553 lines
18 KiB
Python
553 lines
18 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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# Code was based on https://github.com/Sense-X/UniFormer
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# reference: https://arxiv.org/abs/2201.09450
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from collections import OrderedDict
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from functools import partial
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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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import math
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from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity, Mlp
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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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"UniFormer_small":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_pretrained.pdparams",
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"UniFormer_small_plus":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_plus_pretrained.pdparams",
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"UniFormer_small_plus_dim64":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_plus_dim64_pretrained.pdparams",
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"UniFormer_base":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_base_pretrained.pdparams",
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"UniFormer_base_ls":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_base_ls_pretrained.pdparams",
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}
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__all__ = list(MODEL_URLS.keys())
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layer_scale = False
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init_value = 1e-6
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class CMlp(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_conv = nn.Conv2D(in_features, hidden_features, 1)
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self.act = act_layer()
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self.fc2_conv = 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_conv(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2_conv(x)
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x = self.drop(x)
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return x
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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.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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def forward(self, x):
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(
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shape=[B, N, 3, self.num_heads, C // self.num_heads]).transpose(
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perm=[2, 0, 3, 1, 4])
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q, k, v = qkv[0], qkv[1], qkv[2]
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attn = (q @k.transpose(perm=[0, 1, 3, 2])) * self.scale
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attn = nn.Softmax(axis=-1)(attn)
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attn = self.attn_drop(attn)
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x = (attn @v).transpose(perm=[0, 2, 1, 3]).reshape(shape=[B, 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 CBlock(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.pos_embed = nn.Conv2D(dim, dim, 3, padding=1, groups=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.attn = nn.Conv2D(dim, dim, 5, padding=2, groups=dim)
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self.drop_path = DropPath(
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drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = nn.BatchNorm2D(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = CMlp(
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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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def forward(self, x):
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x = x + self.pos_embed(x)
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x = x + self.drop_path(
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self.conv2(self.attn(self.conv1(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 SABlock(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.pos_embed = nn.Conv2D(dim, dim, 3, padding=1, groups=dim)
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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(
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drop_path) if drop_path > 0. else nn.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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global layer_scale
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self.ls = layer_scale
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if self.ls:
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global init_value
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print(f"Use layer_scale: {layer_scale}, init_values: {init_value}")
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self.gamma_1 = self.create_parameter(
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[dim],
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dtype='float32',
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default_initializer=nn.initializer.Constant(value=init_value))
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self.gamma_2 = self.create_parameter(
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[dim],
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dtype='float32',
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default_initializer=nn.initializer.Constant(value=init_value))
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def forward(self, x):
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x = x + self.pos_embed(x)
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B, N, H, W = x.shape
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x = x.flatten(2).transpose(perm=[0, 2, 1])
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if self.ls:
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x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
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x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
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else:
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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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x = x.transpose(perm=[0, 2, 1]).reshape(shape=[B, N, H, W])
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return x
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class HeadEmbedding(nn.Layer):
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def __init__(self, in_channels, out_channels):
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super().__init__()
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self.proj = nn.Sequential(
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nn.Conv2D(
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in_channels,
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out_channels // 2,
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kernel_size=(3, 3),
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stride=(2, 2),
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padding=(1, 1)),
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nn.BatchNorm2D(out_channels // 2),
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nn.GELU(),
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nn.Conv2D(
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out_channels // 2,
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out_channels,
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kernel_size=(3, 3),
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stride=(2, 2),
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padding=(1, 1)),
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nn.BatchNorm2D(out_channels))
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def forward(self, x):
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x = self.proj(x)
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return x
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class MiddleEmbedding(nn.Layer):
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def __init__(self, in_channels, out_channels):
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super().__init__()
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self.proj = nn.Sequential(
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nn.Conv2D(
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in_channels,
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out_channels,
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kernel_size=(3, 3),
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stride=(2, 2),
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padding=(1, 1)),
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nn.BatchNorm2D(out_channels))
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def forward(self, x):
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x = self.proj(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.norm = nn.LayerNorm(embed_dim)
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self.proj_conv = 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_conv(x)
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B, C, H, W = x.shape
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x = x.flatten(2).transpose(perm=[0, 2, 1])
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x = self.norm(x)
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x = x.reshape(shape=[B, H, W, C]).transpose(perm=[0, 3, 1, 2])
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return x
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class UniFormer(nn.Layer):
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""" UniFormer
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A PaddlePaddle impl of : `UniFormer: Unifying Convolution and Self-attention for Visual Recognition` -
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https://arxiv.org/abs/2201.09450
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"""
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def __init__(self,
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depth=[3, 4, 8, 3],
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img_size=224,
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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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head_dim=64,
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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.,
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norm_layer=None,
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conv_stem=False):
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"""
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Args:
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depth (list): depth of each stage
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img_size (int, tuple): input image 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 (list): embedding dimension of each stage
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head_dim (int): head dimension
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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.Module): normalization layer
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conv_stem (bool): whether use overlapped patch stem
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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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if conv_stem:
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self.patch_embed1 = HeadEmbedding(
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in_channels=in_chans, out_channels=embed_dim[0])
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self.patch_embed2 = MiddleEmbedding(
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in_channels=embed_dim[0], out_channels=embed_dim[1])
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self.patch_embed3 = MiddleEmbedding(
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in_channels=embed_dim[1], out_channels=embed_dim[2])
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self.patch_embed4 = MiddleEmbedding(
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in_channels=embed_dim[2], out_channels=embed_dim[3])
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else:
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self.patch_embed1 = PatchEmbed(
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img_size=img_size,
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patch_size=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 = PatchEmbed(
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img_size=img_size // 4,
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patch_size=2,
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in_chans=embed_dim[0],
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embed_dim=embed_dim[1])
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self.patch_embed3 = PatchEmbed(
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img_size=img_size // 8,
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patch_size=2,
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in_chans=embed_dim[1],
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embed_dim=embed_dim[2])
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self.patch_embed4 = PatchEmbed(
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img_size=img_size // 16,
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patch_size=2,
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in_chans=embed_dim[2],
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embed_dim=embed_dim[3])
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self.pos_drop = nn.Dropout(p=drop_rate)
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dpr = [
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x.item() for x in paddle.linspace(0, drop_path_rate, sum(depth))
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] # stochastic depth decay rule
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num_heads = [dim // head_dim for dim in embed_dim]
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self.blocks1 = nn.LayerList([
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CBlock(
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dim=embed_dim[0],
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num_heads=num_heads[0],
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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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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i],
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norm_layer=norm_layer) for i in range(depth[0])
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])
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self.blocks2 = nn.LayerList([
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CBlock(
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dim=embed_dim[1],
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num_heads=num_heads[1],
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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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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i + depth[0]],
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norm_layer=norm_layer) for i in range(depth[1])
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])
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self.blocks3 = nn.LayerList([
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SABlock(
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dim=embed_dim[2],
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num_heads=num_heads[2],
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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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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i + depth[0] + depth[1]],
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norm_layer=norm_layer) for i in range(depth[2])
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])
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self.blocks4 = nn.LayerList([
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SABlock(
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dim=embed_dim[3],
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num_heads=num_heads[3],
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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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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[i + depth[0] + depth[1] + depth[2]],
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norm_layer=norm_layer) for i in range(depth[3])
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])
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self.norm = nn.BatchNorm2D(embed_dim[-1])
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# Representation layer
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if representation_size:
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self.num_features = representation_size
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self.pre_logits = nn.Sequential(
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OrderedDict([('fc', nn.Linear(embed_dim, representation_size)),
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('act', nn.Tanh())]))
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else:
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self.pre_logits = nn.Identity()
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# Classifier head
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self.head = nn.Linear(embed_dim[-1],
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class_num) if class_num > 0 else nn.Identity()
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight)
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if isinstance(m, nn.Linear) and m.bias is not None:
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zeros_(m.bias)
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elif isinstance(m, nn.LayerNorm):
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zeros_(m.bias)
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ones_(m.weight)
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def forward_features(self, x):
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x = self.patch_embed1(x)
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x = self.pos_drop(x)
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for blk in self.blocks1:
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x = blk(x)
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x = self.patch_embed2(x)
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for blk in self.blocks2:
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x = blk(x)
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x = self.patch_embed3(x)
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for blk in self.blocks3:
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x = blk(x)
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x = self.patch_embed4(x)
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for blk in self.blocks4:
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x = blk(x)
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x = self.norm(x)
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x = self.pre_logits(x)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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x = x.flatten(2).mean(-1)
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x = self.head(x)
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return x
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def _load_pretrained(pretrained, model, model_url, use_ssld=False):
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if pretrained is False:
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pass
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elif pretrained is True:
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load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
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elif isinstance(pretrained, str):
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load_dygraph_pretrain(model, pretrained)
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else:
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raise RuntimeError(
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"pretrained type is not available. Please use `string` or `boolean` type."
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)
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def UniFormer_small(pretrained=True, use_ssld=False, **kwargs):
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model = UniFormer(
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depth=[3, 4, 8, 3],
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embed_dim=[64, 128, 320, 512],
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head_dim=64,
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mlp_ratio=4,
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qkv_bias=True,
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norm_layer=partial(
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nn.LayerNorm, epsilon=1e-6),
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drop_path_rate=0.1,
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**kwargs)
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_load_pretrained(
|
|
pretrained, model, MODEL_URLS["UniFormer_small"], use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def UniFormer_small_plus(pretrained=True, use_ssld=False, **kwargs):
|
|
model = UniFormer(
|
|
depth=[3, 5, 9, 3],
|
|
conv_stem=True,
|
|
embed_dim=[64, 128, 320, 512],
|
|
head_dim=32,
|
|
mlp_ratio=4,
|
|
qkv_bias=True,
|
|
norm_layer=partial(
|
|
nn.LayerNorm, epsilon=1e-6),
|
|
drop_path_rate=0.1,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained,
|
|
model,
|
|
MODEL_URLS["UniFormer_small_plus"],
|
|
use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def UniFormer_small_plus_dim64(pretrained=True, use_ssld=False, **kwargs):
|
|
model = UniFormer(
|
|
depth=[3, 5, 9, 3],
|
|
conv_stem=True,
|
|
embed_dim=[64, 128, 320, 512],
|
|
head_dim=64,
|
|
mlp_ratio=4,
|
|
qkv_bias=True,
|
|
norm_layer=partial(
|
|
nn.LayerNorm, epsilon=1e-6),
|
|
drop_path_rate=0.1,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained,
|
|
model,
|
|
MODEL_URLS["UniFormer_small_plus_dim64"],
|
|
use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def UniFormer_base(pretrained=True, use_ssld=False, **kwargs):
|
|
model = UniFormer(
|
|
depth=[5, 8, 20, 7],
|
|
embed_dim=[64, 128, 320, 512],
|
|
head_dim=64,
|
|
mlp_ratio=4,
|
|
qkv_bias=True,
|
|
norm_layer=partial(
|
|
nn.LayerNorm, epsilon=1e-6),
|
|
drop_path_rate=0.3,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained, model, MODEL_URLS["UniFormer_base"], use_ssld=use_ssld)
|
|
return model
|
|
|
|
|
|
def UniFormer_base_ls(pretrained=True, use_ssld=False, **kwargs):
|
|
global layer_scale
|
|
layer_scale = True
|
|
model = UniFormer(
|
|
depth=[5, 8, 20, 7],
|
|
embed_dim=[64, 128, 320, 512],
|
|
head_dim=64,
|
|
mlp_ratio=4,
|
|
qkv_bias=True,
|
|
norm_layer=partial(
|
|
nn.LayerNorm, epsilon=1e-6),
|
|
drop_path_rate=0.3,
|
|
**kwargs)
|
|
_load_pretrained(
|
|
pretrained, model, MODEL_URLS["UniFormer_base_ls"], use_ssld=use_ssld)
|
|
return model
|