🐛 Bug I wrote a custom collect_fn to filter out bad samples. But in some batches, all samples are being removed so it returns an empty list. But in the default_collate(batch) fn, it assumes the batch is not None since it immediately read...
(dataset, batch_size=2, num_workers=3, shuffle=True, collate_fn=custom_collate_fn) assert dataset._datasets[0].shuffle assert dataset._datasets[1].shuffle dataloader_iter = iter(dataloader) assert next(dataloader_iter) == "received" assert dataloader._num_samples_yielded_combined[0] == [2...
--See https://docs.microsoft.com/en-us/sql/t-sql/statements/alter-database-transact-sql?view=sql-server-ver15 before doing this IF NOT EXISTS ( SELECT 1 FROM sys.databases WHERE name= Db_Name() AND collation_name='SQL_Latin1_General_CP1_CI_AI') ALTER DATABASE ...
_memory(data) /opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index) 45 else: 46 data = self.dataset[possibly_batched_index] ---> 47 return self.collate_fn(data) /opt/conda/lib/python3.8/site-packages/transformers/data/data_...
I've included a .zip with said test dataset, in hopes someone can test it on their machine, post their results (I've tried both the 2D and 3D keypoint formats to no avail). test_dataset.zip When checking the collate_fn function, all of the data appears normal (the class tensors an...
❔Question I have a custom dataset that has imbalanced classes: ~10K A, ~10K B, and ~100 C. It is hard to detect C class in the test set. The bounding box works on C object but with wrong lables. Is there anyway to add weight to the C cla...
() ) # collate_fn needs for batch def collate_fn(batch): return tuple(zip(*batch)) # Batch size train_batch_size = 2 # own DataLoader data_loader = torch.utils.data.DataLoader(my_dataset, batch_size=train_batch_size, shuffle=True, num_workers=2, collate_fn=collate_fn, pin_memory=...
CollateFuncion val_dataloader: type: DataLoader dataset: type: CocoDetection img_folder: /data/yourdataset/val ann_file: /data/yourdataset/val/val.json return_masks: False transforms: type: Compose ops: ~ shuffle: False num_workers: 4 drop_last: False collate_fn: type: BatchImageCollateFunc...
export async function CollateResourceDiffs( resources , mutationInfo : StackMutationInfo /* create/update/delete */ ){ const provider = CategoryProviders.CLOUDFORMATION; let resourceDiffs : ResourceDiff[] = []; for await (const resource of resources) { resourceDiffs.push( new ResourceDiff( resourc...
dataloader = DataLoader(dataset_module["eval_dataset"], batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True) criterion = torch.nn.CrossEntropyLoss(reduction="none") total_ppl = 0 perplexities = [] 6 changes: 3 additions & 3 deletions 6 scripts/length_cdf.py Original file...