702 lines
24 KiB
Python
702 lines
24 KiB
Python
# copyright (c) 2022 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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# reference: https://arxiv.org/abs/2105.14734v4
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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 .vision_transformer import to_2tuple, zeros_, ones_, VisionTransformer, Identity, zeros_
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from functools import partial
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from paddle.nn.initializer import TruncatedNormal, Constant, Normal
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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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"DSNet_tiny":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DSNet_tiny_pretrained.pdparams",
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"DSNet_small":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DSNet_small_pretrained.pdparams",
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"DSNet_base":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DSNet_base_pretrained.pdparams",
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}
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__all__ = list(MODEL_URLS.keys())
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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.Conv2D(in_features, hidden_features, 1)
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self.act = act_layer()
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self.fc2 = nn.Conv2D(hidden_features, out_features, 1)
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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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class DWConv(nn.Layer):
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def __init__(self, dim=768):
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super(DWConv, self).__init__()
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self.dwconv = nn.Conv2D(dim, dim, 3, 1, 1, bias=True, groups=dim)
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def forward(self, x):
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x = self.dwconv(x)
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return x
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class DWConvMlp(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.Conv2D(in_features, hidden_features, 1)
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self.dwconv = DWConv(hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Conv2D(hidden_features, out_features, 1)
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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.dwconv(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 drop_path(x, drop_prob=0., training=False):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ...
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"""
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if drop_prob == 0. or not training:
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return x
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keep_prob = paddle.to_tensor(1 - drop_prob)
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shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
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random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
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random_tensor = paddle.floor(random_tensor) # binarize
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output = x.divide(keep_prob) * random_tensor
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return output
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class DropPath(nn.Layer):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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"""
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def __init__(self, drop_prob=None):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training)
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class Attention(nn.Layer):
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def __init__(self,
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dim,
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num_heads=8,
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qkv_bias=False,
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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.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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self.attn_drop = nn.Dropout(attn_drop)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x):
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B, N, C = x.shape
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C = int(C // 3)
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qkv = x.reshape(
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(B, 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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attn = (q.matmul(k.transpose((0, 1, 3, 2)))) * self.scale
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attn = F.softmax(attn, axis=-1)
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attn = self.attn_drop(attn)
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x = (attn.matmul(v)).transpose((0, 2, 1, 3)).reshape((B, N, C))
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x = self.proj_drop(x)
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return x
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class Cross_Attention(nn.Layer):
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def __init__(self,
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dim,
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num_heads=8,
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qkv_bias=False,
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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.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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self.attn_drop = nn.Dropout(attn_drop)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, tokens_q, memory_k, memory_v, shape=None):
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assert shape is not None
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attn = (tokens_q.matmul(memory_k.transpose((0, 1, 3, 2)))) * self.scale
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attn = F.softmax(attn, axis=-1)
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attn = self.attn_drop(attn)
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x = (attn.matmul(memory_v)).transpose((0, 2, 1, 3)).reshape(
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(shape[0], shape[1], shape[2]))
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x = self.proj_drop(x)
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return x
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class MixBlock(nn.Layer):
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def __init__(self,
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dim,
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num_heads,
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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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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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downsample=2,
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conv_ffn=False):
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super().__init__()
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self.pos_embed = nn.Conv2D(dim, dim, 3, padding=1, groups=dim)
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self.dim = dim
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self.norm1 = nn.BatchNorm2D(dim)
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self.conv1 = nn.Conv2D(dim, dim, 1)
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self.conv2 = nn.Conv2D(dim, dim, 1)
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self.dim_conv = int(dim * 0.5)
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self.dim_sa = dim - self.dim_conv
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self.norm_conv1 = nn.BatchNorm2D(self.dim_conv)
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self.norm_sa1 = nn.LayerNorm(self.dim_sa)
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self.conv = nn.Conv2D(
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self.dim_conv, self.dim_conv, 3, padding=1, groups=self.dim_conv)
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self.channel_up = nn.Linear(self.dim_sa, 3 * self.dim_sa)
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self.cross_channel_up_conv = nn.Conv2D(self.dim_conv,
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3 * self.dim_conv, 1)
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self.cross_channel_up_sa = nn.Linear(self.dim_sa, 3 * self.dim_sa)
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self.fuse_channel_conv = nn.Linear(self.dim_conv, self.dim_conv)
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self.fuse_channel_sa = nn.Linear(self.dim_sa, self.dim_sa)
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self.num_heads = num_heads
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self.attn = Attention(
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self.dim_sa,
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num_heads=self.num_heads,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop=0.1,
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proj_drop=drop)
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self.cross_attn = Cross_Attention(
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self.dim_sa,
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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=0.1,
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proj_drop=drop)
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self.norm_conv2 = nn.BatchNorm2D(self.dim_conv)
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self.norm_sa2 = nn.LayerNorm(self.dim_sa)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
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self.norm2 = nn.BatchNorm2D(dim)
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self.downsample = downsample
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mlp_hidden_dim = int(dim * mlp_ratio)
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if conv_ffn:
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self.mlp = DWConvMlp(
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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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else:
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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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def forward(self, x):
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x = x + self.pos_embed(x)
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_, _, H, W = x.shape
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residual = x
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x = self.norm1(x)
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x = self.conv1(x)
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qkv = x[:, :self.dim_sa, :]
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conv = x[:, self.dim_sa:, :, :]
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residual_conv = conv
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conv = residual_conv + self.conv(self.norm_conv1(conv))
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sa = F.interpolate(
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qkv,
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size=(H // self.downsample, W // self.downsample),
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mode='bilinear')
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B, _, H_down, W_down = sa.shape
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sa = sa.flatten(2).transpose([0, 2, 1])
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residual_sa = sa
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sa = self.norm_sa1(sa)
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sa = self.channel_up(sa)
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sa = residual_sa + self.attn(sa)
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# cross attention
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residual_conv_co = conv
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residual_sa_co = sa
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conv_qkv = self.cross_channel_up_conv(self.norm_conv2(conv))
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conv_qkv = conv_qkv.flatten(2).transpose([0, 2, 1])
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sa_qkv = self.cross_channel_up_sa(self.norm_sa2(sa))
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B_conv, N_conv, C_conv = conv_qkv.shape
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C_conv = int(C_conv // 3)
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conv_qkv = conv_qkv.reshape((B_conv, N_conv, 3, self.num_heads,
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C_conv // self.num_heads)).transpose(
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(2, 0, 3, 1, 4))
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conv_q, conv_k, conv_v = conv_qkv[0], conv_qkv[1], conv_qkv[2]
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B_sa, N_sa, C_sa = sa_qkv.shape
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C_sa = int(C_sa // 3)
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sa_qkv = sa_qkv.reshape(
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(B_sa, N_sa, 3, self.num_heads, C_sa // self.num_heads)).transpose(
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(2, 0, 3, 1, 4))
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sa_q, sa_k, sa_v = sa_qkv[0], sa_qkv[1], sa_qkv[2]
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# sa -> conv
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conv = self.cross_attn(
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conv_q, sa_k, sa_v, shape=(B_conv, N_conv, C_conv))
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conv = self.fuse_channel_conv(conv)
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conv = conv.reshape((B, H, W, C_conv)).transpose((0, 3, 1, 2))
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conv = residual_conv_co + conv
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# conv -> sa
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sa = self.cross_attn(sa_q, conv_k, conv_v, shape=(B_sa, N_sa, C_sa))
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sa = residual_sa_co + self.fuse_channel_sa(sa)
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sa = sa.reshape((B, H_down, W_down, C_sa)).transpose((0, 3, 1, 2))
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sa = F.interpolate(sa, size=(H, W), mode='bilinear')
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x = paddle.concat([conv, sa], axis=1)
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x = residual + self.drop_path(self.conv2(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 Block(nn.Layer):
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def __init__(self,
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dim,
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num_heads,
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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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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.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim,
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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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def forward(self, x):
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x = x + self.drop_path(self.attn(self.norm1(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 PatchEmbed(nn.Layer):
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""" Image to Patch Embedding
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
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super().__init__()
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img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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num_patches = (img_size[1] // patch_size[1]) * (img_size[0] //
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patch_size[0])
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self.img_size = img_size
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self.patch_size = patch_size
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self.num_patches = num_patches
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self.proj = nn.Conv2D(
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in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
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def forward(self, x):
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B, C, H, W = x.shape
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assert H == self.img_size[0] and W == self.img_size[1], \
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f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
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x = self.proj(x)
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return x
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class OverlapPatchEmbed(nn.Layer):
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""" Image to Overlapping Patch Embedding
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"""
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def __init__(self,
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img_size=224,
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patch_size=7,
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stride=4,
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in_chans=3,
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embed_dim=768):
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super().__init__()
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img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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self.img_size = img_size
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self.patch_size = patch_size
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self.H, self.W = img_size[0] // patch_size[0], img_size[
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1] // patch_size[1]
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self.num_patches = self.H * self.W
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self.proj = nn.Conv2D(
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in_chans,
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embed_dim,
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kernel_size=patch_size,
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stride=stride,
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padding=(patch_size[0] // 2, patch_size[1] // 2))
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def forward(self, x):
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B, C, H, W = x.shape
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assert H == self.img_size[0] and W == self.img_size[1], \
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f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
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x = self.proj(x)
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return x
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class MixVisionTransformer(nn.Layer):
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""" Mixed Vision Transformer for DSNet
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A PaddlePaddle impl of : `Dual-stream Network for Visual Recognition` - https://arxiv.org/abs/2105.14734v4
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"""
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def __init__(self,
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img_size=224,
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patch_size=16,
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in_chans=3,
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class_num=1000,
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embed_dim=[64, 128, 320, 512],
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depth=[2, 2, 4, 1],
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num_heads=[1, 2, 5, 8],
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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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representation_size=None,
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drop_rate=0.,
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attn_drop_rate=0.,
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drop_path_rate=0.1,
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norm_layer=None,
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overlap_embed=False,
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conv_ffn=False):
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"""
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Args:
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img_size (int, tuple): input image size
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patch_size (int, tuple): patch size
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in_chans (int): number of input channels
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class_num (int): number of classes for classification head
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embed_dim (int): embedding dimension
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depth (int): depth of transformer
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num_heads (int): number of attention heads
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mlp_ratio (int): ratio of mlp hidden dim to embedding dim
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qkv_bias (bool): enable bias for qkv if True
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qk_scale (float): override default qk scale of head_dim ** -0.5 if set
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representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
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drop_rate (float): dropout rate
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attn_drop_rate (float): attention dropout rate
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drop_path_rate (float): stochastic depth rate
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norm_layer: (nn.Layer): normalization layer
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overlap_embed (bool): enable overlapped patch embedding if True
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conv_ffn (bool): enable depthwise convolution for mlp if True
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"""
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super().__init__()
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self.class_num = class_num
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self.num_features = self.embed_dim = embed_dim
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norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
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downsamples = [8, 4, 2, 2]
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if overlap_embed:
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self.patch_embed1 = OverlapPatchEmbed(
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img_size=img_size,
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patch_size=7,
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stride=4,
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in_chans=in_chans,
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embed_dim=embed_dim[0])
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self.patch_embed2 = OverlapPatchEmbed(
|
|
img_size=img_size // 4,
|
|
patch_size=3,
|
|
stride=2,
|
|
in_chans=embed_dim[0],
|
|
embed_dim=embed_dim[1])
|
|
self.patch_embed3 = OverlapPatchEmbed(
|
|
img_size=img_size // 8,
|
|
patch_size=3,
|
|
stride=2,
|
|
in_chans=embed_dim[1],
|
|
embed_dim=embed_dim[2])
|
|
self.patch_embed4 = OverlapPatchEmbed(
|
|
img_size=img_size // 16,
|
|
patch_size=3,
|
|
stride=2,
|
|
in_chans=embed_dim[2],
|
|
embed_dim=embed_dim[3])
|
|
else:
|
|
self.patch_embed1 = PatchEmbed(
|
|
img_size=img_size,
|
|
patch_size=4,
|
|
in_chans=in_chans,
|
|
embed_dim=embed_dim[0])
|
|
self.patch_embed2 = PatchEmbed(
|
|
img_size=img_size // 4,
|
|
patch_size=2,
|
|
in_chans=embed_dim[0],
|
|
embed_dim=embed_dim[1])
|
|
self.patch_embed3 = PatchEmbed(
|
|
img_size=img_size // 8,
|
|
patch_size=2,
|
|
in_chans=embed_dim[1],
|
|
embed_dim=embed_dim[2])
|
|
self.patch_embed4 = PatchEmbed(
|
|
img_size=img_size // 16,
|
|
patch_size=2,
|
|
in_chans=embed_dim[2],
|
|
embed_dim=embed_dim[3])
|
|
|
|
self.pos_drop = nn.Dropout(p=drop_rate)
|
|
self.mixture = False
|
|
dpr = [
|
|
x.item() for x in paddle.linspace(0, drop_path_rate, sum(depth))
|
|
]
|
|
self.blocks1 = nn.LayerList([
|
|
MixBlock(
|
|
dim=embed_dim[0],
|
|
num_heads=num_heads[0],
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop_rate,
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[i],
|
|
norm_layer=norm_layer,
|
|
downsample=downsamples[0],
|
|
conv_ffn=conv_ffn) for i in range(depth[0])
|
|
])
|
|
|
|
self.blocks2 = nn.LayerList([
|
|
MixBlock(
|
|
dim=embed_dim[1],
|
|
num_heads=num_heads[1],
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop_rate,
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[i],
|
|
norm_layer=norm_layer,
|
|
downsample=downsamples[1],
|
|
conv_ffn=conv_ffn) for i in range(depth[1])
|
|
])
|
|
|
|
self.blocks3 = nn.LayerList([
|
|
MixBlock(
|
|
dim=embed_dim[2],
|
|
num_heads=num_heads[2],
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop_rate,
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[i],
|
|
norm_layer=norm_layer,
|
|
downsample=downsamples[2],
|
|
conv_ffn=conv_ffn) for i in range(depth[2])
|
|
])
|
|
|
|
if self.mixture:
|
|
self.blocks4 = nn.LayerList([
|
|
Block(
|
|
dim=embed_dim[3],
|
|
num_heads=16,
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop_rate,
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[i],
|
|
norm_layer=norm_layer,
|
|
downsample=downsamples[3],
|
|
conv_ffn=conv_ffn) for i in range(depth[3])
|
|
])
|
|
self.norm = norm_layer(embed_dim[-1])
|
|
else:
|
|
self.blocks4 = nn.LayerList([
|
|
MixBlock(
|
|
dim=embed_dim[3],
|
|
num_heads=num_heads[3],
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop_rate,
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[i],
|
|
norm_layer=norm_layer,
|
|
downsample=downsamples[3],
|
|
conv_ffn=conv_ffn) for i in range(depth[3])
|
|
])
|
|
self.norm = nn.BatchNorm2D(embed_dim[-1])
|
|
|
|
# Representation layer
|
|
if representation_size:
|
|
self.num_features = representation_size
|
|
self.pre_logits = nn.Sequential(
|
|
OrderedDict([('fc', nn.Linear(embed_dim, representation_size)),
|
|
('act', nn.Tanh())]))
|
|
else:
|
|
self.pre_logits = Identity()
|
|
|
|
# Classifier head
|
|
self.head = nn.Linear(embed_dim[-1],
|
|
class_num) if class_num > 0 else Identity()
|
|
|
|
self.apply(self._init_weights)
|
|
|
|
def _init_weights(self, m):
|
|
if isinstance(m, nn.Linear):
|
|
TruncatedNormal(m.weight, std=.02)
|
|
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 get_classifier(self):
|
|
return self.head
|
|
|
|
def reset_classifier(self, class_num, global_pool=''):
|
|
self.class_num = class_num
|
|
self.head = nn.Linear(self.embed_dim,
|
|
class_num) if class_num > 0 else Identity()
|
|
|
|
def forward_features(self, x):
|
|
B = x.shape[0]
|
|
x = self.patch_embed1(x)
|
|
x = self.pos_drop(x)
|
|
for blk in self.blocks1:
|
|
x = blk(x)
|
|
x = self.patch_embed2(x)
|
|
for blk in self.blocks2:
|
|
x = blk(x)
|
|
x = self.patch_embed3(x)
|
|
for blk in self.blocks3:
|
|
x = blk(x)
|
|
x = self.patch_embed4(x)
|
|
if self.mixture:
|
|
x = x.flatten(2).transpose([0, 2, 1])
|
|
for blk in self.blocks4:
|
|
x = blk(x)
|
|
x = self.norm(x)
|
|
x = self.pre_logits(x)
|
|
return x
|
|
|
|
def forward(self, x):
|
|
x = self.forward_features(x)
|
|
if self.mixture:
|
|
x = x.mean(1)
|
|
else:
|
|
x = x.flatten(2).mean(-1)
|
|
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 DSNet_tiny(pretrained=False, use_ssld=False, **kwargs):
|
|
model = MixVisionTransformer(
|
|
patch_size=16,
|
|
depth=[2, 2, 4, 1],
|
|
mlp_ratio=4,
|
|
qkv_bias=True,
|
|
norm_layer=partial(
|
|
nn.LayerNorm, eps=1e-6),
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained, model, MODEL_URLS["DSNet_tiny"], use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def DSNet_small(pretrained=False, use_ssld=False, **kwargs):
|
|
model = MixVisionTransformer(
|
|
patch_size=16,
|
|
depth=[3, 4, 8, 3],
|
|
mlp_ratio=4,
|
|
qkv_bias=True,
|
|
norm_layer=partial(
|
|
nn.LayerNorm, eps=1e-6),
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained, model, MODEL_URLS["DSNet_small"], use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def DSNet_base(pretrained=False, use_ssld=False, **kwargs):
|
|
model = MixVisionTransformer(
|
|
patch_size=16,
|
|
depth=[3, 4, 28, 3],
|
|
mlp_ratio=4,
|
|
qkv_bias=True,
|
|
norm_layer=partial(
|
|
nn.LayerNorm, eps=1e-6),
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained, model, MODEL_URLS["DSNet_base"], use_ssld=use_ssld)
|
|
return model
|