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Merge pull request #1005 from baiyfbupt/cls_qat 

support quant cls model in slim module
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Double_V 2020-10-26 15:33:15 +08:00 committed by GitHub
parent a978d82f8a a2e932c0fb
commit c77427630c
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3 changed files with 8 additions and 1 deletions
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3
deploy/slim/quantization/export_model.py
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@ -51,6 +51,7 @@ from paddleslim.quant import quant_aware, convert
from paddle.fluid.layer_helper import LayerHelper from paddle.fluid.layer_helper import LayerHelper
from eval_utils.eval_det_utils import eval_det_run from eval_utils.eval_det_utils import eval_det_run
from eval_utils.eval_rec_utils import eval_rec_run from eval_utils.eval_rec_utils import eval_rec_run
from eval_utils.eval_cls_utils import eval_cls_run
def main(): def main():
@ -105,6 +106,8 @@ def main():
if alg_type == 'det': if alg_type == 'det':
final_metrics = eval_det_run(exe, config, quant_info_dict, "eval") final_metrics = eval_det_run(exe, config, quant_info_dict, "eval")
elif alg_type == 'cls':
final_metrics = eval_cls_run(exe, quant_info_dict)
else: else:
final_metrics = eval_rec_run(exe, config, quant_info_dict, "eval") final_metrics = eval_rec_run(exe, config, quant_info_dict, "eval")
print(final_metrics) print(final_metrics)

5
deploy/slim/quantization/quant.py
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@ -178,9 +178,12 @@ def main():
if train_alg_type == 'det': if train_alg_type == 'det':
program.train_eval_det_run( program.train_eval_det_run(
config, exe, train_info_dict, eval_info_dict, is_slim="quant") config, exe, train_info_dict, eval_info_dict, is_slim="quant")
else: elif train_alg_type == 'rec':
program.train_eval_rec_run( program.train_eval_rec_run(
config, exe, train_info_dict, eval_info_dict, is_slim="quant") config, exe, train_info_dict, eval_info_dict, is_slim="quant")
else:
program.train_eval_cls_run(
config, exe, train_info_dict, eval_info_dict, is_slim="quant")
if __name__ == '__main__': if __name__ == '__main__':

1
ppocr/modeling/architectures/cls_model.py
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@ -65,6 +65,7 @@ class ClsModel(object):
labels = None labels = None
loader = None loader = None
image = fluid.data(name='image', shape=image_shape, dtype='float32') image = fluid.data(name='image', shape=image_shape, dtype='float32')
image.stop_gradient = False
return image, labels, loader return image, labels, loader
def __call__(self, mode): def __call__(self, mode):