update config
This commit is contained in:
tink2123
parent
09d8cb6d98
commit
6832ca029f
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@ -17,10 +17,11 @@ Global:
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average_window: 0.15
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max_average_window: 15625
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min_average_window: 10000
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reader_yml: ./configs/rec/rec_srn_reader.yml
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reader_yml: ./configs/rec/rec_benchmark_reader.yml
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pretrain_weights:
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checkpoints:
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save_inference_dir:
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infer_img:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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@ -118,15 +118,14 @@ class LMDBReader(object):
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image_file_list = get_image_file_list(self.infer_img)
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for single_img in image_file_list:
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img = cv2.imread(single_img)
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if img.shape[-1]==1 or len(list(img.shape))==2:
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if img.shape[-1] == 1 or len(list(img.shape)) == 2:
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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if self.loss_type == 'srn':
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norm_img = process_image_srn(
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img=img,
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image_shape=self.image_shape,
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num_heads=self.num_heads,
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max_text_length=self.max_text_length
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)
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max_text_length=self.max_text_length)
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else:
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norm_img = process_image(
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img=img,
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@ -135,20 +134,20 @@ class LMDBReader(object):
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tps=self.use_tps,
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infer_mode=True)
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yield norm_img
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elif self.mode == 'test':
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image_file_list = get_image_file_list(self.infer_img)
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for single_img in image_file_list:
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img = cv2.imread(single_img)
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if img.shape[-1]==1 or len(list(img.shape))==2:
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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norm_img = process_image(
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img=img,
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image_shape=self.image_shape,
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char_ops=self.char_ops,
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tps=self.use_tps,
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infer_mode=True
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)
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yield norm_img
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#elif self.mode == 'eval':
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# image_file_list = get_image_file_list(self.infer_img)
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# for single_img in image_file_list:
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# img = cv2.imread(single_img)
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# if img.shape[-1]==1 or len(list(img.shape))==2:
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# img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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# norm_img = process_image(
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# img=img,
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# image_shape=self.image_shape,
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# char_ops=self.char_ops,
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# tps=self.use_tps,
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# infer_mode=True
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# )
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# yield norm_img
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else:
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lmdb_sets = self.load_hierarchical_lmdb_dataset()
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if process_id == 0:
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@ -169,14 +168,15 @@ class LMDBReader(object):
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img, label = sample_info
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outs = []
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if self.loss_type == "srn":
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outs = process_image_srn(img, self.image_shape, self.num_heads,
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self.max_text_length, label,
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self.char_ops, self.loss_type)
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outs = process_image_srn(
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img, self.image_shape, self.num_heads,
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self.max_text_length, label, self.char_ops,
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self.loss_type)
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else:
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outs = process_image(img, self.image_shape, label,
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self.char_ops, self.loss_type,
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self.max_text_length)
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outs = process_image(
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img, self.image_shape, label, self.char_ops,
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self.loss_type, self.max_text_length)
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if outs is None:
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continue
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yield outs
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@ -184,6 +184,7 @@ class LMDBReader(object):
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if finish_read_num == len(lmdb_sets):
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break
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self.close_lmdb_dataset(lmdb_sets)
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def batch_iter_reader():
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batch_outs = []
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for outs in sample_iter_reader():
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@ -79,17 +79,45 @@ class RecModel(object):
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feed_list = [image, label_in, label_out]
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labels = {'label_in': label_in, 'label_out': label_out}
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elif self.loss_type == "srn":
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encoder_word_pos = fluid.data(name="encoder_word_pos", shape=[-1, int((image_shape[-2] / 8) * (image_shape[-1] / 8)), 1], dtype="int64")
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gsrm_word_pos = fluid.data(name="gsrm_word_pos", shape=[-1, self.max_text_length, 1], dtype="int64")
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gsrm_slf_attn_bias1 = fluid.data(name="gsrm_slf_attn_bias1", shape=[-1, self.num_heads, self.max_text_length, self.max_text_length])
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gsrm_slf_attn_bias2 = fluid.data(name="gsrm_slf_attn_bias2", shape=[-1, self.num_heads, self.max_text_length, self.max_text_length])
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lbl_weight = fluid.layers.data(name="lbl_weight", shape=[-1, 1], dtype='int64')
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encoder_word_pos = fluid.data(
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name="encoder_word_pos",
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shape=[
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-1, int((image_shape[-2] / 8) * (image_shape[-1] / 8)),
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1
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],
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dtype="int64")
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gsrm_word_pos = fluid.data(
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name="gsrm_word_pos",
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shape=[-1, self.max_text_length, 1],
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dtype="int64")
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gsrm_slf_attn_bias1 = fluid.data(
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name="gsrm_slf_attn_bias1",
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shape=[
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-1, self.num_heads, self.max_text_length,
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self.max_text_length
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])
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gsrm_slf_attn_bias2 = fluid.data(
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name="gsrm_slf_attn_bias2",
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shape=[
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-1, self.num_heads, self.max_text_length,
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self.max_text_length
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])
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lbl_weight = fluid.layers.data(
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name="lbl_weight", shape=[-1, 1], dtype='int64')
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label = fluid.data(
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name='label', shape=[-1, 1], dtype='int32', lod_level=1)
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feed_list = [image, label, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2, lbl_weight]
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labels = {'label': label, 'encoder_word_pos': encoder_word_pos,
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'gsrm_word_pos': gsrm_word_pos, 'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1,
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'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2,'lbl_weight':lbl_weight}
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feed_list = [
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image, label, encoder_word_pos, gsrm_word_pos,
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gsrm_slf_attn_bias1, gsrm_slf_attn_bias2, lbl_weight
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]
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labels = {
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'label': label,
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'encoder_word_pos': encoder_word_pos,
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'gsrm_word_pos': gsrm_word_pos,
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'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1,
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'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2,
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'lbl_weight': lbl_weight
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}
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else:
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label = fluid.data(
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name='label', shape=[None, 1], dtype='int32', lod_level=1)
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@ -112,15 +140,41 @@ class RecModel(object):
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"We set img_shape to be the same , it may affect the inference effect"
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)
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image_shape = deepcopy(self.image_shape)
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image = fluid.data(name='image', shape=image_shape, dtype='float32')
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image = fluid.data(name='image', shape=image_shape, dtype='float32')
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if self.loss_type == "srn":
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encoder_word_pos = fluid.data(name="encoder_word_pos", shape=[-1, int((image_shape[-2] / 8) * (image_shape[-1] / 8)), 1], dtype="int64")
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gsrm_word_pos = fluid.data(name="gsrm_word_pos", shape=[-1, self.max_text_length, 1], dtype="int64")
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gsrm_slf_attn_bias1 = fluid.data(name="gsrm_slf_attn_bias1", shape=[-1, self.num_heads, self.max_text_length, self.max_text_length])
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gsrm_slf_attn_bias2 = fluid.data(name="gsrm_slf_attn_bias2", shape=[-1, self.num_heads, self.max_text_length, self.max_text_length])
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feed_list = [image, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2]
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labels = {'encoder_word_pos': encoder_word_pos, 'gsrm_word_pos': gsrm_word_pos,
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'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1, 'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2}
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encoder_word_pos = fluid.data(
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name="encoder_word_pos",
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shape=[
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-1, int((image_shape[-2] / 8) * (image_shape[-1] / 8)),
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1
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],
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dtype="int64")
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gsrm_word_pos = fluid.data(
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name="gsrm_word_pos",
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shape=[-1, self.max_text_length, 1],
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dtype="int64")
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gsrm_slf_attn_bias1 = fluid.data(
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name="gsrm_slf_attn_bias1",
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shape=[
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-1, self.num_heads, self.max_text_length,
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self.max_text_length
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])
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gsrm_slf_attn_bias2 = fluid.data(
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name="gsrm_slf_attn_bias2",
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shape=[
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-1, self.num_heads, self.max_text_length,
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self.max_text_length
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])
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feed_list = [
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image, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2
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]
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labels = {
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'encoder_word_pos': encoder_word_pos,
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'gsrm_word_pos': gsrm_word_pos,
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'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1,
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'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2
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}
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return image, labels, loader
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def __call__(self, mode):
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@ -140,8 +194,13 @@ class RecModel(object):
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label = labels['label']
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if self.loss_type == 'srn':
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total_loss, img_loss, word_loss = self.loss(predicts, labels)
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outputs = {'total_loss':total_loss, 'img_loss':img_loss, 'word_loss':word_loss,
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'decoded_out':decoded_out, 'label':label}
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outputs = {
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'total_loss': total_loss,
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'img_loss': img_loss,
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'word_loss': word_loss,
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'decoded_out': decoded_out,
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'label': label
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}
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else:
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outputs = {'total_loss':loss, 'decoded_out':\
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decoded_out, 'label':label}
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@ -4,8 +4,9 @@ import numpy as np
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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from .desc import *
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from .config import ModelHyperParams,TrainTaskConfig
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# Set seed for CE
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dropout_seed = None
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def wrap_layer_with_block(layer, block_idx):
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"""
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@ -269,23 +270,24 @@ pre_process_layer = partial(pre_post_process_layer, None)
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post_process_layer = pre_post_process_layer
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def prepare_encoder(src_word,#[b,t,c]
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src_pos,
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src_vocab_size,
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src_emb_dim,
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src_max_len,
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dropout_rate=0.,
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bos_idx=0,
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word_emb_param_name=None,
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pos_enc_param_name=None):
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def prepare_encoder(
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src_word, #[b,t,c]
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src_pos,
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src_vocab_size,
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src_emb_dim,
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src_max_len,
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dropout_rate=0.,
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bos_idx=0,
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word_emb_param_name=None,
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pos_enc_param_name=None):
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"""Add word embeddings and position encodings.
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The output tensor has a shape of:
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[batch_size, max_src_length_in_batch, d_model].
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This module is used at the bottom of the encoder stacks.
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"""
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src_word_emb =src_word#layers.concat(res,axis=1)
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src_word_emb=layers.cast(src_word_emb,'float32')
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src_word_emb = src_word #layers.concat(res,axis=1)
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src_word_emb = layers.cast(src_word_emb, 'float32')
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# print("src_word_emb",src_word_emb)
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src_word_emb = layers.scale(x=src_word_emb, scale=src_emb_dim**0.5)
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@ -302,14 +304,14 @@ def prepare_encoder(src_word,#[b,t,c]
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def prepare_decoder(src_word,
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src_pos,
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src_vocab_size,
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src_emb_dim,
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src_max_len,
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dropout_rate=0.,
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bos_idx=0,
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word_emb_param_name=None,
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pos_enc_param_name=None):
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src_pos,
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src_vocab_size,
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src_emb_dim,
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src_max_len,
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dropout_rate=0.,
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bos_idx=0,
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word_emb_param_name=None,
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pos_enc_param_name=None):
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"""Add word embeddings and position encodings.
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The output tensor has a shape of:
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[batch_size, max_src_length_in_batch, d_model].
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@ -323,7 +325,7 @@ def prepare_decoder(src_word,
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name=word_emb_param_name,
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initializer=fluid.initializer.Normal(0., src_emb_dim**-0.5)))
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# print("target_word_emb",src_word_emb)
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src_word_emb = layers.scale(x=src_word_emb, scale=src_emb_dim ** 0.5)
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src_word_emb = layers.scale(x=src_word_emb, scale=src_emb_dim**0.5)
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src_pos_enc = layers.embedding(
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src_pos,
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size=[src_max_len, src_emb_dim],
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@ -335,6 +337,7 @@ def prepare_decoder(src_word,
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enc_input, dropout_prob=dropout_rate, seed=dropout_seed,
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is_test=False) if dropout_rate else enc_input
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# prepare_encoder = partial(
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# prepare_encoder_decoder, pos_enc_param_name=pos_enc_param_names[0])
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# prepare_decoder = partial(
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@ -595,21 +598,9 @@ def transformer(src_vocab_size,
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weights = all_inputs[-1]
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enc_output = wrap_encoder(
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src_vocab_size,
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ModelHyperParams.src_seq_len,
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n_layer,
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n_head,
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d_key,
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d_value,
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d_model,
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d_inner_hid,
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prepostprocess_dropout,
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attention_dropout,
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relu_dropout,
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preprocess_cmd,
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postprocess_cmd,
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weight_sharing,
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enc_inputs)
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src_vocab_size, 64, n_layer, n_head, d_key, d_value, d_model,
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d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout,
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preprocess_cmd, postprocess_cmd, weight_sharing, enc_inputs)
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predict = wrap_decoder(
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trg_vocab_size,
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@ -650,21 +641,21 @@ def transformer(src_vocab_size,
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def wrap_encoder_forFeature(src_vocab_size,
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max_length,
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n_layer,
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n_head,
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d_key,
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d_value,
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d_model,
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d_inner_hid,
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prepostprocess_dropout,
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attention_dropout,
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relu_dropout,
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preprocess_cmd,
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postprocess_cmd,
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weight_sharing,
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enc_inputs=None,
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bos_idx=0):
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max_length,
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n_layer,
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n_head,
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d_key,
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d_value,
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d_model,
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d_inner_hid,
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prepostprocess_dropout,
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attention_dropout,
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relu_dropout,
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preprocess_cmd,
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postprocess_cmd,
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weight_sharing,
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enc_inputs=None,
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bos_idx=0):
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"""
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The wrapper assembles together all needed layers for the encoder.
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img, src_pos, src_slf_attn_bias = enc_inputs
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@ -676,8 +667,8 @@ def wrap_encoder_forFeature(src_vocab_size,
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conv_features, src_pos, src_slf_attn_bias = make_all_inputs(
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encoder_data_input_fields)
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else:
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conv_features, src_pos, src_slf_attn_bias = enc_inputs#
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b,t,c = conv_features.shape
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conv_features, src_pos, src_slf_attn_bias = enc_inputs #
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b, t, c = conv_features.shape
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#"""
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# insert cnn
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#"""
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@ -718,7 +709,7 @@ def wrap_encoder_forFeature(src_vocab_size,
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max_length,
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prepostprocess_dropout,
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bos_idx=bos_idx,
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word_emb_param_name=word_emb_param_names[0])
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word_emb_param_name="src_word_emb_table")
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enc_output = encoder(
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enc_input,
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@ -736,6 +727,7 @@ def wrap_encoder_forFeature(src_vocab_size,
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postprocess_cmd, )
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return enc_output
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def wrap_encoder(src_vocab_size,
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max_length,
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n_layer,
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@ -762,7 +754,7 @@ def wrap_encoder(src_vocab_size,
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src_word, src_pos, src_slf_attn_bias = make_all_inputs(
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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
|
||||
#"""
|
||||
|
|
@ -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,
|
||||
|
|
|
|||
|
|
@ -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 = []
|
||||
|
|
@ -89,10 +90,14 @@ 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
|
||||
|
||||
|
|
@ -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, \
|
||||
|
|
|
|||
|
|
@ -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()
|
||||
|
|
|
|||
|
|
@ -0,0 +1 @@
|
|||
/workspace/PaddleOCR/train_data/
|
||||
Loading…
Reference in New Issue