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Added support for RNN

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HowToSD
2025-03-24 23:21:35 -07:00
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docs/reference/node_reference.md
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| [Ptn Max Pool 2d](ptn_max_pool2d.md) | A maxpool layer. |
| [Ptn Pre Add Channel Axis](ptn_pre_add_channel_axis.md) | Adds a channel axis after the batch axis if the input is rank 3 (bs, h, w) |
| [Ptn Pre Flatten](ptn_pre_flatten.md) | Flattens the input tensor before processing the tensor in the specified model. |
| [Ptn RNN](ptn_rnn.md) | A recurrent neural network (RNN) model consisting of one or more of a recurrent layer. |
| [Ptn RNN Linear](ptn_rnn_linear.md) | A recurrent neural network (RNN) model with a linear head. |
| [Ptn Resnet Model](ptn_resnet_model.md) | A Resnet model consisting of multiple Resnet layers. |
## PyTorch wrapper - Optimizer
| Node | Description |
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| [Pt Train Classification Model](pt_train_classification_model.md) | Trains a classification model using a given dataset, optimizer, and number of epochs. |
| [Pt Train Classification Model Lr](pt_train_classification_model_lr.md) | Trains a classification model using a given dataset, optimizer, and number of epochs with learning rate decay. |
| [Pt Train Model](pt_train_model.md) | Trains a model using a given dataset, loss function, optimizer, and number of epochs with learning rate decay. |
| [Pt Train R N N Model](pt_train_rnn_model.md) | Trains an RNN model using a given dataset, loss function, optimizer, and number of epochs with learning rate decay. |
| [Pt Train Regression Model](pt_train_regression_model.md) | Trains a regression model using a given dataset, optimizer, and number of epochs. |
| [Pto Lr Scheduler Cosine Annealing](pto_lr_scheduler_cosine_annealing.md) | Creates a cosine annealing learning rate scheduler for an optimizer. |
| [Pto Lr Scheduler Reduce On Plateau](pto_lr_scheduler_reduce_on_plateau.md) | Creates a reduce-on-plateau learning rate scheduler for an optimizer. |
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| [Ptv Dataset](ptv_dataset.md) | A Torchvision Dataset class wrapper. |
| [Ptv Dataset Len](ptv_dataset_len.md) | A wrapper class that calls Python len on a dataset. |
| [Ptv Dataset Loader](ptv_dataset_loader.md) | A node to combine the dataset and data loader into a single node. |
| [Ptv Hf Glove Dataset](ptv_hf_glove_dataset.md) | A PyTorch Dataset class wrapper for a Hugging Face dataset that converts text to embedding using Glove. |
| [Ptv Image Folder Dataset](ptv_image_folder_dataset.md) | A Torchvision ImageFolder Dataset class wrapper. |
| [Ptv Sequential Tensor Dataset](ptv_sequential_tensor_dataset.md) | Creates a sequential tensor Dataset. |
| [Ptv Transforms Data Augment](ptv_transforms_data_augment.md) | Applies data augmentation transformations to dataset elements. |
| [Ptv Transforms Resize](ptv_transforms_resize.md) | Resizes and transforms elements of dataset to PyTorch tensors. |
| [Ptv Transforms To Tensor](ptv_transforms_to_tensor.md) | Transforms elements of dataset to PyTorch tensors. |
docs/reference/pt_train_rnn_model.md
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# Pt Train R N N Model
Trains an RNN model using a given dataset, loss function, optimizer, and number of epochs with learning rate decay.
Args:
model (PTMODEL): The PyTorch model to be trained.
linear_head (bool): The RNN model has a Linear head.
train_loader (torch.utils.data.DataLoader): DataLoader containing the training dataset.
optimizer (PTOPTIMIZER): Optimizer used for training.
loss (PTLOSS): The loss class.
epochs (int): Number of training epochs.
use_gpu (bool): Whether to use GPU for training.
early_stopping (bool): Whether to turn on early stopping
early_stopping_rounds (int): Specifies the number of rounds to monitor the evaluation loss when early_stopping is enabled and validation data is specified. If the loss does not decrease compared to the current best loss within this period, training will be stopped.
output_best_val_model (bool): Whether to outputs the model with the best val loss or the model from the last epoch.
classification_metrics (bool): If True and if val_loader is set, print out classification metrics. This is only valid for classification model training.
use_valid_token_mean (bool): Computes the mean of the RNN output for non-zero inputs and feeds to the linear layer.
val_loader (PTDATALOADER): DataLoader for validation dataset (optional).
scheduler (PTLRSCHEDULER): Learning rate scheduler.
## Input
| Name | Data type |
|---|---|
| model | Ptmodel |
| linear_head | Boolean |
| train_loader | Ptdataloader |
| optimizer | Ptoptimizer |
| loss_function | Ptloss |
| epochs | Int |
| use_gpu | Boolean |
| early_stopping | Boolean |
| early_stopping_rounds | Int |
| output_best_val_model | Boolean |
| classification_metrics | Boolean |
| use_valid_token_mean | Boolean |
| scheduler | Ptlrscheduler |
| val_loader | Ptdataloader |
## Output
| Data type |
|---|
| Ptmodel |
| Tensor |
| Tensor |
<HR>
Category: PyTorch wrapper - Training
ComfyUI Pt Wrapper Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/ptn_rnn.md
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# Ptn RNN
A recurrent neural network (RNN) 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.
nonlinearity (str): Activation function to apply after each hidden layer. Specify 'tanh' or 'relu'.
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 RNN 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.
## Input
| Name | Data type |
|---|---|
| input_size | Int |
| hidden_size | Int |
| num_layers | Int |
| nonlinearity | |
| bias | Boolean |
| batch_first | Boolean |
| dropout | Float |
| bidirectional | Boolean |
## Output
| Data type |
|---|
| Ptmodel |
<HR>
Category: PyTorch wrapper - Model
ComfyUI Pt Wrapper Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/ptn_rnn_linear.md
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# Ptn RNN Linear
A recurrent neural network (RNN) model with a linear head.
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 in RNN.
nonlinearity (str): Activation function to apply after each hidden layer. Specify 'tanh' or 'relu'.
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 RNN 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.
linear_output_size (int): The number of output features of the linear layer.
linar_bias (bool): Use bias or not in linear layer.
## Input
| Name | Data type |
|---|---|
| input_size | Int |
| hidden_size | Int |
| num_layers | Int |
| nonlinearity | |
| bias | Boolean |
| batch_first | Boolean |
| dropout | Float |
| bidirectional | Boolean |
## Output
| Data type |
|---|
| Ptmodel |
<HR>
Category: PyTorch wrapper - Model
ComfyUI Pt Wrapper Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/ptv_hf_glove_dataset.md
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# Ptv Hf Glove Dataset
A PyTorch Dataset class wrapper for a Hugging Face dataset that converts text to embedding using Glove.
Args:
name (str): Name of the dataset class (e.g., MNIST, FashionMNIST).
split (str): The name of the subset of dataset to retrieve or the split specifier.
embed_dim (int): Embedding dimension for Glove.
max_seq_len (int): Sequence length.
sample_field_name (str): Field name for text samples.
label_field_name (str): Field name for labels.
## Input
| Name | Data type |
|---|---|
| name | String |
| split | String |
| embed_dim | Int |
| max_seq_len | Int |
| sample_field_name | String |
| label_field_name | String |
## Output
| Data type |
|---|
| Ptvdataset |
<HR>
Category: PyTorch wrapper - Training
ComfyUI Pt Wrapper Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/ptv_sequential_tensor_dataset.md
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# Ptv Sequential Tensor Dataset
Creates a sequential tensor Dataset.
Args:
tens (torch.Tensor): The tensor containing the data.
seq_len (int): Sequence length.
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| seq_len | Int |
## Output
| Data type |
|---|
| Ptvdataset |
<HR>
Category: PyTorch wrapper - Training
ComfyUI Pt Wrapper Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.