head = k[:7]
if head == 'module.':
name = k[7:] # remove `module.`
else:
name = k
new_state_dict[name] = v
net.load_state_dict(new_state_dict)
cudnn.benchmark = True
# okay now we want to add new layers
for params in net.parameters(): # set all layers to false
params.requires_grad = False
net.ClassHead = net._make_class_head(fpn_num=3, inchannels=cfg['out_channel']) # re-initiliaze the layers
net.BboxHead = net._make_bbox_head(fpn_num=5, inchannels=cfg['out_channel'])
for name, param in net.named_parameters():
print(name,param.shape)
# for name, param in net.named_parameters(): # util to print layers that are now trainable
# if param.requires_grad:
# print (name)
Plist = []
for params in net.parameters(): # stores parameters that will be updated in Plist
if params.requires_grad:
Plist.append(params)
if num_gpu > 1 and gpu_train: # now transfer net to gpu if possible
net = torch.nn.DataParallel(net).cuda()
initial_lr = args.lr
gamma = args.gamma
training_dataset = args.training_dataset
validation_dataset = args.validation_dataset
save_folder = args.save_folder
net = RetinaFace(cfg=cfg)
print("Printing net...")
i = 0
for name, params in net.named_parameters():
if (i < 219):
params.requires_grad = False
print(i, name, params.requires_grad)
i += 1
net.ClassHead = net._make_class_head(fpn_num=3,
inchannels=cfg['out_channel'])
net.BboxHead = net._make_bbox_head(fpn_num=3,
inchannels=cfg['out_channel'])
net.LandmarkHead = net._make_landmark_head(fpn_num=3,
inchannels=cfg['out_channel'])
print("printing the previous ones 220 pe jo hain")
i = 0
for name, params in net.named_parameters():
if (i == 219):
print(i, name, params)
i += 1
print(
"===================================================================\nprinting the new ones ones 220 pe jo hain"
)
