652 lines
23 KiB
Python
652 lines
23 KiB
Python
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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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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# Code was based on https://github.com/BR-IDL/PaddleViT/blob/develop/image_classification/CSwin/cswin.py
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# reference: https://arxiv.org/abs/2107.00652
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import copy
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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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from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity
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from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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MODEL_URLS = {
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"CSWinTransformer_tiny_224":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSWinTransformer_tiny_224_pretrained.pdparams",
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"CSWinTransformer_small_224":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSWinTransformer_small_224_pretrained.pdparams",
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"CSWinTransformer_base_224":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSWinTransformer_base_224_pretrained.pdparams",
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"CSWinTransformer_large_224":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSWinTransformer_large_224_pretrained.pdparams",
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"CSWinTransformer_base_384":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSWinTransformer_base_384_pretrained.pdparams",
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"CSWinTransformer_large_384":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSWinTransformer_large_384_pretrained.pdparams",
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}
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__all__ = list(MODEL_URLS.keys())
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class PatchEmbedding(nn.Layer):
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"""CSwin Patch Embedding
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This patch embedding has a 7x7 conv + layernorm, the output tensor
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is reshaped to [Batch, H*W, embed_dim]. Note that the patch is applied
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by a conv with overlap (using patch_stride).
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Args:
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patch_stride: int, patch stride size, default: 4
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in_channels: int, number of channels of input image, default: 3
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embed_dim: int, output feature dimension, default: 96
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"""
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def __init__(self, patch_stride=4, in_channels=3, embed_dim=96):
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super().__init__()
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self.patch_embed = nn.Conv2D(
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in_channels=in_channels,
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out_channels=embed_dim,
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kernel_size=7,
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stride=patch_stride,
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padding=2)
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self.norm = nn.LayerNorm(embed_dim)
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def forward(self, x):
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x = self.patch_embed(
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x) # [batch, embed_dim, h, w], h = w = image_size / 4
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x = x.flatten(start_axis=2, stop_axis=-1) # [batch, embed_dim, h*w]
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x = x.transpose([0, 2, 1]) # [batch, h*w, embed_dim]
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x = self.norm(x)
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return x
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class Mlp(nn.Layer):
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""" MLP module
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Impl using nn.Linear and activation is GELU, dropout is applied.
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Ops: fc -> act -> dropout -> fc -> dropout
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Attributes:
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fc1: nn.Linear
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fc2: nn.Linear
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act: GELU
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dropout1: dropout after fc1
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dropout2: dropout after fc2
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"""
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def __init__(self, in_features, hidden_features, dropout):
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super().__init__()
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.fc2 = nn.Linear(hidden_features, in_features)
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self.act = nn.GELU()
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self.dropout = nn.Dropout(dropout)
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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.dropout(x)
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x = self.fc2(x)
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x = self.dropout(x)
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return x
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def img2windows(img, h_split, w_split):
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"""Convert input tensor into split stripes
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Args:
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img: tensor, image tensor with shape [B, C, H, W]
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h_split: int, splits width in height direction
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w_split: int, splits width in width direction
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Returns:
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out: tensor, splitted image
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"""
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B, C, H, W = img.shape
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out = img.reshape([B, C, H // h_split, h_split, W // w_split, w_split])
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out = out.transpose(
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[0, 2, 4, 3, 5, 1]) # [B, H//h_split, W//w_split, h_split, w_split, C]
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out = out.reshape([-1, h_split * w_split,
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C]) # [B, H//h_split, W//w_split, h_split*w_split, C]
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return out
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def windows2img(img_splits, h_split, w_split, img_h, img_w):
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"""Convert splitted stripes back
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Args:
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img_splits: tensor, image tensor with shape [B, C, H, W]
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h_split: int, splits width in height direction
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w_split: int, splits width in width direction
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img_h: int, original tensor height
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img_w: int, original tensor width
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Returns:
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img: tensor, original tensor
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"""
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B = paddle.to_tensor(img_splits.shape[0] //
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(img_h // h_split * img_w // w_split), "int32")
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img = img_splits.reshape([
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B, img_h // h_split, img_w // w_split, h_split, w_split,
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img_splits.shape[-1]
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])
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img = img.transpose(
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[0, 1, 3, 2, 4,
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5]) #[B,img_h//h_split, h_split, img_w//w_split, w_split,C]
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img = img.reshape(
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[B, img_h, img_w, img_splits.shape[-1]]) # [B, img_h, img_w, C]
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return img
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class LePEAttention(nn.Layer):
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"""Cross Shaped Window self-attention with Locally enhanced positional encoding"""
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def __init__(self,
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dim,
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resolution,
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h_split=7,
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w_split=7,
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num_heads=8,
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attention_dropout=0.,
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dropout=0.,
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qk_scale=None):
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super().__init__()
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self.dim = dim
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self.resolution = resolution
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self.num_heads = num_heads
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self.dim_head = dim // num_heads
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self.scale = qk_scale or self.dim_head**-0.5
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self.h_split = h_split
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self.w_split = w_split
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self.get_v = nn.Conv2D(
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in_channels=dim,
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out_channels=dim,
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kernel_size=3,
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stride=1,
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padding=1,
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groups=dim)
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self.softmax = nn.Softmax(axis=-1)
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self.attn_dropout = nn.Dropout(attention_dropout)
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def im2cswin(self, x):
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B, HW, C = x.shape
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H = W = int(np.sqrt(HW))
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x = x.transpose([0, 2, 1]) # [B, C, H*W]
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x = x.reshape([B, C, H, W]) # [B, C, H, W]
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x = img2windows(x, self.h_split, self.w_split)
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x = x.reshape(
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[-1, self.h_split * self.w_split, self.num_heads, self.dim_head])
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x = x.transpose([0, 2, 1, 3])
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return x
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def get_lepe(self, x, func):
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"""Locally Enhanced Positional Encoding (LePE)
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This module applies a depthwise conv on V and returns the lepe
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Args:
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x: tensor, the input tensor V
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func: nn.Layer, a depth wise conv of kernel 3 stride 1 and padding 1
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"""
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B, HW, C = x.shape
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H = W = int(np.sqrt(HW))
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h_split = self.h_split
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w_split = self.w_split
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x = x.transpose([0, 2, 1]) # [B, C, H*W]
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x = x.reshape([B, C, H, W]) # [B, C, H, W]
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x = x.reshape([B, C, H // h_split, h_split, W // w_split, w_split])
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x = x.transpose(
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[0, 2, 4, 1, 3,
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5]) # [B, H//h_split, W//w_split, C, h_split, w_split]
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x = x.reshape(
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[-1, C, h_split,
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w_split]) # [B*(H//h_split)*(W//w_split), h_split, w_split]
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lepe = func(x) # depth wise conv does not change shape
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#lepe = lepe.reshape([-1, self.num_heads, C // self.num_heads, h_split * w_split])
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lepe = lepe.reshape(
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[-1, self.num_heads, self.dim_head, h_split * w_split])
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lepe = lepe.transpose(
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[0, 1, 3, 2]) # [B, num_heads, h_spllit*w_split, dim_head]
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x = x.reshape([-1, self.num_heads, self.dim_head, h_split * w_split])
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x = x.transpose(
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[0, 1, 3, 2]) # [B, num_heads, h_split*wsplit, dim_head]
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return x, lepe
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def forward(self, q, k, v):
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B, HW, C = q.shape
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H = W = self.resolution
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q = self.im2cswin(q)
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k = self.im2cswin(k)
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v, lepe = self.get_lepe(v, self.get_v)
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q = q * self.scale
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attn = paddle.matmul(q, k, transpose_y=True)
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attn = self.softmax(attn)
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attn = self.attn_dropout(attn)
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z = paddle.matmul(attn, v)
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z = z + lepe
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z = z.transpose([0, 2, 1, 3])
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z = z.reshape([-1, self.h_split * self.w_split, C])
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z = windows2img(z, self.h_split, self.w_split, H, W)
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z = z.reshape([B, z.shape[1] * z.shape[2], C])
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return z
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class CSwinBlock(nn.Layer):
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"""CSwin Block
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CSwin block contains a LePE attention modual, a linear projection,
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a mlp layer, and related norms layers. In the first 3 stages, the
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LePE attention moduals used 2 branches, where horizontal and
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vertical split stripes are used for self attention and a concat
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op is applied to combine the outputs. The last stage does not
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have branche in LePE attention.
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Args:
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dim: int, input feature dimension
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input_resolution: int, input feature spatial size.
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num_heads: int, num of attention heads in current stage
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split_size: int, the split size in current stage
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mlp_ratio: float, mlp ratio, mlp_hidden_dim = mlp_ratio * mlp_in_dim, default: 4.
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qkv_bias: bool, if set True, qkv projection will have bias, default: True
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qk_scale: float, if set, replace the orig qk_scale (dim_head ** -0.5), default: None
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dropout: float, dropout rate for linear projection, default: 0
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attention_dropout: float, dropout rate for attention, default: 0
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droppath: float, drop path rate, default: 0
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split_heads: bool, if True, split heads is applied (True for 1,2,3 stages), default: True
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"""
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def __init__(self,
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dim,
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input_resolution,
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num_heads,
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split_size=7,
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mlp_ratio=4.,
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qkv_bias=False,
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qk_scale=None,
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attention_dropout=0.,
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dropout=0.,
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droppath=0.,
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split_heads=True):
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super().__init__()
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self.dim = dim
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# NOTE: here assume image_h == imgae_w
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self.input_resolution = (input_resolution, input_resolution)
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self.num_heads = num_heads
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self.dim_head = dim // num_heads
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self.mlp_ratio = mlp_ratio
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self.split_size = split_size
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self.norm1 = nn.LayerNorm(dim)
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self.qkv = nn.Linear(
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dim, dim * 3, bias_attr=None if qkv_bias else False)
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self.attns = nn.LayerList()
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self.split_heads = split_heads
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num_branches = 2 if split_heads else 1
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if split_heads: # first 3 stages
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splits = [self.input_resolution[0],
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self.split_size] # horizantal splits
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else: # last stage
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splits = [self.input_resolution[0], self.input_resolution[0]]
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for _ in range(num_branches):
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attn = LePEAttention(
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dim=dim // num_branches,
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resolution=input_resolution,
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h_split=splits[0],
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w_split=splits[1],
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num_heads=num_heads // num_branches,
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qk_scale=qk_scale,
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attention_dropout=attention_dropout,
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dropout=dropout)
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self.attns.append(copy.deepcopy(attn))
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# switch splits from horizantal to vertical
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# NOTE: may need to change for different H and W
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splits[0], splits[1] = splits[1], splits[0]
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self.proj = nn.Linear(dim, dim)
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self.drop_path = DropPath(droppath) if droppath > 0. else Identity()
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self.norm2 = nn.LayerNorm(dim)
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self.mlp = Mlp(in_features=dim,
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hidden_features=int(dim * mlp_ratio),
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dropout=dropout)
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def chunk_qkv(self, x, chunks=1, axis=-1):
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x = x.chunk(chunks, axis=axis)
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return x
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def forward(self, x):
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H, W = self.input_resolution
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B, HW, C = x.shape
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# cswin attention
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h = x
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x = self.norm1(x)
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qkv = self.qkv(x).chunk(3, axis=-1) # qkv is a tuple of [q, k, v]
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if self.split_heads:
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q, k, v = map(self.chunk_qkv, qkv,
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(2, 2, 2)) # map requries list/tuple inputs
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else:
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q, k, v = map(lambda x: [x], qkv)
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if self.split_heads: # first 3 stages
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h_attn = self.attns[0](q[0], k[0], v[0])
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w_attn = self.attns[1](q[1], k[1], v[1])
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attn = paddle.concat([h_attn, w_attn], axis=2)
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else: # last stage
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attn = self.attns[0](q[0], k[0], v[0])
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attn = self.proj(attn)
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attn = self.drop_path(attn)
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x = h + attn
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# mlp + residual
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h = x
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x = self.norm2(x)
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x = self.mlp(x)
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x = self.drop_path(x)
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x = h + x
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return x
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class MergeBlock(nn.Layer):
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def __init__(self, dim_in, dim_out):
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super().__init__()
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self.conv = nn.Conv2D(
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in_channels=dim_in,
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out_channels=dim_out,
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kernel_size=3,
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stride=2,
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padding=1)
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self.norm = nn.LayerNorm(dim_out)
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def forward(self, x):
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B, HW, C = x.shape
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H = W = int(np.sqrt(HW))
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x = x.transpose([0, 2, 1]) # [B, C, HW]
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x = x.reshape([B, C, H, W]) # [B, C, H, W]
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x = self.conv(x)
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new_shape = [x.shape[0], x.shape[1],
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x.shape[2] * x.shape[3]] # [B, C', H*W]
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x = x.reshape(new_shape) # [B, C', H*W]
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x = x.transpose([0, 2, 1]) # [B, H*W, C']
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x = self.norm(x)
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return x
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class CSwinStage(nn.Layer):
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""" CSwin Stage, each stage contains multi blocks
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CSwin has 4 stages, the first 3 stages are using head split. The last
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stage does not have head split. There is a merge block between each
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2 stages.
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Args:
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dim: int, input feature dimension
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depth: int, number of blocks in current stage
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num_heads: int, num of attention heads in current stage
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split_size: int, the split size in current stage
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mlp_ratio: float, mlp ratio, mlp_hidden_dim = mlp_ratio * mlp_in_dim, default: 4.
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qkv_bias: bool, if set True, qkv projection will have bias, default: True
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qk_scale: float, if set, replace the orig qk_scale (dim_head ** -0.5), default: None
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dropout: float, dropout rate for linear projection, default: 0
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attention_dropout: float, dropout rate for attention, default: 0
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droppath: float, drop path rate, default: 0
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last_stage: bool, if current stage is the last stage, default: False
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"""
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def __init__(self,
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dim,
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input_resolution,
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depth,
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num_heads,
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split_size,
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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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dropout=0.,
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attention_dropout=0.,
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droppath=0.,
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last_stage=False):
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super().__init__()
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self.blocks = nn.LayerList()
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for i in range(depth):
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block = CSwinBlock(
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dim=dim,
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input_resolution=input_resolution,
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num_heads=num_heads,
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split_size=split_size,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attention_dropout=attention_dropout,
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dropout=dropout,
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droppath=droppath[i]
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if isinstance(droppath, list) else droppath,
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split_heads=not last_stage)
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self.blocks.append(copy.deepcopy(block))
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# last stage does not need merge layer
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self.merge = MergeBlock(
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dim_in=dim, dim_out=dim * 2) if not last_stage else Identity()
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def forward(self, x):
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for block in self.blocks:
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x = block(x)
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x = self.merge(x)
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return x
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class CSwinTransformer(nn.Layer):
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"""CSwin Transformer class
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Args:
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image_size: int, input image size, default: 224
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patch_stride: int, stride for patch embedding, default: 4
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in_channels: int, num of channels of input image, default: 3
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num_classes: int, num of classes, default: 1000
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embed_dim: int, embedding dim (patch embed out dim), default: 96
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depths: list/tuple(int), number of blocks in each stage, default: [2, 4, 32, 2]
|
|
splits: list/tuple(int), the split number in each stage, default: [1, 2, 7, 7]
|
|
num_heads: list/tuple(int), num of attention heads in each stage, default: [4, 8, 16, 32]
|
|
mlp_ratio: float, mlp ratio, mlp_hidden_dim = mlp_ratio * mlp_in_dim, default: 4.
|
|
qkv_bias: bool, if set True, qkv projection will have bias, default: True
|
|
qk_scale: float, if set, replace the orig qk_scale (dim_head ** -0.5), default: None
|
|
dropout: float, dropout rate for linear projection, default: 0
|
|
attention_dropout: float, dropout rate for attention, default: 0
|
|
droppath: float, drop path rate, default: 0
|
|
"""
|
|
|
|
def __init__(self,
|
|
image_size=224,
|
|
patch_stride=4,
|
|
in_channels=3,
|
|
class_num=1000,
|
|
embed_dim=96,
|
|
depths=[2, 4, 32, 2],
|
|
splits=[1, 2, 7, 7],
|
|
num_heads=[4, 8, 16, 32],
|
|
mlp_ratio=4.,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
dropout=0.,
|
|
attention_dropout=0.,
|
|
droppath=0.):
|
|
super().__init__()
|
|
# token embedding
|
|
self.patch_embedding = PatchEmbedding(
|
|
patch_stride=patch_stride,
|
|
in_channels=in_channels,
|
|
embed_dim=embed_dim)
|
|
# drop path decay by stage
|
|
depth_decay = [
|
|
x.item() for x in paddle.linspace(0, droppath, sum(depths))
|
|
]
|
|
dim = embed_dim
|
|
resolution = image_size // 4
|
|
self.stages = nn.LayerList()
|
|
num_stages = len(depths)
|
|
# construct CSwin stages: each stage has multiple blocks
|
|
for stage_idx in range(num_stages):
|
|
stage = CSwinStage(
|
|
dim=dim,
|
|
input_resolution=resolution,
|
|
depth=depths[stage_idx],
|
|
num_heads=num_heads[stage_idx],
|
|
split_size=splits[stage_idx],
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
dropout=dropout,
|
|
attention_dropout=attention_dropout,
|
|
droppath=depth_decay[sum(depths[:stage_idx]):sum(
|
|
depths[:stage_idx + 1])],
|
|
last_stage=stage_idx == num_stages - 1)
|
|
self.stages.append(stage)
|
|
if stage_idx != num_stages - 1:
|
|
dim = dim * 2
|
|
resolution = resolution // 2
|
|
# last norm and classification head layers
|
|
self.norm = nn.LayerNorm(dim)
|
|
self.head = nn.Linear(dim, class_num)
|
|
|
|
self.apply(self._init_weights)
|
|
|
|
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_features(self, x):
|
|
x = self.patch_embedding(x)
|
|
for stage in self.stages:
|
|
x = stage(x)
|
|
x = self.norm(x)
|
|
return paddle.mean(x, axis=1)
|
|
|
|
def forward(self, x):
|
|
x = self.forward_features(x)
|
|
x = self.head(x)
|
|
return x
|
|
|
|
|
|
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
|
|
if pretrained is False:
|
|
pass
|
|
elif pretrained is True:
|
|
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
|
|
elif isinstance(pretrained, str):
|
|
load_dygraph_pretrain(model, pretrained)
|
|
else:
|
|
raise RuntimeError(
|
|
"pretrained type is not available. Please use `string` or `boolean` type."
|
|
)
|
|
|
|
|
|
def CSWinTransformer_tiny_224(pretrained=False, use_ssld=False, **kwargs):
|
|
model = CSwinTransformer(
|
|
image_size=224,
|
|
embed_dim=64,
|
|
depths=[1, 2, 21, 1],
|
|
splits=[1, 2, 7, 7],
|
|
num_heads=[2, 4, 8, 16],
|
|
droppath=0.2,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained,
|
|
model,
|
|
MODEL_URLS["CSWinTransformer_tiny_224"],
|
|
use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def CSWinTransformer_small_224(pretrained=False, use_ssld=False, **kwargs):
|
|
model = CSwinTransformer(
|
|
image_size=224,
|
|
embed_dim=64,
|
|
depths=[2, 4, 32, 2],
|
|
splits=[1, 2, 7, 7],
|
|
num_heads=[2, 4, 8, 16],
|
|
droppath=0.4,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained,
|
|
model,
|
|
MODEL_URLS["CSWinTransformer_small_224"],
|
|
use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def CSWinTransformer_base_224(pretrained=False, use_ssld=False, **kwargs):
|
|
model = CSwinTransformer(
|
|
image_size=224,
|
|
embed_dim=96,
|
|
depths=[2, 4, 32, 2],
|
|
splits=[1, 2, 7, 7],
|
|
num_heads=[4, 8, 16, 32],
|
|
droppath=0.5,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained,
|
|
model,
|
|
MODEL_URLS["CSWinTransformer_base_224"],
|
|
use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def CSWinTransformer_base_384(pretrained=False, use_ssld=False, **kwargs):
|
|
model = CSwinTransformer(
|
|
image_size=384,
|
|
embed_dim=96,
|
|
depths=[2, 4, 32, 2],
|
|
splits=[1, 2, 12, 12],
|
|
num_heads=[4, 8, 16, 32],
|
|
droppath=0.5,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained,
|
|
model,
|
|
MODEL_URLS["CSWinTransformer_base_384"],
|
|
use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def CSWinTransformer_large_224(pretrained=False, use_ssld=False, **kwargs):
|
|
model = CSwinTransformer(
|
|
image_size=224,
|
|
embed_dim=144,
|
|
depths=[2, 4, 32, 2],
|
|
splits=[1, 2, 7, 7],
|
|
num_heads=[6, 12, 24, 24],
|
|
droppath=0.5,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained,
|
|
model,
|
|
MODEL_URLS["CSWinTransformer_large_224"],
|
|
use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def CSWinTransformer_large_384(pretrained=False, use_ssld=False, **kwargs):
|
|
model = CSwinTransformer(
|
|
image_size=384,
|
|
embed_dim=144,
|
|
depths=[2, 4, 32, 2],
|
|
splits=[1, 2, 12, 12],
|
|
num_heads=[6, 12, 24, 24],
|
|
droppath=0.5,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained,
|
|
model,
|
|
MODEL_URLS["CSWinTransformer_large_384"],
|
|
use_ssld=use_ssld)
|
|
return model
|