def test_ssd_mobilenet_v2_model():
model = DLRModel(MODEL_PATH.as_posix())
data = np.load(DATA_PATH)
assert model.get_input_names() == ['image_tensor']
assert model.get_output_names() == [
'detection_scores:0', 'detection_classes:0', 'num_detections:0'
]
assert model.get_input_dtypes() == ['uint8']
assert model.get_output_dtypes() == ['float32', 'float32', 'float32']
outputs = model.run({"image_tensor": data})
assert outputs[0].shape == (1, 100, 4)
assert outputs[1].shape == (1, 100)
assert outputs[2].shape == (1, 100)
detections = np.multiply(np.ceil(outputs[1]), outputs[2])
expected = np.zeros(detections.shape)
expected[:, :6] = np.array([[1., 1., 1., 2., 3., 1]])
comparison = detections == expected
assert comparison.all()
def test_resnet():
# Load the model
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'resnet18_v1')
device = 'cpu'
model = DLRModel(model_path, device)
# Run the model
image = np.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'dog.npy')).astype(np.float32)
#flatten within a input array
input_data = {'data': image}
print('Testing inference on resnet18...')
probabilities = model.run(input_data) #need to be a list of input arrays matching input names
assert probabilities[0].argmax() == 151
assert model.get_input_names() == ["data"]
assert model.get_input_dtypes() == ["float32"]
assert model.get_output_dtypes() == ["float32"]
assert model.get_input_dtype(0) == "float32"
assert model.get_output_dtype(0) == "float32"
def test_ssd_mobilenet_v2_model():
model = DLRModel(MODEL_PATH.as_posix())
data = np.load(DATA_PATH)
assert model.get_input_names() == ['image_tensor']
assert model.get_output_names() == [
'detection_classes:0', 'num_detections:0', 'detection_boxes:0',
'detection_scores:0'
]
assert model.get_input_dtypes() == ['uint8']
assert model.get_output_dtypes() == [
'float32', 'float32', 'float32', 'float32'
]
outputs = model.run({"image_tensor": data})
assert outputs[0].shape == (1, 100)
assert outputs[1].shape == (1, )
assert outputs[2].shape == (1, 100, 4)
assert outputs[3].shape == (1, 100)
detections = np.multiply(np.ceil(outputs[3]), outputs[0])
expected = np.zeros(detections.shape)
assert np.count_nonzero(detections) == outputs[1][0]
def test_mobilenet_v1_0_75_224_quant():
# Load the model
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'mobilenet_v1_0.75_224_quant')
device = 'cpu'
model = DLRModel(model_path, device)
# load image (dtype: uint8)
image = np.load(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'cat_224_uint8.npy'))
print('Testing inference on mobilenet_v1_0.75_224_quant...')
probabilities = model.run({'input': image})
assert probabilities[0].argmax() == 282
assert model.get_input_names() == ["input"]
assert model.get_input_dtypes() == ["uint8"]
assert model.get_output_dtypes() == ["uint8"]
assert model.get_input_dtype(0) == "uint8"
assert model.get_output_dtype(0) == "uint8"
input2 = model.get_input("input")
assert input2.dtype == 'uint8'
assert input2.shape == (1, 224, 224, 3)
assert (input2 == image).all()