def run_example():
runtime.__full_version__
current_path = Path(__file__).absolute().parent
path = current_path.joinpath("../assets/quantized_models/MNISTnet_uint8_quant_without_softmax.tflite")
# Load a model and compile the model
sess = session.create(str(path))
# Print the model summary
sess.print_summary()
# Print the first input tensor shape and dimensions
input_meta = sess.inputs()[0]
print(input_meta)
# Generate the random input tensor according to the input shape
input = np.random.randint(-128, 127, input_meta.shape(), dtype=np.int8)
# Run the inference
outputs = sess.run(input)
print("== Output ==")
print(outputs)
print(outputs[0].numpy())
def classify(image_path):
from furiosa.runtime import session
from helper import load_labels
image = Image.open(image_path)
current_path = Path(__file__).absolute().parent
path = current_path \
.joinpath("../assets/quantized_models/imagenet_224x224_mobilenet_v1_uint8_quantization-aware-trained_dm_1.0_without_softmax.tflite")
print(f"Loading and compiling the model {path}")
with session.create(str(path)) as sess:
print(f"Model has been compiled successfully")
print("Model input and output:")
print(sess.print_summary())
_, height, width, channel = sess.input(0).shape()
image = image.convert('RGB').resize((width, height))
data = np.zeros((width, height, channel), np.uint8)
data[:width, :height, :channel] = image
data = np.reshape(data, (1, width, height, channel))
start_time = time.time()
outputs = sess.run(data)
print('Prediction elapsed {:.2f} secs'.format(time.time() -
start_time))
classified = np.squeeze(outputs[0].numpy())
imagenet_labels = load_labels(
current_path.joinpath('../assets/labels/ImageNetLabels.txt'))
Class = collections.namedtuple('Class', ['id', 'score'])
objects = []
for idx, n in np.ndenumerate(classified):
objects.append(Class(idx[0], n))
objects.sort(key=lambda x: x[1], reverse=True)
print("[Top 5 scores:]")
for object in objects[:5]:
print("{}: {}".format(imagenet_labels[object.id], object.score))
def random_input_inference(model_path, num_inf):
from furiosa.runtime import session
print(f"Loading and compiling the model {model_path}")
with session.create(str(model_path)) as sess:
print(f"Model has been compiled successfully")
print("Model input and output:")
print(sess.print_summary())
for idx in range(num_inf):
print(f'Iteration {idx}...')
inputs = []
for session_input in sess.inputs():
if session_input.dtype() == DataType.UINT8:
inputs.append(
np.random.randint(0,
255,
session_input.shape(),
dtype=np.uint8))
elif session_input.dtype() == DataType.INT8:
inputs.append(
np.random.randint(-128,
127,
session_input.shape(),
dtype=np.int8))
elif session_input.dtype() == DataType.FLOAT32:
inputs.append(
np.random.random(session_input.shape()).astype(
np.float32))
else:
raise Exception(
f"Unsupported DataType({session_input.dtype()}) of input."
)
sess.run(inputs)
def __init__(self, model_path):
self.nux_sess = session.create(model=model_path)
super().__init__(model_path)
def __init__(self, model_path):
self.session = session.create(model=model_path)