forked from mindspore-Ecosystem/mindspore
!1171 adjust onnx related testcases
Merge pull request !1171 from fary86/adjust_onnx_related_uts
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# Copyright 2020 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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"""ut for model serialize(save/load)"""
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import os
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import stat
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import time
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import numpy as np
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import pytest
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import mindspore.common.dtype as mstype
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.common.parameter import Parameter
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from mindspore.common.tensor import Tensor
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from mindspore.ops import operations as P
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from mindspore.train.serialization import export
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context.set_context(mode=context.GRAPH_MODE)
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def is_enable_onnxruntime():
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val = os.getenv("ENABLE_ONNXRUNTIME", "False")
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if val in ('ON', 'on', 'TRUE', 'True', 'true'):
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return True
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return False
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run_on_onnxruntime = pytest.mark.skipif(not is_enable_onnxruntime(), reason="Only support running on onnxruntime")
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def setup_module():
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pass
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def teardown_module():
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cur_dir = os.path.dirname(os.path.realpath(__file__))
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for filename in os.listdir(cur_dir):
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if filename.find('ms_output_') == 0 and filename.find('.pb') > 0:
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# delete temp files generated by run ut
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os.chmod(filename, stat.S_IWRITE)
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os.remove(filename)
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class BatchNormTester(nn.Cell):
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"used to test exporting network in training mode in onnx format"
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def __init__(self, num_features):
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super(BatchNormTester, self).__init__()
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self.bn = nn.BatchNorm2d(num_features)
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def construct(self, x):
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return self.bn(x)
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def test_batchnorm_train_onnx_export():
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"test onnx export interface does not modify trainable flag of a network"
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input = Tensor(np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01)
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net = BatchNormTester(3)
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net.set_train()
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if not net.training:
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raise ValueError('netowrk is not in training mode')
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onnx_file = 'batch_norm.onnx'
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export(net, input, file_name=onnx_file, file_format='ONNX')
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if not net.training:
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raise ValueError('netowrk is not in training mode')
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# check existence of exported onnx file and delete it
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assert os.path.exists(onnx_file)
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os.chmod(onnx_file, stat.S_IWRITE)
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os.remove(onnx_file)
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class LeNet5(nn.Cell):
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"""LeNet5 definition"""
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def __init__(self):
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super(LeNet5, self).__init__()
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self.conv1 = nn.Conv2d(1, 6, 5, pad_mode='valid')
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self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
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self.fc1 = nn.Dense(16 * 5 * 5, 120)
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self.fc2 = nn.Dense(120, 84)
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self.fc3 = nn.Dense(84, 10)
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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 = P.Flatten()
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def construct(self, x):
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x = self.max_pool2d(self.relu(self.conv1(x)))
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x = self.max_pool2d(self.relu(self.conv2(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 DefinedNet(nn.Cell):
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"""simple Net definition with maxpoolwithargmax."""
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def __init__(self, num_classes=10):
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super(DefinedNet, self).__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=0, weight_init="zeros")
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU()
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self.maxpool = P.MaxPoolWithArgmax(padding="same", ksize=2, strides=2)
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self.flatten = nn.Flatten()
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self.fc = nn.Dense(int(56 * 56 * 64), num_classes)
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def construct(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x, argmax = self.maxpool(x)
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x = self.flatten(x)
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x = self.fc(x)
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return x
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class DepthwiseConv2dAndReLU6(nn.Cell):
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"Net for testing DepthwiseConv2d and ReLU6"
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def __init__(self, input_channel, kernel_size):
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super(DepthwiseConv2dAndReLU6, self).__init__()
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weight_shape = [1, input_channel, kernel_size, kernel_size]
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from mindspore.common.initializer import initializer
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self.weight = Parameter(initializer('ones', weight_shape), name='weight')
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self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=1, kernel_size=(kernel_size, kernel_size))
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self.relu6 = nn.ReLU6()
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def construct(self, x):
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x = self.depthwise_conv(x, self.weight)
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x = self.relu6(x)
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return x
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# generate mindspore Tensor by shape and numpy datatype
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def gen_tensor(shape, dtype=np.float32):
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return Tensor(np.ones(shape).astype(dtype))
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# ut configs in triple: (ut_name, network, network-input)
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net_cfgs = [
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('lenet', LeNet5(), gen_tensor([1, 1, 32, 32])),
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('maxpoolwithargmax', DefinedNet(), gen_tensor([1, 3, 224, 224])),
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('depthwiseconv_relu6', DepthwiseConv2dAndReLU6(3, kernel_size=3), gen_tensor([1, 3, 32, 32])),
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]
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def get_id(cfg):
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return list(map(lambda x: x[0], net_cfgs))
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# use `pytest test_onnx.py::test_onnx_export[name]` or `pytest test_onnx.py::test_onnx_export -k name` to run single ut
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@pytest.mark.parametrize('name, net, inp', net_cfgs, ids=get_id(net_cfgs))
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def test_onnx_export(name, net, inp):
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onnx_file = name + ".onnx"
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export(net, inp, file_name=onnx_file, file_format='ONNX')
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# check existence of exported onnx file and delete it
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assert os.path.exists(onnx_file)
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os.chmod(onnx_file, stat.S_IWRITE)
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os.remove(onnx_file)
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@run_on_onnxruntime
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@pytest.mark.parametrize('name, net, inp', net_cfgs, ids=get_id(net_cfgs))
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def test_onnx_export_load_run(name, net, inp):
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onnx_file = name + ".onnx"
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export(net, inp, file_name=onnx_file, file_format='ONNX')
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import onnx
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import onnxruntime as ort
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print('--------------------- onnx load ---------------------')
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# Load the ONNX model
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model = onnx.load(onnx_file)
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# Check that the IR is well formed
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onnx.checker.check_model(model)
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# Print a human readable representation of the graph
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g = onnx.helper.printable_graph(model.graph)
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print(g)
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print('------------------ onnxruntime run ------------------')
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ort_session = ort.InferenceSession(onnx_file)
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input_map = {'x': inp.asnumpy()}
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# provide only input x to run model
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outputs = ort_session.run(None, input_map)
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print(outputs[0])
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# overwrite default weight to run model
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for item in net.trainable_params():
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default_value = item.default_input.asnumpy()
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input_map[item.name] = np.ones(default_value.shape, dtype=default_value.dtype)
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outputs = ort_session.run(None, input_map)
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print(outputs[0])
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# check existence of exported onnx file and delete it
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assert os.path.exists(onnx_file)
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os.chmod(onnx_file, stat.S_IWRITE)
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os.remove(onnx_file)
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@ -321,171 +321,8 @@ def test_export():
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export(net, input_data, file_name="./me_export.pb", file_format="GEIR")
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class BatchNormTester(nn.Cell):
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"used to test exporting network in training mode in onnx format"
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def __init__(self, num_features):
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super(BatchNormTester, self).__init__()
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self.bn = nn.BatchNorm2d(num_features)
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def construct(self, x):
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return self.bn(x)
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class DepthwiseConv2dAndReLU6(nn.Cell):
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"Net for testing DepthwiseConv2d and ReLU6"
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def __init__(self, input_channel, kernel_size):
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super(DepthwiseConv2dAndReLU6, self).__init__()
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weight_shape = [1, input_channel, kernel_size, kernel_size]
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from mindspore.common.initializer import initializer
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self.weight = Parameter(initializer('ones', weight_shape), name='weight')
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self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=1, kernel_size=(kernel_size, kernel_size))
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self.relu6 = nn.ReLU6()
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def construct(self, x):
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x = self.depthwise_conv(x, self.weight)
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x = self.relu6(x)
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return x
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def test_batchnorm_train_onnx_export():
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input = Tensor(np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01)
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net = BatchNormTester(3)
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net.set_train()
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if not net.training:
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raise ValueError('netowrk is not in training mode')
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export(net, input, file_name='batch_norm.onnx', file_format='ONNX')
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if not net.training:
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raise ValueError('netowrk is not in training mode')
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class LeNet5(nn.Cell):
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"""LeNet5 definition"""
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def __init__(self):
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super(LeNet5, self).__init__()
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self.conv1 = nn.Conv2d(1, 6, 5, pad_mode='valid')
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self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
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self.fc1 = nn.Dense(16 * 5 * 5, 120)
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self.fc2 = nn.Dense(120, 84)
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self.fc3 = nn.Dense(84, 10)
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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 = P.Flatten()
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def construct(self, x):
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x = self.max_pool2d(self.relu(self.conv1(x)))
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x = self.max_pool2d(self.relu(self.conv2(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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def test_lenet5_onnx_export():
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input = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
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net = LeNet5()
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export(net, input, file_name='lenet5.onnx', file_format='ONNX')
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class DefinedNet(nn.Cell):
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"""simple Net definition with maxpoolwithargmax."""
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def __init__(self, num_classes=10):
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super(DefinedNet, self).__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=0, weight_init="zeros")
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU()
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self.maxpool = P.MaxPoolWithArgmax(padding="same", ksize=2, strides=2)
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self.flatten = nn.Flatten()
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self.fc = nn.Dense(int(56 * 56 * 64), num_classes)
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def construct(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x, argmax = self.maxpool(x)
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x = self.flatten(x)
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x = self.fc(x)
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return x
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def test_net_onnx_maxpoolwithargmax_export():
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input = Tensor(np.ones([1, 3, 224, 224]).astype(np.float32) * 0.01)
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net = DefinedNet()
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export(net, input, file_name='definedNet.onnx', file_format='ONNX')
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@run_on_onnxruntime
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def test_lenet5_onnx_load_run():
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onnx_file = 'lenet5.onnx'
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input = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
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net = LeNet5()
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export(net, input, file_name=onnx_file, file_format='ONNX')
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import onnx
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import onnxruntime as ort
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print('--------------------- onnx load ---------------------')
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# Load the ONNX model
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model = onnx.load(onnx_file)
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# Check that the IR is well formed
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onnx.checker.check_model(model)
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# Print a human readable representation of the graph
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g = onnx.helper.printable_graph(model.graph)
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print(g)
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print('------------------ onnxruntime run ------------------')
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ort_session = ort.InferenceSession(onnx_file)
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input_map = {'x': input.asnumpy()}
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# provide only input x to run model
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outputs = ort_session.run(None, input_map)
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print(outputs[0])
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# overwrite default weight to run model
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for item in net.trainable_params():
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input_map[item.name] = np.ones(item.default_input.asnumpy().shape, dtype=np.float32)
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outputs = ort_session.run(None, input_map)
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print(outputs[0])
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@run_on_onnxruntime
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def test_depthwiseconv_relu6_onnx_load_run():
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onnx_file = 'depthwiseconv_relu6.onnx'
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input_channel = 3
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input = Tensor(np.ones([1, input_channel, 32, 32]).astype(np.float32) * 0.01)
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net = DepthwiseConv2dAndReLU6(input_channel, kernel_size=3)
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export(net, input, file_name=onnx_file, file_format='ONNX')
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import onnx
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import onnxruntime as ort
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print('--------------------- onnx load ---------------------')
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# Load the ONNX model
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model = onnx.load(onnx_file)
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# Check that the IR is well formed
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onnx.checker.check_model(model)
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# Print a human readable representation of the graph
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g = onnx.helper.printable_graph(model.graph)
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print(g)
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print('------------------ onnxruntime run ------------------')
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ort_session = ort.InferenceSession(onnx_file)
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input_map = {'x': input.asnumpy()}
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# provide only input x to run model
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outputs = ort_session.run(None, input_map)
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print(outputs[0])
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# overwrite default weight to run model
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for item in net.trainable_params():
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input_map[item.name] = np.ones(item.default_input.asnumpy().shape, dtype=np.float32)
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outputs = ort_session.run(None, input_map)
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print(outputs[0])
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def teardown_module():
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files = ['parameters.ckpt', 'new_ckpt.ckpt', 'lenet5.onnx', 'batch_norm.onnx', 'empty.ckpt']
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files = ['parameters.ckpt', 'new_ckpt.ckpt', 'empty.ckpt']
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for item in files:
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file_name = './' + item
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if not os.path.exists(file_name):
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