class FaceGenderAge(object):
WRN_WEIGHTS_PATH = "https://github.com/Tony607/Keras_age_gender/releases/download/V1.0/weights.18-4.06.hdf5"
def __init__(self, depth=16, width=8, face_size=64):
self.face_size = face_size
self.model = WideResNet(face_size, depth=depth, k=width)()
model_dir = os.path.join(os.getcwd(), "models")
fpath = get_file('weights.18-4.06.hdf5',
self.WRN_WEIGHTS_PATH,
cache_subdir=model_dir)
self.model.load_weights(fpath)
self.model._make_predict_function()
def predict_agge(self, frame):
inference_frame = cv2.resize(frame, (self.face_size, self.face_size))
inference_frame = inference_frame[np.newaxis, :, :, :]
results = self.model.predict(inference_frame)
predicted_genders = results[0]
ages = np.arange(0, 101).reshape(101, 1)
predicted_ages = results[1].dot(ages).flatten()
label = "Age: {}, Gender: {}".format(
int(predicted_ages), "F" if predicted_genders[0][0] > 0.5 else "M")
return label
def load_network(loc):
net_checkpoint = torch.load(loc)
start_epoch = net_checkpoint['epoch']
SavedConv, SavedBlock = what_conv_block(net_checkpoint['conv'],
net_checkpoint['blocktype'], net_checkpoint['module'])
net = WideResNet(args.wrn_depth, args.wrn_width, SavedConv, SavedBlock, num_classes=num_classes, dropRate=0).cuda()
net.load_state_dict(net_checkpoint['net'])
return net, start_epoch
def __init__(self, depth=16, width=8, face_size=64):
self.face_size = face_size
self.model = WideResNet(face_size, depth=depth, k=width)()
model_dir = os.path.join(os.getcwd(), "models")
fpath = get_file('weights.18-4.06.hdf5',
self.WRN_WEIGHTS_PATH,
cache_subdir=model_dir)
self.model.load_weights(fpath)
self.model._make_predict_function()
def __init__(self):
if torch.cuda.is_available():
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
print('WARNING: Found no valid GPU device - Running on CPU')
self.model = WideResNet(DEPTH, cfg.NUM_TRANS)
self.model.cuda(self.device)
self.criterion = torch.nn.CrossEntropyLoss().cuda(self.device)
self.optimizer = None
def build_network(Conv, Block, network):
if network == 'WideResNet':
return WideResNet(28, 10, Conv, Block, num_classes=10, dropRate=0)
elif network == 'WRN_50_2':
return WRN_50_2(Conv)
elif network == 'DARTS':
return DARTS(Conv, num_classes=10, drop_path_prob=0., auxiliary=False)
def load_network(loc, masked=False):
net_checkpoint = torch.load(loc)
start_epoch = net_checkpoint['epoch']
SavedConv, SavedBlock = what_conv_block(net_checkpoint['conv'],
net_checkpoint['blocktype'],
net_checkpoint['module'])
net = WideResNet(args.wrn_depth,
args.wrn_width,
SavedConv,
SavedBlock,
num_classes=num_classes,
dropRate=0,
masked=masked).cuda()
if masked:
new_sd = net.state_dict()
old_sd = net_checkpoint['net']
new_names = [v for v in new_sd]
old_names = [v for v in old_sd]
for i, j in enumerate(new_names):
new_sd[j] = old_sd[old_names[i]]
net.load_state_dict(new_sd)
else:
net.load_state_dict(net_checkpoint['net'])
return net, start_epoch
def load_baseline_model(self):
"""
Load a simple baseline model AND dataset
Note that this sets the model to training mode
"""
if self.args.dataset == DATASET_CIFAR_10:
imsize, in_channel, num_classes = 32, 3, 10
elif self.args.dataset == DATASET_CIFAR_100:
imsize, in_channel, num_classes = 32, 3, 100
elif self.args.dataset == DATASET_MNIST:
imsize, in_channel, num_classes = 28, 1, 10
elif self.args.dataset == DATASET_BOSTON:
imsize, in_channel, num_classes = 13, 1, 1
# init_l2 = -7 # TODO: Important to make sure this is small enough to be unregularized when starting?
if self.args.model == MODEL_RESNET18:
cnn = ResNet18(num_classes=num_classes)
elif self.args.model == MODEL_WIDERESNET:
cnn = WideResNet(depth=28,
num_classes=num_classes,
widen_factor=10,
dropRate=0.3)
elif self.args.model[:3] == MODEL_MLP:
cnn = Net(self.args.num_layers,
0.0,
imsize,
in_channel,
INIT_L2,
num_classes=num_classes,
do_classification=self.args.do_classification)
elif self.args.model == MODEL_CNN_MLP:
cnn = CNN_MLP(learning_rate=0.0001)
checkpoint = None
if self.args.load_baseline_checkpoint:
checkpoint = torch.load(self.args.load_baseline_checkpoint)
cnn.load_state_dict(checkpoint['model_state_dict'])
model = cnn.to(self.device)
if self.args.use_weight_decay:
if self.args.weight_decay_all:
num_p = sum(p.numel() for p in model.parameters())
weights = np.ones(num_p) * INIT_L2
model.weight_decay = Variable(torch.FloatTensor(weights).to(
self.device),
requires_grad=True)
else:
weights = INIT_L2
model.weight_decay = Variable(torch.FloatTensor([weights]).to(
self.device),
requires_grad=True)
model.weight_decay = model.weight_decay.to(self.device)
model.train()
return model, checkpoint
def load_baseline_model(args):
"""
:param args:
:return:
"""
if args.dataset == 'cifar10':
num_classes = 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
elif args.dataset == 'cifar100':
num_classes = 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
elif args.dataset == 'mnist':
args.datasize, args.valsize, args.testsize = 100, 100, 100
num_train = args.datasize
if args.datasize == -1:
num_train = 50000
from data_loaders import load_mnist
train_loader, val_loader, test_loader = load_mnist(args.batch_size,
subset=[args.datasize, args.valsize, args.testsize],
num_train=num_train)
if args.model == 'resnet18':
cnn = ResNet18(num_classes=num_classes)
elif args.model == 'wideresnet':
cnn = WideResNet(depth=28, num_classes=num_classes, widen_factor=10, dropRate=0.3)
checkpoint = None
if args.load_baseline_checkpoint:
checkpoint = torch.load(args.load_baseline_checkpoint)
cnn.load_state_dict(checkpoint['model_state_dict'])
model = cnn.cuda()
model.train()
return model, train_loader, val_loader, test_loader, checkpoint
def build_network(Conv, Block):
if args.network == 'WideResNet':
return WideResNet(args.wrn_depth,
args.wrn_width,
Conv,
Block,
num_classes=num_classes,
dropRate=0,
s=args.AT_split)
elif args.network == 'WRN_50_2':
return WRN_50_2(Conv)
elif args.network == 'MobileNetV2':
return MobileNetV2(Conv)
elif args.network == 'DARTS':
return DARTS(Conv, num_classes=num_classes)
def build_network(Conv, Block):
if args.network == 'WideResNet':
return WideResNet(args.wrn_depth,
args.wrn_width,
Conv,
Block,
num_classes=num_classes,
dropRate=0,
s=args.AT_split,
spectral=args.spectral
and not (args.rank_scale or args.target_ratio))
elif args.network == 'WRN_50_2':
return WRN_50_2(Conv)
elif args.network == 'MobileNetV2':
return MobileNetV2(Conv)
elif args.network == 'DARTS':
return DARTS(Conv, num_classes=num_classes)
def random_conv_sub(net, param_target):
conv_options = get_ordered_conv_options()
while ((get_no_params(net) > param_target)
or (get_no_params(net) < (param_target - (param_target * 0.05)))):
convs = []
for m in net.modules():
conv = choice(conv_options)
convs.append(conv)
net = WideResNet(args.wrn_depth,
args.wrn_width,
Conv,
MaskBlock,
num_classes=num_classes,
dropRate=0,
s=args.AT_split,
convs=convs).cuda()
return convs
def build_network(Conv, Block):
if args.network == 'WideResNet':
return WideResNet(args.wrn_depth,
args.wrn_width,
Conv,
Block,
num_classes=num_classes,
dropRate=0)
elif args.network == 'WRN_50_2':
return WRN_50_2(Conv)
elif args.network == 'DARTS':
assert not args.conv == 'Conv', 'The base network here used' \
' separable convolutions, so you probably did not mean to set this' \
' option.'
return DARTS(Conv,
num_classes=num_classes,
drop_path_prob=0.,
auxiliary=False)
elif args.network == 'MobileNetV2':
return MobileNetV2(Conv)
total_nonzero = 0.0
# With the valid epsilon, we can set sparsities of the remaning layers.
for name, mask in masks.items():
n_param = np.prod(mask.shape)
if name in dense_layers:
density_dict[name] = 1.0
else:
probability_one = epsilon * raw_probabilities[name]
density_dict[name] = probability_one
logging.info(
f"layer: {name}, shape: {mask.shape}, density: {density_dict[name]}"
)
total_nonzero += density_dict[name] * mask.numel()
logging.info(f"Overall sparsity {total_nonzero/total_params}")
return density_dict
if __name__ == "__main__":
from models.wide_resnet import WideResNet
model = WideResNet(depth=22, widen_factor=2)
logging.basicConfig()
logging.getLogger().setLevel(logging.INFO)
tim_ERK(model, density=0.2)
logging.info("========")
googleAI_ERK(model, density=0.2)
emotion_labels = get_labels('fer2013')
gender_labels = get_labels('imdb')
race_labels = get_labels('race')
font = cv2.FONT_HERSHEY_SIMPLEX
# hyper-parameters for bounding boxes shape
frame_window = 10
gender_offsets = (30, 60)
emotion_offsets = (20, 40)
# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
# race_classifier = load_model(race_model_path, compile=False)
age_gender_classifier = WideResNet(64, depth=16, k=8)()
age_gender_classifier.load_weights(age_gender_model_path)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
age_gender_target_size = age_gender_classifier.input_shape[1:3]
# race_target_size = race_classifier.input_shape[1:3]
# starting lists for calculating modes
gender_window = []
emotion_window = []
# starting video streaming
cv2.namedWindow('window_frame')
video_capture = cv2.VideoCapture(video_path)
test_transform = transforms.Compose([transforms.ToTensor(), normalize])
if args.dataset == 'cifar10':
num_classes = 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(
args.batch_size, val_split=True, augmentation=args.data_augmentation)
elif args.dataset == 'cifar100':
num_classes = 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(
args.batch_size, val_split=True, augmentation=args.data_augmentation)
if args.model == 'resnet18':
cnn = ResNet18(num_classes=num_classes)
elif args.model == 'wideresnet':
cnn = WideResNet(depth=28,
num_classes=num_classes,
widen_factor=10,
dropRate=0.3)
cnn = cnn.cuda()
criterion = nn.CrossEntropyLoss().cuda()
if args.optimizer == 'sgdm':
cnn_optimizer = torch.optim.SGD(cnn.parameters(),
lr=args.lr,
momentum=0.9,
nesterov=True,
weight_decay=args.wdecay)
elif args.optimizer == 'sgd':
cnn_optimizer = torch.optim.SGD(cnn.parameters(),
lr=args.lr,
weight_decay=args.wdecay)
class NeuralNet:
def __init__(self):
if torch.cuda.is_available():
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
print('WARNING: Found no valid GPU device - Running on CPU')
self.model = WideResNet(DEPTH, cfg.NUM_TRANS)
self.model.cuda(self.device)
self.criterion = torch.nn.CrossEntropyLoss().cuda(self.device)
self.optimizer = None
def test(self, test_gen):
self.model.eval()
batch_time = self.AverageMeter('Time', ':6.3f')
losses = self.AverageMeter('Loss', ':.4e')
top1 = self.AverageMeter('Acc@1', ':6.2f')
progress = self.ProgressMeter(len(test_gen),
batch_time,
losses,
top1,
prefix='Test: ')
# switch to evaluate mode
self.model.eval()
with torch.no_grad():
end = time.time()
for i, (input, target, _) in enumerate(test_gen):
input = input.cuda(self.device, non_blocking=True)
target = target.cuda(self.device, non_blocking=True)
# Compute output
output, _ = self.model([input, target])
loss = self.criterion(output, target)
# Measure accuracy and record loss
acc1 = self._accuracy(output, target, topk=(1))
losses.update(loss.item(), input.size(0))
top1.update(acc1[0].cpu().detach().item(), input.size(0))
# Measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# Print to screen
if i % 100 == 0:
progress.print(i)
# TODO: this should also be done with the ProgressMeter
print(' * Acc@1 {top1.avg:.3f}'.format(top1=top1))
return top1.avg
def train(self,
train_gen,
test_gen,
epochs,
lr=0.0001,
lr_plan=None,
momentum=0.9,
wd=5e-4):
self.optimizer = torch.optim.SGD(self.model.parameters(),
lr=lr,
momentum=momentum,
weight_decay=wd)
#self.optimizer = torch.optim.Adam(self.model.parameters())
for epoch in range(epochs):
self._adjust_lr_rate(self.optimizer, epoch, lr_plan)
print("=> Training (specific label)")
self._train_step(train_gen, epoch, self.optimizer)
print("=> Validation (entire dataset)")
self.test(test_gen)
def _train_step(self, train_gen, epoch, optimizer):
self.model.train()
batch_time = self.AverageMeter('Time', ':6.3f')
data_time = self.AverageMeter('Data', ':6.3f')
losses = self.AverageMeter('Loss', ':.4e')
top1 = self.AverageMeter('Acc@1', ':6.2f')
progress = self.ProgressMeter(len(train_gen),
batch_time,
data_time,
losses,
top1,
prefix="Epoch: [{}]".format(epoch))
end = time.time()
for i, (input, target, _) in enumerate(train_gen):
# measure data loading time
data_time.update(time.time() - end)
input = input.cuda(self.device, non_blocking=True)
target = target.cuda(self.device, non_blocking=True)
# Compute output
output, trans_out = self.model([input, target])
loss = self.criterion(output, target)
# measure accuracy and record loss
acc1 = self._accuracy(output, target, topk=(1))
losses.update(loss.item(), input.size(0))
top1.update(acc1[0].cpu().detach().item(), input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % 100 == 0:
progress.print(i)
def evaluate(self, eval_gen):
# switch to evaluate mode
self.model.eval()
score_func_list = []
labels_list = []
with torch.no_grad():
for i, (input, target, labels) in enumerate(eval_gen):
input = input.cuda(self.device, non_blocking=True)
#Target- the transforamation class
target = target.cuda(self.device, non_blocking=True)
#The true label
labels = labels[[
cfg.NUM_TRANS * x
for x in range(len(labels) // cfg.NUM_TRANS)
]].cuda(self.device, non_blocking=True)
# Compute output
# #TODO: Rewrite this code section, can be more efficient
output_SM = self.model([input, target])
target_mat = torch.zeros_like(output_SM[0])
target_mat[range(output_SM[0].shape[0]), target] = 1
target_SM = (target_mat * output_SM[0]).sum(dim=1).view(
-1, cfg.NUM_TRANS).sum(dim=1)
score_func_list.append(1 / cfg.NUM_TRANS * target_SM)
labels_list.append(labels)
return torch.cat(score_func_list), torch.cat(labels_list)
def _adjust_lr_rate(self, optimizer, epoch, lr_dict):
if lr_dict is None:
return
for key, value in lr_dict.items():
if epoch == key:
print("=> New learning rate set of {}".format(value))
for param_group in optimizer.param_groups:
param_group['lr'] = value
def summary(self, x_size, print_it=True):
return self.model.summary(x_size, print_it=print_it)
def print_weights(self):
self.model.print_weights()
@staticmethod
def _accuracy(output, target, topk=(1)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = 1
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
correct_k = correct[0].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
return fmtstr.format(**self.__dict__)
class ProgressMeter(object):
def __init__(self, num_batches, *meters, prefix=""):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.prefix = prefix
def print(self, batch):
entries = [self.prefix + self.batch_fmtstr.format(batch)]
entries += [str(meter) for meter in self.meters]
print('\t'.join(entries))
def _get_batch_fmtstr(self, num_batches):
num_digits = len(str(num_batches // 1))
fmt = '{:' + str(num_digits) + 'd}'
return '[' + fmt + '/' + fmt.format(num_batches) + ']'
num_workers=4)
test_set = torchvision.datasets.FashionMNIST(root="/mnt/ds/ryo",
train=False,
download=True,
transform=transform_test)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=100,
shuffle=False,
num_workers=4)
num_class = 10
############## parameter ###################
if str(args.model) == "resnet18":
model = ResNet18(num_class).to(device)
if str(args.model) == "wide-resnet28-10":
model = WideResNet(28, 10, 0, in_channels=3, labels=num_class).to(device)
# optimizing
if str(args.optimizer) == "SGD":
optimizer = optim.SGD(model.parameters(), lr=LEANRATE, momentum=0)
if str(args.optimizer) == "SAM":
base_optimizer = torch.optim.SGD
optimizer = SAM(model.parameters(),
base_optimizer,
rho=0.05,
lr=LEANRATE,
momentum=0,
weight_decay=0.0005)
criterion = nn.CrossEntropyLoss()
net_checkpoint = torch.load(loc)
start_epoch = net_checkpoint['epoch']
SavedConv, SavedBlock = what_conv_block(net_checkpoint['conv'],
net_checkpoint['blocktype'], net_checkpoint['module'])
net = WideResNet(args.wrn_depth, args.wrn_width, SavedConv, SavedBlock, num_classes=num_classes, dropRate=0).cuda()
net.load_state_dict(net_checkpoint['net'])
return net, start_epoch
if args.mode == 'teacher':
if args.resume:
print('Mode Teacher: Loading teacher and continuing training...')
teach, start_epoch = load_network('checkpoints/%s.t7' % args.teacher_checkpoint)
else:
print('Mode Teacher: Making a teacher network from scratch and training it...')
teach = WideResNet(args.wrn_depth, args.wrn_width, Conv, Block, num_classes=num_classes, dropRate=0).cuda()
get_no_params(teach)
optimizer = optim.SGD(teach.parameters(), lr=args.lr, momentum=0.9, weight_decay=args.weightDecay)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=epoch_step, gamma=args.lr_decay_ratio)
# Decay the learning rate depending on the epoch
for e in range(0,start_epoch):
scheduler.step()
for epoch in tqdm(range(start_epoch, args.epochs)):
scheduler.step()
print('Teacher Epoch %d:' % epoch)
print('Learning rate is %s' % [v['lr'] for v in optimizer.param_groups][0])
writer.add_scalar('learning_rate', [v['lr'] for v in optimizer.param_groups][0], epoch)
net.load_state_dict(net_checkpoint['net'])
return net, start_epoch
if args.mode == 'teacher':
if args.resume:
print('Mode Teacher: Loading teacher and continuing training...')
teach, start_epoch = load_network('checkpoints/%s.t7' %
args.teacher_checkpoint)
else:
print(
'Mode Teacher: Making a teacher network from scratch and training it...'
)
teach = WideResNet(args.wrn_depth,
args.wrn_width,
Conv,
Block,
num_classes=num_classes,
dropRate=0).cuda()
get_no_params(teach)
optimizer = optim.SGD(teach.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=args.weightDecay)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=epoch_step,
gamma=args.lr_decay_ratio)
# Decay the learning rate depending on the epoch
for e in range(0, start_epoch):
scheduler.step()
def detection(self):
depth = 16
width = 8
img_size = 64
model = WideResNet(img_size, depth=depth, k=width)()
model.load_weights(r'models/weights.hdf5')
detector = dlib.get_frontal_face_detector()
image_np = cv2.imdecode(np.fromfile(self.fname, dtype=np.uint8), -1)
img_h = image_np.shape[0]
img_w = image_np.shape[1]
detected = detector(image_np, 1)
gender_faces = []
labels = []
original_faces = []
photo_position = []
change_male_to_female_path = r'models/netG_A2B.pth'
change_female_to_male_path = r'models/netG_B2A.pth'
# 加载CycleGAN模型
netG_male2female = Generator(3, 3)
netG_female2male = Generator(3, 3)
netG_male2female.load_state_dict(
torch.load(change_male_to_female_path, map_location='cpu'))
netG_female2male.load_state_dict(
torch.load(change_female_to_male_path, map_location='cpu'))
# 设置模型为预测模式
netG_male2female.eval()
netG_female2male.eval()
transform_list = [
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
transform = transforms.Compose(transform_list)
"""
这段内容为图片数据处理frame
"""
if len(detected) > 0:
for i, d in enumerate(detected):
# weight和height表示原始图片的宽度和高度，因为我们需要不停地resize处理图片
# 最后要贴回到原始图片中去，w和h就用来做最后的resize
x0, y0, x1, y1, w, h = d.left(), d.top(), d.right(), d.bottom(
), d.width(), d.height()
x0 = max(int(x0 - 0.25 * w), 0)
y0 = max(int(y0 - 0.45 * h), 0)
x1 = min(int(x1 + 0.25 * w), img_w - 1)
y1 = min(int(y1 + 0.05 * h), img_h - 1)
w = x1 - x0
h = y1 - y0
if w > h:
x0 = x0 + w // 2 - h // 2
w = h
x1 = x0 + w
else:
y0 = y0 + h // 2 - w // 2
h = w
y1 = y0 + h
original_faces.append(
cv2.resize(image_np[y0:y1, x0:x1, :], (256, 256)))
gender_faces.append(
cv2.resize(image_np[y0:y1, x0:x1, :],
(img_size, img_size)))
photo_position.append([y0, y1, x0, x1, w, h])
gender_faces = np.array(gender_faces)
results = model.predict(gender_faces)
predicted_genders = results[0]
for i in range(len(original_faces)):
labels.append('F' if predicted_genders[i][0] > 0.5 else 'M')
for i, gender in enumerate(labels):
# 这几个变量用于接下来图片缩放和替换
y0, y1, x0, x1, w, h = photo_position[i]
# 将数据转换成Pytorch可以处理的格式
picture = transform(original_faces[i])
picture = Variable(picture)
input_picture = picture.unsqueeze(0)
if gender == "M":
fake_female = 0.5 * (netG_male2female(input_picture).data +
1.0)
out_img = fake_female.detach().squeeze(0)
else:
fake_male = 0.5 * (netG_female2male(input_picture).data +
1.0)
out_img = fake_male.detach().squeeze(0)
# 需要将Pytorch处理之后得到的数据，转换为OpenCV可以处理的格式
# 下面代码就是转换代码
image_numpy = out_img.float().numpy()
image_numpy = (np.transpose(image_numpy,
(1, 2, 0)) + 1) / 2.0 * 255.0
image_numpy = image_numpy.clip(0, 255)
image_numpy = image_numpy.astype(np.uint8)
# 将转换好的性别图片替换到原始图片中去
# 使用泊松融合使生成图像和背景图像浑然一体
# 使用方法：cv2.seamlessClone(src, dst, mask, center, flags)
generate_face = cv2.resize(image_numpy, (w, h))
# Create an all white mask， 感兴趣的需要替换的目标区域，精确地mask可以更好的替换，这里mask就是生成图片的大小
mask = 255 * np.ones((w, h), image_np.dtype)
# center是目标影像的中心在背景图像上的坐标！
center_y = y0 + h // 2
center_x = x0 + w // 2
center = (center_x, center_y)
output_face = cv2.seamlessClone(generate_face, image_np, mask,
center, cv2.NORMAL_CLONE)
self.out_img = output_face
def main(args):
harakiri = Harakiri()
harakiri.set_max_plateau(20)
train_loss_meter = Meter()
val_loss_meter = Meter()
val_accuracy_meter = Meter()
log = JsonLogger(args.log_path, rand_folder=True)
log.update(args.__dict__)
state = args.__dict__
state['exp_dir'] = os.path.dirname(log.path)
state['start_lr'] = state['lr']
print(state)
imagenet_mean = [0.485, 0.456, 0.406]
imagenet_std = [0.229, 0.224, 0.225]
train_dataset = ImageList(args.root_folder,
args.train_listfile,
transform=transforms.Compose([
transforms.Resize(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(imagenet_mean,
imagenet_std)
]))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=False,
num_workers=args.num_workers)
val_dataset = ImageList(args.root_folder,
args.val_listfile,
transform=transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(imagenet_mean,
imagenet_std)
]))
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
pin_memory=False,
num_workers=args.num_workers)
if args.attention_depth == 0:
from models.wide_resnet import WideResNet
model = WideResNet().finetune(args.nlabels).cuda()
else:
from models.wide_resnet_attention import WideResNetAttention
model = WideResNetAttention(args.nlabels, args.attention_depth,
args.attention_width, args.has_gates,
args.reg_weight).finetune(args.nlabels)
# if args.load != "":
# net.load_state_dict(torch.load(args.load), strict=False)
# net = net.cuda()
optimizer = optim.SGD([{
'params': model.get_base_params(),
'lr': args.lr * 0.1
}, {
'params': model.get_classifier_params()
}],
lr=args.lr,
weight_decay=1e-4,
momentum=0.9,
nesterov=True)
if args.ngpu > 1:
model = torch.nn.DataParallel(model, range(args.ngpu)).cuda()
else:
model = model.cuda()
criterion = torch.nn.NLLLoss().cuda()
def train():
"""
"""
model.train()
for data, label in train_loader:
data, label = torch.autograd.Variable(data, requires_grad=False).cuda(async=True), \
torch.autograd.Variable(label, requires_grad=False).cuda()
optimizer.zero_grad()
if args.attention_depth > 0:
output, loss = model(data)
if args.reg_weight > 0:
loss = loss.mean()
else:
loss = 0
else:
loss = 0
output = model(data)
loss += F.nll_loss(output, label)
loss.backward()
optimizer.step()
train_loss_meter.update(loss.data[0], data.size(0))
state['train_loss'] = train_loss_meter.mean()
def val():
"""
"""
model.eval()
for data, label in val_loader:
data, label = torch.autograd.Variable(data, volatile=True).cuda(async=True), \
torch.autograd.Variable(label, volatile=True).cuda()
if args.attention_depth > 0:
output, loss = model(data)
else:
output = model(data)
loss = F.nll_loss(output, label)
val_loss_meter.update(loss.data[0], data.size(0))
preds = output.max(1)[1]
val_accuracy_meter.update((preds == label).float().sum().data[0],
data.size(0))
state['val_loss'] = val_loss_meter.mean()
state['val_accuracy'] = val_accuracy_meter.mean()
best_accuracy = 0
counter = 0
for epoch in range(args.epochs):
train()
val()
harakiri.update(epoch, state['val_accuracy'])
if state['val_accuracy'] > best_accuracy:
counter = 0
best_accuracy = state['val_accuracy']
if args.save:
torch.save(model.state_dict(),
os.path.join(state["exp_dir"], "model.pytorch"))
else:
counter += 1
state['epoch'] = epoch + 1
log.update(state)
print(state)
if (epoch + 1) in args.schedule:
for param_group in optimizer.param_groups:
param_group['lr'] *= 0.1
state['lr'] *= 0.1
