forked from mindspore-Ecosystem/mindspore
140 lines
4.8 KiB
Python
140 lines
4.8 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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import numpy as np
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import mindspore
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from mindspore import Tensor, nn
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from mindspore.rewrite import SymbolTree, ScopedValue, ValueType, Node
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from mindspore.common.initializer import Normal
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from mindspore.common.api import _cell_graph_executor
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class SimpleNet(nn.Cell):
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def __init__(self, num_class=10, num_channel=1):
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super(SimpleNet, 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.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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self.var = 10
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def construct(self, x):
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x = self.conv1(x)
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x = x
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y = self.var
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y = y * 5
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y = y and True
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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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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 MyCell(nn.Cell):
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def __init__(self):
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super().__init__()
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self.conv = nn.Dense(5, 16)
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def construct(self, x, y):
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x = self.conv(x)
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x = mindspore.ops.Add()(x, y)
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return x
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def add_conv_before_flatten(stree: SymbolTree):
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new_conv_node = None
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for node in stree.nodes():
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if node.get_instance_type() == mindspore.nn.Flatten:
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position = stree.before(node)
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new_conv = nn.Conv2d(16, 16, 3)
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new_conv_node = Node.create_call_cell(new_conv, targets=['x_1'], name='new_conv',
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args=[ScopedValue.create_naming_value('self_max_po')])
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stree.insert(position, new_conv_node)
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break
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if new_conv_node is not None:
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for node in stree.nodes():
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if node.get_instance_type() == mindspore.nn.Flatten:
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inputs = node.get_inputs()
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assert len(inputs) == 1
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new_conv_node.set_arg_by_node(0, inputs[0])
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def add_my_cell_after_x_12(stree: SymbolTree):
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for node in stree.nodes():
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targets = node.get_targets()
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if targets is None:
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continue
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assert targets[0].type == ValueType.NamingValue
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target = str(targets[0])
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if target == "x_12":
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position = stree.after(node)
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custom_cell = MyCell()
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bias = Tensor(1, mindspore.int32)
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new_custom_node = Node.create_call_cell(custom_cell, targets=['nx2'],
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args=[ScopedValue.create_naming_value('nx3'),
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ScopedValue.create_variable_value(bias)], name='my_cell')
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stree.insert(position, new_custom_node)
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new_custom_node.set_arg(0, "x_12")
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break
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def erase_node_x_11(stree: SymbolTree):
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return_node = None
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for node in stree.nodes():
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if node.get_targets() is None:
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return_node = node
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break
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assert return_node is not None
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for node in stree.nodes():
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targets = node.get_targets()
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if targets is None:
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continue
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assert targets[0].type == ValueType.NamingValue
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target = str(targets[0])
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if target == "x_11":
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stree.set_output(0, "x_10")
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stree.erase_node(node)
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break
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def transform(stree: SymbolTree):
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add_conv_before_flatten(stree)
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erase_node_x_11(stree)
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def test_simple_net():
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"""
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Feature: Module rewrite.
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Description: Resolve a simple network by rewrite and do some transform on it.
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Expectation: Result of rewrite can be compiled.
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"""
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net = SimpleNet(10)
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stree = SymbolTree.create(net)
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transform(stree)
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print("------------------------------------ keys of global_vars: ", stree.get_handler().get_global_vars().keys())
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net_opt = stree.get_network()
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data_in = Tensor(np.ones([1, 1, 32, 32]), mindspore.float32)
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_cell_graph_executor.compile(net_opt, data_in)
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