本文整理汇总了Python中torch.nn.functional.bilinear方法的典型用法代码示例。如果您正苦于以下问题：Python functional.bilinear方法的具体用法？Python functional.bilinear怎么用？Python functional.bilinear使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch.nn.functional的用法示例。
在下文中一共展示了functional.bilinear方法的7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
# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import bilinear [as 别名]
def forward(self, input_left, input_right):
'''
Args:
input_left: Tensor
the left input tensor with shape = [batch1, batch2, ..., left_features]
input_right: Tensor
the right input tensor with shape = [batch1, batch2, ..., right_features]
Returns:
'''
left_size = input_left.size()
right_size = input_right.size()
assert left_size[:-1] == right_size[:-1], \
"batch size of left and right inputs mis-match: (%s, %s)" % (left_size[:-1], right_size[:-1])
batch = int(np.prod(left_size[:-1]))
# convert left and right input to matrices [batch, left_features], [batch, right_features]
input_left = input_left.view(batch, self.left_features)
input_right = input_right.view(batch, self.right_features)
# output [batch, out_features]
output = F.bilinear(input_left, input_right, self.U, self.bias)
output = output + F.linear(input_left, self.W_l, None) + F.linear(input_right, self.W_r, None)
# convert back to [batch1, batch2, ..., out_features]
return output.view(left_size[:-1] + (self.out_features, )) 示例2: forward
# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import bilinear [as 别名]
def forward(self, input_left, input_right):
"""
Args:
input_left: Tensor
the left input tensor with shape = [batch1, batch2, ..., left_features]
input_right: Tensor
the right input tensor with shape = [batch1, batch2, ..., right_features]
Returns:
"""
batch_size = input_left.size()[:-1]
batch = int(np.prod(batch_size))
# convert left and right input to matrices [batch, left_features], [batch, right_features]
input_left = input_left.view(batch, self.left_features)
input_right = input_right.view(batch, self.right_features)
# output [batch, out_features]
output = F.bilinear(input_left, input_right, self.U, self.bias)
output = output + F.linear(input_left, self.weight_left, None) + F.linear(input_right, self.weight_right, None)
# convert back to [batch1, batch2, ..., out_features]
return output.view(batch_size + (self.out_features, )) 示例3: forward
# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import bilinear [as 别名]
def forward(self, input_left, input_right):
'''
Args:
input_left: Tensor
the left input tensor with shape = [batch1, batch2, ..., left_features]
input_right: Tensor
the right input tensor with shape = [batch1, batch2, ..., right_features]
Returns:
'''
left_size = input_left.size()
right_size = input_right.size()
assert left_size[:-1] == right_size[:-1], \
"batch size of left and right inputs mis-match: (%s, %s)" % (left_size[:-1], right_size[:-1])
batch = int(np.prod(left_size[:-1]))
# convert left and right input to matrices [batch, left_features], [batch, right_features]
input_left = input_left.view(batch, self.left_features)
input_right = input_right.view(batch, self.right_features)
# output [batch, out_features]
output = F.bilinear(input_left, input_right, self.U, self.bias)
output = output + F.linear(input_left, self.W_l, None) + F.linear(input_right, self.W_r, None)
# convert back to [batch1, batch2, ..., out_features]
return output.view(left_size[:-1] + (self.out_features, )) 示例4: forward
# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import bilinear [as 别名]
def forward(self, input1, input2):
dir_ = self.direction
direction = dir_.div(dir_.pow(2).sum(1).sum(1).sqrt()[:,N_,N_])
weight = self.scale[:,N_,N_].mul(direction)
return F.bilinear(input1, input2, weight, self.bias) 示例5: forward
# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import bilinear [as 别名]
def forward(self, input1, input2):
weight = self._regulize_parameter(self.weight)
output = F.bilinear(input1, input2, weight, None)
if self.norm:
output = normalize_prob(output)
return output 示例6: forward
# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import bilinear [as 别名]
def forward(self, input1, input2, params=None):
if params is None:
params = OrderedDict(self.named_parameters())
bias = params.get('bias', None)
return F.bilinear(input1, input2, params['weight'], bias) 示例7: test_grid_sample
# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import bilinear [as 别名]
def test_grid_sample(self):
inp = torch.randn(16, 8, 64, 64, device='cuda', dtype=self.dtype)
grid = torch.randn(16, 32, 32, 2, device='cuda', dtype=self.dtype)
output = F.grid_sample(inp, grid, mode='bilinear', padding_mode='zeros')