[Python] ValueError: too many values to unpack (expected 2) when loading multiple clips for...

i run this script to load multiple clips from dataset for validation

def collate_fn(batch):
batch_clips, batch_targets = zip(*batch)
batch_clips = [clip for multi_clips in batch_clips for clip in multi_clips]
batch_targets = [target for multi_targets in batch_targets for target in multi_targets]

batch_clips = torch.stack(batch_clips, 0)
batch_targets = torch.stack(batch_targets, 0)

return batch_clips, batch_targets
class ActionDatasetMultiClips(ActionDataset):
def __loading(self, path, frame_indices):
clips = []
for clip_frame_indices in frame_indices:
valid_indices = [i for i in clip_frame_indices if i < len(path)]
if not valid_indices:
raise ValueError(f"No valid frame indices for clip: {clip_frame_indices}")
clip = [Image.open(path).convert('RGB') for i in valid_indices]
if self.spatial_transform is not None:
self.spatial_transform.randomize_parameters()
clip = [self.spatial_transform(img) for img in clip]
clips.append(torch.stack(clip, 0))
return clips

def __getitem__(self, idx):
sequence_key = list(self.sequences.keys())[idx]
sequence_data = self.sequences[sequence_key]

frame_paths = [data['image_path'] for data in sequence_data]

if self.temporal_transform:
frame_indices = list(range(len(frame_paths)))
frame_indices = self.temporal_transform(frame_indices)

if isinstance(frame_indices[0], list):
frame_indices = [i for sublist in frame_indices for i in sublist]

frame_indices = [i for i in frame_indices if i < len(frame_paths)]
if not frame_indices:
raise ValueError(f"No valid frame indices for sequence {sequence_key}")

clips = self.__loading(frame_paths, [frame_indices])

action_name = sequence_data[0]['action_name']
condition = sequence_data[0]['condition']
action_name_id = self.action_name_to_id[action_name]
condition_id = self.condition_name_to_id[condition]

targets = [torch.tensor([action_name_id, condition_id], dtype=torch.long) for _ in range(len(clips))]
return clips, targets


and this is the function to train one epoch

def validate_one_epoch(model, dataloader, criterion, device):
model.eval()
total_loss = 0.0
action_correct = 0
condition_correct = 0
total_samples = 0

with torch.no_grad():
for frames, labels in tqdm(dataloader, desc="Validation", leave=False):
frames, labels = frames.to(device), labels.to(device)

action_logits, condition_logits = model(frames)

# Calcul des pertes
action_loss = criterion(action_logits, labels[:, 0])
condition_loss = criterion(condition_logits, labels[:, 1])
loss = action_loss + condition_loss

# Calcul de l'accuracy
accuracy_sequence, accuracy_condition, accuracy_global = calculate_accuracy(
action_logits, condition_logits, labels[:, 0], labels[:, 1]
)

# Accumulation des métriques
total_loss += loss.item()
action_correct += accuracy_sequence * labels.size(0)
condition_correct += accuracy_condition * labels.size(0)
total_samples += labels.size(0)

# Calcul des moyennes
avg_loss = total_loss / len(dataloader)
action_acc = action_correct / total_samples
condition_acc = condition_correct / total_samples

return avg_loss, action_acc, condition_acc


and the dataloader

val_dataset = ActionDatasetMultiClips(
annotation_path="D:/organized_files/val.csv", spatial_transform=spatial_augmentation,
temporal_transform=temporal_augmentation,max_sequence_length=16)

val_loader = DataLoader(val_dataset, batch_size=(BATCH_SIZE//VAL_SAMPLE), shuffle=False,collate_fn=collate_fn,num_workers=8)
return val_loader, val_dataset


but when trying to acces the val dataloader

Traceback (most recent call last):

File ~\anaconda3\envs\cnnsvm\Lib\site-packages\spyder_kernels\customize\utils.py:209 in
exec_encapsulate_locals
exec_fun(compile(code_ast, filename, "exec"), globals)

File d:\organized_files\le\train.py:118
val_loss, val_action_acc, val_condition_acc = validate_one_epoch(

File d:\organized_files\transformer_exemple\utils.py:86 in validate_one_epoch
for frames, labels in tqdm(dataloader, desc="Validation", leave=False):

ValueError: too many values to unpack (expected 2)


I suppose that the loader returns other elements then the clips and labels this the output of the val_dataloader

Batch contents:​

Item 0: <class 'torch.Tensor'> - torch.Size([2, 32, 3, 224, 224]) Item 1: <class 'torch.Tensor'> - torch.Size([2, 2]) Batch 1: Frames shape: torch.Size([2, 32, 3, 224, 224]) Labels shape: torch.Size([2, 2]) First frame values (min, max, mean): -2.0665297508239746, 2.517995834350586, 0.5251008868217468 Labels: tensor([[0, 1], [1, 1]])`

how to fix this ?

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