970b1301b5
* Removed back phase transformations related to IRv7 * Fixed setting value for the input port using the 'set_value' method * Removed front and middle phase transformations related to IRv7 * Cleanup the rest of the Model Optimizer transformations from IRv7 specific transformations * Final cleanup of the deprecated IR v7 related code * Removed 'blobs_as_input' usage in the Model Optimizer. * Removed function '_fuse_add' from the Model Optimizer since it is not used anymore. * Removed 'keep_in_IR' node attribute for FakeQuantize ops in the MO * Disabled failing gpu_engine.user_context test
60 lines
1.8 KiB
Python
60 lines
1.8 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.elemental import copy_shape_infer
|
|
from mo.front.common.partial_infer.utils import mark_input_bins
|
|
from mo.graph.graph import Graph
|
|
from mo.ops.op import Op
|
|
|
|
|
|
class PreluOp(Op):
|
|
op = 'PReLU'
|
|
enabled = True
|
|
|
|
def __init__(self, graph: Graph, attrs: dict):
|
|
super().__init__(graph, {
|
|
'op': self.op,
|
|
'type': self.op,
|
|
'version': 'opset1',
|
|
|
|
'infer': self.infer,
|
|
|
|
'force_precision_in_ports': {1: 'float'},
|
|
|
|
'in_ports_count': 2,
|
|
'out_ports_count': 1,
|
|
}, attrs)
|
|
|
|
def supported_attrs(self):
|
|
if self.ir_version != 10:
|
|
return ['channel_shared', 'filler_type', 'filler_value', 'min', 'max', 'mean', 'std', 'sparse', 'variance_norm']
|
|
else:
|
|
return []
|
|
|
|
@staticmethod
|
|
def infer(node):
|
|
if len(node.in_nodes()) == 2:
|
|
gamma_vector = node.in_node(1)
|
|
if np.all(gamma_vector.shape == [1]):
|
|
node['channel_shared'] = 1
|
|
else:
|
|
node['channel_shared'] = 0
|
|
node.in_node(1)['correct_data_type'] = True
|
|
|
|
copy_shape_infer(node)
|