未验证 提交 6f677d60 编写于 作者: X xiaoting 提交者: GitHub

Fix seed export (#7502)

* fix seed export

* update doc for seed

* add stop_gredient for one_likes
上级 8aa172d8
......@@ -50,7 +50,7 @@ Architecture:
name: AsterHead # AttentionHead
sDim: 512
attDim: 512
max_len_labels: 100
max_len_labels: 20
Loss:
name: AsterLoss
......
......@@ -78,23 +78,34 @@ python3 tools/infer_rec.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Globa
<a name="4-1"></a>
### 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
```
<a name="4-2"></a>
### 4.2 C++推理
coming soon
暂不支持
<a name="4-3"></a>
### 4.3 Serving服务化部署
coming soon
暂不支持
<a name="4-4"></a>
### 4.4 更多推理部署
coming soon
暂不支持
<a name="5"></a>
## 5. FAQ
......
......@@ -77,7 +77,17 @@ python3 tools/infer_rec.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Globa
<a name="4-1"></a>
### 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
```
<a name="4-2"></a>
### 4.2 C++ Inference
......
......@@ -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 <eos> 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 = [
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]) for step in reversed(p)
]
p = paddle.concat(p, -1)[:, 0, :]
return p, paddle.ones_like(p)
shape=[batch_size, beam_width, -1]))
q = paddle.concat(q, -1)[:, 0, :]
return q, paddle.ones_like(q)
class AttentionUnit(nn.Layer):
......@@ -385,8 +381,8 @@ 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)
......
......@@ -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()
......
......@@ -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"),
[
shape=[None, 3, 48, 160], dtype="float32"), [
paddle.static.InputSpec(
shape=[None, ],
dtype="float32"),
shape=[None, ], dtype="float32"),
paddle.static.InputSpec(
shape=[None, max_text_length],
dtype="int64")
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, 3, 64, 256], dtype="float32")
]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] in ["LayoutLM", "LayoutLMv2", "LayoutXLM"]:
......
......@@ -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]],
......
......@@ -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)
......@@ -136,7 +136,8 @@ 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),
img_metas = [
paddle.to_tensor(valid_ratio),
paddle.to_tensor(word_positons),
]
images = np.expand_dims(batch[0], axis=0)
......
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