diff --git a/.style.yapf b/.style.yapf
new file mode 100644
index 0000000000000000000000000000000000000000..286a3f1d7ae2ae8eb6374043fff78b7ff71f1786
--- /dev/null
+++ b/.style.yapf
@@ -0,0 +1,4 @@
+[style]
+BASED_ON_STYLE = pep8
+BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
+SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true
diff --git a/configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py b/configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
index afd31839e5987e76f26364d68ef462d3190f29fe..711231eaac99977a143e8a1a2f5a1354aedcce79 100644
--- a/configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
+++ b/configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
@@ -11,9 +11,7 @@ model = dict(
frozen_stages=1,
style='pytorch',
dcn=dict(
- modulated=False,
- deformable_groups=1,
- fallback_on_stride=False),
+ modulated=False, deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
neck=dict(
type='FPN',
diff --git a/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py b/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py
index 19f281f0cc4cbe5b910be388e4cb28ab089b0f9e..cc00d912f48171ea7c44fd119afe81424f21e21e 100644
--- a/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py
+++ b/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py
@@ -11,9 +11,7 @@ model = dict(
frozen_stages=1,
style='pytorch',
dcn=dict(
- modulated=False,
- deformable_groups=1,
- fallback_on_stride=False),
+ modulated=False, deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
neck=dict(
type='FPN',
diff --git a/configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py b/configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py
index 5d24d81e15c91a48187c8cfe211bb6926cf6b8b5..5e47eaedd7dd05a0df483df0512b6df9da346ab6 100644
--- a/configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py
+++ b/configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py
@@ -10,9 +10,7 @@ model = dict(
frozen_stages=1,
style='pytorch',
dcn=dict(
- modulated=False,
- deformable_groups=1,
- fallback_on_stride=False),
+ modulated=False, deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
neck=dict(
type='FPN',
diff --git a/configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py b/configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py
index 10279acc89376b5353d788f832a5017b8504d71a..73dc579ba3535720f58c7aecf7d8363c376843b1 100644
--- a/configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py
+++ b/configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py
@@ -10,9 +10,7 @@ model = dict(
frozen_stages=1,
style='pytorch',
dcn=dict(
- modulated=True,
- deformable_groups=1,
- fallback_on_stride=False),
+ modulated=True, deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
neck=dict(
type='FPN',
diff --git a/configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py b/configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
index 33259432922adc543204dfaf41343093bba16aed..8135f098e9a00a0b75c14160c29292f4e546b6a3 100644
--- a/configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
+++ b/configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
@@ -10,9 +10,7 @@ model = dict(
frozen_stages=1,
style='pytorch',
dcn=dict(
- modulated=False,
- deformable_groups=1,
- fallback_on_stride=False),
+ modulated=False, deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
neck=dict(
type='FPN',
diff --git a/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py b/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py
index ef635839bb026a747154622285a67cef6a37e600..396993faff4698d3c40d2dc07f154cbfe33f7205 100644
--- a/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py
+++ b/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py
@@ -99,9 +99,7 @@ test_cfg = dict(
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
- score_thr=0.05,
- nms=dict(type='nms', iou_thr=0.5),
- max_per_img=100))
+ score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100))
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
diff --git a/mmdet/apis/inference.py b/mmdet/apis/inference.py
index d2f455ebfa66d9b33b7d6035e4fc4962b0bac32d..222fc8319f1d2d07c4cfdfd7a246131b649489cc 100644
--- a/mmdet/apis/inference.py
+++ b/mmdet/apis/inference.py
@@ -124,8 +124,7 @@ def show_result(img, result, class_names, score_thr=0.3, out_file=None):
segms = mmcv.concat_list(segm_result)
inds = np.where(bboxes[:, -1] > score_thr)[0]
for i in inds:
- color_mask = np.random.randint(
- 0, 256, (1, 3), dtype=np.uint8)
+ color_mask = np.random.randint(0, 256, (1, 3), dtype=np.uint8)
mask = maskUtils.decode(segms[i]).astype(np.bool)
img[mask] = img[mask] * 0.5 + color_mask * 0.5
# draw bounding boxes
diff --git a/mmdet/apis/train.py b/mmdet/apis/train.py
index 6732664b3b049ee10f4337d6bda946b947c4e789..44ec2cc321259433fc87a5fde049b54958b197dc 100644
--- a/mmdet/apis/train.py
+++ b/mmdet/apis/train.py
@@ -91,8 +91,8 @@ def build_optimizer(model, optimizer_cfg):
paramwise_options = optimizer_cfg.pop('paramwise_options', None)
# if no paramwise option is specified, just use the global setting
if paramwise_options is None:
- return obj_from_dict(optimizer_cfg, torch.optim,
- dict(params=model.parameters()))
+ return obj_from_dict(
+ optimizer_cfg, torch.optim, dict(params=model.parameters()))
else:
assert isinstance(paramwise_options, dict)
# get base lr and weight decay
diff --git a/mmdet/core/bbox/assign_sampling.py b/mmdet/core/bbox/assign_sampling.py
index 70d9fdf04257d8b908b654ba2791923b3a7fe2b2..4267174bbece23c9b49a35f81f01533eeeef1e98 100644
--- a/mmdet/core/bbox/assign_sampling.py
+++ b/mmdet/core/bbox/assign_sampling.py
@@ -7,8 +7,7 @@ def build_assigner(cfg, **kwargs):
if isinstance(cfg, assigners.BaseAssigner):
return cfg
elif isinstance(cfg, dict):
- return mmcv.runner.obj_from_dict(
- cfg, assigners, default_args=kwargs)
+ return mmcv.runner.obj_from_dict(cfg, assigners, default_args=kwargs)
else:
raise TypeError('Invalid type {} for building a sampler'.format(
type(cfg)))
@@ -18,8 +17,7 @@ def build_sampler(cfg, **kwargs):
if isinstance(cfg, samplers.BaseSampler):
return cfg
elif isinstance(cfg, dict):
- return mmcv.runner.obj_from_dict(
- cfg, samplers, default_args=kwargs)
+ return mmcv.runner.obj_from_dict(cfg, samplers, default_args=kwargs)
else:
raise TypeError('Invalid type {} for building a sampler'.format(
type(cfg)))
diff --git a/mmdet/core/evaluation/mean_ap.py b/mmdet/core/evaluation/mean_ap.py
index d69eda714560bb447432d32d48e0952b17ac1cc5..8f0474aae21a530efc173ea426a08d1691daab72 100644
--- a/mmdet/core/evaluation/mean_ap.py
+++ b/mmdet/core/evaluation/mean_ap.py
@@ -261,8 +261,8 @@ def eval_map(det_results,
cls_dets, cls_gts, cls_gt_ignore = get_cls_results(
det_results, gt_bboxes, gt_labels, gt_ignore, i)
# calculate tp and fp for each image
- tpfp_func = (tpfp_imagenet
- if dataset in ['det', 'vid'] else tpfp_default)
+ tpfp_func = (
+ tpfp_imagenet if dataset in ['det', 'vid'] else tpfp_default)
tpfp = [
tpfp_func(cls_dets[j], cls_gts[j], cls_gt_ignore[j], iou_thr,
area_ranges) for j in range(len(cls_dets))
diff --git a/mmdet/core/post_processing/bbox_nms.py b/mmdet/core/post_processing/bbox_nms.py
index 01beecd43abc7641d43773b192f988a55f4295d9..cb3fe21e036ee33cabc027567c26f651ac7424ef 100644
--- a/mmdet/core/post_processing/bbox_nms.py
+++ b/mmdet/core/post_processing/bbox_nms.py
@@ -45,9 +45,8 @@ def multiclass_nms(multi_bboxes,
_scores *= score_factors[cls_inds]
cls_dets = torch.cat([_bboxes, _scores[:, None]], dim=1)
cls_dets, _ = nms_op(cls_dets, **nms_cfg_)
- cls_labels = multi_bboxes.new_full((cls_dets.shape[0], ),
- i - 1,
- dtype=torch.long)
+ cls_labels = multi_bboxes.new_full(
+ (cls_dets.shape[0], ), i - 1, dtype=torch.long)
bboxes.append(cls_dets)
labels.append(cls_labels)
if bboxes:
diff --git a/mmdet/datasets/extra_aug.py b/mmdet/datasets/extra_aug.py
index c9e0734da130486c912e07a2043c53fdcf26c442..a9f4f44c5cce27a71bc7af989436e7a0842d1937 100644
--- a/mmdet/datasets/extra_aug.py
+++ b/mmdet/datasets/extra_aug.py
@@ -91,9 +91,7 @@ class Expand(object):
class RandomCrop(object):
- def __init__(self,
- min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
- min_crop_size=0.3):
+ def __init__(self, min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3):
# 1: return ori img
self.sample_mode = (1, *min_ious, 0)
self.min_crop_size = min_crop_size
diff --git a/mmdet/datasets/loader/__init__.py b/mmdet/datasets/loader/__init__.py
index a3d4fdd2cbbe85b26b4c5fa4898315accbe94c0a..1f23fa4436df8587cfa1476d28673117fa0c5e83 100644
--- a/mmdet/datasets/loader/__init__.py
+++ b/mmdet/datasets/loader/__init__.py
@@ -1,6 +1,4 @@
from .build_loader import build_dataloader
from .sampler import GroupSampler, DistributedGroupSampler
-__all__ = [
- 'GroupSampler', 'DistributedGroupSampler', 'build_dataloader'
-]
+__all__ = ['GroupSampler', 'DistributedGroupSampler', 'build_dataloader']
diff --git a/mmdet/datasets/loader/build_loader.py b/mmdet/datasets/loader/build_loader.py
index 248f8f8900dd5217d4128f6058146f968f6c65bc..8759b0612638479247e1a536eca895224aebcbd0 100644
--- a/mmdet/datasets/loader/build_loader.py
+++ b/mmdet/datasets/loader/build_loader.py
@@ -25,10 +25,8 @@ def build_dataloader(dataset,
sampler = DistributedGroupSampler(dataset, imgs_per_gpu,
world_size, rank)
else:
- sampler = DistributedSampler(dataset,
- world_size,
- rank,
- shuffle=False)
+ sampler = DistributedSampler(
+ dataset, world_size, rank, shuffle=False)
batch_size = imgs_per_gpu
num_workers = workers_per_gpu
else:
@@ -36,13 +34,13 @@ def build_dataloader(dataset,
batch_size = num_gpus * imgs_per_gpu
num_workers = num_gpus * workers_per_gpu
- data_loader = DataLoader(dataset,
- batch_size=batch_size,
- sampler=sampler,
- num_workers=num_workers,
- collate_fn=partial(collate,
- samples_per_gpu=imgs_per_gpu),
- pin_memory=False,
- **kwargs)
+ data_loader = DataLoader(
+ dataset,
+ batch_size=batch_size,
+ sampler=sampler,
+ num_workers=num_workers,
+ collate_fn=partial(collate, samples_per_gpu=imgs_per_gpu),
+ pin_memory=False,
+ **kwargs)
return data_loader
diff --git a/mmdet/datasets/loader/sampler.py b/mmdet/datasets/loader/sampler.py
index d2eef23bf780b97ee5839617f75b4b1fc79d1826..1e454b741c8e2dbee10ba25b928a13ba1da18392 100644
--- a/mmdet/datasets/loader/sampler.py
+++ b/mmdet/datasets/loader/sampler.py
@@ -139,8 +139,8 @@ class DistributedGroupSampler(Sampler):
indices = [
indices[j] for i in list(
- torch.randperm(len(indices) // self.samples_per_gpu,
- generator=g))
+ torch.randperm(
+ len(indices) // self.samples_per_gpu, generator=g))
for j in range(i * self.samples_per_gpu, (i + 1) *
self.samples_per_gpu)
]
diff --git a/mmdet/datasets/transforms.py b/mmdet/datasets/transforms.py
index df36590020c98c10cce118a72df60977cb4109aa..ff575dba3f1b13a9df5392c059e2a98985fd4030 100644
--- a/mmdet/datasets/transforms.py
+++ b/mmdet/datasets/transforms.py
@@ -34,8 +34,8 @@ class ImageTransform(object):
else:
img, w_scale, h_scale = mmcv.imresize(
img, scale, return_scale=True)
- scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
- dtype=np.float32)
+ scale_factor = np.array(
+ [w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
img_shape = img.shape
img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
if flip:
diff --git a/mmdet/models/anchor_heads/anchor_head.py b/mmdet/models/anchor_heads/anchor_head.py
index 54096a4f814da76788b88f6e0c0d2355485a2957..881415be267d91d5ab097f2f316cef98b7a4c91d 100644
--- a/mmdet/models/anchor_heads/anchor_head.py
+++ b/mmdet/models/anchor_heads/anchor_head.py
@@ -196,8 +196,9 @@ class AnchorHead(nn.Module):
return None
(labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
num_total_pos, num_total_neg) = cls_reg_targets
- num_total_samples = (num_total_pos if self.cls_focal_loss else
- num_total_pos + num_total_neg)
+ num_total_samples = (
+ num_total_pos
+ if self.cls_focal_loss else num_total_pos + num_total_neg)
losses_cls, losses_reg = multi_apply(
self.loss_single,
cls_scores,
diff --git a/mmdet/models/backbones/resnet.py b/mmdet/models/backbones/resnet.py
index 564b5657cb51810efc2241e6964b9f432c9af811..d1db1dda7d87d810db49819a25de04d0fc3346ae 100644
--- a/mmdet/models/backbones/resnet.py
+++ b/mmdet/models/backbones/resnet.py
@@ -43,12 +43,7 @@ class BasicBlock(nn.Module):
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(
- conv_cfg,
- planes,
- planes,
- 3,
- padding=1,
- bias=False)
+ conv_cfg, planes, planes, 3, padding=1, bias=False)
self.add_module(self.norm2_name, norm2)
self.relu = nn.ReLU(inplace=True)
diff --git a/mmdet/models/bbox_heads/convfc_bbox_head.py b/mmdet/models/bbox_heads/convfc_bbox_head.py
index c0e715415f8b8eb5f519dd25e22d9df757d299c8..2ce687ed07b7c0b66c4b96b796d26ab8c4ec95aa 100644
--- a/mmdet/models/bbox_heads/convfc_bbox_head.py
+++ b/mmdet/models/bbox_heads/convfc_bbox_head.py
@@ -94,8 +94,8 @@ class ConvFCBBoxHead(BBoxHead):
branch_convs = nn.ModuleList()
if num_branch_convs > 0:
for i in range(num_branch_convs):
- conv_in_channels = (last_layer_dim
- if i == 0 else self.conv_out_channels)
+ conv_in_channels = (
+ last_layer_dim if i == 0 else self.conv_out_channels)
branch_convs.append(
ConvModule(
conv_in_channels,
@@ -114,8 +114,8 @@ class ConvFCBBoxHead(BBoxHead):
or self.num_shared_fcs == 0) and not self.with_avg_pool:
last_layer_dim *= (self.roi_feat_size * self.roi_feat_size)
for i in range(num_branch_fcs):
- fc_in_channels = (last_layer_dim
- if i == 0 else self.fc_out_channels)
+ fc_in_channels = (
+ last_layer_dim if i == 0 else self.fc_out_channels)
branch_fcs.append(
nn.Linear(fc_in_channels, self.fc_out_channels))
last_layer_dim = self.fc_out_channels
diff --git a/mmdet/models/detectors/cascade_rcnn.py b/mmdet/models/detectors/cascade_rcnn.py
index 6eb83e8eaa85b50f644225e524fde9c6689fd66f..f0564c9eb16ccf5184f94d29e33c889fbe80c143 100644
--- a/mmdet/models/detectors/cascade_rcnn.py
+++ b/mmdet/models/detectors/cascade_rcnn.py
@@ -186,8 +186,8 @@ class CascadeRCNN(BaseDetector, RPNTestMixin):
gt_labels, rcnn_train_cfg)
loss_bbox = bbox_head.loss(cls_score, bbox_pred, *bbox_targets)
for name, value in loss_bbox.items():
- losses['s{}.{}'.format(
- i, name)] = (value * lw if 'loss' in name else value)
+ losses['s{}.{}'.format(i, name)] = (
+ value * lw if 'loss' in name else value)
# mask head forward and loss
if self.with_mask:
@@ -224,8 +224,8 @@ class CascadeRCNN(BaseDetector, RPNTestMixin):
[res.pos_gt_labels for res in sampling_results])
loss_mask = mask_head.loss(mask_pred, mask_targets, pos_labels)
for name, value in loss_mask.items():
- losses['s{}.{}'.format(
- i, name)] = (value * lw if 'loss' in name else value)
+ losses['s{}.{}'.format(i, name)] = (
+ value * lw if 'loss' in name else value)
# refine bboxes
if i < self.num_stages - 1:
@@ -286,8 +286,9 @@ class CascadeRCNN(BaseDetector, RPNTestMixin):
[] for _ in range(mask_head.num_classes - 1)
]
else:
- _bboxes = (det_bboxes[:, :4] * scale_factor
- if rescale else det_bboxes)
+ _bboxes = (
+ det_bboxes[:, :4] * scale_factor
+ if rescale else det_bboxes)
mask_rois = bbox2roi([_bboxes])
mask_feats = mask_roi_extractor(
x[:len(mask_roi_extractor.featmap_strides)],
@@ -324,8 +325,9 @@ class CascadeRCNN(BaseDetector, RPNTestMixin):
[] for _ in range(self.mask_head[-1].num_classes - 1)
]
else:
- _bboxes = (det_bboxes[:, :4] * scale_factor
- if rescale else det_bboxes)
+ _bboxes = (
+ det_bboxes[:, :4] * scale_factor
+ if rescale else det_bboxes)
mask_rois = bbox2roi([_bboxes])
aug_masks = []
for i in range(self.num_stages):
diff --git a/mmdet/models/detectors/htc.py b/mmdet/models/detectors/htc.py
index b7ac7ae0cc9a9a05029c3a4508b1cfb349780982..0384aa99b110a6ecbf3ad5038ec9b07343fef28c 100644
--- a/mmdet/models/detectors/htc.py
+++ b/mmdet/models/detectors/htc.py
@@ -224,8 +224,8 @@ class HybridTaskCascade(CascadeRCNN):
roi_labels = bbox_targets[0]
for name, value in loss_bbox.items():
- losses['s{}.{}'.format(
- i, name)] = (value * lw if 'loss' in name else value)
+ losses['s{}.{}'.format(i, name)] = (
+ value * lw if 'loss' in name else value)
# mask head forward and loss
if self.with_mask:
@@ -253,8 +253,8 @@ class HybridTaskCascade(CascadeRCNN):
gt_masks, rcnn_train_cfg,
semantic_feat)
for name, value in loss_mask.items():
- losses['s{}.{}'.format(
- i, name)] = (value * lw if 'loss' in name else value)
+ losses['s{}.{}'.format(i, name)] = (
+ value * lw if 'loss' in name else value)
# refine bboxes (same as Cascade R-CNN)
if i < self.num_stages - 1 and not self.interleaved:
@@ -312,8 +312,9 @@ class HybridTaskCascade(CascadeRCNN):
[] for _ in range(mask_head.num_classes - 1)
]
else:
- _bboxes = (det_bboxes[:, :4] * scale_factor
- if rescale else det_bboxes)
+ _bboxes = (
+ det_bboxes[:, :4] * scale_factor
+ if rescale else det_bboxes)
mask_pred = self._mask_forward_test(
i, x, _bboxes, semantic_feat=semantic_feat)
segm_result = mask_head.get_seg_masks(
@@ -345,8 +346,9 @@ class HybridTaskCascade(CascadeRCNN):
[] for _ in range(self.mask_head[-1].num_classes - 1)
]
else:
- _bboxes = (det_bboxes[:, :4] * scale_factor
- if rescale else det_bboxes)
+ _bboxes = (
+ det_bboxes[:, :4] * scale_factor
+ if rescale else det_bboxes)
mask_rois = bbox2roi([_bboxes])
aug_masks = []
diff --git a/mmdet/models/detectors/test_mixins.py b/mmdet/models/detectors/test_mixins.py
index 709ee39ac2446bf78971960cb853fee00d599573..b64f2cda43059b9735d9b8a5beb50606d4192332 100644
--- a/mmdet/models/detectors/test_mixins.py
+++ b/mmdet/models/detectors/test_mixins.py
@@ -105,8 +105,8 @@ class MaskTestMixin(object):
else:
# if det_bboxes is rescaled to the original image size, we need to
# rescale it back to the testing scale to obtain RoIs.
- _bboxes = (det_bboxes[:, :4] * scale_factor
- if rescale else det_bboxes)
+ _bboxes = (
+ det_bboxes[:, :4] * scale_factor if rescale else det_bboxes)
mask_rois = bbox2roi([_bboxes])
mask_feats = self.mask_roi_extractor(
x[:len(self.mask_roi_extractor.featmap_strides)], mask_rois)
diff --git a/mmdet/models/mask_heads/fcn_mask_head.py b/mmdet/models/mask_heads/fcn_mask_head.py
index 9274e7c8cea60d3c3d5a606b5e9f3207aeacb7a5..0a5054c7a0ed38ffde90e8d1ff6c2d3e1810f257 100644
--- a/mmdet/models/mask_heads/fcn_mask_head.py
+++ b/mmdet/models/mask_heads/fcn_mask_head.py
@@ -43,8 +43,8 @@ class FCNMaskHead(nn.Module):
self.convs = nn.ModuleList()
for i in range(self.num_convs):
- in_channels = (self.in_channels
- if i == 0 else self.conv_out_channels)
+ in_channels = (
+ self.in_channels if i == 0 else self.conv_out_channels)
padding = (self.conv_kernel_size - 1) // 2
self.convs.append(
ConvModule(
@@ -54,8 +54,8 @@ class FCNMaskHead(nn.Module):
padding=padding,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg))
- upsample_in_channels = (self.conv_out_channels
- if self.num_convs > 0 else in_channels)
+ upsample_in_channels = (
+ self.conv_out_channels if self.num_convs > 0 else in_channels)
if self.upsample_method is None:
self.upsample = None
elif self.upsample_method == 'deconv':
@@ -69,9 +69,9 @@ class FCNMaskHead(nn.Module):
scale_factor=self.upsample_ratio, mode=self.upsample_method)
out_channels = 1 if self.class_agnostic else self.num_classes
- logits_in_channel = (self.conv_out_channels
- if self.upsample_method == 'deconv' else
- upsample_in_channels)
+ logits_in_channel = (
+ self.conv_out_channels
+ if self.upsample_method == 'deconv' else upsample_in_channels)
self.conv_logits = nn.Conv2d(logits_in_channel, out_channels, 1)
self.relu = nn.ReLU(inplace=True)
self.debug_imgs = None
diff --git a/mmdet/models/mask_heads/fused_semantic_head.py b/mmdet/models/mask_heads/fused_semantic_head.py
index 12b8302ab11507234e3a78146f77a0ba67f4e242..6107423395c27361d8e3da311a106d31769d53af 100644
--- a/mmdet/models/mask_heads/fused_semantic_head.py
+++ b/mmdet/models/mask_heads/fused_semantic_head.py
@@ -85,10 +85,7 @@ class FusedSemanticHead(nn.Module):
for i, feat in enumerate(feats):
if i != self.fusion_level:
feat = F.interpolate(
- feat,
- size=fused_size,
- mode='bilinear',
- align_corners=True)
+ feat, size=fused_size, mode='bilinear', align_corners=True)
x += self.lateral_convs[i](feat)
for i in range(self.num_convs):
diff --git a/mmdet/models/registry.py b/mmdet/models/registry.py
index fc14bc11b6c6ab468e9791c0303b9606c7eed3ec..d5f8e67cfe96af410169af7295ff26ab88498179 100644
--- a/mmdet/models/registry.py
+++ b/mmdet/models/registry.py
@@ -22,8 +22,9 @@ class Registry(object):
module (:obj:`nn.Module`): Module to be registered.
"""
if not issubclass(module_class, nn.Module):
- raise TypeError('module must be a child of nn.Module, but got {}'.
- format(module_class))
+ raise TypeError(
+ 'module must be a child of nn.Module, but got {}'.format(
+ module_class))
module_name = module_class.__name__
if module_name in self._module_dict:
raise KeyError('{} is already registered in {}'.format(
diff --git a/mmdet/models/utils/conv_module.py b/mmdet/models/utils/conv_module.py
index 329b2352767949eab9c1e3d5adda52e1f23558e5..623d691259c8de23dda86d5795e82002e0b49ccd 100644
--- a/mmdet/models/utils/conv_module.py
+++ b/mmdet/models/utils/conv_module.py
@@ -98,15 +98,16 @@ class ConvModule(nn.Module):
warnings.warn('ConvModule has norm and bias at the same time')
# build convolution layer
- self.conv = build_conv_layer(conv_cfg,
- in_channels,
- out_channels,
- kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=groups,
- bias=bias)
+ self.conv = build_conv_layer(
+ conv_cfg,
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride=stride,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ bias=bias)
# export the attributes of self.conv to a higher level for convenience
self.in_channels = self.conv.in_channels
self.out_channels = self.conv.out_channels
diff --git a/mmdet/models/utils/norm.py b/mmdet/models/utils/norm.py
index a912a19b5a3a5de5090865f28cebf230862b6375..d5687cbd9a31be259ee0b202800af53fbba8ca1a 100644
--- a/mmdet/models/utils/norm.py
+++ b/mmdet/models/utils/norm.py
@@ -1,6 +1,5 @@
import torch.nn as nn
-
norm_cfg = {
# format: layer_type: (abbreviation, module)
'BN': ('bn', nn.BatchNorm2d),
diff --git a/mmdet/ops/dcn/setup.py b/mmdet/ops/dcn/setup.py
index 020d5edc4b2308aa1a32092e7d75da599da27f28..96380181d848545d21aca6e33686a7bc33f57d94 100644
--- a/mmdet/ops/dcn/setup.py
+++ b/mmdet/ops/dcn/setup.py
@@ -8,8 +8,8 @@ setup(
'src/deform_conv_cuda.cpp',
'src/deform_conv_cuda_kernel.cu',
]),
- CUDAExtension('deform_pool_cuda', [
- 'src/deform_pool_cuda.cpp', 'src/deform_pool_cuda_kernel.cu'
- ]),
+ CUDAExtension(
+ 'deform_pool_cuda',
+ ['src/deform_pool_cuda.cpp', 'src/deform_pool_cuda_kernel.cu']),
],
cmdclass={'build_ext': BuildExtension})
diff --git a/tools/analyze_logs.py b/tools/analyze_logs.py
index b56f3defbd890926e0207eeefad160f231b4e9a9..c9f603f46fd3bba4226733c953b3d8bd2ef598d8 100644
--- a/tools/analyze_logs.py
+++ b/tools/analyze_logs.py
@@ -51,8 +51,8 @@ def plot_curve(log_dicts, args):
for j, metric in enumerate(metrics):
print('plot curve of {}, metric is {}'.format(
args.json_logs[i], metric))
- assert metric in log_dict[
- epochs[0]], '{} does not contain metric {}'.format(
+ assert metric in log_dict[epochs[
+ 0]], '{} does not contain metric {}'.format(
args.json_logs[i], metric)
if 'mAP' in metric:
diff --git a/tools/test.py b/tools/test.py
index dc94b6dfa0c4e50158281d4bbd255450eb5b3594..9719f45e3f881a7632bbd7f99187efe3181b35a3 100644
--- a/tools/test.py
+++ b/tools/test.py
@@ -63,15 +63,12 @@ def collect_results(result_part, size, tmpdir=None):
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
- dir_tensor = torch.full((MAX_LEN, ),
- 32,
- dtype=torch.uint8,
- device='cuda')
+ dir_tensor = torch.full(
+ (MAX_LEN, ), 32, dtype=torch.uint8, device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
- tmpdir = torch.tensor(bytearray(tmpdir.encode()),
- dtype=torch.uint8,
- device='cuda')
+ tmpdir = torch.tensor(
+ bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
@@ -113,10 +110,11 @@ def parse_args():
help='eval types')
parser.add_argument('--show', action='store_true', help='show results')
parser.add_argument('--tmpdir', help='tmp dir for writing some results')
- parser.add_argument('--launcher',
- choices=['none', 'pytorch', 'slurm', 'mpi'],
- default='none',
- help='job launcher')
+ parser.add_argument(
+ '--launcher',
+ choices=['none', 'pytorch', 'slurm', 'mpi'],
+ default='none',
+ help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
return args
@@ -145,11 +143,12 @@ def main():
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = get_dataset(cfg.data.test)
- data_loader = build_dataloader(dataset,
- imgs_per_gpu=1,
- workers_per_gpu=cfg.data.workers_per_gpu,
- dist=distributed,
- shuffle=False)
+ data_loader = build_dataloader(
+ dataset,
+ imgs_per_gpu=1,
+ workers_per_gpu=cfg.data.workers_per_gpu,
+ dist=distributed,
+ shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
diff --git a/tools/train.py b/tools/train.py
index 97a253fd3a7f78daae5acb0360ae051a69b6034c..3e06d6f56cd6109f6788e84fd7284a852809a612 100644
--- a/tools/train.py
+++ b/tools/train.py
@@ -77,7 +77,8 @@ def main():
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
- mmdet_version=__version__, config=cfg.text,
+ mmdet_version=__version__,
+ config=cfg.text,
classes=train_dataset.CLASSES)
# add an attribute for visualization convenience
model.CLASSES = train_dataset.CLASSES