diff --git a/mmdet/core/eval/__init__.py b/mmdet/core/eval/__init__.py
index c46d860d4b1c027f47ab75090bd13d0332d88efb..b5df6595a0e7e1421e7e0bab322f1ad9d66041c9 100644
--- a/mmdet/core/eval/__init__.py
+++ b/mmdet/core/eval/__init__.py
@@ -1,14 +1,17 @@
from .class_names import (voc_classes, imagenet_det_classes,
imagenet_vid_classes, coco_classes, dataset_aliases,
get_classes)
-from .coco_utils import coco_eval
+from .coco_utils import coco_eval, results2json
+from .eval_hooks import DistEvalHook, DistEvalRecallHook, CocoDistEvalmAPHook
from .mean_ap import average_precision, eval_map, print_map_summary
from .recall import (eval_recalls, print_recall_summary, plot_num_recall,
plot_iou_recall)
__all__ = [
'voc_classes', 'imagenet_det_classes', 'imagenet_vid_classes',
- 'coco_classes', 'dataset_aliases', 'get_classes', 'average_precision',
- 'eval_map', 'print_map_summary', 'eval_recalls', 'print_recall_summary',
- 'plot_num_recall', 'plot_iou_recall', 'coco_eval'
+ 'coco_classes', 'dataset_aliases', 'get_classes', 'coco_eval',
+ 'results2json', 'DistEvalHook', 'DistEvalRecallHook',
+ 'CocoDistEvalmAPHook', 'average_precision', 'eval_map',
+ 'print_map_summary', 'eval_recalls', 'print_recall_summary',
+ 'plot_num_recall', 'plot_iou_recall'
]
diff --git a/mmdet/core/eval/coco_utils.py b/mmdet/core/eval/coco_utils.py
index cff6f678e4fd60cc1c863dcd1ae32e4a3e4db2ab..719e70a75e099859d0d05a483df3b957bf523d4f 100644
--- a/mmdet/core/eval/coco_utils.py
+++ b/mmdet/core/eval/coco_utils.py
@@ -1,4 +1,5 @@
import mmcv
+import numpy as np
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
@@ -24,3 +25,77 @@ def coco_eval(result_file, result_types, coco, max_dets=(100, 300, 1000)):
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
+
+
+def xyxy2xywh(bbox):
+ _bbox = bbox.tolist()
+ return [
+ _bbox[0],
+ _bbox[1],
+ _bbox[2] - _bbox[0] + 1,
+ _bbox[3] - _bbox[1] + 1,
+ ]
+
+
+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)):
+ img_id = dataset.img_ids[idx]
+ result = results[idx]
+ for label in range(len(result)):
+ bboxes = result[label]
+ 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'] = dataset.cat_ids[label]
+ json_results.append(data)
+ return json_results
+
+
+def segm2json(dataset, results):
+ json_results = []
+ for idx in range(len(dataset)):
+ img_id = dataset.img_ids[idx]
+ det, seg = results[idx]
+ for label in range(len(det)):
+ bboxes = det[label]
+ segms = seg[label]
+ 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'] = dataset.cat_ids[label]
+ segms[i]['counts'] = segms[i]['counts'].decode()
+ data['segmentation'] = segms[i]
+ json_results.append(data)
+ return json_results
+
+
+def results2json(dataset, results, out_file):
+ if isinstance(results[0], list):
+ json_results = det2json(dataset, results)
+ elif isinstance(results[0], tuple):
+ json_results = segm2json(dataset, results)
+ elif isinstance(results[0], np.ndarray):
+ json_results = proposal2json(dataset, results)
+ else:
+ raise TypeError('invalid type of results')
+ mmcv.dump(json_results, out_file)
diff --git a/mmdet/core/eval/eval_hooks.py b/mmdet/core/eval/eval_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..2393449bc68582bef7615163d3f24b568860d714
--- /dev/null
+++ b/mmdet/core/eval/eval_hooks.py
@@ -0,0 +1,168 @@
+import os
+import os.path as osp
+import shutil
+import time
+
+import mmcv
+import numpy as np
+import torch
+from mmcv.torchpack import Hook, obj_from_dict
+from pycocotools.cocoeval import COCOeval
+from torch.utils.data import Dataset
+
+from .coco_utils import results2json
+from .recall import eval_recalls
+from ..parallel import scatter
+from mmdet import datasets
+from mmdet.datasets.loader import collate
+
+
+class DistEvalHook(Hook):
+
+ def __init__(self, dataset, interval=1):
+ if isinstance(dataset, Dataset):
+ self.dataset = dataset
+ elif isinstance(dataset, dict):
+ self.dataset = obj_from_dict(dataset, datasets,
+ {'test_mode': True})
+ else:
+ raise TypeError(
+ 'dataset must be a Dataset object or a dict, not {}'.format(
+ type(dataset)))
+ self.interval = interval
+ self.lock_dir = None
+
+ def _barrier(self, rank, world_size):
+ """Due to some issues with `torch.distributed.barrier()`, we have to
+ implement this ugly barrier function.
+ """
+ if rank == 0:
+ for i in range(1, world_size):
+ tmp = osp.join(self.lock_dir, '{}.pkl'.format(i))
+ while not (osp.exists(tmp)):
+ time.sleep(1)
+ for i in range(1, world_size):
+ tmp = osp.join(self.lock_dir, '{}.pkl'.format(i))
+ os.remove(tmp)
+ else:
+ tmp = osp.join(self.lock_dir, '{}.pkl'.format(rank))
+ mmcv.dump([], tmp)
+ while osp.exists(tmp):
+ time.sleep(1)
+
+ def before_run(self, runner):
+ self.lock_dir = osp.join(runner.work_dir, '.lock_map_hook')
+ if runner.rank == 0:
+ if osp.exists(self.lock_dir):
+ shutil.rmtree(self.lock_dir)
+ mmcv.mkdir_or_exist(self.lock_dir)
+
+ def after_train_epoch(self, runner):
+ if not self.every_n_epochs(runner, self.interval):
+ return
+ runner.model.eval()
+ results = [None for _ in range(len(self.dataset))]
+ prog_bar = mmcv.ProgressBar(len(self.dataset))
+ for idx in range(runner.rank, len(self.dataset), runner.world_size):
+ data = self.dataset[idx]
+ data_gpu = scatter(
+ collate([data], samples_per_gpu=1),
+ [torch.cuda.current_device()])[0]
+
+ # compute output
+ with torch.no_grad():
+ result = runner.model(
+ **data_gpu, return_loss=False, rescale=True)
+ results[idx] = result
+
+ batch_size = runner.world_size
+ for _ in range(batch_size):
+ prog_bar.update()
+
+ if runner.rank == 0:
+ print('\n')
+ self._barrier(runner.rank, runner.world_size)
+ for i in range(1, runner.world_size):
+ tmp_file = osp.join(runner.work_dir, 'temp_{}.pkl'.format(i))
+ tmp_results = mmcv.load(tmp_file)
+ for idx in range(i, len(results), runner.world_size):
+ results[idx] = tmp_results[idx]
+ os.remove(tmp_file)
+ self.evaluate(runner, results)
+ else:
+ tmp_file = osp.join(runner.work_dir,
+ 'temp_{}.pkl'.format(runner.rank))
+ mmcv.dump(results, tmp_file)
+ self._barrier(runner.rank, runner.world_size)
+ self._barrier(runner.rank, runner.world_size)
+
+ def evaluate(self):
+ raise NotImplementedError
+
+
+class DistEvalRecallHook(DistEvalHook):
+
+ def __init__(self,
+ dataset,
+ proposal_nums=(100, 300, 1000),
+ iou_thrs=np.arange(0.5, 0.96, 0.05)):
+ super(DistEvalRecallHook, self).__init__(dataset)
+ self.proposal_nums = np.array(proposal_nums, dtype=np.int32)
+ self.iou_thrs = np.array(iou_thrs, dtype=np.float32)
+
+ def evaluate(self, runner, results):
+ # the official coco evaluation is too slow, here we use our own
+ # implementation instead, which may get slightly different results
+ gt_bboxes = []
+ for i in range(len(self.dataset)):
+ img_id = self.dataset.img_ids[i]
+ ann_ids = self.dataset.coco.getAnnIds(imgIds=img_id)
+ ann_info = self.dataset.coco.loadAnns(ann_ids)
+ if len(ann_info) == 0:
+ gt_bboxes.append(np.zeros((0, 4)))
+ continue
+ bboxes = []
+ for ann in ann_info:
+ if ann.get('ignore', False) or ann['iscrowd']:
+ continue
+ x1, y1, w, h = ann['bbox']
+ bboxes.append([x1, y1, x1 + w - 1, y1 + h - 1])
+ bboxes = np.array(bboxes, dtype=np.float32)
+ if bboxes.shape[0] == 0:
+ bboxes = np.zeros((0, 4))
+ gt_bboxes.append(bboxes)
+
+ recalls = eval_recalls(
+ gt_bboxes,
+ results,
+ self.proposal_nums,
+ self.iou_thrs,
+ print_summary=False)
+ ar = recalls.mean(axis=1)
+ for i, num in enumerate(self.proposal_nums):
+ runner.log_buffer.output['AR@{}'.format(num)] = ar[i]
+ runner.log_buffer.ready = True
+
+
+class CocoDistEvalmAPHook(DistEvalHook):
+
+ def evaluate(self, runner, results):
+ tmp_file = osp.join(runner.work_dir, 'temp_0.json')
+ results2json(self.dataset, results, tmp_file)
+
+ res_types = ['bbox',
+ 'segm'] if runner.model.module.with_mask else ['bbox']
+ cocoGt = self.dataset.coco
+ cocoDt = cocoGt.loadRes(tmp_file)
+ imgIds = cocoGt.getImgIds()
+ for res_type in res_types:
+ iou_type = res_type
+ cocoEval = COCOeval(cocoGt, cocoDt, iou_type)
+ cocoEval.params.imgIds = imgIds
+ cocoEval.evaluate()
+ cocoEval.accumulate()
+ cocoEval.summarize()
+ field = '{}_mAP'.format(res_type)
+ runner.log_buffer.output[field] = cocoEval.stats[0]
+ runner.log_buffer.ready = True
+ os.remove(tmp_file)
diff --git a/mmdet/core/utils/__init__.py b/mmdet/core/utils/__init__.py
index 30c9c9e5c83797ddf7ea84d088ad29cb8e4b18cc..e04da6a9a5c82c02c924ecd7c763aaf8b1c02d4e 100644
--- a/mmdet/core/utils/__init__.py
+++ b/mmdet/core/utils/__init__.py
@@ -1,12 +1,9 @@
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
+from .hooks import EmptyCacheHook
+from .misc import tensor2imgs, unmap, multi_apply
__all__ = [
'init_dist', 'reduce_grads', 'DistOptimizerHook', 'DistSamplerSeedHook',
- 'EmptyCacheHook', 'DistEvalHook', 'DistEvalRecallHook',
- 'CocoDistEvalmAPHook', 'tensor2imgs', 'unmap', 'results2json',
- 'multi_apply'
+ 'EmptyCacheHook', 'tensor2imgs', 'unmap', 'multi_apply'
]
diff --git a/mmdet/core/utils/dist_utils.py b/mmdet/core/utils/dist_utils.py
index 4bc986ca73fc6faacb12c6fdbc20f020d6bdb56f..2a5d7659df78055fc068224d789a5d391f73d8e0 100644
--- a/mmdet/core/utils/dist_utils.py
+++ b/mmdet/core/utils/dist_utils.py
@@ -39,7 +39,7 @@ def _init_dist_slurm(backend, **kwargs):
# modified from https://github.com/NVIDIA/apex/blob/master/apex/parallel/distributed.py#L9
-def coalesce_all_reduce(tensors):
+def all_reduce_coalesced(tensors):
buckets = OrderedDict()
for tensor in tensors:
tp = tensor.type()
@@ -64,7 +64,7 @@ def reduce_grads(model, coalesce=True):
if param.requires_grad and param.grad is not None
]
if coalesce:
- coalesce_all_reduce(grads)
+ all_reduce_coalesced(grads)
else:
for tensor in grads:
dist.all_reduce(tensor)
diff --git a/mmdet/core/utils/hooks.py b/mmdet/core/utils/hooks.py
index 9772d4d64f1f5860a7646c23c950b620a052ae20..72eb3438efac1a3d2c8114914c087f0b563272d9 100644
--- a/mmdet/core/utils/hooks.py
+++ b/mmdet/core/utils/hooks.py
@@ -1,17 +1,5 @@
-import os
-import os.path as osp
-import shutil
-import time
-
-import mmcv
-import numpy as np
import torch
from mmcv.torchpack import Hook
-from pycocotools.cocoeval import COCOeval
-
-from ..eval import eval_recalls
-from ..parallel import scatter
-from mmdet.datasets.loader import collate
class EmptyCacheHook(Hook):
@@ -21,220 +9,3 @@ class EmptyCacheHook(Hook):
def after_epoch(self, runner):
torch.cuda.empty_cache()
-
-
-class DistEvalHook(Hook):
-
- def __init__(self, dataset, interval=1):
- self.dataset = dataset
- self.interval = interval
- self.lock_dir = None
-
- def _barrier(self, rank, world_size):
- """Due to some issues with `torch.distributed.barrier()`, we have to
- implement this ugly barrier function.
- """
- if rank == 0:
- for i in range(1, world_size):
- tmp = osp.join(self.lock_dir, '{}.pkl'.format(i))
- while not (osp.exists(tmp)):
- time.sleep(1)
- for i in range(1, world_size):
- tmp = osp.join(self.lock_dir, '{}.pkl'.format(i))
- os.remove(tmp)
- else:
- tmp = osp.join(self.lock_dir, '{}.pkl'.format(rank))
- mmcv.dump([], tmp)
- while osp.exists(tmp):
- time.sleep(1)
-
- def before_run(self, runner):
- self.lock_dir = osp.join(runner.work_dir, '.lock_map_hook')
- if runner.rank == 0:
- if osp.exists(self.lock_dir):
- shutil.rmtree(self.lock_dir)
- mmcv.mkdir_or_exist(self.lock_dir)
-
- def after_train_epoch(self, runner):
- if not self.every_n_epochs(runner, self.interval):
- return
- runner.model.eval()
- results = [None for _ in range(len(self.dataset))]
- prog_bar = mmcv.ProgressBar(len(self.dataset))
- for idx in range(runner.rank, len(self.dataset), runner.world_size):
- data = self.dataset[idx]
- device_id = torch.cuda.current_device()
- imgs_data = tuple(
- scatter(collate([data], samples_per_gpu=1), [device_id])[0])
-
- # compute output
- with torch.no_grad():
- result = runner.model(
- *imgs_data,
- return_loss=False,
- return_bboxes=True,
- rescale=True)
- results[idx] = result
-
- batch_size = runner.world_size
- for _ in range(batch_size):
- prog_bar.update()
-
- if runner.rank == 0:
- print('\n')
- self._barrier(runner.rank, runner.world_size)
- for i in range(1, runner.world_size):
- tmp_file = osp.join(runner.work_dir, 'temp_{}.pkl'.format(i))
- tmp_results = mmcv.load(tmp_file)
- for idx in range(i, len(results), runner.world_size):
- results[idx] = tmp_results[idx]
- os.remove(tmp_file)
- self.evaluate(runner, results)
- else:
- tmp_file = osp.join(runner.work_dir,
- 'temp_{}.pkl'.format(runner.rank))
- mmcv.dump(results, tmp_file)
- self._barrier(runner.rank, runner.world_size)
- self._barrier(runner.rank, runner.world_size)
-
- def evaluate(self):
- raise NotImplementedError
-
-
-class CocoEvalMixin(object):
-
- def _xyxy2xywh(self, bbox):
- _bbox = bbox.tolist()
- return [
- _bbox[0],
- _bbox[1],
- _bbox[2] - _bbox[0] + 1,
- _bbox[3] - _bbox[1] + 1,
- ]
-
- def det2json(self, dataset, results):
- json_results = []
- for idx in range(len(dataset)):
- img_id = dataset.img_ids[idx]
- result = results[idx]
- for label in range(len(result)):
- bboxes = result[label]
- for i in range(bboxes.shape[0]):
- data = dict()
- data['image_id'] = img_id
- data['bbox'] = self._xyxy2xywh(bboxes[i])
- data['score'] = float(bboxes[i][4])
- data['category_id'] = dataset.cat_ids[label]
- json_results.append(data)
- return json_results
-
- def segm2json(self, dataset, results):
- json_results = []
- for idx in range(len(dataset)):
- img_id = dataset.img_ids[idx]
- det, seg = results[idx]
- for label in range(len(det)):
- bboxes = det[label]
- segms = seg[label]
- for i in range(bboxes.shape[0]):
- data = dict()
- data['image_id'] = img_id
- data['bbox'] = self._xyxy2xywh(bboxes[i])
- data['score'] = float(bboxes[i][4])
- data['category_id'] = dataset.cat_ids[label]
- segms[i]['counts'] = segms[i]['counts'].decode()
- data['segmentation'] = segms[i]
- json_results.append(data)
- return json_results
-
- def proposal2json(self, 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'] = self._xyxy2xywh(bboxes[i])
- data['score'] = float(bboxes[i][4])
- data['category_id'] = 1
- json_results.append(data)
- return json_results
-
- def results2json(self, dataset, results, out_file):
- if isinstance(results[0], list):
- json_results = self.det2json(dataset, results)
- elif isinstance(results[0], tuple):
- json_results = self.segm2json(dataset, results)
- elif isinstance(results[0], np.ndarray):
- json_results = self.proposal2json(dataset, results)
- else:
- raise TypeError('invalid type of results')
- mmcv.dump(json_results, out_file, file_format='json')
-
-
-class DistEvalRecallHook(DistEvalHook):
-
- def __init__(self,
- dataset,
- proposal_nums=(100, 300, 1000),
- iou_thrs=np.arange(0.5, 0.96, 0.05)):
- super(DistEvalRecallHook, self).__init__(dataset)
- self.proposal_nums = np.array(proposal_nums, dtype=np.int32)
- self.iou_thrs = np.array(iou_thrs, dtype=np.float32)
-
- def evaluate(self, runner, results):
- # official coco evaluation is too slow, here we use our own
- # implementation, which may get slightly different results
- gt_bboxes = []
- for i in range(len(self.dataset)):
- img_id = self.dataset.img_ids[i]
- ann_ids = self.dataset.coco.getAnnIds(imgIds=img_id)
- ann_info = self.dataset.coco.loadAnns(ann_ids)
- if len(ann_info) == 0:
- gt_bboxes.append(np.zeros((0, 4)))
- continue
- bboxes = []
- for ann in ann_info:
- if ann.get('ignore', False) or ann['iscrowd']:
- continue
- x1, y1, w, h = ann['bbox']
- bboxes.append([x1, y1, x1 + w - 1, y1 + h - 1])
- bboxes = np.array(bboxes, dtype=np.float32)
- if bboxes.shape[0] == 0:
- bboxes = np.zeros((0, 4))
- gt_bboxes.append(bboxes)
-
- recalls = eval_recalls(
- gt_bboxes,
- results,
- self.proposal_nums,
- self.iou_thrs,
- print_summary=False)
- ar = recalls.mean(axis=1)
- for i, num in enumerate(self.proposal_nums):
- runner.log_buffer.output['AR@{}'.format(num)] = ar[i]
- runner.log_buffer.ready = True
-
-
-class CocoDistEvalmAPHook(DistEvalHook, CocoEvalMixin):
-
- def evaluate(self, runner, results):
- tmp_file = osp.join(runner.work_dir, 'temp_0.json')
- self.results2json(self.dataset, results, tmp_file)
-
- res_types = ['bbox', 'segm'] if runner.model.with_mask else ['bbox']
- cocoGt = self.dataset.coco
- cocoDt = cocoGt.loadRes(tmp_file)
- imgIds = cocoGt.getImgIds()
- for res_type in res_types:
- iou_type = res_type
- cocoEval = COCOeval(cocoGt, cocoDt, iou_type)
- cocoEval.params.imgIds = imgIds
- cocoEval.evaluate()
- cocoEval.accumulate()
- cocoEval.summarize()
- field = '{}_mAP'.format(res_type)
- runner.log_buffer.output[field] = cocoEval.stats[0]
- runner.log_buffer.ready = True
- os.remove(tmp_file)
diff --git a/mmdet/core/utils/misc.py b/mmdet/core/utils/misc.py
index d34ff94302c2a5215681a3e81e38ca9aee8070df..fd8211ef68de37a1631135c9f19823653c0176f1 100644
--- a/mmdet/core/utils/misc.py
+++ b/mmdet/core/utils/misc.py
@@ -4,6 +4,7 @@ import mmcv
import numpy as np
from six.moves import map, zip
+
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)
@@ -33,77 +34,3 @@ def unmap(data, count, inds, fill=0):
ret = data.new_full(new_size, fill)
ret[inds, :] = data
return ret
-
-
-def xyxy2xywh(bbox):
- _bbox = bbox.tolist()
- return [
- _bbox[0],
- _bbox[1],
- _bbox[2] - _bbox[0] + 1,
- _bbox[3] - _bbox[1] + 1,
- ]
-
-
-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)):
- img_id = dataset.img_ids[idx]
- result = results[idx]
- for label in range(len(result)):
- bboxes = result[label]
- 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'] = dataset.cat_ids[label]
- json_results.append(data)
- return json_results
-
-
-def segm2json(dataset, results):
- json_results = []
- for idx in range(len(dataset)):
- img_id = dataset.img_ids[idx]
- det, seg = results[idx]
- for label in range(len(det)):
- bboxes = det[label]
- segms = seg[label]
- 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'] = dataset.cat_ids[label]
- segms[i]['counts'] = segms[i]['counts'].decode()
- data['segmentation'] = segms[i]
- json_results.append(data)
- return json_results
-
-
-def results2json(dataset, results, out_file):
- if isinstance(results[0], list):
- json_results = det2json(dataset, results)
- elif isinstance(results[0], tuple):
- json_results = segm2json(dataset, results)
- elif isinstance(results[0], np.ndarray):
- json_results = proposal2json(dataset, results)
- else:
- raise TypeError('invalid type of results')
- mmcv.dump(json_results, out_file)
diff --git a/tools/configs/r50_fpn_frcnn_1x.py b/tools/configs/r50_fpn_frcnn_1x.py
index 156b8b2aa4e2ad7f70d8e1a44682128076e9e34b..23903e084e204812571945bbe83fd626d6bc5922 100644
--- a/tools/configs/r50_fpn_frcnn_1x.py
+++ b/tools/configs/r50_fpn_frcnn_1x.py
@@ -93,16 +93,26 @@ data = dict(
flip_ratio=0.5,
with_mask=False,
with_crowd=True,
- with_label=True,
- test_mode=False),
- test=dict(
+ with_label=True),
+ val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=32,
flip_ratio=0,
+ with_mask=False,
+ with_crowd=True,
+ with_label=True),
+ test=dict(
+ type=dataset_type,
+ ann_file=data_root + 'annotations/instances_val2017.json',
+ img_prefix=data_root + 'val2017/',
+ img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
+ flip_ratio=0,
with_mask=False,
with_label=False,
test_mode=True))
@@ -128,7 +138,7 @@ log_config = dict(
# runtime settings
total_epochs = 12
device_ids = range(8)
-dist_params = dict(backend='nccl', port='29500')
+dist_params = dict(backend='gloo')
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 5697bca4a58cf7343b348371d56e8f631bda7a5a..41c2a1476dd438c685bc882a6d1478c00133e4e3 100644
--- a/tools/configs/r50_fpn_maskrcnn_1x.py
+++ b/tools/configs/r50_fpn_maskrcnn_1x.py
@@ -106,16 +106,26 @@ data = dict(
flip_ratio=0.5,
with_mask=True,
with_crowd=True,
- with_label=True,
- test_mode=False),
- test=dict(
+ with_label=True),
+ val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=32,
flip_ratio=0,
+ with_mask=True,
+ with_crowd=True,
+ with_label=True),
+ test=dict(
+ type=dataset_type,
+ ann_file=data_root + 'annotations/instances_val2017.json',
+ img_prefix=data_root + 'val2017/',
+ img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
+ flip_ratio=0,
with_mask=False,
with_label=False,
test_mode=True))
@@ -141,7 +151,7 @@ log_config = dict(
# runtime settings
total_epochs = 12
device_ids = range(8)
-dist_params = dict(backend='nccl', port='29500')
+dist_params = dict(backend='gloo')
log_level = 'INFO'
work_dir = './work_dirs/fpn_mask_rcnn_r50_1x'
load_from = None
diff --git a/tools/configs/r50_fpn_rpn_1x.py b/tools/configs/r50_fpn_rpn_1x.py
index a00cab9de8013455f498d96cb12c9801aafcc343..1f14f72235b73e7724fad619e4041c453c6e60bc 100644
--- a/tools/configs/r50_fpn_rpn_1x.py
+++ b/tools/configs/r50_fpn_rpn_1x.py
@@ -65,16 +65,26 @@ data = dict(
flip_ratio=0.5,
with_mask=False,
with_crowd=False,
- with_label=False,
- test_mode=False),
- test=dict(
+ with_label=False),
+ val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=32,
flip_ratio=0,
+ with_mask=False,
+ with_crowd=False,
+ with_label=False),
+ test=dict(
+ type=dataset_type,
+ ann_file=data_root + 'annotations/instances_val2017.json',
+ img_prefix=data_root + 'val2017/',
+ img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
+ flip_ratio=0,
with_mask=False,
with_label=False,
test_mode=True))
@@ -103,5 +113,5 @@ dist_params = dict(backend='gloo')
log_level = 'INFO'
work_dir = './work_dirs/fpn_rpn_r50_1x'
load_from = None
-resume_from = None
-workflow = [('train', 1)]
+resume_from = None
+workflow = [('train', 1), ('val', 1)]
diff --git a/tools/dist_train.sh b/tools/dist_train.sh
index 8b79c6158daee7592766f8d9fa42770d7eb17c90..7bb903bd3dcef5c98cd5bcd93ce3e8cfb9de9882 100755
--- a/tools/dist_train.sh
+++ b/tools/dist_train.sh
@@ -2,4 +2,4 @@
PYTHON=${PYTHON:-"python"}
-$PYTHON -m torch.distributed.launch --nproc_per_node=$2 train.py $1 --launcher pytorch
\ No newline at end of file
+$PYTHON -m torch.distributed.launch --nproc_per_node=$2 train.py $1 --launcher pytorch $3
diff --git a/tools/test.py b/tools/test.py
index 0a43cdc316506ecf2b7addb8d11e3dc7dc30507b..4c87f4eeee74c3117f505c38eee13d94201380f5 100644
--- a/tools/test.py
+++ b/tools/test.py
@@ -59,7 +59,7 @@ def main():
cfg.model.pretrained = None
cfg.data.test.test_mode = True
- dataset = obj_from_dict(cfg.data.test, datasets)
+ dataset = obj_from_dict(cfg.data.test, datasets, dict(test_mode=True))
if args.gpus == 1:
model = build_detector(
cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
diff --git a/tools/train.py b/tools/train.py
index fd47b1375622693e7e0fdd31c5e746df9dc5ff5a..6e1b9d91aaf53037e7fe246734ae8dfc7500c7df 100644
--- a/tools/train.py
+++ b/tools/train.py
@@ -9,9 +9,10 @@ from mmcv.torchpack import Runner, obj_from_dict
from mmdet import datasets
from mmdet.core import (init_dist, DistOptimizerHook, DistSamplerSeedHook,
- MMDataParallel, MMDistributedDataParallel)
+ MMDataParallel, MMDistributedDataParallel,
+ DistEvalRecallHook, CocoDistEvalmAPHook)
from mmdet.datasets.loader import build_dataloader
-from mmdet.models import build_detector
+from mmdet.models import build_detector, RPN
def parse_losses(losses):
@@ -109,6 +110,11 @@ def main():
cfg.checkpoint_config, cfg.log_config)
if dist:
runner.register_hook(DistSamplerSeedHook())
+ # register eval hooks
+ if isinstance(model.module, RPN):
+ runner.register_hook(DistEvalRecallHook(cfg.data.val))
+ elif cfg.data.val.type == 'CocoDataset':
+ runner.register_hook(CocoDistEvalmAPHook(cfg.data.val))
if cfg.resume_from:
runner.resume(cfg.resume_from)