Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
test_graph_utils.py 5.24 KB
Newer Older
chicm-ms's avatar
Graph torch14 refactor (#2384)  
chicm-ms committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158 
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

import sys
import os
import math
import uuid
import shutil
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from tensorboard.compat.proto.graph_pb2 import GraphDef
from google.protobuf import text_format
import unittest
from unittest import TestCase, main

from nni._graph_utils import build_module_graph, build_graph

class BackboneModel1(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 1, 1, 1)
    def forward(self, x):
        return self.conv1(x)

class BackboneModel2(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5, 1)
        self.conv2 = nn.Conv2d(20, 50, 5, 1)
        self.bn1 = nn.BatchNorm2d(self.conv1.out_channels)
        self.bn2 = nn.BatchNorm2d(self.conv2.out_channels)
        self.fc1 = nn.Linear(4 * 4 * 50, 500)
        self.fc2 = nn.Linear(500, 10)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(x.size(0), -1)
        
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

class BigModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.backbone1 = BackboneModel1()
        self.backbone2 = BackboneModel2()
        self.fc3 = nn.Linear(10, 2) 
    def forward(self, x):
        x = self.backbone1(x)
        x = self.backbone2(x)
        x = self.fc3(x)
        return x

class GraphUtilsTestCase(TestCase):
    def test_build_module_graph(self):
        big_model = BigModel()
        g = build_module_graph(big_model, torch.randn(2, 1, 28, 28))
        print(g.name_to_node.keys())
        leaf_modules = set([
            'backbone1.conv1', 'backbone2.bn1', 'backbone2.bn2', 'backbone2.conv1',
            'backbone2.conv2', 'backbone2.fc1', 'backbone2.fc2', 'fc3'
        ])

        assert set(g.leaf_modules) == leaf_modules
        assert not leaf_modules - set(g.name_to_node.keys())
        assert g.find_successors('backbone2.conv1') == ['backbone2.bn1']
        assert g.find_successors('backbone2.conv2') == ['backbone2.bn2']
        assert g.find_predecessors('backbone2.bn1') == ['backbone2.conv1']
        assert g.find_predecessors('backbone2.bn2') == ['backbone2.conv2']

    def _test_graph(self, model, dummy_input, expected_file):
        actual_proto, _ = build_graph(model, dummy_input)

        assert os.path.exists(expected_file), expected_file
        with open(expected_file, "r") as f:
            expected_str = f.read()

        expected_proto = GraphDef()
        text_format.Parse(expected_str, expected_proto)

        self.assertEquals(len(expected_proto.node), len(actual_proto.node))
        for i in range(len(expected_proto.node)):
            expected_node = expected_proto.node[i]
            actual_node = actual_proto.node[i]
            self.assertEquals(expected_node.name, actual_node.name)
            self.assertEquals(expected_node.op, actual_node.op)
            self.assertEquals(expected_node.input, actual_node.input)
            self.assertEquals(expected_node.device, actual_node.device)
            self.assertEquals(
                sorted(expected_node.attr.keys()), sorted(actual_node.attr.keys()))

    @unittest.skipIf(torch.__version__ < "1.4.0", "not supported")
    def test_graph_module1(self):
        dummy_input = (torch.zeros(1, 3),)

        class myLinear(torch.nn.Module):
            def __init__(self):
                super(myLinear, self).__init__()
                self.l = torch.nn.Linear(3, 5)

            def forward(self, x):
                return self.l(x)

        self._test_graph(
            myLinear(),
            dummy_input,
            os.path.join(os.path.dirname(__file__), "expect", "test_graph_module1.expect")
        )

    @unittest.skipIf(torch.__version__ < "1.4.0", "not supported")
    def test_graph_module2(self):
        class MyModule(nn.Module):
            def __init__(self):
                super().__init__()
                self.weight = nn.Linear(5, 3)
                self.bias = nn.Linear(5, 3)
                self.module = nn.Linear(6, 1)

            def forward(self, x):
                tensors = [self.weight(x), self.bias(x)]
                self.module(torch.cat(tensors, dim=1))
                return x

        self._test_graph(
            MyModule(),
            torch.randn(4, 5),
            os.path.join(os.path.dirname(__file__), "expect", "test_graph_module2.expect")
        )

    @unittest.skipIf(torch.__version__ < "1.4.0", "not supported")
    def test_graph_module3(self):
        class MyModule(nn.Module):
            def __init__(self):
                super().__init__()
                self.module = nn.ModuleList([
                    nn.Linear(5, 3),
                    nn.Linear(3, 1)
                ])

            def forward(self, x):
                x = self.module[0](x)
                x = self.module[1](x)
                return x

        self._test_graph(
            MyModule(),
            torch.randn(4, 5),
            os.path.join(os.path.dirname(__file__), "expect", "test_graph_module3.expect")
        )

if __name__ == '__main__':
    main()