87.py

import os
import tensorflow as tf
from tensorflow.keras.metrics import CategoricalAccuracy, Mean, CategoricalCrossentropy, TopKCategoricalAccuracy

from hanser.distribute import setup_runtime, distribute_datasets
from hanser.datasets.imagenet import make_imagenet_dataset
from hanser.transform import random_resized_crop, resize, center_crop, normalize, to_tensor

from hanser.train.optimizers import SGD
from hanser.models.imagenet.res2net.resnet_vd import resnet50
from hanser.train.cls import SuperLearner
from hanser.train.lr_schedule import CosineLR
from hanser.losses import CrossEntropy

TASK_NAME = os.environ.get("TASK_NAME", "default")
TASK_ID = os.environ.get("TASK_ID", 0)

TRAIN_RES = 224

def transform(image, label, training):
    if training:
        image = random_resized_crop(image, TRAIN_RES, scale=(0.05, 1.0), ratio=(0.75, 1.33))
        image = tf.image.random_flip_left_right(image)
    else:
        image = resize(image, 256)
        image = center_crop(image, 224)

    image, label = to_tensor(image, label, label_offset=1)
    image = normalize(image, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])

    label = tf.one_hot(label, 1000)
    return image, label

batch_size = 1024
eval_batch_size = 2048

remote_dir, local_dir = os.getenv('REMOTE_DDIR'), os.getenv('LOCAL_DDIR')
ds_train, ds_eval, steps_per_epoch, eval_steps = make_imagenet_dataset(
    batch_size, eval_batch_size, transform, data_dir=(remote_dir, local_dir))

setup_runtime(fp16=True)
ds_train, ds_eval = distribute_datasets(ds_train, ds_eval)

model = resnet50()
model.build((None, TRAIN_RES, TRAIN_RES, 3))
model.summary()

criterion = CrossEntropy()

base_lr = 0.1
epochs = 120
lr_schedule = CosineLR(base_lr * (batch_size // 256), steps_per_epoch, epochs=epochs, min_lr=0,
                       warmup_epoch=5, warmup_min_lr=0)
optimizer = SGD(lr_schedule, momentum=0.9, weight_decay=1e-4, nesterov=True)

train_metrics = {
    'loss': Mean(),
    'acc': CategoricalAccuracy(),
}
eval_metrics = {
    'loss': CategoricalCrossentropy(from_logits=True),
    'acc': CategoricalAccuracy(),
    'acc5': TopKCategoricalAccuracy(k=5),
}

learner = SuperLearner(
    model, criterion, optimizer,
    train_metrics=train_metrics, eval_metrics=eval_metrics,
    work_dir=f"./models/{TASK_NAME}-{TASK_ID}")
learner.load(miss_ok=True)

learner.fit(ds_train, epochs, ds_eval, val_freq=1,
            steps_per_epoch=steps_per_epoch, val_steps=eval_steps,
            save_freq=10)