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!13689 opt warpctc performance 

From: @gengdongjie
Reviewed-by: @wuxuejian,@c_34
Signed-off-by: @c_34
This commit is contained in:
mindspore-ci-bot 2021-03-22 20:45:56 +08:00 committed by Gitee
parent f0016f5574 47085ceefe
commit 61b445ff41
2 changed files with 8 additions and 9 deletions
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16
model_zoo/official/cv/warpctc/src/dataset.py
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@ -67,12 +67,6 @@ def transpose_hwc2whc(image):
return image
def transpose_hwc2chw(image):
"""transpose image from HWC to CHW"""
image = np.transpose(image, (2, 0, 1))
return image
def create_dataset(dataset_path, batch_size=1, num_shards=1, shard_id=0, device_target='Ascend'):
"""
create train or evaluation dataset for warpctc
@ -93,14 +87,20 @@ def create_dataset(dataset_path, batch_size=1, num_shards=1, shard_id=0, device_
vc.Resize((m.ceil(cf.captcha_height / 16) * 16, cf.captcha_width)),
c.TypeCast(mstype.float16)
]
image_trans_gpu = [
vc.Rescale(1.0 / 255.0, 0.0),
vc.Normalize([0.9010, 0.9049, 0.9025], std=[0.1521, 0.1347, 0.1458]),
vc.Resize((m.ceil(cf.captcha_height / 16) * 16, cf.captcha_width)),
vc.HWC2CHW()
]
label_trans = [
c.TypeCast(mstype.int32)
]
data_set = data_set.map(operations=image_trans, input_columns=["image"], num_parallel_workers=8)
if device_target == 'Ascend':
data_set = data_set.map(operations=image_trans, input_columns=["image"], num_parallel_workers=8)
data_set = data_set.map(operations=transpose_hwc2whc, input_columns=["image"], num_parallel_workers=8)
else:
data_set = data_set.map(operations=transpose_hwc2chw, input_columns=["image"], num_parallel_workers=8)
data_set = data_set.map(operations=image_trans_gpu, input_columns=["image"], num_parallel_workers=8)
data_set = data_set.map(operations=label_trans, input_columns=["label"], num_parallel_workers=8)
data_set = data_set.batch(batch_size, drop_remainder=True)

1
model_zoo/official/cv/warpctc/src/warpctc.py
View File

@ -123,7 +123,6 @@ class StackedRNNForGPU(nn.Cell):
self.transpose = P.Transpose()
def construct(self, x):
x = self.cast(x, mstype.float32)
x = self.transpose(x, (3, 0, 2, 1))
x = self.reshape(x, (-1, self.batch_size, self.input_size))
output, _ = self.lstm(x, (self.h, self.c))