195 lines
7.1 KiB
Python
195 lines
7.1 KiB
Python
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import torch.nn as nn
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from mmcv.cnn import ConvModule, constant_init, kaiming_init, normal_init
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from mmcv.utils.parrots_wrapper import _BatchNorm
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from ..builder import BACKBONES
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from .base_backbone import BaseBackbone
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def make_vgg_layer(in_channels,
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out_channels,
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num_blocks,
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conv_cfg=None,
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norm_cfg=None,
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act_cfg=dict(type='ReLU'),
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dilation=1,
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with_norm=False,
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ceil_mode=False):
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layers = []
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for _ in range(num_blocks):
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layer = ConvModule(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=3,
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dilation=dilation,
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padding=dilation,
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bias=True,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg)
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layers.append(layer)
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in_channels = out_channels
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layers.append(nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=ceil_mode))
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return layers
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@BACKBONES.register_module()
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class VGG(BaseBackbone):
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"""VGG backbone.
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Args:
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depth (int): Depth of vgg, from {11, 13, 16, 19}.
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with_norm (bool): Use BatchNorm or not.
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num_classes (int): number of classes for classification.
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num_stages (int): VGG stages, normally 5.
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dilations (Sequence[int]): Dilation of each stage.
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out_indices (Sequence[int]): Output from which stages. If only one
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stage is specified, a single tensor (feature map) is returned,
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otherwise multiple stages are specified, a tuple of tensors will
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be returned. When it is None, the default behavior depends on
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whether num_classes is specified. If num_classes <= 0, the default
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value is (4, ), outputing the last feature map before classifier.
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If num_classes > 0, the default value is (5, ), outputing the
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classification score. Default: None.
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frozen_stages (int): Stages to be frozen (all param fixed). -1 means
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not freezing any parameters.
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norm_eval (bool): Whether to set norm layers to eval mode, namely,
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freeze running stats (mean and var). Note: Effect on Batch Norm
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and its variants only. Default: False.
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ceil_mode (bool): Whether to use ceil_mode of MaxPool. Default: False.
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with_last_pool (bool): Whether to keep the last pooling before
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classifier. Default: True.
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"""
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# Parameters to build layers. Each element specifies the number of conv in
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# each stage. For example, VGG11 contains 11 layers with learnable
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# parameters. 11 is computed as 11 = (1 + 1 + 2 + 2 + 2) + 3,
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# where 3 indicates the last three fully-connected layers.
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arch_settings = {
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11: (1, 1, 2, 2, 2),
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13: (2, 2, 2, 2, 2),
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16: (2, 2, 3, 3, 3),
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19: (2, 2, 4, 4, 4)
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}
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def __init__(self,
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depth,
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num_classes=-1,
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num_stages=5,
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dilations=(1, 1, 1, 1, 1),
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out_indices=None,
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frozen_stages=-1,
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conv_cfg=None,
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norm_cfg=None,
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act_cfg=dict(type='ReLU'),
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norm_eval=False,
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ceil_mode=False,
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with_last_pool=True):
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super(VGG, self).__init__()
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if depth not in self.arch_settings:
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raise KeyError(f'invalid depth {depth} for vgg')
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assert num_stages >= 1 and num_stages <= 5
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stage_blocks = self.arch_settings[depth]
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self.stage_blocks = stage_blocks[:num_stages]
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assert len(dilations) == num_stages
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self.num_classes = num_classes
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self.frozen_stages = frozen_stages
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self.norm_eval = norm_eval
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with_norm = norm_cfg is not None
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if out_indices is None:
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out_indices = (5, ) if num_classes > 0 else (4, )
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assert max(out_indices) <= num_stages
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self.out_indices = out_indices
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self.in_channels = 3
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start_idx = 0
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vgg_layers = []
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self.range_sub_modules = []
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for i, num_blocks in enumerate(self.stage_blocks):
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num_modules = num_blocks + 1
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end_idx = start_idx + num_modules
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dilation = dilations[i]
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out_channels = 64 * 2**i if i < 4 else 512
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vgg_layer = make_vgg_layer(
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self.in_channels,
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out_channels,
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num_blocks,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg,
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dilation=dilation,
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with_norm=with_norm,
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ceil_mode=ceil_mode)
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vgg_layers.extend(vgg_layer)
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self.in_channels = out_channels
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self.range_sub_modules.append([start_idx, end_idx])
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start_idx = end_idx
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if not with_last_pool:
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vgg_layers.pop(-1)
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self.range_sub_modules[-1][1] -= 1
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self.module_name = 'features'
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self.add_module(self.module_name, nn.Sequential(*vgg_layers))
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if self.num_classes > 0:
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self.classifier = nn.Sequential(
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nn.Linear(512 * 7 * 7, 4096),
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nn.ReLU(True),
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nn.Dropout(),
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nn.Linear(4096, 4096),
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nn.ReLU(True),
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nn.Dropout(),
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nn.Linear(4096, num_classes),
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)
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def init_weights(self, pretrained=None):
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super(VGG, self).init_weights(pretrained)
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if pretrained is None:
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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kaiming_init(m)
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elif isinstance(m, _BatchNorm):
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constant_init(m, 1)
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elif isinstance(m, nn.Linear):
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normal_init(m, std=0.01)
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else:
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raise TypeError('pretrained must be a str or None')
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def forward(self, x):
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outs = []
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vgg_layers = getattr(self, self.module_name)
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for i in range(len(self.stage_blocks)):
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for j in range(*self.range_sub_modules[i]):
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vgg_layer = vgg_layers[j]
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x = vgg_layer(x)
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if i in self.out_indices:
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outs.append(x)
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if self.num_classes > 0:
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x = x.view(x.size(0), -1)
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x = self.classifier(x)
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outs.append(x)
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if len(outs) == 1:
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return outs[0]
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else:
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return tuple(outs)
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def _freeze_stages(self):
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vgg_layers = getattr(self, self.module_name)
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for i in range(self.frozen_stages):
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for j in range(*self.range_sub_modules[i]):
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m = vgg_layers[j]
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m.eval()
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for param in m.parameters():
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param.requires_grad = False
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def train(self, mode=True):
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super(VGG, self).train(mode)
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self._freeze_stages()
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if mode and self.norm_eval:
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for m in self.modules():
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# trick: eval have effect on BatchNorm only
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if isinstance(m, _BatchNorm):
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m.eval()
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