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- # copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # Code was based on https://github.com/huawei-noah/CV-Backbones/tree/master/tnt_pytorch
- import math
- import numpy as np
- import paddle
- import paddle.nn as nn
- from paddle.nn.initializer import TruncatedNormal, Constant
- from paddlex.ppcls.arch.backbone.base.theseus_layer import Identity
- from paddlex.ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
- MODEL_URLS = {
- "TNT_small":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_small_pretrained.pdparams"
- }
- __all__ = MODEL_URLS.keys()
- trunc_normal_ = TruncatedNormal(std=.02)
- zeros_ = Constant(value=0.)
- ones_ = Constant(value=1.)
- def drop_path(x, drop_prob=0., training=False):
- """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
- the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
- See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ...
- """
- if drop_prob == 0. or not training:
- return x
- keep_prob = paddle.to_tensor(1 - drop_prob)
- shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
- random_tensor = paddle.add(keep_prob, paddle.rand(shape, dtype=x.dtype))
- random_tensor = paddle.floor(random_tensor) # binarize
- output = x.divide(keep_prob) * random_tensor
- return output
- class DropPath(nn.Layer):
- """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
- """
- def __init__(self, drop_prob=None):
- super(DropPath, self).__init__()
- self.drop_prob = drop_prob
- def forward(self, x):
- return drop_path(x, self.drop_prob, self.training)
- class Mlp(nn.Layer):
- def __init__(self,
- in_features,
- hidden_features=None,
- out_features=None,
- act_layer=nn.GELU,
- drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
- class Attention(nn.Layer):
- def __init__(self,
- dim,
- hidden_dim,
- num_heads=8,
- qkv_bias=False,
- attn_drop=0.,
- proj_drop=0.):
- super().__init__()
- self.hidden_dim = hidden_dim
- self.num_heads = num_heads
- head_dim = hidden_dim // num_heads
- self.head_dim = head_dim
- self.scale = head_dim**-0.5
- self.qk = nn.Linear(dim, hidden_dim * 2, bias_attr=qkv_bias)
- self.v = nn.Linear(dim, dim, bias_attr=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
- def forward(self, x):
- B, N, C = x.shape
- qk = self.qk(x).reshape(
- (B, N, 2, self.num_heads, self.head_dim)).transpose(
- (2, 0, 3, 1, 4))
- q, k = qk[0], qk[1]
- v = self.v(x).reshape(
- (B, N, self.num_heads, x.shape[-1] // self.num_heads)).transpose(
- (0, 2, 1, 3))
- attn = paddle.matmul(q, k.transpose((0, 1, 3, 2))) * self.scale
- attn = nn.functional.softmax(attn, axis=-1)
- attn = self.attn_drop(attn)
- x = paddle.matmul(attn, v)
- x = x.transpose((0, 2, 1, 3)).reshape(
- (B, N, x.shape[-1] * x.shape[-3]))
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
- class Block(nn.Layer):
- def __init__(self,
- dim,
- in_dim,
- num_pixel,
- num_heads=12,
- in_num_head=4,
- mlp_ratio=4.,
- qkv_bias=False,
- drop=0.,
- attn_drop=0.,
- drop_path=0.,
- act_layer=nn.GELU,
- norm_layer=nn.LayerNorm):
- super().__init__()
- # Inner transformer
- self.norm_in = norm_layer(in_dim)
- self.attn_in = Attention(
- in_dim,
- in_dim,
- num_heads=in_num_head,
- qkv_bias=qkv_bias,
- attn_drop=attn_drop,
- proj_drop=drop)
- self.norm_mlp_in = norm_layer(in_dim)
- self.mlp_in = Mlp(in_features=in_dim,
- hidden_features=int(in_dim * 4),
- out_features=in_dim,
- act_layer=act_layer,
- drop=drop)
- self.norm1_proj = norm_layer(in_dim)
- self.proj = nn.Linear(in_dim * num_pixel, dim)
- # Outer transformer
- self.norm_out = norm_layer(dim)
- self.attn_out = Attention(
- dim,
- dim,
- num_heads=num_heads,
- qkv_bias=qkv_bias,
- attn_drop=attn_drop,
- proj_drop=drop)
- self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
- self.norm_mlp = norm_layer(dim)
- self.mlp = Mlp(in_features=dim,
- hidden_features=int(dim * mlp_ratio),
- out_features=dim,
- act_layer=act_layer,
- drop=drop)
- def forward(self, pixel_embed, patch_embed):
- # inner
- pixel_embed = paddle.add(
- pixel_embed,
- self.drop_path(self.attn_in(self.norm_in(pixel_embed))))
- pixel_embed = paddle.add(
- pixel_embed,
- self.drop_path(self.mlp_in(self.norm_mlp_in(pixel_embed))))
- # outer
- B, N, C = patch_embed.shape
- norm1_proj = self.norm1_proj(pixel_embed)
- norm1_proj = norm1_proj.reshape(
- (B, N - 1, norm1_proj.shape[1] * norm1_proj.shape[2]))
- patch_embed[:, 1:] = paddle.add(patch_embed[:, 1:],
- self.proj(norm1_proj))
- patch_embed = paddle.add(
- patch_embed,
- self.drop_path(self.attn_out(self.norm_out(patch_embed))))
- patch_embed = paddle.add(
- patch_embed, self.drop_path(self.mlp(self.norm_mlp(patch_embed))))
- return pixel_embed, patch_embed
- class PixelEmbed(nn.Layer):
- def __init__(self,
- img_size=224,
- patch_size=16,
- in_chans=3,
- in_dim=48,
- stride=4):
- super().__init__()
- num_patches = (img_size // patch_size)**2
- self.img_size = img_size
- self.num_patches = num_patches
- self.in_dim = in_dim
- new_patch_size = math.ceil(patch_size / stride)
- self.new_patch_size = new_patch_size
- self.proj = nn.Conv2D(
- in_chans, self.in_dim, kernel_size=7, padding=3, stride=stride)
- def forward(self, x, pixel_pos):
- B, C, H, W = x.shape
- 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})."
- x = self.proj(x)
- x = nn.functional.unfold(x, self.new_patch_size, self.new_patch_size)
- x = x.transpose((0, 2, 1)).reshape(
- (-1, self.in_dim, self.new_patch_size, self.new_patch_size))
- x = x + pixel_pos
- x = x.reshape((-1, self.in_dim, self.new_patch_size *
- self.new_patch_size)).transpose((0, 2, 1))
- return x
- class TNT(nn.Layer):
- def __init__(self,
- img_size=224,
- patch_size=16,
- in_chans=3,
- embed_dim=768,
- in_dim=48,
- depth=12,
- num_heads=12,
- in_num_head=4,
- mlp_ratio=4.,
- qkv_bias=False,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.,
- norm_layer=nn.LayerNorm,
- first_stride=4,
- class_num=1000):
- super().__init__()
- self.class_num = class_num
- # num_features for consistency with other models
- self.num_features = self.embed_dim = embed_dim
- self.pixel_embed = PixelEmbed(
- img_size=img_size,
- patch_size=patch_size,
- in_chans=in_chans,
- in_dim=in_dim,
- stride=first_stride)
- num_patches = self.pixel_embed.num_patches
- self.num_patches = num_patches
- new_patch_size = self.pixel_embed.new_patch_size
- num_pixel = new_patch_size**2
- self.norm1_proj = norm_layer(num_pixel * in_dim)
- self.proj = nn.Linear(num_pixel * in_dim, embed_dim)
- self.norm2_proj = norm_layer(embed_dim)
- self.cls_token = self.create_parameter(
- shape=(1, 1, embed_dim), default_initializer=zeros_)
- self.add_parameter("cls_token", self.cls_token)
- self.patch_pos = self.create_parameter(
- shape=(1, num_patches + 1, embed_dim), default_initializer=zeros_)
- self.add_parameter("patch_pos", self.patch_pos)
- self.pixel_pos = self.create_parameter(
- shape=(1, in_dim, new_patch_size, new_patch_size),
- default_initializer=zeros_)
- self.add_parameter("pixel_pos", self.pixel_pos)
- self.pos_drop = nn.Dropout(p=drop_rate)
- # stochastic depth decay rule
- dpr = np.linspace(0, drop_path_rate, depth)
- blocks = []
- for i in range(depth):
- blocks.append(
- Block(
- dim=embed_dim,
- in_dim=in_dim,
- num_pixel=num_pixel,
- num_heads=num_heads,
- in_num_head=in_num_head,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- drop=drop_rate,
- attn_drop=attn_drop_rate,
- drop_path=dpr[i],
- norm_layer=norm_layer))
- self.blocks = nn.LayerList(blocks)
- self.norm = norm_layer(embed_dim)
- if class_num > 0:
- self.head = nn.Linear(embed_dim, class_num)
- trunc_normal_(self.cls_token)
- trunc_normal_(self.patch_pos)
- trunc_normal_(self.pixel_pos)
- self.apply(self._init_weights)
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight)
- if isinstance(m, nn.Linear) and m.bias is not None:
- zeros_(m.bias)
- elif isinstance(m, nn.LayerNorm):
- zeros_(m.bias)
- ones_(m.weight)
- def forward_features(self, x):
- B = paddle.shape(x)[0]
- pixel_embed = self.pixel_embed(x, self.pixel_pos)
- patch_embed = self.norm2_proj(
- self.proj(
- self.norm1_proj(
- pixel_embed.reshape((-1, self.num_patches, pixel_embed.
- shape[-1] * pixel_embed.shape[-2])))))
- patch_embed = paddle.concat(
- (self.cls_token.expand((B, -1, -1)), patch_embed), axis=1)
- patch_embed = patch_embed + self.patch_pos
- patch_embed = self.pos_drop(patch_embed)
- for blk in self.blocks:
- pixel_embed, patch_embed = blk(pixel_embed, patch_embed)
- patch_embed = self.norm(patch_embed)
- return patch_embed[:, 0]
- def forward(self, x):
- x = self.forward_features(x)
- if self.class_num > 0:
- 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 TNT_small(pretrained=False, **kwargs):
- model = TNT(patch_size=16,
- embed_dim=384,
- in_dim=24,
- depth=12,
- num_heads=6,
- in_num_head=4,
- qkv_bias=False,
- **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["TNT_small"])
- return model
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