chore(vision): pylint format

official/vision/detection/layers/det/anchor.py

@@ -67,6 +67,7 @@ class AnchorBoxGenerator(BaseAnchorGenerator):
         offset (float): center point offset. default is 0.5.
     """
 
+    # pylint: disable=dangerous-default-value
     def __init__(
         self,
         anchor_scales: list = [[32], [64], [128], [256], [512]],
@@ -135,6 +136,7 @@ class AnchorPointGenerator(BaseAnchorGenerator):
         offset (float): center point offset. default is 0.5.
     """
 
+    # pylint: disable=dangerous-default-value
     def __init__(
         self,
         num_anchors: int = 1,

official/vision/detection/layers/det/box_utils.py

@@ -31,6 +31,7 @@ def decode(self) -> Tensor:
 
 
 class BoxCoder(BoxCoderBase, metaclass=ABCMeta):
+    # pylint: disable=dangerous-default-value
     def __init__(
         self,
         reg_mean=[0.0, 0.0, 0.0, 0.0],

official/vision/detection/layers/det/fpn.py

@@ -40,6 +40,7 @@ class FPN(M.Module):
     are produced by the backbone networks like ResNet.
     """
 
+    # pylint: disable=dangerous-default-value
     def __init__(
         self,
         bottom_up: M.Module,

official/vision/detection/models/faster_rcnn.py

@@ -99,6 +99,7 @@ def inference(self, features, im_info):
 
 
 class FasterRCNNConfig:
+    # pylint: disable=too-many-statements
     def __init__(self):
         self.backbone = "resnet50"
         self.backbone_pretrained = True

official/vision/detection/tools/test.py

@@ -47,7 +47,7 @@ def make_parser():
 
 
 def main():
-    # pylint: disable=import-outside-toplevel
+    # pylint: disable=import-outside-toplevel,too-many-branches,too-many-statements
     from pycocotools.coco import COCO
     from pycocotools.cocoeval import COCOeval
 

official/vision/detection/tools/test_random.py

@@ -16,9 +16,9 @@
 import megengine.distributed as dist
 from megengine.data import DataLoader
 
-from official.vision.detection.tools.data_mapper import data_mapper
 from official.vision.detection.tools.utils import (
     InferenceSampler,
+    PseudoDetectionDataset,
     DetEvaluator,
     import_from_file
 )
@@ -47,7 +47,7 @@ def make_parser():
 
 
 def main():
-    # pylint: disable=import-outside-toplevel
+    # pylint: disable=import-outside-toplevel,too-many-branches,too-many-statements
     from pycocotools.coco import COCO
     from pycocotools.cocoeval import COCOeval
 
@@ -204,6 +204,7 @@ def worker(
             result_list.append(result)
 
 
+# pylint: disable=unused-argument
 def build_dataloader(rank, world_size, dataset_dir, cfg):
     val_dataset = PseudoDetectionDataset(length=5000, order=["image", "info"])
     val_sampler = InferenceSampler(val_dataset, 1, world_size=world_size, rank=rank)

official/vision/detection/tools/train.py

@@ -233,6 +233,7 @@ def build_dataset(dataset_dir, cfg):
     return data_mapper[data_name](**data_cfg)
 
 
+# pylint: disable=dangerous-default-value
 def build_sampler(train_dataset, batch_size, aspect_grouping=[1]):
     def _compute_aspect_ratios(dataset):
         aspect_ratios = []

official/vision/detection/tools/train_random.py

@@ -8,7 +8,6 @@
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 import argparse
 import bisect
-import copy
 import multiprocessing as mp
 import os
 import time
@@ -21,7 +20,6 @@
 # from megengine.jit import trace
 from megengine.optimizer import SGD
 
-from official.vision.detection.tools.data_mapper import data_mapper
 from official.vision.detection.tools.utils import (
     AverageMeter,
     DetectionPadCollator,
@@ -220,10 +218,12 @@ def adjust_learning_rate(optimizer, epoch, step, cfg, args):
         param_group["lr"] = base_lr * lr_factor
 
 
-def build_dataset(*args):
+# pylint: disable=unused-argument
+def build_dataset(dataset_dir, cfg):
     return PseudoDetectionDataset(order=["image", "boxes", "boxes_category", "info"])
 
 
+# pylint: disable=dangerous-default-value
 def build_sampler(train_dataset, batch_size, aspect_grouping=[1]):
     def _compute_aspect_ratios(dataset):
         aspect_ratios = []

official/vision/detection/tools/utils.py

@@ -111,6 +111,9 @@ def __init__(self, dataset, batch_size=1, world_size=None, rank=None):
         end = min(self.num_samples * (self.rank + 1), len(self.dataset))
         self.indices = list(range(begin, end))
 
+    def sample(self):
+        pass
+
     def batch(self):
         step, length = self.batch_size, len(self.indices)
         batch_index = [self.indices[i : i + step] for i in range(0, length, step)]

official/vision/segmentation/tools/test.py

@@ -256,6 +256,7 @@ def compute_metric(result_list, cfg):
         k = (gt >= 0) & (gt < num_classes)
         labeled += np.sum(k)
         correct += np.sum((pred[k] == gt[k]))
+        # pylint: disable=no-member
         hist += np.bincount(
             num_classes * gt[k].astype(int) + pred[k].astype(int),
             minlength=num_classes ** 2

official/vision/segmentation/tools/train.py

@@ -70,6 +70,7 @@ def main():
         worker(None, None, 1, 0, args)
 
 
+# pylint: disable=too-many-branches
 def worker(master_ip, port, world_size, rank, args):
     if world_size > 1:
         dist.init_process_group(

official/vision/segmentation/tools/utils.py

@@ -64,6 +64,9 @@ def __init__(self, dataset, batch_size=1, world_size=None, rank=None):
         end = min(self.num_samples * (self.rank + 1), len(self.dataset))
         self.indices = list(range(begin, end))
 
+    def sample(self):
+        pass
+
     def batch(self):
         step, length = self.batch_size, len(self.indices)
         batch_index = [self.indices[i : i + step] for i in range(0, length, step)]