Hi Team, I’m performing object detection task. I’m getting below error,
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_8392/1841461244.py in <module>
      2     _n = len(train_loader)
      3     for ix, inputs in enumerate(train_loader):
----> 4         loss, losses = train_batch(inputs, model, optimizer)
      5         loc_loss, regr_loss, loss_objectness, loss_rpn_box_reg = \
      6             [losses[k] for k in ['loss_classifier','loss_box_reg','loss_objectness','loss_rpn_box_reg']]

~\AppData\Local\Temp/ipykernel_8392/1903493105.py in train_batch(inputs, model, optimizer)
      8     losses = model(input_, targets)
      9     loss = sum(loss for loss in losses.values())
---> 10     loss.backward()
     11     optimizer.step()
     12     return loss, losses

~\miniconda3\envs\pytorch_gpu\lib\site-packages\torch\_tensor.py in backward(self, gradient, retain_graph, create_graph, inputs)
    253                 create_graph=create_graph,
    254                 inputs=inputs)
--> 255         torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
    256 
    257     def register_hook(self, hook):

~\miniconda3\envs\pytorch_gpu\lib\site-packages\torch\autograd\__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)
    147     Variable._execution_engine.run_backward(
    148         tensors, grad_tensors_, retain_graph, create_graph, inputs,
--> 149         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag
    150 
    151 

RuntimeError: Found dtype Double but expected Float

please find the code which I used,
from torch_snippets import *
from PIL import Image
import glob, numpy as np, cv2, warnings,random, albumentations as A
from albumentations.pytorch.transforms import ToTensorV2
warnings.filterwarnings('ignore')

def seed_everything(seed):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = True
    
seed_everything(42)

IMAGE_ROOT = 'images'
DF_RAW = pd.read_csv('train_labels.csv')
DF_RAW['image_id'] = DF_RAW['filename'].apply(lambda x: x.split('.')[0])
DF_RAW['labels'] = DF_RAW['class'].apply(lambda x: 1 if x=='car' else 0)

label2target = {l:t+1 for t,l in enumerate(DF_RAW['class'].unique())}
label2target['background'] = 0
target2label = {t:l for l,t in label2target.items()}
background_class = label2target['background']
num_classes = len(label2target)

def preprocess_image(img):
    img = torch.tensor(img).permute(2,0,1)
    return img.to(device).float()

class OpenDataset(torch.utils.data.Dataset):
    def __init__(self, df, image_folder=IMAGE_ROOT,transforms = None):
        self.root = image_folder
        self.df = df
        self.unique_images = df['image_id'].unique()
        self.transforms = transforms
    def __len__(self): return len(self.unique_images)
    def __getitem__(self, ix):
        image_id = self.unique_images[ix]
        #image_path = f'{self.root}/{image_id}.jpg'
        image = cv2.imread(f'{self.root}/{image_id}.jpg', cv2.IMREAD_COLOR)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)
        image = np.array(image)/255
        df = self.df.copy()
        df = df[df['image_id'] == image_id]
        boxes = df[['xmin','ymin','xmax','ymax']].values
        classes = df['class'].values
        target = {}
        target["boxes"] = boxes
        #target["labels"] = torch.Tensor([label2target[i] for i in classes]).long()
        target["labels"] = torch.Tensor([label2target[i] for i in classes]).long()

        if self.transforms:
            sample = {
                'image': image,
                'bboxes': target['boxes'],
                'labels': torch.Tensor([label2target[i] for i in classes]).long()
            }
            sample = self.transforms(**sample)

            target["boxes"] = torch.as_tensor(sample['bboxes'])
            #target['boxes'] = torch.stack(tuple(map(torch.tensor, zip(*sample['bboxes'])))).permute(1, 0)
        image = preprocess_image(image)
        
        return image, target
    def collate_fn(self, batch):
        return tuple(zip(*batch)) 


def get_train_transform():
    return A.Compose([
        A.HorizontalFlip(p=0.5),
        ToTensorV2(p=1.0)
    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})

def get_valid_transform():
    return A.Compose([
        ToTensorV2(p=1.0)
    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})

from sklearn.model_selection import train_test_split
trn_ids, val_ids = train_test_split(DF_RAW['image_id'].unique(), test_size=0.1, random_state=99)
trn_df, val_df = DF_RAW[DF_RAW['image_id'].isin(trn_ids)], DF_RAW[DF_RAW['image_id'].isin(val_ids)]
print(len(trn_df), len(val_df))
 
train_ds = OpenDataset(trn_df,transforms = get_train_transform())
test_ds = OpenDataset(val_df,transforms = get_valid_transform())
 
train_loader = DataLoader(train_ds, batch_size=2, collate_fn=train_ds.collate_fn, drop_last=True,shuffle=True)
test_loader = DataLoader(test_ds, batch_size=2, collate_fn=test_ds.collate_fn, drop_last=True,shuffle=False)

import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
 
device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
def get_model():
    model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
    in_features = model.roi_heads.box_predictor.cls_score.in_features
    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
    return model


# Defining training and validation functions for a single batch
def train_batch(inputs, model, optimizer):
    model.train()
    input_, targets = inputs
    input_ = list(image.to(device) for image in input_)
    targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
    optimizer.zero_grad()
    losses = model(input_, targets)
    loss = sum(loss for loss in losses.values())
    loss.backward()
    optimizer.step()
    return loss, losses
 
@torch.no_grad() # this will disable gradient computation in the function below
def validate_batch(inputs, model):
    model.train() # to obtain the losses, model needs to be in train mode only. # #Note that here we are not defining the model's forward method 
#and hence need to work per the way the model class is defined
    input_, targets = inputs
    input_ = list(image.to(device) for image in input_)
    targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
 
    optimizer.zero_grad()
    losses = model(input_, targets)
    loss = sum(loss for loss in losses.values())
    return loss, losses


model = get_model().to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=0.005,
                            momentum=0.9, weight_decay=0.0005)
n_epochs = 5
log = Report(n_epochs)

for epoch in range(n_epochs):
    _n = len(train_loader)
    for ix, inputs in enumerate(train_loader):
        loss, losses = train_batch(inputs, model, optimizer)
        loc_loss, regr_loss, loss_objectness, loss_rpn_box_reg = \
            [losses[k] for k in ['loss_classifier','loss_box_reg','loss_objectness','loss_rpn_box_reg']]
        pos = (epoch + (ix+1)/_n)
        log.record(pos, trn_loss=loss.item(), trn_loc_loss=loc_loss.item(), 
                   trn_regr_loss=regr_loss.item(), trn_objectness_loss=loss_objectness.item(),
                   trn_rpn_box_reg_loss=loss_rpn_box_reg.item(), end='\r')
 
    _n = len(test_loader)
    for ix,inputs in enumerate(test_loader):
        loss, losses = validate_batch(inputs, model)
        loc_loss, regr_loss, loss_objectness, loss_rpn_box_reg = \
          [losses[k] for k in ['loss_classifier','loss_box_reg','loss_objectness','loss_rpn_box_reg']]
        pos = (epoch + (ix+1)/_n)
        log.record(pos, val_loss=loss.item(), val_loc_loss=loc_loss.item(), 
                  val_regr_loss=regr_loss.item(), val_objectness_loss=loss_objectness.item(),
                  val_rpn_box_reg_loss=loss_rpn_box_reg.item(), end='\r')
    if (epoch+1)%(n_epochs//5)==0: log.report_avgs(epoch+1)


I’m trying to fix this, it would be help if anyone can help me here.
Thanks in advance.