def __call__(self, x):
h = F.relu(self.conv1_1(x))
h = F.relu(self.conv1_2(h))
h = F.max_pooling_2d(h, 2, stride=2)
h = F.relu(self.conv2_1(h))
h = F.relu(self.conv2_2(h))
h = F.max_pooling_2d(h, 2, stride=2)
h = F.relu(self.conv3_1(h))
h = F.relu(self.conv3_2(h))
h = F.max_pooling_2d(h, 2, stride=2)
h = F.relu(self.conv4_1(h))
h = F.relu(self.conv4_2(h))
h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
h = F.tanh(self.fc4(h))
h = F.dropout(h, ratio=.5, train=self.train)
h = F.tanh(self.fc5(h))
h = F.dropout(h, ratio=.5, train=self.train)
h = self.fc6(h)
return h
python类MaxPooling2D()的实例源码
spp_discriminator.py 文件源码
项目：Semantic-Segmentation-using-Adversarial-Networks
作者: oyam
项目源码
文件源码
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def __init__(self):
super(VGG, self).__init__()
with self.init_scope():
self.conv1_1 = L.Convolution2D(3, 64, 3, stride=1, pad=1)
self.conv1_2 = L.Convolution2D(64, 64, 3, stride=1, pad=1)
self.conv2_1 = L.Convolution2D(64, 128, 3, stride=1, pad=1)
self.conv2_2 = L.Convolution2D(128, 128, 3, stride=1, pad=1)
self.conv3_1 = L.Convolution2D(128, 256, 3, stride=1, pad=1)
self.conv3_2 = L.Convolution2D(256, 256, 3, stride=1, pad=1)
self.conv3_3 = L.Convolution2D(256, 256, 3, stride=1, pad=1)
self.conv4_1 = L.Convolution2D(256, 512, 3, stride=1, pad=1)
self.conv4_2 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
self.conv4_3 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
self.conv5_1 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
self.conv5_2 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
self.conv5_3 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
self.fc6 = L.Linear(25088, 4096)
self.fc7 = L.Linear(4096, 4096)
self.fc8 = L.Linear(4096, 1000)
# Keep track of the pooling indices inside each function instance
self.conv_blocks = [
[self.conv1_1, self.conv1_2],
[self.conv2_1, self.conv2_2],
[self.conv3_1, self.conv3_2, self.conv3_3],
[self.conv4_1, self.conv4_2, self.conv4_3],
[self.conv5_1, self.conv5_2, self.conv5_3]
]
self.deconv_blocks = []
self.mps = [F.MaxPooling2D(2, 2) for _ in self.conv_blocks]
def __init__(self, train=False):
super(VGG16, self).__init__()
self.trunk = [
('conv1_1', L.Convolution2D(3, 64, 3, 1, 1)),
('_relu1_1', F.ReLU()),
('conv1_2', L.Convolution2D(64, 64, 3, 1, 1)),
('_relu1_2', F.ReLU()),
('_pool1', F.MaxPooling2D(2, 2)),
('conv2_1', L.Convolution2D(64, 128, 3, 1, 1)),
('_relu2_1', F.ReLU()),
('conv2_2', L.Convolution2D(128, 128, 3, 1, 1)),
('_relu2_2', F.ReLU()),
('_pool2', F.MaxPooling2D(2, 2)),
('conv3_1', L.Convolution2D(128, 256, 3, 1, 1)),
('_relu3_1', F.ReLU()),
('conv3_2', L.Convolution2D(256, 256, 3, 1, 1)),
('_relu3_2', F.ReLU()),
('conv3_3', L.Convolution2D(256, 256, 3, 1, 1)),
('_relu3_3', F.ReLU()),
('_pool3', F.MaxPooling2D(2, 2)),
('conv4_1', L.Convolution2D(256, 512, 3, 1, 1)),
('_relu4_1', F.ReLU()),
('conv4_2', L.Convolution2D(512, 512, 3, 1, 1)),
('_relu4_2', F.ReLU()),
('conv4_3', L.Convolution2D(512, 512, 3, 1, 1)),
('_relu4_3', F.ReLU()),
('_pool4', F.MaxPooling2D(2, 2)),
('conv5_1', L.Convolution2D(512, 512, 3, 1, 1)),
('_relu5_1', F.ReLU()),
('conv5_2', L.Convolution2D(512, 512, 3, 1, 1)),
('_relu5_2', F.ReLU()),
('conv5_3', L.Convolution2D(512, 512, 3, 1, 1)),
('_relu5_3', F.ReLU()),
]
for name, link in self.trunk:
if not name.startswith('_'):
self.add_link(name, link)
def __init__(self, in_channel, n_mid=64):
w = math.sqrt(2)
super(EncDec, self).__init__(
enc=L.Convolution2D(in_channel, n_mid, 7, 1, 3, w),
bn_m=L.BatchNormalization(n_mid),
dec=L.Convolution2D(n_mid, n_mid, 7, 1, 3, w),
bn_o=L.BatchNormalization(n_mid),
)
self.p = F.MaxPooling2D(2, 2, use_cudnn=False)
self.inside = None
def setUp(self):
self.x = numpy.random.uniform(-1, 1, self.in_shape).astype('f')
self.p = F.MaxPooling2D(2, 2, use_cudnn=False)
self.pooled_y = self.p(self.x)
self.gy = numpy.random.uniform(
-1, 1, self.in_shape).astype(numpy.float32)
test_spatial_pyramid_pooling_2d.py 文件源码
项目：chainer-deconv
作者: germanRos
项目源码
文件源码
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def check_invalid_dtype(self):
functions.spatial_pyramid_pooling_2d(
self.v, 3, functions.MaxPooling2D)
test_spatial_pyramid_pooling_2d.py 文件源码
项目：chainer-deconv
作者: germanRos
项目源码
文件源码
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def forward(self):
x = chainer.Variable(self.x)
return functions.spatial_pyramid_pooling_2d(
x, 3, functions.MaxPooling2D,
use_cudnn=self.use_cudnn)
def check_backward(self, x_data, y_grad, use_cudnn=True):
gradient_check.check_backward(
functions.MaxPooling2D(
3, stride=2, pad=1, cover_all=self.cover_all,
use_cudnn=use_cudnn),
x_data, y_grad, **self.check_backward_options)
def __call__(self, x):
"""Compute an image-wise score from a batch of images
Args:
x (chainer.Variable): A variable with 4D image array.
Returns:
chainer.Variable:
An image-wise score. Its channel size is :obj:`self.n_class`.
"""
p1 = F.MaxPooling2D(2, 2)
p2 = F.MaxPooling2D(2, 2)
p3 = F.MaxPooling2D(2, 2)
p4 = F.MaxPooling2D(2, 2)
h = F.local_response_normalization(x, 5, 1, 1e-4 / 5., 0.75)
h = _pool_without_cudnn(p1, F.relu(self.conv1_bn(self.conv1(h))))
h = _pool_without_cudnn(p2, F.relu(self.conv2_bn(self.conv2(h))))
h = _pool_without_cudnn(p3, F.relu(self.conv3_bn(self.conv3(h))))
h = _pool_without_cudnn(p4, F.relu(self.conv4_bn(self.conv4(h))))
h = self._upsampling_2d(h, p4)
h = self.conv_decode4_bn(self.conv_decode4(h))
h = self._upsampling_2d(h, p3)
h = self.conv_decode3_bn(self.conv_decode3(h))
h = self._upsampling_2d(h, p2)
h = self.conv_decode2_bn(self.conv_decode2(h))
h = self._upsampling_2d(h, p1)
h = self.conv_decode1_bn(self.conv_decode1(h))
score = self.conv_classifier(h)
return score
def __init__(self, train=False):
super(VGG16, self).__init__()
self.trunk = [
('conv1_1', L.Convolution2D(3, 64, 3, 1, 1)),
('relu1_1', F.ReLU()),
('conv1_2', L.Convolution2D(64, 64, 3, 1, 1)),
('relu1_2', F.ReLU()),
('pool1', F.MaxPooling2D(2, 2)),
('conv2_1', L.Convolution2D(64, 128, 3, 1, 1)),
('relu2_1', F.ReLU()),
('conv2_2', L.Convolution2D(128, 128, 3, 1, 1)),
('relu2_2', F.ReLU()),
('pool2', F.MaxPooling2D(2, 2)),
('conv3_1', L.Convolution2D(128, 256, 3, 1, 1)),
('relu3_1', F.ReLU()),
('conv3_2', L.Convolution2D(256, 256, 3, 1, 1)),
('relu3_2', F.ReLU()),
('conv3_3', L.Convolution2D(256, 256, 3, 1, 1)),
('relu3_3', F.ReLU()),
('pool3', F.MaxPooling2D(2, 2)),
('conv4_1', L.Convolution2D(256, 512, 3, 1, 1)),
('relu4_1', F.ReLU()),
('conv4_2', L.Convolution2D(512, 512, 3, 1, 1)),
('relu4_2', F.ReLU()),
('conv4_3', L.Convolution2D(512, 512, 3, 1, 1)),
('relu4_3', F.ReLU()),
('pool4', F.MaxPooling2D(2, 2)),
('conv5_1', L.Convolution2D(512, 512, 3, 1, 1)),
('relu5_1', F.ReLU()),
('conv5_2', L.Convolution2D(512, 512, 3, 1, 1)),
('relu5_2', F.ReLU()),
('conv5_3', L.Convolution2D(512, 512, 3, 1, 1)),
('relu5_3', F.ReLU()),
('rpn_conv_3x3', L.Convolution2D(512, 512, 3, 1, 1)),
('rpn_relu_3x3', F.ReLU()),
]
for name, link in self.trunk:
if 'conv' in name:
self.add_link(name, link)
def __call__(self, x):
h = F.elu(self.conv1(x))
h = F.max_pooling_2d(h, 3, stride=2)
h = self.res2(h, self.train)
h = self.res3(h, self.train)
h = self.res4(h, self.train)
h = self.res5(h, self.train)
h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
h = F.elu(self.conv2(h))
h = F.dropout(h, ratio=0.5)
h = self.conv3(h)
h = F.reshape(h, (-1, self.num_class))
return h
def _spatial_pyramid_pooling_2d(x):
return F.spatial_pyramid_pooling_2d(x, 4, F.MaxPooling2D)
def _spatial_pyramid_pooling_2d(x):
return F.spatial_pyramid_pooling_2d(x, 4, F.MaxPooling2D)
