batch_size = 10
lr_G = 4e-4
lr_D = 4e-4
max_epoch = 2000
show_step = 5
save_epoch = 1
model_save_path = './saved_models/'
optim_level = 'O0'
# fine_tune_with_identity = False
device = torch.device('cuda')
# torch.set_num_threads(12)
G = AEI_Net(c_id=512).to(device)
D = MultiscaleDiscriminator(input_nc=3, n_layers=6, norm_layer=torch.nn.InstanceNorm2d).to(device)
G.train()
D.train()
arcface = Backbone(50, 0.6, 'ir_se').to(device)
arcface.eval()
arcface.load_state_dict(torch.load('./id_model/model_ir_se50.pth', map_location=device), strict=False)
opt_G = optim.Adam(G.parameters(), lr=lr_G, betas=(0, 0.999))
opt_D = optim.Adam(D.parameters(), lr=lr_D, betas=(0, 0.999))
G, opt_G = amp.initialize(G, opt_G, opt_level=optim_level)
D, opt_D = amp.initialize(D, opt_D, opt_level=optim_level)
try:
lr_D = 4e-4
show_step = 50
save_epoch = 1
model_save_path = './saved_models/'
optim_level = 'O1'
min_iter = 0
max_iter = 2000000
C_adv = 1.0
C_id = 5.0
C_attr = 10.0
C_rec = 10.0
device = torch.device('cuda')
G = AEI_Net(c_id=512,
aad_norm=torch.nn.BatchNorm2d,
encoder_norm=torch.nn.BatchNorm2d).to(device)
#mynorm = lambda x: torch.nn.GroupNorm(x // 16, x)
#G = AEI_Net(c_id=512, encoder_norm=mynorm, aad_norm=mynorm).to(device)
D = MultiscaleDiscriminator(input_nc=3,
n_layers=6,
norm_layer=torch.nn.InstanceNorm2d).to(device)
G.train()
D.train()
arcface = Backbone(50, 0.6, 'ir_se').to(device)
arcface.eval()
arcface.load_state_dict(torch.load('./face_modules/model_ir_se50.pth',
map_location=device),
strict=False)
arcface.requires_grad_(False)
while sucess:
sucess, frame = cap.read()
frame_path = os.path.join(target_frames_path, '%08d.jpg' % frame_count)
cv2.imwrite(frame_path, frame)
if (frame_count % 50 == 0):
print("%dth frame has been processed" % frame_count)
frame_count += 1
except Exception as e:
print("video has been prcessed to frames")
cap.release()
print("start load models")
# load models
detector = MTCNN()
device = torch.device('cuda')
G = AEI_Net(c_id=512)
G.eval()
G.load_state_dict(
torch.load('./saved_models/G_latest.pth',
map_location=torch.device('cpu')))
G = G.cuda()
arcface = Backbone(50, 0.6, 'ir_se').to(device)
arcface.eval()
arcface.load_state_dict(torch.load('./face_modules/model_ir_se50.pth',
map_location=device),
strict=False)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
batch_size = 10
lr_G = 4e-4
lr_D = 4e-4
max_epoch = 2000
show_step = 3
save_epoch = 1
model_save_path = './saved_models/'
optim_level = 'O0'
# fine_tune_with_identity = False
device = torch.device('cuda')
# torch.set_num_threads(12)
G = AEI_Net(c_id=512).to(device)
D = MultiscaleDiscriminator(input_nc=3,
n_layers=6,
norm_layer=torch.nn.InstanceNorm2d).to(device)
G.train()
D.train()
arcface = Backbone(50, 0.65, 'ir_se').to(device)
arcface.eval()
arcface.load_state_dict(torch.load('./id_model/model_ir_se50.pth',
map_location=device),
strict=False)
# weight_decay (float, optional)：权重衰减(如L2惩罚)(默认: 0)
opt_G = optim.Adam(G.parameters(),
lr=lr_G,
betas=(0, 0.999),
vis = visdom.Visdom(server='127.0.0.1', env='faceshifter', port=8099)
batch_size = 16
lr_G = 4e-4
lr_D = 4e-4
max_epoch = 2000
show_step = 100
save_epoch = 1
#model_save_path = './saved_models/'
optim_level = 'O1'
# fine_tune_with_identity = False
device = torch.device('cuda')
# torch.set_num_threads(12)
G = AEI_Net(c_id=512).to(device)
D = MultiscaleDiscriminator(input_nc=3,
n_layers=6,
norm_layer=torch.nn.InstanceNorm2d).to(device)
G.train()
D.train()
arcface = Backbone(50, 0.6, 'ir_se').to(device)
arcface.eval()
arcface.load_state_dict(torch.load('./face_modules/model_ir_se50.pth',
map_location=device),
strict=False)
opt_G = optim.Adam(G.parameters(), lr=lr_G, betas=(0, 0.999))
opt_D = optim.Adam(D.parameters(), lr=lr_D, betas=(0, 0.999))
def serve():
data = {"success": False}
detector = MTCNN()
device = torch.device('cuda')
G = AEI_Net(c_id=512)
G.eval()
G.load_state_dict(
torch.load('./saved_models/G_latest.pth',
map_location=torch.device('cpu')))
G = G.cuda()
arcface = Backbone(50, 0.6, 'ir_se').to(device)
arcface.eval()
arcface.load_state_dict(torch.load('./face_modules/model_ir_se50.pth',
map_location=device),
strict=False)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
if flask.request.method == 'POST':
st = time.time()
metadata = flask.request.form
source_image = metadata['source_image']
target_image = metadata['target_image']
source_image = base64.b64decode(source_image.encode('utf-8'))
Xs_raw = cv2.imdecode(np.frombuffer(source_image, np.uint8),
cv2.IMREAD_COLOR)
target_image = base64.b64decode(target_image.encode('utf-8'))
Xt_raw = cv2.imdecode(np.frombuffer(target_image, np.uint8),
cv2.IMREAD_COLOR)
Xs = detector.align(Image.fromarray(Xs_raw[:, :, ::-1]),
crop_size=(256, 256))
Xs_raw = np.array(Xs)[:, :, ::-1]
Xs = test_transform(Xs)
Xs = Xs.unsqueeze(0).cuda()
with torch.no_grad():
embeds = arcface(
F.interpolate(Xs[:, :, 19:237, 19:237], (112, 112),
mode='bilinear',
align_corners=True))
Xt, trans_inv = detector.align(Image.fromarray(Xt_raw[:, :, ::-1]),
crop_size=(256, 256),
return_trans_inv=True)
Xt_raw = Xt_raw.astype(np.float) / 255.0
Xt = test_transform(Xt)
Xt = Xt.unsqueeze(0).cuda()
mask = np.zeros([256, 256], dtype=np.float)
for i in range(256):
for j in range(256):
dist = np.sqrt((i - 128)**2 + (j - 128)**2) / 128
dist = np.minimum(dist, 1)
mask[i, j] = 1 - dist
mask = cv2.dilate(mask, None, iterations=20)
with torch.no_grad():
Yt, _ = G(Xt, embeds)
Yt = Yt.squeeze().detach().cpu().numpy().transpose([1, 2, 0
]) * 0.5 + 0.5
Yt = Yt[:, :, ::-1]
Yt_trans_inv = cv2.warpAffine(
Yt,
trans_inv, (np.size(Xt_raw, 1), np.size(Xt_raw, 0)),
borderValue=(0, 0, 0))
mask_ = cv2.warpAffine(mask,
trans_inv,
(np.size(Xt_raw, 1), np.size(Xt_raw, 0)),
borderValue=(0, 0, 0))
mask_ = np.expand_dims(mask_, 2)
Yt_trans_inv = mask_ * Yt_trans_inv + (1 - mask_) * Xt_raw
img_data = Yt_trans_inv * 255
retval, buffer = cv2.imencode('.jpg', img_data)
pic_str = base64.b64encode(buffer)
pic_str = pic_str.decode()
data['success'] = True
data['image'] = pic_str
st = time.time() - st
print(f'process time: {st} sec')
return flask.jsonify(data)
from apex import amp
import visdom
if __name__ == '__main__':
# vis = visdom.Visdom(server='127.0.0.1', env='faceshifter', port=8099)
batch_size = 32
lr = 4e-4
max_epoch = 2000
show_step = 10
save_epoch = 1
model_save_path = './saved_models/'
optim_level = 'O0'
device = torch.device('cuda')
G = AEI_Net(c_id=512).to(device)
G.eval()
G.load_state_dict(torch.load('./saved_models/G_latest-e.pth', map_location=torch.device('cpu')), strict=True)
net = HearNet()
net.train()
net.to(device)
arcface = Backbone(50, 0.6, 'ir_se').to(device)
arcface.eval()
arcface.load_state_dict(torch.load('./id_model/model_ir_se50.pth', map_location=device), strict=False)
opt = optim.Adam(net.parameters(), lr=lr, betas=(0, 0.999))
net, opt = amp.initialize(net, opt, opt_level=optim_level)
batch_size = 4
lr_G = 4e-4
lr_D = 4e-4
max_epoch = 2000
show_step = 10
save_epoch = 1
model_save_path = './saved_models/'
optim_level = 'O1'
fine_tune_with_identity = False
device = torch.device('cuda')
# torch.set_num_threads(12)
G = AEI_Net(c_id=512).to(device)
D = MultiscaleDiscriminator(input_nc=3, n_layers=5, norm_layer=torch.nn.InstanceNorm2d).to(device)
G.train()
D.train()
arcface = Backbone(50, 0.6, 'ir_se').to(device)
arcface.eval()
arcface.load_state_dict(torch.load('./face_modules/model_ir_se50.pth', map_location=device), strict=False)
opt_G = optim.Adam(G.parameters(), lr=lr_G, betas=(0, 0.999), weight_decay=1e-4)
opt_D = optim.Adam(D.parameters(), lr=lr_D, betas=(0, 0.999), weight_decay=1e-4)
G, opt_G = amp.initialize(G, opt_G, opt_level=optim_level)
D, opt_D = amp.initialize(D, opt_D, opt_level=optim_level)
try: