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PaddleOCR
提交 6832ca02 编写于 作者: T tink2123
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update config

上级 09d8cb6d
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Showing with 197 addition and 132 deletion
configs/rec/rec_r50fpn_vd_none_srn_pvam_test_all.yml
浏览文件 @ 6832ca02
......@@ -17,11 +17,12 @@ Global:
average_window: 0.15
max_average_window: 15625
min_average_window: 10000
reader_yml: ./configs/rec/rec_srn_reader.yml
reader_yml: ./configs/rec/rec_benchmark_reader.yml
pretrain_weights:
checkpoints:
save_inference_dir:
infer_img:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel
......
ppocr/data/rec/dataset_traversal.py
浏览文件 @ 6832ca02
......@@ -118,15 +118,14 @@ class LMDBReader(object):
image_file_list = get_image_file_list(self.infer_img)
for single_img in image_file_list:
img = cv2.imread(single_img)
if img.shape[-1]==1 or len(list(img.shape))==2:
if img.shape[-1] == 1 or len(list(img.shape)) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
if self.loss_type == 'srn':
norm_img = process_image_srn(
img=img,
image_shape=self.image_shape,
num_heads=self.num_heads,
max_text_length=self.max_text_length
)
max_text_length=self.max_text_length)
else:
norm_img = process_image(
img=img,
......@@ -135,20 +134,20 @@ class LMDBReader(object):
tps=self.use_tps,
infer_mode=True)
yield norm_img
elif self.mode == 'test':
image_file_list = get_image_file_list(self.infer_img)
for single_img in image_file_list:
img = cv2.imread(single_img)
if img.shape[-1]==1 or len(list(img.shape))==2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
norm_img = process_image(
img=img,
image_shape=self.image_shape,
char_ops=self.char_ops,
tps=self.use_tps,
infer_mode=True
)
yield norm_img
#elif self.mode == 'eval':
# image_file_list = get_image_file_list(self.infer_img)
# for single_img in image_file_list:
# img = cv2.imread(single_img)
# if img.shape[-1]==1 or len(list(img.shape))==2:
# img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
# norm_img = process_image(
# img=img,
# image_shape=self.image_shape,
# char_ops=self.char_ops,
# tps=self.use_tps,
# infer_mode=True
# )
# yield norm_img
else:
lmdb_sets = self.load_hierarchical_lmdb_dataset()
if process_id == 0:
......@@ -169,14 +168,15 @@ class LMDBReader(object):
img, label = sample_info
outs = []
if self.loss_type == "srn":
outs = process_image_srn(img, self.image_shape, self.num_heads,
self.max_text_length, label,
self.char_ops, self.loss_type)
outs = process_image_srn(
img, self.image_shape, self.num_heads,
self.max_text_length, label, self.char_ops,
self.loss_type)
else:
outs = process_image(img, self.image_shape, label,
self.char_ops, self.loss_type,
self.max_text_length)
outs = process_image(
img, self.image_shape, label, self.char_ops,
self.loss_type, self.max_text_length)
if outs is None:
continue
yield outs
......@@ -184,6 +184,7 @@ class LMDBReader(object):
if finish_read_num == len(lmdb_sets):
break
self.close_lmdb_dataset(lmdb_sets)
def batch_iter_reader():
batch_outs = []
for outs in sample_iter_reader():
......@@ -311,4 +312,4 @@ class SimpleReader(object):
if self.infer_img is None:
return batch_iter_reader
return sample_iter_reader
\ No newline at end of file
return sample_iter_reader
ppocr/modeling/architectures/rec_model.py
浏览文件 @ 6832ca02
......@@ -79,17 +79,45 @@ class RecModel(object):
feed_list = [image, label_in, label_out]
labels = {'label_in': label_in, 'label_out': label_out}
elif self.loss_type == "srn":
encoder_word_pos = fluid.data(name="encoder_word_pos", shape=[-1, int((image_shape[-2] / 8) * (image_shape[-1] / 8)), 1], dtype="int64")
gsrm_word_pos = fluid.data(name="gsrm_word_pos", shape=[-1, self.max_text_length, 1], dtype="int64")
gsrm_slf_attn_bias1 = fluid.data(name="gsrm_slf_attn_bias1", shape=[-1, self.num_heads, self.max_text_length, self.max_text_length])
gsrm_slf_attn_bias2 = fluid.data(name="gsrm_slf_attn_bias2", shape=[-1, self.num_heads, self.max_text_length, self.max_text_length])
lbl_weight = fluid.layers.data(name="lbl_weight", shape=[-1, 1], dtype='int64')
encoder_word_pos = fluid.data(
name="encoder_word_pos",
shape=[
-1, int((image_shape[-2] / 8) * (image_shape[-1] / 8)),
1
],
dtype="int64")
gsrm_word_pos = fluid.data(
name="gsrm_word_pos",
shape=[-1, self.max_text_length, 1],
dtype="int64")
gsrm_slf_attn_bias1 = fluid.data(
name="gsrm_slf_attn_bias1",
shape=[
-1, self.num_heads, self.max_text_length,
self.max_text_length
])
gsrm_slf_attn_bias2 = fluid.data(
name="gsrm_slf_attn_bias2",
shape=[
-1, self.num_heads, self.max_text_length,
self.max_text_length
])
lbl_weight = fluid.layers.data(
name="lbl_weight", shape=[-1, 1], dtype='int64')
label = fluid.data(
name='label', shape=[-1, 1], dtype='int32', lod_level=1)
feed_list = [image, label, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2, lbl_weight]
labels = {'label': label, 'encoder_word_pos': encoder_word_pos,
'gsrm_word_pos': gsrm_word_pos, 'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1,
'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2,'lbl_weight':lbl_weight}
feed_list = [
image, label, encoder_word_pos, gsrm_word_pos,
gsrm_slf_attn_bias1, gsrm_slf_attn_bias2, lbl_weight
]
labels = {
'label': label,
'encoder_word_pos': encoder_word_pos,
'gsrm_word_pos': gsrm_word_pos,
'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1,
'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2,
'lbl_weight': lbl_weight
}
else:
label = fluid.data(
name='label', shape=[None, 1], dtype='int32', lod_level=1)
......@@ -112,15 +140,41 @@ class RecModel(object):
"We set img_shape to be the same , it may affect the inference effect"
)
image_shape = deepcopy(self.image_shape)
image = fluid.data(name='image', shape=image_shape, dtype='float32')
image = fluid.data(name='image', shape=image_shape, dtype='float32')
if self.loss_type == "srn":
encoder_word_pos = fluid.data(name="encoder_word_pos", shape=[-1, int((image_shape[-2] / 8) * (image_shape[-1] / 8)), 1], dtype="int64")
gsrm_word_pos = fluid.data(name="gsrm_word_pos", shape=[-1, self.max_text_length, 1], dtype="int64")
gsrm_slf_attn_bias1 = fluid.data(name="gsrm_slf_attn_bias1", shape=[-1, self.num_heads, self.max_text_length, self.max_text_length])
gsrm_slf_attn_bias2 = fluid.data(name="gsrm_slf_attn_bias2", shape=[-1, self.num_heads, self.max_text_length, self.max_text_length])
feed_list = [image, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2]
labels = {'encoder_word_pos': encoder_word_pos, 'gsrm_word_pos': gsrm_word_pos,
'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1, 'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2}
encoder_word_pos = fluid.data(
name="encoder_word_pos",
shape=[
-1, int((image_shape[-2] / 8) * (image_shape[-1] / 8)),
1
],
dtype="int64")
gsrm_word_pos = fluid.data(
name="gsrm_word_pos",
shape=[-1, self.max_text_length, 1],
dtype="int64")
gsrm_slf_attn_bias1 = fluid.data(
name="gsrm_slf_attn_bias1",
shape=[
-1, self.num_heads, self.max_text_length,
self.max_text_length
])
gsrm_slf_attn_bias2 = fluid.data(
name="gsrm_slf_attn_bias2",
shape=[
-1, self.num_heads, self.max_text_length,
self.max_text_length
])
feed_list = [
image, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
gsrm_slf_attn_bias2
]
labels = {
'encoder_word_pos': encoder_word_pos,
'gsrm_word_pos': gsrm_word_pos,
'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1,
'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2
}
return image, labels, loader
def __call__(self, mode):
......@@ -140,8 +194,13 @@ class RecModel(object):
label = labels['label']
if self.loss_type == 'srn':
total_loss, img_loss, word_loss = self.loss(predicts, labels)
outputs = {'total_loss':total_loss, 'img_loss':img_loss, 'word_loss':word_loss,
'decoded_out':decoded_out, 'label':label}
outputs = {
'total_loss': total_loss,
'img_loss': img_loss,
'word_loss': word_loss,
'decoded_out': decoded_out,
'label': label
}
else:
outputs = {'total_loss':loss, 'decoded_out':\
decoded_out, 'label':label}
......@@ -156,4 +215,4 @@ class RecModel(object):
predict = predicts['predict']
if self.loss_type == "ctc":
predict = fluid.layers.softmax(predict)
return loader, {'decoded_out': decoded_out, 'predicts': predict}
\ No newline at end of file
return loader, {'decoded_out': decoded_out, 'predicts': predict}
ppocr/modeling/heads/self_attention/model.py
浏览文件 @ 6832ca02
......@@ -4,8 +4,9 @@ import numpy as np
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from .desc import *
from .config import ModelHyperParams,TrainTaskConfig
# Set seed for CE
dropout_seed = None
def wrap_layer_with_block(layer, block_idx):
"""
......@@ -114,7 +115,7 @@ def multi_head_attention(queries,
def __split_heads_qkv(queries, keys, values, n_head, d_key, d_value):
"""
Reshape input tensors at the last dimension to split multi-heads
Reshape input tensors at the last dimension to split multi-heads
and then transpose. Specifically, transform the input tensor with shape
[bs, max_sequence_length, n_head * hidden_dim] to the output tensor
with shape [bs, n_head, max_sequence_length, hidden_dim].
......@@ -269,23 +270,24 @@ pre_process_layer = partial(pre_post_process_layer, None)
post_process_layer = pre_post_process_layer
def prepare_encoder(src_word,#[b,t,c]
src_pos,
src_vocab_size,
src_emb_dim,
src_max_len,
dropout_rate=0.,
bos_idx=0,
word_emb_param_name=None,
pos_enc_param_name=None):
def prepare_encoder(
src_word, #[b,t,c]
src_pos,
src_vocab_size,
src_emb_dim,
src_max_len,
dropout_rate=0.,
bos_idx=0,
word_emb_param_name=None,
pos_enc_param_name=None):
"""Add word embeddings and position encodings.
The output tensor has a shape of:
[batch_size, max_src_length_in_batch, d_model].
This module is used at the bottom of the encoder stacks.
"""
src_word_emb =src_word#layers.concat(res,axis=1)
src_word_emb=layers.cast(src_word_emb,'float32')
src_word_emb = src_word #layers.concat(res,axis=1)
src_word_emb = layers.cast(src_word_emb, 'float32')
# print("src_word_emb",src_word_emb)
src_word_emb = layers.scale(x=src_word_emb, scale=src_emb_dim**0.5)
......@@ -302,14 +304,14 @@ def prepare_encoder(src_word,#[b,t,c]
def prepare_decoder(src_word,
src_pos,
src_vocab_size,
src_emb_dim,
src_max_len,
dropout_rate=0.,
bos_idx=0,
word_emb_param_name=None,
pos_enc_param_name=None):
src_pos,
src_vocab_size,
src_emb_dim,
src_max_len,
dropout_rate=0.,
bos_idx=0,
word_emb_param_name=None,
pos_enc_param_name=None):
"""Add word embeddings and position encodings.
The output tensor has a shape of:
[batch_size, max_src_length_in_batch, d_model].
......@@ -323,7 +325,7 @@ def prepare_decoder(src_word,
name=word_emb_param_name,
initializer=fluid.initializer.Normal(0., src_emb_dim**-0.5)))
# print("target_word_emb",src_word_emb)
src_word_emb = layers.scale(x=src_word_emb, scale=src_emb_dim ** 0.5)
src_word_emb = layers.scale(x=src_word_emb, scale=src_emb_dim**0.5)
src_pos_enc = layers.embedding(
src_pos,
size=[src_max_len, src_emb_dim],
......@@ -335,6 +337,7 @@ def prepare_decoder(src_word,
enc_input, dropout_prob=dropout_rate, seed=dropout_seed,
is_test=False) if dropout_rate else enc_input
# prepare_encoder = partial(
# prepare_encoder_decoder, pos_enc_param_name=pos_enc_param_names[0])
# prepare_decoder = partial(
......@@ -595,21 +598,9 @@ def transformer(src_vocab_size,
weights = all_inputs[-1]
enc_output = wrap_encoder(
src_vocab_size,
ModelHyperParams.src_seq_len,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
enc_inputs)
src_vocab_size, 64, n_layer, n_head, d_key, d_value, d_model,
d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout,
preprocess_cmd, postprocess_cmd, weight_sharing, enc_inputs)
predict = wrap_decoder(
trg_vocab_size,
......@@ -650,34 +641,34 @@ def transformer(src_vocab_size,
def wrap_encoder_forFeature(src_vocab_size,
max_length,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
enc_inputs=None,
bos_idx=0):
max_length,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
enc_inputs=None,
bos_idx=0):
"""
The wrapper assembles together all needed layers for the encoder.
img, src_pos, src_slf_attn_bias = enc_inputs
img
"""
if enc_inputs is None:
# This is used to implement independent encoder program in inference.
conv_features, src_pos, src_slf_attn_bias = make_all_inputs(
encoder_data_input_fields)
else:
conv_features, src_pos, src_slf_attn_bias = enc_inputs#
b,t,c = conv_features.shape
conv_features, src_pos, src_slf_attn_bias = enc_inputs #
b, t, c = conv_features.shape
#"""
# insert cnn
#"""
......@@ -694,11 +685,11 @@ def wrap_encoder_forFeature(src_vocab_size,
#b , c, h, w = feat.shape#h=6
#print(feat)
#layers.Print(feat,message="conv_feat",summarize=10)
#feat =layers.conv2d(feat,c,filter_size =[4 , 1],act="relu")
#feat = layers.pool2d(feat,pool_stride=(3,1),pool_size=(3,1))
#src_word = layers.squeeze(feat,axes=[2]) #src_word [-1,c,ww]
#feat = layers.transpose(feat, [0,3,1,2])
#src_word = layers.reshape(feat,[-1,w, c*h])
#src_word = layers.im2sequence(
......@@ -706,10 +697,10 @@ def wrap_encoder_forFeature(src_vocab_size,
# stride=[1, 1],
# filter_size=[feat.shape[2], 1])
#layers.Print(src_word,message="src_word",summarize=10)
# print('feat',feat)
#print("src_word",src_word)
enc_input = prepare_encoder(
conv_features,
src_pos,
......@@ -718,7 +709,7 @@ def wrap_encoder_forFeature(src_vocab_size,
max_length,
prepostprocess_dropout,
bos_idx=bos_idx,
word_emb_param_name=word_emb_param_names[0])
word_emb_param_name="src_word_emb_table")
enc_output = encoder(
enc_input,
......@@ -736,6 +727,7 @@ def wrap_encoder_forFeature(src_vocab_size,
postprocess_cmd, )
return enc_output
def wrap_encoder(src_vocab_size,
max_length,
n_layer,
......@@ -762,7 +754,7 @@ def wrap_encoder(src_vocab_size,
src_word, src_pos, src_slf_attn_bias = make_all_inputs(
encoder_data_input_fields)
else:
src_word, src_pos, src_slf_attn_bias = enc_inputs#
src_word, src_pos, src_slf_attn_bias = enc_inputs #
#"""
# insert cnn
#"""
......@@ -779,11 +771,11 @@ def wrap_encoder(src_vocab_size,
#b , c, h, w = feat.shape#h=6
#print(feat)
#layers.Print(feat,message="conv_feat",summarize=10)
#feat =layers.conv2d(feat,c,filter_size =[4 , 1],act="relu")
#feat = layers.pool2d(feat,pool_stride=(3,1),pool_size=(3,1))
#src_word = layers.squeeze(feat,axes=[2]) #src_word [-1,c,ww]
#feat = layers.transpose(feat, [0,3,1,2])
#src_word = layers.reshape(feat,[-1,w, c*h])
#src_word = layers.im2sequence(
......@@ -791,7 +783,7 @@ def wrap_encoder(src_vocab_size,
# stride=[1, 1],
# filter_size=[feat.shape[2], 1])
#layers.Print(src_word,message="src_word",summarize=10)
# print('feat',feat)
#print("src_word",src_word)
enc_input = prepare_decoder(
......@@ -802,7 +794,7 @@ def wrap_encoder(src_vocab_size,
max_length,
prepostprocess_dropout,
bos_idx=bos_idx,
word_emb_param_name=word_emb_param_names[0])
word_emb_param_name="src_word_emb_table")
enc_output = encoder(
enc_input,
......@@ -858,8 +850,8 @@ def wrap_decoder(trg_vocab_size,
max_length,
prepostprocess_dropout,
bos_idx=bos_idx,
word_emb_param_name=word_emb_param_names[0]
if weight_sharing else word_emb_param_names[1])
word_emb_param_name="src_word_emb_table"
if weight_sharing else "trg_word_emb_table")
dec_output = decoder(
dec_input,
enc_output,
......@@ -886,7 +878,7 @@ def wrap_decoder(trg_vocab_size,
predict = layers.matmul(
x=dec_output,
y=fluid.default_main_program().global_block().var(
word_emb_param_names[0]),
"trg_word_emb_table"),
transpose_y=True)
else:
predict = layers.fc(input=dec_output,
......@@ -931,12 +923,13 @@ def fast_decode(src_vocab_size,
enc_inputs_len = len(encoder_data_input_fields)
dec_inputs_len = len(fast_decoder_data_input_fields)
enc_inputs = all_inputs[0:enc_inputs_len]#enc_inputs tensor
dec_inputs = all_inputs[enc_inputs_len:enc_inputs_len + dec_inputs_len]#dec_inputs tensor
enc_inputs = all_inputs[0:enc_inputs_len] #enc_inputs tensor
dec_inputs = all_inputs[enc_inputs_len:enc_inputs_len +
dec_inputs_len] #dec_inputs tensor
enc_output = wrap_encoder(
src_vocab_size,
ModelHyperParams.src_seq_len,##to do !!!!!????
64, ##to do !!!!!????
n_layer,
n_head,
d_key,
......
tools/eval_utils/eval_rec_utils.py
浏览文件 @ 6832ca02
......@@ -61,7 +61,7 @@ def eval_rec_run(exe, config, eval_info_dict, mode):
img_list.append(data[ino][0])
label_list.append(data[ino][1])
if config['Global']['loss_type'] != "srn":
if config['Global']['loss_type'] != "srn":
img_list = np.concatenate(img_list, axis=0)
outs = exe.run(eval_info_dict['program'], \
feed={'image': img_list}, \
......@@ -75,7 +75,8 @@ def eval_rec_run(exe, config, eval_info_dict, mode):
preds_lod = outs[0].lod()[0]
labels, labels_lod = convert_rec_label_to_lod(label_list)
acc, acc_num, sample_num = cal_predicts_accuracy(
char_ops, preds, preds_lod, labels, labels_lod, is_remove_duplicate)
char_ops, preds, preds_lod, labels, labels_lod,
is_remove_duplicate)
else:
encoder_word_pos_list = []
gsrm_word_pos_list = []
......@@ -89,15 +90,19 @@ def eval_rec_run(exe, config, eval_info_dict, mode):
img_list = np.concatenate(img_list, axis=0)
label_list = np.concatenate(label_list, axis=0)
encoder_word_pos_list = np.concatenate(encoder_word_pos_list, axis=0).astype(np.int64)
gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list, axis=0).astype(np.int64)
gsrm_slf_attn_bias1_list = np.concatenate(gsrm_slf_attn_bias1_list, axis=0).astype(np.float32)
gsrm_slf_attn_bias2_list = np.concatenate(gsrm_slf_attn_bias2_list, axis=0).astype(np.float32)
encoder_word_pos_list = np.concatenate(
encoder_word_pos_list, axis=0).astype(np.int64)
gsrm_word_pos_list = np.concatenate(
gsrm_word_pos_list, axis=0).astype(np.int64)
gsrm_slf_attn_bias1_list = np.concatenate(
gsrm_slf_attn_bias1_list, axis=0).astype(np.float32)
gsrm_slf_attn_bias2_list = np.concatenate(
gsrm_slf_attn_bias2_list, axis=0).astype(np.float32)
labels = label_list
outs = exe.run(eval_info_dict['program'], \
feed={'image': img_list, 'encoder_word_pos': encoder_word_pos_list,
feed={'image': img_list, 'encoder_word_pos': encoder_word_pos_list,
'gsrm_word_pos': gsrm_word_pos_list, 'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1_list,
'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2_list}, \
fetch_list=eval_info_dict['fetch_varname_list'], \
......@@ -108,7 +113,7 @@ def eval_rec_run(exe, config, eval_info_dict, mode):
total_acc_num += acc_num
total_sample_num += sample_num
logger.info("eval batch id: {}, acc: {}".format(total_batch_num, acc))
#logger.info("eval batch id: {}, acc: {}".format(total_batch_num, acc))
total_batch_num += 1
avg_acc = total_acc_num * 1.0 / total_sample_num
metrics = {'avg_acc': avg_acc, "total_acc_num": total_acc_num, \
......
tools/program.py
浏览文件 @ 6832ca02
......@@ -34,6 +34,7 @@ from ppocr.utils.save_load import save_model
import numpy as np
from ppocr.utils.character import cal_predicts_accuracy, cal_predicts_accuracy_srn, CharacterOps
class ArgsParser(ArgumentParser):
def __init__(self):
super(ArgsParser, self).__init__(
......@@ -196,10 +197,13 @@ def build(config, main_prog, startup_prog, mode):
if config['Global']["loss_type"] == 'srn':
model_average = fluid.optimizer.ModelAverage(
config['Global']['average_window'],
min_average_window=config['Global']['min_average_window'],
max_average_window=config['Global']['max_average_window'])
min_average_window=config['Global'][
'min_average_window'],
max_average_window=config['Global'][
'max_average_window'])
return (dataloader, fetch_name_list, fetch_varname_list, opt_loss_name,model_average)
return (dataloader, fetch_name_list, fetch_varname_list, opt_loss_name,
model_average)
def build_export(config, main_prog, startup_prog):
......@@ -398,6 +402,7 @@ def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
save_model(train_info_dict['train_program'], save_path)
return
def preprocess():
FLAGS = ArgsParser().parse_args()
config = load_config(FLAGS.config)
......@@ -409,8 +414,8 @@ def preprocess():
check_gpu(use_gpu)
alg = config['Global']['algorithm']
assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE']
if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE']:
assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN']
if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN']:
config['Global']['char_ops'] = CharacterOps(config['Global'])
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
......
train_data 0 → 120000
浏览文件 @ 6832ca02
/workspace/PaddleOCR/train_data/
\ No newline at end of file
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