def test_global_avgpool2d(change_ordering): if not tf.test.gpu_device_name() and not change_ordering: pytest.skip( "Skip! Since tensorflow AvgPoolingOp op currently only supports the NHWC tensor format on the CPU" ) model = LayerTest() model.eval() input_np = np.random.uniform(0, 1, (1, 3, 224, 224)) error = convert_and_test(model, input_np, verbose=False, change_ordering=change_ordering)
def test_densenet(change_ordering): if not tf.test.gpu_device_name() and not change_ordering: pytest.skip( "Skip! Since tensorflow Conv2D op currently only supports the NHWC tensor format on the CPU" ) model = densenet121() model.eval() input_np = np.random.uniform(0, 1, (1, 3, 224, 224)) error = convert_and_test(model, input_np, verbose=False, change_ordering=change_ordering)
def test_avgpool2d(change_ordering, kernel_size, padding, stride): if not tf.test.gpu_device_name() and not change_ordering: pytest.skip( "Skip! Since tensorflow AvgPoolingOp op currently only supports the NHWC tensor format on the CPU" ) if padding > kernel_size / 2: # RuntimeError: invalid argument 2: pad should be smaller than half of kernel size, # but got padW = 1, padH = 1, kW = 1, pytest.skip("pad should be smaller than half of kernel size") model = LayerTest(kernel_size=kernel_size, padding=padding, stride=stride) model.eval() input_np = np.random.uniform(0, 1, (1, 3, 224, 224)) error = convert_and_test(model, input_np, verbose=False, change_ordering=change_ordering)
def func(change_ordering, kernel_size, padding, stride, bias, dilation, groups): if not tf.test.gpu_device_name() and not change_ordering: pytest.skip( "Skip! Since tensorflow Conv2D op currently only supports the NHWC tensor format on the CPU" ) if stride > 1 and dilation > 1: pytest.skip( "strides > 1 not supported in conjunction with dilation_rate > 1") model = LayerTest(groups * 3, groups, kernel_size=kernel_size, padding=padding, stride=stride, bias=bias, dilation=dilation, groups=groups) model.eval() input_np = np.random.uniform(0, 1, (1, groups * 3, 224, 224)) error = convert_and_test(model, input_np, verbose=False, change_ordering=change_ordering)
def test_f_sigmoid(change_ordering): model = FSigmoid() model.eval() input_np = np.random.uniform(0, 1, (1, 3, 224, 224)) error = convert_and_test(model, input_np, verbose=False, change_ordering=change_ordering)
def test_convtranspose2d(change_ordering, kernel_size, padding, stride, bias): outs = np.random.choice([1, 3, 7]) model = LayerTest(3, outs, kernel_size=kernel_size, padding=padding, stride=stride, bias=bias) model.eval() input_np = np.random.uniform(0, 1, (1, 3, 224, 224)) error = convert_and_test(model, input_np, verbose=False, change_ordering=change_ordering)
def test_layer_upsamle(change_ordering, mode, size, scale_factor): model = LayerUpsample(mode=mode, size=size, scale_factor=scale_factor) model.eval() input_np = np.random.uniform(0, 1, (1, 3, 64, 64)) error = convert_and_test(model, input_np, verbose=False, change_ordering=change_ordering)
def test_f_interpole(change_ordering, mode, size, scale_factor): model = FInterpolate(mode=mode, size=size, scale_factor=scale_factor) model.eval() input_np = np.random.uniform(0, 1, (1, 3, 64, 64)) error = convert_and_test(model, input_np, verbose=False, change_ordering=change_ordering)
def test_f_softmax(change_ordering, dim): model = FSoftmax(dim) model.eval() input_np = np.random.uniform(0, 1, (1, 3, 224, 224)) error = convert_and_test(model, input_np, verbose=False, change_ordering=change_ordering)
def test_f_hardtanh(change_ordering): model = LayerHardtanh() model.eval() input_np = np.random.uniform(0, 1, (1, 3, 224, 224)) error = convert_and_test(model, input_np, verbose=False, change_ordering=change_ordering)