Merge branch 'develop' into add_examples

paddlers/datasets/clas_dataset.py

@@ -48,8 +48,6 @@ class ClasDataset(BaseDataset):
         self.file_list = list()
         self.labels = list()
 
-        # TODO：非None时，让用户跳转数据集分析生成label_list
-        # 不要在此处分析label file
         if label_list is not None:
             with open(label_list, encoding=get_encoding(label_list)) as f:
                 for line in f:

paddlers/datasets/coco.py

@@ -58,11 +58,11 @@ class COCODetDataset(BaseDataset):
                  allow_empty=False,
                  empty_ratio=1.):
         # matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
-        # or matplotlib.backends is imported for the first time
-        # pycocotools import matplotlib
+        # or matplotlib.backends is imported for the first time.
         import matplotlib
         matplotlib.use('Agg')
         from pycocotools.coco import COCO
+
         super(COCODetDataset, self).__init__(data_dir, label_list, transforms,
                                              num_workers, shuffle)
 
@@ -159,7 +159,7 @@ class COCODetDataset(BaseDataset):
             difficults = []
 
             for inst in instances:
-                # check gt bbox
+                # Check gt bbox
                 if inst.get('ignore', False):
                     continue
                 if 'bbox' not in inst.keys():
@@ -168,7 +168,7 @@ class COCODetDataset(BaseDataset):
                     if not any(np.array(inst['bbox'])):
                         continue
 
-                # read box
+                # Read the box
                 x1, y1, box_w, box_h = inst['bbox']
                 x2 = x1 + box_w
                 y2 = y1 + box_h

paddlers/datasets/seg_dataset.py

@@ -49,8 +49,6 @@ class SegDataset(BaseDataset):
         self.file_list = list()
         self.labels = list()
 
-        # TODO：非None时，让用户跳转数据集分析生成label_list
-        # 不要在此处分析label file
         if label_list is not None:
             with open(label_list, encoding=get_encoding(label_list)) as f:
                 for line in f:

paddlers/datasets/voc.py

@@ -58,8 +58,7 @@ class VOCDetDataset(BaseDataset):
                  allow_empty=False,
                  empty_ratio=1.):
         # matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
-        # or matplotlib.backends is imported for the first time
-        # pycocotools import matplotlib
+        # or matplotlib.backends is imported for the first time.
         import matplotlib
         matplotlib.use('Agg')
         from pycocotools.coco import COCO

paddlers/deploy/predictor.py

@@ -97,7 +97,7 @@ class Predictor(object):
             osp.join(self.model_dir, 'model.pdiparams'))
 
         if use_gpu:
-            # 设置GPU初始显存(单位M)和Device ID
+            # Set memory on GPUs (in MB) and device ID
             config.enable_use_gpu(200, gpu_id)
             config.switch_ir_optim(True)
             if use_trt:
@@ -127,7 +127,7 @@ class Predictor(object):
                     )
                 else:
                     try:
-                        # cache 10 different shapes for mkldnn to avoid memory leak
+                        # Cache 10 different shapes for mkldnn to avoid memory leak.
                         config.set_mkldnn_cache_capacity(10)
                         config.enable_mkldnn()
                         config.set_cpu_math_library_num_threads(mkl_thread_num)

paddlers/models/__init__.py

@@ -13,3 +13,6 @@
 # limitations under the License.
 
 from . import ppcls, ppdet, ppseg, ppgan
+import paddlers.models.ppseg.models.losses as seg_losses
+import paddlers.models.ppdet.modeling.losses as det_losses
+import paddlers.models.ppcls.loss as clas_losses

paddlers/rs_models/cd/changeformer.py

@@ -259,7 +259,7 @@ class EncoderTransformer_v3(nn.Layer):
         self.depths = depths
         self.embed_dims = embed_dims
 
-        # patch embedding definitions
+        # Patch embedding definitions
         self.patch_embed1 = OverlapPatchEmbed(
             img_size=img_size,
             patch_size=7,
@@ -406,7 +406,7 @@ class EncoderTransformer_v3(nn.Layer):
         B = x.shape[0]
         outs = []
 
-        # stage 1
+        # Stage 1
         x1, H1, W1 = self.patch_embed1(x)
         for i, blk in enumerate(self.block1):
             x1 = blk(x1, H1, W1)
@@ -416,7 +416,7 @@ class EncoderTransformer_v3(nn.Layer):
                 [0, 3, 1, 2])
         outs.append(x1)
 
-        # stage 2
+        # Stage 2
         x1, H1, W1 = self.patch_embed2(x1)
         for i, blk in enumerate(self.block2):
             x1 = blk(x1, H1, W1)
@@ -426,7 +426,7 @@ class EncoderTransformer_v3(nn.Layer):
                 [0, 3, 1, 2])
         outs.append(x1)
 
-        # stage 3
+        # Stage 3
         x1, H1, W1 = self.patch_embed3(x1)
         for i, blk in enumerate(self.block3):
             x1 = blk(x1, H1, W1)
@@ -436,7 +436,7 @@ class EncoderTransformer_v3(nn.Layer):
                 [0, 3, 1, 2])
         outs.append(x1)
 
-        # stage 4
+        # Stage 4
         x1, H1, W1 = self.patch_embed4(x1)
         for i, blk in enumerate(self.block4):
             x1 = blk(x1, H1, W1)
@@ -467,11 +467,11 @@ class DecoderTransformer_v3(nn.Layer):
                  decoder_softmax=False,
                  feature_strides=[2, 4, 8, 16]):
         super(DecoderTransformer_v3, self).__init__()
-        # assert
+
         assert len(feature_strides) == len(in_channels)
         assert min(feature_strides) == feature_strides[0]
 
-        # settings
+        # Settings
         self.feature_strides = feature_strides
         self.input_transform = input_transform
         self.in_index = in_index
@@ -491,7 +491,7 @@ class DecoderTransformer_v3(nn.Layer):
         self.linear_c1 = MLP(input_dim=c1_in_channels,
                              embed_dim=self.embedding_dim)
 
-        # convolutional Difference Layers
+        # Convolutional Difference Layers
         self.diff_c4 = conv_diff(
             in_channels=2 * self.embedding_dim, out_channels=self.embedding_dim)
         self.diff_c3 = conv_diff(
@@ -501,7 +501,7 @@ class DecoderTransformer_v3(nn.Layer):
         self.diff_c1 = conv_diff(
             in_channels=2 * self.embedding_dim, out_channels=self.embedding_dim)
 
-        # taking outputs from middle of the encoder
+        # Take outputs from middle of the encoder
         self.make_pred_c4 = make_prediction(
             in_channels=self.embedding_dim, out_channels=self.output_nc)
         self.make_pred_c3 = make_prediction(

paddlers/rs_models/cd/layers/pd_timm.py

@@ -38,7 +38,7 @@ class DropPath(nn.Layer):
         Returns:
             output: output tensor after drop path
         """
-        # if prob is 0 or eval mode, return original input
+        # If prob is 0 or is in eval mode, return original input.
         if self.drop_prob == 0. or not self.training:
             return inputs
         keep_prob = 1 - self.drop_prob
@@ -47,8 +47,8 @@ class DropPath(nn.Layer):
                                                )  # shape=(N, 1, 1, 1)
         random_tensor = keep_prob + paddle.rand(shape, dtype=inputs.dtype)
         random_tensor = random_tensor.floor()  # mask
-        output = inputs.divide(
-            keep_prob) * random_tensor  # divide to keep same output expectation
+        # Make division to keep output expectation same.
+        output = inputs.divide(keep_prob) * random_tensor
         return output
 
     def forward(self, inputs):

paddlers/rs_models/cd/stanet.py

@@ -263,7 +263,7 @@ class PAMBlock(nn.Layer):
 
     def _attend(self, query, key, value):
         energy = paddle.bmm(query.transpose((0, 2, 1)),
-                            key)  # batch matrix multiplication
+                            key)  # Batched matrix multiplication
         energy = (self.key_ch**(-0.5)) * energy
         attention = F.softmax(energy, axis=-1)
         out = paddle.bmm(value, attention.transpose((0, 2, 1)))

paddlers/rs_models/clas/condensenet_v2.py

@@ -87,11 +87,11 @@ class Conv(nn.Sequential):
 def ShuffleLayer(x, groups):
     batchsize, num_channels, height, width = x.shape
     channels_per_group = num_channels // groups
-    # reshape
+    # Reshape
     x = x.reshape((batchsize, groups, channels_per_group, height, width))
-    # transpose
+    # Transpose
     x = x.transpose((0, 2, 1, 3, 4))
-    # reshape
+    # Reshape
     x = x.reshape((batchsize, groups * channels_per_group, height, width))
     return x
 
@@ -99,11 +99,11 @@ def ShuffleLayer(x, groups):
 def ShuffleLayerTrans(x, groups):
     batchsize, num_channels, height, width = x.shape
     channels_per_group = num_channels // groups
-    # reshape
+    # Reshape
     x = x.reshape((batchsize, channels_per_group, groups, height, width))
-    # transpose
+    # Transpose
     x = x.transpose((0, 2, 1, 3, 4))
-    # reshape
+    # Reshape
     x = x.reshape((batchsize, channels_per_group * groups, height, width))
     return x
 
@@ -385,7 +385,7 @@ class CondenseNetV2(nn.Layer):
         return out
 
     def _initialize(self):
-        # initialize
+        # Initialize
         for m in self.sublayers():
             if isinstance(m, nn.Conv2D):
                 nn.initializer.KaimingNormal()(m.weight)

paddlers/rs_models/res/generators/rcan.py

@@ -1,4 +1,4 @@
-# base on https://github.com/kongdebug/RCAN-Paddle
+# Based on https://github.com/kongdebug/RCAN-Paddle
 import math
 
 import paddle
@@ -37,9 +37,9 @@ class MeanShift(nn.Conv2D):
 class CALayer(nn.Layer):
     def __init__(self, channel, reduction=16):
         super(CALayer, self).__init__()
-        # global average pooling: feature --> point
+        # Global average pooling: feature --> point
         self.avg_pool = nn.AdaptiveAvgPool2D(1)
-        # feature channel downscale and upscale --> channel weight
+        # Feature channel downscale and upscale --> channel weight
         self.conv_du = nn.Sequential(
             nn.Conv2D(
                 channel, channel // reduction, 1, padding=0, bias_attr=True),
@@ -157,10 +157,10 @@ class RCAN(nn.Layer):
         rgb_std = (1.0, 1.0, 1.0)
         self.sub_mean = MeanShift(rgb_range, rgb_mean, rgb_std)
 
-        # define head module
+        # Define head module
         modules_head = [conv(n_colors, n_feats, kernel_size)]
 
-        # define body module
+        # Define body module
         modules_body = [
             ResidualGroup(
                 conv, n_feats, kernel_size, reduction, act=act, res_scale= 1, n_resblocks=n_resblocks) \
@@ -168,7 +168,7 @@ class RCAN(nn.Layer):
 
         modules_body.append(conv(n_feats, n_feats, kernel_size))
 
-        # define tail module
+        # Define tail module
         modules_tail = [
             Upsampler(
                 conv, scale, n_feats, act=False),

paddlers/tasks/base.py

@@ -76,10 +76,10 @@ class BaseModel(metaclass=ModelMeta):
         self.eval_metrics = None
         self.best_accuracy = -1.
         self.best_model_epoch = -1
-        # 是否使用多卡间同步BatchNorm均值和方差
+        # Whether to use synchronized BN
         self.sync_bn = False
         self.status = 'Normal'
-        # 已完成迭代轮数，为恢复训练时的起始轮数
+        # The initial epoch when training is resumed
         self.completed_epochs = 0
         self.pruner = None
         self.pruning_ratios = None
@@ -239,7 +239,7 @@ class BaseModel(metaclass=ModelMeta):
                 mode='w') as f:
             yaml.dump(model_info, f)
 
-        # 评估结果保存
+        # Save evaluation details
         if hasattr(self, 'eval_details'):
             with open(osp.join(save_dir, 'eval_details.json'), 'w') as f:
                 json.dump(self.eval_details, f)
@@ -258,7 +258,7 @@ class BaseModel(metaclass=ModelMeta):
                     mode='w') as f:
                 yaml.dump(quant_info, f)
 
-        # 模型保存成功的标志
+        # Success flag
         open(osp.join(save_dir, '.success'), 'w').close()
         logging.info("Model saved in {}.".format(save_dir))
 
@@ -391,7 +391,7 @@ class BaseModel(metaclass=ModelMeta):
                 step_time_tic = step_time_toc
                 current_step += 1
 
-                # 每间隔log_interval_steps，输出loss信息
+                # Log loss info every log_interval_steps
                 if current_step % log_interval_steps == 0 and local_rank == 0:
                     if use_vdl:
                         for k, v in outputs.items():
@@ -399,7 +399,7 @@ class BaseModel(metaclass=ModelMeta):
                                 '{}-Metrics/Training(Step): {}'.format(
                                     task_id, k), v, current_step)
 
-                    # 估算剩余时间
+                    # Estimation remaining time
                     avg_step_time = train_step_time.avg()
                     eta = avg_step_time * (train_total_step - current_step)
                     if eval_dataset is not None:
@@ -427,14 +427,14 @@ class BaseModel(metaclass=ModelMeta):
                 self.net.set_state_dict(ema.apply())
             eval_epoch_tic = time.time()
 
-            # 每间隔save_interval_epochs, 在验证集上评估和对模型进行保存
+            # Every save_interval_epochs, evaluate and save the model
             if (i + 1) % save_interval_epochs == 0 or i == num_epochs - 1:
                 if eval_dataset is not None and eval_dataset.num_samples > 0:
                     eval_result = self.evaluate(
                         eval_dataset,
                         batch_size=eval_batch_size,
                         return_details=True)
-                    # 保存最优模型
+                    # Save the optimial model
                     if local_rank == 0:
                         self.eval_metrics, self.eval_details = eval_result
                         if use_vdl:
@@ -548,7 +548,7 @@ class BaseModel(metaclass=ModelMeta):
                 "Exported inference model does not support quantization-aware training.",
                 exit=True)
         if quant_config is None:
-            # default quantization configuration
+            # Default quantization configuration
             quant_config = {
                 # {None, 'PACT'}. Weight preprocess type. If None, no preprocessing is performed.
                 'weight_preprocess_type': None,
@@ -669,7 +669,7 @@ class BaseModel(metaclass=ModelMeta):
                 mode='w') as f:
             yaml.dump(pipeline_info, f)
 
-        # 模型保存成功的标志
+        # Success flag
         open(osp.join(save_dir, '.success'), 'w').close()
         logging.info("The inference model for deployment is saved in {}.".
                      format(save_dir))

paddlers/tasks/change_detector.py

@@ -25,9 +25,10 @@ import paddle.nn.functional as F
 from paddle.static import InputSpec
 
 import paddlers
+import paddlers.models.ppseg as ppseg
 import paddlers.rs_models.cd as cmcd
 import paddlers.utils.logging as logging
-import paddlers.models.ppseg as paddleseg
+from paddlers.models import seg_losses
 from paddlers.transforms import Resize, decode_image
 from paddlers.utils import get_single_card_bs, DisablePrint
 from paddlers.utils.checkpoint import seg_pretrain_weights_dict
@@ -45,6 +46,7 @@ class BaseChangeDetector(BaseModel):
                  model_name,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  **params):
         self.init_params = locals()
         if 'with_net' in self.init_params:
@@ -56,7 +58,7 @@ class BaseChangeDetector(BaseModel):
         self.model_name = model_name
         self.num_classes = num_classes
         self.use_mixed_loss = use_mixed_loss
-        self.losses = None
+        self.losses = losses
         self.labels = None
         if params.get('with_net', True):
             params.pop('with_net', None)
@@ -144,7 +146,7 @@ class BaseChangeDetector(BaseModel):
             origin_shape = [label.shape[-2:]]
             pred = self._postprocess(
                 pred, origin_shape, transforms=inputs[3])[0]  # NCHW
-            intersect_area, pred_area, label_area = paddleseg.utils.metrics.calculate_area(
+            intersect_area, pred_area, label_area = ppseg.utils.metrics.calculate_area(
                 pred, label, self.num_classes)
             outputs['intersect_area'] = intersect_area
             outputs['pred_area'] = pred_area
@@ -178,16 +180,13 @@ class BaseChangeDetector(BaseModel):
         if isinstance(self.use_mixed_loss, bool):
             if self.use_mixed_loss:
                 losses = [
-                    paddleseg.models.CrossEntropyLoss(),
-                    paddleseg.models.LovaszSoftmaxLoss()
+                    seg_losses.CrossEntropyLoss(),
+                    seg_losses.LovaszSoftmaxLoss()
                 ]
                 coef = [.8, .2]
-                loss_type = [
-                    paddleseg.models.MixedLoss(
-                        losses=losses, coef=coef),
-                ]
+                loss_type = [seg_losses.MixedLoss(losses=losses, coef=coef), ]
             else:
-                loss_type = [paddleseg.models.CrossEntropyLoss()]
+                loss_type = [seg_losses.CrossEntropyLoss()]
         else:
             losses, coef = list(zip(*self.use_mixed_loss))
             if not set(losses).issubset(
@@ -195,11 +194,8 @@ class BaseChangeDetector(BaseModel):
                 raise ValueError(
                     "Only 'CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss' are supported."
                 )
-            losses = [getattr(paddleseg.models, loss)() for loss in losses]
-            loss_type = [
-                paddleseg.models.MixedLoss(
-                    losses=losses, coef=list(coef))
-            ]
+            losses = [getattr(seg_losses, loss)() for loss in losses]
+            loss_type = [seg_losses.MixedLoss(losses=losses, coef=list(coef))]
         loss_coef = [1.0]
         losses = {'types': loss_type, 'coef': loss_coef}
         return losses
@@ -492,13 +488,13 @@ class BaseChangeDetector(BaseModel):
                     pred_area_all = pred_area_all + pred_area
                     label_area_all = label_area_all + label_area
                     conf_mat_all.append(conf_mat)
-        class_iou, miou = paddleseg.utils.metrics.mean_iou(
+        class_iou, miou = ppseg.utils.metrics.mean_iou(
             intersect_area_all, pred_area_all, label_area_all)
         # TODO 确认是按oacc还是macc
-        class_acc, oacc = paddleseg.utils.metrics.accuracy(intersect_area_all,
-                                                           pred_area_all)
-        kappa = paddleseg.utils.metrics.kappa(intersect_area_all, pred_area_all,
-                                              label_area_all)
+        class_acc, oacc = ppseg.utils.metrics.accuracy(intersect_area_all,
+                                                       pred_area_all)
+        kappa = ppseg.utils.metrics.kappa(intersect_area_all, pred_area_all,
+                                          label_area_all)
         category_f1score = metrics.f1_score(intersect_area_all, pred_area_all,
                                             label_area_all)
 
@@ -643,7 +639,7 @@ class BaseChangeDetector(BaseModel):
                     int(xoff), int(yoff), xsize, ysize).transpose((1, 2, 0))
                 im2 = src2_data.ReadAsArray(
                     int(xoff), int(yoff), xsize, ysize).transpose((1, 2, 0))
-                # fill
+                # Fill
                 h, w = im1.shape[:2]
                 im1_fill = np.zeros(
                     (block_size[1], block_size[0], bands), dtype=im1.dtype)
@@ -651,10 +647,10 @@ class BaseChangeDetector(BaseModel):
                 im1_fill[:h, :w, :] = im1
                 im2_fill[:h, :w, :] = im2
                 im_fill = (im1_fill, im2_fill)
-                # predict
+                # Predict
                 pred = self.predict(im_fill,
                                     transforms)["label_map"].astype("uint8")
-                # overlap
+                # Overlap
                 rd_block = band.ReadAsArray(int(xoff), int(yoff), xsize, ysize)
                 mask = (rd_block == pred[:h, :w]) | (rd_block == 255)
                 temp = pred[:h, :w].copy()
@@ -816,11 +812,17 @@ class BaseChangeDetector(BaseModel):
             raise TypeError(
                 "`transforms.arrange` must be an ArrangeChangeDetector object.")
 
+    def set_losses(self, losses, weights=None):
+        if weights is None:
+            weights = [1. for _ in range(len(losses))]
+        self.losses = {'types': losses, 'coef': weights}
+
 
 class CDNet(BaseChangeDetector):
     def __init__(self,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  in_channels=6,
                  **params):
         params.update({'in_channels': in_channels})
@@ -828,6 +830,7 @@ class CDNet(BaseChangeDetector):
             model_name='CDNet',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
@@ -835,6 +838,7 @@ class FCEarlyFusion(BaseChangeDetector):
     def __init__(self,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  in_channels=6,
                  use_dropout=False,
                  **params):
@@ -843,6 +847,7 @@ class FCEarlyFusion(BaseChangeDetector):
             model_name='FCEarlyFusion',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
@@ -850,6 +855,7 @@ class FCSiamConc(BaseChangeDetector):
     def __init__(self,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  in_channels=3,
                  use_dropout=False,
                  **params):
@@ -858,6 +864,7 @@ class FCSiamConc(BaseChangeDetector):
             model_name='FCSiamConc',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
@@ -865,6 +872,7 @@ class FCSiamDiff(BaseChangeDetector):
     def __init__(self,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  in_channels=3,
                  use_dropout=False,
                  **params):
@@ -873,6 +881,7 @@ class FCSiamDiff(BaseChangeDetector):
             model_name='FCSiamDiff',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
@@ -880,6 +889,7 @@ class STANet(BaseChangeDetector):
     def __init__(self,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  in_channels=3,
                  att_type='BAM',
                  ds_factor=1,
@@ -893,6 +903,7 @@ class STANet(BaseChangeDetector):
             model_name='STANet',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
@@ -900,6 +911,7 @@ class BIT(BaseChangeDetector):
     def __init__(self,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  in_channels=3,
                  backbone='resnet18',
                  n_stages=4,
@@ -931,6 +943,7 @@ class BIT(BaseChangeDetector):
             model_name='BIT',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
@@ -938,6 +951,7 @@ class SNUNet(BaseChangeDetector):
     def __init__(self,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  in_channels=3,
                  width=32,
                  **params):
@@ -946,6 +960,7 @@ class SNUNet(BaseChangeDetector):
             model_name='SNUNet',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
@@ -953,6 +968,7 @@ class DSIFN(BaseChangeDetector):
     def __init__(self,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  use_dropout=False,
                  **params):
         params.update({'use_dropout': use_dropout})
@@ -960,13 +976,14 @@ class DSIFN(BaseChangeDetector):
             model_name='DSIFN',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
     def default_loss(self):
         if self.use_mixed_loss is False:
             return {
                 # XXX: make sure the shallow copy works correctly here.
-                'types': [paddleseg.models.CrossEntropyLoss()] * 5,
+                'types': [seg_losses.CrossEntropyLoss()] * 5,
                 'coef': [1.0] * 5
             }
         else:
@@ -979,6 +996,7 @@ class DSAMNet(BaseChangeDetector):
     def __init__(self,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  in_channels=3,
                  ca_ratio=8,
                  sa_kernel=7,
@@ -992,14 +1010,15 @@ class DSAMNet(BaseChangeDetector):
             model_name='DSAMNet',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
     def default_loss(self):
         if self.use_mixed_loss is False:
             return {
                 'types': [
-                    paddleseg.models.CrossEntropyLoss(),
-                    paddleseg.models.DiceLoss(), paddleseg.models.DiceLoss()
+                    seg_losses.CrossEntropyLoss(), seg_losses.DiceLoss(),
+                    seg_losses.DiceLoss()
                 ],
                 'coef': [1.0, 0.05, 0.05]
             }
@@ -1013,6 +1032,7 @@ class ChangeStar(BaseChangeDetector):
     def __init__(self,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  mid_channels=256,
                  inner_channels=16,
                  num_convs=4,
@@ -1028,13 +1048,14 @@ class ChangeStar(BaseChangeDetector):
             model_name='ChangeStar',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
     def default_loss(self):
         if self.use_mixed_loss is False:
             return {
                 # XXX: make sure the shallow copy works correctly here.
-                'types': [paddleseg.models.CrossEntropyLoss()] * 4,
+                'types': [seglosses.CrossEntropyLoss()] * 4,
                 'coef': [1.0] * 4
             }
         else:

paddlers/tasks/classifier.py

@@ -22,17 +22,17 @@ import paddle
 import paddle.nn.functional as F
 from paddle.static import InputSpec
 
-import paddlers.models.ppcls as paddleclas
-import paddlers.rs_models.clas as cmcls
 import paddlers
-from paddlers.utils import get_single_card_bs, DisablePrint
+import paddlers.models.ppcls as ppcls
+import paddlers.rs_models.clas as cmcls
 import paddlers.utils.logging as logging
-from .base import BaseModel
+from paddlers.utils import get_single_card_bs, DisablePrint
 from paddlers.models.ppcls.metric import build_metrics
-from paddlers.models.ppcls.loss import build_loss
+from paddlers.models import clas_losses
 from paddlers.models.ppcls.data.postprocess import build_postprocess
 from paddlers.utils.checkpoint import cls_pretrain_weights_dict
 from paddlers.transforms import Resize, decode_image
+from .base import BaseModel
 
 __all__ = [
     "ResNet50_vd", "MobileNetV3_small_x1_0", "HRNet_W18_C", "CondenseNetV2_b"
@@ -45,12 +45,13 @@ class BaseClassifier(BaseModel):
                  in_channels=3,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  **params):
         self.init_params = locals()
         if 'with_net' in self.init_params:
             del self.init_params['with_net']
         super(BaseClassifier, self).__init__('classifier')
-        if not hasattr(paddleclas.arch.backbone, model_name) and \
+        if not hasattr(ppcls.arch.backbone, model_name) and \
            not hasattr(cmcls, model_name):
             raise ValueError("ERROR: There is no model named {}.".format(
                 model_name))
@@ -59,7 +60,7 @@ class BaseClassifier(BaseModel):
         self.num_classes = num_classes
         self.use_mixed_loss = use_mixed_loss
         self.metrics = None
-        self.losses = None
+        self.losses = losses
         self.labels = None
         self._postprocess = None
         if params.get('with_net', True):
@@ -69,7 +70,7 @@ class BaseClassifier(BaseModel):
 
     def build_net(self, **params):
         with paddle.utils.unique_name.guard():
-            model = dict(paddleclas.arch.backbone.__dict__,
+            model = dict(ppcls.arch.backbone.__dict__,
                          **cmcls.__dict__)[self.model_name]
             # TODO: Determine whether there is in_channels
             try:
@@ -145,7 +146,7 @@ class BaseClassifier(BaseModel):
     def default_loss(self):
         # TODO: use mixed loss and other loss
         default_config = [{"CELoss": {"weight": 1.0}}]
-        return build_loss(default_config)
+        return clas_losses.build_loss(default_config)
 
     def default_optimizer(self,
                           parameters,
@@ -556,36 +557,56 @@ class BaseClassifier(BaseModel):
 
 
 class ResNet50_vd(BaseClassifier):
-    def __init__(self, num_classes=2, use_mixed_loss=False, **params):
+    def __init__(self,
+                 num_classes=2,
+                 use_mixed_loss=False,
+                 losses=None,
+                 **params):
         super(ResNet50_vd, self).__init__(
             model_name='ResNet50_vd',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
 class MobileNetV3_small_x1_0(BaseClassifier):
-    def __init__(self, num_classes=2, use_mixed_loss=False, **params):
+    def __init__(self,
+                 num_classes=2,
+                 use_mixed_loss=False,
+                 losses=None,
+                 **params):
         super(MobileNetV3_small_x1_0, self).__init__(
             model_name='MobileNetV3_small_x1_0',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
 class HRNet_W18_C(BaseClassifier):
-    def __init__(self, num_classes=2, use_mixed_loss=False, **params):
+    def __init__(self,
+                 num_classes=2,
+                 use_mixed_loss=False,
+                 losses=None,
+                 **params):
         super(HRNet_W18_C, self).__init__(
             model_name='HRNet_W18_C',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
 class CondenseNetV2_b(BaseClassifier):
-    def __init__(self, num_classes=2, use_mixed_loss=False, **params):
+    def __init__(self,
+                 num_classes=2,
+                 use_mixed_loss=False,
+                 losses=None,
+                 **params):
         super(CondenseNetV2_b, self).__init__(
             model_name='CondenseNetV2_b',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)

paddlers/tasks/object_detector.py

@@ -12,8 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from __future__ import absolute_import
-
 import collections
 import copy
 import os
@@ -23,18 +21,18 @@ import numpy as np
 import paddle
 from paddle.static import InputSpec
 
+import paddlers
 import paddlers.models.ppdet as ppdet
 from paddlers.models.ppdet.modeling.proposal_generator.target_layer import BBoxAssigner, MaskAssigner
-import paddlers
-import paddlers.utils.logging as logging
 from paddlers.transforms import decode_image
 from paddlers.transforms.operators import _NormalizeBox, _PadBox, _BboxXYXY2XYWH, Resize, Pad
 from paddlers.transforms.batch_operators import BatchCompose, BatchRandomResize, BatchRandomResizeByShort, \
     _BatchPad, _Gt2YoloTarget
-from .base import BaseModel
-from .utils.det_metrics import VOCMetric, COCOMetric
 from paddlers.models.ppdet.optimizer import ModelEMA
+import paddlers.utils.logging as logging
 from paddlers.utils.checkpoint import det_pretrain_weights_dict
+from .base import BaseModel
+from .utils.det_metrics import VOCMetric, COCOMetric
 
 __all__ = [
     "YOLOv3", "FasterRCNN", "PPYOLO", "PPYOLOTiny", "PPYOLOv2", "MaskRCNN"
@@ -291,7 +289,7 @@ class BaseDetector(BaseModel):
         train_dataset.batch_transforms = self._compose_batch_transform(
             train_dataset.transforms, mode='train')
 
-        # build optimizer if not defined
+        # Build optimizer if not defined
         if optimizer is None:
             num_steps_each_epoch = len(train_dataset) // train_batch_size
             self.optimizer = self.default_optimizer(
@@ -305,7 +303,7 @@ class BaseDetector(BaseModel):
         else:
             self.optimizer = optimizer
 
-        # initiate weights
+        # Initiate weights
         if pretrain_weights is not None and not osp.exists(pretrain_weights):
             if pretrain_weights not in det_pretrain_weights_dict['_'.join(
                 [self.model_name, self.backbone_name])]:
@@ -335,7 +333,7 @@ class BaseDetector(BaseModel):
             ema = ModelEMA(model=self.net, decay=.9998, use_thres_step=True)
         else:
             ema = None
-        # start train loop
+        # Start train loop
         self.train_loop(
             num_epochs=num_epochs,
             train_dataset=train_dataset,
@@ -822,7 +820,7 @@ class PicoDet(BaseDetector):
             if isinstance(op, (BatchRandomResize, BatchRandomResizeByShort)):
                 if mode != 'train':
                     raise ValueError(
-                        "{} cannot be present in the {} transforms. ".format(
+                        "{} cannot be present in the {} transforms.".format(
                             op.__class__.__name__, mode) +
                         "Please check the {} transforms.".format(mode))
                 custom_batch_transforms.insert(0, copy.deepcopy(op))

paddlers/tasks/segmenter.py

@@ -23,15 +23,16 @@ import paddle
 import paddle.nn.functional as F
 from paddle.static import InputSpec
 
-import paddlers.models.ppseg as paddleseg
-import paddlers.rs_models.seg as cmseg
 import paddlers
-from paddlers.utils import get_single_card_bs, DisablePrint
+import paddlers.models.ppseg as ppseg
+import paddlers.rs_models.seg as cmseg
 import paddlers.utils.logging as logging
+from paddlers.models import seg_losses
+from paddlers.transforms import Resize, decode_image
+from paddlers.utils import get_single_card_bs, DisablePrint
+from paddlers.utils.checkpoint import seg_pretrain_weights_dict
 from .base import BaseModel
 from .utils import seg_metrics as metrics
-from paddlers.utils.checkpoint import seg_pretrain_weights_dict
-from paddlers.transforms import Resize, decode_image
 
 __all__ = ["UNet", "DeepLabV3P", "FastSCNN", "HRNet", "BiSeNetV2", "FarSeg"]
 
@@ -41,19 +42,20 @@ class BaseSegmenter(BaseModel):
                  model_name,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  **params):
         self.init_params = locals()
         if 'with_net' in self.init_params:
             del self.init_params['with_net']
         super(BaseSegmenter, self).__init__('segmenter')
-        if not hasattr(paddleseg.models, model_name) and \
+        if not hasattr(ppseg.models, model_name) and \
            not hasattr(cmseg, model_name):
             raise ValueError("ERROR: There is no model named {}.".format(
                 model_name))
         self.model_name = model_name
         self.num_classes = num_classes
         self.use_mixed_loss = use_mixed_loss
-        self.losses = None
+        self.losses = losses
         self.labels = None
         if params.get('with_net', True):
             params.pop('with_net', None)
@@ -63,9 +65,8 @@ class BaseSegmenter(BaseModel):
     def build_net(self, **params):
         # TODO: when using paddle.utils.unique_name.guard,
         # DeepLabv3p and HRNet will raise a error
-        net = dict(paddleseg.models.__dict__,
-                   **cmseg.__dict__)[self.model_name](
-                       num_classes=self.num_classes, **params)
+        net = dict(ppseg.models.__dict__, **cmseg.__dict__)[self.model_name](
+            num_classes=self.num_classes, **params)
         return net
 
     def _fix_transforms_shape(self, image_shape):
@@ -143,7 +144,7 @@ class BaseSegmenter(BaseModel):
             origin_shape = [label.shape[-2:]]
             pred = self._postprocess(
                 pred, origin_shape, transforms=inputs[2])[0]  # NCHW
-            intersect_area, pred_area, label_area = paddleseg.utils.metrics.calculate_area(
+            intersect_area, pred_area, label_area = ppseg.utils.metrics.calculate_area(
                 pred, label, self.num_classes)
             outputs['intersect_area'] = intersect_area
             outputs['pred_area'] = pred_area
@@ -161,16 +162,13 @@ class BaseSegmenter(BaseModel):
         if isinstance(self.use_mixed_loss, bool):
             if self.use_mixed_loss:
                 losses = [
-                    paddleseg.models.CrossEntropyLoss(),
-                    paddleseg.models.LovaszSoftmaxLoss()
+                    seg_losses.CrossEntropyLoss(),
+                    seg_losses.LovaszSoftmaxLoss()
                 ]
                 coef = [.8, .2]
-                loss_type = [
-                    paddleseg.models.MixedLoss(
-                        losses=losses, coef=coef),
-                ]
+                loss_type = [seg_losses.MixedLoss(losses=losses, coef=coef), ]
             else:
-                loss_type = [paddleseg.models.CrossEntropyLoss()]
+                loss_type = [seg_losses.CrossEntropyLoss()]
         else:
             losses, coef = list(zip(*self.use_mixed_loss))
             if not set(losses).issubset(
@@ -178,11 +176,8 @@ class BaseSegmenter(BaseModel):
                 raise ValueError(
                     "Only 'CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss' are supported."
                 )
-            losses = [getattr(paddleseg.models, loss)() for loss in losses]
-            loss_type = [
-                paddleseg.models.MixedLoss(
-                    losses=losses, coef=list(coef))
-            ]
+            losses = [getattr(seg_losses, loss)() for loss in losses]
+            loss_type = [seg_losses.MixedLoss(losses=losses, coef=list(coef))]
         if self.model_name == 'FastSCNN':
             loss_type *= 2
             loss_coef = [1.0, 0.4]
@@ -475,13 +470,13 @@ class BaseSegmenter(BaseModel):
                     pred_area_all = pred_area_all + pred_area
                     label_area_all = label_area_all + label_area
                     conf_mat_all.append(conf_mat)
-        class_iou, miou = paddleseg.utils.metrics.mean_iou(
+        class_iou, miou = ppseg.utils.metrics.mean_iou(
             intersect_area_all, pred_area_all, label_area_all)
         # TODO 确认是按oacc还是macc
-        class_acc, oacc = paddleseg.utils.metrics.accuracy(intersect_area_all,
-                                                           pred_area_all)
-        kappa = paddleseg.utils.metrics.kappa(intersect_area_all, pred_area_all,
-                                              label_area_all)
+        class_acc, oacc = ppseg.utils.metrics.accuracy(intersect_area_all,
+                                                       pred_area_all)
+        kappa = ppseg.utils.metrics.kappa(intersect_area_all, pred_area_all,
+                                          label_area_all)
         category_f1score = metrics.f1_score(intersect_area_all, pred_area_all,
                                             label_area_all)
         eval_metrics = OrderedDict(
@@ -613,15 +608,15 @@ class BaseSegmenter(BaseModel):
                     ysize = int(height - yoff)
                 im = src_data.ReadAsArray(int(xoff), int(yoff), xsize,
                                           ysize).transpose((1, 2, 0))
-                # fill
+                # Fill
                 h, w = im.shape[:2]
                 im_fill = np.zeros(
                     (block_size[1], block_size[0], bands), dtype=im.dtype)
                 im_fill[:h, :w, :] = im
-                # predict
+                # Predict
                 pred = self.predict(im_fill,
                                     transforms)["label_map"].astype("uint8")
-                # overlap
+                # Overlap
                 rd_block = band.ReadAsArray(int(xoff), int(yoff), xsize, ysize)
                 mask = (rd_block == pred[:h, :w]) | (rd_block == 255)
                 temp = pred[:h, :w].copy()
@@ -778,12 +773,18 @@ class BaseSegmenter(BaseModel):
             raise TypeError(
                 "`transforms.arrange` must be an ArrangeSegmenter object.")
 
+    def set_losses(self, losses, weights=None):
+        if weights is None:
+            weights = [1. for _ in range(len(losses))]
+        self.losses = {'types': losses, 'coef': weights}
+
 
 class UNet(BaseSegmenter):
     def __init__(self,
                  input_channel=3,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  use_deconv=False,
                  align_corners=False,
                  **params):
@@ -796,6 +797,7 @@ class UNet(BaseSegmenter):
             input_channel=input_channel,
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
@@ -805,6 +807,7 @@ class DeepLabV3P(BaseSegmenter):
                  num_classes=2,
                  backbone='ResNet50_vd',
                  use_mixed_loss=False,
+                 losses=None,
                  output_stride=8,
                  backbone_indices=(0, 3),
                  aspp_ratios=(1, 12, 24, 36),
@@ -818,7 +821,7 @@ class DeepLabV3P(BaseSegmenter):
                 "{'ResNet50_vd', 'ResNet101_vd'}.".format(backbone))
         if params.get('with_net', True):
             with DisablePrint():
-                backbone = getattr(paddleseg.models, backbone)(
+                backbone = getattr(ppseg.models, backbone)(
                     input_channel=input_channel, output_stride=output_stride)
         else:
             backbone = None
@@ -833,6 +836,7 @@ class DeepLabV3P(BaseSegmenter):
             model_name='DeepLabV3P',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
@@ -840,6 +844,7 @@ class FastSCNN(BaseSegmenter):
     def __init__(self,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  align_corners=False,
                  **params):
         params.update({'align_corners': align_corners})
@@ -847,6 +852,7 @@ class FastSCNN(BaseSegmenter):
             model_name='FastSCNN',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
@@ -855,6 +861,7 @@ class HRNet(BaseSegmenter):
                  num_classes=2,
                  width=48,
                  use_mixed_loss=False,
+                 losses=None,
                  align_corners=False,
                  **params):
         if width not in (18, 48):
@@ -864,7 +871,7 @@ class HRNet(BaseSegmenter):
         self.backbone_name = 'HRNet_W{}'.format(width)
         if params.get('with_net', True):
             with DisablePrint():
-                backbone = getattr(paddleseg.models, self.backbone_name)(
+                backbone = getattr(ppseg.models, self.backbone_name)(
                     align_corners=align_corners)
         else:
             backbone = None
@@ -874,6 +881,7 @@ class HRNet(BaseSegmenter):
             model_name='FCN',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
         self.model_name = 'HRNet'
 
@@ -882,6 +890,7 @@ class BiSeNetV2(BaseSegmenter):
     def __init__(self,
                  num_classes=2,
                  use_mixed_loss=False,
+                 losses=None,
                  align_corners=False,
                  **params):
         params.update({'align_corners': align_corners})
@@ -889,13 +898,19 @@ class BiSeNetV2(BaseSegmenter):
             model_name='BiSeNetV2',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)
 
 
 class FarSeg(BaseSegmenter):
-    def __init__(self, num_classes=2, use_mixed_loss=False, **params):
+    def __init__(self,
+                 num_classes=2,
+                 use_mixed_loss=False,
+                 losses=None,
+                 **params):
         super(FarSeg, self).__init__(
             model_name='FarSeg',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            losses=losses,
             **params)

paddlers/tasks/utils/det_metrics/coco_utils.py

@@ -142,20 +142,19 @@ def cocoapi_eval(anns,
         logging.info('Per-category of {} AP: \n{}'.format(style, table.table))
         logging.info("per-category PR curve has output to {} folder.".format(
             style + '_pr_curve'))
-    # flush coco evaluation result
+    # Flush coco evaluation result
     sys.stdout.flush()
     return coco_eval.stats
 
 
 def loadRes(coco_obj, anns):
     # This function has the same functionality as pycocotools.COCO.loadRes,
-    # except that the input anns is list of results rather than a json file.
+    # excepting that the input anns is list of results rather than a json file.
     # Refer to
     # https://github.com/cocodataset/cocoapi/blob/8c9bcc3cf640524c4c20a9c40e89cb6a2f2fa0e9/PythonAPI/pycocotools/coco.py#L305,
 
     # matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
-    # or matplotlib.backends is imported for the first time
-    # pycocotools import matplotlib
+    # or matplotlib.backends is imported for the first time.
     import matplotlib
     matplotlib.use('Agg')
     from pycocotools.coco import COCO
@@ -192,7 +191,7 @@ def loadRes(coco_obj, anns):
         res.dataset['categories'] = copy.deepcopy(coco_obj.dataset[
             'categories'])
         for id, ann in enumerate(anns):
-            # now only support compressed RLE format as segmentation results
+            # Now only supports compressed RLE format as segmentation results.
             ann['area'] = maskUtils.area(ann['segmentation'])
             if not 'bbox' in ann:
                 ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
@@ -291,8 +290,7 @@ def analyze_individual_category(k, cocoDt, cocoGt, catId, iou_type, areas=None):
     """
 
     # matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
-    # or matplotlib.backends is imported for the first time
-    # pycocotools import matplotlib
+    # or matplotlib.backends is imported for the first time.
     import matplotlib
     matplotlib.use('Agg')
     from pycocotools.coco import COCO
@@ -311,7 +309,7 @@ def analyze_individual_category(k, cocoDt, cocoGt, catId, iou_type, areas=None):
             select_dt_anns.append(ann)
     dt.dataset['annotations'] = select_dt_anns
     dt.createIndex()
-    # compute precision but ignore superclass confusion
+    # Compute precision but ignore superclass confusion.
     gt = copy.deepcopy(cocoGt)
     child_catIds = gt.getCatIds(supNms=[nm['supercategory']])
     for idx, ann in enumerate(gt.dataset['annotations']):
@@ -379,8 +377,7 @@ def coco_error_analysis(eval_details_file=None,
 
     import multiprocessing as mp
     # matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
-    # or matplotlib.backends is imported for the first time
-    # pycocotools import matplotlib
+    # or matplotlib.backends is imported for the first time.
     import matplotlib
     matplotlib.use('Agg')
     from pycocotools.coco import COCO
@@ -446,11 +443,11 @@ def coco_error_analysis(eval_details_file=None,
             assert k == analyze_result[0], ""
             ps_supercategory = analyze_result[1]['ps_supercategory']
             ps_allcategory = analyze_result[1]['ps_allcategory']
-            # compute precision but ignore superclass confusion
+            # Compute precision but ignore superclass confusion.
             ps[3, :, k, :, :] = ps_supercategory
-            # compute precision but ignore any class confusion
+            # Compute precision but ignore any class confusion.
             ps[4, :, k, :, :] = ps_allcategory
-            # fill in background and false negative errors and plot
+            # Fill in background and false negative errors and plot.
             ps[ps == -1] = 0
             ps[5, :, k, :, :] = ps[4, :, k, :, :] > 0
             ps[6, :, k, :, :] = 1.0

paddlers/tasks/utils/det_metrics/metrics.py

@@ -41,11 +41,11 @@ class Metric(paddle.metric.Metric):
     # paddle.metric.Metric defined :metch:`update`, :meth:`accumulate`
     # :metch:`reset`, in ppdet, we also need following 2 methods:
 
-    # abstract method for logging metric results
+    # Abstract method for logging metric results
     def log(self):
         pass
 
-    # abstract method for getting metric results
+    # Abstract method for getting metric results
     def get_results(self):
         pass
 
@@ -162,7 +162,7 @@ class COCOMetric(Metric):
         self.reset()
 
     def reset(self):
-        # only bbox and mask evaluation support currently
+        # Only bbox and mask evaluation are supported currently.
         self.details = {
             'gt': copy.deepcopy(self.coco_gt.dataset),
             'bbox': [],
@@ -172,7 +172,7 @@ class COCOMetric(Metric):
 
     def update(self, inputs, outputs):
         outs = {}
-        # outputs Tensor -> numpy.ndarray
+        # Tensor -> numpy.ndarray
         for k, v in outputs.items():
             outs[k] = v.numpy() if isinstance(v, paddle.Tensor) else v
 

paddlers/tasks/utils/visualize.py

@@ -309,7 +309,7 @@ def draw_pr_curve(eval_details_file=None,
         aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]
         mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]
         if ap == 1:
-            # dimension of precision: [TxRxKxAxM]
+            # Dimension of precision: [TxRxKxAxM]
             s = coco_gt.eval['precision']
             # IoU
             if iouThr is not None:
@@ -317,7 +317,7 @@ def draw_pr_curve(eval_details_file=None,
                 s = s[t]
             s = s[:, :, :, aind, mind]
         else:
-            # dimension of recall: [TxKxAxM]
+            # Dimension of recall: [TxKxAxM]
             s = coco_gt.eval['recall']
             if iouThr is not None:
                 t = np.where(iouThr == p.iouThrs)[0]

paddlers/transforms/batch_operators.py

@@ -229,7 +229,7 @@ class _Gt2YoloTarget(Transform):
                     if gw <= 0. or gh <= 0. or score <= 0.:
                         continue
 
-                    # find best match anchor index
+                    # Find best matched anchor index
                     best_iou = 0.
                     best_idx = -1
                     for an_idx in range(an_hw.shape[0]):
@@ -243,8 +243,8 @@ class _Gt2YoloTarget(Transform):
                     gi = int(gx * grid_w)
                     gj = int(gy * grid_h)
 
-                    # gtbox should be regresed in this layes if best match
-                    # anchor index in anchor mask of this layer
+                    # gtbox should be regressed in this layer if best matched
+                    # anchor index is in the anchor mask of this layer.
                     if best_idx in mask:
                         best_n = mask.index(best_idx)
 
@@ -257,14 +257,14 @@ class _Gt2YoloTarget(Transform):
                             gh * h / self.anchors[best_idx][1])
                         target[best_n, 4, gj, gi] = 2.0 - gw * gh
 
-                        # objectness record gt_score
+                        # Record gt_score
                         target[best_n, 5, gj, gi] = score
 
-                        # classification
+                        # Do classification
                         target[best_n, 6 + cls, gj, gi] = 1.
 
                     # For non-matched anchors, calculate the target if the iou
-                    # between anchor and gt is larger than iou_thresh
+                    # between anchor and gt is larger than iou_thresh.
                     if self.iou_thresh < 1:
                         for idx, mask_i in enumerate(mask):
                             if mask_i == best_idx: continue
@@ -282,14 +282,14 @@ class _Gt2YoloTarget(Transform):
                                     gh * h / self.anchors[mask_i][1])
                                 target[idx, 4, gj, gi] = 2.0 - gw * gh
 
-                                # objectness record gt_score
+                                # Record gt_score
                                 target[idx, 5, gj, gi] = score
 
-                                # classification
+                                # Do classification
                                 target[idx, 5 + cls, gj, gi] = 1.
                 sample['target{}'.format(i)] = target
 
-            # remove useless gt_class and gt_score after target calculated
+            # Remove useless gt_class and gt_score items after target has been calculated.
             sample.pop('gt_class')
             sample.pop('gt_score')
 

paddlers/transforms/functions.py

@@ -55,7 +55,6 @@ def center_crop(im, crop_size=224):
     return im
 
 
-# region flip
 def img_flip(im, method=0):
     """
     Flip an image. 
@@ -168,10 +167,6 @@ def lt2rb_flip(im):
     return im
 
 
-# endregion
-
-
-# region rotation
 def img_simple_rotate(im, method=0):
     """
     Rotate an image. 
@@ -255,9 +250,6 @@ def rot_270(im):
     return im
 
 
-# endregion
-
-
 def rgb2bgr(im):
     return im[:, :, ::-1]
 
@@ -405,7 +397,7 @@ def to_uint8(im, is_linear=False):
     # 2% linear stretch
     def _two_percent_linear(image, max_out=255, min_out=0):
         def _gray_process(gray, maxout=max_out, minout=min_out):
-            # get the corresponding gray level at 98% histogram
+            # Get the corresponding gray level at 98% in the histogram.
             high_value = np.percentile(gray, 98)
             low_value = np.percentile(gray, 2)
             truncated_gray = np.clip(gray, a_min=low_value, a_max=high_value)
@@ -422,7 +414,7 @@ def to_uint8(im, is_linear=False):
             result = _gray_process(image)
         return np.uint8(result)
 
-    # simple image standardization
+    # Simple image standardization
     def _sample_norm(image):
         stretches = []
         if len(image.shape) == 3:
@@ -456,7 +448,7 @@ def to_intensity(im):
 
     if len(im.shape) != 2:
         raise ValueError("`len(im.shape) must be 2.")
-    # the type is complex means this is a SAR data
+    # If the type is complex, this is SAR data.
     if isinstance(type(im[0, 0]), complex):
         im = abs(im)
     return im
@@ -475,7 +467,7 @@ def select_bands(im, band_list=[1, 2, 3]):
         np.ndarray: Image with selected bands.
     """
 
-    if len(im.shape) == 2:  # just have one channel
+    if len(im.shape) == 2:  # Image has only one channel
         return im
     if not isinstance(band_list, list) or len(band_list) == 0:
         raise TypeError("band_list must be non empty list.")
@@ -517,7 +509,7 @@ def dehaze(im, gamma=False):
         return m_a * I + m_b
 
     def _dehaze(im, r, w, maxatmo_mask, eps):
-        # im is RGB and range[0, 1]
+        # im is a RGB image and the value ranges in [0, 1].
         atmo_mask = np.min(im, 2)
         dark_channel = cv2.erode(atmo_mask, np.ones((15, 15)))
         atmo_mask = _guided_filter(atmo_mask, dark_channel, r, eps)

paddlers/transforms/operators.py

@@ -212,7 +212,7 @@ class DecodeImg(Transform):
                 raise IOError('Can not open', img_path)
             im_data = dataset.ReadAsArray()
             if im_data.ndim == 2 and self.decode_sar:
-                im_data = to_intensity(im_data)  # is read SAR
+                im_data = to_intensity(im_data)
                 im_data = im_data[:, :, np.newaxis]
             else:
                 if im_data.ndim == 3:
@@ -1376,7 +1376,7 @@ class MixupImage(Transform):
         image = self.apply_im(sample[0]['image'], sample[1]['image'], factor)
         result = copy.deepcopy(sample[0])
         result['image'] = image
-        # apply bbox and score
+        # Apply bbox and score
         if 'gt_bbox' in sample[0]:
             gt_bbox1 = sample[0]['gt_bbox']
             gt_bbox2 = sample[1]['gt_bbox']
@@ -1469,7 +1469,7 @@ class RandomDistort(Transform):
         if np.random.uniform(0., 1.) < self.hue_prob:
             return image
 
-        # it works, but result differ from HSV version
+        # It works, but the result differs from HSV version.
         delta = np.random.uniform(low, high)
         u = np.cos(delta * np.pi)
         w = np.sin(delta * np.pi)
@@ -1505,7 +1505,7 @@ class RandomDistort(Transform):
         for i in range(channel // 3):
             sub_img = image[:, :, 3 * i:3 * (i + 1)]
             sub_img = sub_img.astype(np.float32)
-            # it works, but result differ from HSV version
+            # It works, but the result differs from HSV version.
             gray = sub_img * np.array(
                 [[[0.299, 0.587, 0.114]]], dtype=np.float32)
             gray = gray.sum(axis=2, keepdims=True)
@@ -1720,9 +1720,9 @@ class _PadBox(Transform):
             if gt_num > 0:
                 pad_score[:gt_num] = sample['gt_score'][:gt_num, 0]
             sample['gt_score'] = pad_score
-        # in training, for example in op ExpandImage,
-        # the bbox and gt_class is expanded, but the difficult is not,
-        # so, judging by it's length
+        # In training, for example in op ExpandImage,
+        # bbox and gt_class are expanded, but difficult is not,
+        # so judge by its length.
         if 'difficult' in sample:
             pad_diff = np.zeros((num_max, ), dtype=np.int32)
             if gt_num > 0:

paddlers/utils/checkpoint.py

@@ -441,7 +441,7 @@ def load_pretrain_weights(model, pretrain_weights=None, model_name=None):
         if os.path.exists(pretrain_weights):
             param_state_dict = paddle.load(pretrain_weights)
             model_state_dict = model.state_dict()
-            # hack: fit for faster rcnn. Pretrain weights contain prefix of 'backbone'
+            # HACK: Fit for faster rcnn. Pretrain weights contain prefix of 'backbone'
             # while res5 module is located in bbox_head.head. Replace the prefix of
             # res5 with 'bbox_head.head' to load pretrain weights correctly.
             for k in param_state_dict.keys():

paddlers/utils/download.py

@@ -98,7 +98,7 @@ def download(url, path, md5sum=None):
 
         # For protecting download interupted, download to
         # tmp_fullname firstly, move tmp_fullname to fullname
-        # after download finished
+        # after download finished.
         tmp_fullname = fullname + "_tmp"
         total_size = req.headers.get('content-length')
         with open(tmp_fullname, 'wb') as f:
@@ -181,8 +181,7 @@ def download_and_decompress(url, path='.'):
     local_rank = paddle.distributed.get_rank()
     fname = osp.split(url)[-1]
     fullname = osp.join(path, fname)
-    # if url.endswith(('tgz', 'tar.gz', 'tar', 'zip')):
-    #     fullname = osp.join(path, fname.split('.')[0])
+
     if nranks <= 1:
         dst_dir = url2dir(url, path)
         if dst_dir is not None:

paddlers/utils/utils.py

@@ -154,9 +154,9 @@ class DisablePrint(object):
 class Times(object):
     def __init__(self):
         self.time = 0.
-        # start time
+        # Start time
         self.st = 0.
-        # end time
+        # End time
         self.et = 0.
 
     def start(self):

tests/testing_utils.py

@@ -82,7 +82,7 @@ class _CommonTestNamespace:
             def wrapper(self, *args, **kwargs):
                 with warnings.catch_warnings(record=True) as w:
                     warnings.resetwarnings()
-                    # ignore specified warnings
+                    # Ignore specified warnings
                     warning_white_list = [UserWarning]
                     for warning in warning_white_list:
                         warnings.simplefilter("ignore", warning)

tools/coco2mask.py

@@ -96,7 +96,7 @@ def convert_data(raw_dir, end_dir):
         shutil.copy(img_path, img_save_path)
         if k in anns.keys():
             _save_mask(anns[k], sizes[k], lab_save_path)
-        else:  # have not anns
+        else:
             _save_palette(np.zeros(sizes[k], dtype="uint8"), \
                           lab_save_path)
 

tools/coco_tools/json_AnnoSta.py

@@ -166,7 +166,7 @@ def get_args():
     parser = argparse.ArgumentParser(
         description='Json Images Infomation Statistic')
 
-    # parameters
+    # Parameters
     parser.add_argument(
         '--json_path',
         type=str,

tools/coco_tools/json_Img2Json.py

@@ -63,7 +63,7 @@ def js_test(test_image_path, js_train_path, js_test_path, image_keyname,
 def get_args():
     parser = argparse.ArgumentParser(description='Get Test Json')
 
-    # parameters
+    # Parameters
     parser.add_argument('--test_image_path', type=str, help='test image path')
     parser.add_argument(
         '--json_train_path',

tools/coco_tools/json_ImgSta.py

@@ -74,7 +74,7 @@ def get_args():
     parser = argparse.ArgumentParser(
         description='Json Images Infomation Statistic')
 
-    # parameters
+    # Parameters
     parser.add_argument(
         '--json_path',
         type=str,

tools/coco_tools/json_InfoShow.py

@@ -51,7 +51,7 @@ def js_show(js_path, show_num):
 def get_args():
     parser = argparse.ArgumentParser(description='Json Infomation Show')
 
-    # parameters
+    # Parameters
     parser.add_argument(
         '--json_path', type=str, help='json path to show information')
     parser.add_argument(

tools/coco_tools/json_Merge.py

@@ -56,7 +56,7 @@ def js_merge(js1_path, js2_path, js_merge_path, merge_keys):
 def get_args():
     parser = argparse.ArgumentParser(description='Json Merge')
 
-    # parameters
+    # Parameters
     parser.add_argument('--json1_path', type=str, help='json path1 to merge')
     parser.add_argument('--json2_path', type=str, help='json path2 to merge')
     parser.add_argument(

tools/coco_tools/json_Split.py

@@ -87,7 +87,7 @@ def js_split(js_all_path, js_train_path, js_val_path, val_split_rate,
 def get_args():
     parser = argparse.ArgumentParser(description='Json Merge')
 
-    # parameters
+    # Parameters
     parser.add_argument('--json_all_path', type=str, help='json path to split')
     parser.add_argument(
         '--json_train_path',

tools/raster2geotiff.py

@@ -25,7 +25,8 @@ from utils import Raster, save_geotiff, translate_vector, time_it
 def _gt_convert(x_geo, y_geo, geotf):
     a = np.array([[geotf[1], geotf[2]], [geotf[4], geotf[5]]])
     b = np.array([x_geo - geotf[0], y_geo - geotf[3]])
-    return np.round(np.linalg.solve(a, b)).tolist()  # 解一元二次方程
+    return np.round(np.linalg.solve(a,
+                                    b)).tolist()  # Solve a quadratic equation
 
 
 @time_it
@@ -36,13 +37,13 @@ def convert_data(image_path, geojson_path):
     # vector to EPSG from raster
     temp_geojson_path = translate_vector(geojson_path, raster.proj)
     geo_reader = codecs.open(temp_geojson_path, "r", encoding="utf-8")
-    feats = geojson.loads(geo_reader.read())["features"]  # 所有图像块
+    feats = geojson.loads(geo_reader.read())["features"]  # All image patches
     geo_reader.close()
     for feat in tqdm(feats):
         geo = feat["geometry"]
-        if geo["type"] == "Polygon":  # 多边形
+        if geo["type"] == "Polygon":
             geo_points = geo["coordinates"][0]
-        elif geo["type"] == "MultiPolygon":  # 多面
+        elif geo["type"] == "MultiPolygon":
             geo_points = geo["coordinates"][0][0]
         else:
             raise TypeError(
@@ -52,7 +53,7 @@ def convert_data(image_path, geojson_path):
             _gt_convert(point[0], point[1], raster.geot) for point in geo_points
         ]).astype(np.int32)
         # TODO: Label category
-        cv2.fillPoly(tmp_img, [xy_points], 1)  # 多边形填充
+        cv2.fillPoly(tmp_img, [xy_points], 1)  # Fill with polygons
     ext = "." + geojson_path.split(".")[-1]
     save_geotiff(tmp_img,
                  geojson_path.replace(ext, ".tif"), raster.proj, raster.geot)

tools/utils/__init__.py

@@ -14,7 +14,7 @@
 
 import sys
 import os.path as osp
-sys.path.insert(0, osp.abspath(".."))  # add workspace
+sys.path.insert(0, osp.abspath(".."))  # Add workspace
 
 from .raster import Raster, raster2uint8, save_geotiff
 from .vector import translate_vector