Spaces:
Build error
Build error
File size: 6,544 Bytes
28c256d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 |
# 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.
import logging
from typing import List, Optional, Sequence, Tuple, Union
import torch.nn as nn
from mmengine.model import constant_init, kaiming_init, normal_init
from mmengine.runner import load_checkpoint
from torch import Tensor
def conv3x3(in_planes: int, out_planes: int, dilation: int = 1) -> nn.Module:
"""3x3 convolution with padding."""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
padding=dilation,
dilation=dilation)
def make_vgg_layer(inplanes: int,
planes: int,
num_blocks: int,
dilation: int = 1,
with_bn: bool = False,
ceil_mode: bool = False) -> List[nn.Module]:
layers = []
for _ in range(num_blocks):
layers.append(conv3x3(inplanes, planes, dilation))
if with_bn:
layers.append(nn.BatchNorm2d(planes))
layers.append(nn.ReLU(inplace=True))
inplanes = planes
layers.append(nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=ceil_mode))
return layers
class VGG(nn.Module):
"""VGG backbone.
Args:
depth (int): Depth of vgg, from {11, 13, 16, 19}.
with_bn (bool): Use BatchNorm or not.
num_classes (int): number of classes for classification.
num_stages (int): VGG stages, normally 5.
dilations (Sequence[int]): Dilation of each stage.
out_indices (Sequence[int]): Output from which stages.
frozen_stages (int): Stages to be frozen (all param fixed). -1 means
not freezing any parameters.
bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze
running stats (mean and var).
bn_frozen (bool): Whether to freeze weight and bias of BN layers.
"""
arch_settings = {
11: (1, 1, 2, 2, 2),
13: (2, 2, 2, 2, 2),
16: (2, 2, 3, 3, 3),
19: (2, 2, 4, 4, 4)
}
def __init__(self,
depth: int,
with_bn: bool = False,
num_classes: int = -1,
num_stages: int = 5,
dilations: Sequence[int] = (1, 1, 1, 1, 1),
out_indices: Sequence[int] = (0, 1, 2, 3, 4),
frozen_stages: int = -1,
bn_eval: bool = True,
bn_frozen: bool = False,
ceil_mode: bool = False,
with_last_pool: bool = True):
super().__init__()
if depth not in self.arch_settings:
raise KeyError(f'invalid depth {depth} for vgg')
assert num_stages >= 1 and num_stages <= 5
stage_blocks = self.arch_settings[depth]
self.stage_blocks = stage_blocks[:num_stages]
assert len(dilations) == num_stages
assert max(out_indices) <= num_stages
self.num_classes = num_classes
self.out_indices = out_indices
self.frozen_stages = frozen_stages
self.bn_eval = bn_eval
self.bn_frozen = bn_frozen
self.inplanes = 3
start_idx = 0
vgg_layers = []
self.range_sub_modules = []
for i, num_blocks in enumerate(self.stage_blocks):
num_modules = num_blocks * (2 + with_bn) + 1
end_idx = start_idx + num_modules
dilation = dilations[i]
planes = 64 * 2**i if i < 4 else 512
vgg_layer = make_vgg_layer(
self.inplanes,
planes,
num_blocks,
dilation=dilation,
with_bn=with_bn,
ceil_mode=ceil_mode)
vgg_layers.extend(vgg_layer)
self.inplanes = planes
self.range_sub_modules.append([start_idx, end_idx])
start_idx = end_idx
if not with_last_pool:
vgg_layers.pop(-1)
self.range_sub_modules[-1][1] -= 1
self.module_name = 'features'
self.add_module(self.module_name, nn.Sequential(*vgg_layers))
if self.num_classes > 0:
self.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, num_classes),
)
def init_weights(self, pretrained: Optional[str] = None) -> None:
if isinstance(pretrained, str):
logger = logging.getLogger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
elif isinstance(m, nn.Linear):
normal_init(m, std=0.01)
else:
raise TypeError('pretrained must be a str or None')
def forward(self, x: Tensor) -> Union[Tensor, Tuple[Tensor, ...]]:
outs = []
vgg_layers = getattr(self, self.module_name)
for i in range(len(self.stage_blocks)):
for j in range(*self.range_sub_modules[i]):
vgg_layer = vgg_layers[j]
x = vgg_layer(x)
if i in self.out_indices:
outs.append(x)
if self.num_classes > 0:
x = x.view(x.size(0), -1)
x = self.classifier(x)
outs.append(x)
if len(outs) == 1:
return outs[0]
else:
return tuple(outs)
def train(self, mode: bool = True) -> None:
super().train(mode)
if self.bn_eval:
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
if self.bn_frozen:
for params in m.parameters():
params.requires_grad = False
vgg_layers = getattr(self, self.module_name)
if mode and self.frozen_stages >= 0:
for i in range(self.frozen_stages):
for j in range(*self.range_sub_modules[i]):
mod = vgg_layers[j]
mod.eval()
for param in mod.parameters():
param.requires_grad = False
|