74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
# 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
|
|
# --------------------------------------------------------
|
|
|
|
from functools import partial
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
import timm.models.vision_transformer
|
|
|
|
|
|
class VisionTransformer(timm.models.vision_transformer.VisionTransformer):
|
|
""" Vision Transformer with support for global average pooling
|
|
"""
|
|
def __init__(self, global_pool=False, **kwargs):
|
|
super(VisionTransformer, self).__init__(**kwargs)
|
|
|
|
self.global_pool = global_pool
|
|
if self.global_pool:
|
|
norm_layer = kwargs['norm_layer']
|
|
embed_dim = kwargs['embed_dim']
|
|
self.fc_norm = norm_layer(embed_dim)
|
|
|
|
del self.norm # remove the original norm
|
|
|
|
def forward_features(self, x):
|
|
B = x.shape[0]
|
|
x = self.patch_embed(x)
|
|
|
|
cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
|
|
x = torch.cat((cls_tokens, x), dim=1)
|
|
x = x + self.pos_embed
|
|
x = self.pos_drop(x)
|
|
|
|
for blk in self.blocks:
|
|
x = blk(x)
|
|
|
|
if self.global_pool:
|
|
x = x[:, 1:, :].mean(dim=1) # global pool without cls token
|
|
outcome = self.fc_norm(x)
|
|
else:
|
|
x = self.norm(x)
|
|
outcome = x[:, 0]
|
|
|
|
return outcome
|
|
|
|
|
|
def vit_base_patch16(**kwargs):
|
|
model = VisionTransformer(
|
|
patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
|
return model
|
|
|
|
|
|
def vit_large_patch16(**kwargs):
|
|
model = VisionTransformer(
|
|
patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
|
return model
|
|
|
|
|
|
def vit_huge_patch14(**kwargs):
|
|
model = VisionTransformer(
|
|
patch_size=14, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, qkv_bias=True,
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
|
return model |