Object Detection(Oxford pet)
!pip install mmcv-full!git clone https://github.com/open-mmlab/mmdetection.gitCloning into 'mmdetection'...
remote: Enumerating objects: 21092, done.[K
remote: Counting objects: 100% (5/5), done.[K
remote: Compressing objects: 100% (5/5), done.[K
remote: Total 21092 (delta 0), reused 3 (delta 0), pack-reused 21087[K
Receiving objects: 100% (21092/21092), 24.80 MiB | 39.19 MiB/s, done.
Resolving deltas: 100% (14745/14745), done.!python setup.py install!mkdir checkpoints!wget -O /content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth--2021-09-30 05:25:15-- https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35
Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 167287506 (160M) [application/octet-stream]
Saving to: ‘/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth’
/content/mmdetectio 100%[===================>] 159.54M 9.29MB/s in 18s
2021-09-30 05:25:34 (8.97 MB/s) - ‘/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth’ saved [167287506/167287506]!wget https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz
!wget https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz!nvidia-smiThu Sep 30 06:09:14 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.63.01 Driver Version: 460.32.03 CUDA Version: 11.2 |
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |
| N/A 40C P0 27W / 250W | 0MiB / 16280MiB | 0% Default |
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+-----------------------------------------------------------------------------+!mkdir /content/data!tar -xvf images.tar.gz -C /content/data/
!tar -xvf annotations.tar.gz -C /content/data/import os
import cv2
import matplotlib.pyplot as plt
import xml.etree.ElementTree as ET
img_dir = "/content/data/images/"
xml_file = "/content/data/annotations/xmls/Abyssinian_1.xml"
tree = ET.parse(xml_file)
root = tree.getroot()
img_name = root.find('filename').text
img_size = root.find('size')
img_width = int(img_size.find('width').text)
img_height = int(img_size.find('height').text)
img_full_path = os.path.join(img_dir, img_name)
img = cv2.imread(img_full_path)
dst = img.copy()
objects = root.findall('object')
object_list = []
for obj in objects:
box = obj.find("bndbox")
x1 = int(box.find('xmin').text)
y1 = int(box.find('ymin').text)
x2 = int(box.find('xmax').text)
y2 = int(box.find('ymax').text)
bndbox_coor = (x1, y1, x2, y2)
class_name = obj.find('name').text
cv2.rectangle(dst, (x1, y1), (x2, y2), color=(0, 0, 255), thickness=1)
cv2.putText(dst, class_name, (x1, y1-5), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 255, 0), thickness=1 )
object_dict = {'class_name' : class_name, 'bndbox_coor' : bndbox_coor}
object_list.append(object_dict)
plt.figure(figsize=(10, 10))
plt.imshow(cv2.cvtColor(dst, cv2.COLOR_BGR2RGB))
<matplotlib.image.AxesImage at 0x7f47a6c88090>
import pandas as pd
pet_df = pd.read_csv('./data/annotations/trainval.txt', sep=' ', header=None,
names=["image_name", "class_id", "dummy1", "dummy2" ])
pet_dfclass_id
dummy1
dummy2
0
Abyssinian_100
1
1
1
1
Abyssinian_101
1
1
1
2
Abyssinian_102
1
1
1
3
Abyssinian_103
1
1
1
4
Abyssinian_104
1
1
1
...
...
...
...
...
3675
yorkshire_terrier_187
37
2
25
3676
yorkshire_terrier_188
37
2
25
3677
yorkshire_terrier_189
37
2
25
3678
yorkshire_terrier_18
37
2
25
3679
yorkshire_terrier_190
37
2
25
3680 rows × 4 columns
pet_df["class_id"].value_counts()37 100
22 100
34 100
32 100
30 100
28 100
26 100
24 100
20 100
35 100
18 100
16 100
14 100
10 100
6 100
4 100
36 100
1 100
3 100
19 100
31 100
29 100
27 100
25 100
5 100
21 100
17 100
15 100
11 100
9 100
7 100
2 100
33 99
23 96
13 96
8 96
12 93
Name: class_id, dtype: int64pet_df["class_name"] = pet_df["image_name"].apply(lambda x:x[:x.rfind('_')])from sklearn.model_selection import train_test_split
train_df, val_df = train_test_split(pet_df, test_size=0.1, stratify=pet_df["class_id"], random_state=42)train_df.head()class_id
dummy1
dummy2
class_name
235
beagle_135
5
2
4
beagle
1633
Siamese_145
33
1
11
Siamese
2979
newfoundland_18
23
2
16
newfoundland
137
american_pit_bull_terrier_134
3
2
2
american_pit_bull_terrier
2454
english_cocker_spaniel_15
13
2
7
english_cocker_spaniel
train_df = train_df.sort_values(by="image_name")
val_df = val_df.sort_values(by="image_name")train_df.head()class_id
dummy1
dummy2
class_name
10
Abyssinian_10
1
1
1
Abyssinian
0
Abyssinian_100
1
1
1
Abyssinian
1
Abyssinian_101
1
1
1
Abyssinian
2
Abyssinian_102
1
1
1
Abyssinian
3
Abyssinian_103
1
1
1
Abyssinian
train_df["image_name"].to_csv("./data/train.txt", sep=" ", header=False, index=False)
val_df["image_name"].to_csv("./data/val.txt", sep=" ", header=False, index=False)pet_classes = pet_df["class_name"].unique().tolist()
len(pet_classes)37import xml.etree.ElementTree as ET
def get_bboxes(annor_dir, xml_file):
anno_xml_file = os.path.join(annor_dir, xml_file)
tree = ET.parse(anno_xml_file)
root = tree.getroot()
objects = root.findall('object')
object_list = []
bbox_names = []
bboxes = []
for obj in objects:
bbox_name = xml_file[:xml_file.rfind('_')]
box = obj.find("bndbox")
x1 = int(box.find('xmin').text)
y1 = int(box.find('ymin').text)
x2 = int(box.find('xmax').text)
y2 = int(box.find('ymax').text)
bbox_names.append(bbox_name)
bboxes.append([x1, y1, x2, y2])
return bbox_names, bboxes
ls -latotal 792108
drwxr-xr-x 1 root root 4096 Sep 30 02:09 [0m[01;34m.[0m/
drwxr-xr-x 1 root root 4096 Sep 30 01:36 [01;34m..[0m/
-rw-r--r-- 1 root root 19173078 Jun 30 2012 annotations.tar.gz
drwxr-xr-x 4 root root 4096 Sep 16 13:39 [01;34m.config[0m/
drwxr-xr-x 4 root root 4096 Sep 30 03:13 [01;34mdata[0m/
-rw-r--r-- 1 root root 791918971 Jun 18 2012 images.tar.gz
drwxr-xr-x 1 root root 4096 Sep 16 13:40 [01;34msample_data[0m/import copy
import os.path as osp
import os
import mmcv
import numpy as np
from mmdet.datasets.builder import DATASETS
from mmdet.datasets.custom import CustomDataset
@DATASETS.register_module(force=True)
class PetDataset(CustomDataset):
pet_classes = pet_df["class_name"].unique().tolist()
CLASSES = pet_classes
### self.ann_file : /content/data/train.txt
### self.img_prefix : /content/data/images
### ann_file : /content/data/train.txt
def load_annotations(self, ann_file):
print("self.ann_file : ", self.ann_file)
print("self.img_prefix: ", self.img_prefix)
cat2label = {k: i for i, k in enumerate(self.CLASSES)}
# load image list from file
image_list = mmcv.list_from_file(self.ann_file)
data_infos = []
# convert annotations to middle format
for image_id in image_list:
filename = f'{self.img_prefix}/{image_id}.jpg'
image = mmcv.imread(filename)
height, width = image.shape[:2]
data_info = dict(filename=f'{image_id}.jpg', width=width, height=height)
# load annotations
label_prefix = self.img_prefix.replace('images', 'annotations')
anno_xml_file = os.path.join(label_prefix, 'xmls/' + str(image_id) + '.xml')
if not os.path.exists(anno_xml_file):
continue
anno_xml_dir = os.path.join(label_prefix, 'xmls')
bbox_names, bboxes = get_bboxes(anno_xml_dir, str(image_id) + '.xml')
#lines = mmcv.list_from_file(osp.join(label_prefix, f'{image_id}.txt'))
#content = [line.strip().split(' ') for line in lines]
#bbox_names = [x[0] for x in content]
#bbox_names = [[float(info) for info in x[4:8]] for x in content]
gt_bboxes = []
gt_labels = []
gt_bboxes_ignore = []
gt_labels_ignore = []
# filter 'DontCare'
for bbox_name, bbox in zip(bbox_names, bboxes):
if bbox_name in cat2label:
gt_labels.append(cat2label[bbox_name])
gt_bboxes.append(bbox)
else:
gt_labels_ignore.append(-1)
gt_bboxes_ignore.append(bbox)
data_anno = dict(
bboxes=np.array(gt_bboxes, dtype=np.float32).reshape(-1, 4),
labels=np.array(gt_labels, dtype=np.long),
bboxes_ignore=np.array(gt_bboxes_ignore,
dtype=np.float32).reshape(-1, 4),
labels_ignore=np.array(gt_labels_ignore, dtype=np.long))
data_info.update(ann=data_anno)
data_infos.append(data_info)
return data_infosconfig_file = '/content/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
checkpoint_file = '/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'from mmcv import Config
cfg = Config.fromfile(config_file)print(cfg.pretty_text)model = dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
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',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_train2017.json',
img_prefix='data/coco/train2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]),
val=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
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',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
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',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]from google.colab import drive
drive.mount(("/content/gdrive"))Mounted at /content/gdrive%pwd'/content/mmdetection'ls -la /content/gdrive/MyDrivetotal 13
drwx------ 2 root root 4096 Mar 15 2021 [0m[01;34m'Colab Notebooks'[0m/
lrw------- 1 root root 25 Sep 29 21:54 [01;36m'My Drive'[0m -> [01;34m'/content/gdrive/My Drive/'[0m/
drwx------ 2 root root 4096 Sep 30 07:52 [01;34mpet_checkpoints[0m/
drwx------ 2 root root 4096 Sep 29 21:37 [01;34mpet_work_dir[0m/mkdir /content/gdrive/MyDrive/pet_checkpointsls -la ./tutorial_expstotal 324176
drwxr-xr-x 2 root root 4096 Sep 30 06:58 [0m[01;34m.[0m/
drwxr-xr-x 20 root root 4096 Sep 30 06:08 [01;34m..[0m/
-rw-r--r-- 1 root root 331735441 Sep 30 06:58 epoch_5.pth
lrwxrwxrwx 1 root root 11 Sep 30 06:58 [01;36mlatest.pth[0m -> epoch_5.pth
-rw-r--r-- 1 root root 197208 Sep 30 06:58 None.log.jsonfrom mmdet.apis import set_random_seed
# Modify dataset type and path
cfg.dataset_type = 'PetDataset'
cfg.data_root = '/content/data/'
cfg.data.test.type = 'PetDataset'
cfg.data.test.data_root = '/content/data/'
cfg.data.test.ann_file = 'train.txt'
cfg.data.test.img_prefix = 'images'
cfg.data.train.type = 'PetDataset'
cfg.data.train.data_root = '/content/data/'
cfg.data.train.ann_file = 'train.txt'
cfg.data.train.img_prefix = 'images'
cfg.data.val.type = 'PetDataset'
cfg.data.val.data_root = '/content/data/'
cfg.data.val.ann_file = 'val.txt'
cfg.data.val.img_prefix = 'images'
# modify num classes of the model in box head
cfg.model.roi_head.bbox_head.num_classes = 37
# We can still use the pre-trained Mask RCNN model though we do not need to
# use the mask branch
cfg.load_from = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
# Set up working dir to save files and logs.
cfg.work_dir = './tutorial_exps'
#cfg.work_dir = "/content/gdrive/MyDrive/pet_checkpoints"
# The original learning rate (LR) is set for 8-GPU training.
# We divide it by 8 since we only use one GPU.
cfg.optimizer.lr = 0.02 / 8
cfg.lr_config.warmup = None
cfg.log_config.interval = 5
cfg.runner.max_epochs = 5
cfg.data.samples_per_gpu = 4
cfg.lr_config.policy = 'step'
# Change the evaluation metric since we use customized dataset.
cfg.evaluation.metric = 'mAP'
# We can set the evaluation interval to reduce the evaluation times
cfg.evaluation.interval = 5
# We can set the checkpoint saving interval to reduce the storage cost
cfg.checkpoint_config.interval = 5
# Set seed thus the results are more reproducible
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)
# We can initialize the logger for training and have a look
# at the final config used for training
print(f'Config:\n{cfg.pretty_text}')Config:
model = dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=37,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)))
dataset_type = 'PetDataset'
data_root = '/content/data/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
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',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=2,
train=dict(
type='PetDataset',
ann_file='train.txt',
img_prefix='images',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
],
data_root='/content/data/'),
val=dict(
type='PetDataset',
ann_file='val.txt',
img_prefix='images',
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',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
],
data_root='/content/data/'),
test=dict(
type='PetDataset',
ann_file='train.txt',
img_prefix='images',
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',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
],
data_root='/content/data/'))
evaluation = dict(interval=5, metric='mAP')
optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup=None,
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=5)
checkpoint_config = dict(interval=5)
log_config = dict(interval=5, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
resume_from = None
workflow = [('train', 1)]
work_dir = './tutorial_exps'
seed = 0
gpu_ids = range(0, 1)from mmdet.datasets import build_dataset
from mmdet.models import build_detector
from mmdet.apis import train_detectordatasets = [build_dataset(cfg.data.train)]self.ann_file : /content/data/train.txt
self.img_prefix: /content/data/images
/usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/datasets/custom.py:157: UserWarning: CustomDataset does not support filtering empty gt images.
'CustomDataset does not support filtering empty gt images.')datasets[
PetDataset Train dataset with number of images 3303, and instance counts:
+-----------------------+-------+-------------------------+-------+-------------------------------+-------+---------------------+-------+---------------------------------+-------+
| category | count | category | count | category | count | category | count | category | count |
+-----------------------+-------+-------------------------+-------+-------------------------------+-------+---------------------+-------+---------------------------------+-------+
| 0 [Abyssinian] | 89 | 1 [american_bulldog] | 90 | 2 [american_pit_bull_terrier] | 90 | 3 [basset_hound] | 90 | 4 [beagle] | 90 |
| 5 [Bengal] | 89 | 6 [Birman] | 90 | 7 [Bombay] | 87 | 8 [boxer] | 90 | 9 [British_Shorthair] | 90 |
| 10 [chihuahua] | 90 | 11 [Egyptian_Mau] | 81 | 12 [english_cocker_spaniel] | 86 | 13 [english_setter] | 90 | 14 [german_shorthaired] | 90 |
| 15 [great_pyrenees] | 90 | 16 [havanese] | 90 | 17 [japanese_chin] | 90 | 18 [keeshond] | 90 | 19 [leonberger] | 90 |
| 20 [Maine_Coon] | 90 | 21 [miniature_pinscher] | 90 | 22 [newfoundland] | 86 | 23 [Persian] | 90 | 24 [pomeranian] | 90 |
| 25 [pug] | 90 | 26 [Ragdoll] | 89 | 27 [Russian_Blue] | 90 | 28 [saint_bernard] | 89 | 29 [samoyed] | 89 |
| 30 [scottish_terrier] | 90 | 31 [shiba_inu] | 90 | 32 [Siamese] | 89 | 33 [Sphynx] | 90 | 34 [staffordshire_bull_terrier] | 90 |
| | | | | | | | | | |
| 35 [wheaten_terrier] | 90 | 36 [yorkshire_terrier] | 90 | | | | | | |
+-----------------------+-------+-------------------------+-------+-------------------------------+-------+---------------------+-------+---------------------------------+-------+]model = build_detector(cfg.model, train_cfg=cfg.get('train_cfg'), test_cfg=cfg.get('test_cfg') )
model.CLASSES = datasets[0].CLASSES/usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/core/anchor/builder.py:17: UserWarning: ``build_anchor_generator`` would be deprecated soon, please use ``build_prior_generator``
'``build_anchor_generator`` would be deprecated soon, please use '%cd mmdetection/
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
train_detector(model, datasets, cfg, distributed=False, validate=True)from mmdet.apis import init_detector, inference_detector, show_result_pyplotimport cv2
img = cv2.imread("/content/data/images/yorkshire_terrier_138.jpg")
model.cfg = cfg
result = inference_detector(model, img)
show_result_pyplot(model, img, result, score_thr=0.5)/usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/core/anchor/anchor_generator.py:324: UserWarning: ``grid_anchors`` would be deprecated soon. Please use ``grid_priors``
warnings.warn('``grid_anchors`` would be deprecated soon. '
/usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/core/anchor/anchor_generator.py:361: UserWarning: ``single_level_grid_anchors`` would be deprecated soon. Please use ``single_level_grid_priors``
'``single_level_grid_anchors`` would be deprecated soon. '
resultReference
この問題について(Object Detection(Oxford pet)), 我々は、より多くの情報をここで見つけました https://velog.io/@bbkyoo/Object-DetectionOxford-petテキストは自由に共有またはコピーできます。ただし、このドキュメントのURLは参考URLとして残しておいてください。
Collection and Share based on the CC Protocol