I am trying to resume training monkAI pytorch retinanet. I have loaded with .pt file instead of actual model. The changes are made in Monk_Object_Detection/5_pytorch_retinanet/lib/train_detector.py, check for '# change' in the places where its modified.
def Model(self, model_name="resnet18",gpu_devices=[0]):
'''
User function: Set Model parameters
Available Models
resnet18
resnet34
resnet50
resnet101
resnet152
Args:
model_name (str): Select model from available models
gpu_devices (list): List of GPU Device IDs to be used in training
Returns:
None
'''
num_classes = self.system_dict["local"]["dataset_train"].num_classes();
if model_name == "resnet18":
retinanet = model.resnet18(num_classes=num_classes, pretrained=True)
elif model_name == "resnet34":
retinanet = model.resnet34(num_classes=num_classes, pretrained=True)
elif model_name == "resnet50":
# retinanet = model.resnet50(num_classes=num_classes, pretrained=True)
# change
retinanet = torch.load('/content/drive/MyDrive/Object_detection_retinanet/trained_retinanet_40.pt')
elif model_name == "resnet101":
retinanet = model.resnet101(num_classes=num_classes, pretrained=True)
elif model_name == "resnet152":
retinanet = model.resnet152(num_classes=num_classes, pretrained=True)
if self.system_dict["params"]["use_gpu"]:
self.system_dict["params"]["gpu_devices"] = gpu_devices
if len(self.system_dict["params"]["gpu_devices"])==1:
os.environ["CUDA_VISIBLE_DEVICES"] = str(self.system_dict["params"]["gpu_devices"][0])
else:
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join([str(id) for id in self.system_dict["params"]["gpu_devices"]])
self.system_dict["local"]["device"] = 'cuda' if torch.cuda.is_available() else 'cpu'
# change - added 3 lines below
if isinstance(retinanet,torch.nn.DataParallel):
retinanet = retinanet.module
retinanet.load_state_dict(torch.load('/content/drive/MyDrive/Object_detection_retinanet/trained_retinanet_40.pt'))
retinanet = retinanet.to(self.system_dict["local"]["device"])
retinanet = torch.nn.DataParallel(retinanet).to(self.system_dict["local"]["device"])
retinanet.training = True
retinanet.train()
retinanet.module.freeze_bn()
self.system_dict["local"]["model"] = retinanet;
I am getting attribute error, when I call the Model() from main function as shown below:
from train_detector import Detector
gtf = Detector()
#Loading the dataset
root_dir = './'
coco_dir = 'coco_dir'
img_dir = 'images'
set_dir ='train'
gtf.Train_Dataset(root_dir, coco_dir, img_dir, set_dir, batch_size=8, use_gpu=True)
gtf.Model(model_name="resnet50", gpu_devices=[0, 1, 2, 3])
error:
AttributeError Traceback (most recent call last)
<ipython-input-22-1a0c8d446904> in <module>()
3 if PRE_TRAINED:
4 #Initialising Model
----> 5 gtf.Model(model_name="resnet50", gpu_devices=[0, 1, 2, 3])
6 #Setting up hyperparameters
7 gtf.Set_Hyperparams(lr=0.001, val_interval=1, print_interval=20)
2 frames
/content/Monk_Object_Detection/5_pytorch_retinanet/lib/train_detector.py in Model(self, model_name, gpu_devices)
245 if isinstance(retinanet,torch.nn.DataParallel):
246 retinanet = retinanet.module
--> 247 retinanet.load_state_dict(torch.load('/content/drive/MyDrive/Object_detection_retinanet/trained_retinanet_40.pt'))
248
249 retinanet = retinanet.to(self.system_dict["local"]["device"])
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict)
1453 # copy state_dict so _load_from_state_dict can modify it
1454 metadata = getattr(state_dict, '_metadata', None)
-> 1455 state_dict = state_dict.copy()
1456 if metadata is not None:
1457 # mypy isn't aware that "_metadata" exists in state_dict
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in __getattr__(self, name)
1176 return modules[name]
1177 raise AttributeError("'{}' object has no attribute '{}'".format(
-> 1178 type(self).__name__, name))
1179
1180 def __setattr__(self, name: str, value: Union[Tensor, 'Module']) -> None:
AttributeError: 'DataParallel' object has no attribute 'copy'
kindly, help me with the solution!
