def _Pool3DShape(op):
"""Shape function for Max/AvgPool3D."""
input_shape = op.inputs[0].get_shape().with_rank(5)
ksize_b, ksize_p, ksize_r, ksize_c, ksize_d = op.get_attr("ksize")
assert ksize_b == 1
assert ksize_d == 1
stride_b, stride_p, stride_r, stride_c, stride_d = op.get_attr("strides")
assert stride_b == 1
assert stride_d == 1
batch_size = input_shape[0]
channels = input_shape[4]
padding = op.get_attr("padding")
out_planes, out_rows, out_cols = common_shapes.get_conv_output_size(
input_shape[1:4], (ksize_p, ksize_r, ksize_c),
(stride_p, stride_r, stride_c), padding)
return [tensor_shape.TensorShape([batch_size, out_planes, out_rows, out_cols,
channels])]
def _Pool3DShape(op):
"""Shape function for Max/AvgPool3D."""
input_shape = op.inputs[0].get_shape().with_rank(5)
ksize_b, ksize_p, ksize_r, ksize_c, ksize_d = op.get_attr("ksize")
assert ksize_b == 1
assert ksize_d == 1
stride_b, stride_p, stride_r, stride_c, stride_d = op.get_attr("strides")
assert stride_b == 1
assert stride_d == 1
batch_size = input_shape[0]
channels = input_shape[4]
padding = op.get_attr("padding")
out_planes, out_rows, out_cols = common_shapes.get_conv_output_size(
input_shape[1:4], (ksize_p, ksize_r, ksize_c),
(stride_p, stride_r, stride_c), padding)
return [
tensor_shape.TensorShape(
[batch_size, out_planes, out_rows, out_cols, channels])
]
def _Conv3DShape(op):
"""Shape function for Conv3D."""
input_shape = op.inputs[0].get_shape().with_rank(5)
filter_shape = op.inputs[1].get_shape().with_rank(5)
batch_size = input_shape[0]
out_channels = filter_shape[4]
# Check that the input number of channels is compatible between
# input data and filter size.
input_shape[4].assert_is_compatible_with(filter_shape[3])
stride_b, stride_p, stride_r, stride_c, stride_d = op.get_attr("strides")
assert stride_b == 1
assert stride_d == 1
padding_type = op.get_attr("padding")
out_planes, out_rows, out_cols = common_shapes.get_conv_output_size(
input_shape[1:4], filter_shape[0:3], (stride_p, stride_r, stride_c),
padding_type)
return [tensor_shape.TensorShape([batch_size, out_planes, out_rows, out_cols,
out_channels])]
def _Conv3DShape(op):
"""Shape function for Conv3D."""
input_shape = op.inputs[0].get_shape().with_rank(5)
filter_shape = op.inputs[1].get_shape().with_rank(5)
batch_size = input_shape[0]
out_channels = filter_shape[4]
# Check that the input number of channels is compatible between
# input data and filter size.
input_shape[4].assert_is_compatible_with(filter_shape[3])
stride_b, stride_p, stride_r, stride_c, stride_d = op.get_attr("strides")
assert stride_b == 1
assert stride_d == 1
padding_type = op.get_attr("padding")
out_planes, out_rows, out_cols = common_shapes.get_conv_output_size(
input_shape[1:4], filter_shape[0:3], (stride_p, stride_r, stride_c),
padding_type)
return [
tensor_shape.TensorShape(
[batch_size, out_planes, out_rows, out_cols, out_channels])
]
