Fix typos (#14800)

ppocr/ext_op/roi_align_rotated/roi_align_rotated.cc

@@ -10,7 +10,7 @@
 
 #define PADDLE_WITH_CUDA
 #define CHECK_INPUT_SAME(x1, x2)                                               \
-  PD_CHECK(x1.place() == x2.place(), "input must be smae pacle.")
+  PD_CHECK(x1.place() == x2.place(), "input must be same place.")
 #define CHECK_INPUT_CPU(x) PD_CHECK(x.is_cpu(), #x " must be a CPU Tensor.")
 
 template <typename T> struct PreCalc {

ppocr/losses/distillation_loss.py

@@ -1026,14 +1026,14 @@ class CTCDKDLoss(nn.Layer):
         pred_student = F.softmax(logits_student / self.temperature, axis=-1)
         pred_teacher = F.softmax(logits_teacher / self.temperature, axis=-1)
 
-        # differents with dkd
+        # differences with dkd
         pred_student = paddle.mean(pred_student, axis=1)
         pred_teacher = paddle.mean(pred_teacher, axis=1)
 
         pred_student = self._cat_mask(pred_student, gt_mask, other_mask)
         pred_teacher = self._cat_mask(pred_teacher, gt_mask, other_mask)
 
-        # differents with dkd
+        # differences with dkd
         tckd_loss = self.kl_loss(pred_student, pred_teacher)
 
         gt_mask_ex = paddle.expand_as(gt_mask.unsqueeze(axis=1), logits_teacher)
@@ -1043,11 +1043,11 @@ class CTCDKDLoss(nn.Layer):
         pred_student_part2 = F.softmax(
             logits_student / self.temperature - 1000.0 * gt_mask_ex, axis=-1
         )
-        # differents with dkd
+        # differences with dkd
         pred_teacher_part2 = paddle.mean(pred_teacher_part2, axis=1)
         pred_student_part2 = paddle.mean(pred_student_part2, axis=1)
 
-        # differents with dkd
+        # differences with dkd
         nckd_loss = self.kl_loss(pred_student_part2, pred_teacher_part2)
         loss = self.alpha * tckd_loss + self.beta * nckd_loss
         return loss

ppocr/modeling/architectures/base_model.py

@@ -36,7 +36,7 @@ class BaseModel(nn.Layer):
         model_type = config["model_type"]
         # build transform,
         # for rec, transform can be TPS,None
-        # for det and cls, transform shoule to be None,
+        # for det and cls, transform should to be None,
         # if you make model differently, you can use transform in det and cls
         if "Transform" not in config or config["Transform"] is None:
             self.use_transform = False

ppocr/modeling/backbones/rec_efficientb3_pren.py

@@ -228,7 +228,7 @@ class MbConvBlock(nn.Layer):
             x = F.sigmoid(x_squeezed) * x
         x = self._bn2(self._project_conv(x))
 
-        # skip conntection and drop connect
+        # skip connection and drop connect
         if self.id_skip and self._block_args.stride == 1 and self.inp == self.final_oup:
             if drop_connect_rate:
                 x = self._drop_connect(x, p=drop_connect_rate, training=self.training)

ppocr/modeling/backbones/rec_pphgnetv2.py

@@ -521,7 +521,7 @@ class TheseusLayer(nn.Layer):
                 return_patterns = [stages_pattern[i] for i in return_stages]
 
             if return_patterns:
-                # call update_res function after the __init__ of the object has completed execution, that is, the contructing of layer or model has been completed.
+                # call update_res function after the __init__ of the object has completed execution, that is, the constructing of layer or model has been completed.
                 def update_res_hook(layer, input):
                     self.update_res(return_patterns)
 
@@ -680,7 +680,7 @@ class TheseusLayer(nn.Layer):
 
         res = self.upgrade_sublayer(layer_name, stop_grad)
         if len(res) == 0:
-            msg = "Failed to stop the gradient befor the layer named '{layer_name}'"
+            msg = "Failed to stop the gradient before the layer named '{layer_name}'"
             return False
         return True
 
@@ -734,12 +734,12 @@ def save_sub_res_hook(layer, input, output):
 def set_identity(
     parent_layer: nn.Layer, layer_name: str, layer_index_list: str = None
 ) -> bool:
-    """set the layer specified by layer_name and layer_index_list to Indentity.
+    """set the layer specified by layer_name and layer_index_list to Identity.
 
     Args:
         parent_layer (nn.Layer): The parent layer of target layer specified by layer_name and layer_index_list.
-        layer_name (str): The name of target layer to be set to Indentity.
+        layer_name (str): The name of target layer to be set to Identity.
-        layer_index_list (str, optional): The index of target layer to be set to Indentity in parent_layer. Defaults to None.
+        layer_index_list (str, optional): The index of target layer to be set to Identity in parent_layer. Defaults to None.
 
     Returns:
         bool: True if successfully, False otherwise.
@@ -775,7 +775,7 @@ def parse_pattern_str(
     """parse the string type pattern.
 
     Args:
-        pattern (str): The pattern to discribe layer.
+        pattern (str): The pattern to describe layer.
         parent_layer (nn.Layer): The root layer relative to the pattern.
 
     Returns:
@@ -806,7 +806,7 @@ def parse_pattern_str(
         target_layer = getattr(parent_layer, target_layer_name, None)
 
         if target_layer is None:
-            msg = f"Not found layer named('{target_layer_name}') specifed in pattern('{pattern}')."
+            msg = f"Not found layer named('{target_layer_name}') specified in pattern('{pattern}')."
             return None
 
         if target_layer_index_list:
@@ -814,7 +814,7 @@ def parse_pattern_str(
                 if int(target_layer_index) < 0 or int(target_layer_index) >= len(
                     target_layer
                 ):
-                    msg = f"Not found layer by index('{target_layer_index}') specifed in pattern('{pattern}'). The index should < {len(target_layer)} and > 0."
+                    msg = f"Not found layer by index('{target_layer_index}') specified in pattern('{pattern}'). The index should < {len(target_layer)} and > 0."
                     return None
                 target_layer = target_layer[target_layer_index]
 

ppocr/modeling/heads/rec_abinet_head.py

@@ -78,10 +78,10 @@ class BCNLanguage(nn.Layer):
         embed = self.token_encoder(embed)  # (B, N, C)
         padding_mask = _get_mask(lengths, self.max_length)
         zeros = paddle.zeros_like(embed)  # (B, N, C)
-        qeury = self.pos_encoder(zeros)
+        query = self.pos_encoder(zeros)
         for decoder_layer in self.decoder:
-            qeury = decoder_layer(qeury, embed, cross_mask=padding_mask)
+            query = decoder_layer(query, embed, cross_mask=padding_mask)
-        output = qeury  # (B, N, C)
+        output = query  # (B, N, C)
 
         logits = self.cls(output)  # (B, N, C)
 
@@ -246,7 +246,7 @@ class ABINetHead(nn.Layer):
                 lengths = align_lengths
                 lengths = paddle.clip(
                     lengths, 2, self.max_length
-                )  # TODO:move to langauge model
+                )  # TODO:move to language model
                 l_feature, l_logits = self.language(tokens, lengths)
 
                 # alignment

ppocr/modeling/heads/rec_aster_head.py

@@ -216,7 +216,7 @@ class AttentionRecognitionHead(nn.Layer):
             )
             state = paddle.index_select(state, index=predecessors.squeeze(), axis=1)
 
-            # Update sequence socres and erase scores for <eos> symbol so that they aren't expanded
+            # Update sequence scores 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)

ppocr/modeling/heads/rec_nrtr_head.py

@@ -23,7 +23,7 @@ from paddle.nn.initializer import XavierNormal as xavier_normal_
 
 
 class Transformer(nn.Layer):
-    """A transformer model. User is able to modify the attributes as needed. The architechture
+    """A transformer model. User is able to modify the attributes as needed. The architecture
     is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer,
     Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and
     Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information

ppocr/modeling/heads/rec_parseq_head.py

@@ -386,7 +386,7 @@ class ParseQHead(nn.Layer):
                 )
                 logits = self.head(tgt_out)
 
-        # transfer to probility
+        # transfer to probability
         logits = F.softmax(logits, axis=-1)
 
         final_output = {"predict": logits}

ppocr/modeling/heads/rec_ppformulanet_head.py

@@ -990,7 +990,7 @@ class PPFormulaNet_Head(UniMERNetHead):
         if isinstance(decoder_start_token_id, list):
             if len(decoder_start_token_id) != batch_size:
                 raise ValueError(
-                    f"`decoder_start_token_id` expcted to have length {batch_size} but got {len(decoder_start_token_id)}"
+                    f"`decoder_start_token_id` expected to have length {batch_size} but got {len(decoder_start_token_id)}"
                 )
             decoder_input_ids_start = paddle.to_tensor(
                 decoder_start_token_id,

ppocr/modeling/heads/rec_srn_head.py

@@ -183,7 +183,7 @@ class GSRM(nn.Layer):
         # ===== GSRM Semantic reasoning block =====
         """
         This module is achieved through bi-transformers,
-        ngram_feature1 is the froward one, ngram_fetaure2 is the backward one
+        ngram_feature1 is the forward one, ngram_fetaure2 is the backward one
         """
         pad_idx = self.char_num
 

ppocr/modeling/heads/rec_unimernet_head.py

@@ -2175,7 +2175,7 @@ class UniMERNetHead(nn.Layer):
         if isinstance(decoder_start_token_id, list):
             if len(decoder_start_token_id) != batch_size:
                 raise ValueError(
-                    f"`decoder_start_token_id` expcted to have length {batch_size} but got {len(decoder_start_token_id)}"
+                    f"`decoder_start_token_id` expected to have length {batch_size} but got {len(decoder_start_token_id)}"
                 )
             decoder_input_ids_start = paddle.to_tensor(
                 decoder_start_token_id,

ppocr/modeling/heads/rec_visionlan_head.py

@@ -218,7 +218,7 @@ class Transformer_Encoder(nn.Layer):
         self.layer_norm = nn.LayerNorm(d_model, epsilon=1e-6)
 
     def forward(self, enc_output, src_mask, return_attns=False):
-        enc_output = self.dropout(self.position_enc(enc_output))  # position embeding
+        enc_output = self.dropout(self.position_enc(enc_output))  # position embedding
         for enc_layer in self.layer_stack:
             enc_output = enc_layer(enc_output, slf_attn_mask=src_mask)
         enc_output = self.layer_norm(enc_output)

ppocr/modeling/heads/table_master_head.py

@@ -154,7 +154,7 @@ class TableMasterHead(nn.Layer):
 
 class DecoderLayer(nn.Layer):
     """
-    Decoder is made of self attention, srouce attention and feed forward.
+    Decoder is made of self attention, source attention and feed forward.
     """
 
     def __init__(self, headers, d_model, dropout, d_ff):

ppocr/modeling/transforms/tps_spatial_transformer.py

@@ -115,7 +115,7 @@ class TPSSpatialTransformer(nn.Layer):
         # compute inverse matrix
         inverse_kernel = paddle.inverse(forward_kernel)
 
-        # create target cordinate matrix
+        # create target coordinate matrix
         HW = self.target_height * self.target_width
         target_coordinate = list(
             itertools.product(range(self.target_height), range(self.target_width))

ppocr/postprocess/sast_postprocess.py

@@ -61,7 +61,7 @@ class SASTPostProcess(object):
         """
         Transfer vertical point_pairs into poly point in clockwise.
         """
-        # constract poly
+        # construct poly
         point_num = len(point_pair_list) * 2
         point_list = [0] * point_num
         for idx, point_pair in enumerate(point_pair_list):

ppstructure/pdf2word/pdf2word.py

@@ -464,12 +464,12 @@ class APP_Image2Doc(QWidget):
             # Must set image path list and language before start
             self.output_dir = os.path.join(
                 os.path.dirname(self.imagePaths[0]), "output"
-            )  # output_dir shold be same as imagepath
+            )  # output_dir should be same as imagepath
             self._thread.setOutputDir(self.output_dir)
             self._thread.setImagePath(self.imagePaths)
             self._thread.setLang(lang)
             self._thread.setPDFParser(pdfParser)
-            # disenble buttons
+            # disable buttons
             self.openFileButton.setEnabled(False)
             self.startCNButton.setEnabled(False)
             self.startENButton.setEnabled(False)

ppstructure/table/convert_label2html.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-conver table label to html
+convert table label to html
 """
 
 import json
@@ -84,7 +84,7 @@ def convert(origin_gt_path, save_path):
     for img_name, gt in tqdm(data_dict.items()):
         html = gen_html(gt)
         save_pred_txt(img_name, html, save_path)
-    print("conver finish")
+    print("convert finish")
 
 
 def parse_args():

ppstructure/table/table_metric/table_metric.py

@@ -112,7 +112,7 @@ class TEDS(object):
     def __init__(self, structure_only=False, n_jobs=1, ignore_nodes=None):
         assert isinstance(n_jobs, int) and (
             n_jobs >= 1
-        ), "n_jobs must be an integer greather than 1"
+        ), "n_jobs must be an integer greater than 1"
         self.structure_only = structure_only
         self.n_jobs = n_jobs
         self.ignore_nodes = ignore_nodes

test_tipc/supplementary/data.py

@@ -64,7 +64,7 @@ class DecodeImage(object):
 
 
 class NormalizeImage(object):
-    """normalize image such as substract mean, divide std"""
+    """normalize image such as subtract mean, divide std"""
 
     def __init__(self, scale=None, mean=None, std=None, order="chw", **kwargs):
         if isinstance(scale, str):

test_tipc/supplementary/metric.py

@@ -24,7 +24,7 @@ def create_metric(
         mode(str): mode, train/valid
 
     Returns:
-        fetchs(dict): dict of measures
+        fetches(dict): dict of measures
     """
     # if architecture["name"] == "GoogLeNet":
     #     assert len(out) == 3, "GoogLeNet should have 3 outputs"
@@ -35,10 +35,10 @@ def create_metric(
     #         out = out[1]
     softmax_out = F.softmax(out)
 
-    fetchs = OrderedDict()
+    fetches = OrderedDict()
-    # set top1 to fetchs
+    # set top1 to fetches
     top1 = paddle.metric.accuracy(softmax_out, label=label, k=1)
-    # set topk to fetchs
+    # set topk to fetches
     k = min(topk, classes_num)
     topk = paddle.metric.accuracy(softmax_out, label=label, k=k)
 
@@ -53,8 +53,8 @@ def create_metric(
             / paddle.distributed.get_world_size()
         )
 
-    fetchs["top1"] = top1
+    fetches["top1"] = top1
     topk_name = "top{}".format(k)
-    fetchs[topk_name] = topk
+    fetches[topk_name] = topk
 
-    return fetchs
+    return fetches

tools/infer/utility.py

@@ -282,7 +282,7 @@ def create_predictor(args, mode, logger):
                     workspace_size=1 << 30,
                     precision_mode=precision,
                     max_batch_size=args.max_batch_size,
-                    min_subgraph_size=args.min_subgraph_size,  # skip the minmum trt subgraph
+                    min_subgraph_size=args.min_subgraph_size,  # skip the minimum trt subgraph
                     use_calib_mode=False,
                 )
 

tools/naive_sync_bn.py

@@ -71,7 +71,7 @@ class NaiveSyncBatchNorm(nn.BatchNorm2D):
             mean, meansqr = paddle.split(vec, [C, C])
             momentum = (
                 1 - self._momentum
-            )  # NOTE: paddle has reverse momentum defination
+            )  # NOTE: paddle has reverse momentum definition
         else:
             if B == 0:
                 vec = paddle.zeros([2 * C + 1], dtype=mean.dtype)