# copyright (c) 2022 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/Sense-X/UniFormer # reference: https://arxiv.org/abs/2201.09450 from collections import OrderedDict from functools import partial import paddle import paddle.nn as nn import paddle.nn.functional as F import math from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity, Mlp from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url MODEL_URLS = { "UniFormer_small": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_pretrained.pdparams", "UniFormer_small_plus": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_plus_pretrained.pdparams", "UniFormer_small_plus_dim64": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_plus_dim64_pretrained.pdparams", "UniFormer_base": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_base_pretrained.pdparams", "UniFormer_base_ls": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_base_ls_pretrained.pdparams", } __all__ = list(MODEL_URLS.keys()) layer_scale = False init_value = 1e-6 class CMlp(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_conv = nn.Conv2D(in_features, hidden_features, 1) self.act = act_layer() self.fc2_conv = nn.Conv2D(hidden_features, out_features, 1) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1_conv(x) x = self.act(x) x = self.drop(x) x = self.fc2_conv(x) x = self.drop(x) return x class Attention(nn.Layer): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim**-0.5 self.qkv = nn.Linear(dim, dim * 3, 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 qkv = self.qkv(x).reshape( shape=[B, N, 3, self.num_heads, C // self.num_heads]).transpose( perm=[2, 0, 3, 1, 4]) q, k, v = qkv[0], qkv[1], qkv[2] attn = (q @k.transpose(perm=[0, 1, 3, 2])) * self.scale attn = nn.Softmax(axis=-1)(attn) attn = self.attn_drop(attn) x = (attn @v).transpose(perm=[0, 2, 1, 3]).reshape(shape=[B, N, C]) x = self.proj(x) x = self.proj_drop(x) return x class CBlock(nn.Layer): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.pos_embed = nn.Conv2D(dim, dim, 3, padding=1, groups=dim) self.norm1 = nn.BatchNorm2D(dim) self.conv1 = nn.Conv2D(dim, dim, 1) self.conv2 = nn.Conv2D(dim, dim, 1) self.attn = nn.Conv2D(dim, dim, 5, padding=2, groups=dim) self.drop_path = DropPath( drop_path) if drop_path > 0. else nn.Identity() self.norm2 = nn.BatchNorm2D(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = CMlp( in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) def forward(self, x): x = x + self.pos_embed(x) x = x + self.drop_path( self.conv2(self.attn(self.conv1(self.norm1(x))))) x = x + self.drop_path(self.mlp(self.norm2(x))) return x class SABlock(nn.Layer): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.pos_embed = nn.Conv2D(dim, dim, 3, padding=1, groups=dim) self.norm1 = norm_layer(dim) self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) self.drop_path = DropPath( drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) global layer_scale self.ls = layer_scale if self.ls: global init_value print(f"Use layer_scale: {layer_scale}, init_values: {init_value}") self.gamma_1 = self.create_parameter( [dim], dtype='float32', default_initializer=nn.initializer.Constant(value=init_value)) self.gamma_2 = self.create_parameter( [dim], dtype='float32', default_initializer=nn.initializer.Constant(value=init_value)) def forward(self, x): x = x + self.pos_embed(x) B, N, H, W = x.shape x = x.flatten(2).transpose(perm=[0, 2, 1]) if self.ls: x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x))) x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) else: x = x + self.drop_path(self.attn(self.norm1(x))) x = x + self.drop_path(self.mlp(self.norm2(x))) x = x.transpose(perm=[0, 2, 1]).reshape(shape=[B, N, H, W]) return x class HeadEmbedding(nn.Layer): def __init__(self, in_channels, out_channels): super().__init__() self.proj = nn.Sequential( nn.Conv2D( in_channels, out_channels // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)), nn.BatchNorm2D(out_channels // 2), nn.GELU(), nn.Conv2D( out_channels // 2, out_channels, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)), nn.BatchNorm2D(out_channels)) def forward(self, x): x = self.proj(x) return x class MiddleEmbedding(nn.Layer): def __init__(self, in_channels, out_channels): super().__init__() self.proj = nn.Sequential( nn.Conv2D( in_channels, out_channels, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)), nn.BatchNorm2D(out_channels)) def forward(self, x): x = self.proj(x) return x class PatchEmbed(nn.Layer): """ Image to Patch Embedding """ def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) self.img_size = img_size self.patch_size = patch_size self.num_patches = num_patches self.norm = nn.LayerNorm(embed_dim) self.proj_conv = nn.Conv2D( in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, x): B, C, H, W = x.shape assert H == self.img_size[0] and W == self.img_size[1], \ f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." x = self.proj_conv(x) B, C, H, W = x.shape x = x.flatten(2).transpose(perm=[0, 2, 1]) x = self.norm(x) x = x.reshape(shape=[B, H, W, C]).transpose(perm=[0, 3, 1, 2]) return x class UniFormer(nn.Layer): """ UniFormer A PaddlePaddle impl of : `UniFormer: Unifying Convolution and Self-attention for Visual Recognition` - https://arxiv.org/abs/2201.09450 """ def __init__(self, depth=[3, 4, 8, 3], img_size=224, in_chans=3, class_num=1000, embed_dim=[64, 128, 320, 512], head_dim=64, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, conv_stem=False): """ Args: depth (list): depth of each stage img_size (int, tuple): input image size in_chans (int): number of input channels class_num (int): number of classes for classification head embed_dim (list): embedding dimension of each stage head_dim (int): head dimension mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True qk_scale (float): override default qk scale of head_dim ** -0.5 if set representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate norm_layer (nn.Module): normalization layer conv_stem (bool): whether use overlapped patch stem """ super().__init__() self.class_num = class_num self.num_features = self.embed_dim = embed_dim norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) if conv_stem: self.patch_embed1 = HeadEmbedding( in_channels=in_chans, out_channels=embed_dim[0]) self.patch_embed2 = MiddleEmbedding( in_channels=embed_dim[0], out_channels=embed_dim[1]) self.patch_embed3 = MiddleEmbedding( in_channels=embed_dim[1], out_channels=embed_dim[2]) self.patch_embed4 = MiddleEmbedding( in_channels=embed_dim[2], out_channels=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) dpr = [ x.item() for x in paddle.linspace(0, drop_path_rate, sum(depth)) ] # stochastic depth decay rule num_heads = [dim // head_dim for dim in embed_dim] self.blocks1 = nn.LayerList([ CBlock( 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) for i in range(depth[0]) ]) self.blocks2 = nn.LayerList([ CBlock( 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 + depth[0]], norm_layer=norm_layer) for i in range(depth[1]) ]) self.blocks3 = nn.LayerList([ SABlock( 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 + depth[0] + depth[1]], norm_layer=norm_layer) for i in range(depth[2]) ]) self.blocks4 = nn.LayerList([ SABlock( 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 + depth[0] + depth[1] + depth[2]], norm_layer=norm_layer) 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 = nn.Identity() # Classifier head self.head = nn.Linear(embed_dim[-1], class_num) if class_num > 0 else nn.Identity() 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): 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) 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) 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 UniFormer_small(pretrained=True, use_ssld=False, **kwargs): model = UniFormer( depth=[3, 4, 8, 3], 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"], 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