Added Ptv Transforms Resize node to resize image data. Added workflows and PyTorch model training tutorials.

README.md

@@ -1,45 +1,10 @@
 # ComfyUI-Data-Analysis
-## Update (March 5, 2025)
-Training workflow trains the FashionMNIST model from scratch with a single click of a button.
+## Update (March 7, 2025)
+You can now use newly added nodes to train a machine learning model for image classification with your own image data **without any coding** from scratch.
 
-Experimental PyTorch model training has been added (documentation coming soon).
-No coding required—training is as simple as a single click!
-
-It is designed to be modular. For example, to switch from a linear model to a conv model, you can just replace the model node (see screenshot below).
-
-Or you can just drag and drop below workflows to ComfyUI to check it out. Model is downloaded automatically:
-
-Linear model  
-* Training: examples/fashion_mnist_train.json  
-* Evaluation: examples/fashion_mnist_eval.json
-
-Convolutional model  
-* Training: examples/fashion_mnist_train_conv.json  
-* Evaluation: examples/fashion_mnist_eval_conv.json
-
-These training workflows train a FashionMNIST model from scratch effortlessly.
-
-### Training workflow
-**Linear model**
-![Train](docs/images/fashion_mnist_train.png)
-
-**Conv model**
-![Train](docs/images/conv_train.png)
-
-*Close up of the conv node*
-![Train](docs/images/conv_train2.png)
-
-### Eval workflow
-**Linear model**
-![Eval](docs/images/fashion_mnist_eval.png)
-
-**Conv model**
-![Eval](docs/images/conv_eval.png)
-
-Note that the training feature is a proof of concept and currently supports only the linear and conv models. Additional models may be added based on demand.
-
-<hr>
+Check out [the new tutorial to classify dogs and cats](docs/dog_cat_classification_model_training.md)
 
+---
 ![Baseball tutorial](docs/images/baseball_question.jpg)
 ## How Can We Figure Out Which MLB Player Had the Most Hits Per Year?
 

__init__.py

@@ -243,6 +243,7 @@ from .modules.pytorch_wrapper.pto_adam import PtoAdam
 from .modules.pytorch_wrapper.ptv_dataset import PtvDataset
 from .modules.pytorch_wrapper.ptv_dataset_loader import PtvDatasetLoader
 from .modules.pytorch_wrapper.ptv_image_folder_dataset import PtvImageFolderDataset
+from .modules.pytorch_wrapper.ptv_transforms_resize import PtvTransformsResize
 from .modules.pytorch_wrapper.ptv_transforms_to_tensor import PtvTransformsToTensor
 from .modules.seaborn_wrapper.sns_bar import SNSBar
 from .modules.seaborn_wrapper.sns_boxplot import SNSBoxplot
@@ -494,6 +495,7 @@ NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
     "PtvDataset": PtvDataset,
     "PtvDatasetLoader": PtvDatasetLoader,
     "PtvImageFolderDataset": PtvImageFolderDataset,
+    "PtvTransformsResize": PtvTransformsResize,
     "PtvTransformsToTensor": PtvTransformsToTensor,
     "PyDatetimeToString": PyDatetimeToString,
     "PyFloatToString": PyFloatToString,
@@ -754,6 +756,7 @@ NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
     "PtvDataset": "Ptv Dataset",
     "PtvDatasetLoader": "Ptv Dataset Loader",
     "PtvImageFolderDataset": "Ptv Image Folder Dataset",
+    "PtvTransformsResize": "Ptv Transforms Resize",
     "PtvTransformsToTensor": "Ptv Transforms To Tensor",
     "PyDatetimeToString": "Py Datetime To String",
     "PyFloatToString": "Py Float To String",

dev/scripts/run_tests.sh

@@ -8,5 +8,5 @@
 # the tests in the directory will be skipped.
 export PYTHONPATH=modules:$PYTHONPATH
 echo $PYTHONPATH
-python -m unittest discover -s tests -p "*_test.py" -t .
+RUN_SKIPPED_TESTS=0 python -m unittest discover -s tests -p "*_test.py" -t .
 

docs/dog_cat_classification_model_training.md

@@ -0,0 +1,137 @@
+# Dog cat classification model training tutorial
+This tutorial covers how to use **ComfyUI Data Analysis** to train an image classification model with your own image dataset.  
+We'll use a **dog and cat image dataset** created specifically for this tutorial, which is available publicly on [my GitHub repo](https://github.com/HowToSD/dog_and_cat_dataset).  
+
+Once you complete the tutorial, you can experiment with your own images.
+
+**Disclaimer:** The model architecture is a traditional convolutional neural network (CNN) and is **not state-of-the-art**. This choice keeps the model small, enabling faster training and easier explanation.
+
+---
+
+## Overview of Steps
+
+1. Downloading the dog and cat dataset from the GitHub repo
+2. Edit the training workflow  
+3. Run the training workflow  
+4. Edit the evaluation workflow
+5. Run the evaluation workflow
+
+---
+
+## 1. Download the Dog & Cat Dataset  
+
+Visit [the GitHub repo](https://github.com/HowToSD/dog_and_cat_dataset) and go to the **Releases** page.  
+Click **"Source code (zip)"** to download the zip that contains all the images.  
+Extract the ZIP file using an unarchiver.  
+
+The dataset follows this directory structure:  
+
+```
+dog_and_cat
+  train
+    cats
+    dogs
+
+  val
+    cats
+    dogs
+```
+
+
+Take note of the **full paths** to the `train` and `val` directories, as you'll need them in the next step.
+
+---
+
+## 2. Editing the Training Workflow
+
+Drag and drop the below file into ComfyUI:  
+```
+examples/workflow/dog_cat_classification_train_model.json
+```
+Some nodes will be marked red, indicating they require edits.  
+The only required changes are the **train** and **val** dataset paths.  
+Replace them with the paths noted earlier.
+
+---
+
+## 3. Running the Training Workflow  
+
+Click **Queue** to start training.  
+
+Monitor the console for errors. If training runs smoothly, you should see output like this:
+
+```
+Epoch (train) 1/20, Loss: 0.6865
+Epoch (val) 1/20, Loss: 0.6801
+Epoch (train) 2/20, Loss: 0.6216
+Epoch (val) 2/20, Loss: 0.5710
+Epoch (train) 3/20, Loss: 0.4998
+Epoch (val) 3/20, Loss: 0.4716
+Epoch (train) 4/20, Loss: 0.3827
+Epoch (val) 4/20, Loss: 0.4045
+Epoch (train) 5/20, Loss: 0.2897
+Epoch (val) 5/20, Loss: 0.3297
+Epoch (train) 6/20, Loss: 0.2243
+Epoch (val) 6/20, Loss: 0.2925
+Epoch (train) 7/20, Loss: 0.1603
+Epoch (val) 7/20, Loss: 0.2916
+Epoch (train) 8/20, Loss: 0.1102
+Epoch (val) 8/20, Loss: 0.3216
+Epoch (train) 9/20, Loss: 0.0916
+Epoch (val) 9/20, Loss: 0.2706
+Epoch (train) 10/20, Loss: 0.0522
+Epoch (val) 10/20, Loss: 0.2220
+Epoch (train) 11/20, Loss: 0.0481
+Epoch (val) 11/20, Loss: 0.2820
+Epoch (train) 12/20, Loss: 0.0228
+Epoch (val) 12/20, Loss: 0.2899
+Epoch (train) 13/20, Loss: 0.0130
+Epoch (val) 13/20, Loss: 0.3056
+Epoch (train) 14/20, Loss: 0.0137
+Epoch (val) 14/20, Loss: 0.3070
+Epoch (train) 15/20, Loss: 0.0200
+Epoch (val) 15/20, Loss: 0.2714
+Epoch (train) 16/20, Loss: 0.0313
+Epoch (val) 16/20, Loss: 0.3108
+Epoch (train) 17/20, Loss: 0.0125
+Epoch (val) 17/20, Loss: 0.3388
+Epoch (train) 18/20, Loss: 0.0174
+Epoch (val) 18/20, Loss: 0.2989
+Epoch (train) 19/20, Loss: 0.0017
+Epoch (val) 19/20, Loss: 0.3165
+Epoch (train) 20/20, Loss: 0.0009
+Epoch (val) 20/20, Loss: 0.3582
+Prompt executed in 253.64 seconds
+```
+
+If you encounter an error:  
+- Ensure dataset paths are correct.  
+- Reduce **batch size** if your GPU runs out of memory (OOM error).  
+
+Once training completes, a **line chart** displaying training progress will appear at the bottom right of the canvas. You don’t need to interpret it initially.
+
+---
+
+## 4. Editing the Evaluation Workflow
+Now, let's verify if the model was trained correctly. In this step, we'll feed the validation data into the trained model and compute metrics to assess its performance.
+
+Drag and drop the below file into ComfyUI:  
+```
+examples/workflow/dog_cat_classification_eval_model.json
+```
+
+into **ComfyUI**.  
+
+Update the **val dataset path**.
+
+---
+
+## 5. Running the Evaluation Workflow  
+
+Click **Queue**. The evaluation process runs much faster than training.  
+
+Check the **Accuracy** node—it should display a value around **0.9**, confirming successful model training.
+
+Congratulations! You’ve successfully trained a machine learning model from scratch without writing a single line of code.
+
+Now, you can use your own images to train the model in the same way.

docs/model_training.md

@@ -0,0 +1,49 @@
+# Model Training
+
+Experimental PyTorch model training has been added.  
+No coding is required. Training is as simple as a single click!  
+Currently, only classification is supported.
+
+The system is designed to be modular. For example, to switch from a linear model to a convolutional model, you only need to replace the model node (see screenshot below).
+
+You can choose to:
+* Use your own images
+* Use a public dataset
+
+## Using Your Own Images
+
+Check out [the dog and cat classification tutorial](dog_cat_classification_model_training.md).
+
+## Public Datasets
+
+Currently, only Fashion MNIST has been tested. Other public datasets have not been verified yet, though they may work.  
+If you try one and it doesn't work, please create an issue so I can investigate adding support.
+
+To use the Fashion MNIST dataset, simply drag and drop the workflow files below into ComfyUI. The model will be downloaded automatically.
+
+Linear model  
+* Training: examples/fashion_mnist_train.json  
+* Evaluation: examples/fashion_mnist_eval.json
+
+Convolutional model  
+* Training: examples/fashion_mnist_train_conv.json  
+* Evaluation: examples/fashion_mnist_eval_conv.json
+
+These training workflows train a FashionMNIST model from scratch effortlessly.
+
+### Training workflow
+**Linear model**
+![Train](images/fashion_mnist_train.png)
+
+**Conv model**
+![Train](images/conv_train.png)
+
+*Close up of the conv node*
+![Train](images/conv_train2.png)
+
+### Eval workflow
+**Linear model**
+![Eval](images/fashion_mnist_eval.png)
+
+**Conv model**
+![Eval](images/conv_eval.png)

docs/reference/node_reference.md

@@ -276,6 +276,7 @@
 | [Ptv Dataset](ptv_dataset.md) | A Torchvision Dataset class wrapper. |
 | [Ptv Dataset Loader](ptv_dataset_loader.md) | A node to combine the dataset and data loader into a single node. |
 | [Ptv Image Folder Dataset](ptv_image_folder_dataset.md) | A Torchvision ImageFolder Dataset class wrapper. |
+| [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. |
 ## PyTorch wrapper - Transform
 | Node | Description |

docs/reference/ptv_transforms_resize.md

@@ -0,0 +1,18 @@
+# Ptv Transforms Resize
+Resizes and transforms elements of dataset to PyTorch tensors.
+
+## Input
+| Name | Data type |
+|---|---|
+| height | Int |
+| width | Int |
+
+## Output
+| Data type |
+|---|
+| Ptvtransform |
+
+<HR>
+Category: PyTorch wrapper - Training
+
+ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.

modules/pytorch_wrapper/ptv_transforms_resize.py

@@ -0,0 +1,48 @@
+from typing import Any, Dict
+import torchvision.transforms as transforms
+
+
+class PtvTransformsResize:
+    """
+    Resizes and transforms elements of dataset to PyTorch tensors.
+
+    category: PyTorch wrapper - Training
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the required input types.
+
+        Returns:
+            Dict[str, Any]: An empty dictionary.
+        """
+        return {
+            "required": {
+                "height": ("INT", {"default": 256, "min": 1, "max": 32768}),
+                "width": ("INT", {"default": 256, "min": 1, "max": 32768})
+            },
+        }
+
+    RETURN_TYPES: tuple = ("PTVTRANSFORM",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Training"
+
+    def f(self, height: int, width: int) -> tuple:
+        """
+        Resizes and transforms elements of dataset to PyTorch tensors.
+
+        Args:
+            height (int): Target height of the input image after transform
+            width (int): Target width of the inpt image after transform
+
+        Returns:
+            tuple: A tuple containing the transform callable.
+        """
+        c = transforms.Compose([
+            transforms.Resize((height, width)),
+            transforms.ToTensor()
+        ])
+        return(c,)
+
+

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "data-analysis"
 description = "Data analysis custom modules for ComfyUI – Integrates Pandas, Seaborn and Matplotlib into ComfyUI, featuring extensive PyTorch wrapper nodes for seamless tensor operations. Experimental PyTorch model training was added in v2.0."
-version = "2.0.7"
+version = "2.0.8"
 license = {file = "LICENSE"}
 
 [project.urls]

tests/pytorch_wrapper/pt_evaluate_classification_conv_model_test2.py

@@ -9,17 +9,19 @@ MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
 sys.path.append(MODULE_ROOT)
 from pytorch_wrapper.ptn_conv_model import PtnConvModel
 from pytorch_wrapper.ptv_image_folder_dataset import PtvImageFolderDataset
-from pytorch_wrapper.ptv_transforms_to_tensor import PtvTransformsToTensor
+from pytorch_wrapper.ptv_transforms_resize import PtvTransformsResize
 from pytorch_wrapper.pt_data_loader import PtDataLoader
 from pytorch_wrapper.pt_evaluate_classification_model import PtEvaluateClassificationModel
 from pytorch_wrapper.pt_load_model import PtLoadModel
  
 class TestPtEvaluate(unittest.TestCase):
+
+    @unittest.skipIf(os.getenv("RUN_SKIPPED_TESTS") != "1", "Skipping unless explicitly enabled")
     def setUp(self):
         """Set up test instance."""
         # Transform
-        compose_node = PtvTransformsToTensor()
-        transform = compose_node.f()[0]
+        compose_node = PtvTransformsResize()
+        transform = compose_node.f(256, 256)[0]
         
         # Dataset
         self.node = PtvImageFolderDataset()
@@ -38,10 +40,10 @@ class TestPtEvaluate(unittest.TestCase):
         # Model
         self.model_node = PtnConvModel()
         self.model = self.model_node.f(
-                 input_dim="(3, 512, 512)",
+                 input_dim="(3, 256, 256)",
                  penultimate_dim=0,
                  output_dim=2,
-                 channel_list="[32,64,128,256,512]", # To 256, 128, 64, 32, 16
+                 channel_list="[32,64,128,256,512]", # To 128, 64, 32, 16, 8
                  kernel_size_list="[3,3,3,3,3]",
                  padding_list="[1,1,1,1,1]",
                  downsample_list="[True,True,True,True,True]"
@@ -49,12 +51,13 @@ class TestPtEvaluate(unittest.TestCase):
 
         # Load weights
         self.load_model = PtLoadModel()
-        epochs = 7
+        epochs = 20
         self.loaded_model = self.load_model.f(self.model,f"dog_cat_{epochs}_epochs_conv.pt")
 
         # Instantiates evaluation node
         self.evaluate = PtEvaluateClassificationModel()
 
+    @unittest.skipIf(os.getenv("RUN_SKIPPED_TESTS") != "1", "Skipping unless explicitly enabled")
     def test_evaluation(self):
         """
         Tests evaluation.  

tests/pytorch_wrapper/pt_train_classification_conv_model_test2.py

@@ -11,23 +11,34 @@ sys.path.append(MODULE_ROOT)
 from pytorch_wrapper.ptn_conv_model import PtnConvModel
 from pytorch_wrapper.pto_adam import PtoAdam
 from pytorch_wrapper.ptv_image_folder_dataset import PtvImageFolderDataset
-from pytorch_wrapper.ptv_transforms_to_tensor import PtvTransformsToTensor
+from pytorch_wrapper.ptv_transforms_resize import PtvTransformsResize
 from pytorch_wrapper.pt_data_loader import PtDataLoader
 from pytorch_wrapper.pt_train_classification_model import PtTrainClassificationModel
 from pytorch_wrapper.pt_save_model import PtSaveModel
 
  
 class TestPtTrainConvModel2(unittest.TestCase):
+
+    @unittest.skipIf(os.getenv("RUN_SKIPPED_TESTS") != "1", "Skipping unless explicitly enabled")
     def setUp(self):
         """Set up test instance."""
-        compose_node = PtvTransformsToTensor()
-        transform = compose_node.f()[0]
+        compose_node = PtvTransformsResize()
+        transform = compose_node.f(256, 256)[0]
         self.node = PtvImageFolderDataset()
-        dataset = self.node.f("dog_and_cat/train",
+        train_dataset = self.node.f("dog_and_cat/train",
                     transform=transform)[0]
-        data_loader_node = PtDataLoader()
-        self.train_loader = data_loader_node.f(
-            dataset=dataset,
+        train_data_loader_node = PtDataLoader()
+        val_dataset = self.node.f("dog_and_cat/val",
+                    transform=transform)[0]
+        val_data_loader_node = PtDataLoader()
+        self.train_loader = train_data_loader_node.f(
+            dataset=train_dataset,
+            batch_size=32,
+            shuffle=True,
+            parameters='{"num_workers":1}'
+        )[0]
+        self.val_loader = val_data_loader_node.f(
+            dataset=val_dataset,
             batch_size=32,
             shuffle=True,
             parameters='{"num_workers":1}'
@@ -35,10 +46,10 @@ class TestPtTrainConvModel2(unittest.TestCase):
 
         self.model_node = PtnConvModel()
         self.model = self.model_node.f(
-                 input_dim="(3, 512, 512)",
+                 input_dim="(3, 256, 256)",
                  penultimate_dim=0,
                  output_dim=2,
-                 channel_list="[32,64,128,256,512]", # To 256, 128, 64, 32, 16
+                 channel_list="[32,64,128,256,512]", # To 128, 64, 32, 16, 8
                  kernel_size_list="[3,3,3,3,3]",
                  padding_list="[1,1,1,1,1]",
                  downsample_list="[True,True,True,True,True]"
@@ -48,11 +59,13 @@ class TestPtTrainConvModel2(unittest.TestCase):
         self.trainer = PtTrainClassificationModel()
         self.save_model = PtSaveModel()
 
+    @unittest.skipIf(os.getenv("RUN_SKIPPED_TESTS") != "1", "Skipping unless explicitly enabled")
     def test_1(self):
-        epochs = 7
+        epochs = 20
         trained_model = self.trainer.f(self.model, self.train_loader, self.optimizer,
                        epochs, # epochs
-                       use_gpu=True)[0]
+                       use_gpu=True,
+                       val_loader=self.val_loader)[0]
         self.save_model.f(trained_model, f"dog_cat_{epochs}_epochs_conv.pt")
 
 

tests/pytorch_wrapper/ptv_transforms_resize_test.py

@@ -0,0 +1,43 @@
+import os
+import sys
+import unittest
+import torch
+from PIL import Image
+import numpy as np
+
+PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
+MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
+sys.path.append(MODULE_ROOT)
+from pytorch_wrapper.ptv_transforms_resize import PtvTransformsResize
+
+
+class TestPtvTransformsResize(unittest.TestCase):
+    def setUp(self):
+        """Set up test data."""
+        self.compose_node = PtvTransformsResize()
+        self.height = 128
+        self.width = 128
+
+    def test_with_transform(self):
+        """Tests the transformation output."""
+        transform = self.compose_node.f(self.height, self.width)[0]
+        self.assertTrue(callable(transform), "Transform should be callable.")
+
+        # Create a dummy image
+        img = Image.fromarray(np.random.randint(0, 256, (256, 256, 3), dtype=np.uint8))
+        transformed_img = transform(img)
+
+        # Check output type
+        self.assertIsInstance(transformed_img, torch.Tensor, "Output should be a PyTorch tensor.")
+
+        # Check output shape
+        self.assertEqual(transformed_img.shape, (3, self.height, self.width), "Output shape should match target dimensions.")
+
+    def test_default_parameters(self):
+        """Tests default transform parameters."""
+        transform = self.compose_node.f(256, 256)[0]
+        self.assertTrue(callable(transform), "Default transform should be callable.")
+
+
+if __name__ == "__main__":
+    unittest.main()