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stdc.py
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stdc.py
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import torch
import torch.nn as nn
from torch.nn import init
import math
class ConvX(nn.Module):
def __init__(self, in_planes, out_planes, kernel=3, stride=1):
super(ConvX, self).__init__()
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel, stride=stride, padding=kernel // 2, bias=False)
self.bn = nn.BatchNorm2d(out_planes)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
out = self.relu(self.bn(self.conv(x)))
return out
class AddBottleneck(nn.Module):
def __init__(self, in_planes, out_planes, block_num=3, stride=1):
super(AddBottleneck, self).__init__()
assert block_num > 1, print("block number should be larger than 1.")
self.conv_list = nn.ModuleList()
self.stride = stride
if stride == 2:
self.avd_layer = nn.Sequential(
nn.Conv2d(out_planes // 2, out_planes // 2, kernel_size=3, stride=2, padding=1, groups=out_planes // 2,
bias=False),
nn.BatchNorm2d(out_planes // 2),
)
self.skip = nn.Sequential(
nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=2, padding=1, groups=in_planes, bias=False),
nn.BatchNorm2d(in_planes),
nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False),
nn.BatchNorm2d(out_planes),
)
stride = 1
for idx in range(block_num):
if idx == 0:
self.conv_list.append(ConvX(in_planes, out_planes // 2, kernel=1))
elif idx == 1 and block_num == 2:
self.conv_list.append(ConvX(out_planes // 2, out_planes // 2, stride=stride))
elif idx == 1 and block_num > 2:
self.conv_list.append(ConvX(out_planes // 2, out_planes // 4, stride=stride))
elif idx < block_num - 1:
self.conv_list.append(
ConvX(out_planes // int(math.pow(2, idx)), out_planes // int(math.pow(2, idx + 1))))
else:
self.conv_list.append(ConvX(out_planes // int(math.pow(2, idx)), out_planes // int(math.pow(2, idx))))
def forward(self, x):
out_list = []
out = x
for idx, conv in enumerate(self.conv_list):
if idx == 0 and self.stride == 2:
out = self.avd_layer(conv(out))
else:
out = conv(out)
out_list.append(out)
if self.stride == 2:
x = self.skip(x)
return torch.cat(out_list, dim=1) + x
class CatBottleneck(nn.Module):
def __init__(self, in_planes, out_planes, block_num=3, stride=1):
super(CatBottleneck, self).__init__()
assert block_num > 1, print("block number should be larger than 1.")
self.conv_list = nn.ModuleList()
self.stride = stride
if stride == 2:
self.avd_layer = nn.Sequential(
nn.Conv2d(out_planes // 2, out_planes // 2, kernel_size=3, stride=2, padding=1, groups=out_planes // 2,
bias=False),
nn.BatchNorm2d(out_planes // 2),
)
self.skip = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
stride = 1
for idx in range(block_num):
if idx == 0:
self.conv_list.append(ConvX(in_planes, out_planes // 2, kernel=1))
elif idx == 1 and block_num == 2:
self.conv_list.append(ConvX(out_planes // 2, out_planes // 2, stride=stride))
elif idx == 1 and block_num > 2:
self.conv_list.append(ConvX(out_planes // 2, out_planes // 4, stride=stride))
elif idx < block_num - 1:
self.conv_list.append(
ConvX(out_planes // int(math.pow(2, idx)), out_planes // int(math.pow(2, idx + 1))))
else:
self.conv_list.append(ConvX(out_planes // int(math.pow(2, idx)), out_planes // int(math.pow(2, idx))))
def forward(self, x):
out_list = []
out1 = self.conv_list[0](x)
for idx, conv in enumerate(self.conv_list[1:]):
if idx == 0:
if self.stride == 2:
out = conv(self.avd_layer(out1))
else:
out = conv(out1)
else:
out = conv(out)
out_list.append(out)
if self.stride == 2:
out1 = self.skip(out1)
out_list.insert(0, out1)
out = torch.cat(out_list, dim=1)
return out
# STDC2Net
class STDCNet1446(nn.Module):
def __init__(self, base=64, layers=[4, 5, 3], block_num=4, type="cat", num_classes=1000, dropout=0.20,
pretrain_model='', use_conv_last=False):
super(STDCNet1446, self).__init__()
if type == "cat":
block = CatBottleneck
elif type == "add":
block = AddBottleneck
self.use_conv_last = use_conv_last
self.features = self._make_layers(base, layers, block_num, block)
self.conv_last = ConvX(base * 16, max(1024, base * 16), 1, 1)
self.gap = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(max(1024, base * 16), max(1024, base * 16), bias=False)
self.bn = nn.BatchNorm1d(max(1024, base * 16))
self.relu = nn.ReLU(inplace=True)
self.dropout = nn.Dropout(p=dropout)
self.linear = nn.Linear(max(1024, base * 16), num_classes, bias=False)
self.x2 = nn.Sequential(self.features[:1])
self.x4 = nn.Sequential(self.features[1:2])
self.x8 = nn.Sequential(self.features[2:6])
self.x16 = nn.Sequential(self.features[6:11])
self.x32 = nn.Sequential(self.features[11:])
if pretrain_model:
print('use pretrain model {}'.format(pretrain_model))
self.init_weight(pretrain_model)
else:
self.init_params()
def init_weight(self, pretrain_model):
state_dict = torch.load(pretrain_model)["state_dict"]
self_state_dict = self.state_dict()
for k, v in state_dict.items():
self_state_dict.update({k: v})
self.load_state_dict(self_state_dict)
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)
def _make_layers(self, base, layers, block_num, block):
features = []
features += [ConvX(3, base // 2, 3, 2)]
features += [ConvX(base // 2, base, 3, 2)]
for i, layer in enumerate(layers):
for j in range(layer):
if i == 0 and j == 0:
features.append(block(base, base * 4, block_num, 2))
elif j == 0:
features.append(block(base * int(math.pow(2, i + 1)), base * int(math.pow(2, i + 2)), block_num, 2))
else:
features.append(block(base * int(math.pow(2, i + 2)), base * int(math.pow(2, i + 2)), block_num, 1))
return nn.Sequential(*features)
def forward(self, x):
feat2 = self.x2(x)
feat4 = self.x4(feat2)
feat8 = self.x8(feat4)
feat16 = self.x16(feat8)
feat32 = self.x32(feat16)
if self.use_conv_last:
feat32 = self.conv_last(feat32)
return feat2, feat4, feat8, feat16, feat32
def forward_impl(self, x):
out = self.features(x)
out = self.conv_last(out).pow(2)
out = self.gap(out).flatten(1)
out = self.fc(out)
# out = self.bn(out)
out = self.relu(out)
# out = self.relu(self.bn(self.fc(out)))
out = self.dropout(out)
out = self.linear(out)
return out
# STDC1Net
class STDCNet813(nn.Module):
def __init__(self, base=64, layers=[2, 2, 2], block_num=4, type="cat", num_classes=1000, dropout=0.20,
pretrain_model='', use_conv_last=False):
super(STDCNet813, self).__init__()
if type == "cat":
block = CatBottleneck
elif type == "add":
block = AddBottleneck
self.use_conv_last = use_conv_last
self.features = self._make_layers(base, layers, block_num, block)
self.conv_last = ConvX(base * 16, max(1024, base * 16), 1, 1)
self.gap = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(max(1024, base * 16), max(1024, base * 16), bias=False)
self.bn = nn.BatchNorm1d(max(1024, base * 16))
self.relu = nn.ReLU(inplace=True)
self.dropout = nn.Dropout(p=dropout)
self.linear = nn.Linear(max(1024, base * 16), num_classes, bias=False)
self.x2 = nn.Sequential(self.features[:1])
self.x4 = nn.Sequential(self.features[1:2])
self.x8 = nn.Sequential(self.features[2:4])
self.x16 = nn.Sequential(self.features[4:6])
self.x32 = nn.Sequential(self.features[6:])
self.feat_channels = [base // 2, base, base * 4, base * 8, base * 16]
if pretrain_model:
print('use pretrain model {}'.format(pretrain_model))
self.init_weight(pretrain_model)
else:
self.init_params()
def init_weight(self, pretrain_model):
state_dict = torch.load(pretrain_model)["state_dict"]
self_state_dict = self.state_dict()
for k, v in state_dict.items():
self_state_dict.update({k: v})
self.load_state_dict(self_state_dict)
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)
def _make_layers(self, base, layers, block_num, block):
features = []
features += [ConvX(3, base // 2, 3, 2)]
features += [ConvX(base // 2, base, 3, 2)]
for i, layer in enumerate(layers):
for j in range(layer):
if i == 0 and j == 0:
features.append(block(base, base * 4, block_num, 2))
elif j == 0:
features.append(block(base * int(math.pow(2, i + 1)), base * int(math.pow(2, i + 2)), block_num, 2))
else:
features.append(block(base * int(math.pow(2, i + 2)), base * int(math.pow(2, i + 2)), block_num, 1))
return nn.Sequential(*features)
def forward(self, x):
feat2 = self.x2(x)
feat4 = self.x4(feat2)
feat8 = self.x8(feat4)
feat16 = self.x16(feat8)
feat32 = self.x32(feat16)
if self.use_conv_last:
feat32 = self.conv_last(feat32)
return feat2, feat4, feat8, feat16, feat32
def forward_impl(self, x):
out = self.features(x)
out = self.conv_last(out).pow(2)
out = self.gap(out).flatten(1)
out = self.fc(out)
# out = self.bn(out)
out = self.relu(out)
# out = self.relu(self.bn(self.fc(out)))
out = self.dropout(out)
out = self.linear(out)
return out
if __name__ == "__main__":
model = STDCNet813(num_classes=1000, dropout=0.00, block_num=4)
model.eval()
x = torch.randn(1, 3, 224, 224)
y = model(x)
# torch.save(model.state_dict(), 'cat.pth')
print(model)