140 lines
5.3 KiB
Python
140 lines
5.3 KiB
Python
# ------------------------------------------------------------------------
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# Deformable DETR
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# Copyright (c) 2020 SenseTime. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
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# ------------------------------------------------------------------------
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# Modified from DETR (https://github.com/facebookresearch/detr)
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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# ------------------------------------------------------------------------
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"""
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Backbone modules.
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"""
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from collections import OrderedDict
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import torch
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import torch.nn.functional as F
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import torchvision
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from torch import nn
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from torchvision.models._utils import IntermediateLayerGetter
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from typing import Dict, List
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from util.misc import NestedTensor, is_main_process
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from .position_encoding import build_position_encoding
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class FrozenBatchNorm2d(torch.nn.Module):
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"""
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BatchNorm2d where the batch statistics and the affine parameters are fixed.
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Copy-paste from torchvision.misc.ops with added eps before rqsrt,
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without which any other models than torchvision.models.resnet[18,34,50,101]
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produce nans.
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"""
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def __init__(self, n, eps=1e-5):
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super(FrozenBatchNorm2d, self).__init__()
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self.register_buffer("weight", torch.ones(n))
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self.register_buffer("bias", torch.zeros(n))
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self.register_buffer("running_mean", torch.zeros(n))
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self.register_buffer("running_var", torch.ones(n))
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self.eps = eps
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def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
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missing_keys, unexpected_keys, error_msgs):
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num_batches_tracked_key = prefix + 'num_batches_tracked'
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if num_batches_tracked_key in state_dict:
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del state_dict[num_batches_tracked_key]
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super(FrozenBatchNorm2d, self)._load_from_state_dict(
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state_dict, prefix, local_metadata, strict,
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missing_keys, unexpected_keys, error_msgs)
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def forward(self, x):
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# move reshapes to the beginning
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# to make it fuser-friendly
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w = self.weight.reshape(1, -1, 1, 1)
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b = self.bias.reshape(1, -1, 1, 1)
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rv = self.running_var.reshape(1, -1, 1, 1)
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rm = self.running_mean.reshape(1, -1, 1, 1)
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eps = self.eps
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scale = w * (rv + eps).rsqrt()
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bias = b - rm * scale
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return x * scale + bias
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class BackboneBase(nn.Module):
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# backbone, 是否训练backbone, 是否返回中间值
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def __init__(self, backbone: nn.Module, train_backbone: bool, return_interm_layers: bool):
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super().__init__()
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for name, parameter in backbone.named_parameters():
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if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:
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parameter.requires_grad_(False)
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if return_interm_layers:
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# return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
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return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
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self.strides = [8, 16, 32]
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self.num_channels = [512, 1024, 2048]
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else:
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return_layers = {'layer4': "0"}
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self.strides = [32]
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self.num_channels = [2048]
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self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
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def forward(self, tensor_list: NestedTensor):
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# tensor list
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xs = self.body(tensor_list.tensors)
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out: Dict[str, NestedTensor] = {}
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for name, x in xs.items():
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m = tensor_list.mask
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assert m is not None
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mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
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out[name] = NestedTensor(x, mask)
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return out
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class Backbone(BackboneBase):
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"""ResNet backbone with frozen BatchNorm."""
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def __init__(self, name: str,
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train_backbone: bool,
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return_interm_layers: bool,
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dilation: bool):
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norm_layer = FrozenBatchNorm2d
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backbone = getattr(torchvision.models, name)(
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replace_stride_with_dilation=[False, False, dilation],
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pretrained=is_main_process(), norm_layer=norm_layer)
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assert name not in ('resnet18', 'resnet34'), "number of channels are hard coded"
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super().__init__(backbone, train_backbone, return_interm_layers)
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if dilation:
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self.strides[-1] = self.strides[-1] // 2
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class Joiner(nn.Sequential):
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def __init__(self, backbone, position_embedding):
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super().__init__(backbone, position_embedding)
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self.strides = backbone.strides
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self.num_channels = backbone.num_channels
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def forward(self, tensor_list: NestedTensor):
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xs = self[0](tensor_list)
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out: List[NestedTensor] = []
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pos = []
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for name, x in sorted(xs.items()):
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out.append(x)
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# position encoding
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for x in out:
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pos.append(self[1](x).to(x.tensors.dtype))
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return out, pos
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def build_backbone(args):
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position_embedding = build_position_encoding(args)
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train_backbone = args.lr_backbone > 0
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return_interm_layers = args.masks or (args.num_feature_levels > 1 )
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backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation)
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model = Joiner(backbone, position_embedding)
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return model
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