add paddlex.png

deploy/cpp/model_deploy/common/src/paddle_deploy.cpp

@@ -19,4 +19,4 @@ Model* CreateModel(const std::string& name) {
 	return PaddleDeploy::ModelFactory::CreateObject(name);
 }
 
-}  // namespace PaddleDeploy
+}  // namespace PaddleDeploy

examples/C#_deploy/model_infer.cpp

@@ -41,21 +41,21 @@ extern "C" __declspec(dllexport) void InitModel(const char* model_type, const ch
 	if (init)
 	{
 		std::cout << "init model success" << std::endl;
-	}	
+	}
 }
 /*
 * img: input for predicting.
-* 
+*
 * nWidth: width of img.
-* 
+*
 * nHeight: height of img.
-* 
+*
 * nChannel: channel of img.
-* 
+*
 * output: result of pridict ,include category_id£¬score£¬coordinate¡£
-* 
+*
 * nBoxesNum£º number of box
-* 
+*
 * LabelList: label list of result
 */
 extern "C" __declspec(dllexport) void ModelPredict(const unsigned char* img, int nWidth, int nHeight,int nChannel, float* output, int* nBoxesNum, char* LabelList)
@@ -110,7 +110,7 @@ extern "C" __declspec(dllexport) void ModelPredict(const unsigned char* img, int
 			output[num * 6 + 2] = results[num].det_result->boxes[i].coordinate[0];
 			output[num * 6 + 3] = results[num].det_result->boxes[i].coordinate[1];
 			output[num * 6 + 4] = results[num].det_result->boxes[i].coordinate[2];
-			output[num * 6 + 5] = results[num].det_result->boxes[i].coordinate[3];						
+			output[num * 6 + 5] = results[num].det_result->boxes[i].coordinate[3];
 		}
 	}
 	memcpy(LabelList, label.c_str(), strlen(label.c_str()));

paddlex/cv/models/utils/det_metrics/coco_utils.py

@@ -131,7 +131,7 @@ def cocoapi_eval(anns,
         results_flatten = list(itertools.chain(*results_per_category))
         headers = ['category', 'AP'] * (num_columns // 2)
         results_2d = itertools.zip_longest(
-            *[results_flatten[i::num_columns] for i in range(num_columns)])
+            * [results_flatten[i::num_columns] for i in range(num_columns)])
         table_data = [headers]
         table_data += [result for result in results_2d]
         table = AsciiTable(table_data)

paddlex/paddleseg/core/train.py

@@ -110,8 +110,7 @@ def train(model,
         train_dataset,
         batch_sampler=batch_sampler,
         num_workers=num_workers,
-        return_list=True,
-    )
+        return_list=True, )
 
     if use_vdl:
         from visualdl import LogWriter
@@ -176,9 +175,9 @@ def train(model,
                 eta = calculate_eta(remain_iters, avg_train_batch_cost)
                 logger.info(
                     "[TRAIN] epoch: {}, iter: {}/{}, loss: {:.4f}, lr: {:.6f}, batch_cost: {:.4f}, reader_cost: {:.5f}, ips: {:.4f} samples/sec | ETA {}"
-                    .format((iter - 1) // iters_per_epoch + 1, iter, iters,
-                            avg_loss, lr, avg_train_batch_cost,
-                            avg_train_reader_cost,
+                    .format((iter - 1
+                             ) // iters_per_epoch + 1, iter, iters, avg_loss,
+                            lr, avg_train_batch_cost, avg_train_reader_cost,
                             batch_cost_averager.get_ips_average(), eta))
                 if use_vdl:
                     log_writer.add_scalar('Train/loss', avg_loss, iter)
@@ -201,14 +200,15 @@ def train(model,
                 reader_cost_averager.reset()
                 batch_cost_averager.reset()
 
-            if (iter % save_interval == 0
-                    or iter == iters) and (val_dataset is not None):
+            if (iter % save_interval == 0 or
+                    iter == iters) and (val_dataset is not None):
                 num_workers = 1 if num_workers > 0 else 0
                 mean_iou, acc, _, _, _ = evaluate(
                     model, val_dataset, num_workers=num_workers)
                 model.train()
 
-            if (iter % save_interval == 0 or iter == iters) and local_rank == 0:
+            if (iter % save_interval == 0 or
+                    iter == iters) and local_rank == 0:
                 current_save_dir = os.path.join(save_dir,
                                                 "iter_{}".format(iter))
                 if not os.path.isdir(current_save_dir):

paddlex/paddleseg/core/val.py

@@ -72,8 +72,7 @@ def evaluate(model,
         eval_dataset,
         batch_sampler=batch_sampler,
         num_workers=num_workers,
-        return_list=True,
-    )
+        return_list=True, )
 
     total_iters = len(loader)
     intersect_area_all = 0
@@ -81,9 +80,8 @@ def evaluate(model,
     label_area_all = 0
 
     if print_detail:
-        logger.info(
-            "Start evaluating (total_samples: {}, total_iters: {})...".format(
-                len(eval_dataset), total_iters))
+        logger.info("Start evaluating (total_samples: {}, total_iters: {})...".
+                    format(len(eval_dataset), total_iters))
     progbar_val = progbar.Progbar(target=total_iters, verbose=1)
     reader_cost_averager = TimeAverager()
     batch_cost_averager = TimeAverager()
@@ -167,8 +165,8 @@ def evaluate(model,
 
     if print_detail:
         logger.info(
-            "[EVAL] #Images: {} mIoU: {:.4f} Acc: {:.4f} Kappa: {:.4f} ".format(
-                len(eval_dataset), miou, acc, kappa))
+            "[EVAL] #Images: {} mIoU: {:.4f} Acc: {:.4f} Kappa: {:.4f} ".
+            format(len(eval_dataset), miou, acc, kappa))
         logger.info("[EVAL] Class IoU: \n" + str(np.round(class_iou, 4)))
         logger.info("[EVAL] Class Acc: \n" + str(np.round(class_acc, 4)))
     return miou, acc, class_iou, class_acc, kappa

paddlex/paddleseg/cvlibs/config.py

@@ -67,9 +67,9 @@ class Config(object):
 
     def __init__(self,
                  path: str,
-                 learning_rate: float = None,
-                 batch_size: int = None,
-                 iters: int = None):
+                 learning_rate: float=None,
+                 batch_size: int=None,
+                 iters: int=None):
         if not path:
             raise ValueError('Please specify the configuration file path.')
 
@@ -113,9 +113,9 @@ class Config(object):
         return dic
 
     def update(self,
-               learning_rate: float = None,
-               batch_size: int = None,
-               iters: int = None):
+               learning_rate: float=None,
+               batch_size: int=None,
+               iters: int=None):
         '''Update config'''
         if learning_rate:
             if 'lr_scheduler' in self.dic:
@@ -207,10 +207,9 @@ class Config(object):
 
     @property
     def decay_args(self) -> dict:
-        args = self.dic.get('learning_rate', {}).get('decay', {
-            'type': 'poly',
-            'power': 0.9
-        }).copy()
+        args = self.dic.get('learning_rate', {}).get(
+            'decay', {'type': 'poly',
+                      'power': 0.9}).copy()
 
         if args['type'] == 'poly':
             args.setdefault('decay_steps', self.iters)

paddlex/paddleseg/cvlibs/manager.py

@@ -74,8 +74,8 @@ class ComponentManager:
 
     def __getitem__(self, item):
         if item not in self._components_dict.keys():
-            raise KeyError("{} does not exist in availabel {}".format(
-                item, self))
+            raise KeyError("{} does not exist in availabel {}".format(item,
+                                                                      self))
         return self._components_dict[item]
 
     @property
@@ -100,9 +100,8 @@ class ComponentManager:
 
         # Currently only support class or function type
         if not (inspect.isclass(component) or inspect.isfunction(component)):
-            raise TypeError(
-                "Expect class/function type, but received {}".format(
-                    type(component)))
+            raise TypeError("Expect class/function type, but received {}".
+                            format(type(component)))
 
         # Obtain the internal name of the component
         component_name = component.__name__

paddlex/paddleseg/datasets/cityscapes.py

@@ -82,6 +82,5 @@ class Cityscapes(Dataset):
         img_files = sorted(
             glob.glob(os.path.join(img_dir, mode, '*', '*_leftImg8bit.png')))
 
-        self.file_list = [[
-            img_path, label_path
-        ] for img_path, label_path in zip(img_files, label_files)]
+        self.file_list = [[img_path, label_path] for img_path, label_path in
+                          zip(img_files, label_files)]

paddlex/paddleseg/datasets/cocostuff.py

@@ -75,6 +75,5 @@ class CocoStuff(Dataset):
         img_files = sorted(
             glob.glob(os.path.join(img_dir, mode + '2017', '*.jpg')))
 
-        self.file_list = [[
-            img_path, label_path
-        ] for img_path, label_path in zip(img_files, label_files)]
+        self.file_list = [[img_path, label_path] for img_path, label_path in
+                          zip(img_files, label_files)]

paddlex/paddleseg/datasets/optic_disc_seg.py

@@ -53,8 +53,8 @@ class OpticDiscSeg(Dataset):
 
         if mode not in ['train', 'val', 'test']:
             raise ValueError(
-                "`mode` should be 'train', 'val' or 'test', but got {}.".format(
-                    mode))
+                "`mode` should be 'train', 'val' or 'test', but got {}.".
+                format(mode))
 
         if self.transforms is None:
             raise ValueError("`transforms` is necessary, but it is None.")

paddlex/paddleseg/datasets/voc.py

@@ -38,7 +38,8 @@ class PascalVOC(Dataset):
     """
     NUM_CLASSES = 21
 
-    def __init__(self, transforms, dataset_root=None, mode='train', edge=False):
+    def __init__(self, transforms, dataset_root=None, mode='train',
+                 edge=False):
         self.dataset_root = dataset_root
         self.transforms = Compose(transforms)
         mode = mode.lower()

paddlex/paddleseg/models/ann.py

@@ -143,7 +143,9 @@ class ANNHead(nn.Layer):
                 psp_size=psp_size))
 
         self.cls = nn.Conv2D(
-            in_channels=inter_channels, out_channels=num_classes, kernel_size=1)
+            in_channels=inter_channels,
+            out_channels=num_classes,
+            kernel_size=1)
         self.auxlayer = layers.AuxLayer(
             in_channels=low_in_channels,
             inter_channels=low_in_channels // 2,

paddlex/paddleseg/models/attention_unet.py

@@ -103,16 +103,20 @@ class AttentionBlock(nn.Layer):
     def __init__(self, F_g, F_l, F_out):
         super().__init__()
         self.W_g = nn.Sequential(
-            nn.Conv2D(F_g, F_out, kernel_size=1, stride=1, padding=0),
+            nn.Conv2D(
+                F_g, F_out, kernel_size=1, stride=1, padding=0),
             nn.BatchNorm2D(F_out))
 
         self.W_x = nn.Sequential(
-            nn.Conv2D(F_l, F_out, kernel_size=1, stride=1, padding=0),
+            nn.Conv2D(
+                F_l, F_out, kernel_size=1, stride=1, padding=0),
             nn.BatchNorm2D(F_out))
 
         self.psi = nn.Sequential(
-            nn.Conv2D(F_out, 1, kernel_size=1, stride=1, padding=0),
-            nn.BatchNorm2D(1), nn.Sigmoid())
+            nn.Conv2D(
+                F_out, 1, kernel_size=1, stride=1, padding=0),
+            nn.BatchNorm2D(1),
+            nn.Sigmoid())
 
         self.relu = nn.ReLU()
 
@@ -129,9 +133,12 @@ class UpConv(nn.Layer):
     def __init__(self, ch_in, ch_out):
         super().__init__()
         self.up = nn.Sequential(
-            nn.Upsample(scale_factor=2, mode="bilinear"),
-            nn.Conv2D(ch_in, ch_out, kernel_size=3, stride=1, padding=1),
-            nn.BatchNorm2D(ch_out), nn.ReLU())
+            nn.Upsample(
+                scale_factor=2, mode="bilinear"),
+            nn.Conv2D(
+                ch_in, ch_out, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2D(ch_out),
+            nn.ReLU())
 
     def forward(self, x):
         return self.up(x)
@@ -169,10 +176,14 @@ class ConvBlock(nn.Layer):
     def __init__(self, ch_in, ch_out):
         super(ConvBlock, self).__init__()
         self.conv = nn.Sequential(
-            nn.Conv2D(ch_in, ch_out, kernel_size=3, stride=1, padding=1),
-            nn.BatchNorm2D(ch_out), nn.ReLU(),
-            nn.Conv2D(ch_out, ch_out, kernel_size=3, stride=1, padding=1),
-            nn.BatchNorm2D(ch_out), nn.ReLU())
+            nn.Conv2D(
+                ch_in, ch_out, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2D(ch_out),
+            nn.ReLU(),
+            nn.Conv2D(
+                ch_out, ch_out, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2D(ch_out),
+            nn.ReLU())
 
     def forward(self, x):
         return self.conv(x)

paddlex/paddleseg/models/backbones/hrnet.py

@@ -24,7 +24,8 @@ from paddlex.paddleseg.utils import utils
 
 __all__ = [
     "HRNet_W18_Small_V1", "HRNet_W18_Small_V2", "HRNet_W18", "HRNet_W30",
-    "HRNet_W32", "HRNet_W40", "HRNet_W44", "HRNet_W48", "HRNet_W60", "HRNet_W64"
+    "HRNet_W32", "HRNet_W40", "HRNet_W44", "HRNet_W48", "HRNet_W60",
+    "HRNet_W64"
 ]
 
 

paddlex/paddleseg/models/backbones/mobilenetv3.py

@@ -103,7 +103,8 @@ class MobileNetV3(nn.Layer):
                 [5, 144, 48, True, "hard_swish", 1],  # output 3 -> out_index=7
                 [5, 288, 96, True, "hard_swish", 2],
                 [5, 576, 96, True, "hard_swish", 1],
-                [5, 576, 96, True, "hard_swish", 1],  # output 4 -> out_index=10
+                [5, 576, 96, True, "hard_swish", 1
+                 ],  # output 4 -> out_index=10
             ]
             self.out_indices = [0, 3, 7, 10]
             self.feat_channels = [

paddlex/paddleseg/models/backbones/resnet_vd.py

@@ -35,8 +35,7 @@ class ConvBNLayer(nn.Layer):
             dilation=1,
             groups=1,
             is_vd_mode=False,
-            act=None,
-    ):
+            act=None, ):
         super(ConvBNLayer, self).__init__()
 
         self.is_vd_mode = is_vd_mode
@@ -215,13 +214,13 @@ class ResNet_vd(nn.Layer):
             depth = [3, 8, 36, 3]
         elif layers == 200:
             depth = [3, 12, 48, 3]
-        num_channels = [64, 256, 512, 1024
-                        ] if layers >= 50 else [64, 64, 128, 256]
+        num_channels = [64, 256, 512,
+                        1024] if layers >= 50 else [64, 64, 128, 256]
         num_filters = [64, 128, 256, 512]
 
         # for channels of four returned stages
-        self.feat_channels = [c * 4 for c in num_filters
-                              ] if layers >= 50 else num_filters
+        self.feat_channels = [c * 4 for c in
+                              num_filters] if layers >= 50 else num_filters
 
         dilation_dict = None
         if output_stride == 8:
@@ -230,7 +229,11 @@ class ResNet_vd(nn.Layer):
             dilation_dict = {3: 2}
 
         self.conv1_1 = ConvBNLayer(
-            in_channels=3, out_channels=32, kernel_size=3, stride=2, act='relu')
+            in_channels=3,
+            out_channels=32,
+            kernel_size=3,
+            stride=2,
+            act='relu')
         self.conv1_2 = ConvBNLayer(
             in_channels=32,
             out_channels=32,
@@ -277,8 +280,8 @@ class ResNet_vd(nn.Layer):
                             in_channels=num_channels[block]
                             if i == 0 else num_filters[block] * 4,
                             out_channels=num_filters[block],
-                            stride=2 if i == 0 and block != 0
-                            and dilation_rate == 1 else 1,
+                            stride=2 if i == 0 and block != 0 and
+                            dilation_rate == 1 else 1,
                             shortcut=shortcut,
                             if_first=block == i == 0,
                             dilation=dilation_rate))

paddlex/paddleseg/models/bisenet.py

@@ -109,7 +109,8 @@ class StemBlock(nn.Layer):
 
         self.left = nn.Sequential(
             layers.ConvBNReLU(out_dim, out_dim // 2, 1),
-            layers.ConvBNReLU(out_dim // 2, out_dim, 3, stride=2))
+            layers.ConvBNReLU(
+                out_dim // 2, out_dim, 3, stride=2))
 
         self.right = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
 
@@ -167,12 +168,14 @@ class GatherAndExpansionLayer2(nn.Layer):
 
         self.branch_1 = nn.Sequential(
             layers.ConvBNReLU(in_dim, in_dim, 3),
-            layers.DepthwiseConvBN(in_dim, expand_dim, 3, stride=2),
+            layers.DepthwiseConvBN(
+                in_dim, expand_dim, 3, stride=2),
             layers.DepthwiseConvBN(expand_dim, expand_dim, 3),
             layers.ConvBN(expand_dim, out_dim, 1))
 
         self.branch_2 = nn.Sequential(
-            layers.DepthwiseConvBN(in_dim, in_dim, 3, stride=2),
+            layers.DepthwiseConvBN(
+                in_dim, in_dim, 3, stride=2),
             layers.ConvBN(in_dim, out_dim, 1))
 
     def forward(self, x):
@@ -189,17 +192,19 @@ class DetailBranch(nn.Layer):
 
         self.convs = nn.Sequential(
             # stage 1
-            layers.ConvBNReLU(3, C1, 3, stride=2),
+            layers.ConvBNReLU(
+                3, C1, 3, stride=2),
             layers.ConvBNReLU(C1, C1, 3),
             # stage 2
-            layers.ConvBNReLU(C1, C2, 3, stride=2),
+            layers.ConvBNReLU(
+                C1, C2, 3, stride=2),
             layers.ConvBNReLU(C2, C2, 3),
             layers.ConvBNReLU(C2, C2, 3),
             # stage 3
-            layers.ConvBNReLU(C2, C3, 3, stride=2),
+            layers.ConvBNReLU(
+                C2, C3, 3, stride=2),
             layers.ConvBNReLU(C3, C3, 3),
-            layers.ConvBNReLU(C3, C3, 3),
-        )
+            layers.ConvBNReLU(C3, C3, 3), )
 
     def forward(self, x):
         return self.convs(x)
@@ -252,12 +257,15 @@ class BGA(nn.Layer):
             nn.Conv2D(out_dim, out_dim, 1))
 
         self.db_branch_down = nn.Sequential(
-            layers.ConvBN(out_dim, out_dim, 3, stride=2),
-            nn.AvgPool2D(kernel_size=3, stride=2, padding=1))
+            layers.ConvBN(
+                out_dim, out_dim, 3, stride=2),
+            nn.AvgPool2D(
+                kernel_size=3, stride=2, padding=1))
 
         self.sb_branch_keep = nn.Sequential(
             layers.DepthwiseConvBN(out_dim, out_dim, 3),
-            nn.Conv2D(out_dim, out_dim, 1), layers.Activation(act='sigmoid'))
+            nn.Conv2D(out_dim, out_dim, 1),
+            layers.Activation(act='sigmoid'))
 
         self.sb_branch_up = layers.ConvBN(out_dim, out_dim, 3)
 

paddlex/paddleseg/models/danet.py

@@ -50,7 +50,9 @@ class DANet(nn.Layer):
 
         self.backbone = backbone
         self.backbone_indices = backbone_indices
-        in_channels = [self.backbone.feat_channels[i] for i in backbone_indices]
+        in_channels = [
+            self.backbone.feat_channels[i] for i in backbone_indices
+        ]
 
         self.head = DAHead(num_classes=num_classes, in_channels=in_channels)
 
@@ -205,8 +207,8 @@ class CAM(nn.Layer):
         # sim: n, c, c
         sim = paddle.bmm(query, key)
         # The danet author claims that this can avoid gradient divergence
-        sim = paddle.max(
-            sim, axis=-1, keepdim=True).tile([1, 1, self.channels]) - sim
+        sim = paddle.max(sim, axis=-1, keepdim=True).tile(
+            [1, 1, self.channels]) - sim
         sim = F.softmax(sim, axis=-1)
 
         # feat: from (n, c, h * w) to (n, c, h, w)

paddlex/paddleseg/models/decoupled_segnet.py

@@ -126,14 +126,17 @@ class DecoupledSegNetHead(nn.Layer):
                 in_channels=256,
                 out_channels=48,
                 kernel_size=3,
-                bias_attr=False), nn.Conv2D(48, 1, 1, bias_attr=False))
+                bias_attr=False),
+            nn.Conv2D(
+                48, 1, 1, bias_attr=False))
         self.dsn_seg_body = nn.Sequential(
             layers.ConvBNReLU(
                 in_channels=256,
                 out_channels=256,
                 kernel_size=3,
-                bias_attr=False), nn.Conv2D(
-                    256, num_classes, 1, bias_attr=False))
+                bias_attr=False),
+            nn.Conv2D(
+                256, num_classes, 1, bias_attr=False))
 
         self.final_seg = nn.Sequential(
             layers.ConvBNReLU(
@@ -146,7 +149,8 @@ class DecoupledSegNetHead(nn.Layer):
                 out_channels=256,
                 kernel_size=3,
                 bias_attr=False),
-            nn.Conv2D(256, num_classes, kernel_size=1, bias_attr=False))
+            nn.Conv2D(
+                256, num_classes, kernel_size=1, bias_attr=False))
 
     def forward(self, feat_list):
         fine_fea = feat_list[self.backbone_indices[0]]
@@ -163,7 +167,9 @@ class DecoupledSegNetHead(nn.Layer):
             fine_size[2:],
             mode='bilinear',
             align_corners=self.align_corners)
-        seg_edge = self.edge_fusion(paddle.concat([seg_edge, fine_fea], axis=1))
+        seg_edge = self.edge_fusion(
+            paddle.concat(
+                [seg_edge, fine_fea], axis=1))
         seg_edge_out = self.edge_out(seg_edge)
         seg_edge_out = self.sigmoid_edge(seg_edge_out)  # seg_edge output
         seg_body_out = self.dsn_seg_body(seg_body)  # body out
@@ -218,7 +224,8 @@ class SqueezeBodyEdge(nn.Layer):
         h_grid = h_grid.tile([size[1]])
         w_grid = paddle.linspace(-1.0, 1.0, size[1]).reshape([-1, 1])
         w_grid = w_grid.tile([size[0]]).transpose([1, 0])
-        grid = paddle.concat([w_grid.unsqueeze(2), h_grid.unsqueeze(2)], axis=2)
+        grid = paddle.concat(
+            [w_grid.unsqueeze(2), h_grid.unsqueeze(2)], axis=2)
         grid.unsqueeze(0).tile([input_shape[0], 1, 1, 1])
         grid = grid + paddle.transpose(flow, (0, 2, 3, 1)) / norm
 

paddlex/paddleseg/models/deeplab.py

@@ -117,7 +117,8 @@ class DeepLabV3PHead(nn.Layer):
             align_corners,
             use_sep_conv=True,
             image_pooling=True)
-        self.decoder = Decoder(num_classes, backbone_channels[0], align_corners)
+        self.decoder = Decoder(num_classes, backbone_channels[0],
+                               align_corners)
         self.backbone_indices = backbone_indices
 
     def forward(self, feat_list):

paddlex/paddleseg/models/dnlnet.py

@@ -60,7 +60,9 @@ class DNLNet(nn.Layer):
         super().__init__()
         self.backbone = backbone
         self.backbone_indices = backbone_indices
-        in_channels = [self.backbone.feat_channels[i] for i in backbone_indices]
+        in_channels = [
+            self.backbone.feat_channels[i] for i in backbone_indices
+        ]
         self.head = DNLHead(num_classes, in_channels, reduction, use_scale,
                             mode, temperature, concat_input,
                             enable_auxiliary_loss)
@@ -143,7 +145,8 @@ class DNLHead(nn.Layer):
                 in_channels=1024,
                 out_channels=256,
                 kernel_size=3,
-                bias_attr=False), nn.Dropout2D(p=0.1),
+                bias_attr=False),
+            nn.Dropout2D(p=0.1),
             nn.Conv2D(256, num_classes, 1))
         if self.concat_input:
             self.conv_cat = layers.ConvBNReLU(
@@ -196,14 +199,16 @@ class DisentangledNonLocal2D(layers.NonLocal2D):
             theta_x = paddle.transpose(
                 x.reshape([0, self.in_channels, -1]), [0, 2, 1])
             if self.sub_sample:
-                phi_x = paddle.transpose(self.phi(x), [0, self.in_channels, -1])
+                phi_x = paddle.transpose(
+                    self.phi(x), [0, self.in_channels, -1])
             else:
                 phi_x = paddle.transpose(x, [0, self.in_channels, -1])
 
         elif self.mode == "concatenation":
             theta_x = paddle.reshape(
                 self.theta(x), [0, self.inter_channels, -1, 1])
-            phi_x = paddle.reshape(self.phi(x), [0, self.inter_channels, 1, -1])
+            phi_x = paddle.reshape(
+                self.phi(x), [0, self.inter_channels, 1, -1])
 
         else:
             theta_x = self.theta(x).reshape([0, self.inter_channels,

paddlex/paddleseg/models/emanet.py

@@ -63,10 +63,12 @@ class EMANet(nn.Layer):
 
         self.backbone = backbone
         self.backbone_indices = backbone_indices
-        in_channels = [self.backbone.feat_channels[i] for i in backbone_indices]
-        self.head = EMAHead(num_classes, in_channels, ema_channels, gc_channels,
-                            num_bases, stage_num, momentum, concat_input,
-                            enable_auxiliary_loss)
+        in_channels = [
+            self.backbone.feat_channels[i] for i in backbone_indices
+        ]
+        self.head = EMAHead(num_classes, in_channels, ema_channels,
+                            gc_channels, num_bases, stage_num, momentum,
+                            concat_input, enable_auxiliary_loss)
         self.align_corners = align_corners
         self.pretrained = pretrained
         self.init_weight()
@@ -127,7 +129,8 @@ class EMAHead(nn.Layer):
             in_channels=self.in_channels,
             out_channels=ema_channels,
             kernel_size=3)
-        self.ema_mid_conv = nn.Conv2D(ema_channels, ema_channels, kernel_size=1)
+        self.ema_mid_conv = nn.Conv2D(
+            ema_channels, ema_channels, kernel_size=1)
         self.ema_out_conv = layers.ConvBNReLU(
             in_channels=ema_channels, out_channels=ema_channels, kernel_size=1)
         self.bottleneck = layers.ConvBNReLU(
@@ -137,7 +140,8 @@ class EMAHead(nn.Layer):
         self.aux = nn.Sequential(
             layers.ConvBNReLU(
                 in_channels=1024, out_channels=256, kernel_size=3),
-            nn.Dropout2D(p=0.1), nn.Conv2D(256, num_classes, 1))
+            nn.Dropout2D(p=0.1),
+            nn.Conv2D(256, num_classes, 1))
         if self.concat_input:
             self.conv_cat = layers.ConvBNReLU(
                 self.in_channels + gc_channels, gc_channels, kernel_size=3)

paddlex/paddleseg/models/fast_scnn.py

@@ -304,7 +304,9 @@ class Classifier(nn.Layer):
             padding=1)
 
         self.conv = nn.Conv2D(
-            in_channels=input_channels, out_channels=num_classes, kernel_size=1)
+            in_channels=input_channels,
+            out_channels=num_classes,
+            kernel_size=1)
 
         self.dropout = nn.Dropout(p=0.1)  # dropout_prob
 

paddlex/paddleseg/models/gcnet.py

@@ -188,7 +188,8 @@ class GlobalContextBlock(nn.Layer):
                 in_channels=in_channels,
                 out_channels=inter_channels,
                 kernel_size=1),
-            nn.LayerNorm(normalized_shape=[inter_channels, 1, 1]), nn.ReLU(),
+            nn.LayerNorm(normalized_shape=[inter_channels, 1, 1]),
+            nn.ReLU(),
             nn.Conv2D(
                 in_channels=inter_channels,
                 out_channels=in_channels,

paddlex/paddleseg/models/gscnn.py

@@ -237,9 +237,13 @@ class GatedSpatailConv2d(nn.Layer):
         super().__init__()
         self._gate_conv = nn.Sequential(
             layers.SyncBatchNorm(in_channels + 1),
-            nn.Conv2D(in_channels + 1, in_channels + 1, kernel_size=1),
-            nn.ReLU(), nn.Conv2D(in_channels + 1, 1, kernel_size=1),
-            layers.SyncBatchNorm(1), nn.Sigmoid())
+            nn.Conv2D(
+                in_channels + 1, in_channels + 1, kernel_size=1),
+            nn.ReLU(),
+            nn.Conv2D(
+                in_channels + 1, 1, kernel_size=1),
+            layers.SyncBatchNorm(1),
+            nn.Sigmoid())
         self.conv = nn.Conv2D(
             in_channels,
             out_channels,

paddlex/paddleseg/models/hardnet.py

@@ -255,7 +255,8 @@ class HarDBlock(nn.Layer):
 
             self.links.append(link)
             layers_.append(
-                layers.ConvBNReLU(inch, outch, kernel_size=3, bias_attr=False))
+                layers.ConvBNReLU(
+                    inch, outch, kernel_size=3, bias_attr=False))
             if (i % 2 == 0) or (i == n_layers - 1):
                 self.out_channels += outch
         self.layers = nn.LayerList(layers_)

paddlex/paddleseg/models/isanet.py

@@ -54,9 +54,11 @@ class ISANet(nn.Layer):
 
         self.backbone = backbone
         self.backbone_indices = backbone_indices
-        in_channels = [self.backbone.feat_channels[i] for i in backbone_indices]
-        self.head = ISAHead(num_classes, in_channels, isa_channels, down_factor,
-                            enable_auxiliary_loss)
+        in_channels = [
+            self.backbone.feat_channels[i] for i in backbone_indices
+        ]
+        self.head = ISAHead(num_classes, in_channels, isa_channels,
+                            down_factor, enable_auxiliary_loss)
         self.align_corners = align_corners
         self.pretrained = pretrained
         self.init_weight()
@@ -114,7 +116,8 @@ class ISAHead(nn.Layer):
                 in_channels=1024,
                 out_channels=256,
                 kernel_size=3,
-                bias_attr=False), nn.Dropout2D(p=0.1),
+                bias_attr=False),
+            nn.Dropout2D(p=0.1),
             nn.Conv2D(256, num_classes, 1))
 
     def forward(self, feat_list):
@@ -127,22 +130,24 @@ class ISAHead(nn.Layer):
         pad_h, pad_w = (Q_h * P_h - x_shape[2]).astype('int32'), (
             Q_w * P_w - x_shape[3]).astype('int32')
         if pad_h > 0 or pad_w > 0:
-            padding = paddle.concat([
-                pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2
-            ],
-                                    axis=0)
+            padding = paddle.concat(
+                [
+                    pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
+                    pad_h - pad_h // 2
+                ],
+                axis=0)
             feat = F.pad(x, padding)
         else:
             feat = x
 
         feat = feat.reshape([0, x_shape[1], Q_h, P_h, Q_w, P_w])
-        feat = feat.transpose([0, 3, 5, 1, 2,
-                               4]).reshape([-1, self.inter_channels, Q_h, Q_w])
+        feat = feat.transpose(
+            [0, 3, 5, 1, 2, 4]).reshape([-1, self.inter_channels, Q_h, Q_w])
         feat = self.global_relation(feat)
 
         feat = feat.reshape([x_shape[0], P_h, P_w, x_shape[1], Q_h, Q_w])
-        feat = feat.transpose([0, 4, 5, 3, 1,
-                               2]).reshape([-1, self.inter_channels, P_h, P_w])
+        feat = feat.transpose(
+            [0, 4, 5, 3, 1, 2]).reshape([-1, self.inter_channels, P_h, P_w])
         feat = self.local_relation(feat)
 
         feat = feat.reshape([x_shape[0], Q_h, Q_w, x_shape[1], P_h, P_w])

paddlex/paddleseg/models/layers/activation.py

@@ -60,8 +60,8 @@ class Activation(nn.Layer):
         if act is not None:
             if act in act_dict.keys():
                 act_name = act_dict[act]
-                self.act_func = eval(
-                    "nn.layer.activation.{}()".format(act_name))
+                self.act_func = eval("nn.layer.activation.{}()".format(
+                    act_name))
             else:
                 raise KeyError("{} does not exist in the current {}".format(
                     act, act_dict.keys()))

paddlex/paddleseg/models/layers/layer_libs.py

@@ -21,7 +21,8 @@ import paddle.nn.functional as F
 
 def SyncBatchNorm(*args, **kwargs):
     """In cpu environment nn.SyncBatchNorm does not have kernel so use nn.BatchNorm2D instead"""
-    if paddle.get_device() == 'cpu' or os.environ.get('PADDLESEG_EXPORT_STAGE'):
+    if paddle.get_device() == 'cpu' or os.environ.get(
+            'PADDLESEG_EXPORT_STAGE'):
         return nn.BatchNorm2D(*args, **kwargs)
     else:
         return nn.SyncBatchNorm(*args, **kwargs)

paddlex/paddleseg/models/layers/nonlocal2d.py

@@ -135,7 +135,8 @@ class NonLocal2D(nn.Layer):
         elif self.mode == 'concatenation':
             theta_x = paddle.reshape(
                 self.theta(x), [n, self.inter_channels, -1, 1])
-            phi_x = paddle.reshape(self.phi(x), [n, self.inter_channels, 1, -1])
+            phi_x = paddle.reshape(
+                self.phi(x), [n, self.inter_channels, 1, -1])
 
         else:
             theta_x = paddle.reshape(

paddlex/paddleseg/models/losses/cross_entropy_loss.py

@@ -34,7 +34,8 @@ class CrossEntropyLoss(nn.Layer):
             the top k percent pixels (e.g., the top 20% pixels). This is useful for hard pixel mining.
     """
 
-    def __init__(self, weight=None, ignore_index=255, top_k_percent_pixels=1.0):
+    def __init__(self, weight=None, ignore_index=255,
+                 top_k_percent_pixels=1.0):
         super(CrossEntropyLoss, self).__init__()
         if weight is not None:
             weight = paddle.to_tensor(weight, dtype='float32')

paddlex/paddleseg/models/losses/lovasz_loss.py

@@ -77,8 +77,8 @@ class LovaszHingeLoss(nn.Layer):
         """
         if logits.shape[1] == 2:
             logits = binary_channel_to_unary(logits)
-        loss = lovasz_hinge_flat(
-            *flatten_binary_scores(logits, labels, self.ignore_index))
+        loss = lovasz_hinge_flat(*flatten_binary_scores(logits, labels,
+                                                        self.ignore_index))
         return loss
 
 
@@ -181,8 +181,8 @@ def lovasz_softmax_flat(probas, labels, classes='present'):
         else:
             class_pred = probas[:, c]
         errors = paddle.abs(fg - class_pred)
-        errors_sorted, perm = paddle.fluid.core.ops.argsort(
-            errors, 'axis', 0, 'descending', True)
+        errors_sorted, perm = paddle.fluid.core.ops.argsort(errors, 'axis', 0,
+                                                            'descending', True)
         errors_sorted.stop_gradient = False
 
         fg_sorted = paddle.gather(fg, perm)

paddlex/paddleseg/models/ocrnet.py

@@ -55,7 +55,9 @@ class OCRNet(nn.Layer):
 
         self.backbone = backbone
         self.backbone_indices = backbone_indices
-        in_channels = [self.backbone.feat_channels[i] for i in backbone_indices]
+        in_channels = [
+            self.backbone.feat_channels[i] for i in backbone_indices
+        ]
 
         self.head = OCRHead(
             num_classes=num_classes,
@@ -239,8 +241,8 @@ class ObjectAttentionBlock(nn.Layer):
         # context from (n, h1*w1, key_channels) to (n , out_channels, h1, w1)
         context = paddle.bmm(sim_map, value)
         context = paddle.transpose(context, (0, 2, 1))
-        context = paddle.reshape(context,
-                                 (0, self.key_channels, x_shape[2], x_shape[3]))
+        context = paddle.reshape(
+            context, (0, self.key_channels, x_shape[2], x_shape[3]))
         context = self.f_up(context)
 
         return context

paddlex/paddleseg/models/pspnet.py

@@ -58,8 +58,8 @@ class PSPNet(nn.Layer):
             backbone.feat_channels[i] for i in backbone_indices
         ]
 
-        self.head = PSPNetHead(num_classes, backbone_indices, backbone_channels,
-                               pp_out_channels, bin_sizes,
+        self.head = PSPNetHead(num_classes, backbone_indices,
+                               backbone_channels, pp_out_channels, bin_sizes,
                                enable_auxiliary_loss, align_corners)
         self.align_corners = align_corners
         self.pretrained = pretrained

paddlex/paddleseg/models/sfnet.py

@@ -132,12 +132,15 @@ class SFNetHead(nn.Layer):
         for i in range(len(fpn_inplanes) - 1):
             self.fpn_out.append(
                 nn.Sequential(
-                    layers.ConvBNReLU(fpn_dim, fpn_dim, 3, bias_attr=False)))
+                    layers.ConvBNReLU(
+                        fpn_dim, fpn_dim, 3, bias_attr=False)))
             self.fpn_out_align.append(
-                AlignedModule(inplane=fpn_dim, outplane=fpn_dim // 2))
+                AlignedModule(
+                    inplane=fpn_dim, outplane=fpn_dim // 2))
             if self.enable_auxiliary_loss:
                 self.dsn.append(
-                    nn.Sequential(layers.AuxLayer(fpn_dim, fpn_dim, num_class)))
+                    nn.Sequential(
+                        layers.AuxLayer(fpn_dim, fpn_dim, num_class)))
 
         self.fpn_out = nn.LayerList(self.fpn_out)
         self.fpn_out_align = nn.LayerList(self.fpn_out_align)
@@ -148,7 +151,8 @@ class SFNetHead(nn.Layer):
         self.conv_last = nn.Sequential(
             layers.ConvBNReLU(
                 len(fpn_inplanes) * fpn_dim, fpn_dim, 3, bias_attr=False),
-            nn.Conv2D(fpn_dim, num_class, kernel_size=1))
+            nn.Conv2D(
+                fpn_dim, num_class, kernel_size=1))
 
     def forward(self, conv_out):
         psp_out = self.ppm(conv_out[-1])
@@ -211,8 +215,8 @@ class AlignedModule(nn.Layer):
         norm = paddle.to_tensor([[[[out_w, out_h]]]]).astype('float32')
         h = paddle.linspace(-1.0, 1.0, out_h).reshape([-1, 1]).tile([1, out_w])
         w = paddle.linspace(-1.0, 1.0, out_w).tile([out_h, 1])
-        grid = paddle.concat([paddle.unsqueeze(w, 2),
-                              paddle.unsqueeze(h, 2)], 2)
+        grid = paddle.concat([paddle.unsqueeze(w, 2), paddle.unsqueeze(h, 2)],
+                             2)
         grid = grid.tile([n, 1, 1, 1]).astype('float32')
         grid = grid + flow.transpose([0, 2, 3, 1]) / norm
         output = F.grid_sample(inputs, grid)

paddlex/paddleseg/models/shufflenet_slim.py

@@ -35,15 +35,32 @@ class ShuffleNetV2(nn.Layer):
         self.conv_bn1 = _ConvBNReLU(36, 18, 1, 1, 0)
 
         self.block1 = nn.Sequential(
-            SFNetV2Module(36, stride=2, out_channels=72),
-            SFNetV2Module(72, stride=1), SFNetV2Module(72, stride=1),
-            SFNetV2Module(72, stride=1))
+            SFNetV2Module(
+                36, stride=2, out_channels=72),
+            SFNetV2Module(
+                72, stride=1),
+            SFNetV2Module(
+                72, stride=1),
+            SFNetV2Module(
+                72, stride=1))
 
         self.block2 = nn.Sequential(
-            SFNetV2Module(72, stride=2), SFNetV2Module(144, stride=1),
-            SFNetV2Module(144, stride=1), SFNetV2Module(144, stride=1),
-            SFNetV2Module(144, stride=1), SFNetV2Module(144, stride=1),
-            SFNetV2Module(144, stride=1), SFNetV2Module(144, stride=1))
+            SFNetV2Module(
+                72, stride=2),
+            SFNetV2Module(
+                144, stride=1),
+            SFNetV2Module(
+                144, stride=1),
+            SFNetV2Module(
+                144, stride=1),
+            SFNetV2Module(
+                144, stride=1),
+            SFNetV2Module(
+                144, stride=1),
+            SFNetV2Module(
+                144, stride=1),
+            SFNetV2Module(
+                144, stride=1))
 
         self.depthwise_separable0 = _SeparableConvBNReLU(144, 64, 3, stride=1)
         self.depthwise_separable1 = _SeparableConvBNReLU(82, 64, 3, stride=1)
@@ -227,7 +244,8 @@ class SFNetV2Module(nn.Layer):
         h, w = out_shape[2], out_shape[3]
         output = paddle.reshape(x=output, shape=[0, 2, self.in_channels, h, w])
         output = paddle.transpose(x=output, perm=[0, 2, 1, 3, 4])
-        output = paddle.reshape(x=output, shape=[0, 2 * self.in_channels, h, w])
+        output = paddle.reshape(
+            x=output, shape=[0, 2 * self.in_channels, h, w])
         return output
 
 

paddlex/paddleseg/models/unet_3plus.py

@@ -62,8 +62,10 @@ class UNet3Plus(nn.Layer):
             self.deepsup = DeepSup(self.up_channels, self.filters, num_classes)
             if self.is_CGM:
                 self.cls = nn.Sequential(
-                    nn.Dropout(p=0.5), nn.Conv2D(self.filters[4], 2, 1),
-                    nn.AdaptiveMaxPool2D(1), nn.Sigmoid())
+                    nn.Dropout(p=0.5),
+                    nn.Conv2D(self.filters[4], 2, 1),
+                    nn.AdaptiveMaxPool2D(1),
+                    nn.Sigmoid())
         else:
             self.outconv1 = nn.Conv2D(
                 self.up_channels, num_classes, 3, padding=1)
@@ -93,7 +95,8 @@ class UNet3Plus(nn.Layer):
                 cls_branch = self.cls(hds[-1]).squeeze(3).squeeze(
                     2)  # (B,N,1,1)->(B,N)
                 cls_branch_max = cls_branch.argmax(axis=1)
-                cls_branch_max = cls_branch_max.reshape((-1, 1)).astype('float')
+                cls_branch_max = cls_branch_max.reshape(
+                    (-1, 1)).astype('float')
                 out = [self.dotProduct(d, cls_branch_max) for d in out]
         else:
             out = [self.outconv1(hds[0])]  # d1->320*320*num_classes
@@ -185,7 +188,8 @@ class Decoder(nn.Layer):
         self.hd4_UT_hd1 = nn.Upsample(scale_factor=8, mode='bilinear')  # 14*14
         self.hd4_UT_hd1_cbr = ConvBnReLU2D(up_channels, cat_channels)
         # hd5->20*20, hd1->320*320, Upsample 16 times
-        self.hd5_UT_hd1 = nn.Upsample(scale_factor=16, mode='bilinear')  # 14*14
+        self.hd5_UT_hd1 = nn.Upsample(
+            scale_factor=16, mode='bilinear')  # 14*14
         self.hd5_UT_hd1_cbr = ConvBnReLU2D(filters[4], cat_channels)
         # fusion(h1_Cat_hd1, hd2_UT_hd1, hd3_UT_hd1, hd4_UT_hd1, hd5_UT_hd1)
         self.cbr1d_1 = ConvBnReLU2D(up_channels, up_channels)  # 16
@@ -217,8 +221,9 @@ class Decoder(nn.Layer):
         hd5_UT_hd2 = self.hd5_UT_hd2_cbr(self.hd5_UT_hd2(hd5))
         # hd2->160*160*up_channels
         hd2 = self.cbr2d_1(
-            paddle.concat(
-                [h1_PT_hd2, h2_Cat_hd2, hd3_UT_hd2, hd4_UT_hd2, hd5_UT_hd2], 1))
+            paddle.concat([
+                h1_PT_hd2, h2_Cat_hd2, hd3_UT_hd2, hd4_UT_hd2, hd5_UT_hd2
+            ], 1))
         h1_Cat_hd1 = self.h1_Cat_hd1_cbr(h1)
         hd2_UT_hd1 = self.hd2_UT_hd1_cbr(self.hd2_UT_hd1(hd2))
         hd3_UT_hd1 = self.hd3_UT_hd1_cbr(self.hd3_UT_hd1(hd3))
@@ -226,9 +231,9 @@ class Decoder(nn.Layer):
         hd5_UT_hd1 = self.hd5_UT_hd1_cbr(self.hd5_UT_hd1(hd5))
         # hd1->320*320*up_channels
         hd1 = self.cbr1d_1(
-            paddle.concat(
-                [h1_Cat_hd1, hd2_UT_hd1, hd3_UT_hd1, hd4_UT_hd1, hd5_UT_hd1],
-                1))
+            paddle.concat([
+                h1_Cat_hd1, hd2_UT_hd1, hd3_UT_hd1, hd4_UT_hd1, hd5_UT_hd1
+            ], 1))
         return [hd1, hd2, hd3, hd4, hd5]
 
 
@@ -254,15 +259,19 @@ class DeepSup(nn.Layer):
 class ConvBnReLU2D(nn.Sequential):
     def __init__(self, in_channels, out_channels):
         super(ConvBnReLU2D, self).__init__(
-            nn.Conv2D(in_channels, out_channels, 3, padding=1),
-            nn.BatchNorm(out_channels), nn.ReLU())
+            nn.Conv2D(
+                in_channels, out_channels, 3, padding=1),
+            nn.BatchNorm(out_channels),
+            nn.ReLU())
 
 
 class ConvUp2D(nn.Sequential):
     def __init__(self, in_channels, out_channels, scale_factor):
         super(ConvUp2D, self).__init__(
-            nn.Conv2D(in_channels, out_channels, 3, padding=1),
-            nn.Upsample(scale_factor=scale_factor, mode='bilinear'))
+            nn.Conv2D(
+                in_channels, out_channels, 3, padding=1),
+            nn.Upsample(
+                scale_factor=scale_factor, mode='bilinear'))
 
 
 class MaxPoolConv2D(nn.Sequential):

paddlex/paddleseg/models/unet_plusplus.py

@@ -195,9 +195,10 @@ class DoubleConv(nn.Layer):
         super(DoubleConv, self).__init__()
         self.conv = nn.Sequential(
             nn.Conv2D(in_channels, out_channels, filter_size, stride, padding),
-            SyncBatchNorm(out_channels), nn.ReLU(),
-            nn.Conv2D(out_channels, out_channels, filter_size, stride, padding),
-            SyncBatchNorm(out_channels), nn.ReLU())
+            SyncBatchNorm(out_channels),
+            nn.ReLU(),
+            nn.Conv2D(out_channels, out_channels, filter_size, stride,
+                      padding), SyncBatchNorm(out_channels), nn.ReLU())
 
     def forward(self, inputs):
         conv = self.conv(inputs)

paddlex/paddleseg/transforms/functional.py

@@ -128,11 +128,12 @@ def onehot_to_binary_edge(mask, radius):
 
     edge = np.zeros(mask.shape[1:])
     # pad borders
-    mask = np.pad(
-        mask, ((0, 0), (1, 1), (1, 1)), mode='constant', constant_values=0)
+    mask = np.pad(mask, ((0, 0), (1, 1), (1, 1)),
+                  mode='constant',
+                  constant_values=0)
     for i in range(num_classes):
-        dist = distance_transform_edt(
-            mask[i, :]) + distance_transform_edt(1.0 - mask[i, :])
+        dist = distance_transform_edt(mask[i, :]) + distance_transform_edt(
+            1.0 - mask[i, :])
         dist = dist[1:-1, 1:-1]
         dist[dist > radius] = 0
         edge += dist

paddlex/paddleseg/transforms/transforms.py

@@ -282,7 +282,8 @@ class LimitLong:
         if target != long_edge:
             im = functional.resize_long(im, target)
             if label is not None:
-                label = functional.resize_long(label, target, cv2.INTER_NEAREST)
+                label = functional.resize_long(label, target,
+                                               cv2.INTER_NEAREST)
 
         if label is None:
             return (im, )
@@ -303,8 +304,8 @@ class ResizeRangeScaling:
     def __init__(self, min_value=400, max_value=600):
         if min_value > max_value:
             raise ValueError('min_value must be less than max_value, '
-                             'but they are {} and {}.'.format(
-                                 min_value, max_value))
+                             'but they are {} and {}.'.format(min_value,
+                                                              max_value))
         self.min_value = min_value
         self.max_value = max_value
 
@@ -415,8 +416,9 @@ class Normalize:
     def __init__(self, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)):
         self.mean = mean
         self.std = std
-        if not (isinstance(self.mean, (list, tuple))
-                and isinstance(self.std, (list, tuple))):
+        if not (isinstance(self.mean,
+                           (list, tuple)) and isinstance(self.std,
+                                                         (list, tuple))):
             raise ValueError(
                 "{}: input type is invalid. It should be list or tuple".format(
                     self))
@@ -562,9 +564,10 @@ class PaddingByAspectRatio:
             img_height = int(img_width / self.aspect_ratio)
         else:
             img_width = int(img_height * self.aspect_ratio)
-        padding = Padding((img_width, img_height),
-                          im_padding_value=self.im_padding_value,
-                          label_padding_value=self.label_padding_value)
+        padding = Padding(
+            (img_width, img_height),
+            im_padding_value=self.im_padding_value,
+            label_padding_value=self.label_padding_value)
         return padding(im, label)
 
 
@@ -655,8 +658,8 @@ class RandomPaddingCrop:
                 h_off = np.random.randint(img_height - crop_height + 1)
                 w_off = np.random.randint(img_width - crop_width + 1)
 
-                im = im[h_off:(crop_height + h_off), w_off:(
-                    w_off + crop_width), :]
+                im = im[h_off:(crop_height + h_off), w_off:(w_off + crop_width
+                                                            ), :]
                 if label is not None:
                     label = label[h_off:(crop_height + h_off), w_off:(
                         w_off + crop_width)]

paddlex/paddleseg/utils/config_check.py

@@ -37,7 +37,8 @@ def num_classes_check(cfg, train_dataset, val_dataset):
         num_classes_set.add(train_dataset.num_classes)
     if val_dataset and hasattr(val_dataset, 'num_classes'):
         num_classes_set.add(val_dataset.num_classes)
-    if cfg.dic.get('model', None) and cfg.dic['model'].get('num_classes', None):
+    if cfg.dic.get('model', None) and cfg.dic['model'].get('num_classes',
+                                                           None):
         num_classes_set.add(cfg.dic['model'].get('num_classes'))
     if (not cfg.train_dataset) and (not cfg.val_dataset):
         raise ValueError(

paddlex/paddleseg/utils/download.py

@@ -102,8 +102,8 @@ def _uncompress_file(filepath, extrapath, delete_file, print_progress):
     for total_num, index, rootpath in handler(filepath, extrapath):
         if print_progress:
             done = int(50 * float(index) / total_num)
-            progress(
-                "[%-50s] %.2f%%" % ('=' * done, float(100 * index) / total_num))
+            progress("[%-50s] %.2f%%" %
+                     ('=' * done, float(100 * index) / total_num))
     if print_progress:
         progress("[%-50s] %.2f%%" % ('=' * 50, 100), end=True)
 
@@ -133,8 +133,8 @@ def download_file_and_uncompress(url,
     savepath = os.path.join(savepath, savename)
     savename = ".".join(savename.split(".")[:-1])
     savename = os.path.join(extrapath, savename)
-    extraname = savename if extraname is None else os.path.join(
-        extrapath, extraname)
+    extraname = savename if extraname is None else os.path.join(extrapath,
+                                                                extraname)
 
     if cover:
         if os.path.exists(savepath):

paddlex/paddleseg/utils/logger.py

@@ -27,9 +27,8 @@ def log(level=2, message=""):
         time_array = time.localtime(current_time)
         current_time = time.strftime("%Y-%m-%d %H:%M:%S", time_array)
         if log_level >= level:
-            print(
-                "{} [{}]\t{}".format(current_time, levels[level],
-                                     message).encode("utf-8").decode("latin1"))
+            print("{} [{}]\t{}".format(current_time, levels[level], message)
+                  .encode("utf-8").decode("latin1"))
             sys.stdout.flush()
 
 

paddlex/paddleseg/utils/metrics.py

@@ -38,8 +38,8 @@ def calculate_area(pred, label, num_classes, ignore_index=255):
         label = paddle.squeeze(label, axis=1)
     if not pred.shape == label.shape:
         raise ValueError('Shape of `pred` and `label should be equal, '
-                         'but there are {} and {}.'.format(
-                             pred.shape, label.shape))
+                         'but there are {} and {}.'.format(pred.shape,
+                                                           label.shape))
 
     # Delete ignore_index
     mask = label != ignore_index

paddlex/paddleseg/utils/progbar.py

@@ -52,11 +52,10 @@ class Progbar(object):
         else:
             self.stateful_metrics = set()
 
-        self._dynamic_display = ((hasattr(sys.stderr, 'isatty')
-                                  and sys.stderr.isatty())
-                                 or 'ipykernel' in sys.modules
-                                 or 'posix' in sys.modules
-                                 or 'PYCHARM_HOSTED' in os.environ)
+        self._dynamic_display = (
+            (hasattr(sys.stderr, 'isatty') and
+             sys.stderr.isatty()) or 'ipykernel' in sys.modules or
+            'posix' in sys.modules or 'PYCHARM_HOSTED' in os.environ)
         self._total_width = 0
         self._seen_so_far = 0
         # We use a dict + list to avoid garbage collection
@@ -122,7 +121,8 @@ class Progbar(object):
 
             if self.target is not None:
                 numdigits = int(np.log10(self.target)) + 1
-                bar = ('%' + str(numdigits) + 'd/%d [') % (current, self.target)
+                bar = ('%' + str(numdigits) + 'd/%d [') % (current,
+                                                           self.target)
                 prog = float(current) / self.target
                 prog_width = int(self.width * prog)
                 if prog_width > 0:
@@ -148,14 +148,16 @@ class Progbar(object):
                 if time_per_unit >= 1 or time_per_unit == 0:
                     info += ' %.0fs/%s' % (time_per_unit, self.unit_name)
                 elif time_per_unit >= 1e-3:
-                    info += ' %.0fms/%s' % (time_per_unit * 1e3, self.unit_name)
+                    info += ' %.0fms/%s' % (time_per_unit * 1e3,
+                                            self.unit_name)
                 else:
-                    info += ' %.0fus/%s' % (time_per_unit * 1e6, self.unit_name)
+                    info += ' %.0fus/%s' % (time_per_unit * 1e6,
+                                            self.unit_name)
             else:
                 eta = time_per_unit * (self.target - current)
                 if eta > 3600:
-                    eta_format = '%d:%02d:%02d' % (eta // 3600,
-                                                   (eta % 3600) // 60, eta % 60)
+                    eta_format = '%d:%02d:%02d' % (eta // 3600, (eta % 3600) //
+                                                   60, eta % 60)
                 elif eta > 60:
                     eta_format = '%d:%02d' % (eta // 60, eta % 60)
                 else:
@@ -166,8 +168,8 @@ class Progbar(object):
             for k in self._values_order:
                 info += ' - %s:' % k
                 if isinstance(self._values[k], list):
-                    avg = np.mean(
-                        self._values[k][0] / max(1, self._values[k][1]))
+                    avg = np.mean(self._values[k][0] /
+                                  max(1, self._values[k][1]))
                     if abs(avg) > 1e-3:
                         info += ' %.4f' % avg
                     else:
@@ -188,12 +190,13 @@ class Progbar(object):
         elif self.verbose == 2:
             if finalize:
                 numdigits = int(np.log10(self.target)) + 1
-                count = ('%' + str(numdigits) + 'd/%d') % (current, self.target)
+                count = ('%' + str(numdigits) + 'd/%d') % (current,
+                                                           self.target)
                 info = count + info
                 for k in self._values_order:
                     info += ' - %s:' % k
-                    avg = np.mean(
-                        self._values[k][0] / max(1, self._values[k][1]))
+                    avg = np.mean(self._values[k][0] /
+                                  max(1, self._values[k][1]))
                     if avg > 1e-3:
                         info += ' %.4f' % avg
                     else:

paddlex/paddleseg/utils/utils.py

@@ -26,7 +26,7 @@ from paddlex.paddleseg.utils.download import download_file_and_uncompress
 
 
 @contextlib.contextmanager
-def generate_tempdir(directory: str = None, **kwargs):
+def generate_tempdir(directory: str=None, **kwargs):
     '''Generate a temporary directory'''
     directory = seg_env.TMP_HOME if not directory else directory
     with tempfile.TemporaryDirectory(dir=directory, **kwargs) as _dir:
@@ -43,7 +43,8 @@ def load_entire_model(model, pretrained):
 
 def load_pretrained_model(model, pretrained_model):
     if pretrained_model is not None:
-        logger.info('Loading pretrained model from {}'.format(pretrained_model))
+        logger.info('Loading pretrained model from {}'.format(
+            pretrained_model))
         # download pretrained model from url
         if urlparse(pretrained_model).netloc:
             pretrained_model = unquote(pretrained_model)
@@ -73,24 +74,23 @@ def load_pretrained_model(model, pretrained_model):
             for k in keys:
                 if k not in para_state_dict:
                     logger.warning("{} is not in pretrained model".format(k))
-                elif list(para_state_dict[k].shape) != list(
-                        model_state_dict[k].shape):
+                elif list(para_state_dict[k].shape) != list(model_state_dict[k]
+                                                            .shape):
                     logger.warning(
                         "[SKIP] Shape of pretrained params {} doesn't match.(Pretrained: {}, Actual: {})"
-                        .format(k, para_state_dict[k].shape,
-                                model_state_dict[k].shape))
+                        .format(k, para_state_dict[k].shape, model_state_dict[
+                            k].shape))
                 else:
                     model_state_dict[k] = para_state_dict[k]
                     num_params_loaded += 1
             model.set_dict(model_state_dict)
             logger.info("There are {}/{} variables loaded into {}.".format(
-                num_params_loaded, len(model_state_dict),
-                model.__class__.__name__))
+                num_params_loaded,
+                len(model_state_dict), model.__class__.__name__))
 
         else:
-            raise ValueError(
-                'The pretrained model directory is not Found: {}'.format(
-                    pretrained_model))
+            raise ValueError('The pretrained model directory is not Found: {}'.
+                             format(pretrained_model))
     else:
         logger.info(
             'No pretrained model to load, {} will be trained from scratch.'.

paddlex/ppcls/data/imaug/operators.py

@@ -195,10 +195,18 @@ class NormalizeImage(object):
     """ normalize image such as substract mean, divide std
     """
 
-    def __init__(self, scale=None, mean=None, std=None, order='chw', output_fp16=False, channel_num=3):
+    def __init__(self,
+                 scale=None,
+                 mean=None,
+                 std=None,
+                 order='chw',
+                 output_fp16=False,
+                 channel_num=3):
         if isinstance(scale, str):
             scale = eval(scale)
-        assert channel_num in [3, 4], "channel number of input image should be set to 3 or 4."
+        assert channel_num in [
+            3, 4
+        ], "channel number of input image should be set to 3 or 4."
         self.channel_num = channel_num
         self.output_dtype = 'float16' if output_fp16 else 'float32'
         self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
@@ -217,15 +225,19 @@ class NormalizeImage(object):
 
         assert isinstance(img,
                           np.ndarray), "invalid input 'img' in NormalizeImage"
-        
+
         img = (img.astype('float32') * self.scale - self.mean) / self.std
 
         if self.channel_num == 4:
             img_h = img.shape[1] if self.order == 'chw' else img.shape[0]
             img_w = img.shape[2] if self.order == 'chw' else img.shape[1]
-            pad_zeros = np.zeros((1, img_h, img_w)) if self.order == 'chw' else np.zeros((img_h, img_w, 1))
-            img = (np.concatenate((img, pad_zeros), axis=0) if self.order == 'chw'
-                   else np.concatenate((img, pad_zeros), axis=2))
+            pad_zeros = np.zeros(
+                (1, img_h, img_w)) if self.order == 'chw' else np.zeros(
+                    (img_h, img_w, 1))
+            img = (np.concatenate(
+                (img, pad_zeros), axis=0)
+                   if self.order == 'chw' else np.concatenate(
+                       (img, pad_zeros), axis=2))
         return img.astype(self.output_dtype)
 
 

paddlex/ppcls/modeling/architectures/inception_v3.py

@@ -16,7 +16,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-
 import paddle
 from paddle import ParamAttr
 import paddle.nn as nn
@@ -48,53 +47,60 @@ class ConvBNLayer(nn.Layer):
             stride=stride,
             padding=padding,
             groups=groups,
-            weight_attr=ParamAttr(name=name+"_weights"),
+            weight_attr=ParamAttr(name=name + "_weights"),
             bias_attr=False)
         self.batch_norm = BatchNorm(
             num_filters,
             act=act,
-            param_attr=ParamAttr(name=name+"_bn_scale"),
-            bias_attr=ParamAttr(name=name+"_bn_offset"),
-            moving_mean_name=name+"_bn_mean",
-            moving_variance_name=name+"_bn_variance")
+            param_attr=ParamAttr(name=name + "_bn_scale"),
+            bias_attr=ParamAttr(name=name + "_bn_offset"),
+            moving_mean_name=name + "_bn_mean",
+            moving_variance_name=name + "_bn_variance")
 
     def forward(self, inputs):
         y = self.conv(inputs)
         y = self.batch_norm(y)
         return y
 
+
 class InceptionStem(nn.Layer):
     def __init__(self):
         super(InceptionStem, self).__init__()
-        self.conv_1a_3x3 = ConvBNLayer(num_channels=3,
-                                       num_filters=32,
-                                       filter_size=3,
-                                       stride=2,
-                                       act="relu",
-                                       name="conv_1a_3x3")
-        self.conv_2a_3x3 = ConvBNLayer(num_channels=32,
-                                       num_filters=32,
-                                       filter_size=3,
-                                       stride=1,
-                                       act="relu",
-                                       name="conv_2a_3x3")
-        self.conv_2b_3x3 = ConvBNLayer(num_channels=32,
-                                       num_filters=64,
-                                       filter_size=3,
-                                       padding=1,
-                                       act="relu",
-                                       name="conv_2b_3x3")
+        self.conv_1a_3x3 = ConvBNLayer(
+            num_channels=3,
+            num_filters=32,
+            filter_size=3,
+            stride=2,
+            act="relu",
+            name="conv_1a_3x3")
+        self.conv_2a_3x3 = ConvBNLayer(
+            num_channels=32,
+            num_filters=32,
+            filter_size=3,
+            stride=1,
+            act="relu",
+            name="conv_2a_3x3")
+        self.conv_2b_3x3 = ConvBNLayer(
+            num_channels=32,
+            num_filters=64,
+            filter_size=3,
+            padding=1,
+            act="relu",
+            name="conv_2b_3x3")
         self.maxpool = MaxPool2D(kernel_size=3, stride=2, padding=0)
-        self.conv_3b_1x1 = ConvBNLayer(num_channels=64,
-                                       num_filters=80,
-                                       filter_size=1,
-                                       act="relu",
-                                       name="conv_3b_1x1")        
-        self.conv_4a_3x3 = ConvBNLayer(num_channels=80,
-                                       num_filters=192,
-                                       filter_size=3,
-                                       act="relu",
-                                       name="conv_4a_3x3")
+        self.conv_3b_1x1 = ConvBNLayer(
+            num_channels=64,
+            num_filters=80,
+            filter_size=1,
+            act="relu",
+            name="conv_3b_1x1")
+        self.conv_4a_3x3 = ConvBNLayer(
+            num_channels=80,
+            num_filters=192,
+            filter_size=3,
+            act="relu",
+            name="conv_4a_3x3")
+
     def forward(self, x):
         y = self.conv_1a_3x3(x)
         y = self.conv_2a_3x3(y)
@@ -105,50 +111,59 @@ class InceptionStem(nn.Layer):
         y = self.maxpool(y)
         return y
 
-                         
+
 class InceptionA(nn.Layer):
     def __init__(self, num_channels, pool_features, name=None):
         super(InceptionA, self).__init__()
-        self.branch1x1 = ConvBNLayer(num_channels=num_channels,
-                                     num_filters=64,
-                                     filter_size=1,
-                                     act="relu",
-                                     name="inception_a_branch1x1_"+name) 
-        self.branch5x5_1 = ConvBNLayer(num_channels=num_channels,
-                                       num_filters=48, 
-                                       filter_size=1, 
-                                       act="relu",
-                                       name="inception_a_branch5x5_1_"+name)
-        self.branch5x5_2 = ConvBNLayer(num_channels=48, 
-                                       num_filters=64, 
-                                       filter_size=5, 
-                                       padding=2, 
-                                       act="relu",
-                                       name="inception_a_branch5x5_2_"+name)
-
-        self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels,
-                                       num_filters=64, 
-                                       filter_size=1, 
-                                       act="relu",
-                                       name="inception_a_branch3x3dbl_1_"+name)
-        self.branch3x3dbl_2 = ConvBNLayer(num_channels=64,
-                                       num_filters=96, 
-                                       filter_size=3, 
-                                       padding=1,
-                                       act="relu",
-                                       name="inception_a_branch3x3dbl_2_"+name)
-        self.branch3x3dbl_3 = ConvBNLayer(num_channels=96,
-                               num_filters=96, 
-                               filter_size=3, 
-                               padding=1,
-                               act="relu",
-                               name="inception_a_branch3x3dbl_3_"+name)
-        self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False)
-        self.branch_pool_conv = ConvBNLayer(num_channels=num_channels,
-                               num_filters=pool_features, 
-                               filter_size=1, 
-                               act="relu",
-                               name="inception_a_branch_pool_"+name)
+        self.branch1x1 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=64,
+            filter_size=1,
+            act="relu",
+            name="inception_a_branch1x1_" + name)
+        self.branch5x5_1 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=48,
+            filter_size=1,
+            act="relu",
+            name="inception_a_branch5x5_1_" + name)
+        self.branch5x5_2 = ConvBNLayer(
+            num_channels=48,
+            num_filters=64,
+            filter_size=5,
+            padding=2,
+            act="relu",
+            name="inception_a_branch5x5_2_" + name)
+
+        self.branch3x3dbl_1 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=64,
+            filter_size=1,
+            act="relu",
+            name="inception_a_branch3x3dbl_1_" + name)
+        self.branch3x3dbl_2 = ConvBNLayer(
+            num_channels=64,
+            num_filters=96,
+            filter_size=3,
+            padding=1,
+            act="relu",
+            name="inception_a_branch3x3dbl_2_" + name)
+        self.branch3x3dbl_3 = ConvBNLayer(
+            num_channels=96,
+            num_filters=96,
+            filter_size=3,
+            padding=1,
+            act="relu",
+            name="inception_a_branch3x3dbl_3_" + name)
+        self.branch_pool = AvgPool2D(
+            kernel_size=3, stride=1, padding=1, exclusive=False)
+        self.branch_pool_conv = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=pool_features,
+            filter_size=1,
+            act="relu",
+            name="inception_a_branch_pool_" + name)
+
     def forward(self, x):
         branch1x1 = self.branch1x1(x)
         branch5x5 = self.branch5x5_1(x)
@@ -161,38 +176,43 @@ class InceptionA(nn.Layer):
         branch_pool = self.branch_pool(x)
 
         branch_pool = self.branch_pool_conv(branch_pool)
-        outputs = paddle.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
+        outputs = paddle.concat(
+            [branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
         return outputs
 
-    
+
 class InceptionB(nn.Layer):
     def __init__(self, num_channels, name=None):
         super(InceptionB, self).__init__()
-        self.branch3x3 = ConvBNLayer(num_channels=num_channels,
-                                     num_filters=384,
-                                     filter_size=3,
-                                     stride=2,
-                                     act="relu",
-                                     name="inception_b_branch3x3_"+name) 
-        self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels, 
-                                       num_filters=64, 
-                                       filter_size=1, 
-                                       act="relu",
-                                       name="inception_b_branch3x3dbl_1_"+name)
-        self.branch3x3dbl_2 = ConvBNLayer(num_channels=64, 
-                                       num_filters=96, 
-                                       filter_size=3, 
-                                       padding=1,
-                                       act="relu",
-                                       name="inception_b_branch3x3dbl_2_"+name)
-        self.branch3x3dbl_3 = ConvBNLayer(num_channels=96, 
-                                       num_filters=96, 
-                                       filter_size=3,
-                                       stride=2,
-                                       act="relu",
-                                       name="inception_b_branch3x3dbl_3_"+name)
+        self.branch3x3 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=384,
+            filter_size=3,
+            stride=2,
+            act="relu",
+            name="inception_b_branch3x3_" + name)
+        self.branch3x3dbl_1 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=64,
+            filter_size=1,
+            act="relu",
+            name="inception_b_branch3x3dbl_1_" + name)
+        self.branch3x3dbl_2 = ConvBNLayer(
+            num_channels=64,
+            num_filters=96,
+            filter_size=3,
+            padding=1,
+            act="relu",
+            name="inception_b_branch3x3dbl_2_" + name)
+        self.branch3x3dbl_3 = ConvBNLayer(
+            num_channels=96,
+            num_filters=96,
+            filter_size=3,
+            stride=2,
+            act="relu",
+            name="inception_b_branch3x3dbl_3_" + name)
         self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
-        
+
     def forward(self, x):
         branch3x3 = self.branch3x3(x)
 
@@ -206,72 +226,84 @@ class InceptionB(nn.Layer):
 
         return outputs
 
+
 class InceptionC(nn.Layer):
     def __init__(self, num_channels, channels_7x7, name=None):
         super(InceptionC, self).__init__()
-        self.branch1x1 = ConvBNLayer(num_channels=num_channels, 
-                                       num_filters=192, 
-                                       filter_size=1, 
-                                       act="relu",
-                                       name="inception_c_branch1x1_"+name)
-        self.branch7x7_1 = ConvBNLayer(num_channels=num_channels, 
-                                       num_filters=channels_7x7, 
-                                       filter_size=1, 
-                                       stride=1,
-                                       act="relu",
-                                       name="inception_c_branch7x7_1_"+name)
-        self.branch7x7_2 = ConvBNLayer(num_channels=channels_7x7,
-                                       num_filters=channels_7x7, 
-                                       filter_size=(1, 7), 
-                                       stride=1,
-                                       padding=(0, 3),
-                                       act="relu",
-                                       name="inception_c_branch7x7_2_"+name)
-        self.branch7x7_3 = ConvBNLayer(num_channels=channels_7x7,
-                                       num_filters=192, 
-                                       filter_size=(7, 1), 
-                                       stride=1,
-                                       padding=(3, 0),
-                                       act="relu",
-                                       name="inception_c_branch7x7_3_"+name)
-        
-        self.branch7x7dbl_1 = ConvBNLayer(num_channels=num_channels, 
-                                       num_filters=channels_7x7, 
-                                       filter_size=1, 
-                                       act="relu",
-                                       name="inception_c_branch7x7dbl_1_"+name)
-        self.branch7x7dbl_2 = ConvBNLayer(num_channels=channels_7x7,  
-                                       num_filters=channels_7x7, 
-                                       filter_size=(7, 1), 
-                                       padding = (3, 0),
-                                       act="relu",
-                                       name="inception_c_branch7x7dbl_2_"+name)
-        self.branch7x7dbl_3 = ConvBNLayer(num_channels=channels_7x7, 
-                                       num_filters=channels_7x7, 
-                                       filter_size=(1, 7), 
-                                       padding = (0, 3),
-                                       act="relu",
-                                       name="inception_c_branch7x7dbl_3_"+name)
-        self.branch7x7dbl_4 = ConvBNLayer(num_channels=channels_7x7,  
-                                       num_filters=channels_7x7, 
-                                       filter_size=(7, 1), 
-                                       padding = (3, 0),
-                                       act="relu",
-                                       name="inception_c_branch7x7dbl_4_"+name)
-        self.branch7x7dbl_5 = ConvBNLayer(num_channels=channels_7x7, 
-                                       num_filters=192, 
-                                       filter_size=(1, 7), 
-                                       padding = (0, 3),
-                                       act="relu",
-                                       name="inception_c_branch7x7dbl_5_"+name)
-       
-        self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False)
-        self.branch_pool_conv = ConvBNLayer(num_channels=num_channels,
-                                       num_filters=192, 
-                                       filter_size=1, 
-                                       act="relu",
-                                       name="inception_c_branch_pool_"+name)
-        
+        self.branch1x1 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=192,
+            filter_size=1,
+            act="relu",
+            name="inception_c_branch1x1_" + name)
+        self.branch7x7_1 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=channels_7x7,
+            filter_size=1,
+            stride=1,
+            act="relu",
+            name="inception_c_branch7x7_1_" + name)
+        self.branch7x7_2 = ConvBNLayer(
+            num_channels=channels_7x7,
+            num_filters=channels_7x7,
+            filter_size=(1, 7),
+            stride=1,
+            padding=(0, 3),
+            act="relu",
+            name="inception_c_branch7x7_2_" + name)
+        self.branch7x7_3 = ConvBNLayer(
+            num_channels=channels_7x7,
+            num_filters=192,
+            filter_size=(7, 1),
+            stride=1,
+            padding=(3, 0),
+            act="relu",
+            name="inception_c_branch7x7_3_" + name)
+
+        self.branch7x7dbl_1 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=channels_7x7,
+            filter_size=1,
+            act="relu",
+            name="inception_c_branch7x7dbl_1_" + name)
+        self.branch7x7dbl_2 = ConvBNLayer(
+            num_channels=channels_7x7,
+            num_filters=channels_7x7,
+            filter_size=(7, 1),
+            padding=(3, 0),
+            act="relu",
+            name="inception_c_branch7x7dbl_2_" + name)
+        self.branch7x7dbl_3 = ConvBNLayer(
+            num_channels=channels_7x7,
+            num_filters=channels_7x7,
+            filter_size=(1, 7),
+            padding=(0, 3),
+            act="relu",
+            name="inception_c_branch7x7dbl_3_" + name)
+        self.branch7x7dbl_4 = ConvBNLayer(
+            num_channels=channels_7x7,
+            num_filters=channels_7x7,
+            filter_size=(7, 1),
+            padding=(3, 0),
+            act="relu",
+            name="inception_c_branch7x7dbl_4_" + name)
+        self.branch7x7dbl_5 = ConvBNLayer(
+            num_channels=channels_7x7,
+            num_filters=192,
+            filter_size=(1, 7),
+            padding=(0, 3),
+            act="relu",
+            name="inception_c_branch7x7dbl_5_" + name)
+
+        self.branch_pool = AvgPool2D(
+            kernel_size=3, stride=1, padding=1, exclusive=False)
+        self.branch_pool_conv = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=192,
+            filter_size=1,
+            act="relu",
+            name="inception_c_branch_pool_" + name)
+
     def forward(self, x):
         branch1x1 = self.branch1x1(x)
 
@@ -288,47 +320,55 @@ class InceptionC(nn.Layer):
         branch_pool = self.branch_pool(x)
         branch_pool = self.branch_pool_conv(branch_pool)
 
-        outputs = paddle.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
-        
+        outputs = paddle.concat(
+            [branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
+
         return outputs
-    
+
+
 class InceptionD(nn.Layer):
     def __init__(self, num_channels, name=None):
         super(InceptionD, self).__init__()
-        self.branch3x3_1 = ConvBNLayer(num_channels=num_channels, 
-                                       num_filters=192, 
-                                       filter_size=1, 
-                                       act="relu",
-                                       name="inception_d_branch3x3_1_"+name)
-        self.branch3x3_2 = ConvBNLayer(num_channels=192, 
-                                       num_filters=320, 
-                                       filter_size=3, 
-                                       stride=2,
-                                       act="relu",
-                                       name="inception_d_branch3x3_2_"+name)
-        self.branch7x7x3_1 = ConvBNLayer(num_channels=num_channels, 
-                                       num_filters=192, 
-                                       filter_size=1, 
-                                       act="relu",
-                                       name="inception_d_branch7x7x3_1_"+name)
-        self.branch7x7x3_2 = ConvBNLayer(num_channels=192,
-                                       num_filters=192, 
-                                       filter_size=(1, 7), 
-                                       padding=(0, 3),
-                                       act="relu",
-                                       name="inception_d_branch7x7x3_2_"+name)
-        self.branch7x7x3_3 = ConvBNLayer(num_channels=192, 
-                                       num_filters=192, 
-                                       filter_size=(7, 1), 
-                                       padding=(3, 0),
-                                       act="relu",
-                                       name="inception_d_branch7x7x3_3_"+name)
-        self.branch7x7x3_4 = ConvBNLayer(num_channels=192,  
-                                       num_filters=192, 
-                                       filter_size=3, 
-                                       stride=2,
-                                       act="relu",
-                                       name="inception_d_branch7x7x3_4_"+name)
+        self.branch3x3_1 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=192,
+            filter_size=1,
+            act="relu",
+            name="inception_d_branch3x3_1_" + name)
+        self.branch3x3_2 = ConvBNLayer(
+            num_channels=192,
+            num_filters=320,
+            filter_size=3,
+            stride=2,
+            act="relu",
+            name="inception_d_branch3x3_2_" + name)
+        self.branch7x7x3_1 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=192,
+            filter_size=1,
+            act="relu",
+            name="inception_d_branch7x7x3_1_" + name)
+        self.branch7x7x3_2 = ConvBNLayer(
+            num_channels=192,
+            num_filters=192,
+            filter_size=(1, 7),
+            padding=(0, 3),
+            act="relu",
+            name="inception_d_branch7x7x3_2_" + name)
+        self.branch7x7x3_3 = ConvBNLayer(
+            num_channels=192,
+            num_filters=192,
+            filter_size=(7, 1),
+            padding=(3, 0),
+            act="relu",
+            name="inception_d_branch7x7x3_3_" + name)
+        self.branch7x7x3_4 = ConvBNLayer(
+            num_channels=192,
+            num_filters=192,
+            filter_size=3,
+            stride=2,
+            act="relu",
+            name="inception_d_branch7x7x3_4_" + name)
         self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
 
     def forward(self, x):
@@ -341,65 +381,77 @@ class InceptionD(nn.Layer):
         branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
 
         branch_pool = self.branch_pool(x)
-        
+
         outputs = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
         return outputs
-    
+
+
 class InceptionE(nn.Layer):
     def __init__(self, num_channels, name=None):
         super(InceptionE, self).__init__()
-        self.branch1x1 = ConvBNLayer(num_channels=num_channels,
-                                       num_filters=320, 
-                                       filter_size=1, 
-                                       act="relu",
-                                       name="inception_e_branch1x1_"+name)
-        self.branch3x3_1 = ConvBNLayer(num_channels=num_channels,
-                                       num_filters=384, 
-                                       filter_size=1, 
-                                       act="relu",
-                                       name="inception_e_branch3x3_1_"+name)
-        self.branch3x3_2a = ConvBNLayer(num_channels=384, 
-                                       num_filters=384, 
-                                       filter_size=(1, 3), 
-                                       padding=(0, 1),
-                                       act="relu",
-                                       name="inception_e_branch3x3_2a_"+name)
-        self.branch3x3_2b = ConvBNLayer(num_channels=384, 
-                                       num_filters=384, 
-                                       filter_size=(3, 1), 
-                                       padding=(1, 0),
-                                       act="relu",
-                                       name="inception_e_branch3x3_2b_"+name)
-        
-        self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels, 
-                                       num_filters=448, 
-                                       filter_size=1, 
-                                       act="relu",
-                                       name="inception_e_branch3x3dbl_1_"+name)
-        self.branch3x3dbl_2 = ConvBNLayer(num_channels=448, 
-                                       num_filters=384, 
-                                       filter_size=3, 
-                                       padding=1,
-                                       act="relu",
-                                       name="inception_e_branch3x3dbl_2_"+name)
-        self.branch3x3dbl_3a = ConvBNLayer(num_channels=384,
-                                       num_filters=384, 
-                                       filter_size=(1, 3), 
-                                       padding=(0, 1),
-                                       act="relu",
-                                       name="inception_e_branch3x3dbl_3a_"+name)
-        self.branch3x3dbl_3b = ConvBNLayer(num_channels=384,
-                                       num_filters=384, 
-                                       filter_size=(3, 1), 
-                                       padding=(1, 0),
-                                       act="relu",
-                                       name="inception_e_branch3x3dbl_3b_"+name)
-        self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False)
-        self.branch_pool_conv = ConvBNLayer(num_channels=num_channels, 
-                                       num_filters=192, 
-                                       filter_size=1, 
-                                       act="relu",
-                                       name="inception_e_branch_pool_"+name)
+        self.branch1x1 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=320,
+            filter_size=1,
+            act="relu",
+            name="inception_e_branch1x1_" + name)
+        self.branch3x3_1 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=384,
+            filter_size=1,
+            act="relu",
+            name="inception_e_branch3x3_1_" + name)
+        self.branch3x3_2a = ConvBNLayer(
+            num_channels=384,
+            num_filters=384,
+            filter_size=(1, 3),
+            padding=(0, 1),
+            act="relu",
+            name="inception_e_branch3x3_2a_" + name)
+        self.branch3x3_2b = ConvBNLayer(
+            num_channels=384,
+            num_filters=384,
+            filter_size=(3, 1),
+            padding=(1, 0),
+            act="relu",
+            name="inception_e_branch3x3_2b_" + name)
+
+        self.branch3x3dbl_1 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=448,
+            filter_size=1,
+            act="relu",
+            name="inception_e_branch3x3dbl_1_" + name)
+        self.branch3x3dbl_2 = ConvBNLayer(
+            num_channels=448,
+            num_filters=384,
+            filter_size=3,
+            padding=1,
+            act="relu",
+            name="inception_e_branch3x3dbl_2_" + name)
+        self.branch3x3dbl_3a = ConvBNLayer(
+            num_channels=384,
+            num_filters=384,
+            filter_size=(1, 3),
+            padding=(0, 1),
+            act="relu",
+            name="inception_e_branch3x3dbl_3a_" + name)
+        self.branch3x3dbl_3b = ConvBNLayer(
+            num_channels=384,
+            num_filters=384,
+            filter_size=(3, 1),
+            padding=(1, 0),
+            act="relu",
+            name="inception_e_branch3x3dbl_3b_" + name)
+        self.branch_pool = AvgPool2D(
+            kernel_size=3, stride=1, padding=1, exclusive=False)
+        self.branch_pool_conv = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=192,
+            filter_size=1,
+            act="relu",
+            name="inception_e_branch_pool_" + name)
+
     def forward(self, x):
         branch1x1 = self.branch1x1(x)
 
@@ -421,44 +473,53 @@ class InceptionE(nn.Layer):
         branch_pool = self.branch_pool(x)
         branch_pool = self.branch_pool_conv(branch_pool)
 
-        outputs = paddle.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
-        return outputs   
+        outputs = paddle.concat(
+            [branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
+        return outputs
+
 
-    
 class InceptionV3(nn.Layer):
     def __init__(self, class_dim=1000):
         super(InceptionV3, self).__init__()
         self.inception_a_list = [[192, 256, 288], [32, 64, 64]]
         self.inception_c_list = [[768, 768, 768, 768], [128, 160, 160, 192]]
-        
+
         self.inception_stem = InceptionStem()
         self.inception_block_list = []
         for i in range(len(self.inception_a_list[0])):
-            inception_a = self.add_sublayer("inception_a_"+str(i+1),
-                                           InceptionA(self.inception_a_list[0][i], 
-                                                      self.inception_a_list[1][i], 
-                                                      name=str(i+1)))
+            inception_a = self.add_sublayer(
+                "inception_a_" + str(i + 1),
+                InceptionA(
+                    self.inception_a_list[0][i],
+                    self.inception_a_list[1][i],
+                    name=str(i + 1)))
             self.inception_block_list.append(inception_a)
-        inception_b = self.add_sublayer("nception_b_1",
-                                        InceptionB(288, name="1"))
+        inception_b = self.add_sublayer(
+            "nception_b_1", InceptionB(
+                288, name="1"))
         self.inception_block_list.append(inception_b)
-        
+
         for i in range(len(self.inception_c_list[0])):
-            inception_c = self.add_sublayer("inception_c_"+str(i+1),
-                                           InceptionC(self.inception_c_list[0][i], 
-                                                      self.inception_c_list[1][i], 
-                                                      name=str(i+1)))
+            inception_c = self.add_sublayer(
+                "inception_c_" + str(i + 1),
+                InceptionC(
+                    self.inception_c_list[0][i],
+                    self.inception_c_list[1][i],
+                    name=str(i + 1)))
             self.inception_block_list.append(inception_c)
-        inception_d = self.add_sublayer("inception_d_1",
-                                        InceptionD(768, name="1"))
+        inception_d = self.add_sublayer(
+            "inception_d_1", InceptionD(
+                768, name="1"))
         self.inception_block_list.append(inception_d)
-        inception_e = self.add_sublayer("inception_e_1", 
-                                        InceptionE(1280, name="1"))
+        inception_e = self.add_sublayer(
+            "inception_e_1", InceptionE(
+                1280, name="1"))
         self.inception_block_list.append(inception_e)
-        inception_e = self.add_sublayer("inception_e_2",
-                                        InceptionE(2048, name="2"))
+        inception_e = self.add_sublayer(
+            "inception_e_2", InceptionE(
+                2048, name="2"))
         self.inception_block_list.append(inception_e)
- 
+
         self.gap = AdaptiveAvgPool2D(1)
         self.drop = Dropout(p=0.2, mode="downscale_in_infer")
         stdv = 1.0 / math.sqrt(2048 * 1.0)
@@ -472,10 +533,9 @@ class InceptionV3(nn.Layer):
     def forward(self, x):
         y = self.inception_stem(x)
         for inception_block in self.inception_block_list:
-           y = inception_block(y)
+            y = inception_block(y)
         y = self.gap(y)
         y = paddle.reshape(y, shape=[-1, 2048])
         y = self.drop(y)
         y = self.out(y)
         return y
-    

paddlex/ppcls/modeling/architectures/resnet.py

@@ -183,7 +183,11 @@ class BasicBlock(nn.Layer):
 
 
 class ResNet(nn.Layer):
-    def __init__(self, layers=50, class_dim=1000, input_image_channel=3, data_format="NCHW"):
+    def __init__(self,
+                 layers=50,
+                 class_dim=1000,
+                 input_image_channel=3,
+                 data_format="NCHW"):
         super(ResNet, self).__init__()
 
         self.layers = layers
@@ -216,10 +220,7 @@ class ResNet(nn.Layer):
             name="conv1",
             data_format=self.data_format)
         self.pool2d_max = MaxPool2D(
-            kernel_size=3,
-            stride=2, 
-            padding=1,
-            data_format=self.data_format)
+            kernel_size=3, stride=2, padding=1, data_format=self.data_format)
 
         self.block_list = []
         if layers >= 50:

paddlex/ppcls/modeling/architectures/resnext.py

@@ -135,7 +135,12 @@ class BottleneckBlock(nn.Layer):
 
 
 class ResNeXt(nn.Layer):
-    def __init__(self, layers=50, class_dim=1000, cardinality=32, input_image_channel=3, data_format="NCHW"):
+    def __init__(self,
+                 layers=50,
+                 class_dim=1000,
+                 cardinality=32,
+                 input_image_channel=3,
+                 data_format="NCHW"):
         super(ResNeXt, self).__init__()
 
         self.layers = layers
@@ -168,7 +173,8 @@ class ResNeXt(nn.Layer):
             act='relu',
             name="res_conv1",
             data_format=self.data_format)
-        self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1, data_format=self.data_format)
+        self.pool2d_max = MaxPool2D(
+            kernel_size=3, stride=2, padding=1, data_format=self.data_format)
 
         self.block_list = []
         for block in range(len(depth)):

paddlex/ppcls/modeling/loss.py

@@ -15,7 +15,9 @@
 import paddle
 import paddle.nn.functional as F
 
-__all__ = ['CELoss', 'MixCELoss', 'GoogLeNetLoss', 'JSDivLoss', 'MultiLabelLoss']
+__all__ = [
+    'CELoss', 'MixCELoss', 'GoogLeNetLoss', 'JSDivLoss', 'MultiLabelLoss'
+]
 
 
 class Loss(object):
@@ -41,13 +43,15 @@ class Loss(object):
         soft_target = F.label_smooth(one_hot_target, epsilon=self._epsilon)
         soft_target = paddle.reshape(soft_target, shape=[-1, self._class_dim])
         return soft_target
-    
+
     def _binary_crossentropy(self, input, target):
         if self._label_smoothing:
             target = self._labelsmoothing(target)
-            cost = F.binary_cross_entropy_with_logits(logit=input, label=target)
+            cost = F.binary_cross_entropy_with_logits(
+                logit=input, label=target)
         else:
-            cost = F.binary_cross_entropy_with_logits(logit=input, label=target)
+            cost = F.binary_cross_entropy_with_logits(
+                logit=input, label=target)
 
         avg_cost = paddle.mean(cost)
 
@@ -59,7 +63,7 @@ class Loss(object):
             input = -F.log_softmax(input, axis=-1)
             cost = paddle.sum(target * input, axis=-1)
         else:
-            cost = F.cross_entropy(input=input, label=target) 
+            cost = F.cross_entropy(input=input, label=target)
         avg_cost = paddle.mean(cost)
         return avg_cost
 
@@ -79,8 +83,8 @@ class Loss(object):
 
     def __call__(self, input, target):
         pass
-    
-    
+
+
 class MultiLabelLoss(Loss):
     """
     Multilabel loss based binary cross entropy
@@ -119,7 +123,7 @@ class MixCELoss(Loss):
     def __call__(self, input, target0, target1, lam):
         cost0 = self._crossentropy(input, target0)
         cost1 = self._crossentropy(input, target1)
-        cost = lam * cost0 + (1.0 - lam) * cost1  
+        cost = lam * cost0 + (1.0 - lam) * cost1
         avg_cost = paddle.mean(cost)
         return avg_cost
 

paddlex/ppcls/utils/check.py

@@ -82,7 +82,7 @@ def check_architecture(architecture):
 
 def check_model_with_running_mode(architecture):
     """
-    check whether the model is consistent with the operating mode 
+    check whether the model is consistent with the operating mode
     """
     # some model are not supported in the static mode
     blacklist = get_blacklist_model_in_static_mode()

paddlex/ppcls/utils/logger.py

@@ -83,7 +83,7 @@ def scaler(name, value, step, writer):
     This function will draw a scalar curve generated by the visualdl.
     Usage: Install visualdl: pip3 install visualdl==2.0.0b4
            and then:
-           visualdl --logdir ./scalar --host 0.0.0.0 --port 8830 
+           visualdl --logdir ./scalar --host 0.0.0.0 --port 8830
            to preview loss corve in real time.
     """
     writer.add_scalar(tag=name, step=step, value=value)

paddlex/ppcls/utils/metrics.py

@@ -25,7 +25,10 @@ from sklearn.preprocessing import binarize
 
 import numpy as np
 
-__all__ = ["multi_hot_encode", "hamming_distance", "accuracy_score", "precision_recall_fscore", "mean_average_precision"]
+__all__ = [
+    "multi_hot_encode", "hamming_distance", "accuracy_score",
+    "precision_recall_fscore", "mean_average_precision"
+]
 
 
 def multi_hot_encode(logits, threshold=0.5):
@@ -70,7 +73,8 @@ def accuracy_score(output, target, base="sample"):
         tps = mcm[:, 1, 1]
         fps = mcm[:, 0, 1]
 
-        accuracy = (sum(tps) + sum(tns)) / (sum(tps) + sum(tns) + sum(fns) + sum(fps))
+        accuracy = (sum(tps) + sum(tns)) / (
+            sum(tps) + sum(tns) + sum(fns) + sum(fps))
 
     return accuracy
 
@@ -84,7 +88,8 @@ def precision_recall_fscore(output, target):
         fscores:
     """
 
-    precisions, recalls, fscores, _ = precision_recall_fscore_support(target, output)
+    precisions, recalls, fscores, _ = precision_recall_fscore_support(target,
+                                                                      output)
 
     return precisions, recalls, fscores
 

paddlex/ppdet/core/workspace.py

@@ -155,7 +155,10 @@ def merge_config(config, another_cfg=None):
 
 
 def get_registered_modules():
-    return {k: v for k, v in global_config.items() if isinstance(v, SchemaDict)}
+    return {
+        k: v
+        for k, v in global_config.items() if isinstance(v, SchemaDict)
+    }
 
 
 def make_partial(cls):

paddlex/ppdet/data/__init__.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from . import source

paddlex/ppdet/data/reader.py

@@ -120,7 +120,7 @@ class BaseDataLoader(object):
         collate_batch (bool): whether to collate batch in dataloader.
             If set to True, the samples will collate into batch according
             to the batch size. Otherwise, the ground-truth will not collate,
-            which is used when the number of ground-truch is different in 
+            which is used when the number of ground-truch is different in
             samples.
         use_shared_memory (bool): whether to use shared memory to
                 accelerate data loading, enable this only if you
@@ -146,7 +146,7 @@ class BaseDataLoader(object):
         self._sample_transforms = Compose(
             sample_transforms, num_classes=num_classes)
 
-        # batch transfrom 
+        # batch transfrom
         self._batch_transforms = BatchCompose(batch_transforms, num_classes,
                                               collate_batch)
         self.batch_size = batch_size
@@ -282,9 +282,9 @@ class EvalMOTReader(BaseDataLoader):
                  drop_last=False,
                  num_classes=1,
                  **kwargs):
-        super(EvalMOTReader, self).__init__(sample_transforms, batch_transforms,
-                                            batch_size, shuffle, drop_last,
-                                            num_classes, **kwargs)
+        super(EvalMOTReader, self).__init__(
+            sample_transforms, batch_transforms, batch_size, shuffle,
+            drop_last, num_classes, **kwargs)
 
 
 @register
@@ -299,6 +299,6 @@ class TestMOTReader(BaseDataLoader):
                  drop_last=False,
                  num_classes=1,
                  **kwargs):
-        super(TestMOTReader, self).__init__(sample_transforms, batch_transforms,
-                                            batch_size, shuffle, drop_last,
-                                            num_classes, **kwargs)
+        super(TestMOTReader, self).__init__(
+            sample_transforms, batch_transforms, batch_size, shuffle,
+            drop_last, num_classes, **kwargs)

paddlex/ppdet/data/source/category.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import

paddlex/ppdet/data/source/coco.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 import os
@@ -33,11 +33,11 @@ class COCODataSet(DetDataset):
         anno_path (str): coco annotation file path.
         data_fields (list): key name of data dictionary, at least have 'image'.
         sample_num (int): number of samples to load, -1 means all.
-        load_crowd (bool): whether to load crowded ground-truth. 
+        load_crowd (bool): whether to load crowded ground-truth.
             False as default
         allow_empty (bool): whether to load empty entry. False as default
-        empty_ratio (float): the ratio of empty record number to total 
-            record's, if empty_ratio is out of [0. ,1.), do not sample the 
+        empty_ratio (float): the ratio of empty record number to total
+            record's, if empty_ratio is out of [0. ,1.), do not sample the
             records. 1. as default
     """
 
@@ -90,8 +90,9 @@ class COCODataSet(DetDataset):
 
         if 'annotations' not in coco.dataset:
             self.load_image_only = True
-            logger.warning('Annotation file: {} does not contains ground truth '
-                           'and load image information only.'.format(anno_path))
+            logger.warning(
+                'Annotation file: {} does not contains ground truth '
+                'and load image information only.'.format(anno_path))
 
         for img_id in img_ids:
             img_anno = coco.loadImgs([img_id])[0]
@@ -108,9 +109,9 @@ class COCODataSet(DetDataset):
                 continue
 
             if im_w < 0 or im_h < 0:
-                logger.warning('Illegal width: {} or height: {} in annotation, '
-                               'and im_id: {} will be ignored'.format(
-                                   im_w, im_h, img_id))
+                logger.warning(
+                    'Illegal width: {} or height: {} in annotation, '
+                    'and im_id: {} will be ignored'.format(im_w, im_h, img_id))
                 continue
 
             coco_rec = {
@@ -122,7 +123,8 @@ class COCODataSet(DetDataset):
 
             if not self.load_image_only:
                 ins_anno_ids = coco.getAnnIds(
-                    imgIds=[img_id], iscrowd=None if self.load_crowd else False)
+                    imgIds=[img_id],
+                    iscrowd=None if self.load_crowd else False)
                 instances = coco.loadAnns(ins_anno_ids)
 
                 bboxes = []
@@ -187,7 +189,7 @@ class COCODataSet(DetDataset):
                     if is_rbox_anno:
                         gt_rbox[i, :] = box['clean_rbox']
                     is_crowd[i][0] = box['iscrowd']
-                    # check RLE format 
+                    # check RLE format
                     if 'segmentation' in box and box['iscrowd'] == 1:
                         gt_poly[i] = [[0.0, 0.0], ]
                     elif 'segmentation' in box and box['segmentation']:

paddlex/ppdet/data/source/dataset.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 import os

paddlex/ppdet/data/source/keypoint_coco.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 import os

paddlex/ppdet/data/source/mot.py

@@ -41,10 +41,10 @@ class MOTDataSet(DetDataset):
         MOT datasets root directory following this:
             dataset/mot
             |——————image_lists
-            |        |——————caltech.train  
-            |        |——————caltech.val   
-            |        |——————mot16.train  
-            |        |——————mot17.train  
+            |        |——————caltech.train
+            |        |——————caltech.val
+            |        |——————mot16.train
+            |        |——————mot17.train
             |        ......
             |——————Caltech
             |——————MOT17
@@ -302,7 +302,7 @@ class MOTVideoDataset(DetDataset):
     Args:
         video_file (str): path of the video file
         dataset_dir (str): root directory for dataset.
-        keep_ori_im (bool): whether to keep original image, default False. 
+        keep_ori_im (bool): whether to keep original image, default False.
             Set True when used during MOT model inference while saving
             images or video, or used in DeepSORT.
     """

paddlex/ppdet/data/source/voc.py

@@ -90,8 +90,8 @@ class VOCDataSet(DetDataset):
                         for x in line.strip().split()[:2]]
                 if not os.path.exists(img_file):
                     logger.warn(
-                        'Illegal image file: {}, and it will be ignored'.format(
-                            img_file))
+                        'Illegal image file: {}, and it will be ignored'.
+                        format(img_file))
                     continue
                 if not os.path.isfile(xml_file):
                     logger.warn('Illegal xml file: {}, and it will be ignored'.

paddlex/ppdet/data/transform/autoaugment_utils.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-# Reference: 
+# Reference:
 #   https://github.com/tensorflow/tpu/blob/master/models/official/detection/utils/autoaugment_utils.py
 """AutoAugment util file."""
 
@@ -67,7 +67,7 @@ def policy_v1():
         [('ShearY_Only_BBoxes', 0.8, 2), ('Flip_Only_BBoxes', 0.0, 10)],
         [('Equalize', 0.6, 10), ('TranslateX_BBox', 0.2, 2)],
         [('Color', 1.0, 10), ('TranslateY_Only_BBoxes', 0.4, 6)],
-        [('Rotate_BBox', 0.8, 10), ('Contrast', 0.0, 10)],  # , 
+        [('Rotate_BBox', 0.8, 10), ('Contrast', 0.0, 10)],  # ,
         [('Cutout', 0.2, 2), ('Brightness', 0.8, 10)],
         [('Color', 1.0, 6), ('Equalize', 1.0, 2)],
         [('Cutout_Only_BBoxes', 0.4, 6), ('TranslateY_Only_BBoxes', 0.8, 2)],
@@ -113,7 +113,8 @@ def policy_v2():
         [('Cutout', 0.8, 8), ('Brightness', 0.8, 8), ('Cutout', 0.2, 2)],
         [('Color', 0.8, 4), ('TranslateY_BBox', 1.0, 6),
          ('Rotate_BBox', 0.6, 6)],
-        [('Rotate_BBox', 0.6, 10), ('BBox_Cutout', 1.0, 4), ('Cutout', 0.2, 8)],
+        [('Rotate_BBox', 0.6, 10), ('BBox_Cutout', 1.0, 4), ('Cutout', 0.2, 8)
+         ],
         [('Rotate_BBox', 0.0, 0), ('Equalize', 0.6, 6),
          ('ShearY_BBox', 0.6, 8)],
         [('Brightness', 0.8, 8), ('AutoContrast', 0.4, 2),
@@ -365,8 +366,10 @@ def random_shift_bbox(image,
                                        2.0))
     maxval_y = clip_y(min_y + np.int32(pixel_scaling * float(bbox_height) /
                                        2.0))
-    minval_x = clip_x(min_x - np.int32(pixel_scaling * float(bbox_width) / 2.0))
-    maxval_x = clip_x(min_x + np.int32(pixel_scaling * float(bbox_width) / 2.0))
+    minval_x = clip_x(min_x - np.int32(pixel_scaling * float(bbox_width) /
+                                       2.0))
+    maxval_x = clip_x(min_x + np.int32(pixel_scaling * float(bbox_width) /
+                                       2.0))
 
     # Sample and calculate the new unclipped min/max coordinates of the new bbox.
     if new_min_bbox_coords is None:
@@ -403,8 +406,8 @@ def random_shift_bbox(image,
     bbox_content = image[shifted_min_y:shifted_max_y + 1, shifted_min_x:
                          shifted_max_x + 1, :]
 
-    def mask_and_add_image(min_y_, min_x_, max_y_, max_x_, mask, content_tensor,
-                           image_):
+    def mask_and_add_image(min_y_, min_x_, max_y_, max_x_, mask,
+                           content_tensor, image_):
         """Applies mask to bbox region in image then adds content_tensor to it."""
         mask = np.pad(mask, [[min_y_, (image_height - 1) - max_y_],
                              [min_x_, (image_width - 1) - max_x_], [0, 0]],
@@ -426,8 +429,8 @@ def random_shift_bbox(image,
 
     # Fill in bbox content to new bbox location.
     mask = np.zeros_like(bbox_content)
-    image = mask_and_add_image(new_min_y, new_min_x, new_max_y, new_max_x, mask,
-                               bbox_content, image)
+    image = mask_and_add_image(new_min_y, new_min_x, new_max_y, new_max_x,
+                               mask, bbox_content, image)
 
     return image.astype(np.uint8), new_bbox
 
@@ -609,8 +612,8 @@ def _apply_bbox_augmentation_wrapper(image, bbox, new_bboxes, prob,
             augmented_image, bbox = (image, bbox)
     else:
         if should_apply_op:
-            augmented_image = _apply_bbox_augmentation(image, bbox,
-                                                       augmentation_func, *args)
+            augmented_image = _apply_bbox_augmentation(
+                image, bbox, augmentation_func, *args)
         else:
             augmented_image = image
     new_bboxes = _concat_bbox(bbox, new_bboxes)
@@ -761,16 +764,16 @@ def solarize_only_bboxes(image, bboxes, prob, threshold):
     """Apply solarize to each bbox in the image with probability prob."""
     func_changes_bbox = False
     prob = _scale_bbox_only_op_probability(prob)
-    return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, solarize,
-                                                  func_changes_bbox, threshold)
+    return _apply_multi_bbox_augmentation_wrapper(
+        image, bboxes, prob, solarize, func_changes_bbox, threshold)
 
 
 def equalize_only_bboxes(image, bboxes, prob):
     """Apply equalize to each bbox in the image with probability prob."""
     func_changes_bbox = False
     prob = _scale_bbox_only_op_probability(prob)
-    return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, equalize,
-                                                  func_changes_bbox)
+    return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob,
+                                                  equalize, func_changes_bbox)
 
 
 def cutout_only_bboxes(image, bboxes, prob, pad_size, replace):
@@ -1087,7 +1090,8 @@ def sharpness(image, factor):
     image = image.astype(np.float32)
     # Make image 4D for conv operation.
     # SMOOTH PIL Kernel.
-    kernel = np.array([[1, 1, 1], [1, 5, 1], [1, 1, 1]], dtype=np.float32) / 13.
+    kernel = np.array(
+        [[1, 1, 1], [1, 5, 1], [1, 1, 1]], dtype=np.float32) / 13.
     result = cv2.filter2D(image, -1, kernel).astype(np.uint8)
 
     # Blend the final result.
@@ -1388,9 +1392,10 @@ def _translate_level_to_arg(level, translate_const):
 
 
 def _bbox_cutout_level_to_arg(level, hparams):
-    cutout_pad_fraction = (level /
-                           _MAX_LEVEL) * 0.75  # hparams.cutout_max_pad_fraction
-    return (cutout_pad_fraction, False)  # hparams.cutout_bbox_replace_with_mean
+    cutout_pad_fraction = (
+        level / _MAX_LEVEL) * 0.75  # hparams.cutout_max_pad_fraction
+    return (cutout_pad_fraction, False
+            )  # hparams.cutout_bbox_replace_with_mean
 
 
 def level_to_arg(hparams):

paddlex/ppdet/data/transform/batch_operators.py

@@ -146,7 +146,8 @@ class BatchRandomResize(BaseOperator):
         else:
             interp = self.interp
 
-        resizer = Resize(target_size, keep_ratio=self.keep_ratio, interp=interp)
+        resizer = Resize(
+            target_size, keep_ratio=self.keep_ratio, interp=interp)
         return resizer(samples, context=context)
 
 
@@ -214,7 +215,7 @@ class Gt2YoloTarget(BaseOperator):
                     gi = int(gx * grid_w)
                     gj = int(gy * grid_h)
 
-                    # gtbox should be regresed in this layes if best match 
+                    # gtbox should be regresed in this layes if best match
                     # anchor index in anchor mask of this layer
                     if best_idx in mask:
                         best_n = mask.index(best_idx)
@@ -234,7 +235,7 @@ class Gt2YoloTarget(BaseOperator):
                         # classification
                         target[best_n, 6 + cls, gj, gi] = 1.
 
-                    # For non-matched anchors, calculate the target if the iou 
+                    # For non-matched anchors, calculate the target if the iou
                     # between anchor and gt is larger than iou_thresh
                     if self.iou_thresh < 1:
                         for idx, mask_i in enumerate(mask):
@@ -286,7 +287,8 @@ class Gt2FCOSTarget(BaseOperator):
         object_sizes_of_interest = []
         for i in range(len(self.object_sizes_boundary) - 1):
             object_sizes_of_interest.append([
-                self.object_sizes_boundary[i], self.object_sizes_boundary[i + 1]
+                self.object_sizes_boundary[i],
+                self.object_sizes_boundary[i + 1]
             ])
         self.object_sizes_of_interest = object_sizes_of_interest
         self.norm_reg_targets = norm_reg_targets
@@ -358,7 +360,8 @@ class Gt2FCOSTarget(BaseOperator):
         r_res = clipped_box[:, :, 2] - xs
         t_res = ys - clipped_box[:, :, 1]
         b_res = clipped_box[:, :, 3] - ys
-        clipped_box_reg_targets = np.stack([l_res, t_res, r_res, b_res], axis=2)
+        clipped_box_reg_targets = np.stack(
+            [l_res, t_res, r_res, b_res], axis=2)
         inside_gt_box = np.min(clipped_box_reg_targets, axis=2) > 0
         return inside_gt_box
 
@@ -439,8 +442,10 @@ class Gt2FCOSTarget(BaseOperator):
                 split_sections.append(end)
                 beg = end
             labels_by_level = np.split(labels, split_sections, axis=0)
-            reg_targets_by_level = np.split(reg_targets, split_sections, axis=0)
-            ctn_targets_by_level = np.split(ctn_targets, split_sections, axis=0)
+            reg_targets_by_level = np.split(
+                reg_targets, split_sections, axis=0)
+            ctn_targets_by_level = np.split(
+                ctn_targets, split_sections, axis=0)
             for lvl in range(len(self.downsample_ratios)):
                 grid_w = int(np.ceil(w / self.downsample_ratios[lvl]))
                 grid_h = int(np.ceil(h / self.downsample_ratios[lvl]))
@@ -472,7 +477,7 @@ class Gt2TTFTarget(BaseOperator):
     """
     Gt2TTFTarget
     Generate TTFNet targets by ground truth data
-    
+
     Args:
         num_classes(int): the number of classes.
         down_ratio(int): the down ratio from images to heatmap, 4 by default.
@@ -523,7 +528,8 @@ class Gt2TTFTarget(BaseOperator):
 
             for k in range(len(gt_bbox)):
                 cls_id = gt_class[k]
-                fake_heatmap = np.zeros((feat_size, feat_size), dtype='float32')
+                fake_heatmap = np.zeros(
+                    (feat_size, feat_size), dtype='float32')
                 self.draw_truncate_gaussian(fake_heatmap, ct_inds[k],
                                             h_radiuses_alpha[k],
                                             w_radiuses_alpha[k])
@@ -606,7 +612,8 @@ class Gt2Solov2Target(BaseOperator):
             im_c, im_h, im_w = sample['image'].shape[:]
             gt_masks_raw = sample['gt_segm'].astype(np.uint8)
             mask_feat_size = [
-                int(im_h / self.sampling_ratio), int(im_w / self.sampling_ratio)
+                int(im_h / self.sampling_ratio),
+                int(im_w / self.sampling_ratio)
             ]
             gt_areas = np.sqrt((gt_bboxes_raw[:, 2] - gt_bboxes_raw[:, 0]) *
                                (gt_bboxes_raw[:, 3] - gt_bboxes_raw[:, 1]))
@@ -662,15 +669,18 @@ class Gt2Solov2Target(BaseOperator):
                     top_box = max(0,
                                   int(((center_h - half_h) / upsampled_size[0])
                                       // (1. / num_grid)))
-                    down_box = min(num_grid - 1,
-                                   int(((center_h + half_h) / upsampled_size[0])
-                                       // (1. / num_grid)))
-                    left_box = max(0,
-                                   int(((center_w - half_w) / upsampled_size[1])
-                                       // (1. / num_grid)))
+                    down_box = min(
+                        num_grid - 1,
+                        int(((center_h + half_h) / upsampled_size[0]) //
+                            (1. / num_grid)))
+                    left_box = max(
+                        0,
+                        int(((center_w - half_w) / upsampled_size[1]) //
+                            (1. / num_grid)))
                     right_box = min(num_grid - 1,
                                     int(((center_w + half_w) /
-                                         upsampled_size[1]) // (1. / num_grid)))
+                                         upsampled_size[1]) //
+                                        (1. / num_grid)))
 
                     top = max(top_box, coord_h - 1)
                     down = min(down_box, coord_h + 1)

paddlex/ppdet/data/transform/gridmask_utils.py

@@ -68,8 +68,8 @@ class Gridmask(object):
         mask = Image.fromarray(np.uint8(mask))
         mask = mask.rotate(r)
         mask = np.asarray(mask)
-        mask = mask[(hh - h) // 2:(hh - h) // 2 + h, (ww - w) // 2:(ww - w) // 2
-                    + w].astype(np.float32)
+        mask = mask[(hh - h) // 2:(hh - h) // 2 + h, (ww - w) // 2:(ww - w) //
+                    2 + w].astype(np.float32)
 
         if self.mode == 1:
             mask = 1 - mask

paddlex/ppdet/data/transform/keypoint_operators.py

@@ -334,7 +334,8 @@ class TagGenerate(object):
     def __call__(self, records):
         kpts_lst = records['joints']
         kpts = kpts_lst[0]
-        tagmap = np.zeros((self.max_people, self.num_joints, 4), dtype=np.int64)
+        tagmap = np.zeros(
+            (self.max_people, self.num_joints, 4), dtype=np.int64)
         inds = np.where(kpts[..., 2] > 0)
         p, j = inds[0], inds[1]
         visible = kpts[inds]

paddlex/ppdet/data/transform/mot_operators.py

@@ -130,7 +130,7 @@ class LetterBoxResize(BaseOperator):
 
 @register_op
 class MOTRandomAffine(BaseOperator):
-    """ 
+    """
     Affine transform to image and coords to achieve the rotate, scale and
     shift effect for training image.
 
@@ -225,7 +225,8 @@ class MOTRandomAffine(BaseOperator):
 
             # apply angle-based reduction
             radians = a * math.pi / 180
-            reduction = max(abs(math.sin(radians)), abs(math.cos(radians)))**0.5
+            reduction = max(abs(math.sin(radians)),
+                            abs(math.cos(radians)))**0.5
             x = (xy[:, 2] + xy[:, 0]) / 2
             y = (xy[:, 3] + xy[:, 1]) / 2
             w = (xy[:, 2] - xy[:, 0]) * reduction
@@ -269,7 +270,7 @@ class Gt2JDETargetThres(BaseOperator):
         anchors (list): anchors of JDE model
         anchor_masks (list): anchor_masks of JDE model
         downsample_ratios (list): downsample ratios of JDE model
-        ide_thresh (float): thresh of identity, higher is groud truth 
+        ide_thresh (float): thresh of identity, higher is groud truth
         fg_thresh (float): thresh of foreground, higher is foreground
         bg_thresh (float): thresh of background, lower is background
         num_classes (int): number of classes
@@ -511,8 +512,8 @@ class Gt2JDETargetMax(BaseOperator):
                     # XY coordinates
                     tbox[:, :, :, 0:2][a_i, gj_i, gi_i] = gxy - gxy.astype(int)
                     # Width and height in yolo method
-                    tbox[:, :, :, 2:4][a_i, gj_i, gi_i] = np.log(gwh /
-                                                                 anchor_hw[a_i])
+                    tbox[:, :, :, 2:4][a_i, gj_i, gi_i] = np.log(
+                        gwh / anchor_hw[a_i])
                     tconf[a_i, gj_i, gi_i] = 1
                     tid[a_i, gj_i, gi_i] = t_id
 
@@ -528,7 +529,7 @@ class Gt2FairMOTTarget(Gt2TTFTarget):
     Difference between Gt2FairMOTTarget and Gt2TTFTarget are:
         1. the gaussian kernal radius to generate a heatmap.
         2. the targets needed during traing.
-    
+
     Args:
         num_classes(int): the number of classes.
         down_ratio(int): the down ratio from images to heatmap, 4 by default.
@@ -586,8 +587,8 @@ class Gt2FairMOTTarget(Gt2TTFTarget):
                     radius = max(0, int(radius))
                     ct = np.array([bbox[0], bbox[1]], dtype=np.float32)
                     ct_int = ct.astype(np.int32)
-                    self.draw_truncate_gaussian(heatmap[cls_id], ct_int, radius,
-                                                radius)
+                    self.draw_truncate_gaussian(heatmap[cls_id], ct_int,
+                                                radius, radius)
                     bbox_size[k] = ct[0] - bbox_amodal[0], ct[1] - bbox_amodal[1], \
                             bbox_amodal[2] - ct[0], bbox_amodal[3] - ct[1]
 

paddlex/ppdet/data/transform/op_helper.py

@@ -61,7 +61,10 @@ def is_overlap(object_bbox, sample_bbox):
         return True
 
 
-def filter_and_process(sample_bbox, bboxes, labels, scores=None,
+def filter_and_process(sample_bbox,
+                       bboxes,
+                       labels,
+                       scores=None,
                        keypoints=None):
     new_bboxes = []
     new_labels = []
@@ -233,7 +236,8 @@ def data_anchor_sampling(bbox_labels, image_width, image_height, scale_array,
 
         else:
             w_off_orig = np.random.uniform(image_width - sample_bbox_size, 0.0)
-            h_off_orig = np.random.uniform(image_height - sample_bbox_size, 0.0)
+            h_off_orig = np.random.uniform(image_height - sample_bbox_size,
+                                           0.0)
 
         w_off_orig = math.floor(w_off_orig)
         h_off_orig = math.floor(h_off_orig)

paddlex/ppdet/data/transform/operators.py

@@ -41,11 +41,11 @@ from paddlex.ppdet.core.workspace import serializable
 from paddlex.ppdet.modeling.layers import AnchorGrid
 from paddlex.ppdet.modeling import bbox_utils
 
-from .op_helper import (satisfy_sample_constraint, filter_and_process,
-                        generate_sample_bbox, clip_bbox, data_anchor_sampling,
-                        satisfy_sample_constraint_coverage, crop_image_sampling,
-                        generate_sample_bbox_square, bbox_area_sampling,
-                        is_poly, gaussian_radius, draw_gaussian)
+from .op_helper import (
+    satisfy_sample_constraint, filter_and_process, generate_sample_bbox,
+    clip_bbox, data_anchor_sampling, satisfy_sample_constraint_coverage,
+    crop_image_sampling, generate_sample_bbox_square, bbox_area_sampling,
+    is_poly, gaussian_radius, draw_gaussian)
 
 from paddlex.ppdet.utils.logger import setup_logger
 logger = setup_logger(__name__)
@@ -58,7 +58,8 @@ def register_op(cls):
     if not hasattr(BaseOperator, cls.__name__):
         setattr(BaseOperator, cls.__name__, cls)
     else:
-        raise KeyError("The {} class has been registered.".format(cls.__name__))
+        raise KeyError("The {} class has been registered.".format(
+            cls.__name__))
     return serializable(cls)
 
 
@@ -238,7 +239,9 @@ class RandomErasingImage(BaseOperator):
 
 @register_op
 class NormalizeImage(BaseOperator):
-    def __init__(self, mean=[0.485, 0.456, 0.406], std=[1, 1, 1],
+    def __init__(self,
+                 mean=[0.485, 0.456, 0.406],
+                 std=[1, 1, 1],
                  is_scale=True):
         """
         Args:
@@ -322,7 +325,8 @@ class GridMask(BaseOperator):
             upper_iter=upper_iter)
 
     def apply(self, sample, context=None):
-        sample['image'] = self.gridmask_op(sample['image'], sample['curr_iter'])
+        sample['image'] = self.gridmask_op(sample['image'],
+                                           sample['curr_iter'])
         return sample
 
 
@@ -514,7 +518,8 @@ class RandomFlip(BaseOperator):
         for segm in segms:
             if is_poly(segm):
                 # Polygon format
-                flipped_segms.append([_flip_poly(poly, width) for poly in segm])
+                flipped_segms.append(
+                    [_flip_poly(poly, width) for poly in segm])
             else:
                 # RLE format
                 import pycocotools.mask as mask_util
@@ -582,8 +587,8 @@ class RandomFlip(BaseOperator):
                 sample['gt_segm'] = sample['gt_segm'][:, :, ::-1]
 
             if 'gt_rbox2poly' in sample and sample['gt_rbox2poly'].any():
-                sample['gt_rbox2poly'] = self.apply_rbox(sample['gt_rbox2poly'],
-                                                         width)
+                sample['gt_rbox2poly'] = self.apply_rbox(
+                    sample['gt_rbox2poly'], width)
 
             sample['flipped'] = True
             sample['image'] = im
@@ -594,7 +599,7 @@ class RandomFlip(BaseOperator):
 class Resize(BaseOperator):
     def __init__(self, target_size, keep_ratio, interp=cv2.INTER_LINEAR):
         """
-        Resize image to target size. if keep_ratio is True, 
+        Resize image to target size. if keep_ratio is True,
         resize the image's long side to the maximum of target_size
         if keep_ratio is False, resize the image to target size(h, w)
         Args:
@@ -734,8 +739,8 @@ class Resize(BaseOperator):
 
         # apply polygon
         if 'gt_poly' in sample and len(sample['gt_poly']) > 0:
-            sample['gt_poly'] = self.apply_segm(sample['gt_poly'], im_shape[:2],
-                                                [im_scale_x, im_scale_y])
+            sample['gt_poly'] = self.apply_segm(
+                sample['gt_poly'], im_shape[:2], [im_scale_x, im_scale_y])
 
         # apply semantic
         if 'semantic' in sample and sample['semantic']:
@@ -1241,14 +1246,15 @@ class RandomCrop(BaseOperator):
                             if not isinstance(part, Polygon):
                                 continue
                             part = np.squeeze(
-                                np.array(part.exterior.coords[:-1]).reshape(1,
-                                                                            -1))
+                                np.array(part.exterior.coords[:-1]).reshape(
+                                    1, -1))
                             part[0::2] -= xmin
                             part[1::2] -= ymin
                             crop_segm.append(part.tolist())
                     elif isinstance(inter, Polygon):
                         crop_poly = np.squeeze(
-                            np.array(inter.exterior.coords[:-1]).reshape(1, -1))
+                            np.array(inter.exterior.coords[:-1]).reshape(1,
+                                                                         -1))
                         crop_poly[0::2] -= xmin
                         crop_poly[1::2] -= ymin
                         crop_segm.append(crop_poly.tolist())
@@ -1485,7 +1491,7 @@ class RandomScaledCrop(BaseOperator):
 @register_op
 class Cutmix(BaseOperator):
     def __init__(self, alpha=1.5, beta=1.5):
-        """ 
+        """
         CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features, see https://arxiv.org/abs/1905.04899
         Cutmix image and gt_bbbox/gt_score
         Args:
@@ -1808,7 +1814,9 @@ class DebugVisibleImage(BaseOperator):
                     x1 = round(keypoint[2 * j]).astype(np.int32)
                     y1 = round(keypoint[2 * j + 1]).astype(np.int32)
                     draw.ellipse(
-                        (x1, y1, x1 + 5, y1 + 5), fill='green', outline='green')
+                        (x1, y1, x1 + 5, y1 + 5),
+                        fill='green',
+                        outline='green')
         save_path = os.path.join(self.output_dir, out_file_name)
         image.save(save_path, quality=95)
         return sample
@@ -2015,10 +2023,10 @@ class Rbox2Poly(BaseOperator):
 @register_op
 class AugmentHSV(BaseOperator):
     def __init__(self, fraction=0.50, is_bgr=False):
-        """ 
+        """
         Augment the SV channel of image data.
         Args:
-            fraction (float): the fraction for augment 
+            fraction (float): the fraction for augment
             is_bgr (bool): whether the image is BGR mode
         """
         super(AugmentHSV, self).__init__()

paddlex/ppdet/engine/__init__.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from . import trainer

paddlex/ppdet/engine/callbacks.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import
@@ -97,7 +97,8 @@ class LogPrinter(Callback):
                 logs = training_staus.log()
                 space_fmt = ':' + str(len(str(steps_per_epoch))) + 'd'
                 if step_id % self.model.cfg.log_iter == 0:
-                    eta_steps = (epoches - epoch_id) * steps_per_epoch - step_id
+                    eta_steps = (epoches - epoch_id
+                                 ) * steps_per_epoch - step_id
                     eta_sec = eta_steps * batch_time.global_avg
                     eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
                     ips = float(batch_size) / batch_time.avg
@@ -162,7 +163,8 @@ class Checkpointer(Callback):
                         epoch_id + 1
                 ) % self.model.cfg.snapshot_epoch == 0 or epoch_id == end_epoch - 1:
                     save_name = str(
-                        epoch_id) if epoch_id != end_epoch - 1 else "model_final"
+                        epoch_id
+                    ) if epoch_id != end_epoch - 1 else "model_final"
                     weight = self.weight
             elif mode == 'eval':
                 if 'save_best_model' in status and status['save_best_model']:
@@ -217,7 +219,8 @@ class VisualDLWriter(Callback):
             logger.error('visualdl not found, plaese install visualdl. '
                          'for example: `pip install visualdl`.')
             raise e
-        self.vdl_writer = LogWriter(model.cfg.get('vdl_log_dir', 'vdl_log_dir/scalar'))
+        self.vdl_writer = LogWriter(
+            model.cfg.get('vdl_log_dir', 'vdl_log_dir/scalar'))
         self.vdl_loss_step = 0
         self.vdl_mAP_step = 0
         self.vdl_image_step = 0
@@ -236,8 +239,8 @@ class VisualDLWriter(Callback):
                 ori_image = status['original_image']
                 result_image = status['result_image']
                 self.vdl_writer.add_image(
-                    "original/frame_{}".format(self.vdl_image_frame), ori_image,
-                    self.vdl_image_step)
+                    "original/frame_{}".format(self.vdl_image_frame),
+                    ori_image, self.vdl_image_step)
                 self.vdl_writer.add_image(
                     "result/frame_{}".format(self.vdl_image_frame),
                     result_image, self.vdl_image_step)

paddlex/ppdet/engine/env.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import

paddlex/ppdet/engine/export_utils.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import
@@ -114,4 +114,5 @@ def _dump_infer_config(config, path, image_shape, model):
             infer_cfg['label_list'].insert(0, 'background')
 
     yaml.dump(infer_cfg, open(path, 'w'))
-    logger.info("Export inference config file to {}".format(os.path.join(path)))
+    logger.info("Export inference config file to {}".format(
+        os.path.join(path)))

paddlex/ppdet/metrics/coco_utils.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import

paddlex/ppdet/metrics/json_results.py

@@ -143,8 +143,8 @@ def get_keypoint_res(results, im_id):
             }
             x = kpt[0::3]
             y = kpt[1::3]
-            x0, x1, y0, y1 = np.min(x).item(), np.max(x).item(), np.min(y).item(
-            ), np.max(y).item()
+            x0, x1, y0, y1 = np.min(x).item(), np.max(x).item(), np.min(
+                y).item(), np.max(y).item()
             ann['area'] = (x1 - x0) * (y1 - y0)
             ann['bbox'] = [x0, y0, x1 - x0, y1 - y0]
             anns.append(ann)

paddlex/ppdet/metrics/keypoint_metrics.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 import copy
@@ -61,10 +61,10 @@ class KeyPointTopDownCOCOEval(object):
         num_images = inputs['image'].shape[0]
         self.results['all_preds'][self.idx:self.idx + num_images, :, 0:
                                   3] = kpts[:, :, 0:3]
-        self.results['all_boxes'][self.idx:self.idx + num_images, 0:2] = inputs[
-            'center'].numpy()[:, 0:2]
-        self.results['all_boxes'][self.idx:self.idx + num_images, 2:4] = inputs[
-            'scale'].numpy()[:, 0:2]
+        self.results['all_boxes'][self.idx:self.idx + num_images, 0:
+                                  2] = inputs['center'].numpy()[:, 0:2]
+        self.results['all_boxes'][self.idx:self.idx + num_images, 2:
+                                  4] = inputs['scale'].numpy()[:, 0:2]
         self.results['all_boxes'][self.idx:self.idx + num_images, 4] = np.prod(
             inputs['scale'].numpy() * 200, 1)
         self.results['all_boxes'][self.idx:self.idx + num_images,

paddlex/ppdet/metrics/map_utils.py

@@ -108,8 +108,8 @@ class DetectionMAP(object):
     Args:
         class_num (int): The class number.
         overlap_thresh (float): The threshold of overlap
-            ratio between prediction bounding box and 
-            ground truth bounding box for deciding 
+            ratio between prediction bounding box and
+            ground truth bounding box for deciding
             true/false positive. Default 0.5.
         map_type (str): Calculation method of mean average
             precision, currently support '11point' and
@@ -283,8 +283,9 @@ class DetectionMAP(object):
             num_columns = min(6, len(results_per_category) * 2)
             results_flatten = list(itertools.chain(*results_per_category))
             headers = ['category', 'AP'] * (num_columns // 2)
-            results_2d = itertools.zip_longest(
-                *[results_flatten[i::num_columns] for i in range(num_columns)])
+            results_2d = itertools.zip_longest(* [
+                results_flatten[i::num_columns] for i in range(num_columns)
+            ])
             table_data = [headers]
             table_data += [result for result in results_2d]
             table = AsciiTable(table_data)
@@ -315,7 +316,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls):
     """
     Computes the average precision, given the recall and precision curves.
     Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics.
-    
+
     Args:
         tp (list): True positives.
         conf (list): Objectness value from 0-1.
@@ -369,7 +370,7 @@ def compute_ap(recall, precision):
     """
     Computes the average precision, given the recall and precision curves.
     Code originally from https://github.com/rbgirshick/py-faster-rcnn.
-    
+
     Args:
         recall (list): The recall curve.
         precision (list): The precision curve.

paddlex/ppdet/metrics/metrics.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import
@@ -35,8 +35,8 @@ __all__ = [
 ]
 
 COCO_SIGMAS = np.array([
-    .26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87,
-    .89, .89
+    .26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87,
+    .87, .89, .89
 ]) / 10.0
 CROWD_SIGMAS = np.array(
     [.79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89, .79,
@@ -275,8 +275,8 @@ class VOCMetric(Metric):
 
     def log(self):
         map_stat = 100. * self.detection_map.get_map()
-        logger.info("mAP({:.2f}, {}) = {:.2f}%".format(self.overlap_thresh,
-                                                       self.map_type, map_stat))
+        logger.info("mAP({:.2f}, {}) = {:.2f}%".format(
+            self.overlap_thresh, self.map_type, map_stat))
 
     def get_results(self):
         return {'bbox': [self.detection_map.get_map()]}

paddlex/ppdet/metrics/mot_eval_utils.py

@@ -144,7 +144,8 @@ class MOTEvaluator(object):
             trk_ids = trk_ids[keep]
 
         # get distance matrix
-        iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)
+        iou_distance = mm.distances.iou_matrix(
+            gt_tlwhs, trk_tlwhs, max_iou=0.5)
 
         # acc
         self.acc.update(gt_ids, trk_ids, iou_distance)

paddlex/ppdet/metrics/mot_metrics.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 from __future__ import absolute_import
 from __future__ import division

paddlex/ppdet/metrics/widerface_utils.py

@@ -130,8 +130,8 @@ def multi_scale_test(model, image, max_shrink):
     st = 0.5 if max_shrink >= 0.75 else 0.5 * max_shrink
     det_s = detect_face(model, image, st)
     index = np.where(
-        np.maximum(det_s[:, 2] - det_s[:, 0] + 1, det_s[:, 3] - det_s[:, 1] + 1)
-        > 30)[0]
+        np.maximum(det_s[:, 2] - det_s[:, 0] + 1,
+                   det_s[:, 3] - det_s[:, 1] + 1) > 30)[0]
     det_s = det_s[index, :]
     # Enlarge one times
     bt = min(2, max_shrink) if max_shrink > 1 else (st + max_shrink) / 2
@@ -164,8 +164,8 @@ def multi_scale_test_pyramid(model, image, max_shrink):
     # Use image pyramids to detect faces
     det_b = detect_face(model, image, 0.25)
     index = np.where(
-        np.maximum(det_b[:, 2] - det_b[:, 0] + 1, det_b[:, 3] - det_b[:, 1] + 1)
-        > 30)[0]
+        np.maximum(det_b[:, 2] - det_b[:, 0] + 1,
+                   det_b[:, 3] - det_b[:, 1] + 1) > 30)[0]
     det_b = det_b[index, :]
 
     st = [0.75, 1.25, 1.5, 1.75]
@@ -371,9 +371,11 @@ def lmk2out(results, is_bbox_normalized=False):
                 prior_h_center = (me_prior[3] + me_prior[1]) / 2
                 lmk_decode = np.zeros((10))
                 for j in [0, 2, 4, 6, 8]:
-                    lmk_decode[j] = lmk_pred[j] * 0.1 * prior_w + prior_w_center
+                    lmk_decode[j] = lmk_pred[
+                        j] * 0.1 * prior_w + prior_w_center
                 for j in [1, 3, 5, 7, 9]:
-                    lmk_decode[j] = lmk_pred[j] * 0.1 * prior_h + prior_h_center
+                    lmk_decode[j] = lmk_pred[
+                        j] * 0.1 * prior_h + prior_h_center
                 im_shape = t['im_shape'][0][a].tolist()
                 image_h, image_w = int(im_shape[0]), int(im_shape[1])
                 if is_bbox_normalized:

paddlex/ppdet/model_zoo/__init__.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from . import model_zoo

paddlex/ppdet/model_zoo/model_zoo.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 import os

paddlex/ppdet/model_zoo/tests/test_get_model.py

@@ -21,7 +21,6 @@ import paddle
 import paddlex.ppdet as ppdet
 import unittest
 
-
 # NOTE: weights downloading costs time, we choose
 #       a small model for unittesting
 MODEL_NAME = 'ppyolo/ppyolo_tiny_650e_coco'

paddlex/ppdet/modeling/__init__.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 import warnings

paddlex/ppdet/modeling/architectures/__init__.py

@@ -1,10 +1,10 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
 from . import meta_arch
 from . import faster_rcnn
 from . import mask_rcnn

paddlex/ppdet/modeling/architectures/cascade_rcnn.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import
@@ -96,7 +96,8 @@ class CascadeRCNN(BaseArch):
             bbox_targets = self.bbox_head.get_assigned_targets()
             if self.with_mask:
                 mask_loss = self.mask_head(body_feats, rois, rois_num,
-                                           self.inputs, bbox_targets, bbox_feat)
+                                           self.inputs, bbox_targets,
+                                           bbox_feat)
                 return rpn_loss, bbox_loss, mask_loss
             else:
                 return rpn_loss, bbox_loss, {}

paddlex/ppdet/modeling/architectures/centernet.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import

paddlex/ppdet/modeling/architectures/deepsort.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-# 
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import

paddlex/ppdet/modeling/architectures/fairmot.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import

paddlex/ppdet/modeling/architectures/faster_rcnn.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import

paddlex/ppdet/modeling/architectures/fcos.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import

paddlex/ppdet/modeling/architectures/jde.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-# 
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import
@@ -114,8 +114,9 @@ class JDE(BaseArch):
                 return online_targets
 
             else:
-                raise ValueError("Unknown metric {} for multi object tracking.".
-                                 format(self.metric))
+                raise ValueError(
+                    "Unknown metric {} for multi object tracking.".format(
+                        self.metric))
 
     def get_loss(self):
         return self._forward()

paddlex/ppdet/modeling/architectures/keypoint_hrhrnet.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import
@@ -185,8 +185,8 @@ class HrHRNetPostProcess(object):
         inds_np = inds_k[0].cpu().detach().numpy()
         y = inds_np // W
         x = inds_np % W
-        tags = tagmap[np.arange(J)[None, :].repeat(self.max_num_people),
-                      y.flatten(), x.flatten()].reshape(J, -1, tagmap.shape[-1])
+        tags = tagmap[np.arange(J)[None, :].repeat(self.max_num_people), y.
+                      flatten(), x.flatten()].reshape(J, -1, tagmap.shape[-1])
         coords = np.stack((y, x), axis=2)
         # threshold
         mask = heats > self.heat_thresh

paddlex/ppdet/modeling/architectures/keypoint_hrnet.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License. 
-# You may obtain a copy of the License at 
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and 
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import

paddlex/ppdet/modeling/architectures/mask_rcnn.py

@@ -1,15 +1,15 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import

paddlex/ppdet/modeling/architectures/s2anet.py

@@ -1,15 +1,15 @@
 # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#   
-# Licensed under the Apache License, Version 2.0 (the "License");   
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at   
-#   
-#     http://www.apache.org/licenses/LICENSE-2.0    
-#   
-# Unless required by applicable law or agreed to in writing, software   
-# distributed under the License is distributed on an "AS IS" BASIS, 
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and   
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 from __future__ import absolute_import