From 6c51d5b2546957d150c59602ef21bfe0c870a45b Mon Sep 17 00:00:00 2001 From: jiangjiahui Date: Tue, 16 Apr 2024 15:31:59 +0800 Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=95=B0=E5=AD=97=E8=AF=86?= =?UTF-8?q?=E5=88=AB=E4=BD=BF=E7=94=A8=E7=9A=84=E9=83=A8=E7=BD=B2=E4=BB=A3?= =?UTF-8?q?=E7=A0=81=E5=8F=8A=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .DS_Store | Bin 0 -> 10244 bytes evaluate.py | 60 +++++++ model/saved_model.pb | Bin 0 -> 84275 bytes model/variables/variables.data-00000-of-00001 | Bin 0 -> 1225403 bytes model/variables/variables.index | Bin 0 -> 1452 bytes predict.py | 126 +++++++++++++++ serving/.DS_Store | Bin 0 -> 6148 bytes serving/Dockerfile | 26 +++ serving/serving.py | 100 ++++++++++++ serving/utils.py | 102 ++++++++++++ train.py | 148 ++++++++++++++++++ 11 files changed, 562 insertions(+) create mode 100644 .DS_Store create mode 100644 evaluate.py create mode 100644 model/saved_model.pb create 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b/model/variables/variables.index new file mode 100644 index 0000000000000000000000000000000000000000..cec8d0e7610ba3eb6d8ec111c633906bdb0bfee9 GIT binary patch literal 1452 zcmZQzVB=tvV&Y(AkP(P?_HcFf4)FK%3vqPvagFzP@^W%#Z>E{w<*o8HQAj3TFf4OZUBU(n3o*y&3 zKzi0~6N(a56bD9CUMhi5<^sBW0vp)*8bIfp`hQ*_E^8`HuG^7}Q@8*OSdft(duQG> zlaVIi17w{)ZUDUw)Oq}&sJ5S@WUT8SnF1*JJcZm>j4Lz@er%_1nga-@jfhoDNEii2=ur|-HH;2oom#fba!#5GzhvO9j(p{VZ zXTTY72AqMPVE}iwNOPv>y))npI0GLH$oUY^1Y5(bSdI>Kr33)ZU@ih(Y6-~+hOJ>% zga^W!3e;4#5`#4z_F!?XVOG?1VkyDxU7!(LkTBt6}@)`oPnBwQynhk{=ddA zGg;) 0 and None not in image_files: + image_bytes = [f.read() for f in image_files] + type_ = 'file' + + elif image_base64s and len(image_base64s) > 0 and None not in image_base64s: + image_bytes = [base64.b64decode(s) for s in image_base64s] + type_ = 'base64' + + elif image_urls and len(image_urls) > 0 and None not in image_urls: + image_bytes = [download_image_as_bytes(url, max_file_size=max_file_size) + for url in image_urls] + type_ = 'url' + + else: + raise Exception('no image') + + if max_file_size > 0: + for idx, f in enumerate(image_bytes): + if len(f) > max_file_size: + raise Exception(f'image size must less than {max_file_size / 1024 / 1024}MB, ' + f'but image-{idx + 1} is {len(f) / 1024 / 1024}MB.') + return image_bytes, type_ + + +def mnist_preprocess(images): + results = [] + for img in images: + if isinstance(img, bytes): + image_file = BytesIO(img) + else: + image_file = img + + image = Image.open(image_file) + image = image.resize((28, 28)) + image = image.convert('L') + image = np.asarray(image)[:, :] / 255. + results.append(image) + return np.asarray(results) diff --git a/train.py b/train.py new file mode 100644 index 0000000..745b2cb --- /dev/null +++ b/train.py @@ -0,0 +1,148 @@ +import argparse +import gzip +import json +import os + +import numpy as np +import tensorflow as tf + +num_workers = 1 + + +def load_data(data_dir): + files = [ + 'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz', + 't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz' + ] + paths = [] + for f in files: + paths.append(os.path.join(data_dir, f)) + with gzip.open(paths[0], 'rb') as f: + y_train = np.frombuffer(f.read(), np.uint8, offset=8) + with gzip.open(paths[1], 'rb') as f: + x_train = np.frombuffer(f.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28) + with gzip.open(paths[2], 'rb') as f: + y_test = np.frombuffer(f.read(), np.uint8, offset=8) + with gzip.open(paths[3], 'rb') as f: + x_test = np.frombuffer(f.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28) + return (x_train, y_train), (x_test, y_test) + + +def mnist_dataset(batch_size=64, data_dir=None): + # load dataset + if data_dir: + print(f'Loading mnist data from {data_dir}') + (x_train, y_train), (x_test, y_test) = load_data(data_dir) + else: + print('Loading mnist data from tf.keras.datasets.mnist') + (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() + x_train, x_test = x_train / 255.0, x_test / 255.0 + + train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(60000).repeat().batch(batch_size) + test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size) + + return train_dataset, test_dataset + + +def get_strategy(strategy='off'): + strategy = strategy.lower() + # multiple nodes, every nodes have multiple GPUs + if strategy == "multi_worker_mirrored": + return tf.distribute.experimental.MultiWorkerMirroredStrategy() + # single node with multiple GPUs + if strategy == "mirrored": + return tf.distribute.MirroredStrategy() + # single node with single GPU + return tf.distribute.get_strategy() + + +def setup_env(args): + tf.config.set_soft_device_placement(True) + + # limit the gpu memory usage as much as it need. + try: + gpus = tf.config.experimental.list_physical_devices('GPU') + for gpu in gpus: + tf.config.experimental.set_memory_growth(gpu, True) + logical_gpus = tf.config.list_logical_devices('GPU') + print(f"Detected {len(gpus)} Physical GPUs, {len(logical_gpus)} Logical GPUs") + except RuntimeError as e: + print(e) + + if args.strategy == 'multi_worker_mirrored': + index = int(os.environ['VK_TASK_INDEX']) + task_name = os.environ["VC_TASK_NAME"].upper() + ips = os.environ[f'VC_{task_name}_HOSTS'] + ips = ips.split(',') + global num_workers + num_workers = len(ips) + ips = [f'{ip}:20000' for ip in ips] + os.environ["TF_CONFIG"] = json.dumps({ + "cluster": { + "worker": ips + }, + "task": {"type": "worker", "index": index} + }) + + +def setup_config(): + parser = argparse.ArgumentParser(description='Train MNIST digits classification') + parser.add_argument('--data-dir', help='Directory to MNIST dataset') + parser.add_argument('--output-dir', help='Directory to save models and logs') + parser.add_argument('--epochs', default=2, help='Number of epochs') + parser.add_argument('--eval', action='store_true', help='whether do evaluation after training finished') + parser.add_argument( + '--strategy', + default='off', + choices=['off', 'mirrored', 'multi_worker_mirrored'], + help='TensorFlow distributed training strategies' + ) + args = parser.parse_args() + return args + + +def main(): + args = setup_config() + # tf2 limitation: Collective ops must be configured at program startup + strategy = get_strategy(args.strategy) + setup_env(args) + + with strategy.scope(): + # build dataset + train_dataset, test_dataset = mnist_dataset( + batch_size=64 * num_workers, + data_dir=args.data_dir + ) + + # build model + model = tf.keras.models.Sequential([ + tf.keras.layers.Flatten(input_shape=(28, 28)), + tf.keras.layers.Dense(128, activation='relu'), + tf.keras.layers.Dropout(0.2), + tf.keras.layers.Dense(10) + ]) + model.compile( + optimizer='adam', + loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), + metrics=['accuracy'] + ) + model.summary() + + # training + model.fit(train_dataset, epochs=args.epochs, steps_per_epoch=70) + + # evaluation + if args.eval: + model.evaluate(test_dataset, verbose=2) + + # save model + if args.output_dir: + os.makedirs(args.output_dir, exist_ok=True) + model.save(args.output_dir) + print(f'Saved model to {args.output_dir}') + + +if __name__ == '__main__': + # python train.py --data-dir=/path/to/MNIST/dataset --output-dir=/path/to/output + print("TensorFlow version:", tf.__version__) + main()