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# ------------------------------------------------------------------------
# Grounding DINO
# url: https://github.com/IDEA-Research/GroundingDINO
# Copyright (c) 2023 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Conditional DETR
# Copyright (c) 2021 Microsoft. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Copied from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# ------------------------------------------------------------------------
"""
Backbone modules.
"""
from typing import Dict, List
import torch
import torch.nn.functional as F
import torchvision
from torch import nn
from torchvision.models._utils import IntermediateLayerGetter
from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process
from .position_encoding import build_position_encoding
from .swin_transformer import build_swin_transformer
class FrozenBatchNorm2d(torch.nn.Module):
"""
BatchNorm2d where the batch statistics and the affine parameters are fixed.
Copy-paste from torchvision.misc.ops with added eps before rqsrt,
without which any other models than torchvision.models.resnet[18,34,50,101]
produce nans.
"""
def __init__(self, n):
super(FrozenBatchNorm2d, self).__init__()
self.register_buffer("weight", torch.ones(n))
self.register_buffer("bias", torch.zeros(n))
self.register_buffer("running_mean", torch.zeros(n))
self.register_buffer("running_var", torch.ones(n))
def _load_from_state_dict(
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
):
num_batches_tracked_key = prefix + "num_batches_tracked"
if num_batches_tracked_key in state_dict:
del state_dict[num_batches_tracked_key]
super(FrozenBatchNorm2d, self)._load_from_state_dict(
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
)
def forward(self, x):
# move reshapes to the beginning
# to make it fuser-friendly
w = self.weight.reshape(1, -1, 1, 1)
b = self.bias.reshape(1, -1, 1, 1)
rv = self.running_var.reshape(1, -1, 1, 1)
rm = self.running_mean.reshape(1, -1, 1, 1)
eps = 1e-5
scale = w * (rv + eps).rsqrt()
bias = b - rm * scale
return x * scale + bias
class BackboneBase(nn.Module):
def __init__(
self,
backbone: nn.Module,
train_backbone: bool,
num_channels: int,
return_interm_indices: list,
):
super().__init__()
for name, parameter in backbone.named_parameters():
if (
not train_backbone
or "layer2" not in name
and "layer3" not in name
and "layer4" not in name
):
parameter.requires_grad_(False)
return_layers = {}
for idx, layer_index in enumerate(return_interm_indices):
return_layers.update(
{"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)}
)
# if len:
# if use_stage1_feature:
# return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
# else:
# return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
# else:
# return_layers = {'layer4': "0"}
self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
self.num_channels = num_channels
def forward(self, tensor_list: NestedTensor):
xs = self.body(tensor_list.tensors)
out: Dict[str, NestedTensor] = {}
for name, x in xs.items():
m = tensor_list.mask
assert m is not None
mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
out[name] = NestedTensor(x, mask)
# import ipdb; ipdb.set_trace()
return out
class Backbone(BackboneBase):
"""ResNet backbone with frozen BatchNorm."""
def __init__(
self,
name: str,
train_backbone: bool,
dilation: bool,
return_interm_indices: list,
batch_norm=FrozenBatchNorm2d,
):
if name in ["resnet18", "resnet34", "resnet50", "resnet101"]:
backbone = getattr(torchvision.models, name)(
replace_stride_with_dilation=[False, False, dilation],
pretrained=is_main_process(),
norm_layer=batch_norm,
)
else:
raise NotImplementedError("Why you can get here with name {}".format(name))
# num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available."
assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
num_channels_all = [256, 512, 1024, 2048]
num_channels = num_channels_all[4 - len(return_interm_indices) :]
super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
class Joiner(nn.Sequential):
def __init__(self, backbone, position_embedding):
super().__init__(backbone, position_embedding)
def forward(self, tensor_list: NestedTensor):
xs = self[0](tensor_list)
out: List[NestedTensor] = []
pos = []
for name, x in xs.items():
out.append(x)
# position encoding
pos.append(self[1](x).to(x.tensors.dtype))
return out, pos
def build_backbone(args):
"""
Useful args:
- backbone: backbone name
- lr_backbone:
- dilation
- return_interm_indices: available: [0,1,2,3], [1,2,3], [3]
- backbone_freeze_keywords:
- use_checkpoint: for swin only for now
"""
position_embedding = build_position_encoding(args)
train_backbone = True
if not train_backbone:
raise ValueError("Please set lr_backbone > 0")
return_interm_indices = args.return_interm_indices
assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
args.backbone_freeze_keywords
use_checkpoint = getattr(args, "use_checkpoint", False)
if args.backbone in ["resnet50", "resnet101"]:
backbone = Backbone(
args.backbone,
train_backbone,
args.dilation,
return_interm_indices,
batch_norm=FrozenBatchNorm2d,
)
bb_num_channels = backbone.num_channels
elif args.backbone in [
"swin_T_224_1k",
"swin_B_224_22k",
"swin_B_384_22k",
"swin_L_224_22k",
"swin_L_384_22k",
]:
pretrain_img_size = int(args.backbone.split("_")[-2])
backbone = build_swin_transformer(
args.backbone,
pretrain_img_size=pretrain_img_size,
out_indices=tuple(return_interm_indices),
dilation=False,
use_checkpoint=use_checkpoint,
)
bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
else:
raise NotImplementedError("Unknown backbone {}".format(args.backbone))
assert len(bb_num_channels) == len(
return_interm_indices
), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}"
model = Joiner(backbone, position_embedding)
model.num_channels = bb_num_channels
assert isinstance(
bb_num_channels, List
), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels))
# import ipdb; ipdb.set_trace()
return model
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