Source code for towhee.models.swin_transformer.model
# Copyright 2021 Microsoft . All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This code is modified by Zilliz.
import math
import torch
from torch import nn
from towhee.models.utils.weight_init import trunc_normal_
from towhee.models.layers.patch_embed2d import PatchEmbed2D
from towhee.models.layers.patch_merging import PatchMerging
from towhee.models.utils.init_vit_weights import init_vit_weights
from towhee.models.swin_transformer.basic_layer import BasicLayer
[docs]class SwinTransformer(nn.Module):
r""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
https://arxiv.org/pdf/2103.14030
Args:
img_size (int | tuple(int)): Input image size. Default 224
patch_size (int | tuple(int)): Patch size. Default: 4
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for classification head. Default: 1000
embed_dim (int): Patch embedding dimension. Default: 96
depths (tuple(int)): Depth of each Swin Transformer layer.
num_heads (tuple(int)): Number of attention heads in different layers.
window_size (int): Window size. Default: 7
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
drop_rate (float): Dropout rate. Default: 0
attn_drop_rate (float): Attention dropout rate. Default: 0
drop_path_rate (float): Stochastic depth rate. Default: 0.1
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
patch_norm (bool): If True, add normalization after patch embedding. Default: True
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
pretrained_window_sizes (tuple(int)): Pretrained window sizes of each layer.
"""
[docs] def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24),
window_size=7, mlp_ratio=4., qkv_bias=True,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
use_checkpoint=False, weight_init='', is_v2=False, pretrained_window_sizes=None):
super().__init__()
if pretrained_window_sizes is None:
pretrained_window_sizes = [0, 0, 0, 0]
self.num_classes = num_classes
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.ape = ape
self.patch_norm = patch_norm
self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
self.mlp_ratio = mlp_ratio
# split image into non-overlapping patches
self.patch_embed = PatchEmbed2D(
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
norm_layer=norm_layer if self.patch_norm else None)
num_patches = self.patch_embed.num_patches
self.patch_grid = self.patch_embed.grid_size
# absolute position embedding
if self.ape:
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
trunc_normal_(self.absolute_pos_embed, std=.02)
else:
self.absolute_pos_embed = None
self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
# build layers
layers = []
for i_layer in range(self.num_layers):
layers += [BasicLayer(
dim=int(embed_dim * 2 ** i_layer),
input_resolution=(self.patch_grid[0] // (2 ** i_layer), self.patch_grid[1] // (2 ** i_layer)),
depth=depths[i_layer],
num_heads=num_heads[i_layer],
window_size=window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=qkv_bias,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
norm_layer=norm_layer,
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
use_checkpoint=use_checkpoint,
is_v2=is_v2,
pretrained_window_size=pretrained_window_sizes[i_layer])
]
self.layers = nn.Sequential(*layers)
self.norm = norm_layer(self.num_features)
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
assert weight_init in ('jax', 'jax_nlhb', 'nlhb', '')
head_bias = -math.log(self.num_classes) if 'nlhb' in weight_init else 0.
if weight_init.startswith('jax'):
for n, m in self.named_modules():
init_vit_weights(m, n, head_bias=head_bias, jax_impl=True)
else:
self.apply(init_vit_weights)
@torch.jit.ignore
def no_weight_decay(self):
return {'absolute_pos_embed'}
@torch.jit.ignore
def no_weight_decay_keywords(self):
return {'relative_position_bias_table'}
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes):
self.num_classes = num_classes
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.patch_embed(x)
if self.absolute_pos_embed is not None:
x = x + self.absolute_pos_embed
x = self.pos_drop(x)
x = self.layers(x)
x = self.norm(x) # B L C
x = self.avgpool(x.transpose(1, 2)) # B C 1
x = torch.flatten(x, 1)
return x
# if __name__ == '__main__':
# from towhee.models.swin_transformer.configs import build_configs
# from towhee.models.utils.pretrained_utils import load_pretrained_weights
# img = torch.randn(1, 3, 224, 224)
# name = 'swin_tiny_patch4_window7_224'
#
# arch, model_cfg = build_configs(name)
# model = SwinTransformer(**arch)
#
# load_pretrained_weights(model, name, model_cfg)
# out = model(img)
# print(out.shape)
# pass
