From d00ce3bd14066a984546d18690bb36fec54f17c7 Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@Taigas-MacBook-Air.local>
Date: Mon, 3 Mar 2025 23:02:00 -0800
Subject: [PATCH 01/34] started taking some notes on preprocessing.

---
 figures/CW44_naive_default_crop_img.png    | Bin 0 -> 41429 bytes
 figures/CW44_naive_pre_crop_img.png        | Bin 0 -> 62126 bytes
 figures/preprocessing_difference_alias.png | Bin 0 -> 97671 bytes
 notes/preprocessing.md                     |  13 +++++++++++++
 4 files changed, 13 insertions(+)
 create mode 100644 figures/CW44_naive_default_crop_img.png
 create mode 100644 figures/CW44_naive_pre_crop_img.png
 create mode 100644 figures/preprocessing_difference_alias.png
 create mode 100644 notes/preprocessing.md

diff --git a/figures/CW44_naive_default_crop_img.png b/figures/CW44_naive_default_crop_img.png
new file mode 100644
index 0000000000000000000000000000000000000000..ce119f9507f8dc286c358df2926fa3c61fd49cc9
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HcmV?d00001

diff --git a/notes/preprocessing.md b/notes/preprocessing.md
new file mode 100644
index 0000000..c42d086
--- /dev/null
+++ b/notes/preprocessing.md
@@ -0,0 +1,13 @@
+# Preprocessing description
+
+## Default cropping 
+
+[precrop](../figures/CW44_naive_pre_crop_img.png)
+
+[crop](../figures/CW44_naive_default_crop_img.png)
+
+## Antialiasing 
+
+Differences greater than 1e-3. Due to aliasing I'm pretty sure. 
+
+[alias](../figures/preprocessing_difference_alias.png)

From 7a58426d94cfb84ae673511c315c8afef5d20f6f Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@DN52e64f.SUNet>
Date: Tue, 4 Mar 2025 12:38:49 -0800
Subject: [PATCH 02/34] added and tested pytorch data module

---
 src/behavioral_autoencoder/dataloading.py | 140 ++++++++++++++++++++++
 tests/test_dataloading.py                 | 126 +++++++++++++++++++
 2 files changed, 266 insertions(+)
 create mode 100644 src/behavioral_autoencoder/dataloading.py
 create mode 100644 tests/test_dataloading.py

diff --git a/src/behavioral_autoencoder/dataloading.py b/src/behavioral_autoencoder/dataloading.py
new file mode 100644
index 0000000..e680e97
--- /dev/null
+++ b/src/behavioral_autoencoder/dataloading.py
@@ -0,0 +1,140 @@
+import torchvision
+import numpy as np
+import torch
+from torch.utils.data import Subset,DataLoader
+from behavioral_autoencoder.dataset import SessionFramesTorchvision,CropResizeProportion
+import pytorch_lightning as pl
+from joblib import Memory
+import os
+
+def calculate_mean_image(data_path,dataset_config,subsample_rate,subsample_offset):
+    """
+    Calculate mean image from given training set parameters. Gets parameters from SessionFramesDataModule
+    TODO: implement caching for this function. 
+    """
+    # 1. First construct the right training set indices: 
+    dataset = SessionFramesTorchvision(data_path,**dataset_config)
+    all_indices = np.arange(len(dataset))
+    ## Subsample indices
+    train_inds = all_indices[subsample_offset::subsample_rate]
+    trainset = Subset(dataset,train_inds)
+
+    ## use dataloader to compute sum image 
+    trainloader = DataLoader(trainset,batch_size=10)
+
+    sum_im = torch.zeros(dataset[0].shape)
+    for data in trainloader:
+        sum_im+=data.sum(axis=0)
+    mean=sum_im/len(trainset)    
+    return mean
+
+class SessionFramesDataModule(pl.LightningDataModule):
+    """Lightning data module collects together data loading logic frame subsampling, and then calculates the framewise mean of the training dataset. 
+    This datamodule does the following: 
+        1. Subsamples at a given subsetting rate to generate the training data.  
+
+        2. Checks if we have already calculated the mean image for the given training parameters 
+        3. Generates the training dataset by subsampling at the given subsampling rate and offset. Calculates mean image if does not exist yet. 
+        4. Defines datasets which compose given transformations with a subtraction of the training set mean. 
+
+    """
+    def __init__(
+        self,
+        dataset_config,
+        batch_size,
+        num_workers,
+        trainset_subsample_rate,
+        trainset_subsample_offset,
+        trainset_subtract_mean=True,
+        ):
+        """
+        By default, checks if we have already calculated the image mean on a particular dataset for a particular training set, and if so gets that cached mean.
+
+        Parameters
+        ----------
+        dataset_config : dict
+            dictionary containing configuration parameters for dataset. Must include:
+                data_path: str
+                    path of the top level folder of per-trial frames.
+                transform : any
+                    the transform function that we will apply to all the data. Can be followed by different training, testing transforms for different datasets, and can be None.
+            Can optionally also include:
+                extension : str
+                    extension of files which
+                trial_pattern : str
+                    regex which filters for certain folder prefixes in the trial.
+        batch_size : int
+            frames per batch
+        trainset_subsample_rate : int
+            take one frame for every `trainset_subsample_rate` frames.
+        trainset_subsample_offset : int
+            offset for subsampling.
+        trainset_subtract_mean : bool
+            whether we should calculate and subtract the mean of the training set.
+        """
+        super().__init__()
+        self.data_path = dataset_config.pop("data_path")
+        self.dataset_config = dataset_config
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.subsample_rate = trainset_subsample_rate
+        self.subsample_offset = trainset_subsample_offset
+        self.subtract_mean = trainset_subtract_mean
+
+        if self.subtract_mean:
+            self.mean_image = calculate_mean_image(self.data_path,dataset_config,self.subsample_rate,self.subsample_offset)
+            subtract_mean = torchvision.transforms.Lambda(self.subtract_mean_image)
+            # augment the transformation for our datasets: 
+            if self.dataset_config["transform"] is not None:
+                mean_normed_transform = torchvision.transforms.Compose([
+                    self.dataset_config["transform"],
+                    subtract_mean
+                    ])
+            else:
+                mean_normed_transform = torchvision.transforms.Compose([
+                    subtract_mean
+                    ])
+            self.transform = mean_normed_transform
+        else:
+            self.transform = self.dataset_config["transform"]
+
+        self.setup()    
+
+    def subtract_mean_image(self,x):
+        return x-self.mean_image
+
+    def setup(self):
+        _ = self.dataset_config.pop("transform")
+        dataset = SessionFramesTorchvision(self.data_path,transform = self.transform,**self.dataset_config)
+        all_indices = np.arange(len(dataset))
+        ## Subsample indices
+        train_inds = all_indices[self.subsample_offset::self.subsample_rate]
+        test_inds = [i for i in all_indices if not (i in train_inds)]
+        self.trainset = Subset(dataset,train_inds)
+        self.valset = Subset(dataset,test_inds)
+
+    def train_dataloader(self,shuffle=True):
+        dataloader = DataLoader(
+            self.trainset,
+            batch_size=self.batch_size,
+            num_workers=self.num_workers,
+            shuffle=shuffle,
+            drop_last =False,
+            pin_memory=True
+                )
+        return dataloader
+
+    def val_dataloader(self):
+        dataloader = DataLoader(
+            self.valset,
+            batch_size=self.batch_size,
+            num_workers=self.num_workers,
+            shuffle=False,
+            drop_last =False,
+            pin_memory=True
+                )
+        return dataloader
+
+    def test_dataloader(self):
+        return self.val_dataloader()
+
diff --git a/tests/test_dataloading.py b/tests/test_dataloading.py
new file mode 100644
index 0000000..83224a6
--- /dev/null
+++ b/tests/test_dataloading.py
@@ -0,0 +1,126 @@
+"""
+Test what the dataloader does. 
+"""
+from behavioral_autoencoder.dataset import CropResizeProportion
+import json
+import os
+import cv2
+import numpy as np
+from behavioral_autoencoder.dataloading import SessionFramesDataModule
+
+
+here = os.path.abspath(os.path.dirname(__file__))
+
+def temp_hierarchical_folder_generator(tmp_path, n_trials=3, n_ims_per_trial=10, extra_files=None):
+    """Creates a temporary hierarchical folder structure for testing.
+    
+    This fixture creates a folder structure that mimics a session with multiple trials,
+    where each trial contains randomly sampled images. The structure is:
+    temp_session/
+        ├── 0_trial/
+        │   ├── frame_000000.png
+        │   ├── frame_000001.png
+        │   ├── ...
+        │   └── extra_file.txt
+        ├── 1_trial/
+        │   ├── frame_000000.png
+        │   ├── ...
+        │   └── extra_file.txt
+        └── ...
+    
+    Parameters
+    ----------
+    tmp_path : Path
+        Pytest fixture providing temporary directory path
+    n_trials : int, optional
+        Number of trial folders to create, by default 3
+    n_ims_per_trial : int, optional
+        Number of images to create per trial, by default 10
+    extra_files : list of str, optional
+        List of extra file names to create in each trial directory
+        
+    Returns
+    -------
+    Path
+        Path to the created temporary session directory
+    """
+    # Set random seed for reproducibility
+    np.random.seed(42)
+    
+    # Load example images
+    example_images = np.load('./test_data/example_images.npy')*255
+    
+    # Create session directory
+    session_dir = tmp_path / "temp_session"
+    session_dir.mkdir()
+    
+    # Create trial folders and populate with images
+    for trial in range(n_trials):
+        trial_dir = session_dir / f"{trial}_trial"
+        trial_dir.mkdir()
+        
+        # Randomly sample images
+        selected_images = example_images[
+            np.random.choice(len(example_images), n_ims_per_trial, replace=True)
+        ]
+        
+        # Save images as PNGs
+        for i, img in enumerate(selected_images):
+            img_path = trial_dir / f"frame_{i:06d}.png"
+            cv2.imwrite(str(img_path), img)
+        
+        # Create extra files within each trial folder if specified
+        if extra_files:
+            for filename in extra_files:
+                (trial_dir / filename).touch()
+    
+    return session_dir
+
+class Test_SessionFramesDataModule():
+    config_path = os.path.join(here,"..","configs","crop_configs","alm_side.json")
+    def test_init(self,tmp_path):
+        with open(self.config_path,"r") as f:
+            crop_config = json.load(f)
+        # Create a larger dataset
+        n_trials = 10  # Increased number of trials
+        n_ims_per_trial = 50  # Increased images per trial
+        session_dir = temp_hierarchical_folder_generator(
+            tmp_path,
+            n_trials=n_trials,
+            n_ims_per_trial=n_ims_per_trial
+        )
+        
+        alm_cropping = CropResizeProportion(self.config_path)
+        data_config = {
+                "data_path":session_dir,
+                "transform":alm_cropping,
+                "extension":".png",
+                "trial_pattern":None
+                }
+        sfdm = SessionFramesDataModule(data_config,10,2,10,1)
+        assert sfdm.mean_image.shape == (crop_config["target_h"],crop_config["target_w"])
+
+    def test_train_dataloader(self,tmp_path):
+        with open(self.config_path,"r") as f:
+            crop_config = json.load(f)
+        # Create a larger dataset
+        n_trials = 10  # Increased number of trials
+        n_ims_per_trial = 50  # Increased images per trial
+        session_dir = temp_hierarchical_folder_generator(
+            tmp_path,
+            n_trials=n_trials,
+            n_ims_per_trial=n_ims_per_trial
+        )
+        
+        alm_cropping = CropResizeProportion(self.config_path)
+        data_config = {
+                "data_path":session_dir,
+                "transform":alm_cropping,
+                "extension":".png",
+                "trial_pattern":None
+                }
+        sfdm = SessionFramesDataModule(data_config,10,2,10,1,)
+        dataloader = sfdm.train_dataloader()
+        for batch in dataloader:
+            assert batch.shape[1:] == (crop_config["target_h"],crop_config["target_w"])
+        

From 5d14ac58b19dcf293feb8ef7d39539c9d4c00489 Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@DN52e64f.SUNet>
Date: Tue, 4 Mar 2025 17:23:29 -0800
Subject: [PATCH 03/34] added source code for a minimal implementation of
 single session AE.

---
 src/behavioral_autoencoder/README.md      |   7 +
 src/behavioral_autoencoder/dataloading.py |   8 +-
 src/behavioral_autoencoder/dataset.py     |   9 +-
 src/behavioral_autoencoder/metrics.py     |  13 ++
 src/behavioral_autoencoder/module.py      |  85 +++++++++++
 src/behavioral_autoencoder/networks.py    | 166 ++++++++++++++++++++++
 6 files changed, 281 insertions(+), 7 deletions(-)
 create mode 100644 src/behavioral_autoencoder/README.md
 create mode 100644 src/behavioral_autoencoder/metrics.py
 create mode 100644 src/behavioral_autoencoder/module.py
 create mode 100644 src/behavioral_autoencoder/networks.py

diff --git a/src/behavioral_autoencoder/README.md b/src/behavioral_autoencoder/README.md
new file mode 100644
index 0000000..6363eb8
--- /dev/null
+++ b/src/behavioral_autoencoder/README.md
@@ -0,0 +1,7 @@
+# README 
+This package is organized as follows: 
+- `networks.py` contains actual descriptions of network architectures which can be used as components of an autoencoder model. 
+- `module.py` contains the logic which specifies which combination of network architectures correspond to what kind of models. 
+- `metrics.py` contains custom build evaluation metrics. 
+- `dataloading.py` contains code for the dataloaders with preprocessing. 
+- `dataset.py` contains code to specify how datasets should be structured.
diff --git a/src/behavioral_autoencoder/dataloading.py b/src/behavioral_autoencoder/dataloading.py
index e680e97..67bab10 100644
--- a/src/behavioral_autoencoder/dataloading.py
+++ b/src/behavioral_autoencoder/dataloading.py
@@ -22,9 +22,9 @@ def calculate_mean_image(data_path,dataset_config,subsample_rate,subsample_offse
     ## use dataloader to compute sum image 
     trainloader = DataLoader(trainset,batch_size=10)
 
-    sum_im = torch.zeros(dataset[0].shape)
+    sum_im = torch.zeros(dataset[0].shape[1:]) ## should be 
     for data in trainloader:
-        sum_im+=data.sum(axis=0)
+        sum_im+=data.sum(axis=0).sum(axis=0) ## sum across the batch and sequence dimensions.
     mean=sum_im/len(trainset)    
     return mean
 
@@ -98,12 +98,10 @@ def __init__(
         else:
             self.transform = self.dataset_config["transform"]
 
-        self.setup()    
-
     def subtract_mean_image(self,x):
         return x-self.mean_image
 
-    def setup(self):
+    def setup(self,stage):
         _ = self.dataset_config.pop("transform")
         dataset = SessionFramesTorchvision(self.data_path,transform = self.transform,**self.dataset_config)
         all_indices = np.arange(len(dataset))
diff --git a/src/behavioral_autoencoder/dataset.py b/src/behavioral_autoencoder/dataset.py
index 12f7ac1..cb02b89 100644
--- a/src/behavioral_autoencoder/dataset.py
+++ b/src/behavioral_autoencoder/dataset.py
@@ -95,6 +95,7 @@ class SessionFramesDataset(Dataset):
     cumsum_n_trials : arraylike    
         cumulative index for frame index across all trials. 
 
+
     """
 
     def __init__(self, base_folder, extension=".png", crop_info={'h_coord': 26}, trial_pattern=None):
@@ -299,9 +300,11 @@ class SessionFramesTorchvision(Dataset):
         cumulative index for frame index across all trials. 
     transform : any    
         None or transform function 
+    seq_length : int    
+        we end up outputting data which has a sequence dimension for consistency with other implementations as a singleton dimension preceding the others. 
     """
 
-    def __init__(self, base_folder, extension=".png", trial_pattern=None, transform = None):
+    def __init__(self, base_folder, extension=".png", trial_pattern=None, transform = None, seq_length = 1):
         """
         Parameters
         ----------
@@ -319,6 +322,8 @@ def __init__(self, base_folder, extension=".png", trial_pattern=None, transform
         """
         self.base_folder = base_folder
         self.extension = extension
+        self.seq_length = seq_length
+        assert seq_length == 1, "can't do more than this. "
         
         # Get all items in the base folder
         all_items = os.listdir(base_folder)
@@ -386,6 +391,6 @@ def __getitem__(self, idx, method = "searchsorted"):
         img = Image.open(image_path)
         if self.transform:
             img = self.transform(img)
-        return img
+        return img[None,:]
 
 
diff --git a/src/behavioral_autoencoder/metrics.py b/src/behavioral_autoencoder/metrics.py
new file mode 100644
index 0000000..bbb91b2
--- /dev/null
+++ b/src/behavioral_autoencoder/metrics.py
@@ -0,0 +1,13 @@
+"""
+Metrics and losses for network training
+"""
+import torch
+
+def L2_loss(batch):
+    """Given a batch of latents (shaped batch,sequence,activations), calculates the squared norm of the activations across the entire batch and returns shape batch. 
+
+    """
+    squared_acts = batch**2
+    l2_norm = torch.sum(squared_acts,axis=-1)
+    mean_l2 = torch.mean(l2_norm)
+    return mean_l2
diff --git a/src/behavioral_autoencoder/module.py b/src/behavioral_autoencoder/module.py
new file mode 100644
index 0000000..38a1749
--- /dev/null
+++ b/src/behavioral_autoencoder/module.py
@@ -0,0 +1,85 @@
+import pytorch_lightning as pl
+import torch
+import torch.nn as nn
+from behavioral_autoencoder.metrics import L2_loss
+from behavioral_autoencoder.networks import SingleSessionAutoEncoder
+from torch.optim.lr_scheduler import LinearLR
+
+models = {
+        "single_session_autoencoder":SingleSessionAutoEncoder
+        }
+
+class Autoencoder_Models(pl.LightningModule):
+    """
+    Abstract base class for Autoencoder models. Handles things like loss definition, model choice,  
+    """
+    def __init__(self,hparams):
+        super().__init__()
+        self.save_hyperparameters(hparams)
+        self.reconstruct_criterion = nn.MSELoss()
+        self.shrink_criterion = L2_loss
+    def forward(self,batch):
+        images = batch
+        latents,predictions = self.model(images)
+        return latents,predictions
+    def training_step(self, batch, batch_nb):
+        predictions,latents = self.forward(batch)
+        rec_loss = self.reconstruct_criterion(predictions,batch)
+        shrink_loss = self.shrink_criterion(latents)
+        loss = rec_loss+self.hparams["train_config"]["l2_weight"]*shrink_loss
+        self.log("loss/train", loss)
+        self.log("mse/train", rec_loss)
+        return loss
+    def validation_step(self, batch, batch_nb):
+        predictions,latents = self.forward(batch)
+        rec_loss = self.reconstruct_criterion(predictions,batch)
+        shrink_loss = self.shrink_criterion(latents)
+        loss = rec_loss+self.hparams["train_config"]["l2_weight"]*shrink_loss
+        self.log("loss/val", loss)
+        self.log("mse/val", rec_loss)
+    def test_step(self, batch, batch_nb):
+        predictions,latents = self.forward(batch)
+        rec_loss = self.reconstruct_criterion(predictions,batch)
+        shrink_loss = self.shrink_criterion(latents)
+        loss = rec_loss+self.hparams["train_config"]["l2_weight"]*shrink_loss
+        self.log("loss/val", loss)
+        self.log("mse/val", rec_loss)
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(
+            self.model.parameters(),
+            lr=self.hparams["train_config"]["learning_rate"],
+            weight_decay=self.hparams["train_config"]["weight_decay"],
+        )
+        scheduler = self.setup_scheduler(optimizer)
+        return [optimizer], [scheduler]
+    def setup_scheduler(self,optimizer):
+        """Chooses between the cosine learning rate scheduler, linear scheduler, or step scheduler. 
+
+        """
+        if self.hparams["train_config"]["scheduler"] == "linear":
+            scheduler = {
+                    "scheduler": LinearLR(
+                        optimizer,start_factor=self.hparams["train_config"]["start_factor"],
+                        end_factor=self.hparams["train_config"]["end_factor"],
+                        total_iters=self.hparams["train_config"]["warmup_steps"]
+                        ),
+                    "interval": "step",
+                    "name": "learning_rate"
+                    }
+        elif self.hparams["train_config"]["scheduler"] == "step":    
+            scheduler = {
+                "scheduler": torch.optim.lr_scheduler.MultiStepLR(
+                    optimizer, milestones = [60,120,160], gamma = 0.1, last_epoch=-1
+                ),
+                "interval": "epoch",
+                "frequency":1,
+                "name": "learning_rate",
+                }
+        return scheduler
+
+class SingleSessionModule(Autoencoder_Models):
+    """Class for single session autoencoder. 
+    """
+    def __init__(self,hparams):
+        super().__init__(hparams)
+        self.model = models[hparams["model"]](hparams["model_config"])
diff --git a/src/behavioral_autoencoder/networks.py b/src/behavioral_autoencoder/networks.py
new file mode 100644
index 0000000..41461dc
--- /dev/null
+++ b/src/behavioral_autoencoder/networks.py
@@ -0,0 +1,166 @@
+"""
+Network architectures.  
+"""
+import torch.nn as nn
+
+class PreConvBlock(nn.Module):
+    def __init__(self, 
+                 in_channels = 1, 
+                 out_channels = 16, 
+                 kernel = 3, 
+                 stride=1, 
+                 pool_size = 2,
+                 use_batch_norm = False):
+        """
+        Args:
+            in_channels (int): Number of input channels.
+            out_channels (int): Number of output channels.
+            stride (int): Stride for the convolutional layers.
+        """
+        super(PreConvBlock, self).__init__()
+        
+        self.layers = nn.ModuleList([
+            nn.Conv2d(in_channels, out_channels, 
+                      kernel_size=kernel, stride=stride, 
+                      padding=(kernel - stride) // 2, 
+                      bias= not use_batch_norm),
+        ])
+        if use_batch_norm:
+            self.layers.append(nn.BatchNorm2d(out_channels))
+        
+        self.layers.append(nn.ReLU(inplace=True))
+        self.layers.append(nn.MaxPool2d(kernel_size=pool_size))
+    
+    def forward(self, x):
+        for layer in self.layers:
+            x = layer(x)
+        return x
+    
+class ResidualBlock(nn.Module):
+    def __init__(self, 
+                 n_channels = 16, 
+                 kernel = 3, 
+                 stride = 1, 
+                 use_batch_norm = False, 
+                 downsample=None,
+                 pool_size = 4,
+                 n_layers = 3):
+        """
+        Args:
+            in_channels (int): Number of input channels.
+            out_channels (int): Number of output channels.
+            stride (int): Stride for the convolutional layers.
+            downsample (nn.Module, optional): Downsampling layer if input and output dimensions differ.
+        """
+        super(ResidualBlock, self).__init__()
+        
+        self.layers = nn.ModuleList()
+        for i_layer in range(n_layers):
+            self.layers.append(nn.Conv2d(n_channels, n_channels, 
+                                          kernel_size=kernel, stride=stride, 
+                                          padding=(kernel - stride) // 2, 
+                                          bias= not use_batch_norm))
+            if use_batch_norm:
+                self.layers.append(nn.BatchNorm2d(n_channels))
+            if i_layer!=(n_layers - 1):
+                self.layers.append(nn.ReLU(inplace=True))
+        
+        self.downsample = downsample  # Optional downsampling layer
+        self.post_residual_layers = nn.ModuleList([nn.ReLU(inplace=True)])
+        if pool_size is not None:
+            self.post_residual_layers.append(nn.MaxPool2d(kernel_size=pool_size))
+    
+    def forward(self, x):
+        identity = x
+
+        # Pass through the layers in the ModuleList
+        for layer in self.layers:
+            x = layer(x)
+
+        # Apply downsampling to the identity if necessary
+        if self.downsample:
+            identity = self.downsample(identity)
+
+        # Add the residual connection
+        x += identity
+        for layer in self.post_residual_layers:
+            x = layer(x)
+
+        return x
+    
+class Encoder(nn.Module):
+
+    def __init__(self, 
+                 configs):
+        """
+        Args:
+            in_channels (int): Number of input channels.
+            out_channels (int): Number of output channels.
+            stride (int): Stride for the convolutional layers.
+        """
+        super(Encoder, self).__init__()
+        
+        self.configs = configs
+
+        self.residual_layers = nn.ModuleList()
+
+        for i_blocks in range(configs['num_blocks']):
+            self.residual_layers.append(PreConvBlock(configs['in_channels_%d'%i_blocks],
+                                            configs['out_channels_%d'%i_blocks],
+                                            configs['kernel_preconv'],
+                                            configs['stride_preconv'],
+                                            configs['pool_size_preconv_%d'%i_blocks],
+                                            configs['use_batch_norm_preconv']))
+            self.residual_layers.append(ResidualBlock(configs['out_channels_%d'%i_blocks],
+                                             configs['kernel_residual'],
+                                             configs['stride_residual'],
+                                             configs['use_batch_norm_residual'],
+                                             pool_size=configs['pool_size_residual_%d'%i_blocks],
+                                             n_layers=configs['n_layers_residual']))
+            
+        self.linear_layers = nn.ModuleList([nn.Linear(configs['out_conv'], configs['out_linear'], bias = not configs['use_batch_norm_linear']),])
+        if configs['use_batch_norm_linear']:
+            self.linear_layers.append(nn.BatchNorm1d(configs['out_linear']))
+        self.linear_layers.append(nn.ReLU(inplace=True))
+        self.linear_layers.append(nn.Linear(configs['out_linear'], configs['embed_size']))
+
+    def forward(self, x):
+        bs, seq_length, c, h ,w = x.size()
+        
+        x = x.view(bs*seq_length, c, h, w)
+        for layer in self.residual_layers:
+            x = layer(x)
+        
+        x = x.view(bs*seq_length, -1)
+        for layer in self.linear_layers:
+            x = layer(x)
+        
+        x = x.view(bs, seq_length, -1)
+        return x
+
+class SingleSessionDecoder(nn.Module):
+    def __init__(self, configs):
+        super(SingleSessionDecoder, self).__init__()
+        self.configs = configs
+        self.linear_layer = nn.Linear(configs['embed_size'], configs['image_height']*configs['image_width'])
+
+    def forward(self, x):
+        bs, seq_length, _ = x.size()
+        x = self.linear_layer(x)
+        x = x.view(bs, seq_length, 1, self.configs['image_height'], self.configs['image_width'])
+        return x
+
+class SingleSessionAutoEncoder(nn.Module):
+    """
+    This autoencoder takes in data of shape (batch,sequence (for a sequence of images), channels (1 for grayscale), height, width). Thus, it is set up to work with multidimensional, video structured data, although in practice we work with grayscale and can usually flatten across a sequence of images for the examples that we often care about. The latents are of shape batch,sequence,embedding_size. 
+    """
+    def __init__(self, configs):
+        super(SingleSessionAutoEncoder, self).__init__()
+        self.configs = configs
+        self.encoder = Encoder(configs)
+        self.decoder = SingleSessionDecoder(configs)
+    
+    def forward(self, x):
+        z = self.encoder(x)
+        x = self.decoder(z)
+        return x, z

From 602a8e2fe07cefbd6de429989aa9d3d8ceaccfd3 Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@DN52e64f.SUNet>
Date: Tue, 4 Mar 2025 17:24:26 -0800
Subject: [PATCH 04/34] added rudimentary tests of function for all components.
 Can get a baby training loop to train.

---
 tests/test_dataloading.py |   6 +-
 tests/test_dataset.py     |   4 +-
 tests/test_module.py      | 136 ++++++++++++++++++++++++++++++++++++++
 tests/test_networks.py    |  28 ++++++++
 4 files changed, 170 insertions(+), 4 deletions(-)
 create mode 100644 tests/test_module.py
 create mode 100644 tests/test_networks.py

diff --git a/tests/test_dataloading.py b/tests/test_dataloading.py
index 83224a6..a407112 100644
--- a/tests/test_dataloading.py
+++ b/tests/test_dataloading.py
@@ -98,7 +98,7 @@ def test_init(self,tmp_path):
                 "trial_pattern":None
                 }
         sfdm = SessionFramesDataModule(data_config,10,2,10,1)
-        assert sfdm.mean_image.shape == (crop_config["target_h"],crop_config["target_w"])
+        assert sfdm.mean_image.shape == (1,crop_config["target_h"],crop_config["target_w"])
 
     def test_train_dataloader(self,tmp_path):
         with open(self.config_path,"r") as f:
@@ -121,6 +121,6 @@ def test_train_dataloader(self,tmp_path):
                 }
         sfdm = SessionFramesDataModule(data_config,10,2,10,1,)
         dataloader = sfdm.train_dataloader()
-        for batch in dataloader:
-            assert batch.shape[1:] == (crop_config["target_h"],crop_config["target_w"])
         
+        for batch in dataloader:
+            assert batch.shape[1:] == (1,1,crop_config["target_h"],crop_config["target_w"])
diff --git a/tests/test_dataset.py b/tests/test_dataset.py
index 897aa60..c8781f8 100644
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -285,7 +285,9 @@ def test_init(self, temp_hierarchical_folder):
         # Test image properties
         first_image = dataset[0]
         assert isinstance(first_image, torch.Tensor), "Dataset should return numpy arrays"
-        assert first_image.shape[0] == 1, "Images should be 2D grayscale (H,W)"
+        assert len(first_image.shape) == 4
+        assert first_image.shape[0] == 1, "Sequence dimension should be 0"
+        assert first_image.shape[1] == 1, "Images should be 2D grayscale (H,W)"
         assert first_image.dtype == torch.float32, "Images should be float32"
         
         # Test dataset length
diff --git a/tests/test_module.py b/tests/test_module.py
new file mode 100644
index 0000000..40afcf5
--- /dev/null
+++ b/tests/test_module.py
@@ -0,0 +1,136 @@
+"""
+"""
+from behavioral_autoencoder.module import SingleSessionModule
+from behavioral_autoencoder.dataset import CropResizeProportion
+from behavioral_autoencoder.dataloading import SessionFramesDataModule
+from pytorch_lightning.loggers import TensorBoardLogger
+import numpy as np
+import pytorch_lightning as pl
+import cv2
+import os 
+import json
+
+here = os.path.join(os.path.abspath(os.path.dirname(__file__)))
+
+def temp_hierarchical_folder_generator(tmp_path, n_trials=3, n_ims_per_trial=10, extra_files=None):
+    """Creates a temporary hierarchical folder structure for testing.
+    
+    This fixture creates a folder structure that mimics a session with multiple trials,
+    where each trial contains randomly sampled images. The structure is:
+    temp_session/
+        ├── 0_trial/
+        │   ├── frame_000000.png
+        │   ├── frame_000001.png
+        │   ├── ...
+        │   └── extra_file.txt
+        ├── 1_trial/
+        │   ├── frame_000000.png
+        │   ├── ...
+        │   └── extra_file.txt
+        └── ...
+    
+    Parameters
+    ----------
+    tmp_path : Path
+        Pytest fixture providing temporary directory path
+    n_trials : int, optional
+        Number of trial folders to create, by default 3
+    n_ims_per_trial : int, optional
+        Number of images to create per trial, by default 10
+    extra_files : list of str, optional
+        List of extra file names to create in each trial directory
+        
+    Returns
+    -------
+    Path
+        Path to the created temporary session directory
+    """
+    # Set random seed for reproducibility
+    np.random.seed(42)
+    
+    # Load example images
+    example_images = np.load('./test_data/example_images.npy')*255
+    
+    # Create session directory
+    session_dir = tmp_path / "temp_session"
+    session_dir.mkdir()
+    
+    # Create trial folders and populate with images
+    for trial in range(n_trials):
+        trial_dir = session_dir / f"{trial}_trial"
+        trial_dir.mkdir()
+        
+        # Randomly sample images
+        selected_images = example_images[
+            np.random.choice(len(example_images), n_ims_per_trial, replace=True)
+        ]
+        
+        # Save images as PNGs
+        for i, img in enumerate(selected_images):
+            img_path = trial_dir / f"frame_{i:06d}.png"
+            cv2.imwrite(str(img_path), img)
+        
+        # Create extra files within each trial folder if specified
+        if extra_files:
+            for filename in extra_files:
+                (trial_dir / filename).touch()
+    
+    return session_dir
+
+class Test_SingleSessionModule():
+    model_config_path = os.path.join(here,"..","configs","model_configs","alm_default.json")
+    train_config_path = os.path.join(here,"..","configs","train_configs","alm_default.json")
+    crop_config_path = os.path.join(here,"..","configs","crop_configs","alm_side.json")
+    def test_init(self):
+        with open(self.model_config_path,"r") as f:
+            model_config = json.load(f)
+        with open(self.train_config_path,"r") as f:
+            train_config = json.load(f)
+        hparams = {
+                "model":"single_session_autoencoder",
+                "model_config":model_config,
+                "train_config":train_config
+                }
+        ssm = SingleSessionModule(hparams)
+    def test_baby_train_loop(self,tmp_path):
+        model_config_path = os.path.join(here,"..","configs","model_configs","alm_default.json")
+        train_config_path = os.path.join(here,"..","configs","train_configs","alm_default.json")
+
+        with open(self.model_config_path,"r") as f:
+            model_config = json.load(f)
+        with open(self.train_config_path,"r") as f:
+            train_config = json.load(f)
+        with open(self.crop_config_path,"r") as f:
+            crop_config = json.load(f)
+
+        hparams = {
+                "model":"single_session_autoencoder",
+                "model_config":model_config,
+                "train_config":train_config
+                }
+        ssm = SingleSessionModule(hparams)
+
+        session_dir = temp_hierarchical_folder_generator(
+            tmp_path,
+        )
+
+        alm_cropping = CropResizeProportion(self.crop_config_path)
+        data_config = {
+                "data_path":session_dir,
+                "transform":alm_cropping,
+                "extension":".png",
+                "trial_pattern":None
+                }
+        sfdm = SessionFramesDataModule(data_config,10,2,10,1)
+        logger = TensorBoardLogger("tb_logs",name="test_single_session_auto",log_graph=True)
+        trainer = pl.Trainer(
+            max_epochs=1,
+            accelerator='cpu',  # Explicitly use CPU for testing
+            enable_checkpointing=False,  # Disable for testing
+            logger=logger,  # Disable logging for testing
+            enable_progress_bar=True,  # See training progress
+        )
+        trainer.fit(ssm,sfdm)
+        assert trainer.current_epoch == 1, "Training should complete 2 epochs"
+        assert trainer.global_step > 0, "Should have completed some training steps"
+
diff --git a/tests/test_networks.py b/tests/test_networks.py
new file mode 100644
index 0000000..ed23580
--- /dev/null
+++ b/tests/test_networks.py
@@ -0,0 +1,28 @@
+"""
+Test that networks work as intended. 
+"""
+import os
+import torch
+import json
+from behavioral_autoencoder.networks import SingleSessionAutoEncoder
+
+here = os.path.abspath(os.path.dirname(__file__))
+
+class Test_SingleSessionAutoEncoder():
+    config_path = os.path.join(here,"..","configs","model_configs","alm_default.json")
+    def test_init(self):
+        with open(self.config_path,"r") as f:
+            config = json.load(f)
+        ssae = SingleSessionAutoEncoder(config)
+    def test_forward(self):
+        with open(self.config_path,"r") as f:
+            config = json.load(f)
+        batch_size=10    
+        dummy_input = torch.zeros(10,1,1,config["image_height"],config["image_width"])
+        ssae = SingleSessionAutoEncoder(config)
+        output = ssae(dummy_input)
+        assert output[0].shape == (10,1,1,config["image_height"],config["image_width"])
+        assert output[1].shape == (10,1,config["embed_size"])
+
+
+

From 16ae2584690a660e50057644b4345d4af126ed31 Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@DN52e64f.SUNet>
Date: Tue, 4 Mar 2025 17:24:53 -0800
Subject: [PATCH 05/34] added config params for training of ALM network.

---
 configs/model_configs/alm_default.json | 24 ++++++++++++++++++++++++
 configs/train_configs/alm_default.json | 10 ++++++++++
 2 files changed, 34 insertions(+)
 create mode 100644 configs/model_configs/alm_default.json
 create mode 100644 configs/train_configs/alm_default.json

diff --git a/configs/model_configs/alm_default.json b/configs/model_configs/alm_default.json
new file mode 100644
index 0000000..36d2db6
--- /dev/null
+++ b/configs/model_configs/alm_default.json
@@ -0,0 +1,24 @@
+{
+"num_blocks": 2,
+"in_channels_0": 1,
+"out_channels_0": 16,
+"in_channels_1": 16,
+"out_channels_1": 32,
+"kernel_preconv": 3,
+"stride_preconv": 1,
+"pool_size_preconv_0": 2,
+"pool_size_preconv_1": 4,
+"use_batch_norm_preconv": false,
+"kernel_residual": 3,
+"stride_residual": 1,
+"use_batch_norm_residual": false,
+"pool_size_residual_0": null,
+"pool_size_residual_1": 4,
+"n_layers_residual": 3,
+"out_conv": 288,
+"out_linear": 128,
+"embed_size": 16,
+"use_batch_norm_linear": false,
+"image_height": 120,
+"image_width": 112
+}
diff --git a/configs/train_configs/alm_default.json b/configs/train_configs/alm_default.json
new file mode 100644
index 0000000..b8d6b29
--- /dev/null
+++ b/configs/train_configs/alm_default.json
@@ -0,0 +1,10 @@
+{
+"scheduler":"linear",
+"start_factor":0.1,
+"end_factor":1,
+"warmup_steps":10,
+"max_epochs":500,
+"weight_decay":0,
+"l2_weight":1,
+"learning_rate":0
+}

From f8f0ca6de5e1d8cbc953308631650e850ea05ad3 Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@DN52e64f.SUNet>
Date: Tue, 4 Mar 2025 18:18:35 -0800
Subject: [PATCH 06/34] have a script that should in theory train

---
 scripts/train_single_session.py | 90 +++++++++++++++++++++++++++++++++
 1 file changed, 90 insertions(+)
 create mode 100644 scripts/train_single_session.py

diff --git a/scripts/train_single_session.py b/scripts/train_single_session.py
new file mode 100644
index 0000000..1575149
--- /dev/null
+++ b/scripts/train_single_session.py
@@ -0,0 +1,90 @@
+"""Train a single session autoencoder on provided data. 
+
+Saves checkpoints for the corresponding model, outputs to tensorboard, and finally dumps all predictions and latents into a save directory. 
+
+"""
+import os
+import fire
+import joblib
+import datetime
+from behavioral_autoencoder.module import SingleSessionModule
+from behavioral_autoencoder.dataset import CropResizeProportion
+from behavioral_autoencoder.dataloading import SessionFramesDataModule
+from behavioral_autoencoder.eval import get_all_predicts_latents
+from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.loggers import TensorBoardLogger
+
+here = os.path.join(os.path.abspath(os.path.dirname(__file__)))
+
+def main(model_config_path,train_config_path,data_path,crop_config_path):
+    """This main function takes as input four paths. These paths indicate the model configuration parameters, training configuration parameters, path to the data directory, and cropping configuration, respectively. By default we assume that we are training a single session autoencoder.  
+    """
+    ## Model setup 
+    with open(model_config_path,"r") as f:
+        model_config = json.load(f)
+    with open(train_config_path,"r") as f:
+        train_config = json.load(f)
+    model_name = "single_session_autoencoder"    
+
+    hparams = {
+            "model":model_name,
+            "model_config":model_config,
+            "train_config":train_config
+            }
+
+    ssm = SingleSessionModule(hparams)
+
+    ## Data setup 
+    alm_cropping = CropResizeProportion(crop_config_path)
+    data_config = {
+            "data_path":session_dir,
+            "transform":alm_cropping,
+            "extension":".png",
+            "trial_pattern":None
+            }
+    sfdm = SessionFramesDataModule(
+            data_config,
+            train_config["batch_size"],
+            train_config["num_workers"],
+            train_config["subsample_rate"],
+            train_config["subsample_offset"])
+
+    ## Set up logging and trainer
+    date=datetime.datetime.now().strftime("%m-%d-%y")
+    time=datetime.datetime.now().strftime("%H_%M_%S")
+    timestamp_model = os.path.join(here,"models",model_name,date,time)
+    timestamp_pred = os.path.join(here,"preds",pred_name,date,time)
+    logger = TensorBoardLogger("tb_logs",name="test_single_session_auto",log_graph=True)
+
+    checkpoint = ModelCheckpoint(monitor="acc/mse", mode="max", save_last=False, dirpath = outdir)
+
+    trainer = pl.Trainer(
+        fast_dev_run=train_config["fast_dev_run"],
+        max_epochs=train_config["max_epochs"],
+        accelerator=train_config["accelerator"],  # Explicitly use CPU for testing
+        enable_checkpointing=True,  # Disable for testing
+        checkpoint_callback = checkpoint,
+        log_every_n_steps = 1,
+        logger=logger,  # Disable logging for testing
+        enable_progress_bar=True,  # See training progress
+    )
+
+    ## Fit the model
+    trainer.fit(ssm,sfdm)
+
+    ## Get out predictions 
+    preds,latents = get_all_predicts_latents(ssm,sfdm,train_config["batch_size"],train_config["num_workers"])
+
+    ## Save out all relevant metadata. 
+    os.mkdir(timestamp_pred)
+    joblib.dump(preds,os.path.join(timestamp_pred,"preds"))
+    joblib.dump(latents,os.path.join(timestamp_pred,"latents"))
+    with open(os.path.join(timestamp_pred,"model_config"),"w") as f:
+        json.dump(hparams)
+
+    with open(os.path.join(timestamp_pred,"data_config"),"w") as f:
+        json.dump(data_config)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

From 38cf7136d81e0b7cf4bc603b968df8bf562a296f Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@Taigas-MacBook-Air.local>
Date: Tue, 4 Mar 2025 22:50:09 -0800
Subject: [PATCH 07/34] got training script to run in dev mode.

---
 scripts/train_single_session.py    | 64 ++++++++++++++++++++----------
 src/behavioral_autoencoder/eval.py | 22 ++++++++++
 2 files changed, 66 insertions(+), 20 deletions(-)
 create mode 100644 src/behavioral_autoencoder/eval.py

diff --git a/scripts/train_single_session.py b/scripts/train_single_session.py
index 1575149..6f36ee2 100644
--- a/scripts/train_single_session.py
+++ b/scripts/train_single_session.py
@@ -5,8 +5,10 @@
 """
 import os
 import fire
+import json
 import joblib
 import datetime
+import pytorch_lightning as pl
 from behavioral_autoencoder.module import SingleSessionModule
 from behavioral_autoencoder.dataset import CropResizeProportion
 from behavioral_autoencoder.dataloading import SessionFramesDataModule
@@ -16,32 +18,45 @@
 
 here = os.path.join(os.path.abspath(os.path.dirname(__file__)))
 
-def main(model_config_path,train_config_path,data_path,crop_config_path):
+def main(model_config_path, train_config_path, data_path, data_config_path):
     """This main function takes as input four paths. These paths indicate the model configuration parameters, training configuration parameters, path to the data directory, and cropping configuration, respectively. By default we assume that we are training a single session autoencoder.  
     """
+    print("\n=== Starting Single Session Autoencoder Training ===")
+    
     ## Model setup 
+    print("\nLoading configurations...")
     with open(model_config_path,"r") as f:
         model_config = json.load(f)
     with open(train_config_path,"r") as f:
         train_config = json.load(f)
     model_name = "single_session_autoencoder"    
 
+    print(f"Model config: {model_config}")
+    print(f"Training config: {train_config}")
+
     hparams = {
             "model":model_name,
             "model_config":model_config,
             "train_config":train_config
             }
 
+    print("\nInitializing model...")
     ssm = SingleSessionModule(hparams)
 
     ## Data setup 
-    alm_cropping = CropResizeProportion(crop_config_path)
+    with open(data_config_path,"r") as f:
+        data_process_config = json.load(f)
+    print("\nSetting up data...")
+    alm_cropping = CropResizeProportion(data_config_path)
     data_config = {
-            "data_path":session_dir,
+            "data_path":data_path,
             "transform":alm_cropping,
-            "extension":".png",
-            "trial_pattern":None
+            "extension":data_process_config["extension"],
+            "trial_pattern":data_process_config["trial_pattern"]
             }
+    print(f"Data config: {data_config}")
+    
+    print("Initializing data module...")
     sfdm = SessionFramesDataModule(
             data_config,
             train_config["batch_size"],
@@ -50,41 +65,50 @@ def main(model_config_path,train_config_path,data_path,crop_config_path):
             train_config["subsample_offset"])
 
     ## Set up logging and trainer
+    print("\nSetting up logging and checkpoints...")
     date=datetime.datetime.now().strftime("%m-%d-%y")
     time=datetime.datetime.now().strftime("%H_%M_%S")
     timestamp_model = os.path.join(here,"models",model_name,date,time)
-    timestamp_pred = os.path.join(here,"preds",pred_name,date,time)
+    timestamp_pred = os.path.join(here,"preds",model_name,date,time)
+    print(f"Model will be saved to: {timestamp_model}")
+    print(f"Predictions will be saved to: {timestamp_pred}")
+    
     logger = TensorBoardLogger("tb_logs",name="test_single_session_auto",log_graph=True)
+    checkpoint = ModelCheckpoint(monitor="mse/val", mode="max", save_last=False, dirpath=timestamp_model)
 
-    checkpoint = ModelCheckpoint(monitor="acc/mse", mode="max", save_last=False, dirpath = outdir)
-
+    print("\nInitializing trainer...")
     trainer = pl.Trainer(
         fast_dev_run=train_config["fast_dev_run"],
         max_epochs=train_config["max_epochs"],
-        accelerator=train_config["accelerator"],  # Explicitly use CPU for testing
-        enable_checkpointing=True,  # Disable for testing
-        checkpoint_callback = checkpoint,
-        log_every_n_steps = 1,
-        logger=logger,  # Disable logging for testing
-        enable_progress_bar=True,  # See training progress
+        accelerator=train_config["accelerator"],
+        enable_checkpointing=True,
+        callbacks=[checkpoint],
+        log_every_n_steps=1,
+        logger=logger,
+        enable_progress_bar=True,
     )
 
     ## Fit the model
+    print(f"\nStarting training for {train_config['max_epochs']} epochs...")
     trainer.fit(ssm,sfdm)
+    print("Training completed!")
 
     ## Get out predictions 
+    print("\nGenerating predictions and latents...")
     preds,latents = get_all_predicts_latents(ssm,sfdm,train_config["batch_size"],train_config["num_workers"])
 
-    ## Save out all relevant metadata. 
-    os.mkdir(timestamp_pred)
+    ## Save out all relevant metadata
+    print("\nSaving results...")
+    os.makedirs(timestamp_pred, exist_ok=True)
     joblib.dump(preds,os.path.join(timestamp_pred,"preds"))
     joblib.dump(latents,os.path.join(timestamp_pred,"latents"))
     with open(os.path.join(timestamp_pred,"model_config"),"w") as f:
-        json.dump(hparams)
-
+        json.dump(hparams, f)
     with open(os.path.join(timestamp_pred,"data_config"),"w") as f:
-        json.dump(data_config)
-
+        json.dump(data_config, f)
+    
+    print("\n=== Training Complete ===")
+    print(f"Results saved to: {timestamp_pred}")
 
 if __name__ == "__main__":
     fire.Fire(main)
diff --git a/src/behavioral_autoencoder/eval.py b/src/behavioral_autoencoder/eval.py
new file mode 100644
index 0000000..9b76689
--- /dev/null
+++ b/src/behavioral_autoencoder/eval.py
@@ -0,0 +1,22 @@
+"""
+Functions for two kinds of evaluation:
+    1. Bulk extraction of latents and predictions from the last timepoint. 
+    2. Evaluation of the latent space. 
+"""
+from torch.utils.data import DataLoader
+import torch
+from tqdm import tqdm
+
+def get_all_predicts_latents(trained_model,datamodule,batch_size,num_workers):
+    """
+    Get latents for the entire video, having trained on a subsampled set. 
+
+    """
+    full_dataloader = DataLoader(datamodule.dataset,batch_size=batch_size,num_workers=num_workers)
+    predictions = []
+    latents = []
+    for batch in tqdm(full_dataloader):
+        prediction,latent = trained_model(batch)
+        predictions.append(prediction)
+        latents.append(latent)
+    return torch.concatenate(predictions), torch.concatenate(latents)    

From e7ec537dcc4c216ec8a2588792e14e4c1fc0a26c Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@Taigas-MacBook-Air.local>
Date: Tue, 4 Mar 2025 22:50:42 -0800
Subject: [PATCH 08/34] added configs for debugging and training.

---
 configs/data_configs/alm_side.json         |  8 ++++++++
 configs/train_configs/alm_default_dev.json | 17 +++++++++++++++++
 2 files changed, 25 insertions(+)
 create mode 100644 configs/data_configs/alm_side.json
 create mode 100644 configs/train_configs/alm_default_dev.json

diff --git a/configs/data_configs/alm_side.json b/configs/data_configs/alm_side.json
new file mode 100644
index 0000000..c0f291d
--- /dev/null
+++ b/configs/data_configs/alm_side.json
@@ -0,0 +1,8 @@
+{
+"proportional_h_coord_top": 0.21666666666666667,
+"target_h": 120,
+"target_w": 112,
+"extension": ".png",
+"trial_pattern": "^CW44_20240522153954_side_trial_"
+
+}
diff --git a/configs/train_configs/alm_default_dev.json b/configs/train_configs/alm_default_dev.json
new file mode 100644
index 0000000..cd48064
--- /dev/null
+++ b/configs/train_configs/alm_default_dev.json
@@ -0,0 +1,17 @@
+{
+"scheduler":"linear",
+"start_factor":0.1,
+"end_factor":1,
+"warmup_steps":10,
+"max_epochs":500,
+"weight_decay":0,
+"l2_weight":1,
+"learning_rate":0,
+"batch_size":10,
+"num_workers":2,
+"subsample_rate":10,
+"subsample_offset":0,
+"max_epochs":100,
+"accelerator":"cpu",
+"fast_dev_run":true
+}

From 75d36be39943fcb9a8cc2c870d0808316031c380 Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@Taigas-MacBook-Air.local>
Date: Tue, 4 Mar 2025 22:52:15 -0800
Subject: [PATCH 09/34] fixed some attributes in dataloading.

---
 configs/train_configs/alm_default.json    |  9 ++++++++-
 requirements_cpu.txt                      | 14 ++++++++++----
 src/behavioral_autoencoder/data_utils.py  |  1 -
 src/behavioral_autoencoder/dataloading.py |  8 ++++----
 4 files changed, 22 insertions(+), 10 deletions(-)

diff --git a/configs/train_configs/alm_default.json b/configs/train_configs/alm_default.json
index b8d6b29..5d4e0fd 100644
--- a/configs/train_configs/alm_default.json
+++ b/configs/train_configs/alm_default.json
@@ -6,5 +6,12 @@
 "max_epochs":500,
 "weight_decay":0,
 "l2_weight":1,
-"learning_rate":0
+"learning_rate":0,
+"batch_size":10,
+"num_workers":2,
+"subsample_rate":10,
+"subsample_offset":0,
+"max_epochs":500,
+"accelerator":"gpu"
+"fast_dev_run":false
 }
diff --git a/requirements_cpu.txt b/requirements_cpu.txt
index 7fd84b2..5b3fc31 100644
--- a/requirements_cpu.txt
+++ b/requirements_cpu.txt
@@ -1,4 +1,10 @@
-pip install torch  # should specify a version later
-pip install numpy
-pip install matplotlib
-pip install pytest
+torch  # should specify a version later
+torchvision
+joblib
+pytorch_lightning
+numpy
+matplotlib
+pytest
+opencv-python
+fire
+scikit-image
diff --git a/src/behavioral_autoencoder/data_utils.py b/src/behavioral_autoencoder/data_utils.py
index 2b4823b..308b5d5 100644
--- a/src/behavioral_autoencoder/data_utils.py
+++ b/src/behavioral_autoencoder/data_utils.py
@@ -99,7 +99,6 @@ def main(video_dir,frame_dir,video_suffix=".avi"):
         with open(frame_dir / "annotations.txt", "a") as f:
             f.write(f"{video_dir_name} {first} {last} 0\n")
 
-def npy_to_frames()
 
 if __name__ == "__main__":
     fire.Fire(main)
diff --git a/src/behavioral_autoencoder/dataloading.py b/src/behavioral_autoencoder/dataloading.py
index 67bab10..5fcde8d 100644
--- a/src/behavioral_autoencoder/dataloading.py
+++ b/src/behavioral_autoencoder/dataloading.py
@@ -103,13 +103,13 @@ def subtract_mean_image(self,x):
 
     def setup(self,stage):
         _ = self.dataset_config.pop("transform")
-        dataset = SessionFramesTorchvision(self.data_path,transform = self.transform,**self.dataset_config)
-        all_indices = np.arange(len(dataset))
+        self.dataset = SessionFramesTorchvision(self.data_path,transform = self.transform,**self.dataset_config)
+        all_indices = np.arange(len(self.dataset))
         ## Subsample indices
         train_inds = all_indices[self.subsample_offset::self.subsample_rate]
         test_inds = [i for i in all_indices if not (i in train_inds)]
-        self.trainset = Subset(dataset,train_inds)
-        self.valset = Subset(dataset,test_inds)
+        self.trainset = Subset(self.dataset,train_inds)
+        self.valset = Subset(self.dataset,test_inds)
 
     def train_dataloader(self,shuffle=True):
         dataloader = DataLoader(

From 111a8081f29d0cc59739ce91c931af4001edc119 Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@Taigas-MacBook-Air.local>
Date: Tue, 4 Mar 2025 22:52:41 -0800
Subject: [PATCH 10/34] deleted crop configs

---
 configs/crop_configs/alm_side.json | 5 -----
 1 file changed, 5 deletions(-)
 delete mode 100644 configs/crop_configs/alm_side.json

diff --git a/configs/crop_configs/alm_side.json b/configs/crop_configs/alm_side.json
deleted file mode 100644
index d0419dd..0000000
--- a/configs/crop_configs/alm_side.json
+++ /dev/null
@@ -1,5 +0,0 @@
-{
-"proportional_h_coord_top": 0.21666666666666667,
-"target_h": 120,
-"target_w": 112
-}

From a005bf894102ab0cc38023ec0aa85892d1617ce9 Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@Taigas-MacBook-Air.local>
Date: Tue, 4 Mar 2025 22:53:01 -0800
Subject: [PATCH 11/34] fixed up tests for data configs.

---
 tests/test_dataloading.py |  2 +-
 tests/test_dataset.py     |  2 +-
 tests/test_module.py      | 50 ++++++++++++++++++++++++++++++++++++++-
 3 files changed, 51 insertions(+), 3 deletions(-)

diff --git a/tests/test_dataloading.py b/tests/test_dataloading.py
index a407112..b94103c 100644
--- a/tests/test_dataloading.py
+++ b/tests/test_dataloading.py
@@ -77,7 +77,7 @@ def temp_hierarchical_folder_generator(tmp_path, n_trials=3, n_ims_per_trial=10,
     return session_dir
 
 class Test_SessionFramesDataModule():
-    config_path = os.path.join(here,"..","configs","crop_configs","alm_side.json")
+    config_path = os.path.join(here,"..","configs","data_configs","alm_side.json")
     def test_init(self,tmp_path):
         with open(self.config_path,"r") as f:
             crop_config = json.load(f)
diff --git a/tests/test_dataset.py b/tests/test_dataset.py
index c8781f8..fd263a5 100644
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -264,7 +264,7 @@ def test_getitem_performance(self, tmp_path):
                 f"Methods returned different results for index {idx}"
 
 class Test_SessionFramesTorchvision:
-    config_path = os.path.join(here,"..","configs","crop_configs","alm_side.json")
+    config_path = os.path.join(here,"..","configs","data_configs","alm_side.json")
     def test_init(self, temp_hierarchical_folder):
         """Test the SessionFramesTorchvision initialization and basic properties, including with default cropping given by CropResizeProportion with default.
         
diff --git a/tests/test_module.py b/tests/test_module.py
index 40afcf5..c9d0aca 100644
--- a/tests/test_module.py
+++ b/tests/test_module.py
@@ -5,6 +5,7 @@
 from behavioral_autoencoder.dataloading import SessionFramesDataModule
 from pytorch_lightning.loggers import TensorBoardLogger
 import numpy as np
+from torch.utils.data import DataLoader
 import pytorch_lightning as pl
 import cv2
 import os 
@@ -80,7 +81,7 @@ def temp_hierarchical_folder_generator(tmp_path, n_trials=3, n_ims_per_trial=10,
 class Test_SingleSessionModule():
     model_config_path = os.path.join(here,"..","configs","model_configs","alm_default.json")
     train_config_path = os.path.join(here,"..","configs","train_configs","alm_default.json")
-    crop_config_path = os.path.join(here,"..","configs","crop_configs","alm_side.json")
+    crop_config_path = os.path.join(here,"..","configs","data_configs","alm_side.json")
     def test_init(self):
         with open(self.model_config_path,"r") as f:
             model_config = json.load(f)
@@ -133,4 +134,51 @@ def test_baby_train_loop(self,tmp_path):
         trainer.fit(ssm,sfdm)
         assert trainer.current_epoch == 1, "Training should complete 2 epochs"
         assert trainer.global_step > 0, "Should have completed some training steps"
+    def test_eval_save(self,tmp_path):
+        model_config_path = os.path.join(here,"..","configs","model_configs","alm_default.json")
+        train_config_path = os.path.join(here,"..","configs","train_configs","alm_default.json")
+
+        with open(self.model_config_path,"r") as f:
+            model_config = json.load(f)
+        with open(self.train_config_path,"r") as f:
+            train_config = json.load(f)
+        with open(self.crop_config_path,"r") as f:
+            crop_config = json.load(f)
+
+        hparams = {
+                "model":"single_session_autoencoder",
+                "model_config":model_config,
+                "train_config":train_config
+                }
+        ssm = SingleSessionModule(hparams)
+
+        session_dir = temp_hierarchical_folder_generator(
+            tmp_path,
+        )
+
+        alm_cropping = CropResizeProportion(self.crop_config_path)
+        data_config = {
+                "data_path":session_dir,
+                "transform":alm_cropping,
+                "extension":".png",
+                "trial_pattern":None
+                }
+        sfdm = SessionFramesDataModule(data_config,10,2,10,1)
+        logger = TensorBoardLogger("tb_logs",name="test_single_session_auto",log_graph=True)
+        trainer = pl.Trainer(
+            max_epochs=1,
+            accelerator='cpu',  # Explicitly use CPU for testing
+            enable_checkpointing=False,  # Disable for testing
+            logger=logger,  # Disable logging for testing
+            enable_progress_bar=True,  # See training progress
+        )
+        trainer.fit(ssm,sfdm)
+        # Extract out some latents: 
+        full_dataloader = DataLoader(sfdm.dataset,batch_size=10)
+        predictions = []
+        latents = []
+        for batch in full_dataloader:
+            prediction,latent = ssm(batch)
+            predictions.append()
+        import pdb; pdb.set_trace()    
 

From 7a43ab613b0fff0376094d541f6b78e5420d9e26 Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Wed, 5 Mar 2025 06:57:16 +0000
Subject: [PATCH 12/34] added some docstring for bad stub

---
 src/behavioral_autoencoder/data_utils.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/behavioral_autoencoder/data_utils.py b/src/behavioral_autoencoder/data_utils.py
index 2b4823b..e18f4f6 100644
--- a/src/behavioral_autoencoder/data_utils.py
+++ b/src/behavioral_autoencoder/data_utils.py
@@ -99,7 +99,10 @@ def main(video_dir,frame_dir,video_suffix=".avi"):
         with open(frame_dir / "annotations.txt", "a") as f:
             f.write(f"{video_dir_name} {first} {last} 0\n")
 
-def npy_to_frames()
+def npy_to_frames():
+    """
+    """
+
 
 if __name__ == "__main__":
     fire.Fire(main)

From 8de0168e95905fe55181cd5526b23b253abbc448 Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@Taigas-MacBook-Air.local>
Date: Tue, 4 Mar 2025 23:15:35 -0800
Subject: [PATCH 13/34] some messaging for the dataloading.

---
 src/behavioral_autoencoder/dataloading.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/behavioral_autoencoder/dataloading.py b/src/behavioral_autoencoder/dataloading.py
index 5fcde8d..275fd57 100644
--- a/src/behavioral_autoencoder/dataloading.py
+++ b/src/behavioral_autoencoder/dataloading.py
@@ -82,6 +82,7 @@ def __init__(
         self.subtract_mean = trainset_subtract_mean
 
         if self.subtract_mean:
+            print("Calculating mean image")
             self.mean_image = calculate_mean_image(self.data_path,dataset_config,self.subsample_rate,self.subsample_offset)
             subtract_mean = torchvision.transforms.Lambda(self.subtract_mean_image)
             # augment the transformation for our datasets: 
@@ -97,6 +98,7 @@ def __init__(
             self.transform = mean_normed_transform
         else:
             self.transform = self.dataset_config["transform"]
+            print("Done with setup")
 
     def subtract_mean_image(self,x):
         return x-self.mean_image

From 5c984415946411f5c34090e03144820147b4dafb Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@Taigas-MacBook-Air.local>
Date: Tue, 4 Mar 2025 23:18:37 -0800
Subject: [PATCH 14/34] updated mean image calc.

---
 src/behavioral_autoencoder/dataloading.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/src/behavioral_autoencoder/dataloading.py b/src/behavioral_autoencoder/dataloading.py
index 275fd57..d16fb3b 100644
--- a/src/behavioral_autoencoder/dataloading.py
+++ b/src/behavioral_autoencoder/dataloading.py
@@ -1,4 +1,5 @@
 import torchvision
+from tqdm import tqdm
 import numpy as np
 import torch
 from torch.utils.data import Subset,DataLoader
@@ -7,7 +8,7 @@
 from joblib import Memory
 import os
 
-def calculate_mean_image(data_path,dataset_config,subsample_rate,subsample_offset):
+def calculate_mean_image(data_path,dataset_config,subsample_rate,subsample_offset,batch_size,num_workers):
     """
     Calculate mean image from given training set parameters. Gets parameters from SessionFramesDataModule
     TODO: implement caching for this function. 
@@ -20,10 +21,10 @@ def calculate_mean_image(data_path,dataset_config,subsample_rate,subsample_offse
     trainset = Subset(dataset,train_inds)
 
     ## use dataloader to compute sum image 
-    trainloader = DataLoader(trainset,batch_size=10)
+    trainloader = DataLoader(trainset,batch_size=batch_size,num_workers=num_workers)
 
     sum_im = torch.zeros(dataset[0].shape[1:]) ## should be 
-    for data in trainloader:
+    for data in tqdm(trainloader):
         sum_im+=data.sum(axis=0).sum(axis=0) ## sum across the batch and sequence dimensions.
     mean=sum_im/len(trainset)    
     return mean
@@ -83,7 +84,7 @@ def __init__(
 
         if self.subtract_mean:
             print("Calculating mean image")
-            self.mean_image = calculate_mean_image(self.data_path,dataset_config,self.subsample_rate,self.subsample_offset)
+            self.mean_image = calculate_mean_image(self.data_path,dataset_config,self.subsample_rate,self.subsample_offset,self,batch_size,self.num_workers)
             subtract_mean = torchvision.transforms.Lambda(self.subtract_mean_image)
             # augment the transformation for our datasets: 
             if self.dataset_config["transform"] is not None:

From dd438091da58748d01e11fae07357cd37697d999 Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@Taigas-MacBook-Air.local>
Date: Tue, 4 Mar 2025 23:20:29 -0800
Subject: [PATCH 15/34] fixed argument passing

---
 src/behavioral_autoencoder/dataloading.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/behavioral_autoencoder/dataloading.py b/src/behavioral_autoencoder/dataloading.py
index d16fb3b..04f3631 100644
--- a/src/behavioral_autoencoder/dataloading.py
+++ b/src/behavioral_autoencoder/dataloading.py
@@ -84,7 +84,7 @@ def __init__(
 
         if self.subtract_mean:
             print("Calculating mean image")
-            self.mean_image = calculate_mean_image(self.data_path,dataset_config,self.subsample_rate,self.subsample_offset,self,batch_size,self.num_workers)
+            self.mean_image = calculate_mean_image(self.data_path,dataset_config,self.subsample_rate,self.subsample_offset,self.batch_size,self.num_workers)
             subtract_mean = torchvision.transforms.Lambda(self.subtract_mean_image)
             # augment the transformation for our datasets: 
             if self.dataset_config["transform"] is not None:

From d1e7dc10a5009d6bd1dd6f184d17c18d0eb7d5c7 Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Wed, 5 Mar 2025 07:43:28 +0000
Subject: [PATCH 16/34] trying to keep all predictions in memory is infeasible.
 save out training frames.

---
 scripts/train_single_session.py    |  2 +-
 src/behavioral_autoencoder/eval.py | 13 +++++++++++++
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/scripts/train_single_session.py b/scripts/train_single_session.py
index 6f36ee2..8ec996d 100644
--- a/scripts/train_single_session.py
+++ b/scripts/train_single_session.py
@@ -95,7 +95,7 @@ def main(model_config_path, train_config_path, data_path, data_config_path):
 
     ## Get out predictions 
     print("\nGenerating predictions and latents...")
-    preds,latents = get_all_predicts_latents(ssm,sfdm,train_config["batch_size"],train_config["num_workers"])
+    preds,latents = get_dl_predicts_latents(ssm,sfdm.train_dataloader(),train_config["batch_size"],train_config["num_workers"])
 
     ## Save out all relevant metadata
     print("\nSaving results...")
diff --git a/src/behavioral_autoencoder/eval.py b/src/behavioral_autoencoder/eval.py
index 9b76689..ddafb14 100644
--- a/src/behavioral_autoencoder/eval.py
+++ b/src/behavioral_autoencoder/eval.py
@@ -20,3 +20,16 @@ def get_all_predicts_latents(trained_model,datamodule,batch_size,num_workers):
         predictions.append(prediction)
         latents.append(latent)
     return torch.concatenate(predictions), torch.concatenate(latents)    
+
+def get_dl_predicts_latents(trained_model,dataloader,batch_size,num_workers):
+    """
+    Get latents for the entire video, having trained on a subsampled set. 
+
+    """
+    predictions = []
+    latents = []
+    for batch in tqdm(dataloader):
+        prediction,latent = trained_model(batch)
+        predictions.append(prediction)
+        latents.append(latent)
+    return torch.concatenate(predictions), torch.concatenate(latents)    

From 7acd6f91bdc0127086fee82c372373142d1b7211 Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Wed, 5 Mar 2025 07:52:31 +0000
Subject: [PATCH 17/34] tested additional flags on valset

---
 src/behavioral_autoencoder/dataloading.py | 23 ++++++++++++++---------
 tests/test_dataloading.py                 |  5 +++--
 2 files changed, 17 insertions(+), 11 deletions(-)

diff --git a/src/behavioral_autoencoder/dataloading.py b/src/behavioral_autoencoder/dataloading.py
index 04f3631..fcd7925 100644
--- a/src/behavioral_autoencoder/dataloading.py
+++ b/src/behavioral_autoencoder/dataloading.py
@@ -44,9 +44,11 @@ def __init__(
         dataset_config,
         batch_size,
         num_workers,
-        trainset_subsample_rate,
-        trainset_subsample_offset,
-        trainset_subtract_mean=True,
+        train_subsample_rate,
+        train_subsample_offset,
+        train_subtract_mean=True,
+        val_subsample_rate,
+        val_subsample_offset,
         ):
         """
         By default, checks if we have already calculated the image mean on a particular dataset for a particular training set, and if so gets that cached mean.
@@ -78,13 +80,15 @@ def __init__(
         self.dataset_config = dataset_config
         self.batch_size = batch_size
         self.num_workers = num_workers
-        self.subsample_rate = trainset_subsample_rate
-        self.subsample_offset = trainset_subsample_offset
+        self.train_subsample_rate = train_subsample_rate
+        self.train_subsample_offset = train_subsample_offset
+        self.val_subsample_rate = val_subsample_rate
+        self.val_subsample_offset = val_subsample_offset
         self.subtract_mean = trainset_subtract_mean
 
         if self.subtract_mean:
             print("Calculating mean image")
-            self.mean_image = calculate_mean_image(self.data_path,dataset_config,self.subsample_rate,self.subsample_offset,self.batch_size,self.num_workers)
+            self.mean_image = calculate_mean_image(self.data_path,dataset_config,self.train_subsample_rate,self.train_subsample_offset,self.batch_size,self.num_workers)
             subtract_mean = torchvision.transforms.Lambda(self.subtract_mean_image)
             # augment the transformation for our datasets: 
             if self.dataset_config["transform"] is not None:
@@ -109,10 +113,11 @@ def setup(self,stage):
         self.dataset = SessionFramesTorchvision(self.data_path,transform = self.transform,**self.dataset_config)
         all_indices = np.arange(len(self.dataset))
         ## Subsample indices
-        train_inds = all_indices[self.subsample_offset::self.subsample_rate]
-        test_inds = [i for i in all_indices if not (i in train_inds)]
+        train_inds = all_indices[self.train_subsample_offset::self.train_subsample_rate]
+        val_inds = all_indices[self.val_subsample_offset::self.val_subsample_rate]
+        #test_inds = [i for i in all_indices if not (i in train_inds)]
         self.trainset = Subset(self.dataset,train_inds)
-        self.valset = Subset(self.dataset,test_inds)
+        self.valset = Subset(self.dataset,val_inds)
 
     def train_dataloader(self,shuffle=True):
         dataloader = DataLoader(
diff --git a/tests/test_dataloading.py b/tests/test_dataloading.py
index b94103c..928dbc2 100644
--- a/tests/test_dataloading.py
+++ b/tests/test_dataloading.py
@@ -97,7 +97,7 @@ def test_init(self,tmp_path):
                 "extension":".png",
                 "trial_pattern":None
                 }
-        sfdm = SessionFramesDataModule(data_config,10,2,10,1)
+        sfdm = SessionFramesDataModule(data_config,10,2,10,1,10,1)
         assert sfdm.mean_image.shape == (1,crop_config["target_h"],crop_config["target_w"])
 
     def test_train_dataloader(self,tmp_path):
@@ -119,7 +119,8 @@ def test_train_dataloader(self,tmp_path):
                 "extension":".png",
                 "trial_pattern":None
                 }
-        sfdm = SessionFramesDataModule(data_config,10,2,10,1,)
+        sfdm = SessionFramesDataModule(data_config,10,2,10,1,10,1)
+        sfdm.setup("train")
         dataloader = sfdm.train_dataloader()
         
         for batch in dataloader:

From f15553c4d4eaaf7546ab7cfc126729176d8396d5 Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Wed, 5 Mar 2025 07:53:33 +0000
Subject: [PATCH 18/34] eval test failing for some reason

---
 tests/test_module.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/test_module.py b/tests/test_module.py
index c9d0aca..4e689a8 100644
--- a/tests/test_module.py
+++ b/tests/test_module.py
@@ -122,7 +122,7 @@ def test_baby_train_loop(self,tmp_path):
                 "extension":".png",
                 "trial_pattern":None
                 }
-        sfdm = SessionFramesDataModule(data_config,10,2,10,1)
+        sfdm = SessionFramesDataModule(data_config,10,2,10,1,10,1)
         logger = TensorBoardLogger("tb_logs",name="test_single_session_auto",log_graph=True)
         trainer = pl.Trainer(
             max_epochs=1,
@@ -163,7 +163,7 @@ def test_eval_save(self,tmp_path):
                 "extension":".png",
                 "trial_pattern":None
                 }
-        sfdm = SessionFramesDataModule(data_config,10,2,10,1)
+        sfdm = SessionFramesDataModule(data_config,10,2,10,1,10,1)
         logger = TensorBoardLogger("tb_logs",name="test_single_session_auto",log_graph=True)
         trainer = pl.Trainer(
             max_epochs=1,

From 08c20bbdb993ecfc6d47b09f0683628c6016e1a5 Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Wed, 5 Mar 2025 08:11:12 +0000
Subject: [PATCH 19/34] adapted training for high frame count

---
 scripts/train_single_session.py           | 7 +++++--
 src/behavioral_autoencoder/dataloading.py | 2 +-
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/scripts/train_single_session.py b/scripts/train_single_session.py
index 8ec996d..521b6dc 100644
--- a/scripts/train_single_session.py
+++ b/scripts/train_single_session.py
@@ -12,7 +12,7 @@
 from behavioral_autoencoder.module import SingleSessionModule
 from behavioral_autoencoder.dataset import CropResizeProportion
 from behavioral_autoencoder.dataloading import SessionFramesDataModule
-from behavioral_autoencoder.eval import get_all_predicts_latents
+from behavioral_autoencoder.eval import get_all_predicts_latents,get_dl_predicts_latents
 from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.loggers import TensorBoardLogger
 
@@ -62,7 +62,10 @@ def main(model_config_path, train_config_path, data_path, data_config_path):
             train_config["batch_size"],
             train_config["num_workers"],
             train_config["subsample_rate"],
-            train_config["subsample_offset"])
+            train_config["subsample_offset"],
+            train_config["val_subsample_rate"],
+            train_config["val_subsample_offset"]
+            )
 
     ## Set up logging and trainer
     print("\nSetting up logging and checkpoints...")
diff --git a/src/behavioral_autoencoder/dataloading.py b/src/behavioral_autoencoder/dataloading.py
index fcd7925..8b90f7d 100644
--- a/src/behavioral_autoencoder/dataloading.py
+++ b/src/behavioral_autoencoder/dataloading.py
@@ -46,9 +46,9 @@ def __init__(
         num_workers,
         train_subsample_rate,
         train_subsample_offset,
-        train_subtract_mean=True,
         val_subsample_rate,
         val_subsample_offset,
+        trainset_subtract_mean=True,
         ):
         """
         By default, checks if we have already calculated the image mean on a particular dataset for a particular training set, and if so gets that cached mean.

From 4d8ade96913ff106729dfd35373446654d237aef Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Wed, 5 Mar 2025 08:18:30 +0000
Subject: [PATCH 20/34] added back mean image when predicting

---
 src/behavioral_autoencoder/eval.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/behavioral_autoencoder/eval.py b/src/behavioral_autoencoder/eval.py
index ddafb14..6a3ff60 100644
--- a/src/behavioral_autoencoder/eval.py
+++ b/src/behavioral_autoencoder/eval.py
@@ -17,11 +17,11 @@ def get_all_predicts_latents(trained_model,datamodule,batch_size,num_workers):
     latents = []
     for batch in tqdm(full_dataloader):
         prediction,latent = trained_model(batch)
-        predictions.append(prediction)
+        predictions.append(prediction+full_dataloader.mean_image[None,None,:])
         latents.append(latent)
     return torch.concatenate(predictions), torch.concatenate(latents)    
 
-def get_dl_predicts_latents(trained_model,dataloader,batch_size,num_workers):
+def get_dl_predicts_latents(trained_model,dataloader,mean,batch_size,num_workers):
     """
     Get latents for the entire video, having trained on a subsampled set. 
 
@@ -30,6 +30,6 @@ def get_dl_predicts_latents(trained_model,dataloader,batch_size,num_workers):
     latents = []
     for batch in tqdm(dataloader):
         prediction,latent = trained_model(batch)
-        predictions.append(prediction)
+        predictions.append(prediction+mean[None,None,:])
         latents.append(latent)
     return torch.concatenate(predictions), torch.concatenate(latents)    

From da2ccfa5987f65dec904eaa9b804be82a723bb9e Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Thu, 6 Mar 2025 22:20:03 +0000
Subject: [PATCH 21/34] added learning rate monitoring and fixed checkpointing
 conditions.

---
 scripts/train_single_session.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/scripts/train_single_session.py b/scripts/train_single_session.py
index 521b6dc..e1f223a 100644
--- a/scripts/train_single_session.py
+++ b/scripts/train_single_session.py
@@ -13,7 +13,7 @@
 from behavioral_autoencoder.dataset import CropResizeProportion
 from behavioral_autoencoder.dataloading import SessionFramesDataModule
 from behavioral_autoencoder.eval import get_all_predicts_latents,get_dl_predicts_latents
-from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.callbacks import ModelCheckpoint,LearningRateMonitor
 from pytorch_lightning.loggers import TensorBoardLogger
 
 here = os.path.join(os.path.abspath(os.path.dirname(__file__)))
@@ -77,7 +77,8 @@ def main(model_config_path, train_config_path, data_path, data_config_path):
     print(f"Predictions will be saved to: {timestamp_pred}")
     
     logger = TensorBoardLogger("tb_logs",name="test_single_session_auto",log_graph=True)
-    checkpoint = ModelCheckpoint(monitor="mse/val", mode="max", save_last=False, dirpath=timestamp_model)
+    checkpoint = ModelCheckpoint(monitor="mse/val", mode="min", save_last=True, dirpath=timestamp_model)
+    lr_monitor = LearningRateMonitor(logging_interval='epoch')
 
     print("\nInitializing trainer...")
     trainer = pl.Trainer(
@@ -85,7 +86,7 @@ def main(model_config_path, train_config_path, data_path, data_config_path):
         max_epochs=train_config["max_epochs"],
         accelerator=train_config["accelerator"],
         enable_checkpointing=True,
-        callbacks=[checkpoint],
+        callbacks=[checkpoint,lr_monitor],
         log_every_n_steps=1,
         logger=logger,
         enable_progress_bar=True,
@@ -98,7 +99,7 @@ def main(model_config_path, train_config_path, data_path, data_config_path):
 
     ## Get out predictions 
     print("\nGenerating predictions and latents...")
-    preds,latents = get_dl_predicts_latents(ssm,sfdm.train_dataloader(),train_config["batch_size"],train_config["num_workers"])
+    preds,latents = get_dl_predicts_latents(ssm,sfdm.val_dataloader(),sfdm.mean_image,train_config["batch_size"],train_config["num_workers"])
 
     ## Save out all relevant metadata
     print("\nSaving results...")

From 01cbca0720f092bc25277212c4d1dd09293cfe3d Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Thu, 6 Mar 2025 22:20:36 +0000
Subject: [PATCH 22/34] played with learning rate scheduling.

---
 src/behavioral_autoencoder/module.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/behavioral_autoencoder/module.py b/src/behavioral_autoencoder/module.py
index 38a1749..c73c932 100644
--- a/src/behavioral_autoencoder/module.py
+++ b/src/behavioral_autoencoder/module.py
@@ -69,7 +69,7 @@ def setup_scheduler(self,optimizer):
         elif self.hparams["train_config"]["scheduler"] == "step":    
             scheduler = {
                 "scheduler": torch.optim.lr_scheduler.MultiStepLR(
-                    optimizer, milestones = [60,120,160], gamma = 0.1, last_epoch=-1
+                    optimizer, milestones = [10,20,30], gamma = 0.1, last_epoch=-1
                 ),
                 "interval": "epoch",
                 "frequency":1,

From ca177eeae0f9aa6115293affc7dc13b786cf6e07 Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@Taigas-MacBook-Air.local>
Date: Thu, 6 Mar 2025 14:58:51 -0800
Subject: [PATCH 23/34] added chained lr scheduler

---
 src/behavioral_autoencoder/module.py | 21 ++++++++++++++++++++-
 1 file changed, 20 insertions(+), 1 deletion(-)

diff --git a/src/behavioral_autoencoder/module.py b/src/behavioral_autoencoder/module.py
index 38a1749..f48d78e 100644
--- a/src/behavioral_autoencoder/module.py
+++ b/src/behavioral_autoencoder/module.py
@@ -3,7 +3,7 @@
 import torch.nn as nn
 from behavioral_autoencoder.metrics import L2_loss
 from behavioral_autoencoder.networks import SingleSessionAutoEncoder
-from torch.optim.lr_scheduler import LinearLR
+from torch.optim.lr_scheduler import LinearLR,ChainedScheduler
 
 models = {
         "single_session_autoencoder":SingleSessionAutoEncoder
@@ -75,6 +75,25 @@ def setup_scheduler(self,optimizer):
                 "frequency":1,
                 "name": "learning_rate",
                 }
+        elif self.hparams["train_config"]["scheduler"] == "linear_step":    
+            linear_scheduler = {
+                    "scheduler": LinearLR(
+                        optimizer,start_factor=self.hparams["train_config"]["start_factor"],
+                        end_factor=self.hparams["train_config"]["end_factor"],
+                        total_iters=self.hparams["train_config"]["warmup_steps"]
+                        ),
+                    "interval": "step",
+                    "name": "learning_rate"
+                    }
+            step_scheduler = {
+                "scheduler": torch.optim.lr_scheduler.MultiStepLR(
+                    optimizer, milestones = [7500,15000,22500], gamma = 0.1, last_epoch=-1
+                ),
+                "interval": "step",
+                "frequency":1,
+                "name": "learning_rate",
+                }
+            scheduler = ChainedScheduler([linear_scheduler,step_scheduler],optimizer=optimizer)
         return scheduler
 
 class SingleSessionModule(Autoencoder_Models):

From ff7e23522406ea60e70585918192c8cfea6f0117 Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@Taigas-MacBook-Air.local>
Date: Thu, 6 Mar 2025 15:07:19 -0800
Subject: [PATCH 24/34] arg order wrong for chainedscheduler

---
 src/behavioral_autoencoder/module.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/behavioral_autoencoder/module.py b/src/behavioral_autoencoder/module.py
index 3c72e2d..3f15666 100644
--- a/src/behavioral_autoencoder/module.py
+++ b/src/behavioral_autoencoder/module.py
@@ -93,7 +93,7 @@ def setup_scheduler(self,optimizer):
                 "frequency":1,
                 "name": "learning_rate",
                 }
-            scheduler = ChainedScheduler([linear_scheduler,step_scheduler],optimizer=optimizer)
+            scheduler = ChainedScheduler(optimizer=optimizer,schedulers=[linear_scheduler,step_scheduler])
         return scheduler
 
 class SingleSessionModule(Autoencoder_Models):

From f7113eb5e3fb05cc22f1a1b01a66537d643a28a3 Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Thu, 6 Mar 2025 23:35:27 +0000
Subject: [PATCH 25/34] step scheduler needs to be passed the raw functions,
 not dict

---
 src/behavioral_autoencoder/module.py | 22 ++++++++--------------
 1 file changed, 8 insertions(+), 14 deletions(-)

diff --git a/src/behavioral_autoencoder/module.py b/src/behavioral_autoencoder/module.py
index 3f15666..a1c7182 100644
--- a/src/behavioral_autoencoder/module.py
+++ b/src/behavioral_autoencoder/module.py
@@ -76,24 +76,18 @@ def setup_scheduler(self,optimizer):
                 "name": "learning_rate",
                 }
         elif self.hparams["train_config"]["scheduler"] == "linear_step":    
-            linear_scheduler = {
-                    "scheduler": LinearLR(
+            linear_scheduler = LinearLR(
                         optimizer,start_factor=self.hparams["train_config"]["start_factor"],
                         end_factor=self.hparams["train_config"]["end_factor"],
                         total_iters=self.hparams["train_config"]["warmup_steps"]
-                        ),
-                    "interval": "step",
-                    "name": "learning_rate"
-                    }
-            step_scheduler = {
-                "scheduler": torch.optim.lr_scheduler.MultiStepLR(
+                        )
+            step_scheduler = torch.optim.lr_scheduler.MultiStepLR(
                     optimizer, milestones = [7500,15000,22500], gamma = 0.1, last_epoch=-1
-                ),
-                "interval": "step",
-                "frequency":1,
-                "name": "learning_rate",
-                }
-            scheduler = ChainedScheduler(optimizer=optimizer,schedulers=[linear_scheduler,step_scheduler])
+            scheduler = {
+                "scheduler":ChainedScheduler(optimizer=optimizer,schedulers=[linear_scheduler,step_scheduler]),
+                "interval": "epoch",
+                "frequency": 1,
+                "name": "learning_rate"
         return scheduler
 
 class SingleSessionModule(Autoencoder_Models):

From f54ffc86fcefaeb9ac281495ebd3d4ecb3d8c659 Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Fri, 7 Mar 2025 00:09:49 +0000
Subject: [PATCH 26/34] was scheduling on epochs, not steps!

---
 src/behavioral_autoencoder/module.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/behavioral_autoencoder/module.py b/src/behavioral_autoencoder/module.py
index a1c7182..10d4e96 100644
--- a/src/behavioral_autoencoder/module.py
+++ b/src/behavioral_autoencoder/module.py
@@ -82,12 +82,13 @@ def setup_scheduler(self,optimizer):
                         total_iters=self.hparams["train_config"]["warmup_steps"]
                         )
             step_scheduler = torch.optim.lr_scheduler.MultiStepLR(
-                    optimizer, milestones = [7500,15000,22500], gamma = 0.1, last_epoch=-1
+                        optimizer, milestones = [7500,15000,22500], gamma = 0.1, last_epoch=-1)
             scheduler = {
                 "scheduler":ChainedScheduler(optimizer=optimizer,schedulers=[linear_scheduler,step_scheduler]),
-                "interval": "epoch",
+                "interval": "step",
                 "frequency": 1,
                 "name": "learning_rate"
+                }
         return scheduler
 
 class SingleSessionModule(Autoencoder_Models):

From 5103a0973a3d9e1256ef66fd47e2d0701444ce32 Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Fri, 7 Mar 2025 06:12:11 +0000
Subject: [PATCH 27/34] added string matching to only create some frames.

---
 src/behavioral_autoencoder/data_utils.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/behavioral_autoencoder/data_utils.py b/src/behavioral_autoencoder/data_utils.py
index 308b5d5..f0f6a1c 100644
--- a/src/behavioral_autoencoder/data_utils.py
+++ b/src/behavioral_autoencoder/data_utils.py
@@ -64,7 +64,7 @@ def check_exists(frame_dir: Path) -> bool:
         return response == "y"
     return True
 
-def main(video_dir,frame_dir,video_suffix=".avi"):
+def main(video_dir,frame_dir,video_suffix=".avi",match_str=None):
     """Given a directory of videos, write to a different directory with the following structure:
         1. one subdirectory per video file, as well as a metadata file `annotations.txt`.
         2. within each subdirectory, pngs per individual frames.
@@ -76,12 +76,15 @@ def main(video_dir,frame_dir,video_suffix=".avi"):
             video_dir: directory containing video files. 
             frame_dir: directory to write frames to. 
             video_suffix (default=".avi"): suffix of video files to consider. 
+            match_str: string to find within the video names to write only a subset. 
     """
     video_dir = Path(video_dir)
     frame_dir = Path(frame_dir)
     # 1. Get a directory which contains video files. Store video file names.  
     video_files = os.listdir(video_dir)
     video_files = [f for f in video_files if f.endswith(video_suffix)]
+    if match_str is not None:
+        video_files = [f for f in video_files if match_str in f]
     # 2. Check that the directory we care about exists. If it doesn't create. If it does, ask user.  
     video_files_write = []
     for video_file in video_files:

From 658e3ce3e01945da547bdcd0bdccabf140e45649 Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@Taigas-MacBook-Air.local>
Date: Wed, 19 Mar 2025 12:32:59 -0700
Subject: [PATCH 28/34] started building stub of eval

---
 scripts/eval_single_session.py | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 scripts/eval_single_session.py

diff --git a/scripts/eval_single_session.py b/scripts/eval_single_session.py
new file mode 100644
index 0000000..8465bb4
--- /dev/null
+++ b/scripts/eval_single_session.py
@@ -0,0 +1,5 @@
+"""
+Evaluate a single session autoencoder. 
+
+Assumes that we are given a path to a directory `preds/{modeltype}/date/time/`. Will use the configuration parameters stored there 
+"""

From 8d861ae8cb73fdcfdc00ce90737a780aad3d60eb Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Wed, 19 Mar 2025 20:07:21 +0000
Subject: [PATCH 29/34] make sure we save data path.

---
 scripts/train_single_session.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/scripts/train_single_session.py b/scripts/train_single_session.py
index e1f223a..279a50c 100644
--- a/scripts/train_single_session.py
+++ b/scripts/train_single_session.py
@@ -67,6 +67,7 @@ def main(model_config_path, train_config_path, data_path, data_config_path):
             train_config["val_subsample_offset"]
             )
 
+    import pdb; pdb.set_trace()
     ## Set up logging and trainer
     print("\nSetting up logging and checkpoints...")
     date=datetime.datetime.now().strftime("%m-%d-%y")
@@ -109,6 +110,7 @@ def main(model_config_path, train_config_path, data_path, data_config_path):
     with open(os.path.join(timestamp_pred,"model_config"),"w") as f:
         json.dump(hparams, f)
     with open(os.path.join(timestamp_pred,"data_config"),"w") as f:
+        data_config["data_path"] = data_path
         json.dump(data_config, f)
     
     print("\n=== Training Complete ===")

From 9ea6498c619ba6b8315ac20000968fc68d04a1b6 Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Wed, 19 Mar 2025 22:58:25 +0000
Subject: [PATCH 30/34] added sequence dataset (used for eval for now)

---
 src/behavioral_autoencoder/dataset.py | 113 ++++++++++++++++++++++++++
 tests/test_dataset.py                 |  37 ++++++++-
 2 files changed, 149 insertions(+), 1 deletion(-)

diff --git a/src/behavioral_autoencoder/dataset.py b/src/behavioral_autoencoder/dataset.py
index cb02b89..3c86d6e 100644
--- a/src/behavioral_autoencoder/dataset.py
+++ b/src/behavioral_autoencoder/dataset.py
@@ -9,6 +9,7 @@
 import cv2
 from PIL import Image
 from torchvision.io import read_image
+import torch
 import torchvision.transforms.functional as F
 import os
 import json
@@ -278,6 +279,118 @@ def crop_img_proportional(self,img):
         img = F.crop(img, y_top, x_left, y_bottom - y_top, x_right-x_left)
         return img
 
+class SessionSequenceTorchvision(Dataset):
+    """Quite similar to SessionFramesTorchvision, but returns a whole sequence of frames instead of the single frame. 
+
+    Attributes
+    ----------
+    base_folder : str
+        given by parameter at initialization
+    trial_folders : arraylike    
+        sorted names of per-trial directories 
+    extension : str    
+        extension for frame files.
+    frame_dict : dict    
+        dictionary with keys given by trial_folder names, and entries arraylikes of frames within each folder.  
+    trial_lengths : list    
+        number of frames within each trial dictionary
+    cumsum_n_trials : arraylike    
+        cumulative index for frame index across all trials. 
+    transform : any    
+        None or transform function 
+    frame_subset : any    
+        The indices of frames which we will extract from each trial. If not given, will extract all frames for each trial in each batch.    
+    """
+    def __init__(self, base_folder, extension=".png", trial_pattern=None, transform = None, frame_subset=None):
+        """
+        Parameters
+        ----------
+        base_folder : string
+            path to the base folder which contains folders for each individual trial. 
+        extension : string
+            file extension for frame files (default: ".png")
+        crop_info : dict    
+            cropping information to be passed to `transform_image`, with one expected key, `h_coord`. 
+            Crops out the image in the original space so that ~h_coord pixels to the right of the 
+            image would be cropped following appropriate image transformation. 
+        trial_pattern : string, optional
+            Regular expression pattern to match trial folders. If None, all directories are considered
+            trial folders. Example: r"^\d+_trial$" would match folders like "0_trial", "1_trial", etc.
+        frame_subset : any    
+            Array like of filenames for frames to extract from each trial. If not given, extracts all frames fr each trial in a single batch. 
+            
+        """
+        self.base_folder = base_folder
+        self.extension = extension
+        self.frame_subset = frame_subset
+        
+        # Get all items in the base folder
+        all_items = os.listdir(base_folder)
+        
+        # Filter for directories only
+        self.trial_folders = [
+            item for item in all_items 
+            if os.path.isdir(os.path.join(base_folder, item))
+        ]
+        
+        # Apply regex pattern if provided
+        if trial_pattern is not None:
+            pattern = re.compile(trial_pattern)
+            self.trial_folders = [
+                folder for folder in self.trial_folders 
+                if pattern.match(folder)
+            ]
+        
+        self.trial_folders = np.sort(self.trial_folders)
+        
+        self.frame_dict = {folder: np.sort(self.filter_frames(base_folder,folder)) for folder in self.trial_folders}
+        self.trial_lengths = [len(self.frame_dict[folder]) for folder in self.trial_folders]
+        self.cumsum_n_trials = np.cumsum(self.trial_lengths)
+        self.transform = transform
+
+    def filter_frames(self,base_folder,folder):
+        """
+        Given a trial folder, filters out extra files to return only those with a given extension, and matching a given frame subset names. . 
+        """
+        candidates = os.listdir(os.path.join(base_folder,folder))
+        if self.frame_subset is None:
+            return [f for f in candidates if f.endswith(self.extension)]
+        else:
+            return [f for f in candidates if f.endswith(self.extension) and (f in self.frame_subset)]
+
+    def __len__(self):
+        """
+        Required method for pytorch datasets. 
+        """
+        return np.sum(self.trial_lengths)
+
+    def __getitem__(self, idx, method = "searchsorted"):
+        """
+        Get all frames which match a given
+
+        Parameters 
+        ----------
+        idx: int
+            integer index into the data. 
+        """
+        ## get trial number
+        if method == "argmax":
+            trial_idx = np.argmax(self.cumsum_n_trials > idx)
+        elif method == "searchsorted":    
+            trial_idx = np.searchsorted(self.cumsum_n_trials, idx, side='right')
+
+        batch = []
+        for frame in self.frame_dict[self.trial_folders[trial_idx]]:
+            image_path = os.path.join(
+                    self.base_folder,
+                    self.trial_folders[trial_idx],
+                    frame)
+            img = Image.open(image_path)
+            if self.transform:
+                img = self.transform(img)
+            batch.append(img[None,:])    
+        return torch.cat(batch,dim=0)    
+
 class SessionFramesTorchvision(Dataset): 
     """Essentially the same as SessionFramesDataset above, but factors out image transformations into a separate class. 
     Assumes we have a dataset which is organized as a directory of directories, with one directory per trial.
diff --git a/tests/test_dataset.py b/tests/test_dataset.py
index fd263a5..e67bc32 100644
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -2,7 +2,7 @@
 Test functions within the dataset class.
 Uses test data inside `test_data` folder. 
 """
-from behavioral_autoencoder.dataset import SessionFramesDataset,SessionFramesTorchvision,CropResizeProportion
+from behavioral_autoencoder.dataset import SessionFramesDataset,SessionFramesTorchvision,SessionSequenceTorchvision,CropResizeProportion
 import pytest
 import numpy as np
 import time
@@ -263,6 +263,41 @@ def test_getitem_performance(self, tmp_path):
             assert np.array_equal(result_argmax, result_searchsorted), \
                 f"Methods returned different results for index {idx}"
 
+class Test_SessionSequenceTorchvision:
+    config_path = os.path.join(here,"..","configs","data_configs","alm_side.json")
+    def test_init(self, temp_hierarchical_folder):
+        """Test the SessionFramesTorchvision initialization and basic properties, including with default cropping given by CropResizeProportion with default.
+        
+        Tests:
+        1. Dataset can be initialized
+        2. Number of trials matches fixture
+        3. Image dimensions and format are correct
+        4. Dataset length matches expected total frames
+        """
+        alm_cropping = CropResizeProportion(self.config_path)
+        len_sequence = 3
+        dataset = SessionSequenceTorchvision(temp_hierarchical_folder,transform = alm_cropping,frame_subset = [f"frame_{i:06d}.png" for i in range(len_sequence)])
+
+        # Test number of trials
+        assert len(dataset.trial_folders) == 3, "Should have 3 trials by default"
+        
+        # Test image properties
+        first_image = dataset[0]
+        assert isinstance(first_image, torch.Tensor), "Dataset should return numpy arrays"
+        assert len(first_image.shape) == 4
+        assert first_image.shape[0] == len_sequence, "Sequence dimension should be 0"
+        assert first_image.shape[1] == 1, "Images should be 2D grayscale (H,W)"
+        assert first_image.dtype == torch.float32, "Images should be float32"
+        
+        # Test dataset length
+        expected_length = 3 * len_sequence  # n_trials * n_ims_per_trial
+        assert len(dataset) == expected_length, f"Dataset should have {expected_length} total frames"
+        
+        # Test all images are readable
+        for i in range(3):
+            img = dataset[i]
+            assert img is not None, f"Failed to load sequence at index {i}"
+
 class Test_SessionFramesTorchvision:
     config_path = os.path.join(here,"..","configs","data_configs","alm_side.json")
     def test_init(self, temp_hierarchical_folder):

From 9527a36e7843ef845e50fce0cc9dc04850ce48fe Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Wed, 19 Mar 2025 23:06:24 +0000
Subject: [PATCH 31/34] the parameter for the trainer set up should be fit, not
 train

---
 src/behavioral_autoencoder/dataloading.py | 26 +++++++++++++----------
 tests/test_dataloading.py                 |  2 +-
 2 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/src/behavioral_autoencoder/dataloading.py b/src/behavioral_autoencoder/dataloading.py
index 8b90f7d..5260d30 100644
--- a/src/behavioral_autoencoder/dataloading.py
+++ b/src/behavioral_autoencoder/dataloading.py
@@ -3,7 +3,7 @@
 import numpy as np
 import torch
 from torch.utils.data import Subset,DataLoader
-from behavioral_autoencoder.dataset import SessionFramesTorchvision,CropResizeProportion
+from behavioral_autoencoder.dataset import SessionFramesTorchvision,SessionSequenceTorchvision,CropResizeProportion
 import pytorch_lightning as pl
 from joblib import Memory
 import os
@@ -76,7 +76,7 @@ def __init__(
             whether we should calculate and subtract the mean of the training set.
         """
         super().__init__()
-        self.data_path = dataset_config.pop("data_path")
+        self.data_path = dataset_config.pop("data_path") ## each dataset does not take this argument, so we remove. 
         self.dataset_config = dataset_config
         self.batch_size = batch_size
         self.num_workers = num_workers
@@ -109,15 +109,19 @@ def subtract_mean_image(self,x):
         return x-self.mean_image
 
     def setup(self,stage):
-        _ = self.dataset_config.pop("transform")
-        self.dataset = SessionFramesTorchvision(self.data_path,transform = self.transform,**self.dataset_config)
-        all_indices = np.arange(len(self.dataset))
-        ## Subsample indices
-        train_inds = all_indices[self.train_subsample_offset::self.train_subsample_rate]
-        val_inds = all_indices[self.val_subsample_offset::self.val_subsample_rate]
-        #test_inds = [i for i in all_indices if not (i in train_inds)]
-        self.trainset = Subset(self.dataset,train_inds)
-        self.valset = Subset(self.dataset,val_inds)
+        if stage == "fit":
+            _ = self.dataset_config.pop("transform")
+            self.dataset = SessionFramesTorchvision(self.data_path,transform = self.transform,**self.dataset_config)
+            all_indices = np.arange(len(self.dataset))
+            ## Subsample indices
+            train_inds = all_indices[self.train_subsample_offset::self.train_subsample_rate]
+            val_inds = all_indices[self.val_subsample_offset::self.val_subsample_rate]
+            #test_inds = [i for i in all_indices if not (i in train_inds)]
+            self.trainset = Subset(self.dataset,train_inds)
+            self.valset = Subset(self.dataset,val_inds)
+        if stage == "test":    
+            ### the only way to access  this right now is to pass setup explicitly right now. 
+            self.dataset = SessionSequenceTorchvision(self.data_path,transform = self.transform,**self.dataset_config)
 
     def train_dataloader(self,shuffle=True):
         dataloader = DataLoader(
diff --git a/tests/test_dataloading.py b/tests/test_dataloading.py
index 928dbc2..ae07b80 100644
--- a/tests/test_dataloading.py
+++ b/tests/test_dataloading.py
@@ -120,7 +120,7 @@ def test_train_dataloader(self,tmp_path):
                 "trial_pattern":None
                 }
         sfdm = SessionFramesDataModule(data_config,10,2,10,1,10,1)
-        sfdm.setup("train")
+        sfdm.setup("fit")
         dataloader = sfdm.train_dataloader()
         
         for batch in dataloader:

From 6ac055811c1ab2d80bcc065f115816363ac37be3 Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Thu, 20 Mar 2025 18:34:12 +0000
Subject: [PATCH 32/34] added evaluation script that saves out results per
 trial

---
 scripts/eval_single_session.py | 93 ++++++++++++++++++++++++++++++++++
 1 file changed, 93 insertions(+)

diff --git a/scripts/eval_single_session.py b/scripts/eval_single_session.py
index 8465bb4..da4f7b0 100644
--- a/scripts/eval_single_session.py
+++ b/scripts/eval_single_session.py
@@ -3,3 +3,96 @@
 
 Assumes that we are given a path to a directory `preds/{modeltype}/date/time/`. Will use the configuration parameters stored there 
 """
+import os
+import datetime
+from tqdm import tqdm
+import json
+import numpy as np
+import fire
+import torch
+import pytorch_lightning as pl
+from behavioral_autoencoder.module import SingleSessionModule
+from behavioral_autoencoder.dataloading import SessionFramesDataModule
+from behavioral_autoencoder.dataset import CropResizeProportion
+
+here = os.path.join(os.path.abspath(os.path.dirname(__file__)))
+
+def eval_trialwise(model,test_dataset,mean_image,path):
+    """
+    """
+    for i, image_sequence in tqdm(enumerate(test_dataset)):
+        reconstructs,latents = model(image_sequence[None,:].cuda())
+        reconstructs_centered = reconstructs + mean_image.cuda()
+        folder = test_dataset.trial_folders[i]
+        savepath = os.path.join(path,folder)
+        try:
+            os.mkdir(savepath)
+        except FileExistsError:    
+            pass
+        np.save(os.path.join(savepath,"reconstruct.npy"),reconstructs_centered.cpu().detach().numpy())
+        np.save(os.path.join(savepath,"latents.npy"),latents.cpu().detach().numpy())
+
+def main(data_path,data_config_path,eval_config_path):
+    """
+    get a model path, and use it to load in a given model. 
+    """
+    saved_checkpoint_path = os.path.join(".","models","single_session_autoencoder","03-07-25","18_05_06","epoch=99-step=33100.ckpt")
+    data_dir = os.path.join("home","ubuntu","Data","CW35","2023_12_15","Frames")
+    metadata_dir = os.path.join(".","preds","single_session_autoencoder","03-07-25","18_05_06")
+    video_fps = 400
+    delay_start_time = 2.5+1.3 ## pre-sample and sample time intervals. 
+    delay_end_time = 2.5+1.3+3 ## delay is 3 seconds.
+    subsample = 10
+    eval_batch_size=1
+
+    ## Load in data related stuff
+
+    with open(data_config_path,"r") as f:
+        data_process_config = json.load(f)
+
+    with open(eval_config_path,"r") as f: 
+        eval_config = json.load(f)
+
+    alm_cropping = CropResizeProportion(data_config_path)
+    data_config = {
+            "data_path":data_path,
+            "transform":alm_cropping,
+            "extension":data_process_config["extension"],
+            "trial_pattern":data_process_config["trial_pattern"],
+            "frame_subset":[f"frame_{i:06d}.png" for i in np.arange(int(delay_start_time*video_fps),int(delay_end_time*video_fps),subsample)]
+            }
+
+    date = datetime.datetime.now().strftime("%m-%d-%y")
+    time = datetime.datetime.now().strftime("%H_%M_%S")
+    datestamp_eval = os.path.join(here,"eval",date)
+    timestamp_eval = os.path.join(here,"eval",date,time)
+    for path in [datestamp_eval,timestamp_eval]:
+        try:
+            os.mkdir(path)
+        except FileExistsError:    
+            pass
+
+    sfdm = SessionFramesDataModule(
+            data_config,
+            eval_config["batch_size"],
+            eval_config["num_workers"],
+            eval_config["subsample_rate"],
+            eval_config["subsample_offset"],
+            eval_config["val_subsample_rate"],
+            eval_config["val_subsample_offset"]
+            )
+
+    model = SingleSessionModule.load_from_checkpoint(
+            checkpoint_path=saved_checkpoint_path
+            )
+
+    sfdm.setup("test")
+
+    eval_trialwise(model,sfdm.dataset,sfdm.mean_image,path)
+        
+
+    import pdb; pdb.set_trace()
+
+if __name__ == "__main__":
+    fire.Fire(main)
+

From 665ddc004b4a9c3e241cecb57ea5e9d9eb03e89e Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-24-130.us-west-2.compute.internal>
Date: Thu, 20 Mar 2025 18:35:26 +0000
Subject: [PATCH 33/34] moved things around so we have a preliminary version of
 eval loop

---
 src/behavioral_autoencoder/dataloading.py | 6 +++++-
 src/behavioral_autoencoder/dataset.py     | 6 +-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/behavioral_autoencoder/dataloading.py b/src/behavioral_autoencoder/dataloading.py
index 5260d30..b5e2246 100644
--- a/src/behavioral_autoencoder/dataloading.py
+++ b/src/behavioral_autoencoder/dataloading.py
@@ -14,7 +14,10 @@ def calculate_mean_image(data_path,dataset_config,subsample_rate,subsample_offse
     TODO: implement caching for this function. 
     """
     # 1. First construct the right training set indices: 
-    dataset = SessionFramesTorchvision(data_path,**dataset_config)
+    sub_dataset = {}
+    for field in ["transform","extension","trial_pattern"]:
+        sub_dataset[field] = dataset_config[field]
+    dataset = SessionFramesTorchvision(data_path,**sub_dataset)
     all_indices = np.arange(len(dataset))
     ## Subsample indices
     train_inds = all_indices[subsample_offset::subsample_rate]
@@ -121,6 +124,7 @@ def setup(self,stage):
             self.valset = Subset(self.dataset,val_inds)
         if stage == "test":    
             ### the only way to access  this right now is to pass setup explicitly right now. 
+            _ = self.dataset_config.pop("transform")
             self.dataset = SessionSequenceTorchvision(self.data_path,transform = self.transform,**self.dataset_config)
 
     def train_dataloader(self,shuffle=True):
diff --git a/src/behavioral_autoencoder/dataset.py b/src/behavioral_autoencoder/dataset.py
index 3c86d6e..27a3a99 100644
--- a/src/behavioral_autoencoder/dataset.py
+++ b/src/behavioral_autoencoder/dataset.py
@@ -364,7 +364,7 @@ def __len__(self):
         """
         return np.sum(self.trial_lengths)
 
-    def __getitem__(self, idx, method = "searchsorted"):
+    def __getitem__(self, trial_idx, method = "searchsorted"):
         """
         Get all frames which match a given
 
@@ -374,10 +374,6 @@ def __getitem__(self, idx, method = "searchsorted"):
             integer index into the data. 
         """
         ## get trial number
-        if method == "argmax":
-            trial_idx = np.argmax(self.cumsum_n_trials > idx)
-        elif method == "searchsorted":    
-            trial_idx = np.searchsorted(self.cumsum_n_trials, idx, side='right')
 
         batch = []
         for frame in self.frame_dict[self.trial_folders[trial_idx]]:

From 47d9b16a6ddd06654b5988e832baba45218c3bfb Mon Sep 17 00:00:00 2001
From: Taiga Abe <taigaabe@Taigas-MacBook-Air.local>
Date: Thu, 20 Mar 2025 11:53:55 -0700
Subject: [PATCH 34/34] played with changes to training and added caching

---
 scripts/train_single_session.py           | 11 +++---
 src/behavioral_autoencoder/dataloading.py | 47 +++++++++++++++++++----
 2 files changed, 45 insertions(+), 13 deletions(-)

diff --git a/scripts/train_single_session.py b/scripts/train_single_session.py
index e1f223a..c2a539e 100644
--- a/scripts/train_single_session.py
+++ b/scripts/train_single_session.py
@@ -22,14 +22,14 @@ def main(model_config_path, train_config_path, data_path, data_config_path):
     """This main function takes as input four paths. These paths indicate the model configuration parameters, training configuration parameters, path to the data directory, and cropping configuration, respectively. By default we assume that we are training a single session autoencoder.  
     """
     print("\n=== Starting Single Session Autoencoder Training ===")
-    
+
     ## Model setup 
     print("\nLoading configurations...")
     with open(model_config_path,"r") as f:
         model_config = json.load(f)
     with open(train_config_path,"r") as f:
         train_config = json.load(f)
-    model_name = "single_session_autoencoder"    
+    model_name = "single_session_autoencoder"
 
     print(f"Model config: {model_config}")
     print(f"Training config: {train_config}")
@@ -51,11 +51,12 @@ def main(model_config_path, train_config_path, data_path, data_config_path):
     data_config = {
             "data_path":data_path,
             "transform":alm_cropping,
+            "data_config_path":data_config_path,
             "extension":data_process_config["extension"],
             "trial_pattern":data_process_config["trial_pattern"]
             }
     print(f"Data config: {data_config}")
-    
+
     print("Initializing data module...")
     sfdm = SessionFramesDataModule(
             data_config,
@@ -75,7 +76,7 @@ def main(model_config_path, train_config_path, data_path, data_config_path):
     timestamp_pred = os.path.join(here,"preds",model_name,date,time)
     print(f"Model will be saved to: {timestamp_model}")
     print(f"Predictions will be saved to: {timestamp_pred}")
-    
+
     logger = TensorBoardLogger("tb_logs",name="test_single_session_auto",log_graph=True)
     checkpoint = ModelCheckpoint(monitor="mse/val", mode="min", save_last=True, dirpath=timestamp_model)
     lr_monitor = LearningRateMonitor(logging_interval='epoch')
@@ -110,7 +111,7 @@ def main(model_config_path, train_config_path, data_path, data_config_path):
         json.dump(hparams, f)
     with open(os.path.join(timestamp_pred,"data_config"),"w") as f:
         json.dump(data_config, f)
-    
+
     print("\n=== Training Complete ===")
     print(f"Results saved to: {timestamp_pred}")
 
diff --git a/src/behavioral_autoencoder/dataloading.py b/src/behavioral_autoencoder/dataloading.py
index 8b90f7d..20539f0 100644
--- a/src/behavioral_autoencoder/dataloading.py
+++ b/src/behavioral_autoencoder/dataloading.py
@@ -7,26 +7,57 @@
 import pytorch_lightning as pl
 from joblib import Memory
 import os
+import tempfile
+from pathlib import Path
 
-def calculate_mean_image(data_path,dataset_config,subsample_rate,subsample_offset,batch_size,num_workers):
+# Set up cache location with multiple options for flexibility
+def get_cache_dir():
+    """Get cache directory with the following priority:
+    1. BEHAVIORAL_AUTOENCODER_CACHE env variable if set
+    2. Project's .cache directory if in development mode
+    3. User's home directory under ~/.cache/behavioral_autoencoder
+    4. System temp directory as fallback
     """
-    Calculate mean image from given training set parameters. Gets parameters from SessionFramesDataModule
-    TODO: implement caching for this function. 
+    # Option 1: Environment variable (highest priority)
+    if "BEHAVIORAL_AUTOENCODER_CACHE" in os.environ:
+        cache_dir = Path(os.environ["BEHAVIORAL_AUTOENCODER_CACHE"])
+    
+    # Option 2: Project directory if it exists (development mode)
+    elif (Path(__file__).parent.parent.parent / ".cache").exists():
+        cache_dir = Path(__file__).parent.parent.parent / ".cache"
+    
+    # Option 3: User's home directory
+    else:
+        home_dir = Path.home()
+        cache_dir = home_dir / ".cache" / "behavioral_autoencoder"
+    
+    # Create directory if it doesn't exist
+    os.makedirs(cache_dir, exist_ok=True)
+    return cache_dir
+
+# Initialize memory cache
+memory = Memory(location=get_cache_dir(), verbose=1)
+
+@memory.cache
+def calculate_mean_image(data_path, dataset_config, subsample_rate, subsample_offset, batch_size, num_workers):
+    """
+    Calculate mean image from given training set parameters. 
+    This function is cached to avoid redundant calculations.
     """
     # 1. First construct the right training set indices: 
-    dataset = SessionFramesTorchvision(data_path,**dataset_config)
+    dataset = SessionFramesTorchvision(data_path, **dataset_config)
     all_indices = np.arange(len(dataset))
     ## Subsample indices
     train_inds = all_indices[subsample_offset::subsample_rate]
-    trainset = Subset(dataset,train_inds)
+    trainset = Subset(dataset, train_inds)
 
     ## use dataloader to compute sum image 
-    trainloader = DataLoader(trainset,batch_size=batch_size,num_workers=num_workers)
+    trainloader = DataLoader(trainset, batch_size=batch_size, num_workers=num_workers)
 
     sum_im = torch.zeros(dataset[0].shape[1:]) ## should be 
     for data in tqdm(trainloader):
-        sum_im+=data.sum(axis=0).sum(axis=0) ## sum across the batch and sequence dimensions.
-    mean=sum_im/len(trainset)    
+        sum_im += data.sum(axis=0).sum(axis=0) ## sum across the batch and sequence dimensions.
+    mean = sum_im / len(trainset)    
     return mean
 
 class SessionFramesDataModule(pl.LightningDataModule):