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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 utils 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): |
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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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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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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 = 1e-5 |
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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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def __init__(self, backbone: nn.Module, train_backbone: bool, num_channels: int, 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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else: |
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return_layers = {'layer4': "0"} |
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self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) |
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self.num_channels = num_channels |
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def forward(self, tensor_list: NestedTensor): |
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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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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=FrozenBatchNorm2d) |
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num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 |
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super().__init__(backbone, train_backbone, num_channels, return_interm_layers) |
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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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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 xs.items(): |
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out.append(x) |
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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(config): |
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position_embedding = build_position_encoding(config) |
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train_backbone = config.lr_backbone > 0 |
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return_interm_layers = False |
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backbone = Backbone(config.backbone, train_backbone, return_interm_layers, config.dilation) |
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model = Joiner(backbone, position_embedding) |
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model.num_channels = backbone.num_channels |
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return model |
