def prepare_data_pipeline():
# batch size
config_dict['batch_size'] = 4
# number of train data_loader workers
config_dict['num_train_workers'] = 4
# number of val data_loader workers
config_dict['num_val_workers'] = 4
# construct train data_loader
config_dict['train_dataset_path'] = './COCO_pack/coco_train2017.pkl'
train_dataset = Dataset(load_path=config_dict['train_dataset_path'])
train_dataset_sampler = COCORandomDatasetSampler(
dataset=train_dataset,
batch_size=config_dict['batch_size'],
shuffle=True,
)
train_region_sampler = TypicalCOCOTrainingRegionSampler(
resize_shorter_range=(800, ), resize_longer_limit=1333, pad_divisor=32)
config_dict['train_data_loader'] = DataLoader(
dataset=train_dataset,
dataset_sampler=train_dataset_sampler,
region_sampler=train_region_sampler,
augmentation_pipeline=typical_coco_train_pipeline,
num_workers=config_dict['num_train_workers'])
# construct val data_loader
config_dict['val_dataset_path'] = './COCO_pack/coco_val2017.pkl'
val_dataset = Dataset(load_path=config_dict['val_dataset_path'])
val_dataset_sampler = RandomDatasetSampler(
dataset=val_dataset,
batch_size=config_dict['batch_size'],
shuffle=False,
ignore_last=False)
val_region_sampler = TypicalCOCOTrainingRegionSampler(
resize_shorter_range=(800, ), resize_longer_limit=1333, pad_divisor=32)
config_dict['val_data_loader'] = DataLoader(
dataset=val_dataset,
dataset_sampler=val_dataset_sampler,
region_sampler=val_region_sampler,
augmentation_pipeline=typical_coco_val_pipeline,
num_workers=config_dict['num_val_workers'])
# evaluator
# the evaluator should match the dataset
config_dict[
'val_annotation_path'] = '/home/yonghaohe/datasets/COCO/annotations/instances_val2017.json'
config_dict['evaluator'] = COCOEvaluator(
annotation_path=config_dict['val_annotation_path'],
label_indexes_to_category_ids=val_dataset.
meta_info['label_indexes_to_category_ids'])
def prepare_data_pipeline():
# batch size
config_dict['batch_size'] = 64
# number of train data_loader workers
config_dict['num_train_workers'] = 12
# number of val data_loader workers
config_dict['num_val_workers'] = 0
# construct train data_loader
config_dict['train_dataset_path'] = './WIDERFACE_pack/widerface_train.pkl'
train_dataset = Dataset(load_path=config_dict['train_dataset_path'])
train_dataset_sampler = RandomWithNegDatasetSampler(
train_dataset,
batch_size=config_dict['batch_size'],
neg_ratio=0.2,
shuffle=True,
ignore_last=False)
train_region_sampler = RandomBBoxCropRegionSampler(crop_size=480,
resize_range=(0.5, 1.5),
resize_prob=0.5)
config_dict['train_data_loader'] = DataLoader(
dataset=train_dataset,
dataset_sampler=train_dataset_sampler,
region_sampler=train_region_sampler,
augmentation_pipeline=simple_widerface_train_pipeline,
num_workers=config_dict['num_train_workers'])
def prepare_data_pipeline():
# batch size
config_dict['batch_size'] = 12
# number of train data_loader workers
config_dict['num_train_workers'] = 6
# number of val data_loader workers
config_dict['num_val_workers'] = 0
# construct train data_loader
config_dict['train_dataset_path'] = './WIDERFACE_pack/widerface_train.pkl'
train_dataset = Dataset(load_path=config_dict['train_dataset_path'])
train_dataset_sampler = RandomWithNegDatasetSampler(
train_dataset,
batch_size=config_dict['batch_size'],
neg_ratio=0.2,
shuffle=True,
ignore_last=False)
train_region_sampler = RandomBBoxCropWithRangeSelectionRegionSampler(
crop_size=480,
detection_ranges=config_dict['detection_scales'],
range_selection_probs=[1, 1, 1, 1, 1],
lock_threshold=30)
config_dict['train_data_loader'] = DataLoader(
dataset=train_dataset,
dataset_sampler=train_dataset_sampler,
region_sampler=train_region_sampler,
augmentation_pipeline=simple_widerface_train_pipeline,
num_workers=config_dict['num_train_workers'])
def prepare_data_pipeline():
# batch size
config_dict['batch_size'] = 16
# number of train data_loader workers
config_dict['num_train_workers'] = 4
# number of val data_loader workers
config_dict['num_val_workers'] = 0
# construct train data_loader
config_dict['train_dataset_path'] = 'xxxxxxxxx'
train_dataset = Dataset(load_path=config_dict['train_dataset_path'])
train_dataset_sampler = RandomWithNegDatasetSampler(
train_dataset,
batch_size=config_dict['batch_size'],
neg_ratio=0.1,
shuffle=True,
ignore_last=False)
train_region_sampler = RandomBBoxCropRegionSampler(crop_size=512,
resize_range=(0.5, 1.5))
config_dict['train_data_loader'] = DataLoader(
dataset=train_dataset,
dataset_sampler=train_dataset_sampler,
region_sampler=train_region_sampler,
augmentation_pipeline=simple_widerface_train_pipeline,
num_workers=config_dict['num_train_workers'])
# construct val data_loader
config_dict['val_dataset_path'] = 'xxxxxxxxxx'
val_dataset = Dataset(load_path=config_dict['val_dataset_path'])
val_dataset_sampler = RandomDatasetSampler(
dataset=val_dataset,
batch_size=config_dict['batch_size'],
shuffle=False,
ignore_last=False)
val_region_sampler = IdleRegionSampler()
config_dict['val_data_loader'] = DataLoader(
dataset=val_dataset,
dataset_sampler=val_dataset_sampler,
region_sampler=val_region_sampler,
augmentation_pipeline=simple_widerface_val_pipeline,
num_workers=config_dict['num_val_workers'])
def prepare_data_pipeline():
# batch size
config_dict['batch_size'] = 4
# number of train data_loader workers
config_dict['num_train_workers'] = 4
# number of val data_loader workers
config_dict['num_val_workers'] = 4
# construct train data_loader
config_dict['train_dataset_path'] = './debug_data/train.pkl'
train_dataset = Dataset(load_path=config_dict['train_dataset_path'])
train_dataset_sampler = RandomWithNegDatasetSampler(
train_dataset,
batch_size=config_dict['batch_size'],
neg_ratio=0.2,
shuffle=True,
ignore_last=False)
train_region_sampler = RandomBBoxCropRegionSampler(crop_size=640,
resize_range=(0.5, 1.5),
resize_prob=0.5)
config_dict['train_data_loader'] = DataLoader(
dataset=train_dataset,
dataset_sampler=train_dataset_sampler,
region_sampler=train_region_sampler,
augmentation_pipeline=train_pipeline,
num_workers=config_dict['num_train_workers'])
# construct val data_loader
# config_dict['val_dataset_path'] = './debug_data/train.pkl'
# val_dataset = Dataset(load_path=config_dict['val_dataset_path'])
# val_dataset_sampler = RandomDatasetSampler(dataset=val_dataset,
# batch_size=config_dict['batch_size'],
# shuffle=False,
# ignore_last=False)
# val_region_sampler = IdleRegionSampler()
# config_dict['val_data_loader'] = DataLoader(dataset=val_dataset,
# dataset_sampler=val_dataset_sampler,
# region_sampler=val_region_sampler,
# augmentation_pipeline=val_pipeline,
# num_workers=config_dict['num_val_workers'])
# evaluator
# the evaluator should match the dataset
# validation interval in epochs
config_dict['val_interval'] = 0
# config_dict['val_annotation_path'] = './debug_data/annotations/instances_train2017.json'
config_dict[
'evaluator'] = None # COCOEvaluator(annotation_path=config_dict['val_annotation_path'],
def evaluate():
# set model to be evaluated ----------------------------------------------------------
from TT100K_LFD_S_work_dir_20210127_170801.TT100K_LFD_S import config_dict, prepare_model
weight_file_path = './TT100K_LFD_S_work_dir_20210127_170801/epoch_500.pth'
classification_threshold = 0.1
nms_threshold = 0.1
prepare_model()
load_checkpoint(config_dict['model'],
load_path=weight_file_path,
strict=True)
# predict results and save to json
results_json = dict()
results_json['imgs'] = dict()
test_image_root = '/home/yonghaohe/datasets/TT100K/data/test'
test_image_paths_list = [
os.path.join(test_image_root, file_name)
for file_name in os.listdir(test_image_root)
if file_name.endswith('.jpg')
]
dataset_path = './TT100K_pack/train.pkl'
dataset = Dataset(load_path=dataset_path)
label_indexes_to_category_names = dataset.meta_info[
'label_indexes_to_category_names']
results_json_save_path = os.path.join(
'./TT100K_evaluation/',
os.path.dirname(weight_file_path).split('/')[-1] + '_results.json')
if not os.path.exists(results_json_save_path):
for i, image_path in enumerate(test_image_paths_list):
image_id = os.path.basename(image_path).split('.')[0]
image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
results = config_dict['model'].predict_for_single_image(
image,
aug_pipeline=simple_widerface_val_pipeline,
classification_threshold=classification_threshold,
nms_threshold=nms_threshold,
class_agnostic=True)
temp = dict(id=image_id, objects=list())
for result in results:
cat = label_indexes_to_category_names[result[0]]
score = result[1] * 100 # make score in [0, 100]
xmin = result[2]
ymin = result[3]
xmax = result[4] + result[2]
ymax = result[5] + result[3]
temp_bbox = dict(bbox={
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax
},
category=cat,
score=score)
temp['objects'].append(temp_bbox)
results_json['imgs'][image_id] = temp
print('[%5d] image is predicted.' % i)
if not os.path.exists(os.path.dirname(results_json_save_path)):
os.makedirs(os.path.dirname(results_json_save_path))
json.dump(results_json,
open(results_json_save_path, 'w'),
indent=4,
ensure_ascii=False)
# evaluate
gt_annotation_json_path = '/home/yonghaohe/datasets/TT100K/data/annotations.json'
gt_json = json.load(open(gt_annotation_json_path, 'r'))
results_json = json.load(open(results_json_save_path, 'r'))
eval_result = official_eval.eval_annos(annos_gd=gt_json,
annos_rt=results_json,
iou=0.5,
imgids=None,
check_type=True,
types=official_eval.type45,
minscore=90,
minboxsize=0,
maxboxsize=400,
match_same=True)
print(eval_result['report'])
from lfd.execution.utils import load_checkpoint
from lfd.data_pipeline.augmentation import *
from lfd.data_pipeline.dataset import Dataset
import cv2
from TT100K_LFD_S_work_dir_20210127_170801.TT100K_LFD_S import config_dict, prepare_model
prepare_model()
param_file_path = './TT100K_LFD_S_work_dir_20210127_170801/epoch_500.pth'
load_checkpoint(config_dict['model'], load_path=param_file_path, strict=True)
dataset_path = './TT100K_pack/train.pkl'
dataset = Dataset(load_path=dataset_path)
label_indexes_to_category_names = dataset.meta_info[
'label_indexes_to_category_names']
image_path = '/home/yonghaohe/datasets/TT100K/data/test/66449.jpg'
image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
results = config_dict['model'].predict_for_single_image(
image,
aug_pipeline=simple_widerface_val_pipeline,
classification_threshold=0.5,
nms_threshold=0.1,
class_agnostic=True)
for bbox in results:
print(bbox)
category_name = label_indexes_to_category_names[bbox[0]]
# -*- coding: utf-8 -*-
from lfd.data_pipeline.dataset import Dataset
from lfd.data_pipeline.dataset.coco_parser import COCOParser
train_annotation_file_path = './debug_data/annotations/instances_train2017.json'
train_image_root = './debug_data/images/train2017'
train_pkl_save_path = './debug_data/train.pkl'
train_parser = COCOParser(coco_annotation_path=train_annotation_file_path,
image_root=train_image_root,
filter_no_gt=False,
filter_min_size=32)
train_dataset = Dataset(parser=train_parser, save_path=train_pkl_save_path)
val_annotation_file_path = './debug_data/annotations/instances_val2017.json'
val_image_root = './debug_data/images/val2017'
val_pkl_save_path = './debug_data/val.pkl'
val_parser = COCOParser(coco_annotation_path=val_annotation_file_path,
image_root=val_image_root,
filter_no_gt=False,
filter_min_size=32)
val_dataset = Dataset(parser=val_parser, save_path=val_pkl_save_path)
from TL_augmentation_pipeline import *
from TL_LFD_L_work_dir_20210714_173824.TL_LFD_L import config_dict, prepare_model
prepare_model()
param_file_path = './TL_LFD_L_work_dir_20210714_173824/epoch_100.pth'
load_checkpoint(config_dict['model'], load_path=param_file_path, strict=True)
classification_threshold = 0.1
nms_threshold = 0.3
val_annotation_path = './debug_data/annotations/instances_train2017.json'
val_image_root = './debug_data/images/train2017'
val_dataset_pkl = './debug_data/train.pkl'
val_dataset = Dataset(load_path=val_dataset_pkl)
label_indexes_to_category_ids = val_dataset.meta_info['label_indexes_to_category_ids']
coco_evaluator = COCOEvaluator(annotation_path=val_annotation_path,
label_indexes_to_category_ids=label_indexes_to_category_ids)
coco = COCO(annotation_file=val_annotation_path)
image_ids = coco.getImgIds()
for i, image_id in enumerate(image_ids):
image_info = coco.loadImgs(image_id)
image_path = os.path.join(val_image_root, image_info[0]['file_name'])
