inference代码

import mmcvimport osfrom mmdet.apis import init_detector, inference_detector, show_resultconfig_file = 'configs/mask_rcnn_r50_fpn_1x.py'checkpoint_file = 'checkpoint/epoch_200.pth'model = init_detector(config_file, checkpoint_file, device='cuda:0')path = "data/box_for_label"imgs = []trainimg = os.listdir("data/box_for_label")for lab in range(len(trainimg)):    subname = trainimg[lab]    name = os.path.join(path, subname)    imgs.append(name)import numpy as npimport cv2import pycocotools.mask as maskUtilsscore_thr=0.3for lab in range(len(imgs)):    img = imgs[lab]    result = inference_detector(model, img)    img = mmcv.imread(img)    img = img.copy()    oriimg = img.copy()    if isinstance(result, tuple):        bbox_result, segm_result = result    else:        bbox_result, segm_result = result, None    bboxes = np.vstack(bbox_result)    # draw segmentation masks    if segm_result is not None:        segms = mmcv.concat_list(segm_result)        inds = np.where(bboxes[:, -1] > score_thr)[0]        np.random.seed(42)        color_masks = np.random.randint(0, 256, (1, 3), dtype=np.uint8)        for i in inds:            i = int(i)            mask = maskUtils.decode(segms[i]).astype(np.bool)            img[mask] = img[mask] * 0.5 + color_masks * 0.5    h1, w1 = oriimg.shape[:2]    h2, w2 = img.shape[:2]    vis = np.zeros((max(h1, h2), w1 + w2, 3), np.uint8)    vis[:h1, :w1, :] = oriimg    vis[:h2, w1:w1 + w2, :] = img    out_file = 'result_{}.jpg'.format(lab)    cv2.imwrite(out_file, vis)

训练灰度

如果你想训练灰度图，在这个版本，你应该：

mmdetection/mmdet/datasets/pipelines/loading.py

@PIPELINES.register_moduleclass LoadImageFromFile(object):    def __init__(self, to_float32=False, color_type='color'):        self.to_float32 = to_float32        self.color_type = color_type    def __call__(self, results):        if results['img_prefix'] is not None:            filename = osp.join(results['img_prefix'],                                results['img_info']['filename'])        else:            filename = results['img_info']['filename']        img = mmcv.imread(filename, self.color_type)        if self.to_float32:            img = img.astype(np.float32)        results['filename'] = filename        results['img'] = img        results['img_shape'] = img.shape        results['ori_shape'] = img.shape        return results    def __repr__(self):        return '{} (to_float32={}, color_type={})'.format(            self.__class__.__name__, self.to_float32, self.color_type)

img = mmcv.imread(filename, self.color_type) 后面接一段代码：

import cv2
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

训练期间测试开启

--validate

#!/usr/bin/env bashPYTHON=/home/apple/anaconda3/envs/py36_mmdetection/bin/pythonCONFIG=$1GPUS=$2GPUNAME=$3PORT=${PORT:-29500}$PYTHON -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \    $(dirname "$0")/train.py $CONFIG --gpus $GPUNAME --validate --launcher pytorch ${@:4}

修改多少次进行一次测试：

configs/mask_rcnn_r50_fpn_1x.py

# yapf:enable
evaluation = dict(interval=50)

修改config-batch图像数目，学习率下降等

修改：

train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]

..............................

data = dict(
imgs_per_gpu=4,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline))
..............................

# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[40, 80, 120])