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陈赣
752
rtdetr_paddle/ppdet/modeling/backbones/swin_transformer.py
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752
rtdetr_paddle/ppdet/modeling/backbones/swin_transformer.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This code is based on https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py
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Ths copyright of microsoft/Swin-Transformer is as follows:
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MIT License [see LICENSE for details]
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"""
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import numpy as np
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from ppdet.modeling.shape_spec import ShapeSpec
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from ppdet.core.workspace import register, serializable
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from .transformer_utils import DropPath, Identity
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from .transformer_utils import add_parameter, to_2tuple
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from .transformer_utils import ones_, zeros_, trunc_normal_
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__all__ = ['SwinTransformer']
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MODEL_cfg = {
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# use 22kto1k finetune weights as default pretrained, can set by SwinTransformer.pretrained in config
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'swin_T_224': dict(
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pretrain_img_size=224,
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embed_dim=96,
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depths=[2, 2, 6, 2],
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num_heads=[3, 6, 12, 24],
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window_size=7,
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pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/swin_tiny_patch4_window7_224_22kto1k_pretrained.pdparams',
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),
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'swin_S_224': dict(
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pretrain_img_size=224,
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embed_dim=96,
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depths=[2, 2, 18, 2],
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num_heads=[3, 6, 12, 24],
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window_size=7,
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pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/swin_small_patch4_window7_224_22kto1k_pretrained.pdparams',
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),
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'swin_B_224': dict(
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pretrain_img_size=224,
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embed_dim=128,
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depths=[2, 2, 18, 2],
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num_heads=[4, 8, 16, 32],
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window_size=7,
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pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/swin_base_patch4_window7_224_22kto1k_pretrained.pdparams',
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),
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'swin_L_224': dict(
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pretrain_img_size=224,
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embed_dim=192,
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depths=[2, 2, 18, 2],
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num_heads=[6, 12, 24, 48],
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window_size=7,
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pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/swin_large_patch4_window7_224_22kto1k_pretrained.pdparams',
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),
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'swin_B_384': dict(
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pretrain_img_size=384,
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embed_dim=128,
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depths=[2, 2, 18, 2],
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num_heads=[4, 8, 16, 32],
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window_size=12,
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pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/swin_base_patch4_window12_384_22kto1k_pretrained.pdparams',
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),
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'swin_L_384': dict(
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pretrain_img_size=384,
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embed_dim=192,
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depths=[2, 2, 18, 2],
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num_heads=[6, 12, 24, 48],
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window_size=12,
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pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/swin_large_patch4_window12_384_22kto1k_pretrained.pdparams',
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),
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}
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class Mlp(nn.Layer):
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def __init__(self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.GELU,
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drop=0.):
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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)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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def window_partition(x, window_size):
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"""
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Args:
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x: (B, H, W, C)
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window_size (int): window size
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Returns:
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windows: (num_windows*B, window_size, window_size, C)
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"""
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B, H, W, C = x.shape
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x = x.reshape(
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[-1, H // window_size, window_size, W // window_size, window_size, C])
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windows = x.transpose([0, 1, 3, 2, 4, 5]).reshape(
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[-1, window_size, window_size, C])
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return windows
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def window_reverse(windows, window_size, H, W):
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"""
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Args:
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windows: (num_windows*B, window_size, window_size, C)
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window_size (int): Window size
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H (int): Height of image
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W (int): Width of image
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Returns:
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x: (B, H, W, C)
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"""
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_, _, _, C = windows.shape
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B = int(windows.shape[0] / (H * W / window_size / window_size))
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x = windows.reshape(
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[-1, H // window_size, W // window_size, window_size, window_size, C])
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x = x.transpose([0, 1, 3, 2, 4, 5]).reshape([-1, H, W, C])
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return x
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class WindowAttention(nn.Layer):
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""" Window based multi-head self attention (W-MSA) module with relative position bias.
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It supports both of shifted and non-shifted window.
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Args:
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dim (int): Number of input channels.
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window_size (tuple[int]): The height and width of the window.
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num_heads (int): Number of attention heads.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
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attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
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proj_drop (float, optional): Dropout ratio of output. Default: 0.0
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"""
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def __init__(self,
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dim,
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window_size,
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num_heads,
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qkv_bias=True,
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qk_scale=None,
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attn_drop=0.,
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proj_drop=0.):
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super().__init__()
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self.dim = dim
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self.window_size = window_size # Wh, Ww
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim**-0.5
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# define a parameter table of relative position bias
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self.relative_position_bias_table = add_parameter(
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self,
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paddle.zeros(((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
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num_heads))) # 2*Wh-1 * 2*Ww-1, nH
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# get pair-wise relative position index for each token inside the window
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coords_h = paddle.arange(self.window_size[0])
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coords_w = paddle.arange(self.window_size[1])
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coords = paddle.stack(paddle.meshgrid(
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[coords_h, coords_w])) # 2, Wh, Ww
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coords_flatten = paddle.flatten(coords, 1) # 2, Wh*Ww
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coords_flatten_1 = coords_flatten.unsqueeze(axis=2)
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coords_flatten_2 = coords_flatten.unsqueeze(axis=1)
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relative_coords = coords_flatten_1 - coords_flatten_2
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relative_coords = relative_coords.transpose(
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[1, 2, 0]) # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += self.window_size[
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0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += self.window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
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self.relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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self.qkv = nn.Linear(dim, dim * 3, bias_attr=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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trunc_normal_(self.relative_position_bias_table)
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self.softmax = nn.Softmax(axis=-1)
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def forward(self, x, mask=None):
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""" Forward function.
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Args:
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x: input features with shape of (num_windows*B, N, C)
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mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
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"""
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B_, N, C = x.shape
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qkv = self.qkv(x).reshape(
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[-1, N, 3, self.num_heads, C // self.num_heads]).transpose(
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[2, 0, 3, 1, 4])
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q, k, v = qkv[0], qkv[1], qkv[2]
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q = q * self.scale
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attn = paddle.mm(q, k.transpose([0, 1, 3, 2]))
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index = self.relative_position_index.flatten()
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relative_position_bias = paddle.index_select(
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self.relative_position_bias_table, index)
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relative_position_bias = relative_position_bias.reshape([
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self.window_size[0] * self.window_size[1],
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self.window_size[0] * self.window_size[1], -1
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]) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.transpose(
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[2, 0, 1]) # nH, Wh*Ww, Wh*Ww
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attn = attn + relative_position_bias.unsqueeze(0)
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if mask is not None:
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nW = mask.shape[0]
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attn = attn.reshape([-1, nW, self.num_heads, N, N
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]) + mask.unsqueeze(1).unsqueeze(0)
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attn = attn.reshape([-1, self.num_heads, N, N])
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attn = self.softmax(attn)
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else:
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attn = self.softmax(attn)
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attn = self.attn_drop(attn)
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# x = (attn @ v).transpose(1, 2).reshape([B_, N, C])
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x = paddle.mm(attn, v).transpose([0, 2, 1, 3]).reshape([-1, N, C])
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class SwinTransformerBlock(nn.Layer):
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""" Swin Transformer Block.
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Args:
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dim (int): Number of input channels.
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num_heads (int): Number of attention heads.
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window_size (int): Window size.
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shift_size (int): Shift size for SW-MSA.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
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drop (float, optional): Dropout rate. Default: 0.0
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attn_drop (float, optional): Attention dropout rate. Default: 0.0
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drop_path (float, optional): Stochastic depth rate. Default: 0.0
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act_layer (nn.Layer, optional): Activation layer. Default: nn.GELU
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norm_layer (nn.Layer, optional): Normalization layer. Default: nn.LayerNorm
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"""
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def __init__(self,
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dim,
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num_heads,
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window_size=7,
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shift_size=0,
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mlp_ratio=4.,
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qkv_bias=True,
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qk_scale=None,
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drop=0.,
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attn_drop=0.,
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm):
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super().__init__()
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self.dim = dim
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self.num_heads = num_heads
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self.window_size = window_size
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self.shift_size = shift_size
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self.mlp_ratio = mlp_ratio
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assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
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self.norm1 = norm_layer(dim)
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self.attn = WindowAttention(
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dim,
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window_size=to_2tuple(self.window_size),
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop=attn_drop,
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proj_drop=drop)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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drop=drop)
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self.H = None
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self.W = None
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def forward(self, x, mask_matrix):
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""" Forward function.
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Args:
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x: Input feature, tensor size (B, H*W, C).
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H, W: Spatial resolution of the input feature.
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mask_matrix: Attention mask for cyclic shift.
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"""
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B, L, C = x.shape
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H, W = self.H, self.W
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assert L == H * W, "input feature has wrong size"
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shortcut = x
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x = self.norm1(x)
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x = x.reshape([-1, H, W, C])
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# pad feature maps to multiples of window size
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pad_l = pad_t = 0
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pad_r = (self.window_size - W % self.window_size) % self.window_size
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pad_b = (self.window_size - H % self.window_size) % self.window_size
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x = F.pad(x, [0, pad_l, 0, pad_b, 0, pad_r, 0, pad_t],
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data_format='NHWC')
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_, Hp, Wp, _ = x.shape
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# cyclic shift
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if self.shift_size > 0:
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shifted_x = paddle.roll(
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x, shifts=(-self.shift_size, -self.shift_size), axis=(1, 2))
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attn_mask = mask_matrix
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else:
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shifted_x = x
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attn_mask = None
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# partition windows
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x_windows = window_partition(
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shifted_x, self.window_size) # nW*B, window_size, window_size, C
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x_windows = x_windows.reshape(
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[x_windows.shape[0], self.window_size * self.window_size,
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C]) # nW*B, window_size*window_size, C
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# W-MSA/SW-MSA
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attn_windows = self.attn(
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x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
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# merge windows
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attn_windows = attn_windows.reshape(
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[x_windows.shape[0], self.window_size, self.window_size, C])
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shifted_x = window_reverse(attn_windows, self.window_size, Hp,
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Wp) # B H' W' C
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# reverse cyclic shift
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if self.shift_size > 0:
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x = paddle.roll(
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shifted_x,
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shifts=(self.shift_size, self.shift_size),
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axis=(1, 2))
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else:
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x = shifted_x
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if pad_r > 0 or pad_b > 0:
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x = x[:, :H, :W, :]
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x = x.reshape([-1, H * W, C])
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# FFN
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x = shortcut + self.drop_path(x)
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class PatchMerging(nn.Layer):
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r""" Patch Merging Layer.
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Args:
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dim (int): Number of input channels.
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norm_layer (nn.Layer, optional): Normalization layer. Default: nn.LayerNorm
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"""
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def __init__(self, dim, norm_layer=nn.LayerNorm):
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super().__init__()
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self.dim = dim
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self.reduction = nn.Linear(4 * dim, 2 * dim, bias_attr=False)
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self.norm = norm_layer(4 * dim)
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def forward(self, x, H, W):
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""" Forward function.
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Args:
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x: Input feature, tensor size (B, H*W, C).
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H, W: Spatial resolution of the input feature.
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"""
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B, L, C = x.shape
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assert L == H * W, "input feature has wrong size"
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x = x.reshape([-1, H, W, C])
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# padding
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pad_input = (H % 2 == 1) or (W % 2 == 1)
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if pad_input:
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# paddle F.pad default data_format is 'NCHW'
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x = F.pad(x, [0, 0, 0, H % 2, 0, W % 2, 0, 0], data_format='NHWC')
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H += H % 2
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W += W % 2
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x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
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x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
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x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
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x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
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x = paddle.concat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
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x = x.reshape([-1, H * W // 4, 4 * C]) # B H/2*W/2 4*C
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||||
x = self.norm(x)
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||||
x = self.reduction(x)
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||||
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||||
return x
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||||
|
||||
|
||||
class BasicLayer(nn.Layer):
|
||||
""" A basic Swin Transformer layer for one stage.
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
depth (int): Number of blocks.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Local window size.
|
||||
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 | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||||
norm_layer (nn.Layer, optional): Normalization layer. Default: nn.LayerNorm
|
||||
downsample (nn.Layer | None, optional): Downsample layer at the end of the layer. Default: None
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
dim,
|
||||
depth,
|
||||
num_heads,
|
||||
window_size=7,
|
||||
mlp_ratio=4.,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop=0.,
|
||||
attn_drop=0.,
|
||||
drop_path=0.,
|
||||
norm_layer=nn.LayerNorm,
|
||||
downsample=None):
|
||||
super().__init__()
|
||||
self.window_size = window_size
|
||||
self.shift_size = window_size // 2
|
||||
self.depth = depth
|
||||
|
||||
# build blocks
|
||||
self.blocks = nn.LayerList([
|
||||
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, np.ndarray) 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 = paddle.zeros([1, Hp, Wp, 1], dtype='float32') # 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.reshape(
|
||||
[-1, self.window_size * self.window_size])
|
||||
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
||||
huns = -100.0 * paddle.ones_like(attn_mask)
|
||||
attn_mask = huns * (attn_mask != 0).astype("float32")
|
||||
|
||||
for blk in self.blocks:
|
||||
blk.H, blk.W = H, W
|
||||
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.Layer):
|
||||
""" 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.Layer, 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):
|
||||
# TODO # export dynamic shape
|
||||
B, C, H, W = x.shape
|
||||
# assert [H, W] == self.img_size[:2], "Input image size ({H}*{W}) doesn't match model ({}*{}).".format(H, W, self.img_size[0], self.img_size[1])
|
||||
if W % self.patch_size[1] != 0:
|
||||
x = F.pad(x, [0, self.patch_size[1] - W % self.patch_size[1], 0, 0])
|
||||
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)
|
||||
if self.norm is not None:
|
||||
_, _, Wh, Ww = x.shape
|
||||
x = x.flatten(2).transpose([0, 2, 1])
|
||||
x = self.norm(x)
|
||||
x = x.transpose([0, 2, 1]).reshape([-1, self.embed_dim, Wh, Ww])
|
||||
|
||||
return x
|
||||
|
||||
|
||||
@register
|
||||
@serializable
|
||||
class SwinTransformer(nn.Layer):
|
||||
""" Swin Transformer backbone
|
||||
Args:
|
||||
arch (str): Architecture of FocalNet
|
||||
pretrain_img_size (int | tuple(int)): Input image size. Default 224
|
||||
patch_size (int | tuple(int)): Patch size. Default: 4
|
||||
in_chans (int): Number of input image channels. Default: 3
|
||||
embed_dim (int): Patch embedding dimension. Default: 96
|
||||
depths (tuple(int)): Depth of each Swin Transformer layer.
|
||||
num_heads (tuple(int)): Number of attention heads in different layers.
|
||||
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. Default: None
|
||||
drop_rate (float): Dropout rate. Default: 0
|
||||
attn_drop_rate (float): Attention dropout rate. Default: 0
|
||||
drop_path_rate (float): Stochastic depth rate. Default: 0.1
|
||||
norm_layer (nn.Layer): 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
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
arch='swin_T_224',
|
||||
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.,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop_rate=0.,
|
||||
attn_drop_rate=0.,
|
||||
drop_path_rate=0.2,
|
||||
norm_layer=nn.LayerNorm,
|
||||
ape=False,
|
||||
patch_norm=True,
|
||||
out_indices=(0, 1, 2, 3),
|
||||
frozen_stages=-1,
|
||||
pretrained=None):
|
||||
super(SwinTransformer, self).__init__()
|
||||
assert arch in MODEL_cfg.keys(), "Unsupported arch: {}".format(arch)
|
||||
|
||||
pretrain_img_size = MODEL_cfg[arch]['pretrain_img_size']
|
||||
embed_dim = MODEL_cfg[arch]['embed_dim']
|
||||
depths = MODEL_cfg[arch]['depths']
|
||||
num_heads = MODEL_cfg[arch]['num_heads']
|
||||
window_size = MODEL_cfg[arch]['window_size']
|
||||
if pretrained is None:
|
||||
pretrained = MODEL_cfg[arch]['pretrained']
|
||||
|
||||
self.num_layers = len(depths)
|
||||
self.ape = ape
|
||||
self.patch_norm = patch_norm
|
||||
self.out_indices = out_indices
|
||||
self.frozen_stages = frozen_stages
|
||||
|
||||
# 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 = add_parameter(
|
||||
self,
|
||||
paddle.zeros((1, embed_dim, patches_resolution[0],
|
||||
patches_resolution[1])))
|
||||
trunc_normal_(self.absolute_pos_embed)
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
# stochastic depth
|
||||
dpr = np.linspace(0, drop_path_rate,
|
||||
sum(depths)) # stochastic depth decay rule
|
||||
|
||||
# build layers
|
||||
self.layers = nn.LayerList()
|
||||
for i_layer in range(self.num_layers):
|
||||
layer = BasicLayer(
|
||||
dim=int(embed_dim * 2**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)
|
||||
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_sublayer(layer_name, layer)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
self._freeze_stages()
|
||||
if pretrained:
|
||||
if 'http' in pretrained: #URL
|
||||
path = paddle.utils.download.get_weights_path_from_url(
|
||||
pretrained)
|
||||
else: #model in local path
|
||||
path = pretrained
|
||||
self.set_state_dict(paddle.load(path))
|
||||
|
||||
def _freeze_stages(self):
|
||||
if self.frozen_stages >= 0:
|
||||
self.patch_embed.eval()
|
||||
for param in self.patch_embed.parameters():
|
||||
param.stop_gradient = True
|
||||
|
||||
if self.frozen_stages >= 1 and self.ape:
|
||||
self.absolute_pos_embed.stop_gradient = True
|
||||
|
||||
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.stop_gradient = True
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
zeros_(m.bias)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
zeros_(m.bias)
|
||||
ones_(m.weight)
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
x = self.patch_embed(x['image'])
|
||||
B, _, Wh, Ww = x.shape
|
||||
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([0, 2, 1])
|
||||
else:
|
||||
x = x.flatten(2).transpose([0, 2, 1])
|
||||
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.reshape((-1, H, W, self.num_features[i])).transpose(
|
||||
(0, 3, 1, 2))
|
||||
outs.append(out)
|
||||
|
||||
return outs
|
||||
|
||||
@property
|
||||
def out_shape(self):
|
||||
out_strides = [4, 8, 16, 32]
|
||||
return [
|
||||
ShapeSpec(
|
||||
channels=self.num_features[i], stride=out_strides[i])
|
||||
for i in self.out_indices
|
||||
]
|
||||
Reference in New Issue
Block a user