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Nets_1.py
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Nets_1.py
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import torch.nn as nn
import torch
import torch.nn.functional as F
class Block(nn.Module):
'''Depthwise conv + Pointwise conv'''
def __init__(self, in_planes, out_planes, k=3):
super(Block, self).__init__()
self.conv1 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=1, padding=1, groups=in_planes, bias=False)
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv2 = nn.Conv2d(in_planes, out_planes, kernel_size=k, stride=1, padding=0, bias=False)
self.bn2 = nn.BatchNorm2d(out_planes)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
return out
class PNet(nn.Module):
cfg = [10, 16, 32]
def __init__(self):
super().__init__()
self.layer1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(32)
self.layers = self._make_layers(in_planes=32)
self.conv1 = nn.Sequential(
nn.Conv2d(32, 1, kernel_size=1, stride=1),
nn.Sigmoid(),
)
self.conv2 = nn.Conv2d(32, 4, kernel_size=1, stride=1)
self.conv3 = nn.Conv2d(32, 10, kernel_size=1, stride=1)
def _make_layers(self, in_planes):
layers = []
for x in self.cfg:
out_planes = x if isinstance(x, int) else x[0] # isinstance为内联函数，判断一个对象是否是一个已知的类型。
k = 3 if isinstance(x, int) else x[1]
layers.append(Block(in_planes, out_planes, k))
in_planes = out_planes
return nn.Sequential(*layers)
def forward(self, x):
x = F.relu(self.bn1(self.layer1(x)))
out = self.layers(x)
out = F.max_pool2d(out, 2)
cond = self.conv1(out)
offset = self.conv2(out)
ldmk_off = self.conv3(out)
return cond, offset, ldmk_off
class RNet(nn.Module):
cfg = [28, 48, (64, 2)]
def __init__(self):
super().__init__()
self.layer1 = nn.Conv2d(3, 28, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(28)
self.layers = self._make_layers(in_planes=28)
self.linear1 = nn.Linear(64 * 2 * 2, 128)
self.relu = nn.PReLU()
self.linear2 = nn.Linear(128, 1)
self.linear3 = nn.Linear(128, 4)
self.linear4 = nn.Linear(128, 10)
def _make_layers(self, in_planes):
layers = []
for x in self.cfg:
out_planes = x if isinstance(x, int) else x[0] # isinstance为内联函数，判断一个对象是否是一个已知的类型。
k = 3 if isinstance(x, int) else x[1]
layers.append(Block(in_planes, out_planes, k))
in_planes = out_planes
return nn.Sequential(*layers)
def forward(self, x):
x = F.relu(self.bn1(self.layer1(x)))
x = self.layers(x)
x = F.max_pool2d(x, 2)
x = x.view(x.size(0), -1)
# 讲多维度的图片转换成一维数据，-1是形状自动给，常放在卷积网络和线性网络中间
x = self.linear1(x)
x = self.relu(x)
cond = torch.sigmoid(self.linear2(x))
offset = self.linear3(x)
ldmk_off = self.linear4(x)
return cond, offset, ldmk_off
class ONet(nn.Module):
cfg = [32, 64, 64, (128, 2)]
def __init__(self):
super().__init__()
self.layer1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(32)
self.layers = self._make_layers(in_planes=32)
self.linear1 = nn.Linear(128 * 2 * 2, 256)
self.relu = nn.PReLU()
self.linear2 = nn.Linear(256, 1)
self.linear3 = nn.Linear(256, 4)
self.linear4 = nn.Linear(256, 10)
def _make_layers(self, in_planes):
layers = []
for x in self.cfg:
out_planes = x if isinstance(x, int) else x[0] # isinstance为内联函数，判断一个对象是否是一个已知的类型。
k = 3 if isinstance(x, int) else x[1]
layers.append(Block(in_planes, out_planes, k))
in_planes = out_planes
return nn.Sequential(*layers)
def forward(self, x):
x = F.relu(self.bn1(self.layer1(x)))
x = self.layers(x)
x = F.max_pool2d(x, 2)
x = x.view(x.size(0), -1)
x = self.linear1(x)
x = self.relu(x)
cond = torch.sigmoid(self.linear2(x))
offset = self.linear3(x)
ldmk_off = self.linear4(x)
return cond, offset, ldmk_off