🔧 Batch Count¶
Documentation¶
- Class name:
BatchCount+
- Category:
essentials
- Output node:
False
The BatchCount+ node is designed to count the number of elements in a batch. It can handle various data structures, including tensors, dictionaries, and lists, making it versatile for different types of batched data.
Input types¶
Required¶
batch
- The 'batch' parameter represents the batch of data whose size is to be counted. It can be a tensor, a dictionary containing 'samples', or a list, accommodating a wide range of data structures.
- Comfy dtype:
*
- Python dtype:
Union[torch.Tensor, Dict[str, Any], List[Any]]
Output types¶
int
- Comfy dtype:
INT
- This output represents the count of elements in the input batch, providing a straightforward way to determine the batch size.
- Python dtype:
int
- Comfy dtype:
Usage tips¶
- Infra type:
CPU
- Common nodes: unknown
Source code¶
class BatchCount:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"batch": (any, {}),
},
}
RETURN_TYPES = ("INT",)
FUNCTION = "execute"
CATEGORY = "essentials"
def execute(self, batch):
count = 0
if hasattr(batch, 'shape'):
count = batch.shape[0]
elif isinstance(batch, dict) and 'samples' in batch:
count = batch['samples'].shape[0]
elif isinstance(batch, list) or isinstance(batch, dict):
count = len(batch)
return (count, )