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How to use test_forward method in uiautomator

Best Python code snippet using uiautomator

test_forward.py

Source:test_forward.py

...47                                    + DARKNET_LIB + '?raw=true'48_download(DARKNETLIB_URL, DARKNET_LIB)49LIB = __darknetffi__.dlopen('./' + DARKNET_LIB)5051def test_forward(net):52    '''Test network with given input image on both darknet and tvm'''53    def get_darknet_output(net, img):54        return LIB.network_predict_image(net, img)5556    def get_tvm_output(net, img):57        '''Compute TVM output'''58        dtype = 'float32'59        batch_size = 160        sym, params = frontend.darknet.from_darknet(net, dtype)61        data = np.empty([batch_size, img.c, img.h, img.w], dtype)62        i = 063        for c in range(img.c):64            for h in range(img.h):65                for k in range(img.w):66                    data[0][c][h][k] = img.data[i]67                    i = i + 16869        target = 'llvm'70        shape_dict = {'data': data.shape}71        #with nnvm.compiler.build_config(opt_level=2):72        graph, library, params = nnvm.compiler.build(sym, target, shape_dict, dtype, params=params)73        ######################################################################74        # Execute on TVM75        # ---------------76        # The process is no different from other examples.77        from tvm.contrib import graph_runtime78        ctx = tvm.cpu(0)79        m = graph_runtime.create(graph, library, ctx)80        # set inputs81        m.set_input('data', tvm.nd.array(data.astype(dtype)))82        m.set_input(**params)83        m.run()84        # get outputs85        out_shape = (net.outputs,)86        tvm_out = m.get_output(0, tvm.nd.empty(out_shape, dtype)).asnumpy()87        return tvm_out8889    test_image = 'dog.jpg'90    img_url = 'https://github.com/siju-samuel/darknet/blob/master/data/' + test_image   +'?raw=true'91    _download(img_url, test_image)92    img = LIB.letterbox_image(LIB.load_image_color(test_image.encode('utf-8'), 0, 0), net.w, net.h)93    darknet_output = get_darknet_output(net, img)94    darknet_out = np.zeros(net.outputs, dtype='float32')95    for i in range(net.outputs):96        darknet_out[i] = darknet_output[i]97    tvm_out = get_tvm_output(net, img)98    np.testing.assert_allclose(darknet_out, tvm_out, rtol=1e-3, atol=1e-3)99100def test_forward_extraction():101    '''test extraction model'''102    model_name = 'extraction'103    cfg_name = model_name + '.cfg'104    weights_name = model_name + '.weights'105    cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'106    weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'107    _download(cfg_url, cfg_name)108    _download(weights_url, weights_name)109    net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)110    test_forward(net)111    LIB.free_network(net)112113def test_forward_alexnet():114    '''test alexnet model'''115    model_name = 'alexnet'116    cfg_name = model_name + '.cfg'117    weights_name = model_name + '.weights'118    cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'119    weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'120    _download(cfg_url, cfg_name)121    _download(weights_url, weights_name)122    net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)123    test_forward(net)124    LIB.free_network(net)125126def test_forward_resnet50():127    '''test resnet50 model'''128    model_name = 'resnet50'129    cfg_name = model_name + '.cfg'130    weights_name = model_name + '.weights'131    cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'132    weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'133    _download(cfg_url, cfg_name)134    _download(weights_url, weights_name)135    net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)136    test_forward(net)137    LIB.free_network(net)138139def test_forward_yolo():140    '''test yolo model'''141    model_name = 'yolo'142    cfg_name = model_name + '.cfg'143    weights_name = model_name + '.weights'144    cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'145    weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'146    _download(cfg_url, cfg_name)147    _download(weights_url, weights_name)148    net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)149    test_forward(net)150    LIB.free_network(net)151152def test_forward_convolutional():153    '''test convolutional layer'''154    net = LIB.make_network(1)155    layer = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)156    net.layers[0] = layer157    net.w = net.h = 224158    LIB.resize_network(net, 224, 224)159    test_forward(net)160    LIB.free_network(net)161162def test_forward_dense():163    '''test fully connected layer'''164    net = LIB.make_network(1)165    layer = LIB.make_connected_layer(1, 75, 20, 1, 0, 0)166    net.layers[0] = layer167    net.w = net.h = 5168    LIB.resize_network(net, 5, 5)169    test_forward(net)170    LIB.free_network(net)171172def test_forward_maxpooling():173    '''test maxpooling layer'''174    net = LIB.make_network(1)175    layer = LIB.make_maxpool_layer(1, 224, 224, 3, 2, 2, 0)176    net.layers[0] = layer177    net.w = net.h = 224178    LIB.resize_network(net, 224, 224)179    test_forward(net)180    LIB.free_network(net)181182def test_forward_avgpooling():183    '''test avgerage pooling layer'''184    net = LIB.make_network(1)185    layer = LIB.make_avgpool_layer(1, 224, 224, 3)186    net.layers[0] = layer187    net.w = net.h = 224188    LIB.resize_network(net, 224, 224)189    test_forward(net)190    LIB.free_network(net)191192def test_forward_batch_norm():193    '''test batch normalization layer'''194    net = LIB.make_network(1)195    layer = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 1, 0, 0, 0)196    for i in range(32):197        layer.rolling_mean[i] = np.random.rand(1)198        layer.rolling_variance[i] = np.random.rand(1)199    net.layers[0] = layer200    net.w = net.h = 224201    LIB.resize_network(net, 224, 224)202    test_forward(net)203    LIB.free_network(net)204205def test_forward_shortcut():206    '''test shortcut layer'''207    net = LIB.make_network(3)208    layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)209    layer_2 = LIB.make_convolutional_layer(1, 111, 111, 32, 32, 1, 1, 1, 0, 1, 0, 0, 0, 0)210    layer_3 = LIB.make_shortcut_layer(1, 0, 111, 111, 32, 111, 111, 32)211    layer_3.activation = 1212    net.layers[0] = layer_1213    net.layers[1] = layer_2214    net.layers[2] = layer_3215    net.w = net.h = 224216    LIB.resize_network(net, 224, 224)217    test_forward(net)218    LIB.free_network(net)219220def test_forward_reorg():221    '''test reorg layer'''222    net = LIB.make_network(2)223    layer_1 = LIB.make_convolutional_layer(1, 222, 222, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)224    layer_2 = LIB.make_reorg_layer(1, 110, 110, 32, 2, 0, 0, 0)225    net.layers[0] = layer_1226    net.layers[1] = layer_2227    net.w = net.h = 222228    LIB.resize_network(net, 222, 222)229    test_forward(net)230    LIB.free_network(net)231232def test_forward_region():233    '''test region layer'''234    net = LIB.make_network(2)235    layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 8, 1, 3, 2, 0, 1, 0, 0, 0, 0)236    layer_2 = LIB.make_region_layer(1, 111, 111, 2, 2, 1)237    layer_2.softmax = 1238    net.layers[0] = layer_1239    net.layers[1] = layer_2240    net.w = net.h = 224241    LIB.resize_network(net, 224, 224)242    test_forward(net)243    LIB.free_network(net)244245if __name__ == '__main__':246    test_forward_resnet50()247    test_forward_alexnet()248    test_forward_extraction()249    test_forward_yolo()250    test_forward_convolutional()251    test_forward_maxpooling()252    test_forward_avgpooling()253    test_forward_batch_norm()254    test_forward_shortcut()255    test_forward_dense()256    test_forward_reorg()
...

test_vggtransformer.py

Source:test_vggtransformer.py

...66class VGGTransformerEncoderTest(TestFairseqEncoderBase):67    def setUp(self):68        super().setUp()69        self.setUpInput(get_dummy_input(T=50, D=80, B=5))70    def test_forward(self):71        print("1. test standard vggtransformer")72        self.setUpEncoder(VGGTransformerEncoder(input_feat_per_channel=80))73        super().test_forward()74        print("2. test vggtransformer with limited right context")75        self.setUpEncoder(76            VGGTransformerEncoder(77                input_feat_per_channel=80, transformer_context=(-1, 5)78            )79        )80        super().test_forward()81        print("3. test vggtransformer with limited left context")82        self.setUpEncoder(83            VGGTransformerEncoder(84                input_feat_per_channel=80, transformer_context=(5, -1)85            )86        )87        super().test_forward()88        print("4. test vggtransformer with limited right context and sampling")89        self.setUpEncoder(90            VGGTransformerEncoder(91                input_feat_per_channel=80,92                transformer_context=(-1, 12),93                transformer_sampling=(2, 2),94            )95        )96        super().test_forward()97        print("5. test vggtransformer with windowed context and sampling")98        self.setUpEncoder(99            VGGTransformerEncoder(100                input_feat_per_channel=80,101                transformer_context=(12, 12),102                transformer_sampling=(2, 2),103            )104        )105class TransformerDecoderTest(TestFairseqDecoderBase):106    def setUp(self):107        super().setUp()108        dict = get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE)109        decoder = TransformerDecoder(dict)110        dummy_encoder_output = get_dummy_encoder_output(encoder_out_shape=(50, 5, 256))...

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