4d9fe14ec6
* changed permutation attribute in conv extractor * changed conv get_group parameter * implemented a transformation * updated BOM * specified transformation for in_channels 1 * added unittest and comment string * updated convolution normalizer to convert depthwise convolution with group=1 to group convolution * renamed function * updated IR reader * conversations resolving * condition change
260 lines
10 KiB
Python
260 lines
10 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import numpy as np
|
|
|
|
from extensions.back.ReshapeMutation import ReshapeMutation
|
|
from extensions.back.ReverseInputChannels import ApplyReverseChannels
|
|
from mo.back.replacement import BackReplacementPattern
|
|
from mo.front.common.partial_infer.utils import int64_array
|
|
from mo.front.tf.graph_utils import create_op_node_with_second_input, create_op_with_const_inputs
|
|
from mo.graph.graph import Graph, Node
|
|
from mo.ops.const import Const
|
|
from mo.ops.reshape import Reshape
|
|
from mo.ops.strided_slice import StridedSlice
|
|
from mo.utils.error import Error
|
|
|
|
|
|
def resolve_convolution_with_group(node: Node, group: int, ir_version: str):
|
|
input_shape = node.in_port(0).data.get_shape()
|
|
assert len(input_shape) in [3, 4, 5]
|
|
|
|
weights_shape = node.in_port(1).data.get_shape()
|
|
assert weights_shape is not None
|
|
assert len(weights_shape) in [3, 4, 5]
|
|
assert weights_shape[0] % group == 0
|
|
|
|
assert int64_array(node.output).ndim == 0
|
|
if ir_version == 'V7':
|
|
if weights_shape[0] == node.output:
|
|
# weights are already is in [G*O I X Y] format
|
|
return
|
|
new_shape = int64_array([node.output, -1, *weights_shape[2:]])
|
|
|
|
elif ir_version == 'V10':
|
|
I = input_shape[1]
|
|
new_shape = int64_array([group, node.output / group, I / group, *weights_shape[2:]])
|
|
assert np.prod(weights_shape) == np.prod(new_shape), \
|
|
'Initial weights shape {}, grouped weights shape {}'.format(weights_shape, new_shape)
|
|
del node['group']
|
|
node['type'] = 'GroupConvolution'
|
|
else:
|
|
raise Error("Unknown IR version: {}".format(ir_version))
|
|
|
|
reshape = create_op_node_with_second_input(node.graph, Reshape, int64_array(new_shape),
|
|
{'override_output_shape': True})
|
|
|
|
node.in_port(1).get_connection().insert_node(reshape)
|
|
|
|
|
|
class ConvolutionNormalizer(BackReplacementPattern):
|
|
enabled = True
|
|
|
|
def pattern(self):
|
|
return dict(
|
|
nodes=[
|
|
('node', dict(kind='op', type='Convolution'))
|
|
],
|
|
edges=[]
|
|
)
|
|
|
|
def replace_pattern(self, graph: Graph, match: dict):
|
|
node = match['node']
|
|
if node.has_valid('kernel_spatial'):
|
|
del node['kernel_spatial']
|
|
|
|
|
|
class V7ConvolutionWithGroupsResolver(BackReplacementPattern):
|
|
"""
|
|
Normalizes grouped convolution weights shape to fit special weights format [G*O I X Y]
|
|
"""
|
|
enabled = False
|
|
|
|
def find_and_replace_pattern(self, graph: Graph):
|
|
for node in graph.get_op_nodes(type='Convolution'):
|
|
group = node.soft_get('group', None)
|
|
if group is not None:
|
|
if group != 1 or node.soft_get('op') == 'DepthwiseConv2dNative':
|
|
resolve_convolution_with_group(node, group, ir_version='V7')
|
|
|
|
|
|
class V10ConvolutionWithGroupsResolver(BackReplacementPattern):
|
|
"""
|
|
Normalizes grouped convolution weights shape to fit special weights format
|
|
V10 IR: [G O I X Y]
|
|
"""
|
|
enabled = False
|
|
|
|
def find_and_replace_pattern(self, graph: Graph):
|
|
for node in graph.get_op_nodes(type='Convolution'):
|
|
group = node.soft_get('group', None)
|
|
if group is not None:
|
|
if group != 1 or node.soft_get('op') == 'DepthwiseConv2dNative':
|
|
resolve_convolution_with_group(node, group, ir_version='V10')
|
|
|
|
|
|
class ConvolutionWithGroupsResolver(BackReplacementPattern):
|
|
"""
|
|
Normalizes grouped convolution weights shape to fit special weights format
|
|
V10 IR: [G O I X Y]
|
|
lower IR versions: [G*O I X Y]
|
|
"""
|
|
enabled = True
|
|
force_clean_up = True
|
|
|
|
def run_before(self):
|
|
return [ReshapeMutation]
|
|
|
|
def run_after(self):
|
|
return [ApplyReverseChannels]
|
|
|
|
def find_and_replace_pattern(self, graph: Graph):
|
|
V7ConvolutionWithGroupsResolver().find_and_replace_pattern(graph)
|
|
PullReshapeThroughFQ().find_and_replace_pattern(graph)
|
|
V10ConvolutionWithGroupsResolver().find_and_replace_pattern(graph)
|
|
|
|
|
|
class PullReshapeThroughFQ(BackReplacementPattern):
|
|
"""
|
|
Before:
|
|
... -> FQ -> Reshape -> Convolution -> ...
|
|
|
|
After:
|
|
... -> Reshape -> FQ (with aligned limits) -> Convolution -> ...
|
|
"""
|
|
enabled = False
|
|
|
|
@staticmethod
|
|
def pattern():
|
|
return dict(
|
|
nodes=[
|
|
('FQ', dict(type='FakeQuantize')),
|
|
('FQed', dict()),
|
|
('reshape', dict(type='Reshape')),
|
|
('reshaped', dict()),
|
|
('node', dict(type=lambda t: t in ['Convolution', 'GroupConvolution'])),
|
|
],
|
|
edges=[
|
|
('FQ', 'FQed'),
|
|
('FQed', 'reshape', {'in': 0}),
|
|
('reshape', 'reshaped'),
|
|
('reshaped', 'node', {'in': 1}),
|
|
]
|
|
)
|
|
|
|
def replace_pattern(self, graph: Graph, match: dict):
|
|
FQ = match['FQ']
|
|
reshape = match['reshape']
|
|
conv = match['node']
|
|
|
|
rank_reshape = reshape.in_port(0).data.get_shape().size != reshape.out_port(0).data.get_shape().size
|
|
|
|
if not all([np.prod(FQ.in_port(i).data.get_shape()) == 1 for i in range(1, 5)]):
|
|
# FakeQuantize has limits with multiple values, that should be reshaped too
|
|
# Pulling Reshape through such FQ is a complex procedure because of broadcasting rules
|
|
return
|
|
|
|
new_rank = reshape.out_port(0).data.get_shape().size
|
|
|
|
reshape.in_port(0).disconnect()
|
|
reshape.out_port(0).disconnect()
|
|
|
|
FQ.out_port(0).connect(conv.in_port(1))
|
|
FQ.in_port(0).get_connection().insert_node(reshape)
|
|
|
|
reshape['need_shape_inference'] = True
|
|
reshape['override_output_shape'] = True
|
|
FQ['need_shape_inference'] = True
|
|
FQ['override_output_shape'] = True
|
|
|
|
if rank_reshape:
|
|
# force rank of limit inputs to match 0-input rank
|
|
# reshaping to lower range needs it the most due to FQ inner broadcast semantics
|
|
for i in range(1, 5):
|
|
reshape = create_op_node_with_second_input(graph, Reshape, int64_array([1] * new_rank),
|
|
{'override_output_shape': True})
|
|
FQ.in_port(i).get_connection().insert_node(reshape)
|
|
|
|
|
|
class DeconvolutionNormalizer(BackReplacementPattern):
|
|
enabled = True
|
|
force_clean_up = True
|
|
|
|
def run_before(self):
|
|
return [ReshapeMutation]
|
|
|
|
def run_after(self):
|
|
return [ApplyReverseChannels]
|
|
|
|
@staticmethod
|
|
def pattern():
|
|
return dict(
|
|
nodes=[
|
|
('node', dict(type='Deconvolution'))
|
|
],
|
|
edges=[]
|
|
)
|
|
|
|
def replace_pattern(self, graph: Graph, match: dict):
|
|
node = match['node']
|
|
node_name = node.soft_get('name', node.id)
|
|
|
|
if 2 in node.in_ports() and not node.in_port(2).disconnected():
|
|
# Third input represents output shape. Cutting its value according to scheme:
|
|
# [N, C, spatial_dim_0, ..., spatial_dim_n] -> [spatial_dim_0, ..., spatial_dim_n]
|
|
in_rank = node.in_port(0).data.get_shape().size
|
|
|
|
shape_src = node.in_port(2).get_source()
|
|
node.in_port(2).disconnect()
|
|
|
|
ss_0 = create_op_with_const_inputs(graph, StridedSlice, {1: np.array([2], dtype=np.int32),
|
|
2: np.array([in_rank], dtype=np.int32),
|
|
3: np.array([1], dtype=np.int32)},
|
|
{'name': node_name + '/ss_0_port',
|
|
'begin_mask': np.array([1], dtype=np.int32),
|
|
'end_mask': np.array([0], dtype=np.int32),
|
|
'new_axis_mask': np.array([0], dtype=np.int32),
|
|
'shrink_axis_mask': np.array([0], dtype=np.int32),
|
|
'ellipsis_mask': np.array([0], dtype=np.int32)})
|
|
|
|
shape_src.connect(ss_0.in_port(0))
|
|
ss_0.out_port(0).connect(node.in_port(2))
|
|
|
|
# Specification: *padding amount* is deduced from relation of input and output spatial shapes
|
|
del node['pad']
|
|
|
|
elif node.has_valid('original_output_spatial_shape'):
|
|
# node had fixed output spatial shape set in original framework, so we restore it here
|
|
const = Const(graph, {'value': int64_array(node.original_output_spatial_shape),
|
|
'name': node_name + '/original_spatial_shape'}).create_node()
|
|
node.add_input_port(2, skip_if_exist=True)
|
|
const.out_port(0).connect(node.in_port(2))
|
|
|
|
# Specification: *padding amount* is deduced from relation of input and output spatial shapes
|
|
del node['pad']
|
|
|
|
group = node.soft_get('group', 1)
|
|
|
|
if group != 1:
|
|
assert group > 1
|
|
|
|
weights_shape = node.in_port(1).data.get_shape()
|
|
assert weights_shape is not None
|
|
I = node.in_port(0).data.get_shape()[1]
|
|
assert I % group == 0
|
|
assert node.output % group == 0
|
|
|
|
new_shape = int64_array([group, I / group, node.output / group, *weights_shape[2:]])
|
|
|
|
assert np.prod(weights_shape) == np.prod(new_shape), \
|
|
'Initial weights shape {}, grouped weights shape {}'.format(weights_shape, new_shape)
|
|
reshape = create_op_node_with_second_input(graph, Reshape, int64_array(new_shape),
|
|
{'override_output_shape': True},
|
|
node.in_port(1).get_source().node)
|
|
|
|
node.in_port(1).get_connection().set_source(reshape.out_port(0))
|
|
|
|
node['type'] = 'GroupConvolutionBackpropData'
|
|
else:
|
|
node['type'] = 'ConvolutionBackpropData'
|