support gsam2 image predictor model
This commit is contained in:
rentainhe
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from .backbone import build_backbone
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# ------------------------------------------------------------------------
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# Grounding DINO
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# url: https://github.com/IDEA-Research/GroundingDINO
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# Copyright (c) 2023 IDEA. 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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# Conditional DETR
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# Copyright (c) 2021 Microsoft. 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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# Copied 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 typing import Dict, List
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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 grounding_dino.groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process
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from .position_encoding import build_position_encoding
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from .swin_transformer import build_swin_transformer
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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(
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self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
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):
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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, missing_keys, unexpected_keys, error_msgs
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)
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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 = 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__(
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self,
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backbone: nn.Module,
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train_backbone: bool,
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num_channels: int,
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return_interm_indices: list,
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):
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super().__init__()
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for name, parameter in backbone.named_parameters():
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if (
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not train_backbone
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or "layer2" not in name
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and "layer3" not in name
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and "layer4" not in name
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):
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parameter.requires_grad_(False)
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return_layers = {}
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for idx, layer_index in enumerate(return_interm_indices):
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return_layers.update(
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{"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)}
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)
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# if len:
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# if use_stage1_feature:
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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 = {"layer2": "0", "layer3": "1", "layer4": "2"}
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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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# import ipdb; ipdb.set_trace()
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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__(
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self,
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name: str,
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train_backbone: bool,
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dilation: bool,
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return_interm_indices: list,
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batch_norm=FrozenBatchNorm2d,
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):
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if name in ["resnet18", "resnet34", "resnet50", "resnet101"]:
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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(),
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norm_layer=batch_norm,
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)
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else:
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raise NotImplementedError("Why you can get here with name {}".format(name))
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# num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
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assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available."
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assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
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num_channels_all = [256, 512, 1024, 2048]
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num_channels = num_channels_all[4 - len(return_interm_indices) :]
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super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
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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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# position encoding
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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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"""
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Useful args:
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- backbone: backbone name
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- lr_backbone:
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- dilation
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- return_interm_indices: available: [0,1,2,3], [1,2,3], [3]
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- backbone_freeze_keywords:
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- use_checkpoint: for swin only for now
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"""
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position_embedding = build_position_encoding(args)
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train_backbone = True
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if not train_backbone:
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raise ValueError("Please set lr_backbone > 0")
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return_interm_indices = args.return_interm_indices
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assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
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args.backbone_freeze_keywords
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use_checkpoint = getattr(args, "use_checkpoint", False)
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if args.backbone in ["resnet50", "resnet101"]:
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backbone = Backbone(
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args.backbone,
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train_backbone,
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args.dilation,
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return_interm_indices,
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batch_norm=FrozenBatchNorm2d,
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)
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bb_num_channels = backbone.num_channels
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elif args.backbone in [
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"swin_T_224_1k",
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"swin_B_224_22k",
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"swin_B_384_22k",
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"swin_L_224_22k",
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"swin_L_384_22k",
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]:
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pretrain_img_size = int(args.backbone.split("_")[-2])
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backbone = build_swin_transformer(
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args.backbone,
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pretrain_img_size=pretrain_img_size,
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out_indices=tuple(return_interm_indices),
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dilation=False,
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use_checkpoint=use_checkpoint,
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)
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bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
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else:
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raise NotImplementedError("Unknown backbone {}".format(args.backbone))
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assert len(bb_num_channels) == len(
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return_interm_indices
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), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}"
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model = Joiner(backbone, position_embedding)
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model.num_channels = bb_num_channels
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assert isinstance(
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bb_num_channels, List
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), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels))
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# import ipdb; ipdb.set_trace()
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return model
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@@ -0,0 +1,186 @@
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# ------------------------------------------------------------------------
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# Grounding DINO
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# url: https://github.com/IDEA-Research/GroundingDINO
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# Copyright (c) 2023 IDEA. 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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# DINO
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# Copyright (c) 2022 IDEA. 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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# Conditional DETR
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# Copyright (c) 2021 Microsoft. 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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# Copied 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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Various positional encodings for the transformer.
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"""
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import math
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import torch
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from torch import nn
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from grounding_dino.groundingdino.util.misc import NestedTensor
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class PositionEmbeddingSine(nn.Module):
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"""
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This is a more standard version of the position embedding, very similar to the one
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used by the Attention is all you need paper, generalized to work on images.
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"""
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def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
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super().__init__()
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self.num_pos_feats = num_pos_feats
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self.temperature = temperature
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self.normalize = normalize
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if scale is not None and normalize is False:
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raise ValueError("normalize should be True if scale is passed")
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if scale is None:
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scale = 2 * math.pi
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self.scale = scale
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def forward(self, tensor_list: NestedTensor):
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x = tensor_list.tensors
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mask = tensor_list.mask
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assert mask is not None
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not_mask = ~mask
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y_embed = not_mask.cumsum(1, dtype=torch.float32)
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x_embed = not_mask.cumsum(2, dtype=torch.float32)
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if self.normalize:
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eps = 1e-6
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# if os.environ.get("SHILONG_AMP", None) == '1':
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# eps = 1e-4
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# else:
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# eps = 1e-6
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y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
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x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
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dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
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dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
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pos_x = x_embed[:, :, :, None] / dim_t
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pos_y = y_embed[:, :, :, None] / dim_t
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pos_x = torch.stack(
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(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
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).flatten(3)
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pos_y = torch.stack(
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(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
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).flatten(3)
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pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
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return pos
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class PositionEmbeddingSineHW(nn.Module):
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"""
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This is a more standard version of the position embedding, very similar to the one
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used by the Attention is all you need paper, generalized to work on images.
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"""
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def __init__(
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self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None
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):
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super().__init__()
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self.num_pos_feats = num_pos_feats
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self.temperatureH = temperatureH
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self.temperatureW = temperatureW
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self.normalize = normalize
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if scale is not None and normalize is False:
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raise ValueError("normalize should be True if scale is passed")
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if scale is None:
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scale = 2 * math.pi
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self.scale = scale
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def forward(self, tensor_list: NestedTensor):
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x = tensor_list.tensors
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mask = tensor_list.mask
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assert mask is not None
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not_mask = ~mask
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y_embed = not_mask.cumsum(1, dtype=torch.float32)
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x_embed = not_mask.cumsum(2, dtype=torch.float32)
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# import ipdb; ipdb.set_trace()
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if self.normalize:
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eps = 1e-6
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y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
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x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
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dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
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dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats)
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pos_x = x_embed[:, :, :, None] / dim_tx
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dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
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dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats)
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pos_y = y_embed[:, :, :, None] / dim_ty
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pos_x = torch.stack(
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(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
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).flatten(3)
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pos_y = torch.stack(
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(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
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).flatten(3)
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pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
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# import ipdb; ipdb.set_trace()
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return pos
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class PositionEmbeddingLearned(nn.Module):
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"""
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Absolute pos embedding, learned.
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"""
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def __init__(self, num_pos_feats=256):
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super().__init__()
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self.row_embed = nn.Embedding(50, num_pos_feats)
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self.col_embed = nn.Embedding(50, num_pos_feats)
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self.reset_parameters()
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def reset_parameters(self):
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nn.init.uniform_(self.row_embed.weight)
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nn.init.uniform_(self.col_embed.weight)
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def forward(self, tensor_list: NestedTensor):
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x = tensor_list.tensors
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h, w = x.shape[-2:]
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i = torch.arange(w, device=x.device)
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j = torch.arange(h, device=x.device)
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x_emb = self.col_embed(i)
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y_emb = self.row_embed(j)
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pos = (
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torch.cat(
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[
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x_emb.unsqueeze(0).repeat(h, 1, 1),
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y_emb.unsqueeze(1).repeat(1, w, 1),
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],
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dim=-1,
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)
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.permute(2, 0, 1)
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.unsqueeze(0)
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.repeat(x.shape[0], 1, 1, 1)
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)
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return pos
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def build_position_encoding(args):
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N_steps = args.hidden_dim // 2
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if args.position_embedding in ("v2", "sine"):
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# TODO find a better way of exposing other arguments
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position_embedding = PositionEmbeddingSineHW(
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N_steps,
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temperatureH=args.pe_temperatureH,
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temperatureW=args.pe_temperatureW,
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normalize=True,
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)
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elif args.position_embedding in ("v3", "learned"):
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position_embedding = PositionEmbeddingLearned(N_steps)
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else:
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raise ValueError(f"not supported {args.position_embedding}")
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return position_embedding
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@@ -0,0 +1,802 @@
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# ------------------------------------------------------------------------
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# Grounding DINO
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# url: https://github.com/IDEA-Research/GroundingDINO
|
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# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
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# ------------------------------------------------------------------------
|
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# DINO
|
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# Copyright (c) 2022 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
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# --------------------------------------------------------
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# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
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# --------------------------------------------------------
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint as checkpoint
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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from grounding_dino.groundingdino.util.misc import NestedTensor
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class Mlp(nn.Module):
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"""Multilayer perceptron."""
|
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def __init__(
|
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self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0
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):
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super().__init__()
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out_features = out_features or in_features
|
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
def window_partition(x, window_size):
|
||||
"""
|
||||
Args:
|
||||
x: (B, H, W, C)
|
||||
window_size (int): window size
|
||||
Returns:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
"""
|
||||
B, H, W, C = x.shape
|
||||
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
|
||||
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
||||
return windows
|
||||
|
||||
|
||||
def window_reverse(windows, window_size, H, W):
|
||||
"""
|
||||
Args:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
window_size (int): Window size
|
||||
H (int): Height of image
|
||||
W (int): Width of image
|
||||
Returns:
|
||||
x: (B, H, W, C)
|
||||
"""
|
||||
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
||||
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
|
||||
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
||||
return x
|
||||
|
||||
|
||||
class WindowAttention(nn.Module):
|
||||
"""Window based multi-head self attention (W-MSA) module with relative position bias.
|
||||
It supports both of shifted and non-shifted window.
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
window_size (tuple[int]): The height and width of the window.
|
||||
num_heads (int): Number of attention heads.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
|
||||
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
|
||||
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
window_size,
|
||||
num_heads,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
attn_drop=0.0,
|
||||
proj_drop=0.0,
|
||||
):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.window_size = window_size # Wh, Ww
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim**-0.5
|
||||
|
||||
# define a parameter table of relative position bias
|
||||
self.relative_position_bias_table = nn.Parameter(
|
||||
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
|
||||
) # 2*Wh-1 * 2*Ww-1, nH
|
||||
|
||||
# get pair-wise relative position index for each token inside the window
|
||||
coords_h = torch.arange(self.window_size[0])
|
||||
coords_w = torch.arange(self.window_size[1])
|
||||
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
|
||||
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
||||
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
||||
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
||||
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
|
||||
relative_coords[:, :, 1] += self.window_size[1] - 1
|
||||
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
||||
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||||
self.register_buffer("relative_position_index", relative_position_index)
|
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
trunc_normal_(self.relative_position_bias_table, std=0.02)
|
||||
self.softmax = nn.Softmax(dim=-1)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
"""Forward function.
|
||||
Args:
|
||||
x: input features with shape of (num_windows*B, N, C)
|
||||
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
|
||||
"""
|
||||
B_, N, C = x.shape
|
||||
qkv = (
|
||||
self.qkv(x)
|
||||
.reshape(B_, N, 3, self.num_heads, C // self.num_heads)
|
||||
.permute(2, 0, 3, 1, 4)
|
||||
)
|
||||
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
||||
|
||||
q = q * self.scale
|
||||
attn = q @ k.transpose(-2, -1)
|
||||
|
||||
relative_position_bias = self.relative_position_bias_table[
|
||||
self.relative_position_index.view(-1)
|
||||
].view(
|
||||
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
|
||||
) # Wh*Ww,Wh*Ww,nH
|
||||
relative_position_bias = relative_position_bias.permute(
|
||||
2, 0, 1
|
||||
).contiguous() # nH, Wh*Ww, Wh*Ww
|
||||
attn = attn + relative_position_bias.unsqueeze(0)
|
||||
|
||||
if mask is not None:
|
||||
nW = mask.shape[0]
|
||||
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
|
||||
attn = attn.view(-1, self.num_heads, N, N)
|
||||
attn = self.softmax(attn)
|
||||
else:
|
||||
attn = self.softmax(attn)
|
||||
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class SwinTransformerBlock(nn.Module):
|
||||
"""Swin Transformer Block.
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Window size.
|
||||
shift_size (int): Shift size for SW-MSA.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float, optional): Stochastic depth rate. Default: 0.0
|
||||
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
num_heads,
|
||||
window_size=7,
|
||||
shift_size=0,
|
||||
mlp_ratio=4.0,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop=0.0,
|
||||
attn_drop=0.0,
|
||||
drop_path=0.0,
|
||||
act_layer=nn.GELU,
|
||||
norm_layer=nn.LayerNorm,
|
||||
):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.window_size = window_size
|
||||
self.shift_size = shift_size
|
||||
self.mlp_ratio = mlp_ratio
|
||||
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
|
||||
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = WindowAttention(
|
||||
dim,
|
||||
window_size=to_2tuple(self.window_size),
|
||||
num_heads=num_heads,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
attn_drop=attn_drop,
|
||||
proj_drop=drop,
|
||||
)
|
||||
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = Mlp(
|
||||
in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop
|
||||
)
|
||||
|
||||
self.H = None
|
||||
self.W = None
|
||||
|
||||
def forward(self, x, mask_matrix):
|
||||
"""Forward function.
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
mask_matrix: Attention mask for cyclic shift.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
H, W = self.H, self.W
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
shortcut = x
|
||||
x = self.norm1(x)
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# pad feature maps to multiples of window size
|
||||
pad_l = pad_t = 0
|
||||
pad_r = (self.window_size - W % self.window_size) % self.window_size
|
||||
pad_b = (self.window_size - H % self.window_size) % self.window_size
|
||||
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
|
||||
_, Hp, Wp, _ = x.shape
|
||||
|
||||
# cyclic shift
|
||||
if self.shift_size > 0:
|
||||
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
||||
attn_mask = mask_matrix
|
||||
else:
|
||||
shifted_x = x
|
||||
attn_mask = None
|
||||
|
||||
# partition windows
|
||||
x_windows = window_partition(
|
||||
shifted_x, self.window_size
|
||||
) # nW*B, window_size, window_size, C
|
||||
x_windows = x_windows.view(
|
||||
-1, self.window_size * self.window_size, C
|
||||
) # nW*B, window_size*window_size, C
|
||||
|
||||
# W-MSA/SW-MSA
|
||||
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
|
||||
|
||||
# merge windows
|
||||
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
||||
shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
|
||||
|
||||
# reverse cyclic shift
|
||||
if self.shift_size > 0:
|
||||
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
|
||||
else:
|
||||
x = shifted_x
|
||||
|
||||
if pad_r > 0 or pad_b > 0:
|
||||
x = x[:, :H, :W, :].contiguous()
|
||||
|
||||
x = x.view(B, H * W, C)
|
||||
|
||||
# FFN
|
||||
x = shortcut + self.drop_path(x)
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class PatchMerging(nn.Module):
|
||||
"""Patch Merging Layer
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
|
||||
def __init__(self, dim, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
|
||||
self.norm = norm_layer(4 * dim)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
"""Forward function.
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# padding
|
||||
pad_input = (H % 2 == 1) or (W % 2 == 1)
|
||||
if pad_input:
|
||||
x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
|
||||
|
||||
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
||||
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
||||
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
||||
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
||||
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
||||
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
||||
|
||||
x = self.norm(x)
|
||||
x = self.reduction(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class BasicLayer(nn.Module):
|
||||
"""A basic Swin Transformer layer for one stage.
|
||||
Args:
|
||||
dim (int): Number of feature channels
|
||||
depth (int): Depths of this stage.
|
||||
num_heads (int): Number of attention head.
|
||||
window_size (int): Local window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
depth,
|
||||
num_heads,
|
||||
window_size=7,
|
||||
mlp_ratio=4.0,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop=0.0,
|
||||
attn_drop=0.0,
|
||||
drop_path=0.0,
|
||||
norm_layer=nn.LayerNorm,
|
||||
downsample=None,
|
||||
use_checkpoint=False,
|
||||
):
|
||||
super().__init__()
|
||||
self.window_size = window_size
|
||||
self.shift_size = window_size // 2
|
||||
self.depth = depth
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
# build blocks
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
SwinTransformerBlock(
|
||||
dim=dim,
|
||||
num_heads=num_heads,
|
||||
window_size=window_size,
|
||||
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop,
|
||||
attn_drop=attn_drop,
|
||||
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||||
norm_layer=norm_layer,
|
||||
)
|
||||
for i in range(depth)
|
||||
]
|
||||
)
|
||||
|
||||
# patch merging layer
|
||||
if downsample is not None:
|
||||
self.downsample = downsample(dim=dim, norm_layer=norm_layer)
|
||||
else:
|
||||
self.downsample = None
|
||||
|
||||
def forward(self, x, H, W):
|
||||
"""Forward function.
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
|
||||
# calculate attention mask for SW-MSA
|
||||
Hp = int(np.ceil(H / self.window_size)) * self.window_size
|
||||
Wp = int(np.ceil(W / self.window_size)) * self.window_size
|
||||
img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
|
||||
h_slices = (
|
||||
slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None),
|
||||
)
|
||||
w_slices = (
|
||||
slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None),
|
||||
)
|
||||
cnt = 0
|
||||
for h in h_slices:
|
||||
for w in w_slices:
|
||||
img_mask[:, h, w, :] = cnt
|
||||
cnt += 1
|
||||
|
||||
mask_windows = window_partition(
|
||||
img_mask, self.window_size
|
||||
) # nW, window_size, window_size, 1
|
||||
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
|
||||
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
||||
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
|
||||
attn_mask == 0, float(0.0)
|
||||
)
|
||||
|
||||
for blk in self.blocks:
|
||||
blk.H, blk.W = H, W
|
||||
if self.use_checkpoint:
|
||||
x = checkpoint.checkpoint(blk, x, attn_mask)
|
||||
else:
|
||||
x = blk(x, attn_mask)
|
||||
if self.downsample is not None:
|
||||
x_down = self.downsample(x, H, W)
|
||||
Wh, Ww = (H + 1) // 2, (W + 1) // 2
|
||||
return x, H, W, x_down, Wh, Ww
|
||||
else:
|
||||
return x, H, W, x, H, W
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
"""Image to Patch Embedding
|
||||
Args:
|
||||
patch_size (int): Patch token size. Default: 4.
|
||||
in_chans (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
||||
"""
|
||||
|
||||
def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
|
||||
super().__init__()
|
||||
patch_size = to_2tuple(patch_size)
|
||||
self.patch_size = patch_size
|
||||
|
||||
self.in_chans = in_chans
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
||||
if norm_layer is not None:
|
||||
self.norm = norm_layer(embed_dim)
|
||||
else:
|
||||
self.norm = None
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
# padding
|
||||
_, _, H, W = x.size()
|
||||
if W % self.patch_size[1] != 0:
|
||||
x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
|
||||
if H % self.patch_size[0] != 0:
|
||||
x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
|
||||
|
||||
x = self.proj(x) # B C Wh Ww
|
||||
if self.norm is not None:
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.norm(x)
|
||||
x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SwinTransformer(nn.Module):
|
||||
"""Swin Transformer backbone.
|
||||
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
|
||||
https://arxiv.org/pdf/2103.14030
|
||||
Args:
|
||||
pretrain_img_size (int): Input image size for training the pretrained model,
|
||||
used in absolute postion embedding. Default 224.
|
||||
patch_size (int | tuple(int)): Patch size. Default: 4.
|
||||
in_chans (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
depths (tuple[int]): Depths of each Swin Transformer stage.
|
||||
num_heads (tuple[int]): Number of attention head of each stage.
|
||||
window_size (int): Window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop_rate (float): Dropout rate.
|
||||
attn_drop_rate (float): Attention dropout rate. Default: 0.
|
||||
drop_path_rate (float): Stochastic depth rate. Default: 0.2.
|
||||
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
||||
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
|
||||
patch_norm (bool): If True, add normalization after patch embedding. Default: True.
|
||||
out_indices (Sequence[int]): Output from which stages.
|
||||
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
|
||||
-1 means not freezing any parameters.
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
dilation (bool): if True, the output size if 16x downsample, ow 32x downsample.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
pretrain_img_size=224,
|
||||
patch_size=4,
|
||||
in_chans=3,
|
||||
embed_dim=96,
|
||||
depths=[2, 2, 6, 2],
|
||||
num_heads=[3, 6, 12, 24],
|
||||
window_size=7,
|
||||
mlp_ratio=4.0,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop_rate=0.0,
|
||||
attn_drop_rate=0.0,
|
||||
drop_path_rate=0.2,
|
||||
norm_layer=nn.LayerNorm,
|
||||
ape=False,
|
||||
patch_norm=True,
|
||||
out_indices=(0, 1, 2, 3),
|
||||
frozen_stages=-1,
|
||||
dilation=False,
|
||||
use_checkpoint=False,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.pretrain_img_size = pretrain_img_size
|
||||
self.num_layers = len(depths)
|
||||
self.embed_dim = embed_dim
|
||||
self.ape = ape
|
||||
self.patch_norm = patch_norm
|
||||
self.out_indices = out_indices
|
||||
self.frozen_stages = frozen_stages
|
||||
self.dilation = dilation
|
||||
|
||||
# if use_checkpoint:
|
||||
# print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!")
|
||||
|
||||
# split image into non-overlapping patches
|
||||
self.patch_embed = PatchEmbed(
|
||||
patch_size=patch_size,
|
||||
in_chans=in_chans,
|
||||
embed_dim=embed_dim,
|
||||
norm_layer=norm_layer if self.patch_norm else None,
|
||||
)
|
||||
|
||||
# absolute position embedding
|
||||
if self.ape:
|
||||
pretrain_img_size = to_2tuple(pretrain_img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
patches_resolution = [
|
||||
pretrain_img_size[0] // patch_size[0],
|
||||
pretrain_img_size[1] // patch_size[1],
|
||||
]
|
||||
|
||||
self.absolute_pos_embed = nn.Parameter(
|
||||
torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
|
||||
)
|
||||
trunc_normal_(self.absolute_pos_embed, std=0.02)
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
# stochastic depth
|
||||
dpr = [
|
||||
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
|
||||
] # stochastic depth decay rule
|
||||
|
||||
# build layers
|
||||
self.layers = nn.ModuleList()
|
||||
# prepare downsample list
|
||||
downsamplelist = [PatchMerging for i in range(self.num_layers)]
|
||||
downsamplelist[-1] = None
|
||||
num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
|
||||
if self.dilation:
|
||||
downsamplelist[-2] = None
|
||||
num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2
|
||||
for i_layer in range(self.num_layers):
|
||||
layer = BasicLayer(
|
||||
# dim=int(embed_dim * 2 ** i_layer),
|
||||
dim=num_features[i_layer],
|
||||
depth=depths[i_layer],
|
||||
num_heads=num_heads[i_layer],
|
||||
window_size=window_size,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop_rate,
|
||||
attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
|
||||
norm_layer=norm_layer,
|
||||
# downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
|
||||
downsample=downsamplelist[i_layer],
|
||||
use_checkpoint=use_checkpoint,
|
||||
)
|
||||
self.layers.append(layer)
|
||||
|
||||
# num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
|
||||
self.num_features = num_features
|
||||
|
||||
# add a norm layer for each output
|
||||
for i_layer in out_indices:
|
||||
layer = norm_layer(num_features[i_layer])
|
||||
layer_name = f"norm{i_layer}"
|
||||
self.add_module(layer_name, layer)
|
||||
|
||||
self._freeze_stages()
|
||||
|
||||
def _freeze_stages(self):
|
||||
if self.frozen_stages >= 0:
|
||||
self.patch_embed.eval()
|
||||
for param in self.patch_embed.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 1 and self.ape:
|
||||
self.absolute_pos_embed.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 2:
|
||||
self.pos_drop.eval()
|
||||
for i in range(0, self.frozen_stages - 1):
|
||||
m = self.layers[i]
|
||||
m.eval()
|
||||
for param in m.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
# def init_weights(self, pretrained=None):
|
||||
# """Initialize the weights in backbone.
|
||||
# Args:
|
||||
# pretrained (str, optional): Path to pre-trained weights.
|
||||
# Defaults to None.
|
||||
# """
|
||||
|
||||
# def _init_weights(m):
|
||||
# if isinstance(m, nn.Linear):
|
||||
# trunc_normal_(m.weight, std=.02)
|
||||
# if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
# nn.init.constant_(m.bias, 0)
|
||||
# elif isinstance(m, nn.LayerNorm):
|
||||
# nn.init.constant_(m.bias, 0)
|
||||
# nn.init.constant_(m.weight, 1.0)
|
||||
|
||||
# if isinstance(pretrained, str):
|
||||
# self.apply(_init_weights)
|
||||
# logger = get_root_logger()
|
||||
# load_checkpoint(self, pretrained, strict=False, logger=logger)
|
||||
# elif pretrained is None:
|
||||
# self.apply(_init_weights)
|
||||
# else:
|
||||
# raise TypeError('pretrained must be a str or None')
|
||||
|
||||
def forward_raw(self, x):
|
||||
"""Forward function."""
|
||||
x = self.patch_embed(x)
|
||||
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
if self.ape:
|
||||
# interpolate the position embedding to the corresponding size
|
||||
absolute_pos_embed = F.interpolate(
|
||||
self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
|
||||
)
|
||||
x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
|
||||
else:
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.pos_drop(x)
|
||||
|
||||
outs = []
|
||||
for i in range(self.num_layers):
|
||||
layer = self.layers[i]
|
||||
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
|
||||
# import ipdb; ipdb.set_trace()
|
||||
|
||||
if i in self.out_indices:
|
||||
norm_layer = getattr(self, f"norm{i}")
|
||||
x_out = norm_layer(x_out)
|
||||
|
||||
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(out)
|
||||
# in:
|
||||
# torch.Size([2, 3, 1024, 1024])
|
||||
# outs:
|
||||
# [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
|
||||
# torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
|
||||
return tuple(outs)
|
||||
|
||||
def forward(self, tensor_list: NestedTensor):
|
||||
x = tensor_list.tensors
|
||||
|
||||
"""Forward function."""
|
||||
x = self.patch_embed(x)
|
||||
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
if self.ape:
|
||||
# interpolate the position embedding to the corresponding size
|
||||
absolute_pos_embed = F.interpolate(
|
||||
self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
|
||||
)
|
||||
x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
|
||||
else:
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.pos_drop(x)
|
||||
|
||||
outs = []
|
||||
for i in range(self.num_layers):
|
||||
layer = self.layers[i]
|
||||
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
|
||||
|
||||
if i in self.out_indices:
|
||||
norm_layer = getattr(self, f"norm{i}")
|
||||
x_out = norm_layer(x_out)
|
||||
|
||||
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(out)
|
||||
# in:
|
||||
# torch.Size([2, 3, 1024, 1024])
|
||||
# out:
|
||||
# [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
|
||||
# torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
|
||||
|
||||
# collect for nesttensors
|
||||
outs_dict = {}
|
||||
for idx, out_i in enumerate(outs):
|
||||
m = tensor_list.mask
|
||||
assert m is not None
|
||||
mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0]
|
||||
outs_dict[idx] = NestedTensor(out_i, mask)
|
||||
|
||||
return outs_dict
|
||||
|
||||
def train(self, mode=True):
|
||||
"""Convert the model into training mode while keep layers freezed."""
|
||||
super(SwinTransformer, self).train(mode)
|
||||
self._freeze_stages()
|
||||
|
||||
|
||||
def build_swin_transformer(modelname, pretrain_img_size, **kw):
|
||||
assert modelname in [
|
||||
"swin_T_224_1k",
|
||||
"swin_B_224_22k",
|
||||
"swin_B_384_22k",
|
||||
"swin_L_224_22k",
|
||||
"swin_L_384_22k",
|
||||
]
|
||||
|
||||
model_para_dict = {
|
||||
"swin_T_224_1k": dict(
|
||||
embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7
|
||||
),
|
||||
"swin_B_224_22k": dict(
|
||||
embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7
|
||||
),
|
||||
"swin_B_384_22k": dict(
|
||||
embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12
|
||||
),
|
||||
"swin_L_224_22k": dict(
|
||||
embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7
|
||||
),
|
||||
"swin_L_384_22k": dict(
|
||||
embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12
|
||||
),
|
||||
}
|
||||
kw_cgf = model_para_dict[modelname]
|
||||
kw_cgf.update(kw)
|
||||
model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf)
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
model = build_swin_transformer("swin_L_384_22k", 384, dilation=True)
|
||||
x = torch.rand(2, 3, 1024, 1024)
|
||||
y = model.forward_raw(x)
|
||||
import ipdb
|
||||
|
||||
ipdb.set_trace()
|
||||
x = torch.rand(2, 3, 384, 384)
|
||||
y = model.forward_raw(x)
|
||||
Reference in New Issue
Block a user