cd391389ce
* MO TF: FQPerChannel extractor * [ MO ] Complete weights layout permutation * removed deleted file out of BOM * Bring back stashed changes * Skip if no weights permutation * Conditional permutation * Comments
121 lines
5.5 KiB
Python
121 lines
5.5 KiB
Python
"""
|
|
Copyright (C) 2018-2020 Intel Corporation
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
"""
|
|
|
|
import numpy as np
|
|
|
|
from mo.front.common.partial_infer.utils import mark_input_bins, assign_dims_to_weights, tf_window_op_pad_infer
|
|
from mo.front.extractor import spatial_getter
|
|
from mo.front.onnx.extractors.utils import get_backend_pad
|
|
from mo.graph.graph import Node, Graph
|
|
from mo.graph.perm_inputs import PermuteInputs
|
|
from mo.ops.op import Op, PermuteAttrs
|
|
|
|
|
|
class Deconvolution(Op):
|
|
op = 'Deconvolution'
|
|
|
|
def __init__(self, graph: Graph, attrs: dict):
|
|
super().__init__(graph, {
|
|
'type': self.op,
|
|
'op': self.op,
|
|
'version': 'opset1',
|
|
'infer': self.infer,
|
|
'in_ports_count': 3,
|
|
'out_ports_count': 1,
|
|
}, attrs)
|
|
|
|
def backend_attrs(self):
|
|
return [
|
|
('dilations', lambda node: ','.join(map(str, node['dilation'][node.spatial_dims]))),
|
|
('strides', lambda node: ','.join(map(str, node['stride'][node.spatial_dims]))),
|
|
('pads_begin',
|
|
lambda node: ','.join(map(str, get_backend_pad(node.pad, node.spatial_dims, 0))) if node.has_valid(
|
|
'pad') else None),
|
|
('pads_end',
|
|
lambda node: ','.join(map(str, get_backend_pad(node.pad, node.spatial_dims, 1))) if node.has_valid(
|
|
'pad') else None),
|
|
'auto_pad',
|
|
]
|
|
|
|
@staticmethod
|
|
def infer(node: Node):
|
|
"""
|
|
Deconvolution has an input argument that explicitly determines output shape, so in contrast
|
|
to the forward Conv2d we shouldn't infer output shape. We just use this output shape as
|
|
an input shape and pass it to our utilities that computes numeric values for padding.
|
|
They also deliver output shape that is interpreted here as input shape for convolution.
|
|
We need to check that the real input shape and shape inferred by those utility functions match.
|
|
"""
|
|
output_shape = np.array(node.in_node(2).value)
|
|
batch = np.array(node.in_node(0).shape)[0]
|
|
output_shape[0] = batch
|
|
kernel_shape = node.in_node(1).shape
|
|
node['kernel_shape'] = kernel_shape
|
|
if output_shape is None or kernel_shape is None or node.spatial_dims is None or node.stride is None:
|
|
return
|
|
|
|
if not node.has_valid('kernel_spatial_idx'):
|
|
node['kernel_spatial_idx'] = np.delete([x for x in range(len(kernel_shape))],
|
|
(node.input_feature_channel, node.output_feature_channel))
|
|
|
|
if not node.has_valid('dilation'):
|
|
node['dilation'] = np.full([len(output_shape)], 1, dtype=np.int64)
|
|
|
|
spatial_dims = node.spatial_dims
|
|
output_spatial = np.array(output_shape[spatial_dims])
|
|
stride_spatial = np.array(node.stride[spatial_dims])
|
|
node['kernel_spatial'] = np.array(kernel_shape[node.kernel_spatial_idx])
|
|
node.pad_spatial_shape, input_spatial_for_check = tf_window_op_pad_infer(
|
|
output_spatial, node.kernel_spatial, stride_spatial, node.auto_pad)
|
|
|
|
assert all(input_spatial_for_check == node.in_node(0).shape[spatial_dims])
|
|
|
|
pad = np.zeros((len(output_shape), 2), dtype=np.int64)
|
|
pad[spatial_dims] = node.pad_spatial_shape
|
|
node.pad = pad
|
|
|
|
node.output = output_shape[node.channel_dims][0]
|
|
node.output_shape = output_shape
|
|
node.out_node().shape = output_shape
|
|
|
|
mark_input_bins(node, ['weights'], 1)
|
|
assign_dims_to_weights(node.in_node(1), node.kernel_spatial_idx, node.input_feature_channel,
|
|
node.output_feature_channel, len(kernel_shape))
|
|
|
|
# OK, now we are sure this is a supported Deconvolution layer
|
|
node.type = 'Deconvolution'
|
|
node.op = 'Deconv2D'
|
|
|
|
# Add permute_attrs
|
|
PermuteAttrs.create_permute_attrs(node, attrs=[('pad', 'input:0'),
|
|
('stride', 'input:0'),
|
|
('output_shape', 'input:0'),
|
|
('batch_dims', 'input:0'),
|
|
('channel_dims', 'input:0'),
|
|
('spatial_dims', 'input:0'),
|
|
|
|
('kernel_shape', 'input:1'),
|
|
('kernel_spatial_idx', 'input:1'),
|
|
('input_feature_channel', 'input:1'),
|
|
('output_feature_channel', 'input:1'),
|
|
])
|
|
|
|
PermuteAttrs.set_permutation(node.in_node(1), node, node.soft_get('get_weights_permute', None))
|
|
PermuteInputs().set_input_permutation(node.in_node(1), node, 'input:1', 'transpose')
|
|
PermuteInputs().set_input_permutation(node.in_node(2), node, 'input:0', 'shape')
|
|
|
|
node['force_precision_in_ports'] = {2: 'int64'}
|