Python tensorflow.python.ops.nn_ops.conv3d() Examples
The following are 11
code examples of tensorflow.python.ops.nn_ops.conv3d().
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tensorflow.python.ops.nn_ops
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Example #1
| Source File: nn_grad.py From lambda-packs with MIT License | 5 votes |
|
def _Conv3DBackpropInputGrad(op, grad):
data_format = op.get_attr("data_format")
return [None,
nn_ops.conv3d_backprop_filter_v2(grad,
array_ops.shape(op.inputs[1]),
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format=data_format),
nn_ops.conv3d(grad,
op.inputs[1],
strides=op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format=data_format)]
Example #2
| Source File: nn_grad.py From lambda-packs with MIT License | 5 votes |
|
def _Conv3DBackpropFilterGrad(op, grad):
data_format = op.get_attr("data_format")
return [nn_ops.conv3d_backprop_input_v2(array_ops.shape(op.inputs[0]),
grad,
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format=data_format),
None,
nn_ops.conv3d(op.inputs[0],
grad,
strides=op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format=data_format)]
Example #3
| Source File: nn_grad.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def _Conv3DBackpropInputGrad(op, grad):
return [None,
nn_ops.conv3d_backprop_filter_v2(grad,
array_ops.shape(op.inputs[1]),
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding")),
nn_ops.conv3d(grad,
op.inputs[1],
strides=op.get_attr("strides"),
padding=op.get_attr("padding"))]
Example #4
| Source File: nn_grad.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def _Conv3DBackpropFilterGrad(op, grad):
return [nn_ops.conv3d_backprop_input_v2(array_ops.shape(op.inputs[0]),
grad,
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding")),
None,
nn_ops.conv3d(op.inputs[0],
grad,
strides=op.get_attr("strides"),
padding=op.get_attr("padding"))]
Example #5
| Source File: nn_grad.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def _Conv3DBackpropInputGrad(op, grad):
return [None,
nn_ops.conv3d_backprop_filter_v2(grad,
array_ops.shape(op.inputs[1]),
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding")),
nn_ops.conv3d(grad,
op.inputs[1],
strides=op.get_attr("strides"),
padding=op.get_attr("padding"))]
Example #6
| Source File: nn_grad.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def _Conv3DBackpropFilterGrad(op, grad):
return [nn_ops.conv3d_backprop_input_v2(array_ops.shape(op.inputs[0]),
grad,
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding")),
None,
nn_ops.conv3d(op.inputs[0],
grad,
strides=op.get_attr("strides"),
padding=op.get_attr("padding"))]
Example #7
| Source File: test_forward.py From incubator-tvm with Apache License 2.0 | 5 votes |
|
def _test_convolution3d(opname, tensor_in_sizes, filter_in_sizes,
dilations, strides, padding, data_format,
deconv_output_shape=[]):
""" One iteration of 3D convolution with given shapes and attributes """
total_size_1 = np.prod(tensor_in_sizes)
total_size_2 = np.prod(filter_in_sizes)
# Initializes the input tensor with array containing incrementing
# numbers from 1.
data_array = [f * 1.0 for f in range(1, total_size_1 + 1)]
filter_array = [f * 1.0 for f in range(1, total_size_2 + 1)]
with tf.Graph().as_default():
in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
in_filter = constant_op.constant(
filter_array, shape=filter_in_sizes, dtype='float32')
if data_format == 'NDHWC':
strides = [1] + strides + [1]
dilations = [1] + dilations + [1]
else:
strides = [1, 1] + strides
dilations = [1, 1] + dilations
if opname == 'conv':
nn_ops.conv3d(in_data,
in_filter,
strides=strides,
dilations=dilations,
padding=padding,
data_format=data_format)
compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
'Placeholder:0', 'Conv3D:0', cuda_layout="NCDHW")
Example #8
| Source File: nn_grad.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def _Conv3DBackpropInputGrad(op, grad):
data_format = op.get_attr("data_format")
return [None,
nn_ops.conv3d_backprop_filter_v2(grad,
array_ops.shape(op.inputs[1]),
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format=data_format),
nn_ops.conv3d(grad,
op.inputs[1],
strides=op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format=data_format)]
Example #9
| Source File: nn_grad.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def _Conv3DBackpropFilterGrad(op, grad):
data_format = op.get_attr("data_format")
return [nn_ops.conv3d_backprop_input_v2(array_ops.shape(op.inputs[0]),
grad,
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format=data_format),
None,
nn_ops.conv3d(op.inputs[0],
grad,
strides=op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format=data_format)]
Example #10
| Source File: nn_grad.py From keras-lambda with MIT License | 5 votes |
|
def _Conv3DBackpropInputGrad(op, grad):
return [None,
nn_ops.conv3d_backprop_filter_v2(grad,
array_ops.shape(op.inputs[1]),
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding")),
nn_ops.conv3d(grad,
op.inputs[1],
strides=op.get_attr("strides"),
padding=op.get_attr("padding"))]
Example #11
| Source File: nn_grad.py From keras-lambda with MIT License | 5 votes |
|
def _Conv3DBackpropFilterGrad(op, grad):
return [nn_ops.conv3d_backprop_input_v2(array_ops.shape(op.inputs[0]),
grad,
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding")),
None,
nn_ops.conv3d(op.inputs[0],
grad,
strides=op.get_attr("strides"),
padding=op.get_attr("padding"))]
