def __call__(self, x, t):
self.clear()
test = not self.train
h = F.max_pooling_2d(
F.relu(self.norm1(self.conv1(x), test=test)), 3, stride=2, pad=1)
h = F.max_pooling_2d(
F.relu(self.norm2(self.conv2(h), test=test)), 3, stride=2, pad=1)
h = self.inc3a(h)
h = self.inc3b(h)
h = self.inc3c(h)
h = self.inc4a(h)
a = F.average_pooling_2d(h, 5, stride=3)
a = F.relu(self.norma(self.conva(a), test=test))
a = F.relu(self.norma2(self.lina(a), test=test))
a = self.outa(a)
self.loss1 = F.softmax_cross_entropy(a, t)
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
b = F.average_pooling_2d(h, 5, stride=3)
b = F.relu(self.normb(self.convb(b), test=test))
b = F.relu(self.normb2(self.linb(b), test=test))
b = self.outb(b)
self.loss2 = F.softmax_cross_entropy(b, t)
h = self.inc4e(h)
h = self.inc5a(h)
h = F.average_pooling_2d(self.inc5b(h), 7)
h = self.out(h)
self.loss3 = F.softmax_cross_entropy(h, t)
self.loss = 0.3 * (self.loss1 + self.loss2) + self.loss3
self.accuracy = F.accuracy(h, t)
return self.loss
python类average_pooling_2d()的实例源码
def forward(self):
x = chainer.Variable(self.x)
return functions.average_pooling_2d(
x, 3, stride=2, pad=1, use_cudnn=self.use_cudnn)
def __call__(self, x, subtract_mean=True):
if subtract_mean:
x = x - self._image_mean
# h = super(ModifiedGoogLeNet, self).__call__(
# x, layers=['pool5'], train=train)['pool5']
# h = self.bn_fc(h, test=not train)
# y = self.fc(h)
# return y
h = F.relu(self.conv1(x))
h = F.max_pooling_2d(h, 3, stride=2)
h = F.local_response_normalization(h, n=5, k=1, alpha=1e-4/5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.local_response_normalization(h, n=5, k=1, alpha=1e-4/5)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc3a(h)
h = self.inc3b(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc4a(h)
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
h = self.inc4e(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc5a(h)
h = self.inc5b(h)
h = F.average_pooling_2d(h, 7, stride=1)
h = self.bn_fc(h)
y = self.fc(h)
if self.normalize_output:
y = F.normalize(y)
return y
def __call__(self, x, train):
h = bst.bst(self.b0(self.conv0(x)))
h = bst.bst(self.b1(self.conv1(h)))
h = bst.bst(self.b2(self.conv2(h)))
h = F.max_pooling_2d(h, 2)
h = F.average_pooling_2d(h, 32)
h = self.b3(self.fc0(h))
return h
def __call__(self, x, train):
h = bst.bst(self.b0(self.conv0(x)))
h = bst.bst(self.b1(self.conv1(h)))
h = bst.bst(self.b2(self.conv2(h)))
h = F.max_pooling_2d(h, 2)
h = F.average_pooling_2d(h, 24)
h = self.b3(self.fc0(h))
return h
pyramidal_residual_networks.py 文件源码
项目：pyramidal_residual_networks
作者: nutszebra
项目源码
文件源码
阅读 24
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def maybe_pooling(self, x):
if 2 in self.strides:
return F.average_pooling_2d(x, 1, 2, 0)
return x
pyramidal_residual_networks.py 文件源码
项目：pyramidal_residual_networks
作者: nutszebra
项目源码
文件源码
阅读 21
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def __call__(self, x, train=False):
h = self.conv1(x, train=train)
for i in six.moves.range(len(self.strides)):
for ii in six.moves.range(len(self.strides[i])):
name = 'res_block{}_{}'.format(i, ii)
h = self[name](h, train=train)
batch, channels, height, width = h.data.shape
h = F.reshape(F.average_pooling_2d(h, (height, width)), (batch, channels, 1, 1))
return F.reshape(self.linear(h, train=train), (batch, self.category_num))
def __init__(self, ksize, stride=None, pad=0, use_cudnn=True):
self._function = "average_pooling_2d"
self.ksize = ksize
self.stride = stride
self.pad = pad
self.use_cudnn = use_cudnn
def __call__(self, x):
return F.average_pooling_2d(x, self.ksize, self.stride, self.pad, self.use_cudnn)
def __call__(self, x):
skip = False
if chainer.config.train and self.skip_ratio > 0 and np.random.rand() < self.skip_ratio:
skip = True
sh, sw = self.conv1.stride
c_out, c_in, kh, kw = self.conv1.W.data.shape
b, c, hh, ww = x.data.shape
if sh == 1 and sw == 1:
shape_out = (b, c_out, hh, ww)
else:
hh = (hh + 2 - kh) // sh + 1
ww = (ww + 2 - kw) // sw + 1
shape_out = (b, c_out, hh, ww)
h = x
if x.data.shape != shape_out:
xp = chainer.cuda.get_array_module(x.data)
n, c, hh, ww = x.data.shape
pad_c = shape_out[1] - c
p = xp.zeros((n, pad_c, hh, ww), dtype=xp.float32)
p = chainer.Variable(p)
x = F.concat((p, x))
if x.data.shape[2:] != shape_out[2:]:
x = F.average_pooling_2d(x, 1, 2)
if skip:
return x
h = self.bn1(self.conv1(h))
if self.activation1 is not None:
h = self.activation1(h)
h = self.bn2(self.conv2(h))
if not chainer.config.train:
h = h * (1 - self.skip_ratio)
if self.swapout:
h = F.dropout(h) + F.dropout(x)
else:
h = h + x
if self.activation2 is not None:
return self.activation2(h)
else:
return h
def __call__(self, x):
skip = False
if chainer.config.train and self.skip_ratio > 0 and np.random.rand() < self.skip_ratio:
skip = True
sh, sw = self.conv1.stride
c_out, c_in, kh, kw = self.conv1.W.data.shape
b, c, hh, ww = x.data.shape
if sh == 1 and sw == 1:
shape_out = (b, c_out, hh, ww)
else:
hh = (hh + 2 - kh) // sh + 1
ww = (ww + 2 - kw) // sw + 1
shape_out = (b, c_out, hh, ww)
h = x
if x.data.shape != shape_out:
xp = chainer.cuda.get_array_module(x.data)
n, c, hh, ww = x.data.shape
pad_c = shape_out[1] - c
p = xp.zeros((n, pad_c, hh, ww), dtype=xp.float32)
p = chainer.Variable(p)
x = F.concat((p, x))
if x.data.shape[2:] != shape_out[2:]:
x = F.average_pooling_2d(x, 1, 2)
if skip:
return x
h = self.bn1(h)
if self.activation1 is not None:
h = self.activation1(h)
h = self.conv1(h)
h = self.bn2(h)
if self.activation2 is not None:
h = self.activation2(h)
h = self.conv2(h)
if not chainer.config.train:
h = h * (1 - self.skip_ratio)
if self.swapout:
return F.dropout(h) + F.dropout(x)
else:
return h + x
def __call__(self, x):
xp = chainer.cuda.get_array_module(x.data)
skip = False
if chainer.config.train and self.skip_ratio > 0 and np.random.rand() < self.skip_ratio:
skip = True
sh, sw = self.conv1.stride
c_out, c_in, kh, kw = self.conv1.W.data.shape
b, c, hh, ww = x.data.shape
if sh == 1 and sw == 1:
shape_out = (b, c_out, hh, ww)
else:
hh = (hh + 2 - kh) // sh + 1
ww = (ww + 2 - kw) // sw + 1
shape_out = (b, c_out, hh, ww)
h = x
if x.data.shape[2:] != shape_out[2:]:
x = F.average_pooling_2d(x, 1, 2)
if x.data.shape[1] != c_out:
n, c, hh, ww = x.data.shape
pad_c = c_out - c
p = xp.zeros((n, pad_c, hh, ww), dtype=xp.float32)
p = chainer.Variable(p)
x = F.concat((x, p), axis=1)
if skip:
return x
h = self.bn1(h)
h = self.conv1(h)
h = self.bn2(h)
if self.activation is not None:
h = self.activation(h)
h = self.conv2(h)
h = self.bn3(h)
if self.skip_ratio > 0 and not chainer.config.train:
h = h * (1 - self.skip_ratio)
return h + x
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.average_pooling_2d(h, 4, 1, 0)
h = self.fc(F.dropout(h))
return h
def __call__(self, x):
h = self.bconv1_1(x)
h = F.dropout(h, 0.25)
h = self.bconv1_2(h)
h = F.dropout(h, 0.25)
h = self.bconv1_3(h)
h = F.dropout(h, 0.25)
h = self.bconv1_4(h)
h = F.dropout(F.max_pooling_2d(h, 2), 0.25)
h = self.bconv2_1(h)
h = F.dropout(h, 0.25)
h = self.bconv2_2(h)
h = F.dropout(h, 0.25)
h = self.bconv2_3(h)
h = F.dropout(h, 0.25)
h = self.bconv2_4(h)
h = F.dropout(F.max_pooling_2d(h, 2), 0.25)
h = self.bconv3_1(h)
h = F.dropout(h, 0.25)
h = self.bconv3_2(h)
h = F.dropout(h, 0.25)
h = self.bconv3_3(h)
h = F.dropout(h, 0.25)
h = self.bconv3_4(h)
h = F.dropout(h, 0.25)
h = self.bconv3_5(h)
h = F.dropout(h, 0.25)
h = self.bconv3_6(h)
h = F.dropout(h, 0.25)
h = self.bconv3_7(h)
h = F.dropout(h, 0.25)
h = self.bconv3_8(h)
h = F.dropout(F.max_pooling_2d(h, 2), 0.25)
h = F.average_pooling_2d(h, 4, 1, 0)
h = self.fc(F.dropout(h))
return h
def __call__(self, x):
sh, sw = self.conv1_1.stride
c_out, c_in, kh, kw = self.conv1_1.W.data.shape
b, c, hh, ww = x.data.shape
if sh == 1 and sw == 1:
shape_out = (b, c_out, hh, ww)
else:
hh = (hh + 2 - kh) // sh + 1
ww = (ww + 2 - kw) // sw + 1
shape_out = (b, c_out, hh, ww)
h = x
if x.data.shape != shape_out:
xp = chainer.cuda.get_array_module(x.data)
n, c, hh, ww = x.data.shape
pad_c = shape_out[1] - c
p = xp.zeros((n, pad_c, hh, ww), dtype=xp.float32)
x = F.concat((p, x))
if x.data.shape[2:] != shape_out[2:]:
x = F.average_pooling_2d(x, 1, 2)
h1 = self.bn1_1(self.conv1_1(h))
h2 = self.bn2_1(self.conv2_1(h))
if self.activation1 is not None:
h1 = self.activation1(h1)
h2 = self.activation1(h2)
h1 = self.bn1_2(self.conv1_2(h1))
h2 = self.bn2_2(self.conv2_2(h2))
h = shake_shake(h1, h2) + x
if self.activation2 is not None:
return self.activation2(h)
else:
return h
def __call__(self, x):
return functions.average_pooling_2d(x, self.ksize, self.stride, self.pad)
def forward(self, x):
h = F.relu(self.conv1(x))
h = F.local_response_normalization(
F.max_pooling_2d(h, 3, stride=2), n=5)
h = F.relu(self.conv2_reduce(h))
h = F.relu(self.conv2(h))
h = F.max_pooling_2d(
F.local_response_normalization(h, n=5), 3, stride=2)
h = self.inc3a(h)
h = self.inc3b(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc4a(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss1_conv(l))
l = F.relu(self.loss1_fc1(l))
l = self.loss1_fc2(l)
loss1 = l
h = self.inc4b(h)
h = self.inc4c(h)
h = self.inc4d(h)
l = F.average_pooling_2d(h, 5, stride=3)
l = F.relu(self.loss2_conv(l))
l = F.relu(self.loss2_fc1(l))
l = self.loss2_fc2(l)
loss2 = l
h = self.inc4e(h)
h = F.max_pooling_2d(h, 3, stride=2)
h = self.inc5a(h)
h = self.inc5b(h)
h = F.average_pooling_2d(h, 7, stride=1)
h = self.loss3_fc(F.dropout(h, 0.4, train=self.train))
loss3 = h
return loss1,loss2,loss3
def __call__(self, x, t):
h = self.bn1(self.conv1(x))
h = F.max_pooling_2d(F.relu(h), 3, stride=2)
h = self.res2(h)
h = self.res3(h)
h = self.res4(h)
h = self.res5(h)
h = F.average_pooling_2d(h, 7, stride=1)
h = self.fc(h)
loss = F.softmax_cross_entropy(h, t)
chainer.report({'loss': loss, 'accuracy': F.accuracy(h, t)}, self)
return loss
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)
h = self.fc(h)
loss = F.softmax_cross_entropy(h, t)
chainer.report({'loss': loss, 'accuracy': F.accuracy(h, t)}, self)
return loss
def __call__(self, x_0, x_1):
h = F.concat([self.c0_0(x_0), self.c0_1(x_1)])
h = self.c1(h)
h = self.c2(h)
h = self.c3(h)
h = self.c4(h)
#h = F.average_pooling_2d(h, h.data.shape[2], 1, 0)
return h
