diff --git a/configs/rec/rec_resnet_stn_bilstm_att.yml b/configs/rec/rec_resnet_stn_bilstm_att.yml
index 0bb90b35264b424c58a45685f5a2a066843298a6..5a37ba7541ef38be6ccde61bf64baba9dbd7a71b 100644
--- a/configs/rec/rec_resnet_stn_bilstm_att.yml
+++ b/configs/rec/rec_resnet_stn_bilstm_att.yml
@@ -50,7 +50,7 @@ Architecture:
name: AsterHead # AttentionHead
sDim: 512
attDim: 512
- max_len_labels: 100
+ max_len_labels: 20
Loss:
name: AsterLoss
diff --git a/doc/doc_ch/algorithm_rec_seed.md b/doc/doc_ch/algorithm_rec_seed.md
index 710e92272dc3169bf373d273534441a15c6be01c..ae0c529a666ac795172e3e260d2738ea461611a9 100644
--- a/doc/doc_ch/algorithm_rec_seed.md
+++ b/doc/doc_ch/algorithm_rec_seed.md
@@ -78,23 +78,34 @@ python3 tools/infer_rec.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Globa
### 4.1 Python推理
-coming soon
+首先将SEED文本识别训练过程中保存的模型，转换成inference model。（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) )，可以使用如下命令进行转换：
+
+```
+python3 tools/export_model.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=seed_infer
+```
+
+SEED文本识别模型推理，可以执行如下命令：
+
+```
+python3 tools/infer/predict_rec.py --rec_model_dir=seed_infer --image_dir=doc/imgs_words_en/word_10.png --rec_algorithm="SEED" --rec_char_dict_path=ppocr/utils/EN_symbol_dict.txt --rec_image_shape="3,64,256" --use_space_char=False
+```
+
### 4.2 C++推理
-coming soon
+暂不支持
### 4.3 Serving服务化部署
-coming soon
+暂不支持
### 4.4 更多推理部署
-coming soon
+暂不支持
## 5. FAQ
diff --git a/doc/doc_en/algorithm_rec_seed_en.md b/doc/doc_en/algorithm_rec_seed_en.md
index f8d7ae6d3f34ab8a4f510c88002b22dbce7a10e8..a71d105e0c1f5b3cbe210d7cc78c2b07725ab978 100644
--- a/doc/doc_en/algorithm_rec_seed_en.md
+++ b/doc/doc_en/algorithm_rec_seed_en.md
@@ -77,7 +77,17 @@ python3 tools/infer_rec.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Globa
### 4.1 Python Inference
-Not support
+First, the model saved during the SEED text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) ), you can use the following command to convert:
+
+```
+python3 tools/export_model.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=seed_infer
+```
+
+For SEED text recognition model inference, the following commands can be executed:
+
+```
+python3 tools/infer/predict_rec.py --rec_model_dir=seed_infer --image_dir=doc/imgs_words_en/word_10.png --rec_algorithm="SEED" --rec_char_dict_path=ppocr/utils/EN_symbol_dict.txt --rec_image_shape="3,64,256" --use_space_char=False
+```
### 4.2 C++ Inference
diff --git a/ppocr/modeling/heads/rec_aster_head.py b/ppocr/modeling/heads/rec_aster_head.py
index c95e8fd31f84c26cf58f7fbbdaab6c825b10eea8..1febc320ea835db31d11bba2fe5f0b4ef0c1f7f9 100644
--- a/ppocr/modeling/heads/rec_aster_head.py
+++ b/ppocr/modeling/heads/rec_aster_head.py
@@ -62,10 +62,11 @@ class AsterHead(nn.Layer):
else:
rec_pred, rec_pred_scores = self.decoder.beam_search(
x, self.beam_width, self.eos, embedding_vectors)
+ rec_pred_scores.stop_gradient = True
+ rec_pred.stop_gradient = True
return_dict['rec_pred'] = rec_pred
return_dict['rec_pred_scores'] = rec_pred_scores
return_dict['embedding_vectors'] = embedding_vectors
-
return return_dict
@@ -114,37 +115,13 @@ class AttentionRecognitionHead(nn.Layer):
y_prev = paddle.full(
shape=[batch_size], fill_value=self.num_classes)
else:
+
y_prev = targets[:, i - 1]
output, state = self.decoder(x, state, y_prev)
outputs.append(output)
outputs = paddle.concat([_.unsqueeze(1) for _ in outputs], 1)
return outputs
- # inference stage.
- def sample(self, x):
- x, _, _ = x
- batch_size = x.size(0)
- # Decoder
- state = paddle.zeros([1, batch_size, self.sDim])
-
- predicted_ids, predicted_scores = [], []
- for i in range(self.max_len_labels):
- if i == 0:
- y_prev = paddle.full(
- shape=[batch_size], fill_value=self.num_classes)
- else:
- y_prev = predicted
-
- output, state = self.decoder(x, state, y_prev)
- output = F.softmax(output, axis=1)
- score, predicted = output.max(1)
- predicted_ids.append(predicted.unsqueeze(1))
- predicted_scores.append(score.unsqueeze(1))
- predicted_ids = paddle.concat([predicted_ids, 1])
- predicted_scores = paddle.concat([predicted_scores, 1])
- # return predicted_ids.squeeze(), predicted_scores.squeeze()
- return predicted_ids, predicted_scores
-
def beam_search(self, x, beam_width, eos, embed):
def _inflate(tensor, times, dim):
repeat_dims = [1] * tensor.dim()
@@ -153,7 +130,7 @@ class AttentionRecognitionHead(nn.Layer):
return output
# https://github.com/IBM/pytorch-seq2seq/blob/fede87655ddce6c94b38886089e05321dc9802af/seq2seq/models/TopKDecoder.py
- batch_size, l, d = x.shape
+ batch_size, l, d = paddle.shape(x)
x = paddle.tile(
paddle.transpose(
x.unsqueeze(1), perm=[1, 0, 2, 3]), [beam_width, 1, 1, 1])
@@ -166,21 +143,22 @@ class AttentionRecognitionHead(nn.Layer):
pos_index = paddle.reshape(
paddle.arange(batch_size) * beam_width, shape=[-1, 1])
-
# Initialize the scores
+
sequence_scores = paddle.full(
- shape=[batch_size * beam_width, 1], fill_value=-float('Inf'))
- index = [i * beam_width for i in range(0, batch_size)]
- sequence_scores[index] = 0.0
+ shape=[batch_size, beam_width], fill_value=-float('Inf'))
+ sequence_scores[:, 0] = 0.0
+ sequence_scores = paddle.reshape(
+ sequence_scores, shape=[batch_size * beam_width, 1])
# Initialize the input vector
y_prev = paddle.full(
shape=[batch_size * beam_width], fill_value=self.num_classes)
# Store decisions for backtracking
- stored_scores = list()
- stored_predecessors = list()
- stored_emitted_symbols = list()
+ stored_scores = []
+ stored_predecessors = []
+ stored_emitted_symbols = []
for i in range(self.max_len_labels):
output, state = self.decoder(inflated_encoder_feats, state, y_prev)
@@ -194,15 +172,16 @@ class AttentionRecognitionHead(nn.Layer):
paddle.reshape(sequence_scores, [batch_size, -1]),
beam_width,
axis=1)
-
# Reshape input = (bk, 1) and sequence_scores = (bk, 1)
y_prev = paddle.reshape(
- candidates % self.num_classes, shape=[batch_size * beam_width])
+ candidates % self.num_classes, shape=[batch_size, beam_width])
+ y_prev = paddle.reshape(y_prev, shape=[batch_size * beam_width])
sequence_scores = paddle.reshape(
scores, shape=[batch_size * beam_width, 1])
# Update fields for next timestep
- pos_index = paddle.expand_as(pos_index, candidates)
+ pos_index = paddle.expand(pos_index, paddle.shape(candidates))
+
predecessors = paddle.cast(
candidates / self.num_classes + pos_index, dtype='int64')
predecessors = paddle.reshape(
@@ -213,13 +192,13 @@ class AttentionRecognitionHead(nn.Layer):
# Update sequence socres and erase scores for symbol so that they aren't expanded
stored_scores.append(sequence_scores.clone())
y_prev = paddle.reshape(y_prev, shape=[-1, 1])
- eos_prev = paddle.full_like(y_prev, fill_value=eos)
+
+ eos_prev = paddle.full(paddle.shape(y_prev), fill_value=eos)
mask = eos_prev == y_prev
+ mask = paddle.cast(mask, 'int64')
mask = paddle.nonzero(mask)
- if mask.dim() > 0:
- sequence_scores = sequence_scores.numpy()
- mask = mask.numpy()
- sequence_scores[mask] = -float('inf')
+ if len(mask) > 0:
+ sequence_scores[:] = -float('inf')
sequence_scores = paddle.to_tensor(sequence_scores)
# Cache results for backtracking
@@ -228,11 +207,12 @@ class AttentionRecognitionHead(nn.Layer):
stored_emitted_symbols.append(y_prev)
# Do backtracking to return the optimal values
- #====== backtrak ======#
+ # ====== backtrak ======#
# Initialize return variables given different types
- p = list()
- l = [[self.max_len_labels] * beam_width for _ in range(batch_size)
- ] # Placeholder for lengths of top-k sequences
+ p = []
+
+ # Placeholder for lengths of top-k sequences
+ l = paddle.full([batch_size, beam_width], self.max_len_labels)
# the last step output of the beams are not sorted
# thus they are sorted here
@@ -244,14 +224,18 @@ class AttentionRecognitionHead(nn.Layer):
# initialize the sequence scores with the sorted last step beam scores
s = sorted_score.clone()
- batch_eos_found = [0] * batch_size # the number of EOS found
+ batch_eos_found = paddle.zeros(
+ [batch_size], dtype='int32') # the number of EOS found
# in the backward loop below for each batch
t = self.max_len_labels - 1
+
# initialize the back pointer with the sorted order of the last step beams.
# add pos_index for indexing variable with b*k as the first dimension.
t_predecessors = paddle.reshape(
- sorted_idx + pos_index.expand_as(sorted_idx),
+ sorted_idx + pos_index.expand(paddle.shape(sorted_idx)),
shape=[batch_size * beam_width])
+
+ tmp_beam_width = beam_width
while t >= 0:
# Re-order the variables with the back pointer
current_symbol = paddle.index_select(
@@ -261,26 +245,32 @@ class AttentionRecognitionHead(nn.Layer):
eos_indices = stored_emitted_symbols[t] == eos
eos_indices = paddle.nonzero(eos_indices)
+ stored_predecessors_t = stored_predecessors[t]
+ stored_emitted_symbols_t = stored_emitted_symbols[t]
+ stored_scores_t = stored_scores[t]
+ t_plus = t + 1
+
if eos_indices.dim() > 0:
- for i in range(eos_indices.shape[0] - 1, -1, -1):
+ for j in range(eos_indices.shape[0] - 1, -1, -1):
# Indices of the EOS symbol for both variables
# with b*k as the first dimension, and b, k for
# the first two dimensions
- idx = eos_indices[i]
- b_idx = int(idx[0] / beam_width)
+ idx = eos_indices[j]
+ b_idx = int(idx[0] / tmp_beam_width)
# The indices of the replacing position
# according to the replacement strategy noted above
- res_k_idx = beam_width - (batch_eos_found[b_idx] %
- beam_width) - 1
+ res_k_idx = tmp_beam_width - (batch_eos_found[b_idx] %
+ tmp_beam_width) - 1
batch_eos_found[b_idx] += 1
- res_idx = b_idx * beam_width + res_k_idx
+ res_idx = b_idx * tmp_beam_width + res_k_idx
# Replace the old information in return variables
# with the new ended sequence information
- t_predecessors[res_idx] = stored_predecessors[t][idx[0]]
- current_symbol[res_idx] = stored_emitted_symbols[t][idx[0]]
- s[b_idx, res_k_idx] = stored_scores[t][idx[0], 0]
- l[b_idx][res_k_idx] = t + 1
+
+ t_predecessors[res_idx] = stored_predecessors_t[idx[0]]
+ current_symbol[res_idx] = stored_emitted_symbols_t[idx[0]]
+ s[b_idx, res_k_idx] = stored_scores_t[idx[0], 0]
+ l[b_idx][res_k_idx] = t_plus
# record the back tracked results
p.append(current_symbol)
@@ -289,24 +279,30 @@ class AttentionRecognitionHead(nn.Layer):
# Sort and re-order again as the added ended sequences may change
# the order (very unlikely)
s, re_sorted_idx = s.topk(beam_width)
+
for b_idx in range(batch_size):
- l[b_idx] = [
- l[b_idx][k_idx.item()] for k_idx in re_sorted_idx[b_idx, :]
- ]
+ tmp_tensor = paddle.full_like(l[b_idx], 0)
+ for k_idx in re_sorted_idx[b_idx]:
+ tmp_tensor[k_idx] = l[b_idx][k_idx]
+ l[b_idx] = tmp_tensor
re_sorted_idx = paddle.reshape(
- re_sorted_idx + pos_index.expand_as(re_sorted_idx),
+ re_sorted_idx + pos_index.expand(paddle.shape(re_sorted_idx)),
[batch_size * beam_width])
# Reverse the sequences and re-order at the same time
# It is reversed because the backtracking happens in reverse time order
- p = [
- paddle.reshape(
- paddle.index_select(step, re_sorted_idx, 0),
- shape=[batch_size, beam_width, -1]) for step in reversed(p)
- ]
- p = paddle.concat(p, -1)[:, 0, :]
- return p, paddle.ones_like(p)
+ reversed_p = p[::-1]
+
+ q = []
+ for step in reversed_p:
+ q.append(
+ paddle.reshape(
+ paddle.index_select(step, re_sorted_idx, 0),
+ shape=[batch_size, beam_width, -1]))
+
+ q = paddle.concat(q, -1)[:, 0, :]
+ return q, paddle.ones_like(q)
class AttentionUnit(nn.Layer):
@@ -385,9 +381,9 @@ class DecoderUnit(nn.Layer):
yProj = self.tgt_embedding(yPrev)
concat_context = paddle.concat([yProj, context], 1)
- concat_context = paddle.squeeze(concat_context, 1)
sPrev = paddle.squeeze(sPrev, 0)
+
output, state = self.gru(concat_context, sPrev)
output = paddle.squeeze(output, axis=1)
output = self.fc(output)
- return output, state
\ No newline at end of file
+ return output, state
diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py
index 749060a053f1442f4bf5df6c5f4b56205e893be8..99e7d0c90f24373c97247bcd407d30af2b48a430 100644
--- a/ppocr/postprocess/rec_postprocess.py
+++ b/ppocr/postprocess/rec_postprocess.py
@@ -307,6 +307,11 @@ class SEEDLabelDecode(BaseRecLabelDecode):
label = self.decode(label, is_remove_duplicate=False)
return text, label
"""
+ tmp = {}
+ if isinstance(preds, list):
+ tmp["rec_pred"] = preds[1]
+ tmp["rec_pred_scores"] = preds[0]
+ preds = tmp
preds_idx = preds["rec_pred"]
if isinstance(preds_idx, paddle.Tensor):
preds_idx = preds_idx.numpy()
diff --git a/tools/export_model.py b/tools/export_model.py
index 193988cc1b62a6c4536a8d2ec640e3e5fc81a79c..4b91462a3b40bb6db73d0ad91d6b3cf96673f2a6 100755
--- a/tools/export_model.py
+++ b/tools/export_model.py
@@ -97,7 +97,6 @@ def export_single_model(model,
paddle.static.InputSpec(
shape=[None, 3, 32, 128], dtype="float32"),
]
- # print([None, 3, 32, 128])
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] in ["NRTR", "SPIN"]:
other_shape = [
@@ -115,16 +114,18 @@ def export_single_model(model,
max_text_length = arch_config["Head"]["max_text_length"]
other_shape = [
paddle.static.InputSpec(
- shape=[None, 3, 48, 160], dtype="float32"),
-
- [
- paddle.static.InputSpec(
- shape=[None, ],
- dtype="float32"),
+ shape=[None, 3, 48, 160], dtype="float32"), [
+ paddle.static.InputSpec(
+ shape=[None, ], dtype="float32"),
+ paddle.static.InputSpec(
+ shape=[None, max_text_length], dtype="int64")
+ ]
+ ]
+ model = to_static(model, input_spec=other_shape)
+ elif arch_config["algorithm"] == "SEED":
+ other_shape = [
paddle.static.InputSpec(
- shape=[None, max_text_length],
- dtype="int64")
- ]
+ shape=[None, 3, 64, 256], dtype="float32")
]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] in ["LayoutLM", "LayoutLMv2", "LayoutXLM"]:
diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py
index 176e2c68e2c9b2e08f9b56378c45a57733faf8cd..5a7564e122c7173658c4cc2a57caef13276a304f 100755
--- a/tools/infer/predict_rec.py
+++ b/tools/infer/predict_rec.py
@@ -100,6 +100,12 @@ class TextRecognizer(object):
"use_space_char": args.use_space_char,
"rm_symbol": True
}
+ elif self.rec_algorithm == "SEED":
+ postprocess_params = {
+ 'name': 'SEEDLabelDecode',
+ "character_dict_path": args.rec_char_dict_path,
+ "use_space_char": args.use_space_char
+ }
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = \
utility.create_predictor(args, 'rec', logger)
@@ -161,6 +167,7 @@ class TextRecognizer(object):
if resized_w > self.rec_image_shape[2]:
resized_w = self.rec_image_shape[2]
imgW = self.rec_image_shape[2]
+
resized_image = cv2.resize(img, (resized_w, imgH))
resized_image = resized_image.astype('float32')
resized_image = resized_image.transpose((2, 0, 1)) / 255
@@ -398,6 +405,11 @@ class TextRecognizer(object):
img_list[indices[ino]], self.rec_image_shape)
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
+ elif self.rec_algorithm == "SEED":
+ norm_img = self.resize_norm_img_svtr(img_list[indices[ino]],
+ self.rec_image_shape)
+ norm_img = norm_img[np.newaxis, :]
+ norm_img_batch.append(norm_img)
elif self.rec_algorithm == "RobustScanner":
norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
img_list[indices[ino]],
diff --git a/tools/infer_rec.py b/tools/infer_rec.py
index 14b14544eb11e9fb0a0c2cdf92aff9d7cb4b5ba7..2d2e09a2840da1f73cac613bdd01090050729893 100755
--- a/tools/infer_rec.py
+++ b/tools/infer_rec.py
@@ -75,7 +75,6 @@ def main():
'out_channels_list'] = out_channels_list
else: # base rec model
config['Architecture']["Head"]['out_channels'] = char_num
-
model = build_model(config['Architecture'])
load_model(config, model)
@@ -97,7 +96,8 @@ def main():
elif config['Architecture']['algorithm'] == "SAR":
op[op_name]['keep_keys'] = ['image', 'valid_ratio']
elif config['Architecture']['algorithm'] == "RobustScanner":
- op[op_name]['keep_keys'] = ['image', 'valid_ratio', 'word_positons']
+ op[op_name][
+ 'keep_keys'] = ['image', 'valid_ratio', 'word_positons']
else:
op[op_name]['keep_keys'] = ['image']
transforms.append(op)
@@ -136,9 +136,10 @@ def main():
if config['Architecture']['algorithm'] == "RobustScanner":
valid_ratio = np.expand_dims(batch[1], axis=0)
word_positons = np.expand_dims(batch[2], axis=0)
- img_metas = [paddle.to_tensor(valid_ratio),
- paddle.to_tensor(word_positons),
- ]
+ img_metas = [
+ paddle.to_tensor(valid_ratio),
+ paddle.to_tensor(word_positons),
+ ]
images = np.expand_dims(batch[0], axis=0)
images = paddle.to_tensor(images)
if config['Architecture']['algorithm'] == "SRN":