def __call__(self, x, t, train=True, finetune=False):
h = self.l1(x, train, finetune)
h = F.dropout(h, self.dr, train)
h = self.l2(h, train, finetune)
h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0, cover_all=True, use_cudnn=True)
h = self.l3(h, train, finetune)
h = F.dropout(h, self.dr, train)
h = self.l4(h, train, finetune)
h = F.dropout(h, self.dr, train)
h = self.l5(h, train, finetune)
h = F.dropout(h, self.dr, train)
h = self.l6(h, train, finetune)
h = F.dropout(h, self.dr, train)
h = self.top(h)
h = F.max(h, axis=-1, keepdims=False)
h = F.max(h, axis=-1, keepdims=False)
return F.softmax_cross_entropy(h, t), F.accuracy(h, t)
python类max_pooling_2d()的实例源码
def __call__(self, x, train=True):
h = self.conv1(x, train)
h = self.conv2(h, train)
h = self.conv3(h, train)
h = F.max_pooling_2d(h, ksize=(3, 3), stride=(2, 2), pad=(1, 1))
h = self.conv4(h, train)
h = self.conv5(h, train)
h = self.conv6(h, train)
h = self.inception_f5_1(h, train)
h = self.inception_f5_2(h, train)
h = self.inception_f5_3(h, train)
h = self.inception_f6_1(h, train)
h = self.inception_f6_2(h, train)
h = self.inception_f6_3(h, train)
h = self.inception_f6_4(h, train)
h = self.inception_f6_5(h, train)
h = self.inception_f7_1(h, train)
h = self.inception_f7_2(h, train)
num, categories, y, x = h.data.shape
# global average pooling
h = F.reshape(F.average_pooling_2d(h, (y, x)), (num, categories))
h = F.dropout(h, ratio=0.2, train=train)
h = self.linear(h)
return h
def predict(self, xs):
"""
batch: list of splitted sentences
"""
xs = [self.extractor.process(x) for x in xs]
batchsize = len(xs)
ws, cs, ls = zip(*xs)
ws = map(self.emb_word, ws)
cs = [F.squeeze(
F.max_pooling_2d(
self.conv_char(
F.expand_dims(
self.emb_char(c), 1)), (l, 1)))
for c, l in zip(cs, ls)]
xs_f = [F.dropout(F.concat([w, c]),
self.dropout_ratio, train=self.train) for w, c in zip(ws, cs)]
xs_b = [x[::-1] for x in xs_f]
cx_f, hx_f, cx_b, hx_b = self._init_state(batchsize)
_, _, hs_f = self.lstm_f(hx_f, cx_f, xs_f, train=self.train)
_, _, hs_b = self.lstm_b(hx_b, cx_b, xs_b, train=self.train)
hs_b = [x[::-1] for x in hs_b]
ys = [self.linear2(F.relu(self.linear1(F.concat([h_f, h_b]))))
for h_f, h_b in zip(hs_f, hs_b)]
return [y.data[1:-1] for y in ys]
def __call__(self, x, train=True):
hlist = []
h_0 = self['embed'](x)
if not self.non_static:
h_0 = Variable(h_0.data)
h_1 = F.reshape(h_0, (h_0.shape[0], 1, h_0.shape[1], h_0.shape[2]))
for filter_h in self.filter_sizes:
pool_size = (self.doc_length - filter_h + 1, 1)
h = F.max_pooling_2d(F.relu(self['conv' + str(filter_h)](h_1)), pool_size)
hlist.append(h)
h = F.concat(hlist)
pos = 0
while pos < len(self.hidden_units) - 1:
h = F.dropout(F.relu(self['l' + str(pos)](h)))
pos += 1
y = F.relu(self['l' + str(pos)](h))
return y
def __call__(self, x, t):
self.clear()
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv1(x))), 3, stride=2)
h = F.max_pooling_2d(F.relu(
F.local_response_normalization(self.conv2(h))), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.fc6(h)), train=self.train)
h = F.dropout(F.relu(self.fc7(h)), train=self.train)
h = self.fc8(h)
self.loss = F.softmax_cross_entropy(h, t)
self.accuracy = F.accuracy(h, t)
return self.loss
def _setup_pooling(self, layer):
param = layer.pooling_param
ksize = _get_ksize(param)
stride = _get_stride(param)
pad = _get_pad(param)
if param.pool == param.MAX:
func = functions.max_pooling_2d
elif param.pool == param.AVE:
func = functions.average_pooling_2d
else:
raise RuntimeError('Stochastic pooling is not supported')
fw = _SingleArgumentFunction(func, ksize, stride=stride, pad=pad)
self.forwards[layer.name] = fw
self._add_layer(layer)
def __call__(self, x, t):
self.clear()
h = self.bn1(self.conv1(x), test=not self.train)
h = F.max_pooling_2d(F.relu(h), 3, stride=2)
h = self.bn2(self.conv2(h), test=not self.train)
h = F.max_pooling_2d(F.relu(h), 3, stride=2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.max_pooling_2d(F.relu(self.conv5(h)), 3, stride=2)
h = F.dropout(F.relu(self.fc6(h)), train=self.train)
h = F.dropout(F.relu(self.fc7(h)), train=self.train)
h = self.fc8(h)
self.loss = F.softmax_cross_entropy(h, t)
self.accuracy = F.accuracy(h, t)
return self.loss
def check_forward(self, x_data, use_cudnn=True):
x = chainer.Variable(x_data)
y = functions.max_pooling_2d(x, 3, stride=2, pad=1,
cover_all=self.cover_all,
use_cudnn=use_cudnn)
self.assertEqual(y.data.dtype, self.dtype)
y_data = cuda.to_cpu(y.data)
self.assertEqual(self.gy.shape, y_data.shape)
for k in six.moves.range(2):
for c in six.moves.range(3):
x = self.x[k, c]
if self.cover_all:
expect = numpy.array([
[x[0:2, 0:2].max(), x[0:2, 1:3].max()],
[x[1:4, 0:2].max(), x[1:4, 1:3].max()],
[x[3:4, 0:2].max(), x[3:4, 1:3].max()]])
else:
expect = numpy.array([
[x[0:2, 0:2].max(), x[0:2, 1:3].max()],
[x[1:4, 0:2].max(), x[1:4, 1:3].max()]])
gradient_check.assert_allclose(expect, y_data[k, c])
def predict(self,x):
h = F.leaky_relu(self.c1(x),slope=0.1)
h = F.max_pooling_2d(h,ksize=2,stride=2,pad=0)
h = F.leaky_relu(self.c3(h),slope=0.1)
h = F.max_pooling_2d(h,ksize=2,stride=2,pad=0)
h = F.leaky_relu(self.c5(h),slope=0.1)
h = F.max_pooling_2d(h,ksize=2,stride=2,pad=0)
h = F.leaky_relu(self.c7(h),slope=0.1)
h = F.max_pooling_2d(h,ksize=2,stride=2,pad=0)
h = F.leaky_relu(self.c9(h),slope=0.1)
h = F.max_pooling_2d(h,ksize=2,stride=2,pad=0)
h = F.leaky_relu(self.c11(h),slope=0.1)
h = F.max_pooling_2d(h,ksize=2,stride=2,pad=0)
h = F.leaky_relu(self.c13(h),slope=0.1)
h = F.leaky_relu(self.c14(h),slope=0.1)
h = F.leaky_relu(self.c15(h),slope=0.1)
h = F.leaky_relu(self.l16(h),slope=0.1)
h = F.leaky_relu(self.l17(h),slope=0.1)
# skip dropout
h = self.l19(h)
return h
def predict(self, x):
""" Predict 2D pose from image. """
# layer1
h = F.relu(self.conv1(x))
h = F.max_pooling_2d(h, 3, stride=2)
# layer2
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(h, 3, stride=2)
# layer3-5
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
h = F.relu(self.conv5(h))
h = F.max_pooling_2d(h, 3, stride=2)
# layer6-8
h = F.dropout(F.relu(self.fc6(h)), train=self.train)
h = F.dropout(F.relu(self.fc7(h)), train=self.train)
h = self.fc8(h)
return F.reshape(h, (-1, self.Nj, 2))
convolutional_pose_machine.py 文件源码
项目：convolutional-pose-machines-chainer
作者: tomoyukun
项目源码
文件源码
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def __call__(self, x):
h = self.conv1(x)
h = F.relu(h)
h = F.max_pooling_2d(h, ksize=3, stride=2)
h = self.conv2(h)
h = F.relu(h)
h = F.max_pooling_2d(h, ksize=3, stride=2)
h = self.conv3(h)
h = F.relu(h)
h = F.max_pooling_2d(h, ksize=3, stride=2)
h = self.conv4(h)
h = F.relu(h)
h = self.conv5(h)
h = F.relu(h)
h = self.conv6(h)
h = F.relu(h)
h = self.conv7(h)
return h
__init__.py 文件源码
项目：convolutional-pose-machines-chainer
作者: tomoyukun
项目源码
文件源码
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def __call__(self, x):
h = self.conv1(x)
h = F.relu(h)
h = F.max_pooling_2d(h, ksize=3, stride=2)
h = self.conv2(h)
h = F.relu(h)
h = F.max_pooling_2d(h, ksize=3, stride=2)
h = self.conv3(h)
h = F.relu(h)
h = F.max_pooling_2d(h, ksize=3, stride=2)
h = self.conv4(h)
h = F.relu(h)
h = self.conv5(h)
h = F.relu(h)
h = self.conv6(h)
h = F.relu(h)
h = self.conv7(h)
return h
def __call__(self, xs):
if self.freeze:
self.embed.disable_update()
xs = self.embed(xs)
batchsize, height, width = xs.shape
xs = F.reshape(xs, (batchsize, 1, height, width))
conv3_xs = self.conv3(xs)
conv4_xs = self.conv4(xs)
conv5_xs = self.conv5(xs)
h1 = F.max_pooling_2d(F.relu(conv3_xs), conv3_xs.shape[2])
h2 = F.max_pooling_2d(F.relu(conv4_xs), conv4_xs.shape[2])
h3 = F.max_pooling_2d(F.relu(conv5_xs), conv5_xs.shape[2])
concat_layer = F.concat([h1, h2, h3], axis=1)
with chainer.using_config('train', True):
y = self.l1(F.dropout(F.tanh(concat_layer)))
return y
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, 2)
h = F.relu(self.conv2_1(h))
h = F.relu(self.conv2_2(h))
h = F.max_pooling_2d(h, 2, 2)
h = F.relu(self.conv3_1(h))
h = F.relu(self.conv3_2(h))
h = F.relu(self.conv3_3(h))
h = F.relu(self.conv3_4(h))
h = F.max_pooling_2d(h, 2, 2)
h = F.relu(self.fc4(h))
h = F.relu(self.fc5(h))
h = self.fc6(h)
L_out = h
return L_out
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
def __call__(self, x):
h = self.bconv1_1(x)
h = self.bconv1_2(h)
h = F.dropout(F.max_pooling_2d(h, 2), 0.25)
h = self.bconv2_1(h)
h = self.bconv2_2(h)
h = F.dropout(F.max_pooling_2d(h, 2), 0.25)
h = self.bconv3_1(h)
h = self.bconv3_2(h)
h = self.bconv3_3(h)
h = self.bconv3_4(h)
h = F.dropout(F.max_pooling_2d(h, 2), 0.25)
h = F.relu(self.fc4(F.dropout(h)))
h = F.relu(self.fc5(F.dropout(h)))
h = self.fc6(h)
return h
def __call__(self, x):
h = self.l0(x)
h = self.l1_1(h)
h = self.l1_2(h)
h = F.dropout(F.max_pooling_2d(h, 2), 0.25)
h = self.l2_1(h)
h = self.l2_2(h)
h = F.dropout(F.max_pooling_2d(h, 2), 0.25)
h = self.l3_1(h)
h = self.l3_2(h)
h = F.dropout(F.max_pooling_2d(h, 2), 0.25)
h = self.l4_1(h)
h = self.l4_2(h)
h = F.dropout(h, 0.25)
h = F.average_pooling_2d(h, 4, 1, 0)
h = self.fc(h)
return h
def __call__(self, x):
h = self.bconv1_1(x)
h = self.bconv1_2(h)
h = F.dropout(F.max_pooling_2d(h, 2), 0.25)
h = self.bconv2_1(h)
h = self.bconv2_2(h)
h = F.dropout(F.max_pooling_2d(h, 2), 0.25)
h = self.bconv3_1(h)
h = self.bconv3_2(h)
h = self.bconv3_3(h)
h = self.bconv3_4(h)
h = F.dropout(F.max_pooling_2d(h, 2), 0.25)
h = F.relu(self.fc4(F.dropout(h)))
h = F.relu(self.fc5(F.dropout(h)))
h = self.fc6(h)
return h
def _setup_pooling(self, layer):
param = layer.pooling_param
ksize = _get_ksize(param)
stride = _get_stride(param)
pad = _get_pad(param)
if param.pool == param.MAX:
func = functions.max_pooling_2d
elif param.pool == param.AVE:
func = functions.average_pooling_2d
else:
raise RuntimeError('Stochastic pooling is not supported')
fw = _SingleArgumentFunction(func, ksize, stride=stride, pad=pad)
self.forwards[layer.name] = fw
self._add_layer(layer)
def __call__(self, x, train=True):
h = self.conv1(x, train)
h = F.max_pooling_2d(h, ksize=(3, 3), stride=(2, 2), pad=(1, 1))
h = self.conv2_1x1(h, train)
h = self.conv2_3x3(h, train)
h = F.max_pooling_2d(h, ksize=(3, 3), stride=(2, 2), pad=(1, 1))
h = self.inception3a(h, train)
h = self.inception3b(h, train)
h = self.inception3c(h, train)
h = self.inception4a(h, train)
h = self.inception4b(h, train)
h = self.inception4c(h, train)
h = self.inception4d(h, train)
h = self.inception4e(h, train)
h = self.inception5a(h, train)
h = self.inception5b(h, train)
num, categories, y, x = h.data.shape
# global average pooling
h = F.reshape(F.average_pooling_2d(h, (y, x)), (num, categories))
h = self.linear(h)
return h
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 __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
def __call__(self, x, train=True):
h = F.relu(self.conv1(x))
h = F.max_pooling_2d(h, ksize=(3, 3), stride=(2, 2), pad=(1, 1))
h = F.relu(self.conv2_1x1(h))
h = F.relu(self.conv2_3x3(h))
h = F.max_pooling_2d(h, ksize=(3, 3), stride=(2, 2), pad=(1, 1))
h = self.inception3a(h)
h = self.inception3b(h)
h = F.max_pooling_2d(h, ksize=(3, 3), stride=(2, 2), pad=(1, 1))
h = self.inception4a(h)
h = self.inception4b(h)
h = self.inception4c(h)
h = self.inception4d(h)
h = self.inception4e(h)
h = F.max_pooling_2d(h, ksize=(3, 3), stride=(2, 2), pad=(1, 1))
h = self.inception5a(h)
h = F.relu(self.inception5b(h))
num, categories, y, x = h.data.shape
# global average pooling
h = F.reshape(F.average_pooling_2d(h, (y, x)), (num, categories))
h = F.dropout(h, ratio=0.4, train=train)
h = self.linear(h)
return h
def __call__(self, x, train=False):
h = F.relu(self.conv1(x))
h = F.max_pooling_2d(h, 3, stride=2)
h = self.fire2(h)
h = self.fire3(h)
h = self.fire4(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.fire5(h)
h = self.fire6(h)
h = self.fire7(h)
h = self.fire8(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.fire9(h)
h = F.dropout(h, ratio=0.5, train=train)
h = F.relu(self.conv10(h))
h = F.average_pooling_2d(h, 13)
return F.reshape(h, (-1, 1000))
def __call__(self, x, train=True):
h = F.relu(self.conv1(x))
h = F.max_pooling_2d(h, ksize=(2, 2), stride=(2, 2), pad=(0, 0))
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(h, ksize=(2, 2), stride=(2, 2), pad=(0, 0))
h = F.relu(self.conv3_1(h))
h = F.relu(self.conv3_2(h))
h = F.max_pooling_2d(h, ksize=(2, 2), stride=(2, 2), pad=(0, 0))
h = F.relu(self.conv4_1(h))
h = F.relu(self.conv4_2(h))
h = F.max_pooling_2d(h, ksize=(2, 2), stride=(2, 2), pad=(0, 0))
h = F.relu(self.conv5_1(h))
h = F.relu(self.conv5_2(h))
h = F.max_pooling_2d(h, ksize=(2, 2), stride=(2, 2), pad=(0, 0))
h = F.dropout(h, ratio=0.5, train=train)
h = F.relu(self.fc1(h))
h = F.dropout(h, ratio=0.5, train=train)
h = F.relu(self.fc2(h))
h = self.fc3(h)
num, categories, y, x = h.data.shape
# global average pooling
h = F.reshape(F.average_pooling_2d(h, (y, x)), (num, categories))
return h
def __call__(self, x):
self.clear()
h = self.conv1(x)
h = F.max_pooling_2d(h, (3, 5), stride=2)
h = self.conv2(h)
h = self.conv3(h)
h = F.max_pooling_2d(h, 3, stride=2, pad=1)
h = self.conv4(h)
h = self.fc1(h)
h = self.fc2(h)
return h
def h(self, x, train, finetune):
# First convolution layer.
h = self[0](x)
h = F.dropout(h, ratio=self.dropout, train=train)
# Residual blocks.
for i in range(1, len(self) - 2):
h = self[i](h, train, finetune)
# Batch normalization.
h = self[-2](h, test=not train, finetune=finetune)
h = F.relu(h)
# Average pooling.
h = F.max_pooling_2d(h, ksize=2, pad=0)
# Prediction layer 5.
h = self[-1](h)
h = F.reshape(h, (h.data.shape[0], 5))
return h
def h(self, x, train, finetune):
# First convolution layer.
h = self[0](x)
h = F.dropout(h, ratio=self.dropout, train=train)
# Residual blocks.
for i in range(1, len(self) - 2):
h = self[i](h, train, finetune)
# Batch normalization.
h = self[-2](h, test=not train, finetune=finetune)
h = F.relu(h)
# Average pooling.
h = F.max_pooling_2d(h, ksize=4, pad=0)
# Prediction layer 5.
h = self[-1](h)
h = F.reshape(h, (h.data.shape[0], 5))
return h
def __call__(self, x, t):
self.clear()
h = self.bn1(self.conv1(x), test=not self.train)
h = F.max_pooling_2d(F.relu(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.average_pooling_2d(h, 7, stride=1)
if t=="feature":
return h
h = self.fc(h)
if self.train:
self.loss = F.softmax_cross_entropy(h, t)
self.accuracy = F.accuracy(h, t)
return self.loss
else:
return h
def __call__(self, x, t, predict=False):
h = self.bn1(self.conv1(x), test=not self.train)
h = F.max_pooling_2d(F.relu(h), 2, stride=2)
h = self.bn2(self.conv2(h), test=not self.train)
h = F.max_pooling_2d(F.relu(h), 2, stride=2)
h = F.dropout(F.relu(self.conv3(h)), ratio=0.6, train=self.train)
h = F.max_pooling_2d(F.relu(self.conv4(h)), 2, stride=2)
h = F.average_pooling_2d(F.relu(self.conv5(h)), 3, stride=1)
h = F.dropout(F.relu(self.fc6(h)), ratio=0.6, train=self.train)
h = self.fc7(h)
self.loss = F.softmax_cross_entropy(h, t)
self.accuracy = F.accuracy(h, t)
if predict:
return h
else:
return self.loss
def reduct(self, x):
h = F.relu(self.conv1_1(x))
h = F.relu(self.bn1(self.conv1_2(h)))
# 100 -> 50
h = F.max_pooling_2d(h, 2, stride=2)
h = F.relu(self.conv2_1(h))
h = F.relu(self.bn2(self.conv2_2(h)))
# 50 -> 25
h = F.max_pooling_2d(h, 2, stride=2)
h = F.relu(self.conv3_1(h))
h = F.relu(self.bn3(self.conv3_2(h)))
# 25 -> (25 + 1 * 2 - 3) / 3 + 1 = 9
h = F.max_pooling_2d(h, 3, stride=3, pad=1)
h = F.relu(self.conv4_1(h))
h = F.relu(self.bn4(self.conv4_2(h)))
# 9 -> 1
h = F.average_pooling_2d(h, 9, stride=1)
return h
def reduct(self, x):
h = F.relu(self.conv1_1(x))
h = F.relu(self.conv1_2(h))
h = self.bn1(h)
# 100 -> 50
h = F.max_pooling_2d(h, 2, stride=2)
h = F.relu(self.conv2(h))
h = self.bn2(h)
# 50 -> 25
h = F.max_pooling_2d(h, 2, stride=2)
h = F.relu(self.conv3(h))
h = self.bn3(h)
# 25 -> (25 + 1 * 2 - 3) / 3 + 1 = 9
h = F.max_pooling_2d(h, 3, stride=3, pad=1)
h = F.relu(self.conv4(h))
h = self.bn4(h)
# 9 -> 1
h = F.average_pooling_2d(h, 9, stride=1)
return h
