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| # All rights reserved. | |
| from math import ceil, sqrt | |
| from collections import OrderedDict | |
| import torch | |
| import torch.nn as nn | |
| from functools import partial | |
| from timm.models.vision_transformer import _cfg | |
| from timm.models.layers import trunc_normal_, DropPath, to_2tuple | |
| import os | |
| global_attn = None | |
| token_indices = None | |
| 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): | |
| 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 | |
| global global_attn | |
| 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 | |
| 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): | |
| 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 = x + self.drop_path(self.attn(self.norm1(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 | |
| 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 = self.attn(self.norm1(x)) | |
| 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 | |
| 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): | |
| x = x + self.pos_embed(x) | |
| B, C, T, H, W = x.shape | |
| x = x.flatten(2).transpose(1, 2) | |
| 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, C, T, H, W) | |
| return x | |
| 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): | |
| 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 = attn + self.drop_path(self.t_attn(self.t_norm(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 = residual + self.drop_path(self.attn(self.norm1(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 | |
| 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, depth=[3, 4, 8, 3], in_chans=3, num_classes=400, embed_dim=[64, 128, 320, 512], | |
| head_dim=64, mlp_ratio=[4., 4., 4., 4.], qkv_bias=True, qk_scale=None, representation_size=None, | |
| drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, | |
| prune_ratio=[[], [], [1, 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]] | |
| ): | |
| super().__init__() | |
| 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[0], 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[1], 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[3], 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[3], 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) | |
| 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) | |
| 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 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 | |
| global_attn = 0 | |
| token_indices = torch.arange(x.shape[2] * x.shape[3] * x.shape[4], dtype=torch.long, device=x.device).unsqueeze(0) | |
| token_indices = token_indices.expand(x.shape[0], -1) | |
| for blk in self.blocks3: | |
| cls_token, x = blk(cls_token, x) | |
| # upsample cls_token before stage4 | |
| cls_token = self.cls_upsample(cls_token) | |
| x = self.patch_embed4(x) | |
| # whether reset global attention? Now simple avgpool | |
| token_indices = torch.arange(x.shape[2] * x.shape[3] * x.shape[4], dtype=torch.long, device=x.device).unsqueeze(0) | |
| token_indices = token_indices.expand(x.shape[0], -1) | |
| for blk in self.blocks4: | |
| cls_token, x = blk(cls_token, x) | |
| 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): | |
| 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) | |
| return x | |
| def uniformer_xxs_video(**kwargs): | |
| model = Uniformer_light( | |
| depth=[2, 5, 8, 2], | |
| prune_ratio=[[], [], [1, 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]], | |
| embed_dim=[56, 112, 224, 448], head_dim=28, mlp_ratio=[3, 3, 3, 3], qkv_bias=True, | |
| **kwargs) | |
| model.default_cfg = _cfg() | |
| return model | |
| def uniformer_xs_video(**kwargs): | |
| model = Uniformer_light( | |
| depth=[3, 5, 9, 3], | |
| 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]], | |
| embed_dim=[64, 128, 256, 512], head_dim=32, mlp_ratio=[3, 3, 3, 3], qkv_bias=True, | |
| **kwargs) | |
| model.default_cfg = _cfg() | |
| return model | |
| if __name__ == '__main__': | |
| import time | |
| from fvcore.nn import FlopCountAnalysis | |
| from fvcore.nn import flop_count_table | |
| import numpy as np | |
| seed = 4217 | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| num_frames = 16 | |
| model = uniformer_xxs_video() | |
| # print(model) | |
| flops = FlopCountAnalysis(model, torch.rand(1, 3, num_frames, 160, 160)) | |
| s = time.time() | |
| print(flop_count_table(flops, max_depth=1)) | |
| print(time.time()-s) |