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fcn.py
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fcn.py
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import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.xavier_normal(m.weight)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
# Alexnet feature maps
class alexnet_features(nn.Module):
def __init__(self, learned_billinear=False):
super(alexnet_features, self).__init__()
self.learned_billinear = learned_billinear
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=0),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, stride=1, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, stride=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
)
self.classifier = nn.Sequential(
nn.Dropout2d(),
nn.Conv2d(256, 4096, kernel_size=6, stride=1),
nn.ReLU(inplace=True),
nn.Dropout2d(),
nn.Conv2d(4096, 4096, kernel_size=1, stride=1),
nn.ReLU(inplace=True),
)
if self.learned_billinear:
raise NotImplementedError
def forward(self, x):
conv = self.features(x)
pre_score = self.classifier(conv)
return pre_score
def init_alex_params(self, alexnet):
blocks = [self.features]
features = list(alexnet.features.children())
for idx, conv_block in enumerate(blocks):
for l1, l2 in zip(features, conv_block):
if isinstance(l1, nn.Conv2d) and isinstance(l2, nn.Conv2d):
#print idx, l1, l2
assert l1.weight.size() == l2.weight.size()
assert l1.bias.size() == l2.bias.size()
l2.weight.data = l1.weight.data
l2.bias.data = l1.bias.data
for i1, i2 in zip([1, 4], [1, 4]):
l1 = alexnet.classifier[i1]
l2 = self.classifier[i2]
#print type(l1), dir(l1),
l2.weight.data = l1.weight.data.view(l2.weight.size())
l2.bias.data = l1.bias.data.view(l2.bias.size())
# Alexnet segmenter
class alexnet_segmenter(nn.Module):
def __init__(self, n_classes=21, learned_billinear=False):
super(alexnet_segmenter, self).__init__()
self.learned_billinear = learned_billinear
self.n_classes = n_classes
self.classifier = nn.Sequential(
nn.Conv2d(4096, self.n_classes, kernel_size=1),
)
if self.learned_billinear:
raise NotImplementedError
def forward(self, x):
score = self.classifier(x)
return score
def init_alex_params(self, alexnet, copy_fc8=True):
n_class = self.classifier[0].weight.size()[0]
if copy_fc8:
l1 = alexnet.classifier[6]
l2 = self.classifier[0]
l2.weight.data = l1.weight.data[:n_class, :].view(l2.weight.size())
l2.bias.data = l1.bias.data[:n_class]
# Alexnet
class alexfcn(nn.Module):
def __init__(self, n_classes=21, learned_billinear=False):
super(alexfcn, self).__init__()
self.learned_billinear = learned_billinear
self.n_classes = n_classes
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=100),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, stride=1, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, stride=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
)
self.classifier = nn.Sequential(
nn.Dropout2d(),
nn.Conv2d(256, 4096, kernel_size=6, stride=1),
nn.ReLU(inplace=True),
nn.Dropout2d(),
nn.Conv2d(4096, 4096, kernel_size=1, stride=1),
nn.ReLU(inplace=True),
nn.Conv2d(4096, self.n_classes, kernel_size=1),
)
if self.learned_billinear:
raise NotImplementedError
def forward(self, x):
conv = self.features(x)
score = self.classifier(conv)
out = F.upsample(score, x.size()[2:], mode='bilinear')
return out
def init_alex_params(self, alexnet, copy_fc8=True):
blocks = [self.features]
features = list(alexnet.features.children())
for idx, conv_block in enumerate(blocks):
for l1, l2 in zip(features, conv_block):
if isinstance(l1, nn.Conv2d) and isinstance(l2, nn.Conv2d):
#print idx, l1, l2
assert l1.weight.size() == l2.weight.size()
assert l1.bias.size() == l2.bias.size()
l2.weight.data = l1.weight.data
l2.bias.data = l1.bias.data
for i1, i2 in zip([1, 4], [1, 4]):
l1 = alexnet.classifier[i1]
l2 = self.classifier[i2]
#print type(l1), dir(l1),
l2.weight.data = l1.weight.data.view(l2.weight.size())
l2.bias.data = l1.bias.data.view(l2.bias.size())
n_class = self.classifier[6].weight.size()[0]
if copy_fc8:
l1 = alexnet.classifier[6]
l2 = self.classifier[6]
l2.weight.data = l1.weight.data[:n_class, :].view(l2.weight.size())
l2.bias.data = l1.bias.data[:n_class]
# FCN32s
class fcn32s(nn.Module):
def __init__(self, n_classes=21, learned_billinear=False):
super(fcn32s, self).__init__()
self.learned_billinear = learned_billinear
self.n_classes = n_classes
self.conv_block1 = nn.Sequential(
nn.Conv2d(3, 64, 3, padding=100),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.conv_block2 = nn.Sequential(
nn.Conv2d(64, 128, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.conv_block3 = nn.Sequential(
nn.Conv2d(128, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.conv_block4 = nn.Sequential(
nn.Conv2d(256, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.conv_block5 = nn.Sequential(
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.classifier = nn.Sequential(
nn.Conv2d(512, 4096, 7),
nn.ReLU(inplace=True),
nn.Dropout2d(),
nn.Conv2d(4096, 4096, 1),
nn.ReLU(inplace=True),
nn.Dropout2d(),
nn.Conv2d(4096, self.n_classes, 1),)
# TODO: Add support for learned upsampling
if self.learned_billinear:
raise NotImplementedError
# upscore = nn.ConvTranspose2d(self.n_classes, self.n_classes, 64, stride=32, bias=False)
# upscore.scale_factor = None
def forward(self, x):
conv1 = self.conv_block1(x)
conv2 = self.conv_block2(conv1)
conv3 = self.conv_block3(conv2)
conv4 = self.conv_block4(conv3)
conv5 = self.conv_block5(conv4)
score = self.classifier(conv5)
out = F.upsample_bilinear(score, x.size()[2:])
return out
def init_vgg16_params(self, vgg16, copy_fc8=True):
blocks = [self.conv_block1,
self.conv_block2,
self.conv_block3,
self.conv_block4,
self.conv_block5]
ranges = [[0, 4], [5, 9], [10, 16], [17, 23], [24, 29]]
features = list(vgg16.features.children())
for idx, conv_block in enumerate(blocks):
for l1, l2 in zip(features[ranges[idx][0]:ranges[idx][1]], conv_block):
if isinstance(l1, nn.Conv2d) and isinstance(l2, nn.Conv2d):
# print idx, l1, l2
assert l1.weight.size() == l2.weight.size()
assert l1.bias.size() == l2.bias.size()
l2.weight.data = l1.weight.data
l2.bias.data = l1.bias.data
for i1, i2 in zip([0, 3], [0, 3]):
l1 = vgg16.classifier[i1]
l2 = self.classifier[i2]
# print type(l1), dir(l1),
l2.weight.data = l1.weight.data.view(l2.weight.size())
l2.bias.data = l1.bias.data.view(l2.bias.size())
n_class = self.classifier[6].weight.size()[0]
if copy_fc8:
l1 = vgg16.classifier[6]
l2 = self.classifier[6]
l2.weight.data = l1.weight.data[:n_class, :].view(l2.weight.size())
l2.bias.data = l1.bias.data[:n_class]
class fcn16s(nn.Module):
def __init__(self, n_classes=21, learned_billinear=False):
super(fcn16s, self).__init__()
self.learned_billinear = learned_billinear
self.n_classes = n_classes
self.conv_block1 = nn.Sequential(
nn.Conv2d(3, 64, 3, padding=100),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.conv_block2 = nn.Sequential(
nn.Conv2d(64, 128, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.conv_block3 = nn.Sequential(
nn.Conv2d(128, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.conv_block4 = nn.Sequential(
nn.Conv2d(256, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.conv_block5 = nn.Sequential(
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.classifier = nn.Sequential(
nn.Conv2d(512, 4096, 7),
nn.ReLU(inplace=True),
nn.Dropout2d(),
nn.Conv2d(4096, 4096, 1),
nn.ReLU(inplace=True),
nn.Dropout2d(),
nn.Conv2d(4096, self.n_classes, 1),)
self.score_pool4 = nn.Conv2d(512, self.n_classes, 1)
# TODO: Add support for learned upsampling
if self.learned_billinear:
raise NotImplementedError
# upscore = nn.ConvTranspose2d(self.n_classes, self.n_classes, 64, stride=32, bias=False)
# upscore.scale_factor = None
def forward(self, x):
conv1 = self.conv_block1(x)
conv2 = self.conv_block2(conv1)
conv3 = self.conv_block3(conv2)
conv4 = self.conv_block4(conv3)
conv5 = self.conv_block5(conv4)
score = self.classifier(conv5)
score_pool4 = self.score_pool4(conv4)
score = F.upsample_bilinear(score, score_pool4.size()[2:])
score += score_pool4
out = F.upsample_bilinear(score, x.size()[2:])
return out
def init_vgg16_params(self, vgg16, copy_fc8=True):
blocks = [self.conv_block1,
self.conv_block2,
self.conv_block3,
self.conv_block4,
self.conv_block5]
ranges = [[0, 4], [5, 9], [10, 16], [17, 23], [24, 29]]
features = list(vgg16.features.children())
for idx, conv_block in enumerate(blocks):
for l1, l2 in zip(features[ranges[idx][0]:ranges[idx][1]], conv_block):
if isinstance(l1, nn.Conv2d) and isinstance(l2, nn.Conv2d):
# print idx, l1, l2
assert l1.weight.size() == l2.weight.size()
assert l1.bias.size() == l2.bias.size()
l2.weight.data = l1.weight.data
l2.bias.data = l1.bias.data
for i1, i2 in zip([0, 3], [0, 3]):
l1 = vgg16.classifier[i1]
l2 = self.classifier[i2]
l2.weight.data = l1.weight.data.view(l2.weight.size())
l2.bias.data = l1.bias.data.view(l2.bias.size())
n_class = self.classifier[6].weight.size()[0]
if copy_fc8:
l1 = vgg16.classifier[6]
l2 = self.classifier[6]
l2.weight.data = l1.weight.data[:n_class, :].view(l2.weight.size())
l2.bias.data = l1.bias.data[:n_class]
# FCN 8s
class fcn8s(nn.Module):
def __init__(self, n_classes=21, learned_billinear=False):
super(fcn8s, self).__init__()
self.learned_billinear = learned_billinear
self.n_classes = n_classes
self.conv_block1 = nn.Sequential(
nn.Conv2d(3, 64, 3, padding=100),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.conv_block2 = nn.Sequential(
nn.Conv2d(64, 128, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.conv_block3 = nn.Sequential(
nn.Conv2d(128, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.conv_block4 = nn.Sequential(
nn.Conv2d(256, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.conv_block5 = nn.Sequential(
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2, ceil_mode=True),)
self.classifier = nn.Sequential(
nn.Conv2d(512, 4096, 7),
nn.ReLU(inplace=True),
nn.Dropout2d(),
nn.Conv2d(4096, 4096, 1),
nn.ReLU(inplace=True),
nn.Dropout2d(),
nn.Conv2d(4096, self.n_classes, 1),)
self.score_pool4 = nn.Conv2d(512, self.n_classes, 1)
self.score_pool3 = nn.Conv2d(256, self.n_classes, 1)
# TODO: Add support for learned upsampling
if self.learned_billinear:
raise NotImplementedError
# upscore = nn.ConvTranspose2d(self.n_classes, self.n_classes, 64, stride=32, bias=False)
# upscore.scale_factor = None
def forward(self, x):
conv1 = self.conv_block1(x)
conv2 = self.conv_block2(conv1)
conv3 = self.conv_block3(conv2)
conv4 = self.conv_block4(conv3)
conv5 = self.conv_block5(conv4)
score = self.classifier(conv5)
score_pool4 = self.score_pool4(conv4)
score_pool3 = self.score_pool3(conv3)
score = F.upsample_bilinear(score, score_pool4.size()[2:])
score += score_pool4
score = F.upsample_bilinear(score, score_pool3.size()[2:])
score += score_pool3
out = F.upsample_bilinear(score, x.size()[2:])
return out
def init_vgg16_params(self, vgg16, copy_fc8=True):
blocks = [self.conv_block1,
self.conv_block2,
self.conv_block3,
self.conv_block4,
self.conv_block5]
ranges = [[0, 4], [5, 9], [10, 16], [17, 23], [24, 29]]
features = list(vgg16.features.children())
for idx, conv_block in enumerate(blocks):
for l1, l2 in zip(features[ranges[idx][0]:ranges[idx][1]], conv_block):
if isinstance(l1, nn.Conv2d) and isinstance(l2, nn.Conv2d):
assert l1.weight.size() == l2.weight.size()
assert l1.bias.size() == l2.bias.size()
l2.weight.data = l1.weight.data
l2.bias.data = l1.bias.data
for i1, i2 in zip([0, 3], [0, 3]):
l1 = vgg16.classifier[i1]
l2 = self.classifier[i2]
l2.weight.data = l1.weight.data.view(l2.weight.size())
l2.bias.data = l1.bias.data.view(l2.bias.size())
n_class = self.classifier[6].weight.size()[0]
if copy_fc8:
l1 = vgg16.classifier[6]
l2 = self.classifier[6]
l2.weight.data = l1.weight.data[:n_class, :].view(l2.weight.size())
l2.bias.data = l1.bias.data[:n_class]