diff --git a/.travis.yml b/.travis.yml
index bba12f1c1540d5bafe3039f2f66fa20d78ece973..127b9030a6fcc860a7c72af2d635908dc23eb190 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,7 +2,7 @@ dist: xenial
language: python
install:
- - pip install flake8
+ - pip install flake8 yapf
python:
- "3.5"
@@ -11,3 +11,4 @@ python:
script:
- flake8
+ - yapf -r -d --style .style.yapf mmdet/ tools/
\ No newline at end of file
diff --git a/mmdet/core/anchor/anchor_generator.py b/mmdet/core/anchor/anchor_generator.py
index 8995ea61d75ad38fa747e63deafd4b048a410d76..61304307415392b17f78dd82f53c731dcca1a144 100644
--- a/mmdet/core/anchor/anchor_generator.py
+++ b/mmdet/core/anchor/anchor_generator.py
@@ -33,12 +33,14 @@ class AnchorGenerator(object):
ws = (w * self.scales[:, None] * w_ratios[None, :]).view(-1)
hs = (h * self.scales[:, None] * h_ratios[None, :]).view(-1)
+ # yapf: disable
base_anchors = torch.stack(
[
x_ctr - 0.5 * (ws - 1), y_ctr - 0.5 * (hs - 1),
x_ctr + 0.5 * (ws - 1), y_ctr + 0.5 * (hs - 1)
],
dim=-1).round()
+ # yapf: enable
return base_anchors
diff --git a/mmdet/core/anchor/guided_anchor_target.py b/mmdet/core/anchor/guided_anchor_target.py
index 2e9540644b995ba74f5e8e261cf880f9f687c596..7284a73c3ade0c62862e8cf009609274d4dcc0fe 100644
--- a/mmdet/core/anchor/guided_anchor_target.py
+++ b/mmdet/core/anchor/guided_anchor_target.py
@@ -62,12 +62,13 @@ def ga_loc_target(gt_bboxes_list,
all_ignore_map = []
for lvl_id in range(num_lvls):
h, w = featmap_sizes[lvl_id]
- loc_targets = torch.zeros(img_per_gpu,
- 1,
- h,
- w,
- device=gt_bboxes_list[0].device,
- dtype=torch.float32)
+ loc_targets = torch.zeros(
+ img_per_gpu,
+ 1,
+ h,
+ w,
+ device=gt_bboxes_list[0].device,
+ dtype=torch.float32)
loc_weights = torch.full_like(loc_targets, -1)
ignore_map = torch.zeros_like(loc_targets)
all_loc_targets.append(loc_targets)
@@ -175,17 +176,18 @@ def ga_shape_target(approx_list,
if gt_bboxes_ignore_list is None:
gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
(all_bbox_anchors, all_bbox_gts, all_bbox_weights, pos_inds_list,
- neg_inds_list) = multi_apply(ga_shape_target_single,
- approx_flat_list,
- inside_flag_flat_list,
- square_flat_list,
- gt_bboxes_list,
- gt_bboxes_ignore_list,
- img_metas,
- approxs_per_octave=approxs_per_octave,
- cfg=cfg,
- sampling=sampling,
- unmap_outputs=unmap_outputs)
+ neg_inds_list) = multi_apply(
+ ga_shape_target_single,
+ approx_flat_list,
+ inside_flag_flat_list,
+ square_flat_list,
+ gt_bboxes_list,
+ gt_bboxes_ignore_list,
+ img_metas,
+ approxs_per_octave=approxs_per_octave,
+ cfg=cfg,
+ sampling=sampling,
+ unmap_outputs=unmap_outputs)
# no valid anchors
if any([bbox_anchors is None for bbox_anchors in all_bbox_anchors]):
return None
diff --git a/mmdet/core/bbox/assigners/approx_max_iou_assigner.py b/mmdet/core/bbox/assigners/approx_max_iou_assigner.py
index 1283f7f525e5678eb8793ca112d851273056f346..867a56b87e869d6b38ca379a4ff1231ffd9f7e20 100644
--- a/mmdet/core/bbox/assigners/approx_max_iou_assigner.py
+++ b/mmdet/core/bbox/assigners/approx_max_iou_assigner.py
@@ -101,14 +101,12 @@ class ApproxMaxIoUAssigner(MaxIoUAssigner):
if (self.ignore_iof_thr > 0) and (gt_bboxes_ignore is not None) and (
gt_bboxes_ignore.numel() > 0):
if self.ignore_wrt_candidates:
- ignore_overlaps = bbox_overlaps(bboxes,
- gt_bboxes_ignore,
- mode='iof')
+ ignore_overlaps = bbox_overlaps(
+ bboxes, gt_bboxes_ignore, mode='iof')
ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
else:
- ignore_overlaps = bbox_overlaps(gt_bboxes_ignore,
- bboxes,
- mode='iof')
+ ignore_overlaps = bbox_overlaps(
+ gt_bboxes_ignore, bboxes, mode='iof')
ignore_max_overlaps, _ = ignore_overlaps.max(dim=0)
overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1
diff --git a/mmdet/core/bbox/assigners/max_iou_assigner.py b/mmdet/core/bbox/assigners/max_iou_assigner.py
index 57a1e750456da7cd5fa251aed3f36416563ce7d0..99dcbe8e0f640fb84f0d9e995212405b0fe628ef 100644
--- a/mmdet/core/bbox/assigners/max_iou_assigner.py
+++ b/mmdet/core/bbox/assigners/max_iou_assigner.py
@@ -107,8 +107,9 @@ class MaxIoUAssigner(BaseAssigner):
num_gts, num_bboxes = overlaps.size(0), overlaps.size(1)
# 1. assign -1 by default
- assigned_gt_inds = overlaps.new_full(
- (num_bboxes, ), -1, dtype=torch.long)
+ assigned_gt_inds = overlaps.new_full((num_bboxes, ),
+ -1,
+ dtype=torch.long)
# for each anchor, which gt best overlaps with it
# for each anchor, the max iou of all gts
diff --git a/mmdet/core/bbox/bbox_target.py b/mmdet/core/bbox/bbox_target.py
index aa1fbc67430672185a1a01cbc5338a1912928b84..20b3957bcfe303225a3f5704e46c1a2832d85935 100644
--- a/mmdet/core/bbox/bbox_target.py
+++ b/mmdet/core/bbox/bbox_target.py
@@ -62,10 +62,10 @@ def bbox_target_single(pos_bboxes,
def expand_target(bbox_targets, bbox_weights, labels, num_classes):
- bbox_targets_expand = bbox_targets.new_zeros((bbox_targets.size(0),
- 4 * num_classes))
- bbox_weights_expand = bbox_weights.new_zeros((bbox_weights.size(0),
- 4 * num_classes))
+ bbox_targets_expand = bbox_targets.new_zeros(
+ (bbox_targets.size(0), 4 * num_classes))
+ bbox_weights_expand = bbox_weights.new_zeros(
+ (bbox_weights.size(0), 4 * num_classes))
for i in torch.nonzero(labels > 0).squeeze(-1):
start, end = labels[i] * 4, (labels[i] + 1) * 4
bbox_targets_expand[i, start:end] = bbox_targets[i, :]
diff --git a/mmdet/core/evaluation/mean_ap.py b/mmdet/core/evaluation/mean_ap.py
index 8f0474aae21a530efc173ea426a08d1691daab72..74a972b1f6c901c246eb68419528f9683bd061b2 100644
--- a/mmdet/core/evaluation/mean_ap.py
+++ b/mmdet/core/evaluation/mean_ap.py
@@ -279,8 +279,8 @@ def eval_map(det_results,
bbox[:, 3] - bbox[:, 1] + 1)
for k, (min_area, max_area) in enumerate(area_ranges):
num_gts[k] += np.sum(
- np.logical_not(cls_gt_ignore[j]) &
- (gt_areas >= min_area) & (gt_areas < max_area))
+ np.logical_not(cls_gt_ignore[j])
+ & (gt_areas >= min_area) & (gt_areas < max_area))
# sort all det bboxes by score, also sort tp and fp
cls_dets = np.vstack(cls_dets)
num_dets = cls_dets.shape[0]
@@ -312,11 +312,12 @@ def eval_map(det_results,
all_ap = np.vstack([cls_result['ap'] for cls_result in eval_results])
all_num_gts = np.vstack(
[cls_result['num_gts'] for cls_result in eval_results])
- mean_ap = [
- all_ap[all_num_gts[:, i] > 0, i].mean()
- if np.any(all_num_gts[:, i] > 0) else 0.0
- for i in range(num_scales)
- ]
+ mean_ap = []
+ for i in range(num_scales):
+ if np.any(all_num_gts[:, i] > 0):
+ mean_ap.append(all_ap[all_num_gts[:, i] > 0, i].mean())
+ else:
+ mean_ap.append(0.0)
else:
aps = []
for cls_result in eval_results:
@@ -368,8 +369,8 @@ def print_map_summary(mean_ap, results, dataset=None):
for j in range(num_classes):
row_data = [
label_names[j], num_gts[i, j], results[j]['num_dets'],
- '{:.3f}'.format(recalls[i, j]), '{:.3f}'.format(
- precisions[i, j]), '{:.3f}'.format(aps[i, j])
+ '{:.3f}'.format(recalls[i, j]),
+ '{:.3f}'.format(precisions[i, j]), '{:.3f}'.format(aps[i, j])
]
table_data.append(row_data)
table_data.append(['mAP', '', '', '', '', '{:.3f}'.format(mean_ap[i])])
diff --git a/mmdet/core/post_processing/bbox_nms.py b/mmdet/core/post_processing/bbox_nms.py
index cb3fe21e036ee33cabc027567c26f651ac7424ef..01beecd43abc7641d43773b192f988a55f4295d9 100644
--- a/mmdet/core/post_processing/bbox_nms.py
+++ b/mmdet/core/post_processing/bbox_nms.py
@@ -45,8 +45,9 @@ 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/__init__.py b/mmdet/datasets/__init__.py
index 786b0593f00ce85ee695f66d6c46fa0ecf5afdf1..6d0f12f60b2f64bb9cee039a8e3e593ad5b131cd 100644
--- a/mmdet/datasets/__init__.py
+++ b/mmdet/datasets/__init__.py
@@ -15,6 +15,6 @@ __all__ = [
'CustomDataset', 'XMLDataset', 'CocoDataset', 'VOCDataset',
'CityscapesDataset', 'GroupSampler', 'DistributedGroupSampler',
'build_dataloader', 'to_tensor', 'random_scale', 'show_ann',
- 'ConcatDataset', 'RepeatDataset', 'ExtraAugmentation',
- 'WIDERFaceDataset', 'DATASETS', 'build_dataset'
+ 'ConcatDataset', 'RepeatDataset', 'ExtraAugmentation', 'WIDERFaceDataset',
+ 'DATASETS', 'build_dataset'
]
diff --git a/mmdet/datasets/builder.py b/mmdet/datasets/builder.py
index 3d4e2caafe38a5831620f93ac60399869cafa1d0..a8b0e29c5dac8cccf7a8ea125dad20354ddd141e 100644
--- a/mmdet/datasets/builder.py
+++ b/mmdet/datasets/builder.py
@@ -29,8 +29,8 @@ def _concat_dataset(cfg, default_args=None):
def build_dataset(cfg, default_args=None):
if cfg['type'] == 'RepeatDataset':
- dataset = RepeatDataset(build_dataset(cfg['dataset'], default_args),
- cfg['times'])
+ dataset = RepeatDataset(
+ build_dataset(cfg['dataset'], default_args), cfg['times'])
elif isinstance(cfg['ann_file'], (list, tuple)):
dataset = _concat_dataset(cfg, default_args)
else:
diff --git a/mmdet/datasets/cityscapes.py b/mmdet/datasets/cityscapes.py
index fcbd43c12412644a1e81087891372f78659041b8..51ca049873fe8af2a62085cdbfd779564552793d 100644
--- a/mmdet/datasets/cityscapes.py
+++ b/mmdet/datasets/cityscapes.py
@@ -5,5 +5,5 @@ from .registry import DATASETS
@DATASETS.register_module
class CityscapesDataset(CocoDataset):
- CLASSES = ('person', 'rider', 'car', 'truck', 'bus',
- 'train', 'motorcycle', 'bicycle')
+ CLASSES = ('person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
+ 'bicycle')
diff --git a/mmdet/datasets/extra_aug.py b/mmdet/datasets/extra_aug.py
index a9f4f44c5cce27a71bc7af989436e7a0842d1937..c1bda3eec3ad1e55f674c8a64e2a66169d8b6cc5 100644
--- a/mmdet/datasets/extra_aug.py
+++ b/mmdet/datasets/extra_aug.py
@@ -115,8 +115,8 @@ class RandomCrop(object):
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
- patch = np.array((int(left), int(top), int(left + new_w),
- int(top + new_h)))
+ patch = np.array(
+ (int(left), int(top), int(left + new_w), int(top + new_h)))
overlaps = bbox_overlaps(
patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1)
if overlaps.min() < min_iou:
diff --git a/mmdet/models/anchor_heads/ga_retina_head.py b/mmdet/models/anchor_heads/ga_retina_head.py
index c39ab8d684f1aa3383b798095bd9ebc433fc45ba..40ad0499f0b3ef99859ddf796c1197f1d0dd0f76 100644
--- a/mmdet/models/anchor_heads/ga_retina_head.py
+++ b/mmdet/models/anchor_heads/ga_retina_head.py
@@ -30,21 +30,23 @@ class GARetinaHead(GuidedAnchorHead):
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
self.cls_convs.append(
- ConvModule(chn,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg))
+ ConvModule(
+ chn,
+ self.feat_channels,
+ 3,
+ stride=1,
+ padding=1,
+ conv_cfg=self.conv_cfg,
+ norm_cfg=self.norm_cfg))
self.reg_convs.append(
- ConvModule(chn,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg))
+ ConvModule(
+ chn,
+ self.feat_channels,
+ 3,
+ stride=1,
+ padding=1,
+ conv_cfg=self.conv_cfg,
+ norm_cfg=self.norm_cfg))
self.conv_loc = nn.Conv2d(self.feat_channels, 1, 1)
self.conv_shape = nn.Conv2d(self.feat_channels, self.num_anchors * 2,
@@ -59,15 +61,13 @@ class GARetinaHead(GuidedAnchorHead):
self.feat_channels,
kernel_size=3,
deformable_groups=self.deformable_groups)
- self.retina_cls = MaskedConv2d(self.feat_channels,
- self.num_anchors *
- self.cls_out_channels,
- 3,
- padding=1)
- self.retina_reg = MaskedConv2d(self.feat_channels,
- self.num_anchors * 4,
- 3,
- padding=1)
+ self.retina_cls = MaskedConv2d(
+ self.feat_channels,
+ self.num_anchors * self.cls_out_channels,
+ 3,
+ padding=1)
+ self.retina_reg = MaskedConv2d(
+ self.feat_channels, self.num_anchors * 4, 3, padding=1)
def init_weights(self):
for m in self.cls_convs:
diff --git a/mmdet/models/anchor_heads/ga_rpn_head.py b/mmdet/models/anchor_heads/ga_rpn_head.py
index b7788b6a9c2b3ebb4febc43a5024ad92a174b494..aecef2b2169b9acd5fd767c2a4696b68f6ac9511 100644
--- a/mmdet/models/anchor_heads/ga_rpn_head.py
+++ b/mmdet/models/anchor_heads/ga_rpn_head.py
@@ -17,10 +17,8 @@ class GARPNHead(GuidedAnchorHead):
super(GARPNHead, self).__init__(2, in_channels, **kwargs)
def _init_layers(self):
- self.rpn_conv = nn.Conv2d(self.in_channels,
- self.feat_channels,
- 3,
- padding=1)
+ self.rpn_conv = nn.Conv2d(
+ self.in_channels, self.feat_channels, 3, padding=1)
super(GARPNHead, self)._init_layers()
def init_weights(self):
@@ -43,19 +41,21 @@ class GARPNHead(GuidedAnchorHead):
img_metas,
cfg,
gt_bboxes_ignore=None):
- losses = super(GARPNHead, self).loss(cls_scores,
- bbox_preds,
- shape_preds,
- loc_preds,
- gt_bboxes,
- None,
- img_metas,
- cfg,
- gt_bboxes_ignore=gt_bboxes_ignore)
- return dict(loss_rpn_cls=losses['loss_cls'],
- loss_rpn_bbox=losses['loss_bbox'],
- loss_anchor_shape=losses['loss_shape'],
- loss_anchor_loc=losses['loss_loc'])
+ losses = super(GARPNHead, self).loss(
+ cls_scores,
+ bbox_preds,
+ shape_preds,
+ loc_preds,
+ gt_bboxes,
+ None,
+ img_metas,
+ cfg,
+ gt_bboxes_ignore=gt_bboxes_ignore)
+ return dict(
+ loss_rpn_cls=losses['loss_cls'],
+ loss_rpn_bbox=losses['loss_bbox'],
+ loss_anchor_shape=losses['loss_shape'],
+ loss_anchor_loc=losses['loss_loc'])
def get_bboxes_single(self,
cls_scores,
diff --git a/mmdet/models/detectors/cascade_rcnn.py b/mmdet/models/detectors/cascade_rcnn.py
index f0564c9eb16ccf5184f94d29e33c889fbe80c143..f76bdc53293370348449f0c204d8a0b535a23791 100644
--- a/mmdet/models/detectors/cascade_rcnn.py
+++ b/mmdet/models/detectors/cascade_rcnn.py
@@ -282,13 +282,12 @@ class CascadeRCNN(BaseDetector, RPNTestMixin):
mask_roi_extractor = self.mask_roi_extractor[i]
mask_head = self.mask_head[i]
if det_bboxes.shape[0] == 0:
- segm_result = [
- [] for _ in range(mask_head.num_classes - 1)
- ]
+ mask_classes = mask_head.num_classes - 1
+ segm_result = [[] for _ in range(mask_classes)]
else:
_bboxes = (
- det_bboxes[:, :4] * scale_factor
- if rescale else det_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)],
@@ -321,13 +320,12 @@ class CascadeRCNN(BaseDetector, RPNTestMixin):
if self.with_mask:
if det_bboxes.shape[0] == 0:
- segm_result = [
- [] for _ in range(self.mask_head[-1].num_classes - 1)
- ]
+ mask_classes = self.mask_head[-1].num_classes - 1
+ segm_result = [[] for _ in range(mask_classes)]
else:
_bboxes = (
- det_bboxes[:, :4] * scale_factor
- if rescale else det_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 0384aa99b110a6ecbf3ad5038ec9b07343fef28c..bcf410f35cb35e628ca8f9fc4bb47242ae8c0c93 100644
--- a/mmdet/models/detectors/htc.py
+++ b/mmdet/models/detectors/htc.py
@@ -205,9 +205,10 @@ class HybridTaskCascade(CascadeRCNN):
gt_bboxes_ignore = [None for _ in range(num_imgs)]
for j in range(num_imgs):
- assign_result = bbox_assigner.assign(
- proposal_list[j], gt_bboxes[j], gt_bboxes_ignore[j],
- gt_labels[j])
+ assign_result = bbox_assigner.assign(proposal_list[j],
+ gt_bboxes[j],
+ gt_bboxes_ignore[j],
+ gt_labels[j])
sampling_result = bbox_sampler.sample(
assign_result,
proposal_list[j],
@@ -308,13 +309,12 @@ class HybridTaskCascade(CascadeRCNN):
if self.with_mask:
mask_head = self.mask_head[i]
if det_bboxes.shape[0] == 0:
- segm_result = [
- [] for _ in range(mask_head.num_classes - 1)
- ]
+ mask_classes = mask_head.num_classes - 1
+ segm_result = [[] for _ in range(mask_classes)]
else:
_bboxes = (
- det_bboxes[:, :4] * scale_factor
- if rescale else det_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(
@@ -342,13 +342,12 @@ class HybridTaskCascade(CascadeRCNN):
if self.with_mask:
if det_bboxes.shape[0] == 0:
- segm_result = [
- [] for _ in range(self.mask_head[-1].num_classes - 1)
- ]
+ mask_classes = self.mask_head[-1].num_classes - 1
+ segm_result = [[] for _ in range(mask_classes)]
else:
_bboxes = (
- det_bboxes[:, :4] * scale_factor
- if rescale else det_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 9a179e9e1b44cbb5284c3b1c1c1ebfea09177c68..6924818d0b6bf0b8340c3ef509b60ab1b0e9ba85 100644
--- a/mmdet/models/detectors/test_mixins.py
+++ b/mmdet/models/detectors/test_mixins.py
@@ -91,9 +91,10 @@ class BBoxTestMixin(object):
# after merging, bboxes will be rescaled to the original image size
merged_bboxes, merged_scores = merge_aug_bboxes(
aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
- det_bboxes, det_labels = multiclass_nms(
- merged_bboxes, merged_scores, rcnn_test_cfg.score_thr,
- rcnn_test_cfg.nms, rcnn_test_cfg.max_per_img)
+ det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
+ rcnn_test_cfg.score_thr,
+ rcnn_test_cfg.nms,
+ rcnn_test_cfg.max_per_img)
return det_bboxes, det_labels
@@ -121,9 +122,11 @@ class MaskTestMixin(object):
if self.with_shared_head:
mask_feats = self.shared_head(mask_feats)
mask_pred = self.mask_head(mask_feats)
- segm_result = self.mask_head.get_seg_masks(
- mask_pred, _bboxes, det_labels, self.test_cfg.rcnn, ori_shape,
- scale_factor, rescale)
+ segm_result = self.mask_head.get_seg_masks(mask_pred, _bboxes,
+ det_labels,
+ self.test_cfg.rcnn,
+ ori_shape, scale_factor,
+ rescale)
return segm_result
def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
diff --git a/mmdet/models/detectors/two_stage.py b/mmdet/models/detectors/two_stage.py
index c30047648c11b1bb903e5496ddc7db72e1417891..f70510cecaf633bc140f5d0280b6040db7788e73 100644
--- a/mmdet/models/detectors/two_stage.py
+++ b/mmdet/models/detectors/two_stage.py
@@ -125,9 +125,10 @@ class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
gt_bboxes_ignore = [None for _ in range(num_imgs)]
sampling_results = []
for i in range(num_imgs):
- assign_result = bbox_assigner.assign(
- proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
- gt_labels[i])
+ assign_result = bbox_assigner.assign(proposal_list[i],
+ gt_bboxes[i],
+ gt_bboxes_ignore[i],
+ gt_labels[i])
sampling_result = bbox_sampler.sample(
assign_result,
proposal_list[i],
@@ -146,8 +147,9 @@ class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
bbox_feats = self.shared_head(bbox_feats)
cls_score, bbox_pred = self.bbox_head(bbox_feats)
- bbox_targets = self.bbox_head.get_target(
- sampling_results, gt_bboxes, gt_labels, self.train_cfg.rcnn)
+ bbox_targets = self.bbox_head.get_target(sampling_results,
+ gt_bboxes, gt_labels,
+ self.train_cfg.rcnn)
loss_bbox = self.bbox_head.loss(cls_score, bbox_pred,
*bbox_targets)
losses.update(loss_bbox)
@@ -179,8 +181,9 @@ class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
mask_feats = bbox_feats[pos_inds]
mask_pred = self.mask_head(mask_feats)
- mask_targets = self.mask_head.get_target(
- sampling_results, gt_masks, self.train_cfg.rcnn)
+ mask_targets = self.mask_head.get_target(sampling_results,
+ gt_masks,
+ self.train_cfg.rcnn)
pos_labels = torch.cat(
[res.pos_gt_labels for res in sampling_results])
loss_mask = self.mask_head.loss(mask_pred, mask_targets,
diff --git a/mmdet/models/mask_heads/fused_semantic_head.py b/mmdet/models/mask_heads/fused_semantic_head.py
index 550e08e61f7ab336bb6d377dcaf0bb2644656b6d..ee6910f48d7512f2149e97809390a19b654d3c79 100644
--- a/mmdet/models/mask_heads/fused_semantic_head.py
+++ b/mmdet/models/mask_heads/fused_semantic_head.py
@@ -98,7 +98,7 @@ class FusedSemanticHead(nn.Module):
x = self.conv_embedding(x)
return mask_pred, x
- @force_fp32(apply_to=('mask_pred',))
+ @force_fp32(apply_to=('mask_pred', ))
def loss(self, mask_pred, labels):
labels = labels.squeeze(1).long()
loss_semantic_seg = self.criterion(mask_pred, labels)
diff --git a/mmdet/models/roi_extractors/single_level.py b/mmdet/models/roi_extractors/single_level.py
index 8731126440367178265209c3e1d3fca473556c02..7eaab120e6e1a6a2b1243752b23348f3b0cca713 100644
--- a/mmdet/models/roi_extractors/single_level.py
+++ b/mmdet/models/roi_extractors/single_level.py
@@ -72,7 +72,7 @@ class SingleRoIExtractor(nn.Module):
target_lvls = target_lvls.clamp(min=0, max=num_levels - 1).long()
return target_lvls
- @force_fp32(apply_to=('feats',), out_fp16=True)
+ @force_fp32(apply_to=('feats', ), out_fp16=True)
def forward(self, feats, rois):
if len(feats) == 1:
return self.roi_layers[0](feats[0], rois)
diff --git a/tools/analyze_logs.py b/tools/analyze_logs.py
index c9f603f46fd3bba4226733c953b3d8bd2ef598d8..2810c98f104e954a764b8add4aa38a577a92a27b 100644
--- a/tools/analyze_logs.py
+++ b/tools/analyze_logs.py
@@ -51,9 +51,9 @@ 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(
- args.json_logs[i], metric)
+ if metric not in log_dict[epochs[0]]:
+ raise KeyError('{} does not contain metric {}'.format(
+ args.json_logs[i], metric))
if 'mAP' in metric:
xs = np.arange(1, max(epochs) + 1)
diff --git a/tools/convert_datasets/pascal_voc.py b/tools/convert_datasets/pascal_voc.py
index 5fb5cb4b7080f134287494f7f0283bed42b351cb..029eeb0a974bdba573727d5f321b5bcf5b52425e 100644
--- a/tools/convert_datasets/pascal_voc.py
+++ b/tools/convert_datasets/pascal_voc.py
@@ -69,16 +69,17 @@ def cvt_annotations(devkit_path, years, split, out_file):
years = [years]
annotations = []
for year in years:
- filelist = osp.join(devkit_path, 'VOC{}/ImageSets/Main/{}.txt'.format(
- year, split))
+ filelist = osp.join(devkit_path,
+ 'VOC{}/ImageSets/Main/{}.txt'.format(year, split))
if not osp.isfile(filelist):
print('filelist does not exist: {}, skip voc{} {}'.format(
filelist, year, split))
return
img_names = mmcv.list_from_file(filelist)
xml_paths = [
- osp.join(devkit_path, 'VOC{}/Annotations/{}.xml'.format(
- year, img_name)) for img_name in img_names
+ osp.join(devkit_path,
+ 'VOC{}/Annotations/{}.xml'.format(year, img_name))
+ for img_name in img_names
]
img_paths = [
'VOC{}/JPEGImages/{}.jpg'.format(year, img_name)