wehub-resource-sync
168 lines
4.6 KiB
Python
168 lines
4.6 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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import numpy as np
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import paddle
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from paddle.static import InputSpec
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from paddle.vision import models
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class TestVisionModels(unittest.TestCase):
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def models_infer(self, arch, pretrained=False, batch_norm=False):
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x = np.array(np.random.random((2, 3, 224, 224)), dtype=np.float32)
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if batch_norm:
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net = models.__dict__[arch](pretrained=pretrained, batch_norm=True)
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else:
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net = models.__dict__[arch](pretrained=pretrained)
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input = InputSpec([None, 3, 224, 224], 'float32', 'image')
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model = paddle.Model(net, input)
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model.prepare()
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model.predict_batch(x)
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def test_mobilenetv2_pretrained(self):
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self.models_infer('mobilenet_v2', pretrained=False)
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def test_mobilenetv1(self):
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self.models_infer('mobilenet_v1')
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def test_mobilenetv3_small(self):
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self.models_infer('mobilenet_v3_small')
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def test_mobilenetv3_large(self):
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self.models_infer('mobilenet_v3_large')
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def test_vgg11(self):
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self.models_infer('vgg11')
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def test_vgg13(self):
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self.models_infer('vgg13')
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def test_vgg16(self):
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self.models_infer('vgg16')
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def test_vgg16_bn(self):
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self.models_infer('vgg16', batch_norm=True)
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def test_vgg19(self):
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self.models_infer('vgg19')
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def test_resnet18(self):
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self.models_infer('resnet18')
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def test_resnet34(self):
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self.models_infer('resnet34')
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def test_resnet50(self):
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self.models_infer('resnet50')
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def test_resnet101(self):
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self.models_infer('resnet101')
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def test_resnet152(self):
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self.models_infer('resnet152')
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def test_wide_resnet50_2(self):
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self.models_infer('wide_resnet50_2')
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def test_wide_resnet101_2(self):
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self.models_infer('wide_resnet101_2')
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def test_densenet121(self):
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self.models_infer('densenet121')
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def test_densenet161(self):
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self.models_infer('densenet161')
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def test_densenet169(self):
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self.models_infer('densenet169')
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def test_densenet201(self):
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self.models_infer('densenet201')
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def test_densenet264(self):
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self.models_infer('densenet264')
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def test_squeezenet1_0(self):
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self.models_infer('squeezenet1_0')
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def test_squeezenet1_1(self):
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self.models_infer('squeezenet1_1')
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def test_alexnet(self):
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self.models_infer('alexnet')
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def test_shufflenetv2_swish(self):
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self.models_infer('shufflenet_v2_swish')
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def test_resnext50_32x4d(self):
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self.models_infer('resnext50_32x4d')
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def test_resnext50_64x4d(self):
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self.models_infer('resnext50_64x4d')
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def test_resnext101_32x4d(self):
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self.models_infer('resnext101_32x4d')
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def test_resnext101_64x4d(self):
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self.models_infer('resnext101_64x4d')
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def test_resnext152_32x4d(self):
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self.models_infer('resnext152_32x4d')
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def test_resnext152_64x4d(self):
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self.models_infer('resnext152_64x4d')
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def test_inception_v3(self):
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self.models_infer('inception_v3')
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def test_googlenet(self):
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self.models_infer('googlenet')
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def test_shufflenetv2_x0_25(self):
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self.models_infer('shufflenet_v2_x0_25')
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def test_shufflenetv2_x0_33(self):
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self.models_infer('shufflenet_v2_x0_33')
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def test_shufflenetv2_x0_5(self):
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self.models_infer('shufflenet_v2_x0_5')
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def test_shufflenetv2_x1_0(self):
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self.models_infer('shufflenet_v2_x1_0')
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def test_shufflenetv2_x1_5(self):
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self.models_infer('shufflenet_v2_x1_5')
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def test_shufflenetv2_x2_0(self):
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self.models_infer('shufflenet_v2_x2_0')
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def test_vgg16_num_classes(self):
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vgg16 = models.__dict__['vgg16'](pretrained=False, num_classes=10)
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def test_lenet(self):
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input = InputSpec([None, 1, 28, 28], 'float32', 'x')
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lenet = paddle.Model(models.__dict__['LeNet'](), input)
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lenet.prepare()
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x = np.array(np.random.random((2, 1, 28, 28)), dtype=np.float32)
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lenet.predict_batch(x)
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if __name__ == '__main__':
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unittest.main()
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