Add definition class

uniformer_finetune/uniformer_finetune_config.py

@@ -0,0 +1,15 @@
+from transformers import PretrainedConfig
+
+class UniformerXXSFinetuneConfig(PretrainedConfig):
+    model_type = "uniformer_finetuned"
+
+    def __init__(
+        self,
+        pretrained: str = 'uniformer_xxs_400',
+        out_class: int = 20,
+        **kwargs
+    ):
+        self.pretrained = pretrained
+        self.out_class = out_class
+        super().__init__(**kwargs)
+

uniformer_finetune/uniformer_finetune_model.py

@@ -0,0 +1,14 @@
+from transformers import PreTrainedModel
+from uniformer_finetune_config import UniformerXXSFinetuneConfig
+from uniformer_xs import UniformerXXSFinetune
+
+class UniformerXXSFinetuneModel(PreTrainedModel):
+    config_class = UniformerXXSFinetuneConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = UniformerXXSFinetune(
+            out_class=config.out_class
+        )
+    def forward(self, tensor):
+        return self.model.forward(tensor)

uniformer_finetune/uniformer_xs.py

@@ -0,0 +1,625 @@
+# All rights reserved.
+from math import ceil, sqrt
+from collections import OrderedDict
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from functools import partial
+from timm.models.vision_transformer import _cfg
+from timm.models.layers import trunc_normal_, DropPath, to_2tuple
+# from .build import MODEL_REGISTRY
+import os
+
+import slowfast.utils.logging as logging
+
+logger = logging.get_logger(__name__)
+
+model_path = 'path_to_models'
+model_path = {
+    'uniformer_xxs_128_in1k': os.path.join(model_path, 'uniformer_xxs_128_in1k.pth'),
+    'uniformer_xxs_160_in1k': os.path.join(model_path, 'uniformer_xxs_160_in1k.pth'),
+    'uniformer_xxs_192_in1k': os.path.join(model_path, 'uniformer_xxs_192_in1k.pth'),
+    'uniformer_xxs_224_in1k': os.path.join(model_path, 'uniformer_xxs_224_in1k.pth'),
+    'uniformer_xs_192_in1k': os.path.join(model_path, 'uniformer_xs_192_in1k.pth'),
+    'uniformer_xs_224_in1k': os.path.join(model_path, 'uniformer_xs_224_in1k.pth'),
+}
+
+
+def conv_3xnxn(inp, oup, kernel_size=3, stride=3, groups=1):
+    return nn.Conv3d(inp, oup, (3, kernel_size, kernel_size), (2, stride, stride), (1, 0, 0), groups=groups)
+    
+def conv_1xnxn(inp, oup, kernel_size=3, stride=3, groups=1):
+    return nn.Conv3d(inp, oup, (1, kernel_size, kernel_size), (1, stride, stride), (0, 0, 0), groups=groups)
+
+def conv_3xnxn_std(inp, oup, kernel_size=3, stride=3, groups=1):
+    return nn.Conv3d(inp, oup, (3, kernel_size, kernel_size), (1, stride, stride), (1, 0, 0), groups=groups)
+
+def conv_1x1x1(inp, oup, groups=1):
+    return nn.Conv3d(inp, oup, (1, 1, 1), (1, 1, 1), (0, 0, 0), groups=groups)
+
+def conv_3x3x3(inp, oup, groups=1):
+    return nn.Conv3d(inp, oup, (3, 3, 3), (1, 1, 1), (1, 1, 1), groups=groups)
+
+def conv_5x5x5(inp, oup, groups=1):
+    return nn.Conv3d(inp, oup, (5, 5, 5), (1, 1, 1), (2, 2, 2), groups=groups)
+
+def bn_3d(dim):
+    return nn.BatchNorm3d(dim)
+
+
+# code is from https://github.com/YifanXu74/Evo-ViT
+def easy_gather(x, indices):
+    # x => B x N x C
+    # indices => B x N
+    B, N, C = x.shape
+    N_new = indices.shape[1]
+    offset = torch.arange(B, dtype=torch.long, device=x.device).view(B, 1) * N
+    indices = indices + offset
+    # only select the informative tokens
+    out = x.reshape(B * N, C)[indices.view(-1)].reshape(B, N_new, C)
+    return out
+
+
+# code is from https://github.com/YifanXu74/Evo-ViT
+def merge_tokens(x_drop, score):
+    # x_drop => B x N_drop
+    # score => B x N_drop
+    weight = score / torch.sum(score, dim=1, keepdim=True)
+    x_drop = weight.unsqueeze(-1) * x_drop
+    return torch.sum(x_drop, dim=1, keepdim=True)
+
+
+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 Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., trade_off=1):
+        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)
+        # updating weight for global score
+        self.trade_off = trade_off
+
+    def forward(self, x, global_attn):
+        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)
+
+        # update global score
+        tradeoff = self.trade_off
+        if isinstance(global_attn, int):
+            global_attn = torch.mean(attn[:, :, 0, 1:], dim=1)
+        elif global_attn.shape[1] == N - 1:
+            # no additional token and no pruning, update all global scores
+            cls_attn = torch.mean(attn[:, :, 0, 1:], dim=1)
+            global_attn = (1 - tradeoff) * global_attn + tradeoff * cls_attn
+        else:
+            # only update the informative tokens
+            # the first one is class token
+            # the last one is rrepresentative token
+            cls_attn = torch.mean(attn[:, :, 0, 1:-1], dim=1)
+            if self.training:
+                temp_attn = (1 - tradeoff) * global_attn[:, :(N - 2)] + tradeoff * cls_attn
+                global_attn = torch.cat((temp_attn, global_attn[:, (N - 2):]), dim=1)
+            else:
+                # no use torch.cat() for fast inference
+                global_attn[:, :(N - 2)] = (1 - tradeoff) * global_attn[:, :(N - 2)] + tradeoff * cls_attn
+
+        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, global_attn
+
+
+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 = conv_1x1x1(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = conv_1x1x1(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 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 = conv_3x3x3(dim, dim, groups=dim)
+        self.norm1 = bn_3d(dim)
+        self.conv1 = conv_1x1x1(dim, dim, 1)
+        self.conv2 = conv_1x1x1(dim, dim, 1)
+        self.attn = conv_5x5x5(dim, dim, 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 = bn_3d(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 EvoSABlock(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, prune_ratio=1,
+                 trade_off=0, downsample=False):
+        super().__init__()
+        self.pos_embed = conv_3x3x3(dim, dim, 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, trade_off=trade_off)
+        # 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)
+        self.prune_ratio = prune_ratio
+        self.downsample = downsample
+        # if downsample:
+        self.avgpool = nn.AvgPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
+
+    def forward(self, cls_token, x, global_attn, token_indices):
+        x = x + self.pos_embed(x)
+        B, C, T, H, W = x.shape
+        x = x.flatten(2).transpose(1, 2)  
+
+        if self.prune_ratio == 1:
+            x = torch.cat([cls_token, x], dim=1)
+            x , global_attn = self.attn(self.norm1(x), global_attn)
+            x = x + self.drop_path(x)
+            x = x + self.drop_path(self.mlp(self.norm2(x)))
+            cls_token, x = x[:, :1], x[:, 1:]
+            x = x.transpose(1, 2).reshape(B, C, T, H, W)
+            return cls_token, x, global_attn, token_indices
+        else:
+            # global global_attn, token_indices
+            # calculate the number of informative tokens
+            N = x.shape[1]
+            N_ = int(N * self.prune_ratio)
+            # sort global attention
+            indices = torch.argsort(global_attn, dim=1, descending=True)
+
+            # concatenate x, global attention and token indices => x_ga_ti
+            # rearrange the tensor according to new indices 
+            x_ga_ti = torch.cat((x, global_attn.unsqueeze(-1), token_indices.unsqueeze(-1)), dim=-1)
+            x_ga_ti = easy_gather(x_ga_ti, indices)
+            x_sorted, global_attn, token_indices = x_ga_ti[:, :, :-2], x_ga_ti[:, :, -2], x_ga_ti[:, :, -1]
+
+            # informative tokens
+            x_info = x_sorted[:, :N_]
+            # merge dropped tokens
+            x_drop = x_sorted[:, N_:]
+            score = global_attn[:, N_:]
+            #  B x N_drop x C => B x 1 x C
+            rep_token = merge_tokens(x_drop, score)
+            # concatenate new tokens
+            x = torch.cat((cls_token, x_info, rep_token), dim=1)
+
+            # slow update
+            fast_update = 0
+            tmp_x, global_attn = self.attn(self.norm1(x), global_attn)
+            fast_update = fast_update + tmp_x[:, -1:]
+            x = x + self.drop_path(tmp_x)
+            tmp_x = self.mlp(self.norm2(x))
+            fast_update = fast_update + tmp_x[:, -1:]
+            x = x + self.drop_path(tmp_x)
+            # fast update
+            x_drop = x_drop + fast_update.expand(-1, N - N_, -1)
+
+            cls_token, x = x[:, :1, :], x[:, 1:-1, :]
+            if self.training:
+                x_sorted = torch.cat((x, x_drop), dim=1)
+            else:
+                x_sorted[:, N_:] = x_drop
+                x_sorted[:, :N_] = x
+
+            # recover token
+            # scale for normalization
+            old_global_scale = torch.sum(global_attn, dim=1, keepdim=True)
+            # recover order
+            indices = torch.argsort(token_indices, dim=1)
+            x_ga_ti = torch.cat((x_sorted, global_attn.unsqueeze(-1), token_indices.unsqueeze(-1)), dim=-1)
+            x_ga_ti = easy_gather(x_ga_ti, indices)
+            x_patch, global_attn, token_indices = x_ga_ti[:, :, :-2], x_ga_ti[:, :, -2], x_ga_ti[:, :, -1]
+            x_patch = x_patch.transpose(1, 2).reshape(B, C, T, H, W)
+
+            if self.downsample:
+                # downsample global attention
+                global_attn = global_attn.reshape(B, 1, T, H, W)
+                global_attn = self.avgpool(global_attn).view(B, -1)
+                # normalize global attention
+                new_global_scale = torch.sum(global_attn, dim=1, keepdim=True)
+                scale = old_global_scale / new_global_scale
+                global_attn = global_attn * scale
+            
+            return cls_token, x_patch, global_attn, token_indices
+
+
+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 = conv_3x3x3(dim, dim, 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)
+
+    def forward(self, x, global_attn):
+        x = x + self.pos_embed(x)
+        B, C, T, H, W = x.shape
+        x = x.flatten(2).transpose(1, 2)
+        x , global_attn = self.attn(self.norm1(x),global_attn)
+        x = x + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        x = x.transpose(1, 2).reshape(B, C, T, H, W)
+        return x, global_attn
+
+
+class SplitSABlock(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 = conv_3x3x3(dim, dim, groups=dim)
+        self.t_norm = norm_layer(dim)
+        self.t_attn = Attention(
+            dim,
+            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
+            attn_drop=attn_drop, proj_drop=drop)
+        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)
+
+    def forward(self, x, global_attn):
+        x = x + self.pos_embed(x)
+        B, C, T, H, W = x.shape
+        attn = x.view(B, C, T, H * W).permute(0, 3, 2, 1).contiguous()
+        attn = attn.view(B * H * W, T, C)
+        attn, global_attn = self.t_attn(self.t_norm(attn),global_attn)
+        attn = attn + self.drop_path(attn)
+        attn = attn.view(B, H * W, T, C).permute(0, 2, 1, 3).contiguous()
+        attn = attn.view(B * T, H * W, C)
+        residual = x.view(B, C, T, H * W).permute(0, 2, 3, 1).contiguous()
+        residual = residual.view(B * T, H * W, C)
+        attn, global_attn = self.attn(self.norm1(attn), global_attn)
+        attn = residual + self.drop_path(attn)
+        attn = attn.view(B, T * H * W, C)
+        out = attn + self.drop_path(self.mlp(self.norm2(attn)))
+        out = out.transpose(1, 2).reshape(B, C, T, H, W)
+        return out, global_attn
+
+
+class SpeicalPatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+    def __init__(self, patch_size=16, in_chans=3, embed_dim=768):
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        self.patch_size = patch_size
+        
+        self.proj = nn.Sequential(
+            nn.Conv3d(in_chans, embed_dim // 2, kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=(1, 1, 1)),
+            nn.BatchNorm3d(embed_dim // 2),
+            nn.GELU(),
+            nn.Conv3d(embed_dim // 2, embed_dim, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1)),
+            nn.BatchNorm3d(embed_dim),
+        )
+
+    def forward(self, x):
+        B, C, T, 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, T, H, W = x.shape
+        x = x.flatten(2).transpose(1, 2)
+        x = x.reshape(B, T, H, W, -1).permute(0, 4, 1, 2, 3).contiguous()
+        return x
+    
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+    def __init__(self, patch_size=16, in_chans=3, embed_dim=768):
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        self.patch_size = patch_size
+        self.norm = nn.LayerNorm(embed_dim)
+        self.proj = conv_1xnxn(in_chans, embed_dim, kernel_size=patch_size[0], stride=patch_size[0])
+
+    def forward(self, x):
+        B, C, T, 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, T, H, W = x.shape
+        x = x.flatten(2).transpose(1, 2)
+        x = self.norm(x)
+        x = x.reshape(B, T, H, W, -1).permute(0, 4, 1, 2, 3).contiguous()
+        return x
+
+class Uniformer_light(nn.Module):
+    """ Vision Transformer
+    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
+        https://arxiv.org/abs/2010.11929
+    """
+
+    # def __init__(self, cfg):
+    #     super().__init__()
+
+    def __init__(self, depth=[3, 5, 9, 3], num_classes=400, img_size=224, in_chans=3, embed_dim=[64, 128, 256, 512],
+                 head_dim=32, mlp_ratio=3., qkv_bias=True, qk_scale=None, representation_size=None,
+                 prune_ratio=[[], [], [1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5]],
+                 trade_off=[[], [], [1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5]],
+                 drop_rate=0.3, attn_drop_rate=0., drop_path_rate=0., norm_layer=None, split=False, std=False):
+        super().__init__()
+
+        # depth = cfg.UNIFORMER.DEPTH
+        # num_classes = cfg.MODEL.NUM_CLASSES 
+        # in_chans = cfg.DATA.INPUT_CHANNEL_NUM[0]
+        # embed_dim = cfg.UNIFORMER.EMBED_DIM
+        # head_dim = cfg.UNIFORMER.HEAD_DIM
+        # mlp_ratio = cfg.UNIFORMER.MLP_RATIO
+        # qkv_bias = cfg.UNIFORMER.QKV_BIAS
+        # qk_scale = cfg.UNIFORMER.QKV_SCALE
+        # representation_size = cfg.UNIFORMER.REPRESENTATION_SIZE
+        # drop_rate = cfg.UNIFORMER.DROPOUT_RATE
+        # attn_drop_rate = cfg.UNIFORMER.ATTENTION_DROPOUT_RATE
+        # drop_path_rate = cfg.UNIFORMER.DROP_DEPTH_RATE
+        # prune_ratio = cfg.UNIFORMER.PRUNE_RATIO
+        # trade_off = cfg.UNIFORMER.TRADE_OFF
+
+        self.num_classes = num_classes
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        norm_layer = partial(nn.LayerNorm, eps=1e-6) 
+        
+        self.patch_embed1 = SpeicalPatchEmbed(
+            patch_size=4, in_chans=in_chans, embed_dim=embed_dim[0])
+        self.patch_embed2 = PatchEmbed(
+            patch_size=2, in_chans=embed_dim[0], embed_dim=embed_dim[1])
+        self.patch_embed3 = PatchEmbed(
+            patch_size=2, in_chans=embed_dim[1], embed_dim=embed_dim[2])
+        self.patch_embed4 = PatchEmbed(
+            patch_size=2, in_chans=embed_dim[2], embed_dim=embed_dim[3])
+
+        # class token
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim[2]))
+        self.cls_upsample = nn.Linear(embed_dim[2], 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([
+            EvoSABlock(
+                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,
+                prune_ratio=prune_ratio[2][i], trade_off=trade_off[2][i],
+                downsample=True if i == depth[2] - 1 else False)
+            for i in range(depth[2])])
+        self.blocks4 = nn.ModuleList([
+            EvoSABlock(
+                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,
+                prune_ratio=prune_ratio[3][i], trade_off=trade_off[3][i])
+        for i in range(depth[3])])
+        self.norm = bn_3d(embed_dim[-1])
+        self.norm_cls = nn.LayerNorm(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.head_cls = nn.Linear(embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity()
+        
+        self.apply(self._init_weights)
+
+        self.global_attn = None
+        self.token_indices = None
+
+        for name, p in self.named_parameters():
+            # fill proj weight with 1 here to improve training dynamics. Otherwise temporal attention inputs
+            # are multiplied by 0*0, which is hard for the model to move out of.
+            if 't_attn.qkv.weight' in name:
+                nn.init.constant_(p, 0)
+            if 't_attn.qkv.bias' in name:
+                nn.init.constant_(p, 0)
+            if 't_attn.proj.weight' in name:
+                nn.init.constant_(p, 1)
+            if 't_attn.proj.bias' in name:
+                nn.init.constant_(p, 0)
+
+    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 inflate_weight(self, weight_2d, time_dim, center=False):
+        if center:
+            weight_3d = torch.zeros(*weight_2d.shape)
+            weight_3d = weight_3d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
+            middle_idx = time_dim // 2
+            weight_3d[:, :, middle_idx, :, :] = weight_2d
+        else:
+            weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
+            weight_3d = weight_3d / time_dim
+        return weight_3d
+
+    def get_pretrained_model(self, cfg):
+        if cfg.UNIFORMER.PRETRAIN_NAME:
+            checkpoint = torch.load(model_path[cfg.UNIFORMER.PRETRAIN_NAME], map_location='cpu')
+
+            state_dict_3d = self.state_dict()
+            for k in checkpoint.keys():
+                if checkpoint[k].shape != state_dict_3d[k].shape:
+                    if len(state_dict_3d[k].shape) <= 2:
+                        logger.info(f'Ignore: {k}')
+                        continue
+                    logger.info(f'Inflate: {k}, {checkpoint[k].shape} => {state_dict_3d[k].shape}')
+                    time_dim = state_dict_3d[k].shape[2]
+                    checkpoint[k] = self.inflate_weight(checkpoint[k], time_dim)
+
+            if self.num_classes != checkpoint['head.weight'].shape[0]:
+                del checkpoint['head.weight'] 
+                del checkpoint['head.bias'] 
+                del checkpoint['head_cls.weight'] 
+                del checkpoint['head_cls.bias'] 
+            return checkpoint
+        else:
+            return None
+
+    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)
+        # add cls_token in stage3
+        cls_token = self.cls_token.expand(x.shape[0], -1, -1)
+        # global global_attn, token_indices
+        self.global_attn = 0
+        self.token_indices = torch.arange(x.shape[2] * x.shape[3] * x.shape[4], dtype=torch.long, device=x.device).unsqueeze(0)
+        self.token_indices = self.token_indices.expand(x.shape[0], -1)
+        for blk in self.blocks3:
+            cls_token, x, self.global_attn, self.token_indices = blk(cls_token, x, self.global_attn, self.token_indices)
+        # upsample cls_token before stage4
+        cls_token = self.cls_upsample(cls_token)
+        x = self.patch_embed4(x)
+        # whether reset global attention? Now simple avgpool
+        self.token_indices = torch.arange(x.shape[2] * x.shape[3] * x.shape[4], dtype=torch.long, device=x.device).unsqueeze(0)
+        self.token_indices = self.token_indices.expand(x.shape[0], -1)
+        for blk in self.blocks4:
+            cls_token, x, self.global_attn, self.token_indices = blk(cls_token, x, self.global_attn, self.token_indices)
+        if self.training:
+            # layer normalization for cls_token
+            cls_token = self.norm_cls(cls_token)
+        x = self.norm(x)
+        x = self.pre_logits(x)
+        return cls_token, x
+
+    def forward(self, x):
+        # x = x[0]
+        cls_token, x = self.forward_features(x)
+        x = x.flatten(2).mean(-1)
+        # if self.training:
+        #     x = self.head(x), self.head_cls(cls_token.squeeze(1))
+        # else:
+        #     x = self.head(x)
+        x = self.head(x), self.head_cls(cls_token.squeeze(1))
+        return x
+
+def uniformer_xs():
+    return Uniformer_light(
+        depth=[3, 5, 9, 3], embed_dim=[64, 128, 256, 512],
+        head_dim=32, drop_rate=0.1
+    )
+
+def uniformer_xxs():
+    return Uniformer_light(
+        depth=[2, 5, 8, 2], embed_dim=[56, 112, 224, 448],
+        head_dim=28, drop_rate=0.05
+    )
+
+class UniformerXXSFinetune(torch.nn.Module):
+
+    def __init__(self, out_class=20):
+        super(UniformerXXSFinetune, self).__init__()
+        self.pretrained = uniformer_xxs()
+        self.fc = torch.nn.Linear(400,out_class)
+
+    def forward(self, x):
+        x = self.pretrained(x)[0]
+        x = self.fc(x)
+        return F.softmax(x,dim=-1)