forked from mindspore-Ecosystem/mindspore
92 lines
3.5 KiB
Python
92 lines
3.5 KiB
Python
# 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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