forked from mindspore-Ecosystem/mindspore
!34567 fix bug in class_def_parser
Merge pull request !34567 from hangq/rewrite-pr
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77726dd5eb
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@ -120,6 +120,7 @@
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"mindspore/tests/ut/python/rewrite/test_node.py" "syntax-error"
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"mindspore/tests/ut/python/rewrite/test_node.py" "protected-access"
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"mindspore/tests/ut/python/rewrite/test_symbol_tree.py" "len-as-condition"
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"mindspore/tests/ut/python/rewrite/test_lenet.py" "protected-access"
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"mindspore/tests/ut/python/test_log.py" "possibly-unused-variable"
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"mindspore/tests/ut/python/test_log.py" "protected-access"
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"mindspore/tests/ut/python/train/summary/test_summary_collector.py" "protected-access"
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@ -762,7 +762,7 @@ class Conv2dBnFoldQuantOneConv(Cell):
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self.fake_quant_weight = quant_config.weight(ema=False,
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channel_axis=channel_axis,
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num_channels=out_channels)
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self.freeze_bn = True
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self.freeze_bn = False
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self.bn_train = P.BatchNorm(is_training=True, epsilon=self.eps,
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momentum=self.momentum, data_format=self.format)
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@ -168,7 +168,7 @@ class ClassDefParser(Parser):
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if isinstance(body, ast.If):
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ClassDefParser._remove_empty_ast_in_init_func(body.body)
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ClassDefParser._remove_empty_ast_in_init_func(body.orelse)
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if not body.body or not body.orelse:
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if not body.body and not body.orelse:
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body_index_to_be_deleted.append(body_index)
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continue
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if isinstance(body, ast.For):
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@ -23,7 +23,7 @@ from .symbol_tree import SymbolTree
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from .node import TreeNode
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from .parser_register import ParserRegister
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from .parser import Parser
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from .ast_transformers import FlattenRecursiveStmt, RemoveReturnOutOfIf
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from .ast_transformers import FlattenRecursiveStmt
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from .ast_helpers import AstModifier
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from .ast_helpers import AstFinder
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@ -71,7 +71,7 @@ class SymbolTreeBuilder:
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Returns:
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An instance of ast been optimized.
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"""
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transform_list = [FlattenRecursiveStmt(), RemoveReturnOutOfIf()]
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transform_list = [FlattenRecursiveStmt()]
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for transformer in transform_list:
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ast_root = transformer.transform(ast_root)
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return ast_root
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@ -0,0 +1,91 @@
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# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""LeNet."""
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from collections import OrderedDict
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import mindspore.nn as nn
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from mindspore.common.initializer import Normal
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from mindspore.rewrite import SymbolTree, PatternEngine, Replacement, PatternNode, Node
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class LeNet5(nn.Cell):
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def __init__(self, num_class=10, num_channel=1, include_top=True):
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super(LeNet5, self).__init__()
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self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
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self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.include_top = include_top
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if self.include_top:
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self.flatten = nn.Flatten()
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self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02))
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self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02))
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self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02))
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def construct(self, x):
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x = self.conv1(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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if not self.include_top:
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return x
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x = self.flatten(x)
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x = self.relu(self.fc1(x))
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x = self.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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class ConvActReplace(Replacement):
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def build(self, pattern: PatternNode, is_chain_pattern: bool, matched: OrderedDict) -> [Node]:
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conv_p = pattern.get_inputs()[0]
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conv_node: Node = matched.get(conv_p.name())
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conv: nn.Conv2d = conv_node.get_instance()
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newconv = nn.Conv2dBnAct(conv.in_channels,
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conv.out_channels,
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conv.kernel_size,
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conv.stride,
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conv.pad_mode,
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conv.padding,
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conv.dilation,
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conv.group,
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conv.has_bias,
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conv.weight_init,
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conv.bias_init,
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False,
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activation="relu")
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newconv_node = Node.create_call_cell(newconv, conv_node.get_targets(), conv_node.get_args(),
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conv_node.get_kwargs(), "Conv2dBnAct")
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return [newconv_node]
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class ConvReLUPattern(PatternEngine):
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def __init__(self):
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super().__init__([nn.Conv2d, nn.ReLU], ConvActReplace())
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def test_lenet():
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"""
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Feature: Test PatternEngine.
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Description: Test PatternEngine on Lenet5.
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Expectation: Success.
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"""
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net = LeNet5(10)
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stree = SymbolTree.create(net)
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original_nodes_size = len(stree.get_handler()._nodes)
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ConvReLUPattern().apply(stree)
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assert len(stree.get_handler()._nodes) == original_nodes_size - 2
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