uniformer_video / uniformer.py

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	from collections import OrderedDict
	import torch
	import torch.nn as nn
	from functools import partial
	from timm.models.layers import trunc_normal_, DropPath, to_2tuple


	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)


	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.):
	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 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 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, 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_3xnxn(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 PatchEmbed(nn.Module):
	""" Image to Patch Embedding
	"""
	def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, std=False):
	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)
	if std:
	self.proj = conv_3xnxn_std(in_chans, embed_dim, kernel_size=patch_size[0], stride=patch_size[0])
	else:
	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(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=[5, 8, 20, 7], num_classes=400, img_size=224, in_chans=3, embed_dim=[64, 128, 320, 512],
	head_dim=64, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
	drop_rate=0.3, attn_drop_rate=0., drop_path_rate=0., norm_layer=None, split=False, std=False):
	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(
	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], std=std)
	self.patch_embed3 = PatchEmbed(
	img_size=img_size // 8, patch_size=2, in_chans=embed_dim[1], embed_dim=embed_dim[2], std=std)
	self.patch_embed4 = PatchEmbed(
	img_size=img_size // 16, patch_size=2, in_chans=embed_dim[2], embed_dim=embed_dim[3], std=std)

	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])])
	if split:
	self.blocks3 = nn.ModuleList([
	SplitSABlock(
	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([
	SplitSABlock(
	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])])
	else:
	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 = bn_3d(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)

	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 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 uniformer_small():
	return Uniformer(
	depth=[3, 4, 8, 3], embed_dim=[64, 128, 320, 512],
	head_dim=64, drop_rate=0.1)

	def uniformer_base():
	return Uniformer(
	depth=[5, 8, 20, 7], embed_dim=[64, 128, 320, 512],
	head_dim=64, drop_rate=0.3)