提交 41407cac 编写于 作者: S Steffy-zxf

Merge branch 'release/v1.7' of https://github.com/Steffy-zxf/PaddleHub into release/v1.7

......@@ -18,7 +18,7 @@ parser.add_argument(
default="mobilenet",
help="Module used as feature extractor.")
# the name of hyperparameters to be searched should keep with hparam.py
# the name of hyper-parameters to be searched should keep with hparam.py
parser.add_argument(
"--batch_size",
type=int,
......@@ -27,7 +27,7 @@ parser.add_argument(
parser.add_argument(
"--learning_rate", type=float, default=1e-4, help="learning_rate.")
# saved_params_dir and model_path are needed by auto finetune
# saved_params_dir and model_path are needed by auto fine-tune
parser.add_argument(
"--saved_params_dir",
type=str,
......@@ -76,7 +76,7 @@ def finetune(args):
img = input_dict["image"]
feed_list = [img.name]
# Select finetune strategy, setup config and finetune
# Select fine-tune strategy, setup config and fine-tune
strategy = hub.DefaultFinetuneStrategy(learning_rate=args.learning_rate)
config = hub.RunConfig(
use_cuda=True,
......@@ -100,7 +100,7 @@ def finetune(args):
task.load_parameters(args.model_path)
logger.info("PaddleHub has loaded model from %s" % args.model_path)
# Finetune by PaddleHub's API
# Fine-tune by PaddleHub's API
task.finetune()
# Evaluate by PaddleHub's API
run_states = task.eval()
......@@ -114,7 +114,7 @@ def finetune(args):
shutil.copytree(best_model_dir, args.saved_params_dir)
shutil.rmtree(config.checkpoint_dir)
# acc on dev will be used by auto finetune
# acc on dev will be used by auto fine-tune
hub.report_final_result(eval_avg_score["acc"])
......
......@@ -13,7 +13,7 @@ from paddlehub.common.logger import logger
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--epochs", type=int, default=3, help="epochs.")
# the name of hyperparameters to be searched should keep with hparam.py
# the name of hyper-parameters to be searched should keep with hparam.py
parser.add_argument("--batch_size", type=int, default=32, help="batch_size.")
parser.add_argument(
"--learning_rate", type=float, default=5e-5, help="learning_rate.")
......@@ -33,7 +33,7 @@ parser.add_argument(
default=None,
help="Directory to model checkpoint")
# saved_params_dir and model_path are needed by auto finetune
# saved_params_dir and model_path are needed by auto fine-tune
parser.add_argument(
"--saved_params_dir",
type=str,
......@@ -82,14 +82,14 @@ if __name__ == '__main__':
inputs["input_mask"].name,
]
# Select finetune strategy, setup config and finetune
# Select fine-tune strategy, setup config and fine-tune
strategy = hub.AdamWeightDecayStrategy(
warmup_proportion=args.warmup_prop,
learning_rate=args.learning_rate,
weight_decay=args.weight_decay,
lr_scheduler="linear_decay")
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
checkpoint_dir=args.checkpoint_dir,
use_cuda=True,
......@@ -98,7 +98,7 @@ if __name__ == '__main__':
enable_memory_optim=True,
strategy=strategy)
# Define a classfication finetune task by PaddleHub's API
# Define a classfication fine-tune task by PaddleHub's API
cls_task = hub.TextClassifierTask(
data_reader=reader,
feature=pooled_output,
......@@ -125,5 +125,5 @@ if __name__ == '__main__':
shutil.copytree(best_model_dir, args.saved_params_dir)
shutil.rmtree(config.checkpoint_dir)
# acc on dev will be used by auto finetune
# acc on dev will be used by auto fine-tune
hub.report_final_result(eval_avg_score["acc"])
......@@ -14,7 +14,7 @@ parser.add_argument("--use_gpu", type=ast.literal_eval, default=True
parser.add_argument("--checkpoint_dir", type=str, default="paddlehub_finetune_ckpt", help="Path to save log data.")
parser.add_argument("--batch_size", type=int, default=16, help="Total examples' number in batch for training.")
parser.add_argument("--module", type=str, default="resnet50", help="Module used as feature extractor.")
parser.add_argument("--dataset", type=str, default="flowers", help="Dataset to finetune.")
parser.add_argument("--dataset", type=str, default="flowers", help="Dataset to fine-tune.")
parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=True, help="Whether use data parallel.")
# yapf: enable.
......@@ -60,7 +60,7 @@ def finetune(args):
# Setup feed list for data feeder
feed_list = [input_dict["image"].name]
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=args.use_data_parallel,
use_cuda=args.use_gpu,
......@@ -69,7 +69,7 @@ def finetune(args):
checkpoint_dir=args.checkpoint_dir,
strategy=hub.finetune.strategy.DefaultFinetuneStrategy())
# Define a reading comprehension finetune task by PaddleHub's API
# Define a image classification task by PaddleHub Fine-tune API
task = hub.ImageClassifierTask(
data_reader=data_reader,
feed_list=feed_list,
......@@ -77,7 +77,7 @@ def finetune(args):
num_classes=dataset.num_labels,
config=config)
# Finetune by PaddleHub's API
# Fine-tune by PaddleHub's API
task.finetune_and_eval()
......
......@@ -13,7 +13,7 @@ parser.add_argument("--use_gpu", type=ast.literal_eval, default=True
parser.add_argument("--checkpoint_dir", type=str, default="paddlehub_finetune_ckpt", help="Path to save log data.")
parser.add_argument("--batch_size", type=int, default=16, help="Total examples' number in batch for training.")
parser.add_argument("--module", type=str, default="resnet50", help="Module used as a feature extractor.")
parser.add_argument("--dataset", type=str, default="flowers", help="Dataset to finetune.")
parser.add_argument("--dataset", type=str, default="flowers", help="Dataset to fine-tune.")
# yapf: enable.
module_map = {
......@@ -58,7 +58,7 @@ def predict(args):
# Setup feed list for data feeder
feed_list = [input_dict["image"].name]
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=False,
use_cuda=args.use_gpu,
......@@ -66,7 +66,7 @@ def predict(args):
checkpoint_dir=args.checkpoint_dir,
strategy=hub.finetune.strategy.DefaultFinetuneStrategy())
# Define a reading comprehension finetune task by PaddleHub's API
# Define a image classification task by PaddleHub Fine-tune API
task = hub.ImageClassifierTask(
data_reader=data_reader,
feed_list=feed_list,
......
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
"""Fine-tuning on classification task """
import argparse
import ast
......@@ -23,7 +23,7 @@ import paddlehub as hub
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for fine-tuning, input should be True or False")
parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
parser.add_argument("--warmup_proportion", type=float, default=0.1, help="Warmup proportion params for warmup strategy")
......@@ -56,13 +56,13 @@ if __name__ == '__main__':
# Use "pooled_output" for classification tasks on an entire sentence.
pooled_output = outputs["pooled_output"]
# Select finetune strategy, setup config and finetune
# Select fine-tune strategy, setup config and fine-tune
strategy = hub.AdamWeightDecayStrategy(
warmup_proportion=args.warmup_proportion,
weight_decay=args.weight_decay,
learning_rate=args.learning_rate)
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_cuda=args.use_gpu,
num_epoch=args.num_epoch,
......@@ -70,7 +70,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# Define a classfication finetune task by PaddleHub's API
# Define a classfication fine-tune task by PaddleHub's API
multi_label_cls_task = hub.MultiLabelClassifierTask(
data_reader=reader,
feature=pooled_output,
......@@ -78,6 +78,6 @@ if __name__ == '__main__':
num_classes=dataset.num_labels,
config=config)
# Finetune and evaluate by PaddleHub's API
# Fine-tune and evaluate by PaddleHub's API
# will finish training, evaluation, testing, save model automatically
multi_label_cls_task.finetune_and_eval()
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
"""Fine-tuning on classification task """
from __future__ import absolute_import
from __future__ import division
......@@ -35,7 +35,7 @@ parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--batch_size", type=int, default=1, help="Total examples' number in batch for training.")
parser.add_argument("--max_seq_len", type=int, default=128, help="Number of words of the longest seqence.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for fine-tuning, input should be True or False")
args = parser.parse_args()
# yapf: enable.
......@@ -65,7 +65,7 @@ if __name__ == '__main__':
# Use "sequence_output" for token-level output.
pooled_output = outputs["pooled_output"]
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=False,
use_cuda=args.use_gpu,
......@@ -73,7 +73,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=hub.finetune.strategy.DefaultFinetuneStrategy())
# Define a classfication finetune task by PaddleHub's API
# Define a classfication fine-tune task by PaddleHub's API
multi_label_cls_task = hub.MultiLabelClassifierTask(
data_reader=reader,
feature=pooled_output,
......
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
"""Fine-tuning on classification task """
import argparse
import ast
......@@ -23,7 +23,7 @@ import paddlehub as hub
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for fine-tuning, input should be True or False")
parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
parser.add_argument("--warmup_proportion", type=float, default=0.0, help="Warmup proportion params for warmup strategy")
......@@ -61,13 +61,13 @@ if __name__ == '__main__':
inputs["input_mask"].name,
]
# Select finetune strategy, setup config and finetune
# Select fine-tune strategy, setup config and fine-tune
strategy = hub.AdamWeightDecayStrategy(
warmup_proportion=args.warmup_proportion,
weight_decay=args.weight_decay,
learning_rate=args.learning_rate)
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=args.use_data_parallel,
use_cuda=args.use_gpu,
......@@ -76,7 +76,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# Define a classfication finetune task by PaddleHub's API
# Define a classfication fine-tune task by PaddleHub's API
cls_task = hub.TextClassifierTask(
data_reader=reader,
feature=pooled_output,
......@@ -84,6 +84,6 @@ if __name__ == '__main__':
num_classes=dataset.num_labels,
config=config)
# Finetune and evaluate by PaddleHub's API
# Fine-tune and evaluate by PaddleHub's API
# will finish training, evaluation, testing, save model automatically
cls_task.finetune_and_eval()
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
"""Fine-tuning on classification task """
from __future__ import absolute_import
from __future__ import division
......@@ -33,7 +33,7 @@ parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--batch_size", type=int, default=1, help="Total examples' number in batch for training.")
parser.add_argument("--max_seq_len", type=int, default=128, help="Number of words of the longest seqence.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for fine-tuning, input should be True or False")
args = parser.parse_args()
# yapf: enable.
......@@ -63,7 +63,7 @@ if __name__ == '__main__':
inputs["input_mask"].name,
]
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=False,
use_cuda=args.use_gpu,
......@@ -71,7 +71,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=hub.finetune.strategy.DefaultFinetuneStrategy())
# Define a classfication finetune task by PaddleHub's API
# Define a classfication fine-tune task by PaddleHub's API
cls_task = hub.TextClassifierTask(
data_reader=reader,
feature=pooled_output,
......
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
"""Fine-tuning on classification task """
from __future__ import absolute_import
from __future__ import division
......@@ -28,7 +28,7 @@ hub.common.logger.logger.setLevel("INFO")
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--num_epoch", type=int, default=1, help="Number of epoches for fine-tuning.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for fine-tuning, input should be True or False")
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint.")
parser.add_argument("--max_seq_len", type=int, default=384, help="Number of words of the longest seqence.")
parser.add_argument("--batch_size", type=int, default=8, help="Total examples' number in batch for training.")
......@@ -64,7 +64,7 @@ if __name__ == '__main__':
inputs["input_mask"].name,
]
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=False,
use_cuda=args.use_gpu,
......@@ -72,7 +72,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=hub.AdamWeightDecayStrategy())
# Define a reading comprehension finetune task by PaddleHub's API
# Define a reading comprehension fine-tune task by PaddleHub's API
reading_comprehension_task = hub.ReadingComprehensionTask(
data_reader=reader,
feature=seq_output,
......
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
"""Fine-tuning on classification task """
import argparse
import ast
......@@ -25,7 +25,7 @@ hub.common.logger.logger.setLevel("INFO")
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--num_epoch", type=int, default=1, help="Number of epoches for fine-tuning.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for fine-tuning, input should be True or False")
parser.add_argument("--learning_rate", type=float, default=3e-5, help="Learning rate used to train with warmup.")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
parser.add_argument("--warmup_proportion", type=float, default=0.0, help="Warmup proportion params for warmup strategy")
......@@ -64,13 +64,13 @@ if __name__ == '__main__':
inputs["input_mask"].name,
]
# Select finetune strategy, setup config and finetune
# Select fine-tune strategy, setup config and fine-tune
strategy = hub.AdamWeightDecayStrategy(
weight_decay=args.weight_decay,
learning_rate=args.learning_rate,
warmup_proportion=args.warmup_proportion)
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
eval_interval=300,
use_data_parallel=args.use_data_parallel,
......@@ -80,7 +80,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# Define a reading comprehension finetune task by PaddleHub's API
# Define a reading comprehension fine-tune task by PaddleHub's API
reading_comprehension_task = hub.ReadingComprehensionTask(
data_reader=reader,
feature=seq_output,
......@@ -89,5 +89,5 @@ if __name__ == '__main__':
sub_task="squad",
)
# Finetune by PaddleHub's API
# Fine-tune by PaddleHub's API
reading_comprehension_task.finetune_and_eval()
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
"""Fine-tuning on classification task """
from __future__ import absolute_import
from __future__ import division
......@@ -33,7 +33,7 @@ parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--batch_size", type=int, default=1, help="Total examples' number in batch for training.")
parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for fine-tuning, input should be True or False")
args = parser.parse_args()
# yapf: enable.
......@@ -64,7 +64,7 @@ if __name__ == '__main__':
inputs["input_mask"].name,
]
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=False,
use_cuda=args.use_gpu,
......@@ -72,7 +72,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=hub.AdamWeightDecayStrategy())
# Define a regression finetune task by PaddleHub's API
# Define a regression fine-tune task by PaddleHub's API
reg_task = hub.RegressionTask(
data_reader=reader,
feature=pooled_output,
......
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
"""Fine-tuning on classification task """
import argparse
import ast
......@@ -23,7 +23,7 @@ import paddlehub as hub
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for fine-tuning, input should be True or False")
parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
parser.add_argument("--warmup_proportion", type=float, default=0.1, help="Warmup proportion params for warmup strategy")
......@@ -62,13 +62,13 @@ if __name__ == '__main__':
inputs["input_mask"].name,
]
# Select finetune strategy, setup config and finetune
# Select fine-tune strategy, setup config and fine-tune
strategy = hub.AdamWeightDecayStrategy(
warmup_proportion=args.warmup_proportion,
weight_decay=args.weight_decay,
learning_rate=args.learning_rate)
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
eval_interval=300,
use_data_parallel=args.use_data_parallel,
......@@ -78,13 +78,13 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# Define a regression finetune task by PaddleHub's API
# Define a regression fine-tune task by PaddleHub's API
reg_task = hub.RegressionTask(
data_reader=reader,
feature=pooled_output,
feed_list=feed_list,
config=config)
# Finetune and evaluate by PaddleHub's API
# Fine-tune and evaluate by PaddleHub's API
# will finish training, evaluation, testing, save model automatically
reg_task.finetune_and_eval()
......@@ -16,7 +16,7 @@ import paddlehub as hub
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for fine-tuning, input should be True or False")
parser.add_argument("--batch_size", type=int, default=1, help="Total examples' number in batch when the program predicts.")
args = parser.parse_args()
# yapf: enable.
......@@ -37,7 +37,7 @@ if __name__ == '__main__':
# Must feed all the tensor of senta's module need
feed_list = [inputs["words"].name]
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=False,
use_cuda=args.use_gpu,
......@@ -45,7 +45,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=hub.AdamWeightDecayStrategy())
# Define a classfication finetune task by PaddleHub's API
# Define a classfication fine-tune task by PaddleHub's API
cls_task = hub.TextClassifierTask(
data_reader=reader,
feature=sent_feature,
......
......@@ -8,7 +8,7 @@ import paddlehub as hub
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for fine-tuning, input should be True or False")
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--batch_size", type=int, default=32, help="Total examples' number in batch for training.")
args = parser.parse_args()
......@@ -30,7 +30,7 @@ if __name__ == '__main__':
# Must feed all the tensor of senta's module need
feed_list = [inputs["words"].name]
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_cuda=args.use_gpu,
use_pyreader=False,
......@@ -40,7 +40,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=hub.AdamWeightDecayStrategy())
# Define a classfication finetune task by PaddleHub's API
# Define a classfication fine-tune task by PaddleHub's API
cls_task = hub.TextClassifierTask(
data_reader=reader,
feature=sent_feature,
......@@ -48,6 +48,6 @@ if __name__ == '__main__':
num_classes=dataset.num_labels,
config=config)
# Finetune and evaluate by PaddleHub's API
# Fine-tune and evaluate by PaddleHub's API
# will finish training, evaluation, testing, save model automatically
cls_task.finetune_and_eval()
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on sequence labeling task """
"""Fine-tuning on sequence labeling task """
from __future__ import absolute_import
from __future__ import division
......@@ -27,14 +27,13 @@ import time
import paddle
import paddle.fluid as fluid
import paddlehub as hub
from paddlehub.finetune.evaluate import chunk_eval, calculate_f1
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
parser.add_argument("--batch_size", type=int, default=1, help="Total examples' number in batch for training.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for fine-tuning, input should be True or False")
args = parser.parse_args()
# yapf: enable.
......@@ -67,7 +66,7 @@ if __name__ == '__main__':
inputs["input_mask"].name,
]
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=False,
use_cuda=args.use_gpu,
......@@ -75,7 +74,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=hub.finetune.strategy.DefaultFinetuneStrategy())
# Define a sequence labeling finetune task by PaddleHub's API
# Define a sequence labeling fine-tune task by PaddleHub's API
# if add crf, the network use crf as decoder
seq_label_task = hub.SequenceLabelTask(
data_reader=reader,
......@@ -84,7 +83,7 @@ if __name__ == '__main__':
max_seq_len=args.max_seq_len,
num_classes=dataset.num_labels,
config=config,
add_crf=True)
add_crf=False)
# Data to be predicted
# If using python 2, prefix "u" is necessary
......
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on sequence labeling task."""
"""Fine-tuning on sequence labeling task."""
import argparse
import ast
......@@ -23,7 +23,7 @@ import paddlehub as hub
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for fine-tuning, input should be True or False")
parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
parser.add_argument("--warmup_proportion", type=float, default=0.1, help="Warmup proportion params for warmup strategy")
......@@ -60,13 +60,13 @@ if __name__ == '__main__':
inputs["segment_ids"].name, inputs["input_mask"].name
]
# Select a finetune strategy
# Select a fine-tune strategy
strategy = hub.AdamWeightDecayStrategy(
warmup_proportion=args.warmup_proportion,
weight_decay=args.weight_decay,
learning_rate=args.learning_rate)
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=args.use_data_parallel,
use_cuda=args.use_gpu,
......@@ -75,7 +75,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# Define a sequence labeling finetune task by PaddleHub's API
# Define a sequence labeling fine-tune task by PaddleHub's API
# If add crf, the network use crf as decoder
seq_label_task = hub.SequenceLabelTask(
data_reader=reader,
......@@ -84,8 +84,8 @@ if __name__ == '__main__':
max_seq_len=args.max_seq_len,
num_classes=dataset.num_labels,
config=config,
add_crf=True)
add_crf=False)
# Finetune and evaluate model by PaddleHub's API
# Fine-tune and evaluate model by PaddleHub's API
# will finish training, evaluation, testing, save model automatically
seq_label_task.finetune_and_eval()
#coding:utf-8
import os
import paddlehub as hub
import cv2
if __name__ == "__main__":
ssd = hub.Module(name="ssd_mobilenet_v1_pascal")
test_img_path = os.path.join("test", "test_img_bird.jpg")
# get the input keys for signature 'object_detection'
data_format = ssd.processor.data_format(sign_name='object_detection')
key = list(data_format.keys())[0]
# set input dict
input_dict = {key: [test_img_path]}
# execute predict and print the result
results = ssd.object_detection(data=input_dict)
results = ssd.object_detection(images=[cv2.imread(test_img_path)])
for result in results:
hub.logger.info(result)
print(result)
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
"""Fine-tuning on classification task """
from __future__ import absolute_import
from __future__ import division
......@@ -32,7 +32,7 @@ parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--batch_size", type=int, default=1, help="Total examples' number in batch for training.")
parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for fine-tuning, input should be True or False")
parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=False, help="Whether use data parallel.")
args = parser.parse_args()
# yapf: enable.
......@@ -70,7 +70,7 @@ if __name__ == '__main__':
inputs["input_mask"].name,
]
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=args.use_data_parallel,
use_cuda=args.use_gpu,
......@@ -78,7 +78,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=hub.AdamWeightDecayStrategy())
# Define a classfication finetune task by PaddleHub's API
# Define a classfication fine-tune task by PaddleHub's API
cls_task = hub.TextClassifierTask(
data_reader=reader,
feature=pooled_output,
......
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
"""Fine-tuning on classification task """
from __future__ import absolute_import
from __future__ import division
......@@ -32,7 +32,7 @@ parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--batch_size", type=int, default=1, help="Total examples' number in batch for training.")
parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for fine-tuning, input should be True or False")
parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=False, help="Whether use data parallel.")
parser.add_argument("--network", type=str, default='bilstm', help="Pre-defined network which was connected after Transformer model, such as ERNIE, BERT ,RoBERTa and ELECTRA.")
args = parser.parse_args()
......@@ -71,7 +71,7 @@ if __name__ == '__main__':
inputs["input_mask"].name,
]
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=args.use_data_parallel,
use_cuda=args.use_gpu,
......@@ -79,7 +79,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=hub.AdamWeightDecayStrategy())
# Define a classfication finetune task by PaddleHub's API
# Define a classfication fine-tune task by PaddleHub's API
# network choice: bilstm, bow, cnn, dpcnn, gru, lstm (PaddleHub pre-defined network)
# If you wanna add network after ERNIE/BERT/RoBERTa/ELECTRA module,
# you must use the outputs["sequence_output"] as the token_feature of TextClassifierTask,
......
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
"""Fine-tuning on classification task """
import argparse
import ast
......@@ -21,7 +21,7 @@ import paddlehub as hub
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for fine-tuning, input should be True or False")
parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
parser.add_argument("--warmup_proportion", type=float, default=0.1, help="Warmup proportion params for warmup strategy")
......@@ -68,13 +68,13 @@ if __name__ == '__main__':
inputs["input_mask"].name,
]
# Select finetune strategy, setup config and finetune
# Select fine-tune strategy, setup config and fine-tune
strategy = hub.AdamWeightDecayStrategy(
warmup_proportion=args.warmup_proportion,
weight_decay=args.weight_decay,
learning_rate=args.learning_rate)
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=args.use_data_parallel,
use_cuda=args.use_gpu,
......@@ -83,7 +83,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# Define a classfication finetune task by PaddleHub's API
# Define a classfication fine-tune task by PaddleHub's API
cls_task = hub.TextClassifierTask(
data_reader=reader,
feature=pooled_output,
......@@ -92,6 +92,6 @@ if __name__ == '__main__':
config=config,
metrics_choices=metrics_choices)
# Finetune and evaluate by PaddleHub's API
# Fine-tune and evaluate by PaddleHub's API
# will finish training, evaluation, testing, save model automatically
cls_task.finetune_and_eval()
......@@ -12,7 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification task """
"""Fine-tuning on classification task """
import argparse
import ast
......@@ -21,7 +21,7 @@ import paddlehub as hub
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for fine-tuning, input should be True or False")
parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
parser.add_argument("--warmup_proportion", type=float, default=0.1, help="Warmup proportion params for warmup strategy")
......@@ -69,13 +69,13 @@ if __name__ == '__main__':
inputs["input_mask"].name,
]
# Select finetune strategy, setup config and finetune
# Select fine-tune strategy, setup config and fine-tune
strategy = hub.AdamWeightDecayStrategy(
warmup_proportion=args.warmup_proportion,
weight_decay=args.weight_decay,
learning_rate=args.learning_rate)
# Setup runing config for PaddleHub Finetune API
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=args.use_data_parallel,
use_cuda=args.use_gpu,
......@@ -84,7 +84,7 @@ if __name__ == '__main__':
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# Define a classfication finetune task by PaddleHub's API
# Define a classfication fine-tune task by PaddleHub's API
# network choice: bilstm, bow, cnn, dpcnn, gru, lstm (PaddleHub pre-defined network)
# If you wanna add network after ERNIE/BERT/RoBERTa/ELECTRA module,
# you must use the outputs["sequence_output"] as the token_feature of TextClassifierTask,
......@@ -98,6 +98,6 @@ if __name__ == '__main__':
config=config,
metrics_choices=metrics_choices)
# Finetune and evaluate by PaddleHub's API
# Fine-tune and evaluate by PaddleHub's API
# will finish training, evaluation, testing, save model automatically
cls_task.finetune_and_eval()
......@@ -22,6 +22,8 @@ if six.PY2:
reload(sys) # noqa
sys.setdefaultencoding("UTF-8")
from .version import hub_version as __version__
from . import module
from . import common
from . import io
......
......@@ -13,5 +13,5 @@
# See the License for the specific language governing permissions and
# limitations under the License.
""" PaddleHub version string """
hub_version = "1.6.2"
hub_version = "1.7.0"
module_proto_version = "1.0.0"
pre-commit
protobuf >= 3.6.0
yapf == 0.26.0
pyyaml
Pillow
six >= 1.10.0
chardet == 3.0.4
requests
flask >= 1.1.0
flake8
visualdl == 2.0.0a0
cma == 2.7.0
cma >= 2.7.0
sentencepiece
nltk
colorlog
opencv-python
# numpy no longer support python2 in version 1.17 and above
numpy ; python_version >= "3"
numpy < 1.17.0 ; python_version < "3"
# pandas no longer support python2 in version 0.25 and above
pandas ; python_version >= "3"
pandas < 0.25.0 ; python_version < "3"
# gunicorn not support windows
gunicorn >= 19.10.0; sys_platform != "win32"
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