# 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. import math import numpy as np import paddle import paddle.nn as nn from paddle.nn.initializer import TruncatedNormal, Constant from ppcls.arch.backbone.base.theseus_layer import Identity from 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