def finetune(args):
# Load Paddlehub pretrained model, default as mobilenet
module = hub.Module(name=args.module)
input_dict, output_dict, program = module.context(trainable=True)
# Download dataset and use ImageClassificationReader to read dataset
dataset = hub.dataset.Flowers()
data_reader = hub.reader.ImageClassificationReader(
image_width=module.get_expected_image_width(),
image_height=module.get_expected_image_height(),
images_mean=module.get_pretrained_images_mean(),
images_std=module.get_pretrained_images_std(),
dataset=dataset)
# The last 2 layer of resnet_v2_101_imagenet network
feature_map = output_dict["feature_map"]
img = input_dict["image"]
feed_list = [img.name]
# Select finetune strategy, setup config and finetune
strategy = hub.DefaultFinetuneStrategy(learning_rate=args.learning_rate)
config = hub.RunConfig(
use_cuda=True,
num_epoch=args.epochs,
batch_size=args.batch_size,
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# Construct transfer learning network
task = hub.ImageClassifierTask(
data_reader=data_reader,
feed_list=feed_list,
feature=feature_map,
num_classes=dataset.num_labels,
config=config)
# Load model from the defined model path or not
if args.model_path != "":
with task.phase_guard(phase="train"):
task.init_if_necessary()
task.load_parameters(args.model_path)
logger.info("PaddleHub has loaded model from %s" % args.model_path)
# Finetune by PaddleHub's API
task.finetune()
# Evaluate by PaddleHub's API
run_states = task.eval()
# Get acc score on dev
eval_avg_score, eval_avg_loss, eval_run_speed = task._calculate_metrics(
run_states)
# Move ckpt/best_model to the defined saved parameters directory
best_model_dir = os.path.join(config.checkpoint_dir, "best_model")
if is_path_valid(args.saved_params_dir) and os.path.exists(best_model_dir):
shutil.copytree(best_model_dir, args.saved_params_dir)
shutil.rmtree(config.checkpoint_dir)
# acc on dev will be used by auto finetune
hub.report_final_result(eval_avg_score["acc"])
TransformerModule.context = TransformerModule_pat.context
module = hub.Module(name="chinese-roberta-wwm-ext-large")
inputs, outputs, program = module.context(trainable=True, max_seq_len=128)
program.random_seed = 1
reader = hub.reader.ClassifyReader(
dataset=dataset,
vocab_path=module.get_vocab_path(),
max_seq_len=128,
random_seed=1)
print("learning rate: ", eval(args.lr))
print("max epoch: ", args.max_epoch)
strategy = hub.DefaultFinetuneStrategy(learning_rate=eval(args.lr), optimizer_name="sgd")
config = hub.RunConfig(use_cuda=True, num_epoch=args.max_epoch, batch_size=32, strategy=strategy, log_interval=100,
eval_interval=1400,save_ckpt_interval=1400, checkpoint_dir='./checkpoint_aug')
pooled_output = outputs["pooled_output"]
feed_list = [
inputs["input_ids"].name,
inputs["position_ids"].name,
inputs["segment_ids"].name,
inputs["input_mask"].name
]
cls_task = hub.TextClassifierTask(
predict_file_with_header=False,
label_list=["Yes", "Depends", "No"])
dataset = Dataset()
module = hub.Module(name="chinese-roberta-wwm-ext-large")
inputs, outputs, program = module.context(trainable=False, max_seq_len=128)
program.random_seed = 1
print(program.random_seed)
reader = hub.reader.ClassifyReader(dataset=dataset,
vocab_path=module.get_vocab_path(),
max_seq_len=128)
strategy = hub.DefaultFinetuneStrategy(learning_rate=0.002,
optimizer_name="sgd")
config = hub.RunConfig(use_cuda=True,
num_epoch=3,
batch_size=32,
strategy=strategy,
log_interval=100,
eval_interval=1400,
save_ckpt_interval=1400,
checkpoint_dir='./checkpoint_aug')
pooled_output = outputs["pooled_output"]
feed_list = [
inputs["input_ids"].name, inputs["position_ids"].name,
inputs["segment_ids"].name, inputs["input_mask"].name