PaddlePaddle モデルを OpenVINO™ IR に変換#

classifier = PaddleClas(inference_model_dir=MODEL_DIR / "{}_infer".format(MODEL_NAME)) 
result = next(classifier.predict(IMAGE_FILENAME)) 
class_names = result[0]["label_names"] 
scores = result[0]["scores"] 
image = Image.open(IMAGE_FILENAME) 
plt.imshow(image) 
for class_name, softmax_probability in zip(class_names, scores): 
    print(f"{class_name}, {softmax_probability:.5f}")

# classifier??

# classifier.get_config()

preprocess_ops = classifier.predictor.preprocess_ops 

def process_image(image): 
    for op in preprocess_ops: 
        image = op(image) 
    return image

pil_image = Image.open(IMAGE_FILENAME) 
processed_image = process_image(np.array(pil_image)) 
print(f"Processed image shape: {processed_image.shape}") 
# 処理された画像は (C、H、W) 形式です。画像を表示するには (H、W、C) に変換します 
plt.imshow(np.transpose(processed_image, (1, 2, 0)))

class_id_map_file = classifier.get_config()["PostProcess"]["Topk"]["class_id_map_file"] 
class_id_map = {} 
with open(class_id_map_file, "r") as fin: 
    lines = fin.readlines() 
    for line in lines: 
        partition = line.split("\n")[0].partition(" ") 
        class_id_map[int(partition[0])] = str(partition[-1])

model_xml = Path(MODEL_NAME).with_suffix(".xml") 
if not model_xml.exists(): 
    ov_model = ov.convert_model("model/MobileNetV3_large_x1_0_infer/inference.pdmodel") 
    ov.save_model(ov_model, str(model_xml)) 
else: 
    print(f"{model_xml} already exists.")

import ipywidgets as widgets 

core = ov.Core() 
device = widgets.Dropdown( 
    options=core.available_devices + ["AUTO"], 
    value="AUTO", 
    description="Device:", 
    disabled=False, 
) 

device

# OpenVINO ランタイムと OpenVINO IR モデルをロード 
core = ov.Core() 
model = core.read_model(model_xml) 
compiled_model = core.compile_model(model=model, device_name=device.value) 

# モデル出力を取得 
output_layer = compiled_model.output(0) 

# 入力画像の読み取り、表示、前処理 
# process_image のソースについては、"PaddlePaddle モデルの推論を表示" セクションを参照 
image = Image.open(IMAGE_FILENAME) 
plt.imshow(image) 
input_image = process_image(np.array(image))[None,] 

# 推論を実行 
ov_result = compiled_model([input_image])[output_layer][0] 

# 上位 3 つの値を見つける 
top_indices = np.argsort(ov_result)[-3:][::-1] 
top_scores = ov_result[top_indices] 

# PaddlePaddle 分類器と同じラベルを使用して、推論結果をクラス名に変換 
for index, softmax_probability in zip(top_indices, top_scores): 
    print(f"{class_id_map[index]}, {softmax_probability:.5f}")

num_images = 50 

image = Image.open(fp=IMAGE_FILENAME)

# デバイス情報を表示 
core = ov.Core() 
devices = core.available_devices 

for device_name in devices: 
    device_full_name = core.get_property(device_name, "FULL_DEVICE_NAME") 
    print(f"{device_name}: {device_full_name}")

# PaddlePaddle モデルでの推論速度を表示 
start = time.perf_counter() 
for _ in range(num_images): 
    result = next(classifier.predict(np.array(image))) 
end = time.perf_counter() 
time_ir = end - start 
print(f"PaddlePaddle model on CPU: {time_ir/num_images:.4f} " f"seconds per image, FPS: {num_images/time_ir:.2f}\n") 
print("PaddlePaddle result:") 
class_names = result[0]["label_names"] 
scores = result[0]["scores"] 
for class_name, softmax_probability in zip(class_names, scores): 
    print(f"{class_name}, {softmax_probability:.5f}") 
plt.imshow(image);

device

# OpenVINO IR モデルの推論速度を表示 
compiled_model = core.compile_model(model=model, device_name=device.value) 
output_layer = compiled_model.output(0) 

start = time.perf_counter() 
input_image = process_image(np.array(image))[None,] 
for _ in range(num_images): 
    ie_result = compiled_model([input_image])[output_layer][0] 
    top_indices = np.argsort(ie_result)[-5:][::-1] 
    top_softmax = ie_result[top_indices] 

end = time.perf_counter() 
time_ir = end - start 

print(f"OpenVINO IR model in OpenVINO Runtime ({device.value}): {time_ir/num_images:.4f} " f"seconds per image, FPS: {num_images/time_ir:.2f}") 
print() 
print("OpenVINO result:") 
for index, softmax_probability in zip(top_indices, top_softmax): 
    print(f"{class_id_map[index]}, {softmax_probability:.5f}") 
plt.imshow(image);