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Merge pull request #1139 from WenmuZhou/dygraph_rc 

新增功能
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dyning 2020-11-09 19:19:57 +08:00 committed by GitHub
parent b863528dcd b2004fe586
commit dc6e724efb
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10 changed files with 84 additions and 74 deletions
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13
ppocr/modeling/backbones/det_resnet_vd.py
View File

@ -19,6 +19,7 @@ from __future__ import print_function
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
__all__ = ["ResNet"]
@ -37,9 +38,9 @@ class ConvBNLayer(nn.Layer):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
self._pool2d_avg = nn.AvgPool2d(
self._pool2d_avg = nn.AvgPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
self._conv = nn.Conv2d(
self._conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
@ -118,7 +119,8 @@ class BottleneckBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
y = paddle.elementwise_add(x=short, y=conv2, act='relu')
y = paddle.add(x=short, y=conv2)
y = F.relu(y)
return y
@ -165,7 +167,8 @@ class BasicBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
y = paddle.elementwise_add(x=short, y=conv1, act='relu')
y = paddle.add(x=short, y=conv1)
y = F.relu(y)
return y
@ -214,7 +217,7 @@ class ResNet(nn.Layer):
stride=1,
act='relu',
name="conv1_3")
self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
self.stages = []
self.out_channels = []

15
ppocr/modeling/backbones/rec_resnet_vd.py
View File

@ -19,6 +19,7 @@ from __future__ import print_function
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
__all__ = ["ResNet"]
@ -37,9 +38,9 @@ class ConvBNLayer(nn.Layer):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
self._pool2d_avg = nn.AvgPool2d(
self._pool2d_avg = nn.AvgPool2D(
kernel_size=stride, stride=stride, padding=0, ceil_mode=True)
self._conv = nn.Conv2d(
self._conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
@ -119,7 +120,8 @@ class BottleneckBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
y = paddle.elementwise_add(x=short, y=conv2, act='relu')
y = paddle.add(x=short, y=conv2)
y = F.relu(y)
return y
@ -166,7 +168,8 @@ class BasicBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
y = paddle.elementwise_add(x=short, y=conv1, act='relu')
y = paddle.add(x=short, y=conv1)
y = F.relu(y)
return y
@ -215,7 +218,7 @@ class ResNet(nn.Layer):
stride=1,
act='relu',
name="conv1_3")
self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
if layers >= 50:
@ -270,7 +273,7 @@ class ResNet(nn.Layer):
shortcut = True
self.block_list.append(basic_block)
self.out_channels = num_filters[block]
self.out_pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.out_pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
def forward(self, inputs):
y = self.conv1_1(inputs)

7
ppocr/postprocess/db_postprocess.py
View File

@ -18,6 +18,7 @@ from __future__ import print_function
import numpy as np
import cv2
import paddle
from shapely.geometry import Polygon
import pyclipper
@ -130,7 +131,9 @@ class DBPostProcess(object):
return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
def __call__(self, pred, shape_list):
pred = pred.numpy()[:, 0, :, :]
if isinstance(pred, paddle.Tensor):
pred = pred.numpy()
pred = pred[:, 0, :, :]
segmentation = pred > self.thresh
boxes_batch = []
@ -140,4 +143,4 @@ class DBPostProcess(object):
pred[batch_index], segmentation[batch_index], width, height)
boxes_batch.append({'points': boxes})
return boxes_batch
return boxes_batch

7
ppocr/postprocess/db_postprocess_torch.py
View File

@ -1,4 +1,5 @@
import cv2
import paddle
import numpy as np
import pyclipper
from shapely.geometry import Polygon
@ -23,7 +24,9 @@ class DBPostProcess():
pred:
binary: text region segmentation map, with shape (N, 1,H, W)
'''
pred = pred.numpy()[:, 0, :, :]
if isinstance(pred, paddle.Tensor):
pred = pred.numpy()
pred = pred[:, 0, :, :]
segmentation = self.binarize(pred)
batch_out = []
for batch_index in range(pred.shape[0]):
@ -130,4 +133,4 @@ class DBPostProcess():
box[:, 0] = box[:, 0] - xmin
box[:, 1] = box[:, 1] - ymin
cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]

12
ppocr/postprocess/rec_postprocess.py
View File

@ -100,9 +100,10 @@ class CTCLabelDecode(BaseRecLabelDecode):
character_type, use_space_char)
def __call__(self, preds, label=None, *args, **kwargs):
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
# out = self.decode_preds(preds)
preds = F.softmax(preds, axis=2).numpy()
preds_idx = preds.argmax(axis=2)
preds_prob = preds.max(axis=2)
text = self.decode(preds_idx, preds_prob)
@ -116,19 +117,18 @@ class CTCLabelDecode(BaseRecLabelDecode):
return dict_character
def decode_preds(self, preds):
probs = F.softmax(preds, axis=2).numpy()
probs_ind = np.argmax(probs, axis=2)
probs_ind = np.argmax(preds, axis=2)
B, N, _ = preds.shape
l = np.ones(B).astype(np.int64) * N
length = paddle.to_variable(l)
length = paddle.to_tensor(l)
out = paddle.fluid.layers.ctc_greedy_decoder(preds, 0, length)
batch_res = [
x[:idx[0]] for x, idx in zip(out[0].numpy(), out[1].numpy())
]
result_list = []
for sample_idx, ind, prob in zip(batch_res, probs_ind, probs):
for sample_idx, ind, prob in zip(batch_res, probs_ind, preds):
char_list = [self.character[idx] for idx in sample_idx]
valid_ind = np.where(ind != 0)[0]
if len(valid_ind) == 0:
@ -172,4 +172,4 @@ class AttnLabelDecode(BaseRecLabelDecode):
else:
assert False, "unsupport type %s in get_beg_end_flag_idx" \
% beg_or_end
return idx
return idx

8
ppocr/utils/save_load.py
View File

@ -68,11 +68,11 @@ def load_dygraph_pretrain(model, logger, path=None, load_static_weights=False):
param_state_dict[key] = pre_state_dict[weight_name]
else:
param_state_dict[key] = model_dict[key]
model.set_dict(param_state_dict)
model.set_state_dict(param_state_dict)
return
param_state_dict, optim_state_dict = paddle.load(path)
model.set_dict(param_state_dict)
param_state_dict = paddle.load(path + '.pdparams')
model.set_state_dict(param_state_dict)
return
@ -91,7 +91,7 @@ def init_model(config, model, logger, optimizer=None, lr_scheduler=None):
"Given dir {}.pdopt not exist.".format(checkpoints)
para_dict = paddle.load(checkpoints + '.pdparams')
opti_dict = paddle.load(checkpoints + '.pdopt')
model.set_dict(para_dict)
model.set_state_dict(para_dict)
if optimizer is not None:
optimizer.set_state_dict(opti_dict)

24
tools/export_model.py
View File

@ -12,6 +12,13 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
import argparse
import paddle
@ -20,14 +27,11 @@ from paddle.jit import to_static
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model
from ppocr.utils.logging import get_logger
from tools.program import load_config
from tools.program import merge_config
def parse_args():
def str2bool(v):
return v.lower() in ("true", "t", "1")
parser = argparse.ArgumentParser()
parser.add_argument("-c", "--config", help="configuration file to use")
parser.add_argument(
@ -43,7 +47,7 @@ class Model(paddle.nn.Layer):
# Please modify the 'shape' according to actual needs
@to_static(input_spec=[
paddle.static.InputSpec(
shape=[None, 3, 32, None], dtype='float32')
shape=[None, 3, 640, 640], dtype='float32')
])
def forward(self, inputs):
x = self.pre_model(inputs)
@ -53,14 +57,13 @@ class Model(paddle.nn.Layer):
def main():
FLAGS = parse_args()
config = load_config(FLAGS.config)
merge_config(FLAGS.opt)
logger = get_logger()
# build post process
post_process_class = build_post_process(config['PostProcess'],
config['Global'])
# build model
#for rec algorithm
# for rec algorithm
if hasattr(post_process_class, 'character'):
char_num = len(getattr(post_process_class, 'character'))
config['Architecture']["Head"]['out_channels'] = char_num
@ -69,7 +72,10 @@ def main():
model.eval()
model = Model(model)
paddle.jit.save(model, FLAGS.output_path)
save_path = '{}/{}'.format(FLAGS.output_path,
config['Architecture']['model_type'])
paddle.jit.save(model, save_path)
logger.info('inference model is saved to {}'.format(save_path))
if __name__ == "__main__":

28
tools/infer/predict_det.py
View File

@ -22,7 +22,6 @@ import cv2
import numpy as np
import time
import sys
import paddle
import tools.infer.utility as utility
@ -39,7 +38,7 @@ class TextDetector(object):
postprocess_params = {}
if self.det_algorithm == "DB":
pre_process_list = [{
'ResizeForTest': {
'DetResizeForTest': {
'limit_side_len': args.det_limit_side_len,
'limit_type': args.det_limit_type
}
@ -53,7 +52,7 @@ class TextDetector(object):
}, {
'ToCHWImage': None
}, {
'keepKeys': {
'KeepKeys': {
'keep_keys': ['image', 'shape']
}
}]
@ -68,8 +67,9 @@ class TextDetector(object):
self.preprocess_op = create_operators(pre_process_list)
self.postprocess_op = build_post_process(postprocess_params)
self.predictor = paddle.jit.load(args.det_model_dir)
self.predictor.eval()
self.predictor, self.input_tensor, self.output_tensors = utility.create_predictor(
args, 'det', logger) # paddle.jit.load(args.det_model_dir)
# self.predictor.eval()
def order_points_clockwise(self, pts):
"""
@ -133,11 +133,23 @@ class TextDetector(object):
return None, 0
img = np.expand_dims(img, axis=0)
shape_list = np.expand_dims(shape_list, axis=0)
img = img.copy()
starttime = time.time()
preds = self.predictor(img)
post_result = self.postprocess_op(preds, shape_list)
if self.use_zero_copy_run:
self.input_tensor.copy_from_cpu(img)
self.predictor.zero_copy_run()
else:
im = paddle.fluid.core.PaddleTensor(img)
self.predictor.run([im])
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
preds = outputs[0]
# preds = self.predictor(img)
post_result = self.postprocess_op(preds, shape_list)
dt_boxes = post_result[0]['points']
dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
elapse = time.time() - starttime
@ -146,8 +158,6 @@ class TextDetector(object):
if __name__ == "__main__":
args = utility.parse_args()
place = paddle.CPUPlace()
paddle.disable_static(place)
image_file_list = get_image_file_list(args.image_dir)
logger = get_logger()

23
tools/infer_det.py
View File

@ -29,12 +29,11 @@ import cv2
import json
import paddle
from ppocr.utils.logging import get_logger
from ppocr.data import create_operators, transform
from ppocr.modeling import build_model
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model
from ppocr.utils.utility import print_dict, get_image_file_list
from ppocr.utils.utility import get_image_file_list
import tools.program as program
@ -67,11 +66,11 @@ def main():
# create data ops
transforms = []
for op in config['EVAL']['dataset']['transforms']:
for op in config['Eval']['dataset']['transforms']:
op_name = list(op)[0]
if 'Label' in op_name:
continue
elif op_name == 'keepKeys':
elif op_name == 'KeepKeys':
op[op_name]['keep_keys'] = ['image', 'shape']
transforms.append(op)
@ -92,8 +91,7 @@ def main():
images = np.expand_dims(batch[0], axis=0)
shape_list = np.expand_dims(batch[1], axis=0)
images = paddle.to_variable(images)
print(images.shape)
images = paddle.to_tensor(images)
preds = model(images)
post_result = post_process_class(preds, shape_list)
boxes = post_result[0]['points']
@ -109,14 +107,7 @@ def main():
draw_det_res(boxes, config, src_img, file)
logger.info("success!")
# save inference model
# paddle.jit.save(model, 'output/model')
if __name__ == '__main__':
place, config = program.preprocess()
paddle.disable_static(place)
logger = get_logger()
print_dict(config, logger)
main()
config, device, logger, vdl_writer = program.preprocess()
main()

21
tools/infer_rec.py
View File

@ -27,12 +27,11 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
import paddle
from ppocr.utils.logging import get_logger
from ppocr.data import create_operators, transform
from ppocr.modeling import build_model
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model
from ppocr.utils.utility import print_dict, get_image_file_list
from ppocr.utils.utility import get_image_file_list
import tools.program as program
@ -54,13 +53,13 @@ def main():
# create data ops
transforms = []
for op in config['EVAL']['dataset']['transforms']:
for op in config['Eval']['dataset']['transforms']:
op_name = list(op)[0]
if 'Label' in op_name:
continue
elif op_name in ['RecResizeImg']:
op[op_name]['infer_mode'] = True
elif op_name == 'keepKeys':
elif op_name == 'KeepKeys':
op[op_name]['keep_keys'] = ['image']
transforms.append(op)
global_config['infer_mode'] = True
@ -75,22 +74,14 @@ def main():
batch = transform(data, ops)
images = np.expand_dims(batch[0], axis=0)
images = paddle.to_variable(images)
images = paddle.to_tensor(images)
preds = model(images)
post_result = post_process_class(preds)
for rec_reuslt in post_result:
logger.info('\t result: {}'.format(rec_reuslt))
logger.info("success!")
# save inference model
# currently, paddle.jit.to_static not support rnn
# paddle.jit.save(model, 'output/rec/model')
if __name__ == '__main__':
place, config = program.preprocess()
paddle.disable_static(place)
logger = get_logger()
print_dict(config, logger)
config, device, logger, vdl_writer = program.preprocess()
main()