diff --git a/tests/test_models.py b/tests/test_models.py index b1b2bf195a..2c4f4a2927 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -41,7 +41,7 @@ 'vit_*', 'tnt_*', 'pit_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*', 'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit*', 'poolformer_*', 'volo_*', 'sequencer2d_*', 'pvt_v2*', 'mvitv2*', 'gcvit*', 'efficientformer*', - 'eva_*', 'flexivit*', 'eva02*', 'samvit_*', 'efficientvit_m*', 'tiny_vit_*' + 'eva_*', 'flexivit*', 'eva02*', 'samvit_*', 'efficientvit_m*', 'tiny_vit_*','uniformer*' ] NUM_NON_STD = len(NON_STD_FILTERS) diff --git a/timm/models/__init__.py b/timm/models/__init__.py index 0eb9561d54..10cfe7dab8 100644 --- a/timm/models/__init__.py +++ b/timm/models/__init__.py @@ -62,6 +62,7 @@ from .tnt import * from .tresnet import * from .twins import * +from .uniformer import * from .vgg import * from .visformer import * from .vision_transformer import * diff --git a/timm/models/uniformer.py b/timm/models/uniformer.py new file mode 100644 index 0000000000..d34b947076 --- /dev/null +++ b/timm/models/uniformer.py @@ -0,0 +1,420 @@ +# All rights reserved. +import logging +from collections import OrderedDict +from functools import partial + +import torch +import torch.nn as nn + +from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD + +from timm.models.layers import trunc_normal_, DropPath, to_2tuple +from .vision_transformer import checkpoint_filter_fn +from ._builder import build_model_with_cfg + +from ._registry import generate_default_cfgs, register_model + +layer_scale = False +init_value = 1e-6 +_logger = logging.getLogger(__name__) + + +class Mlp(nn.Module): + 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 CMlp(nn.Module): + 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.Conv2d(in_features, hidden_features, 1) + self.act = act_layer() + self.fc2 = nn.Conv2d(hidden_features, out_features, 1) + 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.Module): + 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 + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=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(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class CBlock(nn.Module): + 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) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + 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.Module): + 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) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + 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 = nn.Parameter(init_value * torch.ones((dim)), requires_grad=True) + self.gamma_2 = nn.Parameter(init_value * torch.ones((dim)), requires_grad=True) + + def forward(self, x): + x = x + self.pos_embed(x) + B, N, H, W = x.shape + x = x.flatten(2).transpose(1, 2) + 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(1, 2).reshape(B, N, H, W) + return x + + +class head_embedding(nn.Module): + def __init__(self, in_channels, out_channels): + super(head_embedding, self).__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 middle_embedding(nn.Module): + def __init__(self, in_channels, out_channels): + super(middle_embedding, self).__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.Module): + """ 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 = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + 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(x) + B, C, H, W = x.shape + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() + return x + + +class UniFormer(nn.Module): + """ Vision Transformer + A PyTorch impl of : `UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning` - + https://arxiv.org/abs/2201.04676 + """ + + def __init__(self, depth=[3, 4, 8, 3], img_size=224, in_chans=3, num_classes=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 + num_classes (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.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) + print(img_size) + if isinstance(img_size, tuple): + img_size = img_size[0] + if conv_stem: + self.patch_embed1 = head_embedding(in_channels=in_chans, out_channels=embed_dim[0]) + self.patch_embed2 = middle_embedding(in_channels=embed_dim[0], out_channels=embed_dim[1]) + self.patch_embed3 = middle_embedding(in_channels=embed_dim[1], out_channels=embed_dim[2]) + self.patch_embed4 = middle_embedding(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 torch.linspace(0, drop_path_rate, sum(depth))] # stochastic depth decay rule + num_heads = [dim // head_dim for dim in embed_dim] + self.blocks1 = nn.ModuleList([ + 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.ModuleList([ + 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.ModuleList([ + 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.ModuleList([ + 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], num_classes) if num_classes > 0 else nn.Identity() + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + 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 _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, + 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, + 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, + 'first_conv': 'patch_embed.proj', 'classifier': 'head', + **kwargs + } + + +default_cfgs = generate_default_cfgs({ + 'uniformer_small': _cfg( + url='https://drive.google.com/file/d/1-uepH3Q3BhTmWU6HK-sGAGQC_MpfIiPD/view?usp=sharing'), + 'uniformer_small_plus': _cfg( + url='https://drive.google.com/file/d/10IN5ULcjz0Ld_lDokkTGOSmRFXLzUkEs/view'), + 'uniformer_base': _cfg( + url='https://drive.google.com/file/d/1-wT39QazTGELxgrQIu6J12D3qcla3hui/view' + ), + 'uniformer_base_ls': _cfg( + url='https://drive.google.com/file/d/1-wT39QazTGELxgrQIu6J12D3qcla3hui/view' + ) +}) + + +def _create_uniformer(variant, pretrained=False, **kwargs): + default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1)))) + out_indices = kwargs.pop('out_indices', default_out_indices) + + model = build_model_with_cfg( + UniFormer, variant, pretrained, + pretrained_filter_fn=checkpoint_filter_fn, + feature_cfg=dict(flatten_sequential=True, out_indices=out_indices), + **kwargs) + + return model + + +@register_model +def uniformer_small(pretrained=True, **kwargs): + model_args = dict(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, eps=1e-6)) + + return _create_uniformer('uniformer_small', pretrained=pretrained, **dict(model_args, **kwargs)) + + +@register_model +def uniformer_small_plus(pretrained=True, **kwargs): + model_args = dict(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, eps=1e-6)) + + return _create_uniformer('uniformer_small_plus', pretrained=pretrained, **dict(model_args, **kwargs)) + + +@register_model +def uniformer_small_plus_dim64(pretrained=True, **kwargs): + model_args = dict(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, eps=1e-6)) + return _create_uniformer('uniformer_small_plus_dim64', pretrained=pretrained, **dict(model_args, **kwargs)) + + +@register_model +def uniformer_base(pretrained=True, **kwargs): + model_args = dict(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, eps=1e-6)) + return _create_uniformer('uniformer_base', pretrained=pretrained, **dict(model_args, **kwargs)) + + +@register_model +def uniformer_base_ls(pretrained=True, **kwargs): + global layer_scale + layer_scale = True + model_args = dict(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, eps=1e-6)) + return _create_uniformer('uniformer_base_ls', pretrained=pretrained, **dict(model_args, **kwargs))