self.classifier = nn.Sequential(
nn.Linear(16 * 5 * 5, 120),
nn.Dropout(p=0.5),
nn.Linear(120, 84),
nn.Linear(84, 10),
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
transforms = transforms.Compose([
transforms.Resize(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
dataset = torchvision.datasets.MNIST(root=data_root,
transform=transforms,
download=True,
train=True)
train_data = utils.data.DataLoader(dataset,
shuffle=True,
batch_size=100,
num_workers=2)
test_dataset = torchvision.datasets.MNIST(root=data_root,
transform=transforms,
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
net = DarkNet19(100)
train_set = CIFAR100(config.dataset,
train=True,
transform=transform,
download=True)
test_set = CIFAR100(config.dataset,
train=False,
transform=transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
download=True)
train_set, val_set = random_split(train_set, [
int(len(train_set) * 0.9),
len(train_set) - int(len(train_set) * 0.9)
])
# train_set = ImageFolder(config.train_dir, transform=transform)
# val_set = ImageFolder(config.val_dir, transform=transform)
train_loader = torch.utils.data.DataLoader(train_set,
config.batch_size,
normalize,
]))
train_sampler = None
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(ImageFolder_iid(
valdir,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
class Identity(nn.Module):
def __init__(self, ):
super(Identity, self).__init__()
def forward(self, input):
class Config:
data_root = args.root_dir
#data_root = '/data1/xuyilun/LUNA16/data'
training_size = 12500
test_size = 12500
#training_size = 6484
#test_size = 1622
#as_expertise = np.array([[0.6, 0.8, 0.7, 0.6, 0.7],[0.6,0.6,0.7,0.9,0.6]])
#-----------------------------
lexpert = [[0.6, 0.4], [0.4, 0.6]]
hexpert = [[.8, .2], [.2, .8]]
if args.expertise == 0:
as_expertise = np.array([lexpert, lexpert, lexpert, lexpert, lexpert])
senior_num = 5
elif args.expertise == 1:
'''
as_expertise = np.array(
[[[0.9,0.1],[0.1,0.9]], [[0.8,0.2],[0.2,0.8]], [[0.6,0.4],[0.4,0.6]], [[0.7,0.3],[0.3,0.7]], [[0.7,0.3],[0.3,0.7]]])
'''
as_expertise = np.array([[[0.6, 0.4], [0.2, 0.8]],
[[0.7, 0.3], [0.4, 0.6]],
[[0.6, 0.4], [0.4, 0.6]],
[[0.7, 0.3], [0.3, 0.7]],
[[0.7, 0.3], [0.4, 0.6]]])
as_expertise_lambda = np.zeros((as_expertise.shape[0], 2))
for i in range(as_expertise.shape[0]):
as_expertise_lambda[i][0] = 4 * np.log(as_expertise[i][0][0] /
(1 - as_expertise[i][0][0]))
as_expertise_lambda[i][1] = 4 * np.log(as_expertise[i][1][1] /
(1 - as_expertise[i][1][1]))
senior_num = 5
save = args.save
missing_label = np.array([0, 0, 0, 0, 0])
missing = False
num_classes = 2
left_input_size = 28 * 28
batch_size = 16
left_learning_rate = 1e-4
right_learning_rate = 1e-4
epoch_num = 30
#########################
expert_num = args.expert_num
device_id = args.device
experiment_case = args.case
#########################
train_transform = transforms.Compose([
transforms.Resize((150, 150), interpolation=2),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(45),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
test_transform = transforms.Compose([
transforms.Resize((150, 150), interpolation=2),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
if torch.is_tensor(idx):
idx = idx.tolist()
print(idx)
path, label, pos = self.samples[idx]
img = Image.open(path)
img_arr = np.asarray(img)
img_arr = (img_arr - self.mean) / self.var
if self.transform:
img = self.transform(img)
return img, label, pos
train_dataset = AngleData(split=path_train, transform=transforms.Compose([transforms.Resize((640, 480), 2),
transforms.ToTensor()]))
test_dataset = AngleData(split=path_test, transform=transforms.Compose([transforms.Resize((640, 480), 2),
transforms.ToTensor()]))
#train_loader = DataLoader(dataset=train_dataset, batch_size=4, shuffle=True, num_workers=4)
#test_loader = DataLoader(dataset=test_dataset, batch_size=4, shuffle=False, num_workers=4)
def set_parameters_require_grad(model, feature_extracting):
if feature_extracting:
for name, param in model.named_parameters():
param.requires_grad = False
class MyModel(nn.Module):
def __init__(self):
def main():
global args, best_prec1
args = parser.parse_args()
print(args)
lrs = []
args.epochs = 0
for lr, epoch in zip(args.lr[0::2], args.lr[1::2]):
lrs.extend([lr] * int(epoch))
args.epochs += int(epoch)
args.lr = lrs
# replicable results from fixed seed.
random.seed(args.manualSeed)
torch.cuda.manual_seed_all(args.manualSeed)
torch.manual_seed(args.manualSeed)
model = resnet50(pretrained=args.pretrained,
num_classes=args.nclasses,
pool_name=args.pool_name)
print(model)
# define loss function (criterion) and optimizer
criterion = (nn.CrossEntropyLoss().cuda(), EntropyLoss().cuda())
optimizer = torch.optim.SGD(model.parameters(),
0,
momentum=args.momentum,
weight_decay=args.weight_decay)
#optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'], strict=False)
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
model = model.cuda()
cudnn.benchmark = True
# Data loading code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.Resize(512),
transforms.RandomResizedCrop(448, scale=(0.1, 1)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
train_sampler = None
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(datasets.ImageFolder(
valdir,
transforms.Compose([
transforms.Resize(512),
transforms.CenterCrop(448),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.evaluate:
if args.train_val == "train":
validate(train_loader, model, criterion, args.epochs - 1)
elif args.train_val == "val":
validate(val_loader, model, criterion, args.epochs - 1)
else:
print("wrong train_val flag")
return
return
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
prec1 = validate(val_loader, model, criterion, epoch)
best_prec1 = max(prec1, best_prec1)
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
})
from torchvision.transforms import transforms
def_train_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
brightness_jitter_transform = transforms.Compose([
transforms.ColorJitter(brightness=[0.5, 1.5]),
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
hflip_data_transform = transforms.Compose([
transforms.RandomHorizontalFlip(p=1.0),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
darkness_jitter_transform = transforms.Compose([
transforms.ColorJitter(brightness=[0.5, 0.9]),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
lightness_jitter_transform = transforms.Compose([
transforms.ColorJitter(brightness=[1.1, 1.5]),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
class CUBDataset(MNIST):
urls = [
'http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz'
]
train_transform = test_transform = transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor(), normalize])
def download(self):
"""Download the MNIST data if it doesn't exist in processed_folder already."""
from six.moves import urllib
if self._check_exists():
return
try:
os.makedirs(self.raw_folder)
os.makedirs(self.processed_folder)
except:
print("yeeted")
folder = self.raw_folder
for url in self.urls:
print('Downloading ' + url)
data = urllib.request.urlopen(url)
filename = url.rpartition('/')[2]
file_path = os.path.join(self.raw_folder, filename)
with open(file_path, 'wb') as f:
f.write(data.read())
tar = tarfile.open(file_path, "r:gz")
tar.extractall(folder)
tar.close()
folder += '/CUB_200_2011'
# process and save as torch files
print('Processing...')
training_set = []
testing_set = []
with open(folder + '/train_test_split.txt', 'r') as f:
lines = f.readlines()
for line in lines:
image_id, cat = line.split(' ')
cat = cat[0]
if cat == '1':
training_set.append(image_id)
else:
testing_set.append(image_id)
class_dict = {}
with open(folder + '/classes.txt', 'r') as f:
lines = f.readlines()
for line in lines:
class_id, class_name = line.split(' ')
class_dict[class_id] = class_name[:-1]
images_dict = {}
with open(folder + '/images.txt', 'r') as f:
lines = f.readlines()
for line in lines:
img_id, img_name = line.split(' ')
images_dict[img_id] = img_name[:-1]
image_class_dict = {}
with open(folder + '/image_class_labels.txt', 'r') as f:
lines = f.readlines()
for line in lines:
img_id, class_id = line.split(' ')
image_class_dict[img_id] = class_id[:-1]
bbox_dict = {}
with open(folder + '/bounding_boxes.txt', 'r') as f:
lines = f.readlines()
for line in lines:
img_id, x, y, w, h = line.split(' ')
bbox_dict[img_id] = (float(x), float(y), float(w),
float(h[:-1]))
train_data, train_label = [], []
test_data, test_label = [], []
for i, img_id in enumerate(training_set):
# print(i, img_id)
x = np.array(Image.open(folder + '/images/' + images_dict[img_id]))
bbox = tuple(int(x) for x in bbox_dict[img_id])
if x.shape[-1] == 1:
x = x.repeat(3, -1)
elif len(x.shape) == 2:
x = x.reshape(x.shape[0], x.shape[1], 1)
x = x.repeat(3, -1)
x = x[bbox[1]:bbox[1] + bbox[3], bbox[0]:bbox[0] + bbox[2]]
train_data.append(torch.from_numpy(x))
train_label.append(
torch.LongTensor([int(image_class_dict[img_id])]))
for i, img_id in enumerate(testing_set):
# print(i, img_id)
x = np.array(Image.open(folder + '/images/' + images_dict[img_id]))
bbox = tuple(int(x) for x in bbox_dict[img_id])
if x.shape[-1] == 1:
x = x.repeat(3, -1)
elif len(x.shape) == 2:
x = x.reshape(x.shape[0], x.shape[1], 1)
x = x.repeat(3, -1)
x = x[bbox[1]:bbox[1] + bbox[3], bbox[0]:bbox[0] + bbox[2]]
test_data.append(torch.from_numpy(x))
test_label.append(torch.LongTensor([int(image_class_dict[img_id])
]))
training_set = (train_data, train_label)
test_set = (test_data, test_label)
with open(os.path.join(self.processed_folder, self.training_file),
'wb') as f:
torch.save(training_set, f)
with open(os.path.join(self.processed_folder, self.test_file),
'wb') as f:
torch.save(test_set, f)
print('Done!')
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
if self.train:
img, target = self.train_data[index], self.train_labels[index]
else:
img, target = self.test_data[index], self.test_labels[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
if img.shape[-1] != 3:
print(img.shape)
print(index)
img = Image.fromarray(img.numpy())
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
target = target - 1
if img.size(0) == 1:
img = img.expand(3, 224, 224)
return img, target
class ImagenetPartDataset(MNIST):
urls = ['https://github.com/zqs1022/detanimalpart.git']
train_transform = test_transform = transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor(), normalize])
def download(self):
"""Download the MNIST data if it doesn't exist in processed_folder already."""
from six.moves import urllib
if self._check_exists():
return
# download files
try:
os.makedirs(os.path.join(self.root, self.raw_folder))
os.makedirs(os.path.join(self.root, self.processed_folder))
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
folder = os.path.join(self.root, self.raw_folder)
for url in self.urls:
print('Downloading ' + url)
current = os.getcwd()
os.chdir(folder)
call(['git', 'clone', url])
os.chdir(current)
# process and save as torch files
print('Processing...')
folder1 = folder
folder = os.path.join(folder, 'detanimalpart')
training_set = []
testing_set = []
for f in os.listdir(folder):
img_folder = folder + '/' + f + '/img/img'
data_file = folder + '/' + f + '/img/data.mat'
# with h5py.File(data_file, 'r') as f:
# files = list(f)
#pass
with open(folder + '/train_test_split.txt', 'r') as f:
lines = f.readlines()
for line in lines:
image_id, cat = line.split(' ')
cat = cat[0]
if cat == '1':
training_set.append(image_id)
else:
testing_set.append(image_id)
class_dict = {}
with open(folder + '/classes.txt', 'r') as f:
lines = f.readlines()
for line in lines:
class_id, class_name = line.split(' ')
class_dict[class_id] = class_name[:-1]
images_dict = {}
with open(folder + '/images.txt', 'r') as f:
lines = f.readlines()
for line in lines:
img_id, img_name = line.split(' ')
images_dict[img_id] = img_name[:-1]
image_class_dict = {}
with open(folder + '/image_class_labels.txt', 'r') as f:
lines = f.readlines()
for line in lines:
img_id, class_id = line.split(' ')
image_class_dict[img_id] = class_id[:-1]
bbox_dict = {}
with open(folder + '/bounding_boxes.txt', 'r') as f:
lines = f.readlines()
for line in lines:
img_id, x, y, w, h = line.split(' ')
bbox_dict[img_id] = (float(x), float(y), float(w),
float(h[:-1]))
train_data, train_label = [], []
test_data, test_label = [], []
for i, img_id in enumerate(training_set):
# print(i, img_id)
x = np.array(Image.open(folder + '/images/' + images_dict[img_id]))
bbox = tuple(int(x) for x in bbox_dict[img_id])
if x.shape[-1] == 1:
x = x.repeat(3, -1)
x = x[bbox[1]:bbox[1] + bbox[3], bbox[0]:bbox[0] + bbox[2]]
train_data.append(torch.from_numpy(x))
train_label.append(
torch.LongTensor([int(image_class_dict[img_id])]))
for i, img_id in enumerate(testing_set):
# print(i, img_id)
x = np.array(Image.open(folder + '/images/' + images_dict[img_id]))
bbox = tuple(int(x) for x in bbox_dict[img_id])
x = x[bbox[1]:bbox[1] + bbox[3], bbox[0]:bbox[0] + bbox[2]]
test_data.append(torch.from_numpy(x))
test_label.append(torch.LongTensor([int(image_class_dict[img_id])
]))
training_set = (train_data, train_label)
test_set = (test_data, test_label)
with open(os.path.join(self.processed_folder, self.training_file),
'wb') as f:
torch.save(training_set, f)
with open(os.path.join(self.processed_folder, self.test_file),
'wb') as f:
torch.save(test_set, f)
print('Done!')
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
if self.train:
img, target = self.train_data[index], self.train_labels[index]
else:
img, target = self.test_data[index], self.test_labels[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img.numpy())
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
target = target - 1
if img.size(0) == 1:
img = img.expand(3, 224, 224)
return img, target
def __init__(self,
path_root,
n_way,
k_shot,
k_query,
x_dim,
split,
augment='0',
test=None,
shuffle=True,
fetch_global=False):
self.n_way = n_way
self.k_shot = k_shot
self.k_query = k_query
self.x_dim = list(map(int, x_dim.split(',')))
self.split = split
self.shuffle = shuffle
self.path_root = path_root
self.fet_global = fetch_global
if augment == '0':
self.transform = transforms.Compose([
transforms.Lambda(f1),
transforms.Resize(self.x_dim[:2]),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))
])
elif augment == '1':
if self.split == 'train':
self.transform = transforms.Compose([
# lambda x: Image.open(x).convert('RGB'),
transforms.Lambda(f1),
transforms.Resize(
(self.x_dim[0] + 20, self.x_dim[1] + 20)),
transforms.RandomCrop(self.x_dim[:2]),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=.1,
contrast=.1,
saturation=.1,
hue=.1),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))
])
else:
self.transform = transforms.Compose([
# lambda x: Image.open(x).convert('RGB'),
transforms.Lambda(f1),
transforms.Resize(
(self.x_dim[0] + 20, self.x_dim[1] + 20)),
transforms.RandomCrop(self.x_dim[:2]),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))
])
self.path = os.path.join(path_root, 'images')
self.lmdb_file = os.path.join(path_root, "lmdb_data",
"%s.lmdb" % self.split)
if not os.path.exists(self.lmdb_file):
print("lmdb_file is not found, start to generate %s" %
self.lmdb_file)
self._generate_lmdb()
# read lmdb_file
self.env = lmdb.open(self.lmdb_file,
subdir=False,
readonly=True,
lock=False,
readahead=False,
meminit=False)
with self.env.begin(write=False) as txn:
self.total_sample = pyarrow.deserialize(txn.get(b'__len__'))
self.keys = pyarrow.deserialize(txn.get(b'__keys__'))
self.label2num = pyarrow.deserialize(txn.get(b'__label2num__'))
self.num2label = pyarrow.deserialize(txn.get(b'__num2label__'))
self.image_labels = [i.decode() for i in self.keys]
self.total_cls = len(self.num2label)
self.dic_img_label = defaultdict(list)
for i in self.image_labels:
self.dic_img_label[i[:9]].append(i)
self.support_set_size = self.n_way * self.k_shot # num of samples per support set
self.query_set_size = self.n_way * self.k_query
self.episode = self.total_sample // (
self.support_set_size + self.query_set_size) # how many episode
if platform.system().lower() == 'windows':
self.platform = "win"
del self.env
elif platform.system().lower() == 'linux':
self.platform = "linux"
class VOCPartDataset(MNIST):
urls = [
'http://www.stat.ucla.edu/~xianjie.chen/pascal_part_dataset/trainval.tar.gz',
'http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar'
]
train_transform = test_transform = transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor(), normalize])
def download(self):
"""Download the MNIST data if it doesn't exist in processed_folder already."""
from six.moves import urllib
if self._check_exists():
return
# download files
try:
os.makedirs(os.path.join(self.root, self.raw_folder))
os.makedirs(os.path.join(self.root, self.processed_folder))
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
folder = os.path.join(self.root, self.raw_folder)
# for url in self.urls:
# print('Downloading ' + url)
# data = urllib.request.urlopen(url)
# filename = url.rpartition('/')[2]
# file_path = os.path.join(self.root, self.raw_folder, filename)
# with open(file_path, 'wb') as f:
# f.write(data.read())
#
# if file_path.endswith('tar.gz'):
# tar = tarfile.open(file_path, "r:gz")
# else:
# tar = tarfile.open(file_path, 'r:')
# tar.extractall(folder)
# tar.close()
# process and save as torch files
print('Processing...')
image_folder = folder + '/VOCdevkit/VOC2010/JPEGImages'
annotation_folder = folder + '/Annotations_Part'
training_set = []
testing_set = []
for f in os.listdir(annotation_folder):
file_name = annotation_folder + '/' + f
file = sio.loadmat(file_name, squeeze_me=True)
pass
with open(folder + '/train_test_split.txt', 'r') as f:
lines = f.readlines()
for line in lines:
image_id, cat = line.split(' ')
cat = cat[0]
if cat == '1':
training_set.append(image_id)
else:
testing_set.append(image_id)
class_dict = {}
with open(folder + '/classes.txt', 'r') as f:
lines = f.readlines()
for line in lines:
class_id, class_name = line.split(' ')
class_dict[class_id] = class_name[:-1]
images_dict = {}
with open(folder + '/images.txt', 'r') as f:
lines = f.readlines()
for line in lines:
img_id, img_name = line.split(' ')
images_dict[img_id] = img_name[:-1]
image_class_dict = {}
with open(folder + '/image_class_labels.txt', 'r') as f:
lines = f.readlines()
for line in lines:
img_id, class_id = line.split(' ')
image_class_dict[img_id] = class_id[:-1]
bbox_dict = {}
with open(folder + '/bounding_boxes.txt', 'r') as f:
lines = f.readlines()
for line in lines:
img_id, x, y, w, h = line.split(' ')
bbox_dict[img_id] = (float(x), float(y), float(w),
float(h[:-1]))
train_data, train_label = [], []
test_data, test_label = [], []
for i, img_id in enumerate(training_set):
# print(i, img_id)
x = np.array(Image.open(folder + '/images/' + images_dict[img_id]))
bbox = tuple(int(x) for x in bbox_dict[img_id])
if x.shape[-1] == 1:
x = x.repeat(3, -1)
x = x[bbox[1]:bbox[1] + bbox[3], bbox[0]:bbox[0] + bbox[2]]
train_data.append(torch.from_numpy(x))
train_label.append(
torch.LongTensor([int(image_class_dict[img_id])]))
for i, img_id in enumerate(testing_set):
# print(i, img_id)
x = np.array(Image.open(folder + '/images/' + images_dict[img_id]))
bbox = tuple(int(x) for x in bbox_dict[img_id])
x = x[bbox[1]:bbox[1] + bbox[3], bbox[0]:bbox[0] + bbox[2]]
test_data.append(torch.from_numpy(x))
test_label.append(torch.LongTensor([int(image_class_dict[img_id])
]))
training_set = (train_data, train_label)
test_set = (test_data, test_label)
with open(
os.path.join(self.root, self.processed_folder,
self.training_file), 'wb') as f:
torch.save(training_set, f)
with open(
os.path.join(self.root, self.processed_folder, self.test_file),
'wb') as f:
torch.save(test_set, f)
print('Done!')
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
if self.train:
img, target = self.train_data[index], self.train_labels[index]
else:
img, target = self.test_data[index], self.test_labels[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img.numpy())
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
target = target - 1
if img.size(0) == 1:
img = img.expand(3, 224, 224)
return img, target
def __init__(self,
root,
mode,
resize=84,
startidx=0,
memorize=False,
elem_per_class=600,
test=False,
classes=list(range(400)),
seed=10):
"""
:param root: root path of mini-imagenet
:param mode: train, val or test
:param batchsz: batch size of sets, not batch of imgs
:param n_way:
:param k_shot:
:param k_query: num of qeruy imgs per class
:param resize: resize to
:param startidx: start to index label from startidx
"""
self.resize = resize # resize to
self.startidx = startidx # index label not from 0, but from startidx
self.mode = mode
self.memorize = memorize
# if test:
# self.elem_per_class = 600 - elem_per_class
# else:
self.elem_per_class = elem_per_class
if mode == 'train':
self.transform = transforms.Compose([
lambda x: Image.open(x).convert('RGB'),
transforms.Resize((self.resize, self.resize)),
# transforms.RandomHorizontalFlip(),
# transforms.RandomRotation(5),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))
])
else:
self.transform = transforms.Compose([
lambda x: Image.open(x).convert('RGB'),
transforms.Resize((self.resize, self.resize)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))
])
self.path = os.path.join(root, 'images') # image path
csvdata = self.loadCSV(os.path.join(root, mode + '.csv')) # csv path
self.data = []
self.labels = []
self.img2label = {}
cur_class = 0
for i, (k, v) in enumerate(csvdata.items()):
# print(classes)
if i in classes:
# print(i)
# print(len(v))
if test:
v = v[elem_per_class:2 * elem_per_class]
else:
v = v[0:elem_per_class]
self.data.append(v) # [[img1, img2, ...], [img111, ...]]
self.img2label[k] = i + self.startidx # {"img_name[:9]":label}
self.labels.append(i)
cur_class += 1
# from random import shuffle
import random
random.seed(seed)
# random.shuffle(self.data)
self.cls_num = len(self.data)
print("Total classes = ", self.cls_num)
def infer_from_img_url(self, img_url):
tik = time.time()
response = requests.get(img_url, timeout=20)
if response.status_code in [403, 404, 500]:
return {
'status': 2,
'message': 'Invalid URL',
'elapse': time.time() - tik,
'results': None
}
else:
img_content = response.content
import io
from PIL import Image
img_np = np.array(Image.open(io.BytesIO(img_content)))
img = Image.fromarray(img_np.astype(np.uint8))
preprocess = transforms.Compose([
transforms.Resize(227),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
img = preprocess(img)
img.unsqueeze_(0)
img = img.to(self.device)
outputs = self.model.forward(img)
outputs = F.softmax(outputs, dim=1)
# get TOP-K output labels and corresponding probabilities
topK_prob, topK_label = torch.topk(outputs, self.topK)
prob = topK_prob.to("cpu").detach().numpy().tolist()
_, predicted = torch.max(outputs.data, 1)
tok = time.time()
if prob[0][0] >= cfg['thresholds']['plant_recognition']:
return {
'status':
0,
'message':
'success',
'elapse':
tok - tik,
'results': [{
'name':
self.key_type[int(topK_label[0][i].to("cpu"))],
'category_id':
int(topK_label[0][i].data.to("cpu").numpy()) + 1,
'prob':
round(prob[0][i], 4)
} for i in range(self.topK)]
}
else:
return {
'status':
0,
'message':
'success',
'elapse':
tok - tik,
'results': [{
'name': "Unknown",
'category_id': -1,
'prob': round(prob[0][0], 4)
}]
}
def viz_feature_map(image_file,
hmtnet_model_file='../main/model/hmt-net-fbp.pth'):
"""
visualize the activation heat map of HMT-Net
:param image_file:
:param hmtnet_model_file:
:return:
"""
hmt_net = HMTNet()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.device_count() > 1:
print("We are running on", torch.cuda.device_count(), "GPUs!")
hmt_net = nn.DataParallel(hmt_net)
hmt_net.load_state_dict(torch.load(hmtnet_model_file))
else:
state_dict = torch.load(hmtnet_model_file)
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
# load params
hmt_net.load_state_dict(new_state_dict)
img = io.imread(image_file)
img = Image.fromarray(img.astype(np.uint8))
preprocess = transforms.Compose([
transforms.Resize(227),
transforms.RandomCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
img = preprocess(img)
img.unsqueeze_(0)
input = img.to(device)
hmt_net = hmt_net.to(device)
for idx, module in hmt_net.named_children():
if idx != 'conv2':
input = module.forward(input)
else:
break
mat = np.transpose(input[0, :, :, :].data.cpu().numpy(), [1, 2, 0])
mat = cv2.resize(mat, (224, 224))
mat = np.average(mat, axis=2)
# mat = 0.9 * mat + 0.1 * np.average(np.transpose(image, [1, 2, 0]), axis=2)
fig, ax = plt.subplots()
im = ax.imshow(mat)
plt.show()
# newly added
heatmap = np.maximum(mat, 0)
heatmap /= np.max(heatmap)
plt.matshow(heatmap)
plt.show()
img = cv2.imread(image_file)
heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
heatmap = np.uint8(255 * heatmap)
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
overlap_heatmap_img = heatmap * 0.7 + img * 0.3
cv2.imwrite('./heatmap.jpg', overlap_heatmap_img)
FASTAI_TRANSFORM = True
TRAIN_TEST_SPLIT = 0.99
BATCH_SIZE = 32
BATCH_ACCUM = 4
NUM_EPOCHS = 30
IMAGE_RESIZE = 256
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
EXPERIMENT_NAME = f"{MODEL_TYPE}_PT={PRETRAINED}_BS={BATCH_SIZE}_FAT={FASTAI_TRANSFORM}_IRS={IMAGE_RESIZE}"
MODEL_SAVE_DIR = Path(f"saved_models/{EXPERIMENT_NAME}")
MODEL_SAVE_DIR.mkdir(exist_ok=True)
tb_writer = SummaryWriter(log_dir=f"saved_models/tensorboard/{EXPERIMENT_NAME}")
if TORCHVISION_TRANSFORM:
INPUT_DIM = 256
tv_t = transforms.Compose([
transforms.Resize(INPUT_DIM),
transforms.ToTensor()])
else:
tv_t = None
if FASTAI_TRANSFORM:
fa_t_train, fa_t_eval = get_transforms(
do_flip=False,
max_rotate=25,
xtra_tfms= [
RandTransform(jitter, kwargs={'magnitude':(-0.02, 0.02)}, p=0.2, do_run=True, is_random=True),
RandTransform(perspective_warp, kwargs={'magnitude': (-0.5, 0.5)}, p=0.2, do_run=True, is_random=True),
RandTransform(squish, kwargs={}, p=0.2, do_run=True, is_random=True),
RandTransform(skew, kwargs={'magnitude': (-0.5, 0.5), 'direction': (0,7)}, p=0.2, do_run=True, is_random=True),
RandTransform(tilt, kwargs={'magnitude': (-0.5, 0.5), 'direction': (0,3)}, p=0.2, do_run=True, is_random=True),
]
manual_morph = False
output_path = init_experiment_output_dir(
"celeba64" if not args.frgc else "frgc64", "model_evaluation", args)
if args.eval:
Gx.eval()
Gz.eval()
if args.train:
Gx.train()
Gz.train()
trans = []
if args.res != 64:
trans.append(transforms.Resize(args.res))
trans.append(transforms.ToTensor())
if args.tanh:
trans.append(transforms.Lambda(lambda img: img * 2.0 - 1.0))
split = "valid"
if args.test:
split = "test"
if args.frgc:
dataset = FRGCPairsLookAlike(transform=transforms.Compose(trans))
else:
dataset = CelebaCroppedPairsLookAlike(split=split,
transform=transforms.Compose(trans))
def __init__(self, image_path, mat_file, n_way, k_shot, target_class):
self.image_path = image_path
self.mat_file = mat_file
self.images = listdir(self.image_path)
self.labels = loadmat(mat_file)['labels'][0]
self.label2img = {}
for i, j in enumerate(self.images):
if self.labels[i] in target_class:
if self.labels[i] in self.label2img:
self.label2img[self.labels[i]].append(
join(self.image_path, j))
else:
self.label2img[self.labels[i]] = [join(self.image_path, j)]
self.support_set_x = []
self.query_set_x = []
self.support_set_y = []
self.query_set_y = []
for i in range(100):
cls = np.random.choice(target_class, n_way, False)
sup_tmp_x = []
q_tmp_x = []
sup_tmp_y = []
q_tmp_y = []
for k, j in enumerate(cls):
imgs = np.random.choice(len(self.label2img[j]), 2 * k_shot,
False)
sup_tmp_x.append(
np.array(self.label2img[j])[np.array(
imgs[:k_shot])].tolist())
q_tmp_x.append(
np.array(self.label2img[j])[np.array(
imgs[k_shot:])].tolist())
ch = torch.zeros(n_way)
ch[k] = 1
ch = ch.unsqueeze(0).repeat(k_shot, 1)
sup_tmp_y.append(ch.numpy())
q_tmp_y.append(ch.numpy())
self.support_set_x.append(sup_tmp_x)
self.query_set_x.append(q_tmp_x)
self.support_set_y.append(sup_tmp_y)
self.query_set_y.append(q_tmp_y)
self.transform = transforms.Compose([
lambda x: Image.open(x).convert('RGB'),
transforms.Resize((128, 128)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
def __getitem__(self, idx):
image_path = self.file_info['path'][idx]
if not os.path.isfile(image_path):
print(image_path + ' does not exist!')
return None
image = Image.open(image_path).convert('RGB')
label_class = int(self.file_info.iloc[idx]['classes'])
label_species = int(self.file_info.iloc[idx]['species'])
sample = {'image': image, 'classes': label_class, 'species': label_species}
if self.transform:
sample['image'] = self.transform(image)
return sample
train_transforms = transforms.Compose([transforms.Resize((500, 500)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
val_transforms = transforms.Compose([transforms.Resize((500, 500)),
transforms.ToTensor()
])
train_dataset = MyDataset(root_dir= ROOT_DIR + TRAIN_DIR,
annotations_file= TRAIN_ANNO,
transform=train_transforms)
test_dataset = MyDataset(root_dir= ROOT_DIR + VAL_DIR,
annotations_file= VAL_ANNO,
transform=val_transforms)
def ext_face_feats(sphere_face,
img_path,
pretrained_model=os.path.join(cfg['model_zoo_base'],
'sphere20a.pth')):
"""
extract face features
:param sphere_face:
:param img_path:
:param pretrained_model:
:return:
"""
assert os.path.exists(pretrained_model)
if sphere_face is None:
sphere_face = SphereFaceNet()
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
sphere_face.load_state_dict(torch.load(pretrained_model))
sphere_face = sphere_face.to(device)
img = cv2.imread(img_path)
mtcnn_result = detect_face(img_path)
print(mtcnn_result)
if len(mtcnn_result) > 0:
bbox = mtcnn_result[0]['box']
margin_pixel = 10
face_region = img[bbox[0] - margin_pixel:bbox[0] + bbox[2] +
margin_pixel, bbox[1] - margin_pixel:bbox[1] +
bbox[3] + margin_pixel]
ratio = max(face_region.shape[0], face_region.shape[1]) / min(
face_region.shape[0], face_region.shape[1])
if face_region.shape[0] < face_region.shape[1]:
face_region = cv2.resize(face_region, (int(ratio * 64), 64))
face_region = face_region[:,
int((face_region.shape[0] - 64) /
2):int((face_region.shape[0] - 64) /
2) + 64]
else:
face_region = cv2.resize(face_region, (64, int(ratio * 64)))
face_region = face_region[int((face_region.shape[1] - 64) /
2):int((face_region.shape[1] - 64) /
2) + 64, :]
face_region = Image.fromarray(face_region.astype(np.uint8))
preprocess = transforms.Compose([
transforms.Resize((96, 112)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
face_region = preprocess(face_region)
face_region.unsqueeze_(0)
face_region = face_region.to(device)
x = face_region.to(device)
x = sphere_face.forward(x)
x = x.to("cpu").detach().numpy().flatten()
return {
'status': 0,
'message': 'extracted feature',
'feature': x / np.linalg.norm(x)
}
else:
return {'status': 0, 'message': 'No face detected!', 'feature': None}
image_path = self.file_info['data'][idx]
if not os.path.isfile(image_path):
print(image_path + 'does no exist')
return None
image = Image.open(image_path).convert('RGB')
label_species = int(self.file_info.iloc[idx]['label'])
sample = {'image': image, 'species': label_species}
if self.transform:
sample['image'] = self.transform(image)
return sample
train_transforms = transforms.Compose([
transforms.Resize((500, 500)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
val_transforms = transforms.Compose(
[transforms.Resize((500, 500)),
transforms.ToTensor()])
train_dataset = MyDataset(root_dir=ROOT_DIR,
annotations_file=TRAIN_ANNO,
transform=train_transforms)
test_dataset = MyDataset(root_dir=ROOT_DIR,
annotations_file=VAL_ANNO,
transform=val_transforms)
train_loader = DataLoader(dataset=train_dataset, batch_size=24, shuffle=True)
def main():
parser = argparse.ArgumentParser(
description='A demo of the ViolenceDetector')
parser.add_argument(
'--video',
type=str,
help='The path to the video or the folder containing videos',
required=True)
parser.add_argument('--lstm-weight',
type=str,
help='lstm weight file',
required=True)
parser.add_argument('--hrnet-weight',
type=str,
help='hrnet weight file',
default='weight/pose_hrnet_w48_384x288.pth')
parser.add_argument('--series-length',
type=int,
help='the length of a pose series',
default=10)
parser.add_argument(
'--min-poses',
type=int,
help=
'minimum number of poses detected for a series to be considered valid',
default=7)
parser.add_argument('--out',
type=str,
help='output file path',
required=True)
parser.add_argument('--mode',
type=str,
help="'video' or 'folder'",
default='video')
args = parser.parse_args()
if args.mode not in ['video', 'folder']:
raise ValueError('mode must be video or folder')
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
# Load yolov5
yolov5x = torch.hub.load('ultralytics/yolov5', 'yolov5x', pretrained=True)
yolov5x.to(device)
# transform
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((384, 288)),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
# load hrnet
hrnet = HRNet(48)
hrnet.to(device)
hrnet.eval()
checkpoint = torch.load(args.hrnet_weight, map_location=device)
hrnet.load_state_dict(checkpoint)
# Load pretrained lstm model
checkpoint = torch.load(args.lstm_weight)
lstm_model = DatLSTM(39, 64, 2, args.series_length)
lstm_model.to(device)
lstm_model.eval()
lstm_model.load_state_dict(checkpoint)
# Load the detector
violence_detector = ViolenceDetector(yolov5x, hrnet, lstm_model,
args.series_length, args.min_poses,
device)
if args.mode == 'video':
# Load a video
vid = Video(args.video, yolov5x, transform, device, hrnet, 'video')
# inference
violence_detector.predict_and_save(vid, args.out)
else:
for video in tqdm(os.listdir(args.video)):
vid = Video(os.path.join(args.video, video), yolov5x, transform,
device, hrnet, 'video')
violence_detector.predict_and_save(vid,
os.path.join(args.out, video))
import matplotlib.pyplot as plt
import torch
from torchvision.models import resnet18
from torch.nn import CrossEntropyLoss
from torch.optim import Adam
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
from utils import classify, evaluate, generate_datasets, train
if __name__ == '__main__':
transformations = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize([224, 224]),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
"""
train_data, val_data, test_data = generate_datasets(
train_path="P:/face-mask-dataset/face-mask-dataset/train",
test_path="P:/face-mask-dataset/face-mask-dataset/test",
train_val_ratio=0.2,
transformations=transformations
)
train_loader = DataLoader(dataset=train_data, batch_size=8, shuffle=True, drop_last=True)
val_loader = DataLoader(dataset=val_data, batch_size=8, shuffle=True, drop_last=True)
model = resnet18(pretrained=True)
]
std = [
0.15201123862047256, 0.14087982537762958, 0.139965362113942,
0.10123220339551285
]
transform['train'] = transforms.Compose([
transforms.RandomAffine(20, shear=20, resample=PIL.Image.BILINEAR),
#transforms.RandomRotation(20),
transforms.RandomResizedCrop(512),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
transform['val'] = transforms.Compose([
transforms.Resize(570),
transforms.CenterCrop(512),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
class ProteinDataset(torch.utils.data.Dataset):
def __init__(self,
root,
phase,
image_labels=None,
size=None,
transform=None):
self.root = os.path.expanduser(root)
self.phase = phase
import os
import torch
from torchvision.transforms import transforms
from torch.utils.data import Dataset
from PIL import Image
import numpy as np
transformer = transforms.Compose([
#transforms.RandomRotation(90),
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.2], std=[0.58])
])
#transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
img_path = './dataset/img/'
label_path = './dataset/label/label.txt'
def read_label(path):
label = []
txt_file = open(path, "r")
xtlines = txt_file.readlines()
for item in xtlines:
single_label = []
item = item.split(" ")
for i in range(1, 31):
if i == 30:
item[i] = item[i][:-1]
item[i] = float(item[i])
single_label.append(item[i])
from torch import Tensor
from torch.autograd import Variable, grad
from torchvision import datasets
from torchvision.transforms import transforms
from models import Generator, Discriminator, FeatureExtractor
iterations = 100
use_cuda = True
batch_size = 16
models_path = Path("models/")
models_path.mkdir(exist_ok=True, parents=True)
scale = transforms.Compose(
[transforms.ToPILImage(),
transforms.Resize(16),
transforms.ToTensor()])
transform = transforms.Compose([transforms.ToTensor()])
dataset = datasets.CIFAR100(root="data/",
train=True,
download=True,
transform=transform)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=2)
G = Generator(16, 2)
D = Discriminator()
feature_extractor = FeatureExtractor(torchvision.models.vgg19(pretrained=True))
content_criterion = nn.MSELoss()
def __init__(self,
mode,
batchsz,
n_way,
k_shot,
k_query,
imsize,
data_path,
startidx=0,
verbose=True):
"""
:param mode: train, val or test
:param batchsz: batch size of sets, not batch of imgs
:param n_way:
:param k_shot:
:param k_query: num of query imgs per class
:param imsize: resize to
:param startidx: start to index label from startidx
"""
self.batchsz = batchsz # batch of set, not batch of imgs
self.n_way = n_way # n-way
self.k_shot = k_shot # k-shot
self.k_query = k_query # for evaluation
self.support_size = self.n_way * self.k_shot # num of samples per set
self.query_size = self.n_way * self.k_query # number of samples per set for evaluation
self.imsize = imsize # resize to
self.startidx = startidx # index label not from 0, but from startidx
self.data_path = data_path
if verbose:
print(
'shuffle DB :%s, b:%d, %d-way, %d-shot, %d-query, resize:%d' %
(mode, batchsz, n_way, k_shot, k_query, imsize))
self.transform = transforms.Compose([
lambda x: Image.open(x).convert('RGB'),
transforms.Resize((self.imsize, self.imsize), Image.LANCZOS),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
# check if images are all in one folder or separated into train/val/test folders
if os.path.exists(os.path.join(data_path, 'images')):
self.subfolder_split = False
self.path_images = os.path.join(data_path, 'images') # image path
self.path_preprocessed = os.path.join(
data_path, 'images_preprocessed') # preprocessed image path
elif os.path.exists(os.path.join(data_path, 'train')):
self.subfolder_split = True
self.path_images = os.path.join(data_path, mode)
self.path_preprocessed = os.path.join(data_path,
'images_preprocessed')
if not os.path.exists(self.path_preprocessed):
os.mkdir(self.path_preprocessed)
self.path_preprocessed = os.path.join(data_path,
'images_preprocessed', mode)
if not os.path.exists(self.path_preprocessed):
os.mkdir(self.path_preprocessed)
else:
raise FileNotFoundError(
'Mini-Imagenet data not found. '
'Please add images in one of the following folder structures:'
'./data/miniimagenet/images'
'./data/miniimagenet/{train}{test}{val}'
'or specify --data_path in the arguments.')
csvdata = [self.loadCSV(os.path.join(data_path,
mode + '.csv'))] # csv path
# check if we have the images
if not os.listdir(self.path_images):
raise FileNotFoundError(
'Mini-Imagenet data not found. '
'Please add images in one of the following folder structures:'
'./data/miniimagenet/images'
'./data/miniimagenet/{train}{test}{val}'
'or specify --data_path in the arguments.')
self.data = []
self.img2label = {}
for c in csvdata:
for i, (k, v) in enumerate(c.items()):
self.data.append(v) # [[img1, img2, ...], [img111, ...]]
self.img2label[k] = i + self.startidx # {"img_name[:9]":label}
self.startidx += i + 1
self.num_classes = len(self.data)
self.create_batch(self.batchsz)
if random.uniform(0, 1) > 0.5:
i = random.randint(0, self.max_size - 1)
to_return.append(self.data[i].clone())
self.data[i] = element
else:
to_return.append(element)
return Variable(torch.cat(to_return))
#%%
save_path = './L2H_intput'
size = 256
batchSize = 10
transforms_ = [
transforms.Resize(int(size * 1.12)),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(size)
]
transforms_256 = [transforms.ToTensor()]
transforms_128 = [transforms.Resize(int(size / 2)), transforms.ToTensor()]
transforms_64 = [transforms.Resize(int(size / 4)), transforms.ToTensor()]
dataloader = DataLoader(ImageDataset(save_path,
transforms_=transforms_,
unaligned=True),
batch_size=batchSize,
shuffle=True,
drop_last=True)
#%%
files_d = sorted(glob.glob(os.path.join(save_path) + '/*.*'))
def __init__(self,
c,
nof_joints,
checkpoint_path,
resolution=(384, 288),
interpolation=cv2.INTER_CUBIC,
multiperson=True,
return_bounding_boxes=False,
max_batch_size=32,
yolo_model_def="./models/detectors/yolo/config/yolov3.cfg",
yolo_class_path="./models/detectors/yolo/data/coco.names",
yolo_weights_path="./models/detectors/yolo/weights/yolov3.weights",
device=torch.device("cpu")):
"""
Initializes a new SimpleHRNet object.
HRNet (and YOLOv3) are initialized on the torch.device("device") and
its (their) pre-trained weights will be loaded from disk.
Args:
c (int): number of channels.
nof_joints (int): number of joints.
checkpoint_path (str): path to an official hrnet checkpoint or a checkpoint obtained with `train_coco.py`.
resolution (tuple): hrnet input resolution - format: (height, width).
Default: (384, 288)
interpolation (int): opencv interpolation algorithm.
Default: cv2.INTER_CUBIC
multiperson (bool): if True, multiperson detection will be enabled.
This requires the use of a people detector (like YOLOv3).
Default: True
return_bounding_boxes (bool): if True, bounding boxes will be returned along with poses by self.predict.
Default: False
max_batch_size (int): maximum batch size used in hrnet inference.
Useless without multiperson=True.
Default: 16
yolo_model_def (str): path to yolo model definition file.
Default: "./models/detectors/yolo/config/yolov3.cfg"
yolo_class_path (str): path to yolo class definition file.
Default: "./models/detectors/yolo/data/coco.names"
yolo_weights_path (str): path to yolo pretrained weights file.
Default: "./models/detectors/yolo/weights/yolov3.weights.cfg"
device (:class:`torch.device`): the hrnet (and yolo) inference will be run on this device.
Default: torch.device("cpu")
"""
self.c = c
self.nof_joints = nof_joints
self.checkpoint_path = checkpoint_path
self.resolution = resolution # in the form (height, width) as in the original implementation
self.interpolation = interpolation
self.multiperson = multiperson
self.return_bounding_boxes = return_bounding_boxes
self.max_batch_size = max_batch_size
self.yolo_model_def = yolo_model_def
self.yolo_class_path = yolo_class_path
self.yolo_weights_path = yolo_weights_path
self.device = device
self.model = HRNet(c=c, nof_joints=nof_joints).to(device)
checkpoint = torch.load(checkpoint_path, map_location=self.device)
if 'model' in checkpoint:
self.model.load_state_dict(checkpoint['model'])
else:
self.model.load_state_dict(checkpoint)
self.model.eval()
if not self.multiperson:
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
else:
self.detector = YOLOv3(model_def=yolo_model_def,
class_path=yolo_class_path,
weights_path=yolo_weights_path,
classes=('person',),
max_batch_size=self.max_batch_size,
device=device)
self.transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((self.resolution[0], self.resolution[1])), # (height, width)
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
])
TrainLabelTransform = transforms.Compose([
transforms.RandomAffine(degrees=(-10, 10),
translate=(0.1, 0.1),
scale=(0.5, 2.),
shear=10),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.RandomResizedCrop(ImgResize,
scale=(1., 1.),
interpolation=Image.NEAREST),
transforms.ToTensor(),
])
ValImgTransform = transforms.Compose([
transforms.Resize(ImgResize),
transforms.ToTensor(),
transforms.Normalize(mean=[0.46], std=[0.10]),
])
ValLabelTransform = transforms.Compose([
transforms.Resize(ImgResize, interpolation=Image.NEAREST),
transforms.ToTensor(),
])
class PipeDataset(Dataset):
def __init__(
self,
DatasetFolderPath,
ImgTransform,
LabelTransform,
def pil_img_to_tensor_of_with_size(pil_image, img_shape):
transform = transforms.Compose(
[transforms.Resize((img_shape[1], img_shape[2])),
ToTensor()])
return transform(pil_image)
