def create_test_data_loader(args, name, dataset):
train_transformer = T.Compose([
T.RectScale(args.height, args.width),
T.ToTensor(),
T.Normalize(mean=[0.486, 0.459, 0.408], std=[0.229, 0.224, 0.225])
])
test_transformer = T.Compose([
T.RectScale(args.height, args.width),
T.ToTensor(),
T.Normalize(mean=[0.486, 0.459, 0.408], std=[0.229, 0.224, 0.225])
])
train_loader = DataLoader(
Preprocessor(dataset.train, root=os.path.join(dataset.images_dir, dataset.train_path),
transform=train_transformer),
batch_size=args.batch_size, num_workers=args.workers,
shuffle=False, pin_memory=True, drop_last=False)
query_loader = DataLoader(
Preprocessor(dataset.query, root=os.path.join(dataset.images_dir, dataset.query_path),
transform=test_transformer),
batch_size=args.batch_size*4, num_workers=args.workers,
shuffle=False, pin_memory=True, drop_last=False)
gallery_loader = DataLoader(
Preprocessor(dataset.gallery, root=os.path.join(dataset.images_dir, dataset.gallery_path),
transform=test_transformer),
batch_size=args.batch_size*4, num_workers=args.workers,
shuffle=False, pin_memory=True, drop_last=False)
print('{} Datasets Has beed loaded.'.format(name))
return train_loader, query_loader, gallery_loader
def __init__(self, dataset, name, root=None, with_pose=False, pose_root=None, is_test=False, test_root = None,
pid_imgs=None, height=256, width=128, pose_aug='no', transform=None):
super(Preprocessor, self).__init__()
self.dataset = dataset
self.root = root
self.with_pose = with_pose
self.pose_root = pose_root
self.is_test = is_test
self.test_root = test_root
self.pid_imgs = pid_imgs
self.height = height
self.width = width
self.pose_aug = pose_aug
self.name = name
normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
if transform==None:
self.transform = transforms.Compose([
transforms.RectScale(height, width),
transforms.RandomSizedEarser(),
#transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalizer,
])
else:
self.transform = transform
self.transform_p = transforms.Compose([
transforms.RectScale(height, width),
transforms.ToTensor(),
normalizer,
])
def get_dataloader(dataset,data_dir,
training=False, height=256,
width=128, batch_size=64, workers=1):
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])#(0.1307,), (0.3081,)
# normalizer = T.Normalize(mean=[0.1307, 0.1307, 0.1307],
# std=[0.3081, 0.3081, 0.3081])#, (0.3081,)
if training:
transformer = T.Compose([
T.RectScale(config.input_size + 32, config.input_size + 32),
torchvision.transforms.RandomCrop(config.input_size),
torchvision.transforms.RandomHorizontalFlip(),
# RandomRotation(degrees=20),
T.ToTensor(),
normalizer,
])
else:
transformer = T.Compose([
T.RectScale(config.input_size, config.input_size),
T.ToTensor(),
normalizer,
])
data_loader = DataLoader(
Preprocessor(dataset, root=data_dir,
transform=transformer),
batch_size=batch_size, num_workers=workers,
shuffle=training, pin_memory=True, drop_last=training)
return data_loader
def get_data(name, data_dir, height, width, batch_size, workers, pose_aug,
skip, rate, eraser):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root)
video_dict = None
if osp.isfile(osp.join(root, 'video.json')):
video_dict = read_json(osp.join(root, 'video.json'))
if eraser:
train_transformer = T.Compose([
T.RectScale(height, width),
T.RandomSizedEarser(),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
else:
train_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# use combined trainval set for training as default
train_loader = DataLoader(Preprocessor(dataset.trainval,
name,
root=dataset.images_dir,
with_pose=True,
pose_root=dataset.poses_dir,
pid_imgs=dataset.trainval_query,
height=height,
width=width,
pose_aug=pose_aug,
transform=train_transformer),
sampler=RandomTripletSampler(
dataset.trainval,
video_dict=video_dict,
skip_frames=skip,
inter_rate=rate),
batch_size=batch_size,
num_workers=workers,
pin_memory=False)
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
test_loader = DataLoader(Preprocessor(
list(set(dataset.query) | set(dataset.gallery)),
name,
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=False)
return dataset, train_loader, test_loader
def get_data(name, data_dir, height, width, batch_size, workers):
root = osp.join(data_dir, name)
root = data_dir
dataset = datasets.create(name, root)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
num_classes = dataset.num_train_ids
train_transformer = T.Compose([
T.RectScale(height, width),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalizer,
])
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
train_loader = DataLoader(Preprocessor(dataset.train,
root=osp.join(
dataset.images_dir,
dataset.train_path),
transform=train_transformer,
random_mask=False),
batch_size=batch_size,
num_workers=workers,
shuffle=True,
pin_memory=True,
drop_last=True)
query_loader = DataLoader(Preprocessor(dataset.query,
root=osp.join(
dataset.images_dir,
dataset.query_path),
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
gallery_loader = DataLoader(Preprocessor(dataset.gallery,
root=osp.join(
dataset.images_dir,
dataset.gallery_path),
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
return dataset, num_classes, train_loader, query_loader, gallery_loader
def get_data(name, split_id, data_dir, height, width, batch_size,
num_instances, workers, combine_trainval):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root, split_id=split_id)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_set = dataset.trainval if combine_trainval else dataset.train
num_classes = (dataset.num_trainval_ids
if combine_trainval else dataset.num_train_ids)
train_transformer = T.Compose([
T.RectScale(height, width),
T.RandomSizedEarser(),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalizer,
])
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
train_loader = DataLoader(Preprocessor(train_set,
root=dataset.images_dir,
transform=train_transformer),
batch_size=batch_size,
num_workers=workers,
sampler=RandomMultipleGallerySampler(
train_set, num_instances),
pin_memory=True,
drop_last=True)
val_loader = DataLoader(Preprocessor(dataset.val,
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
test_loader = DataLoader(Preprocessor(
list(set(dataset.query) | set(dataset.gallery)),
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
return dataset, num_classes, train_loader, val_loader, test_loader
def get_data(name, data_dir, height, width, ratio, batch_size, workers, num_instances=8):
root = osp.join(data_dir, name)
root = data_dir
dataset = datasets.create(name, root)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
listnormalizer = T.ListNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
num_classes = dataset.num_train_ids + 1 # plus 1 more label for the zero-padded feature
train_transformer = T.Compose([
T.RectScale(height, width),
T.RandomHorizontalFlip(),
T.RandomVerticalCropCont(height,width),
T.ListToTensor(),
listnormalizer,
])
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
query_transformer = T.Compose([
T.ContVerticalCropDiscret(height,width, ratio),
T.ToTensor(),
normalizer,
])
train_loader = DataLoader(
Preprocessor(dataset.train, root=osp.join(dataset.images_dir,dataset.train_path),
transform=train_transformer),
batch_size=batch_size, num_workers=workers,
sampler=RandomIdentitySampler(dataset.train, num_instances),
# shuffle=True,
pin_memory=True, drop_last=True)
query_loader = DataLoader(
Preprocessor(dataset.query, root=osp.join(dataset.images_dir,dataset.query_path),
transform=query_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
gallery_loader = DataLoader(
Preprocessor(dataset.gallery, root=osp.join(dataset.images_dir,dataset.gallery_path),
transform=test_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
return dataset, num_classes, train_loader, query_loader, gallery_loader
def get_data(name,
split_id,
data_dir,
height,
width,
crop_height,
crop_width,
batch_size,
caffe_sampler=False,
workers=4):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root, split_id=split_id)
train_set = dataset.trainval
num_classes = dataset.num_trainval_ids
# transforms
train_transformer = T.Compose([
T.RectScale(height, width),
# T.CenterCrop((crop_height, crop_width)),
T.RandomHorizontalFlip(),
T.ToTensor(),
T.RGB_to_BGR(),
T.NormalizeBy(255),
])
test_transformer = T.Compose([
T.RectScale(height, width),
# T.CenterCrop((crop_height, crop_width)),
T.ToTensor(),
T.RGB_to_BGR(),
T.NormalizeBy(255),
])
# dataloaders
sampler = caffeSampler(train_set, name, batch_size=batch_size, root=dataset.images_dir) if caffe_sampler else \
RandomIdentitySampler(train_set, 10) #TODO
train_loader = DataLoader(Preprocessor(train_set,
root=dataset.images_dir,
transform=train_transformer),
batch_size=batch_size,
num_workers=workers,
sampler=sampler,
pin_memory=True,
drop_last=True)
test_loader = DataLoader(Preprocessor(
list(set(dataset.query) | set(dataset.gallery)),
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
return dataset, num_classes, train_loader, test_loader
def get_data(dataset_name, split_id, data_dir, batch_size, workers,
num_instances, combine_trainval=False):
root = osp.join(data_dir, dataset_name)
dataset = get_dataset(dataset_name, root,
split_id=split_id, num_val=1, download=True)
normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_set = dataset.trainval if combine_trainval else dataset.train
num_classes = (dataset.num_trainval_ids if combine_trainval
else dataset.num_train_ids)
train_processor = Preprocessor(train_set, root=dataset.images_dir,
transform=transforms.Compose([
transforms.RandomSizedRectCrop(256, 128),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalizer,
]))
if num_instances > 0:
train_loader = DataLoader(
train_processor, batch_size=batch_size, num_workers=workers,
sampler=RandomIdentitySampler(train_set, num_instances),
pin_memory=True)
else:
train_loader = DataLoader(
train_processor, batch_size=batch_size, num_workers=workers,
shuffle=True, pin_memory=True)
val_loader = DataLoader(
Preprocessor(dataset.val, root=dataset.images_dir,
transform=transforms.Compose([
transforms.RectScale(256, 128),
transforms.ToTensor(),
normalizer,
])),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
test_loader = DataLoader(
Preprocessor(list(set(dataset.query) | set(dataset.gallery)),
root=dataset.images_dir,
transform=transforms.Compose([
transforms.RectScale(256, 128),
transforms.ToTensor(),
normalizer,
])),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
return dataset, num_classes, train_loader, val_loader, test_loader
def get_data(split_id, data_dir, height, width, batchSize, workers, combine_trainval, train_list, \
val_list, query_list, gallery_list):
root = data_dir
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]) # RGB imagenet
train_set = train_list + val_list if combine_trainval else train_list # a list
train_transformer = T.Compose([
T.RectScale(height, width),
T.RandomHorizontalFlip(),
T.ToTensor(), # [0, 255] to [0.0, 1.0]
normalizer, # normalize each channel of the input
])
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
train_loader = DataLoader(
Preprocessor(train_set, root=root, transform=train_transformer),
batch_size=batchSize,
num_workers=workers,
sampler=RandomSampler(train_set),
# shuffle=True,
pin_memory=True,
drop_last=True)
val_loader = DataLoader(Preprocessor(val_list,
root=root,
transform=test_transformer),
batch_size=batchSize,
num_workers=workers,
shuffle=False,
pin_memory=True)
test_loader = DataLoader(Preprocessor(
list(set(query_list) | set(gallery_list)),
root=root,
transform=test_transformer),
batch_size=batchSize,
num_workers=workers,
shuffle=False,
pin_memory=True)
return train_loader, val_loader, test_loader
def get_data(name, split_id, data_dir, height, width, batch_size, workers,
combine_trainval):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root, split_id=split_id)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_set = dataset.trainval if combine_trainval else dataset.train
num_classes = (dataset.num_trainval_ids
if combine_trainval else dataset.num_train_ids)
train_transformer = T.Compose([
T.RandomSizedRectCrop(height, width),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalizer,
])
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
train_loader = DataLoader(Preprocessor(train_set,
root=dataset.images_dir,
transform=train_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=True,
pin_memory=True,
drop_last=True)
val_loader = DataLoader(Preprocessor(dataset.val,
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
test_loader = DataLoader(Preprocessor(
list(set(dataset.query) | set(dataset.gallery)),
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
pid_train = np.array(list(pid for _, pid, _ in train_set))
class_weight = np.array([(pid_train == i).sum()
for i in range(num_classes)])
assert np.all(class_weight != 0)
class_weight = pid_train.shape[0] / num_classes / class_weight
class_weight = torch.Tensor(class_weight).cuda()
return dataset, num_classes, class_weight, train_loader, val_loader, test_loader
def get_data2(args):
(name, split_id, data_dir, height, width, batch_size, num_instances,
workers, combine_trainval) = (
args.dataset,
args.split,
args.data_dir,
args.height,
args.width,
args.batch_size,
args.num_instances,
args.workers,
args.combine_trainval,
)
pin_memory = args.pin_mem
name_val = args.dataset_val or args.dataset
npy = args.has_npy
rand_ratio = args.random_ratio
dataset_train = datasets.CUB2('train')
dataset_test = datasets.CUB2('test')
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
num_classes = np.unique(dataset_train.train_labels).shape[0]
train_transformer = T.Compose([
T.ToPILImage(),
T.RandomCropFlip(height, width, area=args.area),
T.ToTensor(),
normalizer,
])
test_transformer = T.Compose([
T.ToPILImage(),
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
def get_data(name, data_dir, height, width, batch_size, workers, pose_aug):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root)
# use combined trainval set for training as default
train_loader = DataLoader(Preprocessor(dataset.trainval,
root=dataset.images_dir,
with_pose=True,
pose_root=dataset.poses_dir,
pid_imgs=dataset.trainval_query,
height=height,
width=width,
pose_aug=pose_aug),
sampler=RandomPairSampler(dataset.trainval,
neg_pos_ratio=3),
batch_size=batch_size,
num_workers=workers,
pin_memory=False)
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
test_loader = DataLoader(Preprocessor(
list(set(dataset.query) | set(dataset.gallery)),
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=False)
return dataset, train_loader, test_loader
def get_real_test_data(query_dir, gallery_dir, target_height, target_width,
batch_size, workers):
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
test_transformer = T.Compose([
T.RectScale(target_height, target_width),
T.ToTensor(),
normalizer,
])
query_loader = DataLoader(Flip_Preprocessor(data_dir=query_dir,
is_flip=False,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
gallery_loader = DataLoader(Flip_Preprocessor(data_dir=gallery_dir,
is_flip=False,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
return query_loader, gallery_loader
def get_data(data_dir, big_height, big_width, target_height, target_width, batch_size, workers, is_train=True):
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_transformer = T.Compose([
T.ResizeRandomCrop(big_height, big_width, target_height, target_width),
T.ToTensor(),
normalizer,
])
test_transformer = T.Compose([
T.RectScale(target_height, target_width),
T.ToTensor(),
normalizer,
])
if is_train:
transformer = train_transformer
else:
transformer = test_transformer
data_loader = DataLoader(
Direct_Preprocessor(data_dir=data_dir,
transform=transformer, is_train=is_train),
batch_size=batch_size, num_workers=workers,
shuffle=is_train, pin_memory=True)
return data_loader
def get_loader(data,
root,
height=256,
width=128,
batch_size=32,
workers=0,
training=False):
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
if training:
transformer = T.Compose([
T.RandomSizedRectCrop(height, width), # 对图像进行随机裁剪并缩放.
T.RandomHorizontalFlip(), # 对给定的PIL.Image进行随机水平翻转,概率为0.5,属于数据增强.
T.ToTensor(), # 将numpy图像转换为torch图像.
normalizer,
])
else:
transformer = T.Compose([
T.RectScale(height, width), # 缩放图像.
T.ToTensor(),
normalizer,
])
batch_size = batch_size * 8
data_loader = DataLoader(Preprocessor(data,
root=root,
transform=transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=training,
pin_memory=True)
return data_loader
def get_dataloader(dataset,data_dir,
training=False, height=256,
width=128, batch_size=64, workers=1):
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
if training:
transformer = T.Compose([
T.RandomSizedRectCrop(height, width),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalizer,
])
else:
transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
data_loader = DataLoader(
Preprocessor(dataset, root=data_dir,
transform=transformer),
batch_size=batch_size, num_workers=workers,
shuffle=training, pin_memory=True, drop_last=training)
return data_loader
def get_dataloader(self, dataset, training=False) :
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
if training:
transformer = T.Compose([
T.RandomSizedRectCrop(self.data_height, self.data_width),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalizer,
])
batch_size = self.batch_size
else:
transformer = T.Compose([
T.RectScale(self.data_height, self.data_width),
T.ToTensor(),
normalizer,
])
batch_size = self.eval_bs
data_loader = DataLoader(
Preprocessor(dataset, root=self.data_dir,
transform=transformer, is_training=training, max_frames=self.max_frames),
batch_size=batch_size, num_workers=self.data_workers,
shuffle=training, pin_memory=True, drop_last=training)
current_status = "Training" if training else "Test"
print("create dataloader for {} with batch_size {}".format(current_status, batch_size))
return data_loader
def test(dataset, net, perturbation, args, evaluator, epoch, name,saveRank=False):
print(">> Evaluating network on test datasets...")
net = net.cuda()
net.eval()
normalize = T.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
mean = torch.Tensor(normalize.mean).view(1, 3, 1, 1).cuda()
std = torch.Tensor(normalize.std).view(1, 3, 1, 1).cuda()
test_transformer = T.Compose([
T.RectScale(args.height, args.width),
T.ToTensor()
])
query_loader = DataLoader(
Preprocessor(dataset.query, name, root=dataset.images_dir, transform=test_transformer),
batch_size=args.batch_size, num_workers=0, shuffle=False, pin_memory=True
)
gallery_loader = DataLoader(
Preprocessor(dataset.gallery, name, root=dataset.images_dir, transform=test_transformer),
batch_size=args.batch_size, num_workers=8, shuffle=False, pin_memory=True
)
qFeats, gFeats, testQImage,noiseQIamge, qnames, gnames = [], [], [], [], [],[]
qCams, gCams = [], []
import random
with torch.no_grad():
for batch_index, (inputs, qname, _, qCam) in enumerate(query_loader):
inputs = inputs.cuda()
perturted_input = perturbation(inputs)
successful_diffs = ((perturted_input - inputs) * 3 + 0.5).clamp(0, 1)
if len(testQImage) < 8:
testQImage.append(perturted_input[0, ...])
noiseQIamge.append(successful_diffs[0, ...])
perturted_input = torch.clamp(perturted_input, 0, 1)
###normalization
norm_perturted_input = (perturted_input - mean) / std
perturbed_feature = net(norm_perturted_input)[0]
qFeats.append(perturbed_feature)
qnames.extend(qname)
qCams.append(qCam.cuda())
qFeats = torch.cat(qFeats, 0)
for (inputs, gname, _, gCam) in gallery_loader:
###normalize####
inputs = inputs.cuda()
inputs = (inputs - mean) / std
gFeats.append(net(inputs)[0])
gnames.extend(gname)
gCams.append(gCam.cuda())
gFeats = torch.cat(gFeats, 0)
gCams, qCams = torch.cat(gCams).view(1, -1), torch.cat(qCams).view(-1, 1)
distMat = calDist(qFeats, gFeats)
# evaluate on test datasets
s= evaluator.evaMat(distMat, dataset.query, dataset.gallery)
return testQImage, noiseQIamge,s
def __init__(self, config):
self.model_path = config.args['model_path']
self.width = config.args['width']
self.height = config.args['height']
self.model = init_model(self.model_path)
self.normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
self.test_transformer = T.Compose([
T.RectScale(self.height, self.width),
T.ToTensor(),
self.normalizer,
])
def get_data(name, split_id, data_dir, height, width, batch_size,
num_instances, workers, combine_trainval):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root, split_id=split_id)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# load train_set, query_set, gallery_set
mt_train_set = dataset.train
mt_num_classes = dataset.num_train_tids_sub
query_set = dataset.query
gallery_set = dataset.gallery
train_transformer = T.Compose([
T.RandomSizedRectCrop(height, width),
T.RandomHorizontalFlip(),
T.ToTensor(), normalizer
])
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
# Random ID
mt_train_set = flatten_dataset(mt_train_set)
num_task = len(
mt_num_classes) # num_task equals camera number, each camera is a task
mt_train_loader = DataLoader(
Preprocessor_Image(mt_train_set,
root=dataset.dataset_dir,
transform=train_transformer),
batch_size=batch_size,
num_workers=workers,
sampler=RandomIdentitySampler(
mt_train_set, num_instances,
num_task), # Here is different between softmax_loss
pin_memory=True,
drop_last=True)
query_set = flatten_dataset(query_set)
gallery_set = flatten_dataset(gallery_set)
test_set = list(set(query_set) | set(gallery_set))
test_loader = DataLoader(Preprocessor_Image(test_set,
root=dataset.dataset_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
return mt_train_loader, mt_num_classes, test_loader, query_set, gallery_set
def get_data(name, split_id, data_dir, height, width, batch_size, workers,
combine_trainval, np_ratio):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root, split_id=split_id)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_set = dataset.trainval if combine_trainval else dataset.train
train_transformer = T.Compose([
T.RandomSizedRectCrop(height, width),
T.RandomSizedEarser(),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalizer,
])
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
train_loader = DataLoader(Preprocessor(train_set,
root=dataset.images_dir,
transform=train_transformer),
sampler=RandomPairSampler(
train_set, neg_pos_ratio=np_ratio),
batch_size=batch_size,
num_workers=workers,
pin_memory=False)
val_loader = DataLoader(Preprocessor(dataset.val,
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=0,
shuffle=False,
pin_memory=False)
test_loader = DataLoader(Preprocessor(
list(set(dataset.query) | set(dataset.gallery)),
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=0,
shuffle=False,
pin_memory=False)
return dataset, train_loader, val_loader, test_loader
def get_dataloader(self, dataset, training=False):
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
if training: # import transforms as T
transformer = T.Compose([
T.RandomSizedRectCrop(
self.data_height,
self.data_width), # data_height = 256 data_width = 128
T.RandomHorizontalFlip(), # 随机水平翻转
T.ToTensor(
), # Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
normalizer, # normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225])
])
batch_size = self.batch_size # batch_size = 16
else:
transformer = T.Compose([
T.RectScale(self.data_height,
self.data_width), # RectScale():三角缩放(?)
T.ToTensor(
), # Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
normalizer, # normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
]) # std=[0.229, 0.224, 0.225])
batch_size = self.eval_bs # batch_size = 64
data_dir = self.data_dir # data_dir = dataset_all.image_dir
data_loader = DataLoader( # DataLoader()
Preprocessor(
dataset,
root=data_dir,
num_samples=self.
frames_per_video, # root = dataset_all.image_dir num_samples = 1
transform=transformer,
is_training=training,
max_frames=self.max_frames
), # transform = T.compose()返回值 is_training = False max_frames = 900
batch_size=batch_size,
num_workers=self.
data_workers, # batch_size = 16 data_workers = 6
shuffle=training,
pin_memory=True,
drop_last=training) # shuffle = True drop_last = True
current_status = "Training" if training else "Testing" # current_status = 'Training'
print("Create dataloader for {} with batch_size {}".format(
current_status,
batch_size)) # Create dataloader for Training with batch_size 16
return data_loader
def get_data(name, split_id, data_dir, big_height, big_width, target_height,
target_width, batch_size, num_instances, workers,
combine_trainval):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root, split_id=split_id, download=True)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_set = dataset.trainval if combine_trainval else dataset.train
num_classes = (dataset.num_trainval_ids
if combine_trainval else dataset.num_train_ids)
train_transformer = T.Compose([
T.ResizeRandomCrop(big_height, big_width, target_height, target_width),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalizer,
T.RandomErasing(0.5),
])
test_transformer = T.Compose([
T.RectScale(target_height, target_width),
T.ToTensor(),
normalizer,
])
train_loader = DataLoader(
Attribute_Preprocessor(train_set,
root=dataset.images_dir,
transform=train_transformer),
batch_size=batch_size,
num_workers=workers,
sampler=RandomIdentityAttributeSampler(train_set, num_instances),
pin_memory=True,
drop_last=True)
test_loader = DataLoader(Attribute_Preprocessor(
list(set(dataset.query) | set(dataset.gallery)),
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
return dataset, num_classes, train_loader, test_loader
def get_data(name, split_id, data_dir, height, width, batch_size, workers,
combine_trainval):
root = os.path.join(data_dir, name)
dataset = datasets.create(name, root, split_id=split_id)
train_set = dataset.trainval if combine_trainval else dataset.train
num_classes = (dataset.num_trainval_ids
if combine_trainval else dataset.num_train_ids)
train_transformer = t.Compose([
t.RandomSizedRectCrop(height, width),
t.RandomHorizontalFlip(),
ColorHistograms()
])
test_transformer = t.Compose(
[t.RectScale(height, width),
ColorHistograms()])
train_loader = DataLoader(Preprocessor(train_set,
root=dataset.images_dir,
transform=train_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=True,
pin_memory=True,
drop_last=True)
val_loader = DataLoader(Preprocessor(dataset.val,
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
test_loader = DataLoader(Preprocessor(
list(set(dataset.query) | set(dataset.gallery)),
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
return dataset, num_classes, train_loader, val_loader, test_loader
def get_data(sourceName, mteName, split_id, data_dir, height, width,
batch_size, workers, combine,num_instances=8):
root = osp.join(data_dir, sourceName)
rootMte = osp.join(data_dir, mteName)
sourceSet = datasets.create(sourceName, root, num_val=0.1, split_id=split_id)
mteSet = datasets.create(mteName, rootMte, num_val=0.1, split_id=split_id)
num_classes = sourceSet.num_trainval_ids if combine else sourceSet.num_train_ids
class_meta = mteSet.num_trainval_ids if combine else mteSet.num_train_ids
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
defen_train_transformer = T.Compose([
Resize((height, width)),
T.RandomSizedRectCrop(height, width),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalizer,
T.RandomErasing(probability=0.5, sh=0.2, r1=0.3)
])
meta_train_loader = DataLoader(
Preprocessor(sourceSet.trainval, root=sourceSet.images_dir,
transform=defen_train_transformer),
batch_size=batch_size, num_workers=workers,
sampler=RandomIdentitySampler(sourceSet.trainval, num_instances),
pin_memory=True, drop_last=True)
meta_test_loader=DataLoader(
Preprocessor(mteSet.trainval, root=mteSet.images_dir,
transform=defen_train_transformer),
batch_size=batch_size, num_workers=workers,
sampler=RandomIdentitySampler(mteSet.trainval, num_instances),
pin_memory=True, drop_last=True)
sc_test_loader = DataLoader(
Preprocessor(list(set(sourceSet.query) | set(sourceSet.gallery)),
root=sourceSet.images_dir, transform=test_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
return sourceSet, mteSet, num_classes, meta_train_loader, meta_test_loader,sc_test_loader,class_meta
def main(argv):
#parser
parser = argparse.ArgumentParser(description='test part bilinear network')
parser.add_argument('--exp-dir', type=str, default='logs/market1501/exp1')
parser.add_argument('--target-epoch', type=int, default=750)
parser.add_argument('--gpus', type=str, default='0')
args = parser.parse_args(argv)
# Settings
exp_dir = args.exp_dir
target_epoch = args.target_epoch
batch_size = 50
gpu_ids = args.gpus
set_paths('paths')
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_ids
args = json.load(open(osp.join(exp_dir, "args.json"), "r"))
# Load data
t = T.Compose([
T.RectScale(args['height'], args['width']),
T.CenterCrop((args['crop_height'], args['crop_width'])),
T.ToTensor(),
T.RGB_to_BGR(),
T.NormalizeBy(255),
])
dataset = datasets.create(args['dataset'],
'data/{}'.format(args['dataset']))
dataset_ = Preprocessor(list(set(dataset.query) | set(dataset.gallery)),
root=dataset.images_dir,
transform=t)
dataloader = DataLoader(dataset_, batch_size=batch_size, shuffle=False)
# Load model
model = models.create(args['arch'],
dilation=args['dilation'],
use_relu=args['use_relu'],
initialize=False).cuda()
weight_file = osp.join(exp_dir, 'epoch_{}.pth.tar'.format(target_epoch))
model.load(load_checkpoint(weight_file))
model.eval()
# Evaluate
evaluator = Evaluator(model)
evaluator.evaluate(dataloader, dataset.query, dataset.gallery)
def get_data(args):
(name, split_id, data_dir, height, width, batch_size, num_instances,
workers, combine_trainval) = (
args.dataset,
args.split,
args.data_dir,
args.height,
args.width,
args.batch_size,
args.num_instances,
args.workers,
args.combine_trainval,
)
pin_memory = args.pin_mem
name_val = args.dataset_val or args.dataset
npy = args.has_npy
rand_ratio = args.random_ratio
if isinstance(name, list):
dataset = datasets.creates(name, split_id=split_id)
else:
dataset = datasets.create(
name,
split_id=split_id,
)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
test_loader = DataLoader(
Preprocessor(dataset.query, transform=test_transformer, has_npy=npy),
batch_size=batch_size, # * 2
num_workers=workers,
shuffle=False,
pin_memory=False)
return test_loader
def get_data(name, data_dir, height, width, ratios, batch_size, workers):
root = osp.join(data_dir, name)
root = data_dir
dataset = datasets.create(name, root)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
num_classes = dataset.num_train_ids + 1 # plus 1 more label for the zero-padded feature
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
query_transformers = []
for ratio in ratios:
query_transformers.append(T.Compose([
T.ContVerticalCropDiscret(height, width, ratio),
T.ToTensor(),
normalizer]))
query_loaders = []
for query_transformer in query_transformers:
query_loaders.append(DataLoader(
Preprocessor(dataset.query, root=osp.join(dataset.images_dir,dataset.query_path),
transform=query_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True))
gallery_loader = DataLoader(
Preprocessor(dataset.gallery, root=osp.join(dataset.images_dir,dataset.gallery_path),
transform=test_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
return dataset, num_classes, query_loaders, gallery_loader
def evaluate(args):
# Settings
exp_dir = './logs/{}/{}'.format(args.dataset, args.exp)
target_epoch = args.epoch
batch_size = args.batchsize
gpu_ids = args.gpus
set_paths('paths')
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_ids
args = json.load(open(osp.join(exp_dir, "args.json"), "r"))
# Load data
t = T.Compose([
T.RectScale(args['height'], args['width']),
T.CenterCrop((args['crop_height'], args['crop_width'])),
T.ToTensor(),
T.RGB_to_BGR(),
T.NormalizeBy(255),
])
dataset = datasets.create(args['dataset'],
'data/{}'.format(args['dataset']))
dataset_ = Preprocessor(list(set(dataset.query) | set(dataset.gallery)),
root=dataset.images_dir,
transform=t)
dataloader = DataLoader(dataset_, batch_size=batch_size, shuffle=False)
# Load model
model = models.create(args['arch'],
dilation=args['dilation'],
use_relu=args['use_relu'],
initialize=False).cuda()
weight_file = osp.join(exp_dir, 'epoch_{}.pth.tar'.format(target_epoch))
model.load(load_checkpoint(weight_file))
model.eval()
# Evaluate
evaluator = Evaluator(model)
evaluator.evaluate(dataloader, dataset.query, dataset.gallery)
