diff --git a/mmdet/core/bbox_ops/sampling.py b/mmdet/core/bbox_ops/sampling.py
index d751f8ede433b593c3b75193440a546fa3f8ce56..bcee761e10e5ef8537235cd629d187f874bb08fd 100644
--- a/mmdet/core/bbox_ops/sampling.py
+++ b/mmdet/core/bbox_ops/sampling.py
@@ -255,38 +255,3 @@ def bbox_sampling(assigned_gt_inds,
neg_hard_fraction)
neg_inds = neg_inds.unique()
return pos_inds, neg_inds
-
-
-def sample_proposals(proposals_list, gt_bboxes_list, gt_crowds_list,
- gt_labels_list, cfg):
- cfg_list = [cfg for _ in range(len(proposals_list))]
- results = map(sample_proposals_single, proposals_list, gt_bboxes_list,
- gt_crowds_list, gt_labels_list, cfg_list)
- # list of tuple to tuple of list
- return tuple(map(list, zip(*results)))
-
-
-def sample_proposals_single(proposals, gt_bboxes, gt_crowds, gt_labels, cfg):
- proposals = proposals[:, :4]
- assigned_gt_inds, assigned_labels, argmax_overlaps, max_overlaps = \
- bbox_assign(
- proposals, gt_bboxes, gt_crowds, gt_labels, cfg.pos_iou_thr,
- cfg.neg_iou_thr, cfg.pos_iou_thr, cfg.crowd_thr)
- if cfg.add_gt_as_proposals:
- proposals = torch.cat([gt_bboxes, proposals], dim=0)
- gt_assign_self = torch.arange(
- 1, len(gt_labels) + 1, dtype=torch.long, device=proposals.device)
- assigned_gt_inds = torch.cat([gt_assign_self, assigned_gt_inds])
- assigned_labels = torch.cat([gt_labels, assigned_labels])
-
- pos_inds, neg_inds = bbox_sampling(
- assigned_gt_inds, cfg.roi_batch_size, cfg.pos_fraction, cfg.neg_pos_ub,
- cfg.pos_balance_sampling, max_overlaps, cfg.neg_balance_thr)
- pos_proposals = proposals[pos_inds]
- neg_proposals = proposals[neg_inds]
- pos_assigned_gt_inds = assigned_gt_inds[pos_inds] - 1
- pos_gt_bboxes = gt_bboxes[pos_assigned_gt_inds, :]
- pos_gt_labels = assigned_labels[pos_inds]
-
- return (pos_inds, neg_inds, pos_proposals, neg_proposals,
- pos_assigned_gt_inds, pos_gt_bboxes, pos_gt_labels)
diff --git a/mmdet/core/post_processing/merge_augs.py b/mmdet/core/post_processing/merge_augs.py
index 0472aaf80fd8f0c923c6afcdf3902df6037c3edb..2b8d861a6745b90dd33b77ae4bda65bfd825d9a7 100644
--- a/mmdet/core/post_processing/merge_augs.py
+++ b/mmdet/core/post_processing/merge_augs.py
@@ -54,9 +54,9 @@ def merge_aug_bboxes(aug_bboxes, aug_scores, img_metas, rcnn_test_cfg):
"""
recovered_bboxes = []
for bboxes, img_info in zip(aug_bboxes, img_metas):
- img_shape = img_info['img_shape']
- scale_factor = img_info['scale_factor']
- flip = img_info['flip']
+ img_shape = img_info[0]['img_shape']
+ scale_factor = img_info[0]['scale_factor']
+ flip = img_info[0]['flip']
bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip)
recovered_bboxes.append(bboxes)
bboxes = torch.stack(recovered_bboxes).mean(dim=0)
@@ -75,7 +75,7 @@ def merge_aug_scores(aug_scores):
return np.mean(aug_scores, axis=0)
-def merge_aug_masks(aug_masks, bboxes, img_metas, rcnn_test_cfg, weights=None):
+def merge_aug_masks(aug_masks, img_metas, rcnn_test_cfg, weights=None):
"""Merge augmented mask prediction.
Args:
@@ -87,7 +87,7 @@ def merge_aug_masks(aug_masks, bboxes, img_metas, rcnn_test_cfg, weights=None):
tuple: (bboxes, scores)
"""
recovered_masks = [
- mask if not img_info['flip'][0] else mask[..., ::-1]
+ mask if not img_info[0]['flip'] else mask[..., ::-1]
for mask, img_info in zip(aug_masks, img_metas)
]
if weights is None:
diff --git a/mmdet/core/utils/__init__.py b/mmdet/core/utils/__init__.py
index 2b6e79d62e60b5e1efaac985e039b36840f86397..30c9c9e5c83797ddf7ea84d088ad29cb8e4b18cc 100644
--- a/mmdet/core/utils/__init__.py
+++ b/mmdet/core/utils/__init__.py
@@ -1,3 +1,12 @@
-from .dist_utils import *
-from .hooks import *
-from .misc import *
+from .dist_utils import (init_dist, reduce_grads, DistOptimizerHook,
+ DistSamplerSeedHook)
+from .hooks import (EmptyCacheHook, DistEvalHook, DistEvalRecallHook,
+ CocoDistEvalmAPHook)
+from .misc import tensor2imgs, unmap, results2json, multi_apply
+
+__all__ = [
+ 'init_dist', 'reduce_grads', 'DistOptimizerHook', 'DistSamplerSeedHook',
+ 'EmptyCacheHook', 'DistEvalHook', 'DistEvalRecallHook',
+ 'CocoDistEvalmAPHook', 'tensor2imgs', 'unmap', 'results2json',
+ 'multi_apply'
+]
diff --git a/mmdet/core/utils/dist_utils.py b/mmdet/core/utils/dist_utils.py
index 7ffa7a093481098e937cfa1b287b57980dd3d185..4bc986ca73fc6faacb12c6fdbc20f020d6bdb56f 100644
--- a/mmdet/core/utils/dist_utils.py
+++ b/mmdet/core/utils/dist_utils.py
@@ -8,10 +8,6 @@ from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from torch.nn.utils import clip_grad
from mmcv.torchpack import Hook, OptimizerHook
-__all__ = [
- 'init_dist', 'reduce_grads', 'DistOptimizerHook', 'DistSamplerSeedHook'
-]
-
def init_dist(launcher, backend='nccl', **kwargs):
if mp.get_start_method(allow_none=True) is None:
diff --git a/mmdet/core/utils/hooks.py b/mmdet/core/utils/hooks.py
index 8a52d11ba411f20d05a2f7ee5d532e5f50412edd..05441601ba792e44b47d89e5f405bb5092286d3f 100644
--- a/mmdet/core/utils/hooks.py
+++ b/mmdet/core/utils/hooks.py
@@ -13,11 +13,6 @@ from pycocotools.cocoeval import COCOeval
from ..eval import eval_recalls
-__all__ = [
- 'EmptyCacheHook', 'DistEvalHook', 'DistEvalRecallHook',
- 'CocoDistEvalmAPHook'
-]
-
class EmptyCacheHook(Hook):
diff --git a/mmdet/core/utils/misc.py b/mmdet/core/utils/misc.py
index 02d0b40c1e79d289c832d1e15243d862416cb51d..d34ff94302c2a5215681a3e81e38ca9aee8070df 100644
--- a/mmdet/core/utils/misc.py
+++ b/mmdet/core/utils/misc.py
@@ -4,9 +4,6 @@ import mmcv
import numpy as np
from six.moves import map, zip
-__all__ = ['tensor2imgs', 'multi_apply', 'unmap', 'results2json']
-
-
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
num_imgs = tensor.size(0)
mean = np.array(mean, dtype=np.float32)
@@ -48,6 +45,21 @@ def xyxy2xywh(bbox):
]
+def proposal2json(dataset, results):
+ json_results = []
+ for idx in range(len(dataset)):
+ img_id = dataset.img_ids[idx]
+ bboxes = results[idx]
+ for i in range(bboxes.shape[0]):
+ data = dict()
+ data['image_id'] = img_id
+ data['bbox'] = xyxy2xywh(bboxes[i])
+ data['score'] = float(bboxes[i][4])
+ data['category_id'] = 1
+ json_results.append(data)
+ return json_results
+
+
def det2json(dataset, results):
json_results = []
for idx in range(len(dataset)):
@@ -85,21 +97,6 @@ def segm2json(dataset, results):
return json_results
-def proposal2json(dataset, results):
- json_results = []
- for idx in range(len(dataset)):
- img_id = dataset.img_ids[idx]
- bboxes = results[idx]
- for i in range(bboxes.shape[0]):
- data = dict()
- data['image_id'] = img_id
- data['bbox'] = xyxy2xywh(bboxes[i])
- data['score'] = float(bboxes[i][4])
- data['category_id'] = 1
- json_results.append(data)
- return json_results
-
-
def results2json(dataset, results, out_file):
if isinstance(results[0], list):
json_results = det2json(dataset, results)
diff --git a/mmdet/models/bbox_heads/bbox_head.py b/mmdet/models/bbox_heads/bbox_head.py
index da923ecf2d03f3abedee00b526b2e062866e62d0..941903aba544b2d5ee7f7f6685664f4ab6f27df4 100644
--- a/mmdet/models/bbox_heads/bbox_head.py
+++ b/mmdet/models/bbox_heads/bbox_head.py
@@ -109,7 +109,7 @@ class BBoxHead(nn.Module):
# TODO: add clip here
if rescale:
- bboxes /= scale_factor.float()
+ bboxes /= scale_factor
if nms_cfg is None:
return bboxes, scores
diff --git a/mmdet/models/detectors/rpn.py b/mmdet/models/detectors/rpn.py
index 8d3dfd17c6c32f11726bba34d4ae438370ce10fe..29173cce7a7baa9620af3924d5ffb02b74dd0c6a 100644
--- a/mmdet/models/detectors/rpn.py
+++ b/mmdet/models/detectors/rpn.py
@@ -2,7 +2,7 @@ import mmcv
from mmdet.core import tensor2imgs, bbox_mapping
from .base import BaseDetector
-from .testing_mixins import RPNTestMixin
+from .test_mixins import RPNTestMixin
from .. import builder
diff --git a/mmdet/models/detectors/testing_mixins.py b/mmdet/models/detectors/test_mixins.py
similarity index 82%
rename from mmdet/models/detectors/testing_mixins.py
rename to mmdet/models/detectors/test_mixins.py
index 364fd4e6d1ad3ff64cd0104abe1906aaa15d2619..2fd3b18d09361e2edc28b67e14b558eb3901fbb7 100644
--- a/mmdet/models/detectors/testing_mixins.py
+++ b/mmdet/models/detectors/test_mixins.py
@@ -50,7 +50,7 @@ class BBoxTestMixin(object):
nms_cfg=rcnn_test_cfg)
return det_bboxes, det_labels
- def aug_test_bboxes(self, feats, img_metas, proposals, rcnn_test_cfg):
+ def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg):
aug_bboxes = []
aug_scores = []
for x, img_meta in zip(feats, img_metas):
@@ -58,8 +58,9 @@ class BBoxTestMixin(object):
img_shape = img_meta[0]['img_shape']
scale_factor = img_meta[0]['scale_factor']
flip = img_meta[0]['flip']
- proposals = bbox_mapping(proposals[:, :4], img_shape, scale_factor,
- flip)
+ # TODO more flexible
+ proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
+ scale_factor, flip)
rois = bbox2roi([proposals])
# recompute feature maps to save GPU memory
roi_feats = self.bbox_roi_extractor(
@@ -70,16 +71,17 @@ class BBoxTestMixin(object):
cls_score,
bbox_pred,
img_shape,
+ scale_factor,
rescale=False,
nms_cfg=None)
aug_bboxes.append(bboxes)
aug_scores.append(scores)
# after merging, bboxes will be rescaled to the original image size
merged_bboxes, merged_scores = merge_aug_bboxes(
- aug_bboxes, aug_scores, img_metas, self.rcnn_test_cfg)
+ aug_bboxes, aug_scores, img_metas, self.test_cfg.rcnn)
det_bboxes, det_labels = multiclass_nms(
- merged_bboxes, merged_scores, self.rcnn_test_cfg.score_thr,
- self.rcnn_test_cfg.nms_thr, self.rcnn_test_cfg.max_per_img)
+ merged_bboxes, merged_scores, self.test_cfg.rcnn.score_thr,
+ self.test_cfg.rcnn.nms_thr, self.test_cfg.rcnn.max_per_img)
return det_bboxes, det_labels
@@ -92,7 +94,7 @@ class MaskTestMixin(object):
det_labels,
rescale=False):
# image shape of the first image in the batch (only one)
- img_shape = img_meta[0]['img_shape']
+ ori_shape = img_meta[0]['ori_shape']
scale_factor = img_meta[0]['scale_factor']
if det_bboxes.shape[0] == 0:
segm_result = [[] for _ in range(self.mask_head.num_classes - 1)]
@@ -106,21 +108,11 @@ class MaskTestMixin(object):
x[:len(self.mask_roi_extractor.featmap_strides)], mask_rois)
mask_pred = self.mask_head(mask_feats)
segm_result = self.mask_head.get_seg_masks(
- mask_pred, det_bboxes, det_labels, img_shape,
- self.rcnn_test_cfg, rescale)
+ mask_pred, det_bboxes, det_labels, self.test_cfg.rcnn,
+ ori_shape)
return segm_result
- def aug_test_mask(self,
- feats,
- img_metas,
- det_bboxes,
- det_labels,
- rescale=False):
- if rescale:
- _det_bboxes = det_bboxes
- else:
- _det_bboxes = det_bboxes.clone()
- _det_bboxes[:, :4] *= img_metas[0][0]['scale_factor']
+ def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
if det_bboxes.shape[0] == 0:
segm_result = [[] for _ in range(self.mask_head.num_classes - 1)]
else:
@@ -139,8 +131,10 @@ class MaskTestMixin(object):
# convert to numpy array to save memory
aug_masks.append(mask_pred.sigmoid().cpu().numpy())
merged_masks = merge_aug_masks(aug_masks, img_metas,
- self.rcnn_test_cfg)
+ self.test_cfg.rcnn)
+
+ ori_shape = img_metas[0][0]['ori_shape']
segm_result = self.mask_head.get_seg_masks(
- merged_masks, _det_bboxes, det_labels,
- img_metas[0]['shape_scale'][0], self.rcnn_test_cfg, rescale)
+ merged_masks, det_bboxes, det_labels, self.test_cfg.rcnn,
+ ori_shape)
return segm_result
diff --git a/tools/configs/r50_fpn_frcnn_1x.py b/tools/configs/r50_fpn_frcnn_1x.py
index 4ce93e623e3d66aaf7c1520bc818e6e33d29e184..156b8b2aa4e2ad7f70d8e1a44682128076e9e34b 100644
--- a/tools/configs/r50_fpn_frcnn_1x.py
+++ b/tools/configs/r50_fpn_frcnn_1x.py
@@ -90,7 +90,11 @@ data = dict(
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
- flip_ratio=0.5),
+ flip_ratio=0.5,
+ with_mask=False,
+ with_crowd=True,
+ with_label=True,
+ test_mode=False),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
@@ -98,7 +102,10 @@ data = dict(
img_scale=(1333, 800),
flip_ratio=0,
img_norm_cfg=img_norm_cfg,
- size_divisor=32))
+ size_divisor=32,
+ with_mask=False,
+ with_label=False,
+ test_mode=True))
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
@@ -112,7 +119,7 @@ lr_config = dict(
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
- interval=50,
+ interval=20,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook', log_dir=work_dir + '/log')
@@ -120,7 +127,8 @@ log_config = dict(
# yapf:enable
# runtime settings
total_epochs = 12
-dist_params = dict(backend='nccl')
+device_ids = range(8)
+dist_params = dict(backend='nccl', port='29500')
log_level = 'INFO'
work_dir = './work_dirs/fpn_faster_rcnn_r50_1x'
load_from = None
diff --git a/tools/configs/r50_fpn_maskrcnn_1x.py b/tools/configs/r50_fpn_maskrcnn_1x.py
index 931f051b356c4f01119d20d397ceecd4d3babcf5..5697bca4a58cf7343b348371d56e8f631bda7a5a 100644
--- a/tools/configs/r50_fpn_maskrcnn_1x.py
+++ b/tools/configs/r50_fpn_maskrcnn_1x.py
@@ -103,7 +103,11 @@ data = dict(
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
- flip_ratio=0.5),
+ flip_ratio=0.5,
+ with_mask=True,
+ with_crowd=True,
+ with_label=True,
+ test_mode=False),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
@@ -111,7 +115,10 @@ data = dict(
img_scale=(1333, 800),
flip_ratio=0,
img_norm_cfg=img_norm_cfg,
- size_divisor=32))
+ size_divisor=32,
+ with_mask=False,
+ with_label=False,
+ test_mode=True))
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
@@ -120,12 +127,12 @@ lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
- warmup_ratio=0.333,
+ warmup_ratio=1.0 / 3,
step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
- interval=50,
+ interval=20,
hooks=[
dict(type='TextLoggerHook'),
# ('TensorboardLoggerHook', dict(log_dir=work_dir + '/log')),
@@ -133,7 +140,8 @@ log_config = dict(
# yapf:enable
# runtime settings
total_epochs = 12
-dist_params = dict(backend='nccl')
+device_ids = range(8)
+dist_params = dict(backend='nccl', port='29500')
log_level = 'INFO'
work_dir = './work_dirs/fpn_mask_rcnn_r50_1x'
load_from = None