Repository: Project-MONAI/MONAI
File: monai/apps/auto3dseg/data_analyzer.py (Python)

Code before:
                allowed_shape_difference = self.extra_params.pop("allowed_shape_difference", 5)
                transform_list.append(
                    EnsureSameShaped(
                        keys=self.label_key,
                        source_key=self.image_key,
                        allowed_shape_difference=allowed_shape_difference,
                    )
                )

        transform = Compose(transform_list)
        files, _ = datafold_read(datalist=self.datalist, basedir=self.dataroot, fold=-1, key=key)
        if world_size <= len(files):
            files = partition_dataset(data=files, num_partitions=world_size)[rank]
        else:
            files = partition_dataset(data=files, num_partitions=len(files))[rank] if rank < len(files) else []
        dataset = Dataset(data=files, transform=transform)
        dataloader = DataLoader(
            dataset,
            batch_size=1,
            shuffle=False,
            num_workers=self.worker,
            collate_fn=no_collation,
            pin_memory=self.device.type == "cuda",
        )
        result_bycase: dict[DataStatsKeys, Any] = {DataStatsKeys.SUMMARY: {}, DataStatsKeys.BY_CASE: []}
        device = self.device if self.device.type == "cpu" else torch.device(f"cuda", rank)
        if not has_tqdm:
            warnings.warn("tqdm is not installed. not displaying the caching progress.")

        for batch_data in tqdm(dataloader) if (has_tqdm and rank == 0) else dataloader:
            batch_data = batch_data[0]
            try:
                batch_data[self.image_key] = batch_data[self.image_key].to(device)
                if self.label_key is not None:
                    label = batch_data[self.label_key]
                    label = torch.argmax(label, dim=0) if label.shape[0] > 1 else label[0]
                    batch_data[self.label_key] = label.to(device)

                d = summarizer(batch_data)
            except BaseException:
                logger.info(f"Unable to process data {batch_data['image_meta_dict']['filename_or_obj']} on {device}.")
                if self.device.type == "cuda":
                    logger.info("DataAnalyzer `device` set to GPU execution hit an exception. Falling back to `cpu`.")
                    batch_data[self.image_key] = batch_data[self.image_key].to("cpu")
                    if self.label_key is not None:
                        label = batch_data[self.label_key]
                        label = torch.argmax(label, dim=0) if label.shape[0] > 1 else label[0]
                        batch_data[self.label_key] = label.to("cpu")
                    d = summarizer(batch_data)

            stats_by_cases = {

Review comment (suggestion):
this looks like an issue, could you pls confirm @heyufan1995 
```suggestion
        device = self.device if self.device.type == "cpu" else torch.device(f"cuda:{rank}")
```

Code after:
            transform_list = [
                LoadImaged(keys=keys, ensure_channel_first=True, image_only=True),
                EnsureTyped(keys=keys, data_type="tensor", dtype=torch.float),
                Orientationd(keys=keys, axcodes="RAS"),
            ]
            if self.label_key is not None:
                allowed_shape_difference = self.extra_params.pop("allowed_shape_difference", 5)
                transform_list.append(
                    EnsureSameShaped(
                        keys=self.label_key,
                        source_key=self.image_key,
                        allowed_shape_difference=allowed_shape_difference,
                    )
                )

        transform = Compose(transform_list)
        files, _ = datafold_read(datalist=self.datalist, basedir=self.dataroot, fold=-1, key=key)
        if world_size <= len(files):
            files = partition_dataset(data=files, num_partitions=world_size)[rank]
        else:
            files = partition_dataset(data=files, num_partitions=len(files))[rank] if rank < len(files) else []
        dataset = Dataset(data=files, transform=transform)
        dataloader = DataLoader(
            dataset,
            batch_size=1,
            shuffle=False,
            num_workers=self.worker,
            collate_fn=no_collation,
            pin_memory=self.device.type == "cuda",
        )
        result_bycase: dict[DataStatsKeys, Any] = {DataStatsKeys.SUMMARY: {}, DataStatsKeys.BY_CASE: []}
        device = self.device if self.device.type == "cpu" else torch.device("cuda", rank)
        if not has_tqdm:
            warnings.warn("tqdm is not installed. not displaying the caching progress.")

        for batch_data in tqdm(dataloader) if (has_tqdm and rank == 0) else dataloader:
            batch_data = batch_data[0]
            try:
                batch_data[self.image_key] = batch_data[self.image_key].to(device)
                if self.label_key is not None:
                    label = batch_data[self.label_key]
                    label = torch.argmax(label, dim=0) if label.shape[0] > 1 else label[0]
                    batch_data[self.label_key] = label.to(device)
                d = summarizer(batch_data)
            except BaseException:
                if "image_meta_dict" in batch_data.keys():
                    filename = batch_data["image_meta_dict"]["filename_or_obj"]
                else:
                    filename = batch_data[self.image_key].meta["filename_or_obj"]
                logger.info(f"Unable to process data {filename} on {device}.")
                if self.device.type == "cuda":