本文整理汇总了Python中torch.nn.functional.max_pool2d函数的典型用法代码示例。如果您正苦于以下问题：Python max_pool2d函数的具体用法？Python max_pool2d怎么用？Python max_pool2d使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了max_pool2d函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: forward
def forward(self, x):
x = self.conv1(x)
x = F.max_pool2d(x, 2) + F.avg_pool2d(x, 2)
x = self.block1(x)
x = self.group1(x)
x = F.max_pool2d(x, 2) + F.avg_pool2d(x, 2)
x = self.block2(x)
x = self.group2(x)
x = F.max_pool2d(x, 2) + F.avg_pool2d(x, 2)
x = self.block3(x)
x = self.group3(x)
x = self.block4(x)
x = self.group4(x)
x = F.max_pool2d(x, 2) + F.avg_pool2d(x, 2)
x = x.view(x.size(0), -1)
fc = self.fc(x)
x = F.dropout(fc, training=self.training)
output = list()
for name, fun in self.fc_dict.iteritems():
out = fun(x)
output.append(out)
return output, fc示例2: forward
def forward(self, X):
h = F.relu(self.conv1_1(X), inplace=True)
h = F.relu(self.conv1_2(h), inplace=True)
# relu1_2 = h
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv2_1(h), inplace=True)
h = F.relu(self.conv2_2(h), inplace=True)
# relu2_2 = h
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv3_1(h), inplace=True)
h = F.relu(self.conv3_2(h), inplace=True)
h = F.relu(self.conv3_3(h), inplace=True)
# relu3_3 = h
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv4_1(h), inplace=True)
h = F.relu(self.conv4_2(h), inplace=True)
h = F.relu(self.conv4_3(h), inplace=True)
# relu4_3 = h
h = F.relu(self.conv5_1(h), inplace=True)
h = F.relu(self.conv5_2(h), inplace=True)
h = F.relu(self.conv5_3(h), inplace=True)
relu5_3 = h
return relu5_3示例3: forward
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), 2)
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, 64 * 7 * 7) # reshape Variable
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=-1)示例4: forward
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
return F.log_softmax(self.fc2(x))示例5: forward
def forward(self, x):
out = F.relu(F.max_pool2d(self.conv1(x), 2))
out = F.relu(F.max_pool2d(self.conv2(out), 2))
out = out.view(-1, 320)
out = F.relu(self.fc1(out))
out = self.fc2(out)
return F.log_softmax(out, dim=1)示例6: forward
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x示例7: forward
def forward(self, x):
if self.transform_input:
x = x.clone()
x[:, 0] = x[:, 0] * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
x[:, 1] = x[:, 1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
x[:, 2] = x[:, 2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
else: warn("Input isn't transformed")
x = self.Conv2d_1a_3x3(x)
x = self.Conv2d_2a_3x3(x)
x = self.Conv2d_2b_3x3(x)
x = F.max_pool2d(x, kernel_size=3, stride=2)
x = self.Conv2d_3b_1x1(x)
x = self.Conv2d_4a_3x3(x)
x = F.max_pool2d(x, kernel_size=3, stride=2)
x = self.Mixed_5b(x)
x = self.Mixed_5c(x)
x = self.Mixed_5d(x)
x = self.Mixed_6a(x)
x = self.Mixed_6b(x)
x = self.Mixed_6c(x)
x = self.Mixed_6d(x)
x = self.Mixed_6e(x)
x = self.Mixed_7a(x)
x = self.Mixed_7b(x)
x_for_attn = x = self.Mixed_7c(x)
# 8 x 8 x 2048
x = F.avg_pool2d(x, kernel_size=8)
# 1 x 1 x 2048
x_for_capt = x = x.view(x.size(0), -1)
# 2048
x = self.fc(x)
# 1000 (num_classes)
return x_for_attn, x_for_capt, x示例8: forward
def forward(self, x):
x1 = self.conv1(x)
x1 = F.max_pool2d(x1, 3, stride=2)
x2 = self.fire2(x1)
x3 = self.fire3(x2)
if self.bypass:
x3 = x3 + x2
x4 = self.fire4(x3)
x4 = F.max_pool2d(x4, 3, stride=2)
x5 = self.fire5(x4)
if self.bypass:
x5 = x5 + x4
x6 = self.fire6(x5)
x7 = self.fire7(x6)
if self.bypass:
x7 = x7 + x6
x8 = self.fire8(x7)
x8 = F.max_pool2d(x8, 3, stride=2)
x9 = self.fire9(x8)
if self.bypass:
x9 = x9 + x8
x9 = F.dropout(x9, training=self.training)
x10 = F.relu(self.conv10(x9))
f = F.avg_pool2d(x10, x10.size()[2:]).view(x10.size(0), -1)
if not self.training:
return f
if self.loss == {'xent'}:
return f
elif self.loss == {'xent', 'htri'}:
return f, f
else:
raise KeyError("Unsupported loss: {}".format(self.loss))示例9: forward
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 7*7*64)
x = F.relu(self.fc1(x))
x = F.dropout(x, 0.4)
x = self.fc2(x)
return F.log_softmax(x, dim=1)示例10: forward
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 1600)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return th.abs(10 - x)示例11: forward
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) # max pooling over a 2x2 window
x = F.max_pool2d(F.relu(self.conv2(x)), 2) # square x can only specify single number
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x示例12: deepcompare_2ch
def deepcompare_2ch(input, params):
o = conv2d(input, params, 'conv0', stride=3)
o = F.max_pool2d(F.relu(o), 2, 2)
o = conv2d(o, params, 'conv1')
o = F.max_pool2d(F.relu(o), 2, 2)
o = conv2d(o, params, 'conv2')
o = F.relu(o).view(o.size(0), -1)
return linear(o, params, 'fc')示例13: forward
def forward(self, x):
x = F.max_pool2d(F.relu(self.convolution_0(x)), (2, 2))
x = F.max_pool2d(F.relu(self.convolution_1(x)), (2, 2))
x = x.view(-1, 32 * 5 * 5)
x = F.relu(self.fully_connected_0(x))
x = F.relu(self.fully_connected_1(x))
x = self.fully_connected_2(x)
return x示例14: forward
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) # Max pooling over a (2, 2) window
x = F.max_pool2d(F.relu(self.conv2(x)), 2) # If the size is a square you can only specify a single number
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x示例15: forward
def forward(self, x, y, z):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 1600)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x), F.log_softmax(x), F.log_softmax(x)