388 lines
13 KiB
Python
388 lines
13 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/huawei-noah/CV-Backbones/tree/master/tnt_pytorch
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# reference: https://arxiv.org/abs/2103.00112
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import math
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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 paddle.nn.initializer import TruncatedNormal, Constant
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from ..base.theseus_layer import 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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"TNT_small":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_small_pretrained.pdparams"
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}
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__all__ = MODEL_URLS.keys()
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trunc_normal_ = TruncatedNormal(std=.02)
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zeros_ = Constant(value=0.)
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ones_ = Constant(value=1.)
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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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the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
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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 = paddle.add(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 Mlp(nn.Layer):
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def __init__(self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.GELU,
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drop=0.):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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class Attention(nn.Layer):
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def __init__(self,
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dim,
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hidden_dim,
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num_heads=8,
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qkv_bias=False,
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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.hidden_dim = hidden_dim
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self.num_heads = num_heads
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head_dim = hidden_dim // num_heads
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self.head_dim = head_dim
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self.scale = head_dim**-0.5
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self.qk = nn.Linear(dim, hidden_dim * 2, bias_attr=qkv_bias)
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self.v = nn.Linear(dim, dim, 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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qk = self.qk(x).reshape(
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(B, N, 2, self.num_heads, self.head_dim)).transpose(
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(2, 0, 3, 1, 4))
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q, k = qk[0], qk[1]
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v = self.v(x).reshape(
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(B, N, self.num_heads, x.shape[-1] // self.num_heads)).transpose(
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(0, 2, 1, 3))
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attn = paddle.matmul(q, k.transpose((0, 1, 3, 2))) * self.scale
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attn = nn.functional.softmax(attn, axis=-1)
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attn = self.attn_drop(attn)
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x = paddle.matmul(attn, v)
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x = x.transpose((0, 2, 1, 3)).reshape(
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(B, N, x.shape[-1] * x.shape[-3]))
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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 Block(nn.Layer):
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def __init__(self,
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dim,
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in_dim,
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num_pixel,
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num_heads=12,
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in_num_head=4,
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mlp_ratio=4.,
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qkv_bias=False,
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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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# Inner transformer
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self.norm_in = norm_layer(in_dim)
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self.attn_in = Attention(
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in_dim,
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in_dim,
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num_heads=in_num_head,
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qkv_bias=qkv_bias,
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attn_drop=attn_drop,
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proj_drop=drop)
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self.norm_mlp_in = norm_layer(in_dim)
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self.mlp_in = Mlp(in_features=in_dim,
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hidden_features=int(in_dim * 4),
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out_features=in_dim,
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act_layer=act_layer,
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drop=drop)
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self.norm1_proj = norm_layer(in_dim)
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self.proj = nn.Linear(in_dim * num_pixel, dim)
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# Outer transformer
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self.norm_out = norm_layer(dim)
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self.attn_out = Attention(
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dim,
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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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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.norm_mlp = norm_layer(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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out_features=dim,
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act_layer=act_layer,
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drop=drop)
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def forward(self, pixel_embed, patch_embed):
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# inner
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pixel_embed = paddle.add(
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pixel_embed,
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self.drop_path(self.attn_in(self.norm_in(pixel_embed))))
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pixel_embed = paddle.add(
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pixel_embed,
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self.drop_path(self.mlp_in(self.norm_mlp_in(pixel_embed))))
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# outer
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B, N, C = patch_embed.shape
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norm1_proj = self.norm1_proj(pixel_embed)
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norm1_proj = norm1_proj.reshape(
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(B, N - 1, norm1_proj.shape[1] * norm1_proj.shape[2]))
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patch_embed[:, 1:] = paddle.add(patch_embed[:, 1:],
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self.proj(norm1_proj))
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patch_embed = paddle.add(
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patch_embed,
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self.drop_path(self.attn_out(self.norm_out(patch_embed))))
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patch_embed = paddle.add(
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patch_embed, self.drop_path(self.mlp(self.norm_mlp(patch_embed))))
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return pixel_embed, patch_embed
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class PixelEmbed(nn.Layer):
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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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in_dim=48,
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stride=4):
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super().__init__()
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num_patches = (img_size // patch_size)**2
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self.img_size = img_size
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self.num_patches = num_patches
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self.in_dim = in_dim
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new_patch_size = math.ceil(patch_size / stride)
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self.new_patch_size = new_patch_size
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self.proj = nn.Conv2D(
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in_chans, self.in_dim, kernel_size=7, padding=3, stride=stride)
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def forward(self, x, pixel_pos):
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B, C, H, W = x.shape
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assert H == self.img_size and W == self.img_size, f"Input image size ({H}*{W}) doesn't match model ({self.img_size}*{self.img_size})."
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x = self.proj(x)
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x = nn.functional.unfold(x, self.new_patch_size, self.new_patch_size)
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x = x.transpose((0, 2, 1)).reshape(
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(-1, self.in_dim, self.new_patch_size, self.new_patch_size))
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x = x + pixel_pos
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x = x.reshape((-1, self.in_dim, self.new_patch_size *
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self.new_patch_size)).transpose((0, 2, 1))
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return x
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class TNT(nn.Layer):
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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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embed_dim=768,
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in_dim=48,
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depth=12,
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num_heads=12,
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in_num_head=4,
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mlp_ratio=4.,
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qkv_bias=False,
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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=nn.LayerNorm,
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first_stride=4,
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class_num=1000):
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super().__init__()
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self.class_num = class_num
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# num_features for consistency with other models
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self.num_features = self.embed_dim = embed_dim
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self.pixel_embed = PixelEmbed(
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img_size=img_size,
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patch_size=patch_size,
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in_chans=in_chans,
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in_dim=in_dim,
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stride=first_stride)
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num_patches = self.pixel_embed.num_patches
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self.num_patches = num_patches
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new_patch_size = self.pixel_embed.new_patch_size
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num_pixel = new_patch_size**2
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self.norm1_proj = norm_layer(num_pixel * in_dim)
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self.proj = nn.Linear(num_pixel * in_dim, embed_dim)
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self.norm2_proj = norm_layer(embed_dim)
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self.cls_token = self.create_parameter(
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shape=(1, 1, embed_dim), default_initializer=zeros_)
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self.add_parameter("cls_token", self.cls_token)
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self.patch_pos = self.create_parameter(
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shape=(1, num_patches + 1, embed_dim), default_initializer=zeros_)
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self.add_parameter("patch_pos", self.patch_pos)
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self.pixel_pos = self.create_parameter(
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shape=(1, in_dim, new_patch_size, new_patch_size),
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default_initializer=zeros_)
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self.add_parameter("pixel_pos", self.pixel_pos)
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self.pos_drop = nn.Dropout(p=drop_rate)
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# stochastic depth decay rule
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dpr = np.linspace(0, drop_path_rate, depth)
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blocks = []
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for i in range(depth):
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blocks.append(
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Block(
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dim=embed_dim,
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in_dim=in_dim,
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num_pixel=num_pixel,
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num_heads=num_heads,
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in_num_head=in_num_head,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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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))
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self.blocks = nn.LayerList(blocks)
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self.norm = norm_layer(embed_dim)
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if class_num > 0:
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self.head = nn.Linear(embed_dim, class_num)
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trunc_normal_(self.cls_token)
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trunc_normal_(self.patch_pos)
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trunc_normal_(self.pixel_pos)
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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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B = paddle.shape(x)[0]
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pixel_embed = self.pixel_embed(x, self.pixel_pos)
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patch_embed = self.norm2_proj(
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self.proj(
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self.norm1_proj(
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pixel_embed.reshape((-1, self.num_patches, pixel_embed.
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shape[-1] * pixel_embed.shape[-2])))))
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patch_embed = paddle.concat(
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(self.cls_token.expand((B, -1, -1)), patch_embed), axis=1)
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patch_embed = patch_embed + self.patch_pos
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patch_embed = self.pos_drop(patch_embed)
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for blk in self.blocks:
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pixel_embed, patch_embed = blk(pixel_embed, patch_embed)
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patch_embed = self.norm(patch_embed)
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return patch_embed[:, 0]
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def forward(self, x):
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x = self.forward_features(x)
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if self.class_num > 0:
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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 TNT_small(pretrained=False, use_ssld=False, **kwargs):
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model = TNT(patch_size=16,
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embed_dim=384,
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in_dim=24,
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depth=12,
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num_heads=6,
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in_num_head=4,
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qkv_bias=False,
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**kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["TNT_small"], use_ssld=use_ssld)
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return model
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