def get_loader(json_path, data_dir, mask_dir, inp_size, feat_stride, preprocess,
batch_size, params_transform, training=True, shuffle=True, num_workers=3):
""" Build a COCO dataloader
:param json_path: string, path to jso file
:param datadir: string, path to coco data
:returns : the data_loader
"""
with open(json_path) as data_file:
data_this = json.load(data_file)
data = data_this['root']
# num_samples = len(data)
num_samples = 1000
print(num_samples)
train_indexes = []
val_indexes = []
for count in range(num_samples):
if data[count]['isValidation'] != 0.:
val_indexes.append(count)
else:
train_indexes.append(count)
coco_data = Cocokeypoints(root=data_dir, mask_dir=mask_dir,
index_list=train_indexes if training else val_indexes,
data=data, inp_size=inp_size, feat_stride=feat_stride,
preprocess=preprocess, transform=ToTensor(), params_transform=params_transform)
data_loader = sDataLoader(coco_data, batch_size=batch_size,
shuffle=shuffle, num_workers=num_workers)
return data_loader
def get_loader(json_path,
data_dir,
mask_dir,
inp_size,
feat_stride,
preprocess,
batch_size,
params_transform,
training=True,
shuffle=True,
num_workers=3):
""" Build a COCO dataloader
:param json_path: string, path to jso file
:param datadir: string, path to coco data
:returns : the data_loader
"""
with open(json_path) as data_file:
data_this = json.load(data_file)
data = data_this['root']
num_samples = len(data)
num_train = 200
ctr_train = 0
num_val = 40
ctr_val = 0
train_indexes = []
val_indexes = []
count = 0
while ctr_train < num_train:
if data[count]['isValidation'] == 0:
ctr_train += 1
train_indexes.append(count)
count += 1
while count < num_samples and ctr_val < num_val:
if data[count]['isValidation'] != 0.:
ctr_val += 1
val_indexes.append(count)
count += 1
# for count in range(num_samples):
# if data[count]['isValidation'] != 0.:
# val_indexes.append(count)
# else:
# train_indexes.append(count)
coco_data = Cocokeypoints(
root=data_dir,
mask_dir=mask_dir,
index_list=train_indexes if training else val_indexes,
data=data,
inp_size=inp_size,
feat_stride=feat_stride,
preprocess=preprocess,
transform=ToTensor(),
params_transform=params_transform)
data_loader = sDataLoader(coco_data,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
return data_loader
def get_loader(json_path,
data_dir,
mask_dir,
inp_size,
feat_stride,
preprocess,
batch_size,
params_transform,
training=True,
shuffle=True,
num_workers=3,
coco=True):
""" Build a COCO dataloader
:param json_path: string, path to jso file
:param datadir: string, path to coco data
:returns : the data_loader
"""
with open(json_path) as data_file:
data_this = json.load(data_file)
data = data_this['root']
num_samples = len(data)
train_indexes = []
val_indexes = []
for count in range(num_samples):
if data[count]['isValidation'] != 0.:
val_indexes.append(count)
else:
train_indexes.append(count)
# fix some format problems
set1 = set(train_indexes) - set([
291, 430, 514, 560, 611, 613, 787, 840, 841, 926, 981, 984, 1167, 1453,
1695, 2092, 2589, 2813, 2826, 3467, 3585, 4274, 4633, 4664, 5042, 5678,
5697, 6084, 6172, 6202, 6459, 7080, 7259, 8003, 8105, 8448, 8458, 8501,
9664, 9776, 11157, 11286, 11682, 12603, 12720, 12727, 13598, 13629,
13631, 13633, 14133, 14309, 15450, 16056, 16062, 16203, 16273, 16315,
16835, 17210, 17302, 17545, 18100, 18402, 18458, 18483, 18918, 19879,
20303, 20500, 20738, 20813, 20837, 21248, 21875, 22130, 22146, 22242,
23114, 23416, 23452, 23640, 23803, 24343, 24424, 24613, 25494, 26421,
26487, 27043, 27086, 27089, 27090, 27092, 27094, 27100, 27184, 27526,
27786, 27819, 28604, 28638, 28721, 28764, 28807
])
set2 = set(val_indexes) - set([
904, 4965, 14758, 15187, 15962, 17133, 20015, 20210, 23740, 25349,
27474
])
train_indexes = list(set1)
val_indexes = list(set2)
print("train num: ", len(train_indexes))
print(" val num: ", len(val_indexes))
coco_data = Cocokeypoints(
root=data_dir,
mask_dir=mask_dir,
index_list=train_indexes if training else val_indexes,
data=data,
inp_size=inp_size,
feat_stride=feat_stride, # feat_stride=8
preprocess=preprocess,
transform=ToTensor(),
params_transform=params_transform)
data_loader = sDataLoader(coco_data,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
return data_loader
def get_loader(json_path,
data_dir,
mask_dir,
inp_size,
feat_stride,
preprocess,
batch_size,
params_transform,
training=True,
shuffle=True,
num_workers=1,
aug=False,
classification=False):
""" Build a COCO dataloader
:param json_path: string, path to jso file
:param datadir: string, path to coco data
:returns : the data_loader
"""
json_data = list()
if isinstance(json_path, list): # 将多个root
root = os.path.dirname(json_path[0])
for json_i in json_path:
print(json_i)
with open(json_i) as data_file:
json_data_i = json.load(data_file)
json_data.extend(json_data_i)
# print(len(json_data_i), type(json_data_i))
# print(len(json_data))
elif isinstance(json_path, str):
root = os.path.dirname(json_path)
with open(json_path) as data_file:
json_data = json.load(data_file)
# data_this = json.load(data_file)
# data = data_this['root']
num_samples = len(json_data)
train_indexes = []
val_indexes = []
for count in range(num_samples):
if json_data[count]['isValidation'] != 0.:
val_indexes.append(count)
else:
train_indexes.append(count)
# print('train dataset len:', len(train_indexes), ' val dataset len:', len(val_indexes))
# root = data_dir
# root = os.path.dirname(json_path)
kik_data = UGVKeyPoints(
root=root,
index_list=train_indexes if training else val_indexes,
data=json_data,
feat_stride=feat_stride,
preprocess=preprocess,
transform=ToTensor(),
params_transform=params_transform,
numkeypoints=4,
numlims=4,
aug=aug,
classification=classification) # Mod by Jie.
data_loader = sDataLoader(kik_data,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
return data_loader