Python load_test_imageの例

コード例 #1

tflite_model_file = os.path.join(model_dir, model_name)
tflite_model_buf = open(tflite_model_file, "rb").read()

# Get TFLite model from buffer
try:
    import tflite
    tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
except AttributeError:
    import tflite.Model
    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)

dtype = "float32"
width = 224
height = 224
image_data = load_test_image(dtype, width, height)

input_tensor = "input"
input_shape = (1, 224, 224, 3)
input_dtype = dtype

# Parse TFLite model and convert it to a Relay module
mod, params = relay.frontend.from_tflite(
    tflite_model,
    shape_dict={input_tensor: input_shape},
    dtype_dict={input_tensor: input_dtype})

tvm_target = get_tvm_target(device, get_device_type(), get_device_arch(),
                            get_device_attributes())

tvm_targets = tvm.target.Target(tvm_target)

コード例 #2

ファイル: inception-v1-quant.py プロジェクト: tom-gall/tvm-bench

model_dir = download_model_zoo(model_dir, model_name)

tflite_model_file = os.path.join(model_dir, model_name)
tflite_model_buf = open(tflite_model_file, "rb").read()

# Get TFLite model from buffer
try:
    import tflite
    tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
except AttributeError:
    import tflite.Model
    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)

dtype = "uint8"
image_data = load_test_image(dtype)

input_tensor = "input"
input_shape = (1, 224, 224, 3)
#input_dtype = "float32"
input_dtype = dtype

# Parse TFLite model and convert it to a Relay module
mod, params = relay.frontend.from_tflite(
    tflite_model,
    shape_dict={input_tensor: input_shape},
    dtype_dict={input_tensor: input_dtype})
desired_layouts = {'qnn.conv2d': ['NCHW', 'default']}
seq = tvm.transform.Sequential([
    relay.transform.RemoveUnusedFunctions(),
    relay.transform.ConvertLayout(desired_layouts)

コード例 #3

ファイル: mobilenet-v1.1-224-quant.py プロジェクト: tom-gall/tf-bench

model_name = 'mobilenet_v1_1.0_224_quant.tflite'
repeat = 10

model_dir = download_model_zoo(model_dir, model_name)
tflite_model_file = os.path.join(model_dir, model_name)
tflite_model_buf = open(tflite_model_file, "rb").read()
try:
    import tflite
    tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
except AttributeError:
    import tflite.Model
    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)

interpreter = Interpreter(tflite_model_file, num_threads=get_cpu_count())
interpreter.allocate_tensors()

_, height, width, _ = interpreter.get_input_details()[0]['shape']
image = load_test_image('uint8', height, width)

numpy_time = np.zeros(repeat)

for i in range(0, repeat):
    start_time = time.time()
    results = classify_image(interpreter, image)

    elapsed_ms = (time.time() - start_time) * 1000
    numpy_time[i] = elapsed_ms

print("tflite %-20s %-19s (%s)" % (model_name, "%.2f ms" % np.mean(numpy_time),
                                   "%.2f ms" % np.std(numpy_time)))

コード例 #4

ファイル: inception-v3-float.py プロジェクト: tom-gall/tf-bench

model_name = 'inception_v3.tflite'
repeat = 10

model_dir = download_model_zoo(model_dir, model_name)
tflite_model_file = os.path.join(model_dir, model_name)
tflite_model_buf = open(tflite_model_file, "rb").read()
try:
    import tflite
    tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
except AttributeError:
    import tflite.Model
    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)

interpreter = Interpreter(tflite_model_file, num_threads=get_cpu_count())
interpreter.allocate_tensors()

_, height, width, _ = interpreter.get_input_details()[0]['shape']
image = load_test_image('float32', height, width)

numpy_time = np.zeros(repeat)

for i in range(0, repeat):
    start_time = time.time()
    results = classify_image(interpreter, image)

    elapsed_ms = (time.time() - start_time) * 1000
    numpy_time[i] = elapsed_ms

print("tflite %-20s %-19s (%s)" % (model_name, "%.2f ms" % np.mean(numpy_time),
                                   "%.2f ms" % np.std(numpy_time)))

コード例 #5

ファイル: resnet_v2_101.py プロジェクト: K1504296/tvm-bench

model_dir = download_model_zoo(model_dir, model_name)

tflite_model_file = os.path.join(model_dir, model_name)
tflite_model_buf = open(tflite_model_file, "rb").read()

# Get TFLite model from buffer
try:
    import tflite
    tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
except AttributeError:
    import tflite.Model
    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)

dtype = "float32"
image_data = load_test_image(dtype, 299, 299)

input_tensor = "input"
input_shape = (1, 299, 299, 3)
input_dtype = dtype

# Parse TFLite model and convert it to a Relay module
mod, params = relay.frontend.from_tflite(
    tflite_model,
    shape_dict={input_tensor: input_shape},
    dtype_dict={input_tensor: input_dtype})
desired_layouts = {'nn.conv2d': ['NCHW', 'default']}
seq = tvm.transform.Sequential([
    relay.transform.RemoveUnusedFunctions(),
    relay.transform.ConvertLayout(desired_layouts)
])

コード例 #6

model_dir = download_model_zoo(model_dir, model_name)

tflite_model_file = os.path.join(model_dir, model_name)
tflite_model_buf = open(tflite_model_file, "rb").read()

# Get TFLite model from buffer
try:
    import tflite
    tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
except AttributeError:
    import tflite.Model
    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)

dtype="float32"
image_data = load_test_image(dtype, 416, 416)

input_tensor = "input_1"
input_shape = (1, 416, 416, 3)
input_dtype = dtype

# Parse TFLite model and convert it to a Relay module
mod, params = relay.frontend.from_tflite(tflite_model,
                                         shape_dict={input_tensor: input_shape},
                                         dtype_dict={input_tensor: input_dtype})
desired_layouts = {'nn.conv2d': ['NCHW', 'default']}
seq = tvm.transform.Sequential([relay.transform.RemoveUnusedFunctions(),relay.transform.ConvertLayout(desired_layouts)])

with tvm.transform.PassContext(opt_level=3):
    mod = seq(mod)