def call(self, inputs):
stride = self.strides[0]
output_length, feature_dim, filters = self.kernel_shape
xs = []
for i in range(output_length):
slice_length = slice(i * stride, i * stride + self.kernel_size[0])
xs.append(K.reshape(inputs[:, slice_length, :], (1, -1, feature_dim)))
x_aggregate = K.concatenate(xs, axis=0)
# Shape: `(output_length, batch_size, filters)`.
output = K.batch_dot(x_aggregate, self.kernel)
output = K.permute_dimensions(output, (1, 0, 2))
if self.use_bias:
output += K.reshape(self.bias, (1, output_length, filters))
if self.activation is not None:
output = self.activation(output)
return output
def call(self, inputs):
stride = self.strides[0]
output_length, feature_dim, filters = self.kernel_shape
xs = []
for i in range(output_length):
slice_length = slice(i * stride, i * stride + self.kernel_size[0])
xs.append(K.reshape(inputs[:, slice_length, :], (1, -1, feature_dim)))
x_aggregate = K.concatenate(xs, axis=0)
# Shape: `(output_length, batch_size, filters)`.
output = K.batch_dot(x_aggregate, self.kernel)
output = K.permute_dimensions(output, (1, 0, 2))
if self.use_bias:
output += K.reshape(self.bias, (1, output_length, filters))
if self.activation is not None:
output = self.activation(output)
return output
def call(self, inputs):
x1 = inputs[0]
x2 = inputs[1]
if isinstance(self.axes, int):
if self.axes < 0:
axes = [self.axes % K.ndim(x1), self.axes % K.ndim(x2)]
else:
axes = [self.axes] * 2
else:
axes = []
for i in range(len(self.axes)):
if self.axes[i] < 0:
axes.append(self.axes[i] % K.ndim(inputs[i]))
else:
axes.append(self.axes[i])
if self.normalize:
x1 = K.l2_normalize(x1, axis=axes[0])
x2 = K.l2_normalize(x2, axis=axes[1])
output = K.batch_dot(x1, x2, axes)
return output
def call(self, inputs):
stride_row, stride_col = self.strides
_, feature_dim, filters = self.kernel_shape
if self.data_format == 'channels_first':
if K.backend() == 'theano':
output = []
for i in range(self.output_row):
for j in range(self.output_col):
slice_row = slice(i * stride_row,
i * stride_row + self.kernel_size[0])
slice_col = slice(j * stride_col,
j * stride_col + self.kernel_size[1])
x_flatten = K.reshape(inputs[:, :, slice_row, slice_col],
(1, -1, feature_dim))
output.append(
K.dot(x_flatten, self.kernel[i * self.output_col + j, :, :]))
output = K.concatenate(output, axis=0)
else:
xs = []
for i in range(self.output_row):
for j in range(self.output_col):
slice_row = slice(i * stride_row,
i * stride_row + self.kernel_size[0])
slice_col = slice(j * stride_col,
j * stride_col + self.kernel_size[1])
xs.append(
K.reshape(inputs[:, :, slice_row, slice_col], (1, -1,
feature_dim)))
x_aggregate = K.concatenate(xs, axis=0)
output = K.batch_dot(x_aggregate, self.kernel)
output = K.reshape(output, (self.output_row, self.output_col, -1,
filters))
output = K.permute_dimensions(output, (2, 3, 0, 1))
elif self.data_format == 'channels_last':
xs = []
for i in range(self.output_row):
for j in range(self.output_col):
slice_row = slice(i * stride_row,
i * stride_row + self.kernel_size[0])
slice_col = slice(j * stride_col,
j * stride_col + self.kernel_size[1])
xs.append(
K.reshape(inputs[:, slice_row, slice_col, :], (1, -1, feature_dim
)))
x_aggregate = K.concatenate(xs, axis=0)
output = K.batch_dot(x_aggregate, self.kernel)
output = K.reshape(output, (self.output_row, self.output_col, -1,
filters))
output = K.permute_dimensions(output, (2, 0, 1, 3))
if self.use_bias:
if self.data_format == 'channels_first':
output += K.reshape(self.bias, (1, filters, self.output_row,
self.output_col))
elif self.data_format == 'channels_last':
output += K.reshape(self.bias, (1, self.output_row, self.output_col,
filters))
output = self.activation(output)
return output
def call(self, inputs):
stride_row, stride_col = self.strides
_, feature_dim, filters = self.kernel_shape
if self.data_format == 'channels_first':
if K.backend() == 'theano':
output = []
for i in range(self.output_row):
for j in range(self.output_col):
slice_row = slice(i * stride_row,
i * stride_row + self.kernel_size[0])
slice_col = slice(j * stride_col,
j * stride_col + self.kernel_size[1])
x_flatten = K.reshape(inputs[:, :, slice_row, slice_col],
(1, -1, feature_dim))
output.append(
K.dot(x_flatten, self.kernel[i * self.output_col + j, :, :]))
output = K.concatenate(output, axis=0)
else:
xs = []
for i in range(self.output_row):
for j in range(self.output_col):
slice_row = slice(i * stride_row,
i * stride_row + self.kernel_size[0])
slice_col = slice(j * stride_col,
j * stride_col + self.kernel_size[1])
xs.append(
K.reshape(inputs[:, :, slice_row, slice_col], (1, -1,
feature_dim)))
x_aggregate = K.concatenate(xs, axis=0)
output = K.batch_dot(x_aggregate, self.kernel)
output = K.reshape(output, (self.output_row, self.output_col, -1,
filters))
output = K.permute_dimensions(output, (2, 3, 0, 1))
elif self.data_format == 'channels_last':
xs = []
for i in range(self.output_row):
for j in range(self.output_col):
slice_row = slice(i * stride_row,
i * stride_row + self.kernel_size[0])
slice_col = slice(j * stride_col,
j * stride_col + self.kernel_size[1])
xs.append(
K.reshape(inputs[:, slice_row, slice_col, :], (1, -1, feature_dim
)))
x_aggregate = K.concatenate(xs, axis=0)
output = K.batch_dot(x_aggregate, self.kernel)
output = K.reshape(output, (self.output_row, self.output_col, -1,
filters))
output = K.permute_dimensions(output, (2, 0, 1, 3))
if self.use_bias:
if self.data_format == 'channels_first':
output += K.reshape(self.bias, (1, filters, self.output_row,
self.output_col))
elif self.data_format == 'channels_last':
output += K.reshape(self.bias, (1, self.output_row, self.output_col,
filters))
output = self.activation(output)
return output