Python源码示例：maskrcnn.apply()
示例1
def forward(self, x):
if x.numel() > 0:
return nn.functional.conv2d(
x,
self.kernel,
self.bias,
(self.stride, self.stride),
(self.padding, self.padding),
self.dilation,
self.C, # groups
)
output_shape = [
(i + 2 * p - (di * (k - 1) + 1)) // d + 1
for i, p, di, k, d in zip(
x.shape[-2:],
(self.padding, self.dilation),
(self.dilation, self.dilation),
(self.kernel_size, self.kernel_size),
(self.stride, self.stride),
)
]
output_shape = [x.shape[0], self.C] + output_shape
return _NewEmptyTensorOp.apply(x, output_shape)
示例2
def forward(self, x):
if x.numel() > 0:
return nn.functional.conv2d(
x,
self.kernel,
self.bias,
(self.stride, self.stride),
(self.padding, self.padding),
self.dilation,
self.C, # groups
)
output_shape = [
(i + 2 * p - (di * (k - 1) + 1)) // d + 1
for i, p, di, k, d in zip(
x.shape[-2:],
(self.padding, self.dilation),
(self.dilation, self.dilation),
(self.kernel_size, self.kernel_size),
(self.stride, self.stride),
)
]
output_shape = [x.shape[0], self.C] + output_shape
return _NewEmptyTensorOp.apply(x, output_shape)
示例3
def forward(self, x):
if x.numel() > 0:
return nn.functional.conv2d(
x,
self.kernel,
self.bias,
(self.stride, self.stride),
(self.padding, self.padding),
self.dilation,
self.C, # groups
)
output_shape = [
(i + 2 * p - (di * (k - 1) + 1)) // d + 1
for i, p, di, k, d in zip(
x.shape[-2:],
(self.padding, self.dilation),
(self.dilation, self.dilation),
(self.kernel_size, self.kernel_size),
(self.stride, self.stride),
)
]
output_shape = [x.shape[0], self.C] + output_shape
return _NewEmptyTensorOp.apply(x, output_shape)
示例4
def forward(self, x):
if x.numel() > 0:
return nn.functional.conv2d(
x,
self.kernel,
self.bias,
(self.stride, self.stride),
(self.padding, self.padding),
self.dilation,
self.C, # groups
)
output_shape = [
(i + 2 * p - (di * (k - 1) + 1)) // d + 1
for i, p, di, k, d in zip(
x.shape[-2:],
(self.padding, self.dilation),
(self.dilation, self.dilation),
(self.kernel_size, self.kernel_size),
(self.stride, self.stride),
)
]
output_shape = [x.shape[0], self.C] + output_shape
return _NewEmptyTensorOp.apply(x, output_shape)
示例5
def forward(self, x):
if x.numel() > 0:
return nn.functional.conv2d(
x,
self.kernel,
self.bias,
(self.stride, self.stride),
(self.padding, self.padding),
self.dilation,
self.C, # groups
)
output_shape = [
(i + 2 * p - (di * (k - 1) + 1)) // d + 1
for i, p, di, k, d in zip(
x.shape[-2:],
(self.padding, self.dilation),
(self.dilation, self.dilation),
(self.kernel_size, self.kernel_size),
(self.stride, self.stride),
)
]
output_shape = [x.shape[0], self.C] + output_shape
return _NewEmptyTensorOp.apply(x, output_shape)
示例6
def forward(self, x):
if x.numel() > 0:
return nn.functional.conv2d(
x,
self.kernel,
self.bias,
(self.stride, self.stride),
(self.padding, self.padding),
self.dilation,
self.C, # groups
)
output_shape = [
(i + 2 * p - (di * (k - 1) + 1)) // d + 1
for i, p, di, k, d in zip(
x.shape[-2:],
(self.padding, self.dilation),
(self.dilation, self.dilation),
(self.kernel_size, self.kernel_size),
(self.stride, self.stride),
)
]
output_shape = [x.shape[0], self.C] + output_shape
return _NewEmptyTensorOp.apply(x, output_shape)
示例7
def forward(self, x):
if x.numel() > 0:
return nn.functional.conv2d(
x,
self.kernel,
self.bias,
(self.stride, self.stride),
(self.padding, self.padding),
self.dilation,
self.C, # groups
)
output_shape = [
(i + 2 * p - (di * (k - 1) + 1)) // d + 1
for i, p, di, k, d in zip(
x.shape[-2:],
(self.padding, self.dilation),
(self.dilation, self.dilation),
(self.kernel_size, self.kernel_size),
(self.stride, self.stride),
)
]
output_shape = [x.shape[0], self.C] + output_shape
return _NewEmptyTensorOp.apply(x, output_shape)
示例8
def forward(self, x):
if x.numel() > 0:
return nn.functional.conv2d(
x,
self.kernel,
self.bias,
(self.stride, self.stride),
(self.padding, self.padding),
self.dilation,
self.C, # groups
)
output_shape = [
(i + 2 * p - (di * (k - 1) + 1)) // d + 1
for i, p, di, k, d in zip(
x.shape[-2:],
(self.padding, self.dilation),
(self.dilation, self.dilation),
(self.kernel_size, self.kernel_size),
(self.stride, self.stride),
)
]
output_shape = [x.shape[0], self.C] + output_shape
return _NewEmptyTensorOp.apply(x, output_shape)
示例9
def forward(self, x):
if x.numel() > 0:
return nn.functional.conv2d(
x,
self.kernel,
self.bias,
(self.stride, self.stride),
(self.padding, self.padding),
self.dilation,
self.C, # groups
)
output_shape = [
(i + 2 * p - (di * (k - 1) + 1)) // d + 1
for i, p, di, k, d in zip(
x.shape[-2:],
(self.padding, self.dilation),
(self.dilation, self.dilation),
(self.kernel_size, self.kernel_size),
(self.stride, self.stride),
)
]
output_shape = [x.shape[0], self.C] + output_shape
return _NewEmptyTensorOp.apply(x, output_shape)
