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models_vit.py
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models_vit.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
# DeiT: https://github.com/facebookresearch/deit
# MAE: https://github.com/facebookresearch/mae
# --------------------------------------------------------
from functools import partial
import torch
import torch.nn as nn
from util.logging import master_print as print
from util.video_vit import Attention, Block, PatchEmbed
class VisionTransformer(nn.Module):
"""Vision Transformer with support for global average pooling"""
def __init__(
self,
num_frames=16,
t_patch_size=2,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=None,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.0,
no_qkv_bias=False,
drop_path_rate=0.1,
norm_layer=nn.LayerNorm,
dropout=0.3,
sep_pos_embed=True,
cls_embed=True,
**kwargs,
):
super().__init__()
print(locals())
self.sep_pos_embed = sep_pos_embed
# --------------------------------------------------------------------------
# MAE encoder specifics
self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim, num_frames, t_patch_size)
num_patches = self.patch_embed.num_patches
input_size = self.patch_embed.input_size
self.input_size = input_size
self.cls_embed = cls_embed
if self.cls_embed:
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
if sep_pos_embed:
self.pos_embed_spatial = nn.Parameter(torch.zeros(1, input_size[1] * input_size[2], embed_dim))
self.pos_embed_temporal = nn.Parameter(torch.zeros(1, input_size[0], embed_dim))
if self.cls_embed:
self.pos_embed_class = nn.Parameter(torch.zeros(1, 1, embed_dim))
else:
if self.cls_embed:
_num_patches = num_patches + 1
else:
_num_patches = num_patches
self.pos_embed = nn.Parameter(torch.zeros(1, _num_patches, embed_dim), requires_grad=True) # fixed or not?
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList(
[
Block(
embed_dim,
num_heads,
mlp_ratio,
qkv_bias=not no_qkv_bias,
qk_scale=None,
norm_layer=norm_layer,
drop_path=dpr[i],
attn_func=partial(Attention, input_size=self.patch_embed.input_size),
)
for i in range(depth)
]
)
self.norm = norm_layer(embed_dim)
# --------------------------------------------------------------------------
self.dropout = nn.Dropout(dropout)
self.head = nn.Linear(embed_dim, num_classes) if num_classes is not None else nn.Identity()
if num_classes is not None:
torch.nn.init.normal_(self.head.weight, std=0.02)
@torch.jit.ignore
def no_weight_decay(self):
return {
"cls_token",
"pos_embed",
"pos_embed_spatial",
"pos_embed_temporal",
"pos_embed_class",
}
def forward(self, x):
# embed patches
x = self.patch_embed(x)
N, T, L, C = x.shape # T: temporal; L: spatial
x = x.view([N, T * L, C])
# append cls token
if self.cls_embed:
cls_token = self.cls_token
cls_tokens = cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
if self.sep_pos_embed:
pos_embed = self.pos_embed_spatial.repeat(1, self.input_size[0], 1) + torch.repeat_interleave(self.pos_embed_temporal, self.input_size[1] * self.input_size[2], dim=1)
if self.cls_embed:
pos_embed = torch.cat([self.pos_embed_class.expand(pos_embed.shape[0], -1, -1), pos_embed], 1)
else:
pos_embed = self.pos_embed[:, :, :]
x = x + pos_embed
# reshape to [N, T, L, C] or [N, T*L, C]
requires_t_shape = (
len(self.blocks) > 0 # support empty decoder
and hasattr(self.blocks[0].attn, "requires_t_shape")
and self.blocks[0].attn.requires_t_shape
)
if requires_t_shape:
x = x.view([N, T, L, C])
# apply Transformer blocks
for blk in self.blocks:
x = blk(x)
if requires_t_shape:
x = x.view([N, T * L, C])
# classifier (TODO: FIX THIS)
x = x[:, 0, :] # CLS token
# x = x[:, 1:, :].mean(dim=1) # global pool
x = self.norm(x)
x = self.dropout(x)
x = self.head(x)
return x
def get_last_selfattention(self, x):
# embed patches
x = self.patch_embed(x)
N, T, L, C = x.shape # T: temporal; L: spatial
x = x.view([N, T * L, C])
# append cls token
if self.cls_embed:
cls_token = self.cls_token
cls_tokens = cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
if self.sep_pos_embed:
pos_embed = self.pos_embed_spatial.repeat(1, self.input_size[0], 1) + torch.repeat_interleave(self.pos_embed_temporal, self.input_size[1] * self.input_size[2], dim=1)
if self.cls_embed:
pos_embed = torch.cat([self.pos_embed_class.expand(pos_embed.shape[0], -1, -1), pos_embed], 1)
else:
pos_embed = self.pos_embed[:, :, :]
x = x + pos_embed
# reshape to [N, T, L, C] or [N, T*L, C]
requires_t_shape = (
len(self.blocks) > 0 # support empty decoder
and hasattr(self.blocks[0].attn, "requires_t_shape")
and self.blocks[0].attn.requires_t_shape
)
if requires_t_shape:
x = x.view([N, T, L, C])
for i, blk in enumerate(self.blocks):
if i < len(self.blocks) - 1:
x = blk(x)
else:
# return attention of the last block
return blk(x, return_attention=True)
def vit_huge_patch14(**kwargs):
model = VisionTransformer(patch_size=14, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model