
| inner_block = "fpn_inner{}".format(idx) | |
| layer_block = "fpn_layer{}".format(idx) | |
| if in_channels == 0: | |
| continue | |
| inner_block_module = conv_block(in_channels, out_channels, 1) | |
| layer_block_module = conv_block(out_channels, out_channels, 3, 1) | |
| self.add_module(inner_block, inner_block_module) | |
| self.add_module(layer_block, layer_block_module) | |
| self.inner_blocks.append(inner_block) | |
| self.layer_blocks.append(layer_block) | |
| self.top_blocks = top_blocks | |
| def forward(self, x): | |
| """ | |
| Arguments: | |
| x (list[Tensor]): feature maps for each feature level. | |
| Returns: | |
| results (tuple[Tensor]): feature maps after FPN layers. | |
| They are ordered from highest resolution first. | |
| """ | |
| last_inner = getattr(self, self.inner_blocks[-1])(x[-1]) | |
| results = [] | |
| results.append(getattr(self, self.layer_blocks[-1])(last_inner)) | |
| for feature, inner_block, layer_block in zip( | |
| x[:-1][::-1], self.inner_blocks[:-1][::-1], self.layer_blocks[:-1][::-1] | |
| ): | |
| if not inner_block: | |
| continue | |
| inner_top_down = F.interpolate(last_inner, scale_factor=2, mode="nearest") | |
| inner_lateral = getattr(self, inner_block)(feature) | |
| # TODO use size instead of scale to make it robust to different sizes | |
| # inner_top_down = F.upsample(last_inner, size=inner_lateral.shape[-2:], | |
| # mode='bilinear', align_corners=False) | |
| last_inner = inner_lateral + inner_top_down | |
| results.insert(0, getattr(self, layer_block)(last_inner)) | |
| if isinstance(self.top_blocks, LastLevelP6P7): | |
| last_results = self.top_blocks(x[-1], results[-1]) | |
| results.extend(last_results) | |
| elif isinstance(self.top_blocks, LastLevelMaxPool): | |
| last_results = self.top_blocks(results[-1]) | |
| results.extend(last_results) | |
| return tuple(results) | |
| class LastLevelMaxPool(nn.Module): | |
| def forward(self, x): | |
| return [F.max_pool2d(x, 1, 2, 0)] | |
| class LastLevelP6P7(nn.Module): | |
| """ | |
| This module is used in RetinaNet to generate extra layers, P6 and P7. | |
| """ | |
| def __init__(self, in_channels, out_channels): | |
| super(LastLevelP6P7, self).__init__() | |
| self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1) | |
| self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1) | |
| for module in [self.p6, self.p7]: | |
| nn.init.kaiming_uniform_(module.weight, a=1) | |
| nn.init.constant_(module.bias, 0) | |
| self.use_P5 = in_channels == out_channels | |
| def forward(self, c5, p5): | |
| x = p5 if self.use_P5 else c5 | |
| p6 = self.p6(x) | |
| p7 = self.p7(F.relu(p6)) | |
| return [p6, p7] | |