115 lines
3.6 KiB
Python
Executable File
115 lines
3.6 KiB
Python
Executable File
import torch.nn as nn
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import torch.nn.functional as F
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from ssd.layers import L2Norm
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from ssd.modeling import registry
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from ssd.utils.model_zoo import load_state_dict_from_url
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model_urls = {
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'vgg': 'https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth',
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}
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# borrowed from https://github.com/amdegroot/ssd.pytorch/blob/master/ssd.py
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def add_vgg(cfg, batch_norm=False):
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layers = []
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in_channels = 3
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for v in cfg:
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if v == 'M':
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layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
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elif v == 'C':
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layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
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else:
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conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
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if batch_norm:
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layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
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else:
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layers += [conv2d, nn.ReLU(inplace=True)]
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in_channels = v
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pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
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conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)
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conv7 = nn.Conv2d(1024, 1024, kernel_size=1)
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layers += [pool5, conv6,
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nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]
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return layers
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def add_extras(cfg, i, size=300):
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# Extra layers added to VGG for feature scaling
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layers = []
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in_channels = i
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flag = False
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for k, v in enumerate(cfg):
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if in_channels != 'S':
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if v == 'S':
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layers += [nn.Conv2d(in_channels, cfg[k + 1], kernel_size=(1, 3)[flag], stride=2, padding=1)]
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else:
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layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])]
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flag = not flag
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in_channels = v
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if size == 512:
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layers.append(nn.Conv2d(in_channels, 128, kernel_size=1, stride=1))
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layers.append(nn.Conv2d(128, 256, kernel_size=4, stride=1, padding=1))
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return layers
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vgg_base = {
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'300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
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512, 512, 512],
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'512': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
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512, 512, 512],
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}
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extras_base = {
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'300': [256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256],
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'512': [256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256],
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}
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class VGG(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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size = cfg.INPUT.IMAGE_SIZE
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vgg_config = vgg_base[str(size)]
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extras_config = extras_base[str(size)]
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self.vgg = nn.ModuleList(add_vgg(vgg_config))
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self.extras = nn.ModuleList(add_extras(extras_config, i=1024, size=size))
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self.l2_norm = L2Norm(512, scale=20)
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self.reset_parameters()
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def reset_parameters(self):
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for m in self.extras.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.xavier_uniform_(m.weight)
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nn.init.zeros_(m.bias)
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def init_from_pretrain(self, state_dict):
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self.vgg.load_state_dict(state_dict)
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def forward(self, x):
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features = []
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for i in range(23):
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x = self.vgg[i](x)
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s = self.l2_norm(x) # Conv4_3 L2 normalization
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features.append(s)
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# apply vgg up to fc7
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for i in range(23, len(self.vgg)):
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x = self.vgg[i](x)
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features.append(x)
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for k, v in enumerate(self.extras):
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x = F.relu(v(x), inplace=True)
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if k % 2 == 1:
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features.append(x)
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return tuple(features)
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@registry.BACKBONES.register('vgg')
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def vgg(cfg, pretrained=True):
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model = VGG(cfg)
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if pretrained:
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model.init_from_pretrain(load_state_dict_from_url(model_urls['vgg']))
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return model
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