92 lines
4.0 KiB
Python
92 lines
4.0 KiB
Python
from typing import Any, Dict, Tuple
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
|
|
class PtnLSTM:
|
|
"""
|
|
Ptn LSTM:
|
|
A long short-term memory (LSTM) model consisting of one or more of a recurrent layer.
|
|
|
|
Args:
|
|
input_size (int): The number of input features.
|
|
hidden_size (int): The number of output features of the hidden layer matrix.
|
|
num_layers (int): Number of hidden layers.
|
|
bias (bool): Use bias or not.
|
|
batch_first (bool): Indicates the shape of the input. Input is assumed to be a rank-3 tensor. If True, the shape is [Batch, Seq, Token]. If False, the shape is [Seq, Batch, Token]. Note that default is set to `True` unlike PyTorch's LSTM default model parameter.
|
|
dropout (float): Dropout probability to be applied to the output of intermediate layers if non-zero. This will not be applied to the last layer's output.
|
|
bidirectional (bool): Processes the input in a backward direction and append the result to the output of the forward direction.
|
|
proj_size (int): If set to greater than 0, applies a projection to each output hidden state, reducing its dimensionality to proj_size.
|
|
|
|
category: PyTorch wrapper - Model
|
|
"""
|
|
|
|
@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": {
|
|
"input_size": ("INT", {"default": 1, "min": 1, "max": 1e6}),
|
|
"hidden_size": ("INT", {"default": 1, "min": 1, "max": 1e6}),
|
|
"num_layers": ("INT", {"default": 1, "min": 1, "max": 1e3}),
|
|
"bias": ("BOOLEAN", {"default": True}),
|
|
"batch_first": ("BOOLEAN", {"default": True}),
|
|
"dropout": ("FLOAT", {"default": 0, "min": 0, "max": 1, "step": 0.001}),
|
|
"bidirectional": ("BOOLEAN", {"default": False}),
|
|
"proj_size": ("INT", {"default": 0, "min": 0, "max": 1e6}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES: Tuple[str] = ("PTMODEL",)
|
|
FUNCTION: str = "f"
|
|
CATEGORY: str = "Training"
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, **kw):
|
|
return float("NaN")
|
|
|
|
def f(self,
|
|
input_size: int,
|
|
hidden_size: int,
|
|
num_layers: int,
|
|
bias: bool,
|
|
batch_first: bool,
|
|
dropout: float,
|
|
bidirectional: bool,
|
|
proj_size: int
|
|
) -> Tuple[nn.Module]:
|
|
"""
|
|
Constructs an LSTM model.
|
|
|
|
Args:
|
|
input_size (int): The number of input features.
|
|
hidden_size (int): The number of output features of the hidden layer matrix.
|
|
num_layers (int): Number of hidden layers.
|
|
bias (bool): Use bias or not.
|
|
batch_first (bool): Indicates the shape of the input. Input is assumed to be a rank-3 tensor. If True, the shape is [Batch, Seq, Token]. If False, the shape is [Seq, Batch, Token].
|
|
dropout (float): Dropout probability to be applied to the output of intermediate layers if non-zero. This will not be applied to the last layer's output.
|
|
bidirectional (bool): Processes the input in a backward direction and append the result to the output of the forward direction.
|
|
proj_size (int): If set to greater than 0, applies a projection to each output hidden state, reducing its dimensionality to proj_size.
|
|
|
|
Returns:
|
|
Tuple[nn.Module]: A tuple containing the instantiated PyTorch model.
|
|
"""
|
|
with torch.inference_mode(False):
|
|
model = nn.LSTM(
|
|
input_size=input_size,
|
|
hidden_size=hidden_size,
|
|
num_layers=num_layers,
|
|
bias=bias,
|
|
batch_first=batch_first,
|
|
dropout=dropout,
|
|
bidirectional=bidirectional,
|
|
proj_size=proj_size
|
|
)
|
|
|
|
return (model,)
|