73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
import time
|
|
from typing import Any, Dict
|
|
import torch
|
|
import ast
|
|
import copy
|
|
from .utils import DTYPE_MAPPING
|
|
|
|
class PtRandInt:
|
|
"""
|
|
Creates a PyTorch tensor filled with random integers within a specified range using the size entered in the text field.
|
|
For example, if you want to create a 4D tensor of batch size=2, channel=3, height=96, width=32 with random integers in the range [0, 10),
|
|
enter [2,3,96,32] for size, 0 for min_value, and 10 for max_value.
|
|
|
|
category: PyTorch wrapper - Tensor creation
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls) -> Dict[str, Any]:
|
|
"""
|
|
Defines the input types for the function.
|
|
|
|
Returns:
|
|
Dict[str, Any]: A dictionary specifying required input types.
|
|
"""
|
|
return {
|
|
"required": {
|
|
"min_value": ("INT", {"default": 0, "min": -2**31, "max": 2**31}),
|
|
"max_value": ("INT", {"default": 1, "min": -2**31, "max": 2**31}),
|
|
"size": ("STRING", {"multiline": True}),
|
|
"data_type": (("uint8", "int8", "int16", "int32", "int64"),)
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES: tuple = ("TENSOR",)
|
|
FUNCTION: str = "f"
|
|
CATEGORY: str = "Data Analysis"
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, **kw):
|
|
return float("NaN")
|
|
|
|
def __init__(self):
|
|
# Initialize a separate generator with a different seed
|
|
self.gen = torch.Generator()
|
|
self.gen.manual_seed(int(time.time() * 1000000) % (2**32)) # Unique seed
|
|
|
|
def f(self, min_value: int, max_value: int, size: str, data_type: str) -> tuple:
|
|
"""
|
|
Creates a PyTorch tensor filled with random integers in the range [min_value, max_value).
|
|
|
|
Args:
|
|
min_value (int): Minimum value (inclusive).
|
|
max_value (int): Maximum value (exclusive).
|
|
size (str): String representation of the tensor size.
|
|
data_type (str): Desired integer data type of the tensor.
|
|
|
|
Returns:
|
|
tuple: A tuple containing the PyTorch tensor.
|
|
"""
|
|
# Convert string to Python list
|
|
list_size = ast.literal_eval(size)
|
|
|
|
# Convert list to PyTorch Size
|
|
sz = torch.Size(list_size)
|
|
|
|
# Ensure max_value is greater than min_value
|
|
if max_value <= min_value:
|
|
raise ValueError("max_value must be greater than min_value.")
|
|
|
|
# Generate random integers within the specified range
|
|
tens = torch.randint(min_value, max_value, sz, dtype=DTYPE_MAPPING[data_type], generator=self.gen)
|
|
return (tens,)
|