diff --git a/notebooks/visualize_filters_keras.ipynb b/notebooks/visualize_filters_keras.ipynb
index 4a5918309ac20d7c55e1dee9ddaafee9256fcba4..ebc01d7dbd4fccad0e5a8aef10fa4148814dec19 100644
--- a/notebooks/visualize_filters_keras.ipynb
+++ b/notebooks/visualize_filters_keras.ipynb
@@ -2,11 +2,51 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "0603e9bd-ac66-4984-b1de-f09367956968",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-05-11 15:04:36.370067: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2022-05-11 15:04:36.854948: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:36.856782: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:36.858553: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:36.859189: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:36.860944: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:36.862626: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:36.864627: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:36.865225: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:36.866944: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:36.868675: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:36.870354: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:36.870963: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:38.397852: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:38.399684: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:38.401460: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:38.402104: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:38.403785: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:38.405478: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:38.407158: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:38.407767: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:38.409466: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:38.411164: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43667 MB memory:  -> device: 0, name: NVIDIA A40, pci bus id: 0000:01:00.0, compute capability: 8.6\n",
+      "2022-05-11 15:04:38.415567: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:38.417449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 43667 MB memory:  -> device: 1, name: NVIDIA A40, pci bus id: 0000:02:00.0, compute capability: 8.6\n",
+      "2022-05-11 15:04:38.426976: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:38.427576: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 88 MB memory:  -> device: 2, name: NVIDIA A40, pci bus id: 0000:03:00.0, compute capability: 8.6\n",
+      "2022-05-11 15:04:38.436514: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2022-05-11 15:04:38.438192: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 43667 MB memory:  -> device: 3, name: NVIDIA A40, pci bus id: 0000:04:00.0, compute capability: 8.6\n"
+     ]
+    }
+   ],
    "source": [
+    "import os\n",
+    "os.chdir('..')\n",
+    "\n",
     "from sparse_coding_torch.keras_model import SparseCode, ReconSparse\n",
     "from sparse_coding_torch.load_data import load_pnb_videos\n",
     "import tensorflow.keras as keras\n",
@@ -17,48 +57,7236 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "a02865e4-6685-4401-98bd-e1a71675d122",
    "metadata": {},
    "outputs": [],
    "source": [
-    "image_height = 360\n",
-    "image_width = 304\n",
+    "image_height = 285\n",
+    "image_width = 400\n",
+    "clip_depth = 5\n",
     "batch_size = 1\n",
-    "kernel_size = 15\n",
-    "num_kernels = 48\n",
-    "stride=1\n",
+    "kernel_size = 5\n",
+    "kernel_depth = 5\n",
+    "num_kernels = 20\n",
+    "stride=4\n",
     "max_activation_iter = 150\n",
     "activation_lr=1e-2\n",
     "lam=0.05\n",
     "run_2d=False\n",
-    "sparse_checkpoint = '../sparse_coding_torch/output/sparse_pnb_48/sparse_conv3d_model-best.pt/'"
+    "sparse_checkpoint = 'sparse_coding_torch/output/needle_codes/sparse_conv3d_model-best.pt/'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "72e88838-67a8-4deb-86b0-82f5e4eca9db",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
+     ]
+    }
+   ],
    "source": [
     "inputs = keras.Input(shape=(5, image_height, image_width, 1))\n",
     "        \n",
     "filter_inputs = keras.Input(shape=(5, kernel_size, kernel_size, 1, num_kernels), dtype='float32')\n",
     "\n",
-    "output = SparseCode(batch_size=batch_size, image_height=image_height, image_width=image_width, in_channels=1, out_channels=num_kernels, kernel_size=kernel_size, stride=stride, lam=lam, activation_lr=activation_lr, max_activation_iter=max_activation_iter, run_2d=run_2d)(inputs, filter_inputs)\n",
+    "output = SparseCode(batch_size=batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=num_kernels, kernel_size=kernel_size, kernel_depth=kernel_depth, stride=stride, lam=lam, activation_lr=activation_lr, max_activation_iter=max_activation_iter, run_2d=run_2d)(inputs, filter_inputs)\n",
     "\n",
     "sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)\n",
     "\n",
-    "recon_model = keras.models.load_model(sparse_checkpoint)"
+    "# recon_model = keras.models.load_model(sparse_checkpoint)\n",
+    "\n",
+    "recon_inputs = keras.Input(shape=(1, (image_height - kernel_size) // stride + 1, (image_width - kernel_size) // stride + 1, num_kernels))\n",
+    "    \n",
+    "recon_outputs = ReconSparse(batch_size=batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=num_kernels, kernel_size=kernel_size, kernel_depth=kernel_depth, stride=stride, lam=lam, activation_lr=activation_lr, max_activation_iter=max_activation_iter, run_2d=run_2d)(recon_inputs)\n",
+    "\n",
+    "recon_model = keras.Model(inputs=recon_inputs, outputs=recon_outputs)\n",
+    "\n",
+    "recon_model.set_weights(keras.models.load_model(sparse_checkpoint).get_weights())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "0ed41c99-07a4-4ffb-964c-34bf7ab50a2d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x1584 with 33 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ani = plot_filters(recon_model.get_weights()[0])\n",
     "HTML(ani.to_html5_video())"
@@ -66,29 +7294,864 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "ef944b87-dfb2-4aff-a0b7-497cb86b9742",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded 572 positive examples.\n",
+      "Loaded 1752 negative examples.\n"
+     ]
+    }
+   ],
    "source": [
-    "train_loader, _ = load_pnb_videos(batch_size, classify_mode=True, mode='all_train', device=None, n_splits=1, sparse_model=None)"
+    "import torch\n",
+    "splits, dataset = load_pnb_videos(yolo_model, 1, input_size=(image_height, image_width, clip_depth), crop_size=(image_height, image_width, clip_depth), classify_mode=True, balance_classes=False, mode='default', device=None, n_splits=1, sparse_model=None, frames_to_skip=1)\n",
+    "train_idx, test_idx = list(splits)[0]\n",
+    "\n",
+    "train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)\n",
+    "# train_sampler = SubsetWeightedRandomSampler(get_sample_weights(train_idx, dataset), train_idx, replacement=True)\n",
+    "train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n",
+    "                                       sampler=train_sampler)\n",
+    "\n",
+    "test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)\n",
+    "test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n",
+    "                                       sampler=test_sampler)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "d69a8ad5-4037-490c-bef5-50d93190ece8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Negatives',)\n",
+      "('Negatives',)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "  Your browser does not support the video tag.\n",
+       "</video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 284x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "pos_label, pos_clip, vid = list(train_loader)[1]\n",
-    "neg_label, neg_clip, vid = list(train_loader)[2]\n",
+    "pos_label, pos_clip, vid = next(iter(train_loader))\n",
+    "neg_label, neg_clip, vid = next(iter(train_loader))\n",
     "print(pos_label)\n",
     "print(neg_label)\n",
     "ani = plot_video(neg_clip.squeeze(0))\n",
     "HTML(ani.to_html5_video())"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "30cdbf7d-9f8a-4c2d-9b72-f504b357ddb6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 284x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "clip = neg_clip.permute(0, 2, 3, 4, 1).numpy()\n",
+    "activations = sparse_model([clip, tf.expand_dims(recon_model.trainable_weights[0], axis=0)])\n",
+    "recon = recon_model(activations)\n",
+    "ani = plot_video(recon)\n",
+    "HTML(ani.to_html5_video())"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/run_pnb.py b/run_pnb.py
index 52a023c56604b9849a37eca5afe99f27ff306e5d..fd49440a962cc3b5144c04208f888d13950ec6e1 100644
--- a/run_pnb.py
+++ b/run_pnb.py
@@ -17,9 +17,10 @@ import tensorflow.keras as keras
 from sparse_coding_torch.keras_model import SparseCode, PNBClassifier, PTXClassifier, ReconSparse
 import glob
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument('--input_dir', default='/shared_data/bamc_pnb_data/full_training_data', type=str)
+    parser.add_argument('--input_dir', default='/shared_data/bamc_pnb_data/revised_training_data', type=str)
     parser.add_argument('--kernel_size', default=15, type=int)
     parser.add_argument('--kernel_depth', default=5, type=int)
     parser.add_argument('--num_kernels', default=48, type=int)
@@ -28,7 +29,7 @@ if __name__ == "__main__":
     parser.add_argument('--activation_lr', default=1e-2, type=float)
     parser.add_argument('--lam', default=0.05, type=float)
     parser.add_argument('--sparse_checkpoint', default='sparse_coding_torch/output/sparse_pnb_48/sparse_conv3d_model-best.pt/', type=str)
-    parser.add_argument('--checkpoint', default='sparse_coding_torch/classifier_outputs/48_filters_6/best_classifier.pt/', type=str)
+    parser.add_argument('--checkpoint', default='sparse_coding_torch/classifier_outputs/48_filters_grouped/best_classifier.pt/', type=str)
     parser.add_argument('--run_2d', action='store_true')
     
     args = parser.parse_args()
@@ -37,6 +38,7 @@ if __name__ == "__main__":
 
     image_height = 285
     image_width = 235
+    clip_depth = 5
 
     if args.run_2d:
         inputs = keras.Input(shape=(image_height, image_width, 5))
@@ -45,7 +47,7 @@ if __name__ == "__main__":
 
     filter_inputs = keras.Input(shape=(5, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
 
-    output = SparseCode(batch_size=batch_size, image_height=image_height, image_width=image_width, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d, padding='VALID')(inputs, filter_inputs)
+    output = SparseCode(batch_size=batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d, padding='VALID')(inputs, filter_inputs)
 
     sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)
 
@@ -58,7 +60,7 @@ if __name__ == "__main__":
     transform = torchvision.transforms.Compose(
     [VideoGrayScaler(),
      MinMaxScaler(0, 255),
-     torchvision.transforms.Resize((285, 235))
+     torchvision.transforms.Resize((image_height, image_width))
     ])
 
     all_predictions = []
@@ -71,39 +73,37 @@ if __name__ == "__main__":
         vc = tv.io.read_video(f)[0].permute(3, 0, 1, 2)
         is_right = classify_nerve_is_right(yolo_model, vc)
         
-        vc_sub = vc[:, -5:, :, :]
-        if vc_sub.size(1) < 5:
-            print(f + ' does not contain enough frames for processing')
-            continue
-            
-        ### START time after loading video ###
-        start_time = time.time()
-        clip = None
-        i = 1
-        while not clip and i < 5:
-            if vc_sub.size(1) < 5:
-                break
-            clip = get_yolo_regions(yolo_model, vc_sub, is_right)
+        all_preds = []
+        for i in range(1, 5):
             vc_sub = vc[:, -5*(i+1):-5*i, :, :]
-            i += 1
+            if vc_sub.size(1) < 5:
+                print(f + ' does not contain enough frames for processing')
+                continue
             
-        if clip:
-            clip = clip[0]
-            clip = transform(clip)
-            clip = tf.expand_dims(clip, axis=4) 
-
-            activations = tf.stop_gradient(sparse_model([clip, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
-
-            pred = tf.math.sigmoid(classifier_model(activations))
-
-            final_pred = tf.math.round(pred)
-
+            ### START time after loading video ###
+            start_time = time.time()
+            clip = None
+        
+            clip = get_yolo_regions(yolo_model, vc_sub, is_right, crop_width=image_width, crop_height=image_height)
+            
+            if clip:
+                clip = clip[0]
+                clip = transform(clip)
+                clip = tf.expand_dims(clip, axis=4) 
+
+                activations = tf.stop_gradient(sparse_model([clip, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
+
+                pred = tf.math.sigmoid(classifier_model(activations))
+                
+                all_preds.append(pred)
+                
+        if all_preds:
+            final_pred = np.round(np.mean(np.array(all_preds)))
             if final_pred == 1:
                 str_pred = 'Positive'
             else:
                 str_pred = 'Negative'
         else:
-            print('here')
             str_pred = "Positive"
 
         end_time = time.time()
diff --git a/run_pnb_old.py b/run_pnb_old.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ee5656f7e1db3638e5352dbf1e8e171c506b8ac
--- /dev/null
+++ b/run_pnb_old.py
@@ -0,0 +1,181 @@
+import torch
+import os
+from sparse_coding_torch.keras_model import SparseCode, PNBClassifier, PTXClassifier, ReconSparse
+import time
+import numpy as np
+import torchvision
+from sparse_coding_torch.video_loader import VideoGrayScaler, MinMaxScaler
+from torchvision.datasets.video_utils import VideoClips
+import csv
+from datetime import datetime
+from yolov4.get_bounding_boxes import YoloModel
+import argparse
+import tensorflow as tf
+import tensorflow.keras as keras
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_dir', default='/shared_data/bamc_data/PTX_Sliding', type=str)
+    parser.add_argument('--kernel_size', default=15, type=int)
+    parser.add_argument('--kernel_depth', default=5, type=int)
+    parser.add_argument('--num_kernels', default=64, type=int)
+    parser.add_argument('--stride', default=2, type=int)
+    parser.add_argument('--max_activation_iter', default=100, type=int)
+    parser.add_argument('--activation_lr', default=1e-2, type=float)
+    parser.add_argument('--lam', default=0.05, type=float)
+    parser.add_argument('--sparse_checkpoint', default='converted_checkpoints/sparse.pt', type=str)
+    parser.add_argument('--checkpoint', default='converted_checkpoints/classifier.pt', type=str)
+    parser.add_argument('--run_2d', action='store_true')
+    parser.add_argument('--dataset', default='ptx', type=str)
+    
+    args = parser.parse_args()
+    #print(args.accumulate(args.integers))
+    batch_size = 1
+    
+    if args.dataset == 'pnb':
+        image_height = 250
+        image_width = 600
+    elif args.dataset == 'ptx':
+        image_height = 100
+        image_width = 200
+    else:
+        raise Exception('Invalid dataset')
+
+    if args.run_2d:
+        inputs = keras.Input(shape=(image_height, image_width, 5))
+    else:
+        inputs = keras.Input(shape=(5, image_height, image_width, 1))
+
+    filter_inputs = keras.Input(shape=(5, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
+
+    output = SparseCode(batch_size=batch_size, image_height=image_height, image_width=image_width, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d, padding='SAME')(inputs, filter_inputs)
+
+    sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)
+    
+    recon_inputs = keras.Input(shape=(1, image_height // args.stride, image_width // args.stride, args.num_kernels))
+    
+    recon_outputs = ReconSparse(batch_size=batch_size, image_height=image_height, image_width=image_width, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(recon_inputs)
+    
+    recon_model = keras.Model(inputs=recon_inputs, outputs=recon_outputs)
+
+    if args.sparse_checkpoint:
+        recon_model = keras.models.load_model(args.sparse_checkpoint)
+        
+    if args.checkpoint:
+        classifier_model = keras.models.load_model(args.checkpoint)
+    else:
+        classifier_inputs = keras.Input(shape=(1, image_height // args.stride, image_width // args.stride, args.num_kernels))
+
+        if args.dataset == 'pnb':
+            classifier_outputs = PNBClassifier()(classifier_inputs)
+        elif args.dataset == 'ptx':
+            classifier_outputs = PTXClassifier()(classifier_inputs)
+        else:
+            raise Exception('No classifier exists for that dataset')
+
+        classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
+        
+    yolo_model = YoloModel()
+        
+    transform = torchvision.transforms.Compose(
+    [VideoGrayScaler(),
+     MinMaxScaler(0, 255),
+     torchvision.transforms.Normalize((0.2592,), (0.1251,)),
+     torchvision.transforms.CenterCrop((100, 200))
+    ])
+    
+    all_predictions = []
+    
+    all_files = list(os.listdir(args.input_dir))
+    
+    for f in all_files:
+        print('Processing', f)
+        #start_time = time.time()
+        
+        clipstride = 15
+        
+        vc = VideoClips([os.path.join(args.input_dir, f)],
+                        clip_length_in_frames=5,
+                        frame_rate=20,
+                       frames_between_clips=clipstride)
+    
+        ### START time after loading video ###
+        start_time = time.time()
+        clip_predictions = []
+        i = 0
+        cliplist = []
+        countclips = 0
+        for i in range(vc.num_clips()):
+
+            clip, _, _, _ = vc.get_clip(i)
+            clip = clip.swapaxes(1, 3).swapaxes(0, 1).swapaxes(2, 3).numpy()
+            
+            bounding_boxes, classes = yolo_model.get_bounding_boxes(clip[:, 2, :, :].swapaxes(0, 2).swapaxes(0, 1))
+            bounding_boxes = bounding_boxes.squeeze(0)
+            if bounding_boxes.size == 0:
+                continue
+            #widths = []
+            countclips = countclips + len(bounding_boxes)
+            
+            widths = [(bounding_boxes[i][3] - bounding_boxes[i][1]) for i in range(len(bounding_boxes))]
+            
+            #for i in range(len(bounding_boxes)):
+            #    widths.append(bounding_boxes[i][3] - bounding_boxes[i][1])
+
+            ind =  np.argmax(np.array(widths))
+            #for bb in bounding_boxes:
+            bb = bounding_boxes[ind]
+            center_x = (bb[3] + bb[1]) / 2 * 1920
+            center_y = (bb[2] + bb[0]) / 2 * 1080
+
+            width=400
+            height=400
+
+            lower_y = round(center_y - height / 2)
+            upper_y = round(center_y + height / 2)
+            lower_x = round(center_x - width / 2)
+            upper_x = round(center_x + width / 2)
+
+            trimmed_clip = clip[:, :, lower_y:upper_y, lower_x:upper_x]
+
+            trimmed_clip = torch.tensor(trimmed_clip).to(torch.float)
+
+            trimmed_clip = transform(trimmed_clip)
+            trimmed_clip.pin_memory()
+            cliplist.append(trimmed_clip)
+
+        if len(cliplist) > 0:
+            with torch.no_grad():
+                trimmed_clip = torch.stack(cliplist)
+                images = trimmed_clip.permute(0, 2, 3, 4, 1).numpy()
+                activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
+
+                pred = classifier_model(activations)
+                #print(torch.nn.Sigmoid()(pred))
+                clip_predictions = tf.math.round(tf.math.sigmoid(pred))
+
+            final_pred = torch.mode(torch.tensor(clip_predictions.numpy()).view(-1))[0].item()
+            if len(clip_predictions) % 2 == 0 and tf.math.reduce_sum(clip_predictions) == len(clip_predictions)//2:
+                #print("I'm here")
+                final_pred = torch.mode(torch.tensor(clip_predictions.numpy()).view(-1))[0].item()
+                
+            if final_pred == 1:
+                str_pred = 'No Sliding'
+            else:
+                str_pred = 'Sliding'
+
+        else:
+            str_pred = "No Sliding"
+            
+        print(str_pred)
+            
+        end_time = time.time()
+        
+        all_predictions.append({'FileName': f, 'Prediction': str_pred, 'TotalTimeSec': end_time - start_time})
+        
+    with open('output_' + datetime.now().strftime("%Y%m%d-%H%M%S") + '.csv', 'w+', newline='') as csv_out:
+        writer = csv.DictWriter(csv_out, fieldnames=all_predictions[0].keys())
+        
+        writer.writeheader()
+        writer.writerows(all_predictions)
diff --git a/run_pnb_only_yolo.py b/run_pnb_only_yolo.py
new file mode 100644
index 0000000000000000000000000000000000000000..b75ce5c6dffa11942a42c1c073169ff33102a7af
--- /dev/null
+++ b/run_pnb_only_yolo.py
@@ -0,0 +1,87 @@
+import os
+import time
+import numpy as np
+from yolov4.get_bounding_boxes import YoloModel
+import argparse
+import glob
+from sklearn.linear_model import LogisticRegression
+import torchvision as tv
+from tqdm import tqdm
+import random
+
+def calc_yolo_dist(yolo_model, frame):
+    bounding_boxes, classes = yolo_model.get_bounding_boxes(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
+    bounding_boxes = bounding_boxes.squeeze(0)
+    classes = classes.squeeze(0)
+    
+    needle = None
+    nerve = None
+
+    for bb, class_pred in zip(bounding_boxes, classes):
+        if class_pred == 0 and nerve is None:
+            nerve = np.array(bb)
+        elif class_pred == 2 and needle is None:
+            needle = np.array(bb)
+            
+    if nerve is None or needle is None:
+        return None
+
+    return np.concatenate([nerve, needle])
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_dir', default='/shared_data/bamc_pnb_data/revised_training_data', type=str)
+    
+    args = parser.parse_args()
+        
+    yolo_model = YoloModel()
+
+    all_predictions = []
+
+    all_files = glob.glob(pathname=os.path.join(args.input_dir, '**', '*.mp4'), recursive=True)
+    
+    all_data = []
+    
+    print('Building dataset...')
+
+    for f in tqdm(all_files):
+        vc = tv.io.read_video(f)[0].permute(3, 0, 1, 2)
+        
+        x = None
+        i = -1
+        while x is None and i >= -5:
+            x = calc_yolo_dist(yolo_model, vc[:, i, :, :])
+            i -= 1
+            
+        if x is None:
+            continue
+            
+        y = 1 if f.split('/')[-3] == 'Positives' else 0
+        
+        all_data.append((x, y))
+        
+    random.shuffle(all_data)
+    
+    split = int(len(all_data) * 0.8)
+    
+    train_data = all_data[:split]
+    test_data = all_data[split:]
+    
+    print('Loaded {} train examples.'.format(len(train_data)))
+    print('Loaded {} test examples.'.format(len(test_data)))
+    
+    train_x = [ex[0] for ex in train_data]
+    train_y = [ex[1] for ex in train_data]
+    
+    test_x = [ex[0] for ex in test_data]
+    test_y = [ex[1] for ex in test_data]
+    
+    print('Fitting to data...')
+    
+    clf = LogisticRegression(random_state=0, max_iter=1000).fit(train_x, train_y)
+    
+    print('Evaluating model...')
+    
+    score = clf.score(test_x, test_y)
+    
+    print(score)
\ No newline at end of file
diff --git a/run_tflite_pnb.py b/run_tflite_pnb.py
index ce690e34454a332b3bf191d63e18eb4f883a13a7..ce42f0de78f646b1f0711b75b39028fb57716024 100644
--- a/run_tflite_pnb.py
+++ b/run_tflite_pnb.py
@@ -19,7 +19,10 @@ if __name__ == "__main__":
 
     parser = argparse.ArgumentParser(description='Python program for processing PNB data')
     parser.add_argument('--classifier', type=str, default='sparse_coding_torch/mobile_output/pnb.tflite')
-    parser.add_argument('--input_dir', default='/shared_data/bamc_pnb_data/full_training_data', type=str)
+    parser.add_argument('--input_dir', default='/shared_data/bamc_pnb_data/revised_training_data', type=str)
+    parser.add_argument('--stride', default=30, type=int)
+    parser.add_argument('--image_width', default=400, type=int)
+    parser.add_argument('--image_height', default=285, type=int)
     args = parser.parse_args()
 
     interpreter = tf.lite.Interpreter(args.classifier)
@@ -31,9 +34,9 @@ if __name__ == "__main__":
     yolo_model = YoloModel()
 
     transform = torchvision.transforms.Compose(
-    [#VideoGrayScaler(),
-     MinMaxScaler(0, 255),
-#      torchvision.transforms.Resize((285, 235))
+    [VideoGrayScaler(),
+#      MinMaxScaler(0, 255),
+     torchvision.transforms.Resize((args.image_height, args.image_width))
     ])
 
     all_predictions = []
@@ -46,24 +49,22 @@ if __name__ == "__main__":
         vc = tv.io.read_video(f)[0].permute(3, 0, 1, 2)
         is_right = classify_nerve_is_right(yolo_model, vc)
         
-        vc_sub = vc[:, -5:, :, :]
-        if vc_sub.size(1) < 5:
-            print(f + ' does not contain enough frames for processing')
-            continue
-            
-        ### START time after loading video ###
-        start_time = time.time()
+        all_preds = []
         
-        clip = None
-        i = 1
-        while not clip and i < 5:
+        for j in range(0, vc.size(1) - 5, args.stride):
+            vc_sub = vc[:, j:j+5, :, :]
+            
             if vc_sub.size(1) < 5:
-                break
-            clip = get_yolo_regions(yolo_model, vc_sub, is_right)
-            vc_sub = vc[:, -5*(i+1):-5*i, :, :]
-            i += 1
+                continue
+            
+            ### START time after loading video ###
+            start_time = time.time()
+            
+            clip = get_yolo_regions(yolo_model, vc_sub, is_right, args.image_width, args.image_height)
+            
+            if not clip:
+                continue
 
-        if clip:
             clip = clip[0]
             clip = transform(clip).to(torch.float32)
 
@@ -74,11 +75,18 @@ if __name__ == "__main__":
             output_array = np.array(interpreter.get_tensor(output_details[0]['index']))
 
             pred = output_array[0][0]
-            print(pred)
 
             final_pred = pred.round()
+            
+            all_preds.append(final_pred)
+            
+        print(all_preds)
+            
+            
+        if all_preds[-5:-2]:
+            video_pred = np.round(sum(all_preds[-5:-2]) / len(all_preds[-5:-2]))
 
-            if final_pred == 1:
+            if video_pred == 1:
                 str_pred = 'Positive'
             else:
                 str_pred = 'Negative'
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/arguments.txt b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/arguments.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c91ee97a439ab63eea75112fe259e3608a5de365
--- /dev/null
+++ b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/arguments.txt
@@ -0,0 +1 @@
+Namespace(batch_size=24, kernel_size=15, kernel_depth=5, num_kernels=32, stride=4, max_activation_iter=150, activation_lr=0.01, lr=0.003, epochs=20, lam=0.05, output_dir='sparse_coding_torch/classifier_outputs/32_filters_no_aug_9', sparse_checkpoint='sparse_coding_torch/output/sparse_pnb_32/sparse_conv3d_model-best.pt/', checkpoint=None, splits='k_fold', seed=26, train=True, num_positives=100, n_splits=5, save_train_test_splits=False, run_2d=False, balance_classes=False, dataset='pnb', train_sparse=False, mixing_ratio=1.0, sparse_lr=0.003, crop_height=285, crop_width=400, scale_factor=1, clip_depth=5, frames_to_skip=1)
\ No newline at end of file
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_classifier_0.pt/keras_metadata.pb b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_classifier_0.pt/keras_metadata.pb
new file mode 100644
index 0000000000000000000000000000000000000000..ece3308b71a16a8995d8ba0960be42cbab4787b3
--- /dev/null
+++ b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_classifier_0.pt/keras_metadata.pb
@@ -0,0 +1,10 @@
+
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+�
root.layer-0"_tf_keras_input_layer*�{"class_name": "InputLayer", "name": "input_4", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_4"}}2

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diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_2.pt/variables/variables.index b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_2.pt/variables/variables.index
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diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_3.pt/keras_metadata.pb b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_3.pt/keras_metadata.pb
new file mode 100644
index 0000000000000000000000000000000000000000..d815c94c6837f3edb35435f7471e1608a6fb9663
--- /dev/null
+++ b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_3.pt/keras_metadata.pb
@@ -0,0 +1,4 @@
+
+�root"_tf_keras_network*�{"name": "model_1", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Functional", "config": {"name": "model_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_3"}, "name": "input_3", "inbound_nodes": []}, {"class_name": "ReconSparse", "config": {"layer was saved without config": true}, "name": "recon_sparse", "inbound_nodes": [[["input_3", 0, 0, {}]]]}], "input_layers": [["input_3", 0, 0]], "output_layers": [["recon_sparse", 0, 0]]}, "shared_object_id": 1, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "ndim": 5, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", "input_3"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", "input_3"]}, "keras_version": "2.8.0", "backend": "tensorflow", "model_config": {"class_name": "Functional"}}2

+�
root.layer-0"_tf_keras_input_layer*�{"class_name": "InputLayer", "name": "input_3", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_3"}}2

+�root.layer_with_weights-0"_tf_keras_model*�{"name": "recon_sparse", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "ReconSparse", "config": {"layer was saved without config": true}, "is_graph_network": false, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", null]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", null]}, "keras_version": "2.8.0", "backend": "tensorflow", "model_config": {"class_name": "ReconSparse"}}2

\ No newline at end of file
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_3.pt/saved_model.pb b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_3.pt/saved_model.pb
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index 0000000000000000000000000000000000000000..dee5bbfe31b86b25080b58d6b0c794fb1b76df47
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diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_3.pt/variables/variables.data-00000-of-00001 b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_3.pt/variables/variables.data-00000-of-00001
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index 0000000000000000000000000000000000000000..3bca0584a099f92be0ea672fc8ed447b6f9d0a29
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diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_3.pt/variables/variables.index b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_3.pt/variables/variables.index
new file mode 100644
index 0000000000000000000000000000000000000000..b03b6ee9a87da1005cb075bbe5946be65244d5e4
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diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_4.pt/keras_metadata.pb b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_4.pt/keras_metadata.pb
new file mode 100644
index 0000000000000000000000000000000000000000..d815c94c6837f3edb35435f7471e1608a6fb9663
--- /dev/null
+++ b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_4.pt/keras_metadata.pb
@@ -0,0 +1,4 @@
+
+�root"_tf_keras_network*�{"name": "model_1", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Functional", "config": {"name": "model_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_3"}, "name": "input_3", "inbound_nodes": []}, {"class_name": "ReconSparse", "config": {"layer was saved without config": true}, "name": "recon_sparse", "inbound_nodes": [[["input_3", 0, 0, {}]]]}], "input_layers": [["input_3", 0, 0]], "output_layers": [["recon_sparse", 0, 0]]}, "shared_object_id": 1, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "ndim": 5, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", "input_3"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", "input_3"]}, "keras_version": "2.8.0", "backend": "tensorflow", "model_config": {"class_name": "Functional"}}2

+�
root.layer-0"_tf_keras_input_layer*�{"class_name": "InputLayer", "name": "input_3", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 68, 97, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_3"}}2

+�root.layer_with_weights-0"_tf_keras_model*�{"name": "recon_sparse", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "ReconSparse", "config": {"layer was saved without config": true}, "is_graph_network": false, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", null]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 68, 97, 32]}, "float32", null]}, "keras_version": "2.8.0", "backend": "tensorflow", "model_config": {"class_name": "ReconSparse"}}2

\ No newline at end of file
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_4.pt/saved_model.pb b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_4.pt/saved_model.pb
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diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_4.pt/variables/variables.data-00000-of-00001 b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_4.pt/variables/variables.data-00000-of-00001
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index 0000000000000000000000000000000000000000..3bca0584a099f92be0ea672fc8ed447b6f9d0a29
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diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_4.pt/variables/variables.index b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/best_sparse_model_4.pt/variables/variables.index
new file mode 100644
index 0000000000000000000000000000000000000000..b03b6ee9a87da1005cb075bbe5946be65244d5e4
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diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/fp_fn.txt b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/fp_fn.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8c528efbfe240cecd9c748d1f07d65412a3cfd23
--- /dev/null
+++ b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/fp_fn.txt
@@ -0,0 +1,6 @@
+FP:
+['/shared_data/bamc_pnb_data/full_training_data/Negatives/211/1. 211 AC_Video 1.mp4', '/shared_data/bamc_pnb_data/full_training_data/Negatives/178/3. 178_Video 3.mp4', '/shared_data/bamc_pnb_data/full_training_data/Negatives/207/1. 207 AC_Video 1.mp4', '/shared_data/bamc_pnb_data/full_training_data/Negatives/46/2. 46_Video 2.mp4']
+
+FN:
+['/shared_data/bamc_pnb_data/full_training_data/Positives/240/5. 240 AC_Video 3 (mislabeed as Pop).mp4', '/shared_data/bamc_pnb_data/full_training_data/Positives/238/3. 238 AC_Video 2.mp4', '/shared_data/bamc_pnb_data/full_training_data/Positives/189/3. 189 AC_Video 2.mp4', '/shared_data/bamc_pnb_data/full_training_data/Positives/217/3. 217 AC_Video 2.mp4', '/shared_data/bamc_pnb_data/full_training_data/Positives/205/3. 205 AC_Video 2.mp4', '/shared_data/bamc_pnb_data/full_training_data/Positives/134/3. 134 AC_Video 2.mp4', '/shared_data/bamc_pnb_data/full_training_data/Positives/193/7. 193 AC Video 4.mp4', '/shared_data/bamc_pnb_data/full_training_data/Positives/54/5. 54 AC_Video 3.mp4', '/shared_data/bamc_pnb_data/full_training_data/Positives/222/4. 222A_Video 2.mp4']
+
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_0.pt b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_0.pt
new file mode 100644
index 0000000000000000000000000000000000000000..fea19be68f738b43a11c81c6e2e553c9fbab3b34
Binary files /dev/null and b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_0.pt differ
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_1.pt b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_1.pt
new file mode 100644
index 0000000000000000000000000000000000000000..0d4755e1d3f21f3ff4ef6b24eeb411fcb4d8cce6
Binary files /dev/null and b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_1.pt differ
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_2.pt b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_2.pt
new file mode 100644
index 0000000000000000000000000000000000000000..4580a015f61f7af3f37a99c123cb5ba84ab36118
Binary files /dev/null and b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_2.pt differ
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_3.pt b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_3.pt
new file mode 100644
index 0000000000000000000000000000000000000000..a9b6109c4db9794bc1e167442904f79f31de805c
Binary files /dev/null and b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_3.pt differ
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_4.pt b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_4.pt
new file mode 100644
index 0000000000000000000000000000000000000000..f9d961fa183e0c09eb501b270ef2d0a9a5b40ada
Binary files /dev/null and b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/optimizer_4.pt differ
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_0.txt b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_0.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2323e4d02691cecc297f1d9df4977970208c7c7e
--- /dev/null
+++ b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_0.txt
@@ -0,0 +1 @@
+/shared_data/bamc_pnb_data/full_training_data/Negatives/204/1. 204 AC Video 1.mp4/shared_data/bamc_pnb_data/full_training_data/Negatives/57/1. 57 AC_Video 1.mp4
\ No newline at end of file
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_1.txt b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dfc835609423ae4e1dad8ca5aea482edb52239a4
--- /dev/null
+++ b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_1.txt
@@ -0,0 +1 @@
+/shared_data/bamc_pnb_data/full_training_data/Positives/211/4. 211 AC_Video 3.mp4/shared_data/bamc_pnb_data/full_training_data/Negatives/211/1. 211 AC_Video 1.mp4/shared_data/bamc_pnb_data/full_training_data/Negatives/238/1. 238 AC_Video 1.mp4/shared_data/bamc_pnb_data/full_training_data/Negatives/178/3. 178_Video 3.mp4/shared_data/bamc_pnb_data/full_training_data/Positives/67/3. 67 AC_Video 2.mp4/shared_data/bamc_pnb_data/full_training_data/Positives/240/5. 240 AC_Video 3 (mislabeed as Pop).mp4/shared_data/bamc_pnb_data/full_training_data/Positives/238/3. 238 AC_Video 2.mp4
\ No newline at end of file
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_2.txt b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ca72117d829cf47ff66e9d573ef70157e8504c4f
--- /dev/null
+++ b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_2.txt
@@ -0,0 +1 @@
+/shared_data/bamc_pnb_data/full_training_data/Positives/93/3. 93 AC_Video 2.mp4/shared_data/bamc_pnb_data/full_training_data/Positives/189/3. 189 AC_Video 2.mp4/shared_data/bamc_pnb_data/full_training_data/Positives/11/3. 11 AC_Video 2.mp4/shared_data/bamc_pnb_data/full_training_data/Negatives/189/1. 189 AC_Video 1.mp4/shared_data/bamc_pnb_data/full_training_data/Negatives/191/5. 191 AC_Video 5.mp4/shared_data/bamc_pnb_data/full_training_data/Negatives/39/2. 39 AC_Video 2.mp4
\ No newline at end of file
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_3.txt b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_3.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d8bf3fcff3924af82ce46c6a2c43e02e92e8abc9
--- /dev/null
+++ b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_3.txt
@@ -0,0 +1 @@
+/shared_data/bamc_pnb_data/full_training_data/Negatives/207/1. 207 AC_Video 1.mp4/shared_data/bamc_pnb_data/full_training_data/Positives/217/3. 217 AC_Video 2.mp4/shared_data/bamc_pnb_data/full_training_data/Positives/205/3. 205 AC_Video 2.mp4/shared_data/bamc_pnb_data/full_training_data/Negatives/217/1. 217 AC_Video 1.mp4/shared_data/bamc_pnb_data/full_training_data/Negatives/134/1. 134 AC_Video 1.mp4/shared_data/bamc_pnb_data/full_training_data/Positives/134/3. 134 AC_Video 2.mp4/shared_data/bamc_pnb_data/full_training_data/Negatives/205/1. 205 AC_Video 1.mp4/shared_data/bamc_pnb_data/full_training_data/Positives/94/3. 94 AC_Video 2.mp4
\ No newline at end of file
diff --git a/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_4.txt b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_4.txt
new file mode 100644
index 0000000000000000000000000000000000000000..723ca9f6b381ca41a8a1cbe836f78fe1f8b4723d
--- /dev/null
+++ b/sparse_coding_torch/classifier_outputs/32_filters_no_aug_9/test_videos_4.txt
@@ -0,0 +1 @@
+/shared_data/bamc_pnb_data/full_training_data/Positives/193/7. 193 AC Video 4.mp4/shared_data/bamc_pnb_data/full_training_data/Negatives/136/1. 136 AC_Video 1.mp4/shared_data/bamc_pnb_data/full_training_data/Negatives/46/2. 46_Video 2.mp4/shared_data/bamc_pnb_data/full_training_data/Positives/54/5. 54 AC_Video 3.mp4/shared_data/bamc_pnb_data/full_training_data/Positives/222/4. 222A_Video 2.mp4/shared_data/bamc_pnb_data/full_training_data/Positives/153/5. 153 AC_Video 3.mp4
\ No newline at end of file
diff --git a/sparse_coding_torch/generate_tflite.py b/sparse_coding_torch/generate_tflite.py
index 9903f002392c96c0d172a3a1a65edfd46cb53f1f..2a8b2a245fd602208b954873d0c9a7701307993b 100644
--- a/sparse_coding_torch/generate_tflite.py
+++ b/sparse_coding_torch/generate_tflite.py
@@ -12,25 +12,29 @@ import argparse
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument('--input_dir', default='/shared_data/bamc_pnb_data/full_training_data', type=str)
+    parser.add_argument('--input_dir', default='/shared_data/bamc_pnb_data/revised_training_data', type=str)
     parser.add_argument('--kernel_size', default=15, type=int)
     parser.add_argument('--kernel_depth', default=5, type=int)
     parser.add_argument('--num_kernels', default=32, type=int)
-    parser.add_argument('--stride', default=2, type=int)
-    parser.add_argument('--max_activation_iter', default=100, type=int)
+    parser.add_argument('--stride', default=4, type=int)
+    parser.add_argument('--max_activation_iter', default=150, type=int)
     parser.add_argument('--activation_lr', default=1e-2, type=float)
     parser.add_argument('--lam', default=0.05, type=float)
-    parser.add_argument('--sparse_checkpoint', default='sparse_coding_torch/output/sparse_pnb_32/sparse_conv3d_model-best.pt/', type=str)
-    parser.add_argument('--checkpoint', default='sparse_coding_torch/classifier_outputs/32_filters/best_classifier.pt/', type=str)
+    parser.add_argument('--sparse_checkpoint', default='sparse_coding_torch/output/sparse_pnb_32_long_train/sparse_conv3d_model-best.pt/', type=str)
+    parser.add_argument('--checkpoint', default='sparse_coding_torch/classifier_outputs/32_filters_no_aug_3/best_classifier.pt/', type=str)
     parser.add_argument('--run_2d', action='store_true')
     parser.add_argument('--batch_size', default=1, type=int)
+    parser.add_argument('--image_height', type=int, default=285)
+    parser.add_argument('--image_width', type=int, default=400)
+    parser.add_argument('--clip_depth', type=int, default=5)
     
     args = parser.parse_args()
     #print(args.accumulate(args.integers))
     batch_size = args.batch_size
 
-    image_height = 285
-    image_width = 235
+    image_height = args.image_height
+    image_width = args.image_width
+    clip_depth = args.clip_depth
     
     recon_model = keras.models.load_model(args.sparse_checkpoint)
         
@@ -38,7 +42,7 @@ if __name__ == "__main__":
 
     inputs = keras.Input(shape=(5, image_height, image_width))
 
-    outputs = MobileModelPNB(sparse_weights=recon_model.weights[0], classifier_model=classifier_model, batch_size=batch_size, image_height=image_height, image_width=image_width, out_channels=args.num_kernels, kernel_size=args.kernel_size, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=True)(inputs)
+    outputs = MobileModelPNB(sparse_weights=recon_model.weights[0], classifier_model=classifier_model, batch_size=batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(inputs)
 
     model = keras.Model(inputs=inputs, outputs=outputs)
 
diff --git a/sparse_coding_torch/keras_model.py b/sparse_coding_torch/keras_model.py
index a55afc3750772a9541bed63f7ff9604872ad50db..c73b1423eb1abb9e4b20d1cd4ffe21e88133acdd 100644
--- a/sparse_coding_torch/keras_model.py
+++ b/sparse_coding_torch/keras_model.py
@@ -28,12 +28,12 @@ def do_recon(filters_1, filters_2, filters_3, filters_4, filters_5, activations,
     return recon
 
 # @tf.function
-def do_recon_3d(filters, activations, image_height, image_width, stride, padding='VALID'):
+def do_recon_3d(filters, activations, image_height, image_width, clip_depth, stride, padding='VALID'):
 #     activations = tf.pad(activations, paddings=[[0,0], [2, 2], [0, 0], [0, 0], [0, 0]])
     batch_size = tf.shape(activations)[0]
     if padding == 'SAME':
         activations = tf.pad(activations, paddings=[[0,0],[2,2],[0,0],[0,0],[0,0]])
-    recon = tf.nn.conv3d_transpose(activations, filters, output_shape=(batch_size, 5, image_height, image_width, 1), strides=[1, stride, stride], padding=padding)
+    recon = tf.nn.conv3d_transpose(activations, filters, output_shape=(batch_size, clip_depth, image_height, image_width, 1), strides=[1, stride, stride], padding=padding)
 
     return recon
 
@@ -84,12 +84,11 @@ def normalize_weights_3d(filters, out_channels):
     return adjusted
 
 class SparseCode(keras.layers.Layer):
-    def __init__(self, batch_size, image_height, image_width, in_channels, out_channels, kernel_size, stride, lam, activation_lr, max_activation_iter, run_2d, padding='VALID'):
+    def __init__(self, batch_size, image_height, image_width, clip_depth, in_channels, out_channels, kernel_size, kernel_depth, stride, lam, activation_lr, max_activation_iter, run_2d, padding='VALID'):
         super(SparseCode, self).__init__()
 
         self.out_channels = out_channels
         self.in_channels = in_channels
-        self.kernel_size = kernel_size
         self.stride = stride
         self.lam = lam
         self.activation_lr = activation_lr
@@ -97,7 +96,9 @@ class SparseCode(keras.layers.Layer):
         self.batch_size = batch_size
         self.image_height = image_height
         self.image_width = image_width
+        self.clip_depth = clip_depth
         self.kernel_size = kernel_size
+        self.kernel_depth = kernel_depth
         self.run_2d = run_2d
         self.padding = padding
 
@@ -108,7 +109,7 @@ class SparseCode(keras.layers.Layer):
         if self.run_2d:
             recon = do_recon(filters[0], filters[1], filters[2], filters[3], filters[4], activations, self.image_height, self.image_width, self.stride, self.padding)
         else:
-            recon = do_recon_3d(filters, activations, self.image_height, self.image_width, self.stride, self.padding)
+            recon = do_recon_3d(filters, activations, self.image_height, self.image_width, self.clip_depth, self.stride, self.padding)
 
         e = images - recon
         g = -1 * u
@@ -153,7 +154,7 @@ class SparseCode(keras.layers.Layer):
             if self.run_2d:
                 output_shape = (len(images), (self.image_height - self.kernel_size) // self.stride + 1, (self.image_width - self.kernel_size) // self.stride + 1, self.out_channels)
             else:
-                output_shape = (len(images), 1, (self.image_height - self.kernel_size) // self.stride + 1, (self.image_width - self.kernel_size) // self.stride + 1, self.out_channels)
+                output_shape = (len(images), (self.clip_depth - self.kernel_depth) // 1 + 1, (self.image_height - self.kernel_size) // self.stride + 1, (self.image_width - self.kernel_size) // self.stride + 1, self.out_channels)
 
         u = tf.stop_gradient(tf.zeros(shape=output_shape))
         m = tf.stop_gradient(tf.zeros(shape=output_shape))
@@ -176,7 +177,7 @@ class SparseCode(keras.layers.Layer):
         return u
     
 class ReconSparse(keras.Model):
-    def __init__(self, batch_size, image_height, image_width, in_channels, out_channels, kernel_size, stride, lam, activation_lr, max_activation_iter, run_2d, padding='VALID'):
+    def __init__(self, batch_size, image_height, image_width, clip_depth, in_channels, out_channels, kernel_size, kernel_depth, stride, lam, activation_lr, max_activation_iter, run_2d, padding='VALID'):
         super().__init__()
         
         self.out_channels = out_channels
@@ -188,6 +189,7 @@ class ReconSparse(keras.Model):
         self.batch_size = batch_size
         self.image_height = image_height
         self.image_width = image_width
+        self.clip_depth = clip_depth
         self.run_2d = run_2d
         self.padding = padding
 
@@ -201,7 +203,9 @@ class ReconSparse(keras.Model):
         else:
 #             pytorch_weights = load_pytorch_weights('sparse.pt')
 #             self.filters = tf.Variable(initial_value=pytorch_weights, dtype='float32', trainable=False)
-            self.filters = tf.Variable(initial_value=initializer(shape=(5, kernel_size, kernel_size, in_channels, out_channels), dtype='float32'), trainable=True)
+            initial_values = initializer(shape=(1, kernel_size, kernel_size, in_channels, out_channels), dtype='float32')
+            initial_values = tf.concat([initial_values]*kernel_depth, axis=0)
+            self.filters = tf.Variable(initial_value=initial_values, trainable=True)
 
         if run_2d:
             weights = normalize_weights(self.get_weights(), out_channels)
@@ -214,7 +218,7 @@ class ReconSparse(keras.Model):
         if self.run_2d:
             recon = do_recon(self.filters_1, self.filters_2, self.filters_3, self.filters_4, self.filters_5, activations, self.image_height, self.image_width, self.stride, self.padding)
         else:
-            recon = do_recon_3d(self.filters, activations, self.image_height, self.image_width, self.stride, self.padding)
+            recon = do_recon_3d(self.filters, activations, self.image_height, self.image_width, self.clip_depth, self.stride, self.padding)
             
         return recon
 
@@ -256,36 +260,42 @@ class PNBClassifier(keras.layers.Layer):
     def __init__(self):
         super(PNBClassifier, self).__init__()
 
-        self.max_pool = keras.layers.MaxPooling2D(pool_size=8, strides=8)
-#         self.conv_1 = keras.layers.Conv2D(32, kernel_size=8, strides=4, activation='relu', padding='valid')
-        self.conv_2 = keras.layers.Conv2D(48, kernel_size=(8, 16), strides=4, activation='relu', padding='valid')
-#         self.conv_3 = keras.layers.Conv2D(24, kernel_size=4, strides=2, activation='relu', padding='valid')
-#         self.conv_4 = keras.layers.Conv2D(24, kernel_size=4, strides=2, activation='relu', padding='valid')
+#         self.max_pool = keras.layers.MaxPooling2D(pool_size=(8, 8), strides=(2, 2))
+        self.conv_1 = keras.layers.Conv2D(32, kernel_size=(8, 8), strides=(4, 4), activation='relu', padding='valid')
+        self.conv_2 = keras.layers.Conv2D(32, kernel_size=4, strides=2, activation='relu', padding='valid')
+#         self.conv_3 = keras.layers.Conv2D(12, kernel_size=4, strides=1, activation='relu', padding='valid')
+#         self.conv_4 = keras.layers.Conv2D(16, kernel_size=4, strides=2, activation='relu', padding='valid')
 
         self.flatten = keras.layers.Flatten()
 
-        self.dropout = keras.layers.Dropout(0.5)
+#         self.dropout = keras.layers.Dropout(0.5)
 
-        self.ff_1 = keras.layers.Dense(1000, activation='relu', use_bias=True)
-        self.ff_2 = keras.layers.Dense(500, activation='relu', use_bias=True)
-        self.ff_3 = keras.layers.Dense(100, activation='relu', use_bias=True)
+#         self.ff_1 = keras.layers.Dense(1000, activation='relu', use_bias=True)
+        self.ff_2 = keras.layers.Dense(40, activation='relu', use_bias=True)
+        self.ff_3 = keras.layers.Dense(20, activation='relu', use_bias=True)
         self.ff_4 = keras.layers.Dense(1)
 
 #     @tf.function
     def call(self, activations):
-        activations = tf.squeeze(activations, axis=1)
-        x = self.max_pool(activations)
-#         x = self.conv_1(x)
+        x = tf.squeeze(activations, axis=1)
+#         x = self.max_pool(x)
+#         print(x.shape)
+        x = self.conv_1(x)
+#         print(x.shape)
         x = self.conv_2(x)
+#         print(x.shape)
+#         raise Exception
 #         x = self.conv_3(x)
+#         print(x.shape)
 #         x = self.conv_4(x)
+#         raise Exception
         x = self.flatten(x)
-        x = self.ff_1(x)
-        x = self.dropout(x)
+#         x = self.ff_1(x)
+#         x = self.dropout(x)
         x = self.ff_2(x)
-        x = self.dropout(x)
+#         x = self.dropout(x)
         x = self.ff_3(x)
-        x = self.dropout(x)
+#         x = self.dropout(x)
         x = self.ff_4(x)
 
         return x
@@ -334,9 +344,9 @@ class MobileModelPTX(keras.Model):
         return pred
     
 class MobileModelPNB(keras.Model):
-    def __init__(self, sparse_weights, classifier_model, batch_size, image_height, image_width, out_channels, kernel_size, stride, lam, activation_lr, max_activation_iter, run_2d):
+    def __init__(self, sparse_weights, classifier_model, batch_size, image_height, image_width, clip_depth, out_channels, kernel_size, kernel_depth, stride, lam, activation_lr, max_activation_iter, run_2d):
         super().__init__()
-        self.sparse_code = SparseCode(batch_size=batch_size, image_height=image_height, image_width=image_width, in_channels=1, out_channels=out_channels, kernel_size=kernel_size, stride=stride, lam=lam, activation_lr=activation_lr, max_activation_iter=max_activation_iter, run_2d=run_2d, padding='VALID')
+        self.sparse_code = SparseCode(batch_size=batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=out_channels, kernel_size=kernel_size, kernel_depth=kernel_depth, stride=stride, lam=lam, activation_lr=activation_lr, max_activation_iter=max_activation_iter, run_2d=run_2d, padding='VALID')
         self.classifier = classifier_model
 
         self.out_channels = out_channels
@@ -369,6 +379,7 @@ class MobileModelPNB(keras.Model):
         else:
             activations = self.sparse_code(images, tf.stop_gradient(self.filters))
 
-        pred = tf.math.sigmoid(self.classifier(activations))
+        pred = tf.math.round(tf.math.sigmoid(self.classifier(activations)))
+#         pred = tf.math.reduce_sum(activations)
 
         return pred
diff --git a/sparse_coding_torch/load_data.py b/sparse_coding_torch/load_data.py
index 967d60f1fa05bc6ccda6792a331124b3d0adb14a..96ef3fa885a4a84dfba9f3966ca5a67b75673608 100644
--- a/sparse_coding_torch/load_data.py
+++ b/sparse_coding_torch/load_data.py
@@ -3,7 +3,7 @@ import torchvision
 import torch
 from sklearn.model_selection import train_test_split
 from sparse_coding_torch.video_loader import MinMaxScaler
-from sparse_coding_torch.video_loader import YoloClipLoader, get_ptx_participants, PNBLoader, get_pnb_participants
+from sparse_coding_torch.video_loader import YoloClipLoader, get_ptx_participants, PNBLoader, get_participants, NeedleLoader, ONSDLoader
 from sparse_coding_torch.video_loader import VideoGrayScaler
 from typing import Sequence, Iterator
 import csv
@@ -111,64 +111,127 @@ class SubsetWeightedRandomSampler(torch.utils.data.Sampler[int]):
     def __len__(self) -> int:
         return len(self.indicies)
     
-def load_pnb_videos(batch_size, input_size, mode=None, classify_mode=False, balance_classes=False, device=None, n_splits=None, sparse_model=None):   
+def load_pnb_videos(yolo_model, batch_size, input_size, crop_size=None, mode=None, classify_mode=False, balance_classes=False, device=None, n_splits=None, sparse_model=None, frames_to_skip=1):   
     video_path = "/shared_data/bamc_pnb_data/full_training_data"
+#     video_path = '/home/dwh48@drexel.edu/pnb_videos_for_testing/train'
+#     video_path = '/home/dwh48@drexel.edu/special_splits/train'
+
+    if not crop_size:
+        crop_size = input_size
     
     transforms = torchvision.transforms.Compose(
     [VideoGrayScaler(),
      MinMaxScaler(0, 255),
-     torchvision.transforms.Resize(input_size)
+     torchvision.transforms.Resize(input_size[:2])
     ])
     augment_transforms = torchvision.transforms.Compose(
-    [torchvision.transforms.RandomRotation(20),
-     torchvision.transforms.RandomHorizontalFlip(),
+#     [torchvision.transforms.Resize(input_size[:2]),
+    [torchvision.transforms.RandomRotation(15),
+#      torchvision.transforms.RandomHorizontalFlip(),
 #      torchvision.transforms.RandomVerticalFlip(),
-     torchvision.transforms.ColorJitter(brightness=0.1),     
+#      torchvision.transforms.ColorJitter(brightness=0.02),     
 #      torchvision.transforms.RandomAdjustSharpness(0, p=0.15),
-     torchvision.transforms.RandomAffine(degrees=0, translate=(0.01, 0))
+#      torchvision.transforms.RandomAffine(degrees=0, translate=(0.01, 0))
 #      torchvision.transforms.CenterCrop((100, 200))
+#      torchvision.transforms.Resize(input_size[:2])
     ])
-    dataset = PNBLoader(video_path, input_size[1], input_size[0], classify_mode, balance_classes=balance_classes, num_frames=5, frame_rate=20, transform=transforms, augmentation=augment_transforms)
+    dataset = PNBLoader(yolo_model, video_path, crop_size[1], crop_size[0], crop_size[2], classify_mode, balance_classes=balance_classes, num_frames=5, transform=transforms, augmentation=augment_transforms, frames_to_skip=frames_to_skip)
     
     targets = dataset.get_labels()
     
     if mode == 'leave_one_out':
         gss = LeaveOneGroupOut()
 
-        groups = get_pnb_participants(dataset.get_filenames())
+        groups = get_participants(dataset.get_filenames())
         
         return gss.split(np.arange(len(targets)), targets, groups), dataset
     elif mode == 'all_train':
         train_idx = np.arange(len(targets))
-        train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
-        train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                               sampler=train_sampler)
-        test_loader = None
+        test_idx = None
         
-        return train_loader, test_loader, dataset
+        return [(train_idx, test_idx)], dataset
     elif mode == 'k_fold':
         gss = StratifiedGroupKFold(n_splits=n_splits, shuffle=True)
 
-        groups = get_pnb_participants(dataset.get_filenames())
+        groups = get_participants(dataset.get_filenames())
         
         return gss.split(np.arange(len(targets)), targets, groups), dataset
     else:
 #         gss = ShuffleSplit(n_splits=n_splits, test_size=0.2)
         gss = GroupShuffleSplit(n_splits=n_splits, test_size=0.2)
 
-        groups = get_pnb_participants(dataset.get_filenames())
+        groups = get_participants(dataset.get_filenames())
         
-        train_idx, test_idx = list(gss.split(np.arange(len(targets)), targets, groups))[0]
+#         train_idx, test_idx = list(gss.split(np.arange(len(targets)), targets, groups))[0]
 #         train_idx, test_idx = list(gss.split(np.arange(len(targets)), targets))[0]
 
         
-        train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+#         train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
 #         train_sampler = SubsetWeightedRandomSampler(get_sample_weights(train_idx, dataset), train_idx, replacement=True)
-        train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                               sampler=train_sampler)
+#         train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+#                                                sampler=train_sampler)
         
-        test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
-        test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-                                               sampler=test_sampler)
+#         test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
+#         test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+#                                                sampler=test_sampler)
+        
+        return gss.split(np.arange(len(targets)), targets, groups), dataset
+    
+def load_onsd_videos(batch_size, input_size, mode=None, n_splits=None):   
+    video_path = "/shared_data/bamc_onsd_data/preliminary_onsd_data"
+    
+    transforms = torchvision.transforms.Compose(
+    [VideoGrayScaler(),
+     MinMaxScaler(0, 255),
+     torchvision.transforms.Resize(input_size[:2])
+    ])
+    augment_transforms = torchvision.transforms.Compose(
+    [torchvision.transforms.RandomRotation(15)
+    ])
+    dataset = ONSDLoader(video_path, input_size[1], input_size[0], input_size[2], num_frames=5, transform=transforms, augmentation=augment_transforms)
+    
+    targets = dataset.get_labels()
+    
+    if mode == 'leave_one_out':
+        gss = LeaveOneGroupOut()
+
+        groups = get_participants(dataset.get_filenames())
+        
+        return gss.split(np.arange(len(targets)), targets, groups), dataset
+    elif mode == 'all_train':
+        train_idx = np.arange(len(targets))
+        test_idx = None
         
-        return train_loader, test_loader, dataset
\ No newline at end of file
+        return [(train_idx, test_idx)], dataset
+    elif mode == 'k_fold':
+        gss = StratifiedGroupKFold(n_splits=n_splits, shuffle=True)
+
+        groups = get_participants(dataset.get_filenames())
+        
+        return gss.split(np.arange(len(targets)), targets, groups), dataset
+    else:
+#         gss = ShuffleSplit(n_splits=n_splits, test_size=0.2)
+        gss = GroupShuffleSplit(n_splits=n_splits, test_size=0.2)
+
+        groups = get_participants(dataset.get_filenames())
+        
+        return gss.split(np.arange(len(targets)), targets, groups), dataset
+    
+def load_needle_clips(batch_size, input_size):   
+    video_path = "/shared_data/bamc_pnb_data/needle_data/non_needle"
+    
+    transforms = torchvision.transforms.Compose(
+    [VideoGrayScaler(),
+     MinMaxScaler(0, 255),
+     torchvision.transforms.Resize(input_size[:2])
+    ])
+
+    dataset = NeedleLoader(video_path, transform=transforms, augmentation=None)
+
+    train_idx = np.arange(len(dataset))
+    train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+    train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+                                           sampler=train_sampler)
+    test_loader = None
+
+    return train_loader, test_loader, dataset
\ No newline at end of file
diff --git a/sparse_coding_torch/output/sparse_pnb_32/arguments.txt b/sparse_coding_torch/output/sparse_pnb_32/arguments.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cc840b7056a325ff69b2a80b74bc8e867a6e0643
--- /dev/null
+++ b/sparse_coding_torch/output/sparse_pnb_32/arguments.txt
@@ -0,0 +1 @@
+Namespace(batch_size=32, kernel_size=15, num_kernels=32, stride=1, max_activation_iter=150, activation_lr=0.01, lr=0.003, epochs=20, lam=0.05, output_dir='output/sparse_pnb_32', seed=42, run_2d=False, save_filters=True, optimizer='sgd', dataset='pnb')
\ No newline at end of file
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diff --git a/sparse_coding_torch/output/sparse_pnb_32/loss_graph.png b/sparse_coding_torch/output/sparse_pnb_32/loss_graph.png
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diff --git a/sparse_coding_torch/output/sparse_pnb_32/sparse_conv3d_model-best.pt/keras_metadata.pb b/sparse_coding_torch/output/sparse_pnb_32/sparse_conv3d_model-best.pt/keras_metadata.pb
new file mode 100644
index 0000000000000000000000000000000000000000..9c0e7c985563738cbced69475a809e8dbb069a23
--- /dev/null
+++ b/sparse_coding_torch/output/sparse_pnb_32/sparse_conv3d_model-best.pt/keras_metadata.pb
@@ -0,0 +1,4 @@
+
+�root"_tf_keras_network*�{"name": "model_1", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Functional", "config": {"name": "model_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 346, 290, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_3"}, "name": "input_3", "inbound_nodes": []}, {"class_name": "ReconSparse", "config": {"layer was saved without config": true}, "name": "recon_sparse", "inbound_nodes": [[["input_3", 0, 0, {}]]]}], "input_layers": [["input_3", 0, 0]], "output_layers": [["recon_sparse", 0, 0]]}, "shared_object_id": 1, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 1, 346, 290, 32]}, "ndim": 5, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 346, 290, 32]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 346, 290, 32]}, "float32", "input_3"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 346, 290, 32]}, "float32", "input_3"]}, "keras_version": "2.8.0", "backend": "tensorflow", "model_config": {"class_name": "Functional"}}2

+�
root.layer-0"_tf_keras_input_layer*�{"class_name": "InputLayer", "name": "input_3", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 346, 290, 32]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 1, 346, 290, 32]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_3"}}2

+�root.layer_with_weights-0"_tf_keras_model*�{"name": "recon_sparse", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "ReconSparse", "config": {"layer was saved without config": true}, "is_graph_network": false, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 346, 290, 32]}, "float32", "input_1"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 1, 346, 290, 32]}, "float32", "input_1"]}, "keras_version": "2.8.0", "backend": "tensorflow", "model_config": {"class_name": "ReconSparse"}}2

\ No newline at end of file
diff --git a/sparse_coding_torch/output/sparse_pnb_32/sparse_conv3d_model-best.pt/saved_model.pb b/sparse_coding_torch/output/sparse_pnb_32/sparse_conv3d_model-best.pt/saved_model.pb
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diff --git a/sparse_coding_torch/output/sparse_pnb_32/sparse_conv3d_model-best.pt/variables/variables.data-00000-of-00001 b/sparse_coding_torch/output/sparse_pnb_32/sparse_conv3d_model-best.pt/variables/variables.data-00000-of-00001
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diff --git a/sparse_coding_torch/output/sparse_pnb_32/sparse_conv3d_model-best.pt/variables/variables.index b/sparse_coding_torch/output/sparse_pnb_32/sparse_conv3d_model-best.pt/variables/variables.index
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diff --git a/sparse_coding_torch/train_classifier.py b/sparse_coding_torch/train_classifier.py
index a18886236609a34e53fd8cf00612db4810a15f7b..812769c676852d472491168fca65d770e1bbb76d 100644
--- a/sparse_coding_torch/train_classifier.py
+++ b/sparse_coding_torch/train_classifier.py
@@ -4,8 +4,8 @@ import torch.nn.functional as F
 from tqdm import tqdm
 import argparse
 import os
-from sparse_coding_torch.load_data import load_yolo_clips, load_pnb_videos, SubsetWeightedRandomSampler
-from sparse_coding_torch.keras_model import SparseCode, PNBClassifier, PTXClassifier, ReconSparse
+from sparse_coding_torch.load_data import load_yolo_clips, load_pnb_videos, SubsetWeightedRandomSampler, get_sample_weights
+from sparse_coding_torch.keras_model import SparseCode, PNBClassifier, PTXClassifier, ReconSparse, normalize_weights, normalize_weights_3d
 import time
 import numpy as np
 from sklearn.metrics import f1_score, accuracy_score, confusion_matrix
@@ -13,6 +13,12 @@ import random
 import pickle
 import tensorflow.keras as keras
 import tensorflow as tf
+from sparse_coding_torch.train_sparse_model import sparse_loss
+from sparse_coding_torch.utils import calculate_pnb_scores, calculate_pnb_scores_skipped_frames
+from yolov4.get_bounding_boxes import YoloModel
+import torchvision
+from sparse_coding_torch.video_loader import VideoGrayScaler, MinMaxScaler
+import glob
 
 configproto = tf.compat.v1.ConfigProto()
 configproto.gpu_options.polling_inactive_delay_msecs = 5000
@@ -40,22 +46,36 @@ if __name__ == "__main__":
     parser.add_argument('--seed', default=26, type=int)
     parser.add_argument('--train', action='store_true')
     parser.add_argument('--num_positives', default=100, type=int)
-    parser.add_argument('--n_splits', default=1, type=int)
+    parser.add_argument('--n_splits', default=5, type=int)
     parser.add_argument('--save_train_test_splits', action='store_true')
     parser.add_argument('--run_2d', action='store_true')
     parser.add_argument('--balance_classes', action='store_true')
     parser.add_argument('--dataset', default='pnb', type=str)
+    parser.add_argument('--train_sparse', action='store_true')
+    parser.add_argument('--mixing_ratio', type=float, default=1.0)
+    parser.add_argument('--sparse_lr', type=float, default=0.003)
+    parser.add_argument('--crop_height', type=int, default=285)
+    parser.add_argument('--crop_width', type=int, default=350)
+    parser.add_argument('--scale_factor', type=int, default=1)
+    parser.add_argument('--clip_depth', type=int, default=5)
+    parser.add_argument('--frames_to_skip', type=int, default=1)
     
     args = parser.parse_args()
     
     if args.dataset == 'pnb':
-        image_height = 285
-        image_width = 470
+        crop_height = args.crop_height
+        crop_width = args.crop_width
+
+        image_height = int(crop_height / args.scale_factor)
+        image_width = int(crop_width / args.scale_factor)
+        clip_depth = args.clip_depth
     elif args.dataset == 'ptx':
         image_height = 100
         image_width = 200
     else:
         raise Exception('Invalid dataset')
+        
+    batch_size = args.batch_size
     
     random.seed(args.seed)
     np.random.seed(args.seed)
@@ -67,324 +87,341 @@ if __name__ == "__main__":
         
     with open(os.path.join(output_dir, 'arguments.txt'), 'w+') as out_f:
         out_f.write(str(args))
+    
+    yolo_model = YoloModel()
 
     all_errors = []
     
     if args.run_2d:
-        inputs = keras.Input(shape=(image_height, image_width, 5))
+        inputs = keras.Input(shape=(image_height, image_width, clip_depth))
     else:
-        inputs = keras.Input(shape=(5, image_height, image_width, 1))
+        inputs = keras.Input(shape=(clip_depth, image_height, image_width, 1))
 
-    filter_inputs = keras.Input(shape=(5, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
+    filter_inputs = keras.Input(shape=(args.kernel_depth, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
 
-    output = SparseCode(batch_size=args.batch_size, image_height=image_height, image_width=image_width, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(inputs, filter_inputs)
+    output = SparseCode(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(inputs, filter_inputs)
 
     sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)
     
-    recon_inputs = keras.Input(shape=(1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
+    recon_inputs = keras.Input(shape=((clip_depth - args.kernel_depth) // 1 + 1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
     
-    recon_outputs = ReconSparse(batch_size=args.batch_size, image_height=image_height, image_width=image_width, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(recon_inputs)
+    recon_outputs = ReconSparse(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(recon_inputs)
     
     recon_model = keras.Model(inputs=recon_inputs, outputs=recon_outputs)
 
     if args.sparse_checkpoint:
-        recon_model = keras.models.load_model(args.sparse_checkpoint)
+        recon_model.set_weights(keras.models.load_model(args.sparse_checkpoint).get_weights())
         
     positive_class = None
     if args.dataset == 'pnb':
-        train_loader, test_loader, dataset = load_pnb_videos(args.batch_size, input_size=(image_height, image_width), classify_mode=True, balance_classes=args.balance_classes, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None)
-        print(len([labels for labels, _, _ in test_loader for label in labels if label == 'Positives']))
-        print(len(test_loader))
+        splits, dataset = load_pnb_videos(yolo_model, args.batch_size, input_size=(image_height, image_width, clip_depth), crop_size=(crop_height, crop_width, clip_depth), classify_mode=True, balance_classes=args.balance_classes, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, frames_to_skip=args.frames_to_skip)
         positive_class = 'Positives'
     elif args.dataset == 'ptx':
         train_loader, test_loader, dataset = load_yolo_clips(args.batch_size, num_clips=1, num_positives=15, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, whole_video=False, positive_videos='positive_videos.json')
         positive_class = 'No_Sliding'
     else:
         raise Exception('Invalid dataset')
-    
+
     overall_true = []
     overall_pred = []
     fn_ids = []
     fp_ids = []
+    
+    i_fold = 0
+    for train_idx, test_idx in splits:
+#         train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+        train_sampler = SubsetWeightedRandomSampler(get_sample_weights(train_idx, dataset), train_idx, replacement=True)
+        train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+                                               sampler=train_sampler)
         
-    if args.checkpoint:
-        classifier_model = keras.models.load_model(args.checkpoint)
-    else:
-        if os.path.exists(os.path.join(args.output_dir, 'best_classifier.pt')):
-            classifier_model = keras.models.load_model(os.path.join(output_dir, 'best_classifier.pt'))
-
-        classifier_inputs = keras.Input(shape=(1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
-
-        if args.dataset == 'pnb':
-            classifier_outputs = PNBClassifier()(classifier_inputs)
-        elif args.dataset == 'ptx':
-            classifier_outputs = PTXClassifier()(classifier_inputs)
+        if test_idx is not None:
+            test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
+            test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+                                                   sampler=test_sampler)
+            
+            with open(os.path.join(args.output_dir, 'test_videos_{}.txt'.format(i_fold)), 'w+') as test_videos_out:
+                test_set = set([x for tup in test_loader for x in tup[2]])
+                test_videos_out.writelines(test_set)
+        else:
+            test_loader = None
+        
+        if args.checkpoint:
+            classifier_model = keras.models.load_model(args.checkpoint)
         else:
-            raise Exception('No classifier exists for that dataset')
+            classifier_inputs = keras.Input(shape=((clip_depth - args.kernel_depth) // 1 + 1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
 
-        classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
+            if args.dataset == 'pnb':
+                classifier_outputs = PNBClassifier()(classifier_inputs)
+            elif args.dataset == 'ptx':
+                classifier_outputs = PTXClassifier()(classifier_inputs)
+            else:
+                raise Exception('No classifier exists for that dataset')
 
-    with open(os.path.join(output_dir, 'classifier_summary.txt'), 'w+') as out_f:
-        out_f.write(str(classifier_model.summary()))
-    
-    prediction_optimizer = keras.optimizers.Adam(learning_rate=args.lr)
+            classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
 
-    best_so_far = float('inf')
+        prediction_optimizer = keras.optimizers.Adam(learning_rate=args.lr)
+        filter_optimizer = tf.keras.optimizers.SGD(learning_rate=args.sparse_lr)
 
-    criterion = keras.losses.BinaryCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
+        best_so_far = float('-inf')
 
-    if args.train:
-        for epoch in range(args.epochs):
-            epoch_loss = 0
-            t1 = time.perf_counter()
+        criterion = keras.losses.BinaryCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
 
-            y_true_train = None
-            y_pred_train = None
+        if args.train:
+            for epoch in range(args.epochs):
+                epoch_loss = 0
+                t1 = time.perf_counter()
 
-            for labels, local_batch, vid_f in tqdm(train_loader):
-                images = local_batch.permute(0, 2, 3, 4, 1).numpy()
+                y_true_train = None
+                y_pred_train = None
 
-                torch_labels = np.zeros(len(labels))
-                torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
-                torch_labels = np.expand_dims(torch_labels, axis=1)
+                for labels, local_batch, vid_f in tqdm(train_loader):
+                    images = local_batch.permute(0, 2, 3, 4, 1).numpy()
 
-                activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
-#                     print(tf.math.reduce_sum(activations))
+                    torch_labels = np.zeros(len(labels))
+                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
+                    torch_labels = np.expand_dims(torch_labels, axis=1)
 
-                with tf.GradientTape() as tape:
-                    pred = classifier_model(activations)
-                    loss = criterion(torch_labels, pred)
+                    if args.train_sparse:
+                        with tf.GradientTape() as tape:
+                            activations = sparse_model([images, tf.expand_dims(recon_model.trainable_weights[0], axis=0)])
+                            pred = classifier_model(activations)
+                            loss = criterion(torch_labels, pred)
 
-#                         print(pred)
-#                         print(tf.math.sigmoid(pred))
-#                         print(loss)
-#                     print(torch_labels)
+                            print(loss)
+                    else:
+                        activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
+    #                     raise Exception
 
-                epoch_loss += loss * local_batch.size(0)
+                        with tf.GradientTape() as tape:
+                            pred = classifier_model(activations)
+                            loss = criterion(torch_labels, pred)
 
-                gradients = tape.gradient(loss, classifier_model.trainable_weights)
+                    epoch_loss += loss * local_batch.size(0)
 
-                prediction_optimizer.apply_gradients(zip(gradients, classifier_model.trainable_weights))
+                    if args.train_sparse:
+                        sparse_gradients, classifier_gradients = tape.gradient(loss, [recon_model.trainable_weights, classifier_model.trainable_weights])
 
-                if y_true_train is None:
-                    y_true_train = torch_labels
-                    y_pred_train = tf.math.round(tf.math.sigmoid(pred))
-                else:
-                    y_true_train = tf.concat((y_true_train, torch_labels), axis=0)
-                    y_pred_train = tf.concat((y_pred_train, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+                        prediction_optimizer.apply_gradients(zip(classifier_gradients, classifier_model.trainable_weights))
 
-            t2 = time.perf_counter()
+                        filter_optimizer.apply_gradients(zip(sparse_gradients, recon_model.trainable_weights))
 
-            y_true = None
-            y_pred = None
-            test_loss = 0.0
-            for labels, local_batch, vid_f in tqdm(test_loader):
-                images = local_batch.permute(0, 2, 3, 4, 1).numpy()
+                        if args.run_2d:
+                            weights = normalize_weights(recon_model.get_weights(), args.num_kernels)
+                        else:
+                            weights = normalize_weights_3d(recon_model.get_weights(), args.num_kernels)
+                        recon_model.set_weights(weights)
+                    else:
+                        gradients = tape.gradient(loss, classifier_model.trainable_weights)
 
-                torch_labels = np.zeros(len(labels))
-                torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
-                torch_labels = np.expand_dims(torch_labels, axis=1)
+                        prediction_optimizer.apply_gradients(zip(gradients, classifier_model.trainable_weights))
 
-                activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
 
-                pred = classifier_model(activations)
-                loss = criterion(torch_labels, pred)
+                    if y_true_train is None:
+                        y_true_train = torch_labels
+                        y_pred_train = tf.math.round(tf.math.sigmoid(pred))
+                    else:
+                        y_true_train = tf.concat((y_true_train, torch_labels), axis=0)
+                        y_pred_train = tf.concat((y_pred_train, tf.math.round(tf.math.sigmoid(pred))), axis=0)
 
-                test_loss += loss
+                t2 = time.perf_counter()
 
-                if y_true is None:
-                    y_true = torch_labels
-                    y_pred = tf.math.round(tf.math.sigmoid(pred))
-                else:
-                    y_true = tf.concat((y_true, torch_labels), axis=0)
-                    y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
-
-            t2 = time.perf_counter()
+                y_true = None
+                y_pred = None
+                test_loss = 0.0
+                for labels, local_batch, vid_f in tqdm(test_loader):
+                    images = local_batch.permute(0, 2, 3, 4, 1).numpy()
 
-            y_true = tf.cast(y_true, tf.int32)
-            y_pred = tf.cast(y_pred, tf.int32)
+                    torch_labels = np.zeros(len(labels))
+                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
+                    torch_labels = np.expand_dims(torch_labels, axis=1)
 
-            y_true_train = tf.cast(y_true_train, tf.int32)
-            y_pred_train = tf.cast(y_pred_train, tf.int32)
+                    activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
 
-            f1 = f1_score(y_true, y_pred, average='macro')
-            accuracy = accuracy_score(y_true, y_pred)
+                    pred = classifier_model(activations)
+                    loss = criterion(torch_labels, pred)
 
-            train_accuracy = accuracy_score(y_true_train, y_pred_train)
+                    test_loss += loss
 
-            print('epoch={}, time={:.2f}, train_loss={:.2f}, test_loss={:.2f}, train_acc={:.2f}, test_f1={:.2f}, test_acc={:.2f}'.format(epoch, t2-t1, epoch_loss, test_loss, train_accuracy, f1, accuracy))
-#             print(epoch_loss)
-            if epoch_loss <= best_so_far:
-                print("found better model")
-                # Save model parameters
-                classifier_model.save(os.path.join(output_dir, "best_classifier.pt"))
-                pickle.dump(prediction_optimizer.get_weights(), open(os.path.join(output_dir, 'optimizer.pt'), 'wb+'))
-                best_so_far = epoch_loss
+                    if y_true is None:
+                        y_true = torch_labels
+                        y_pred = tf.math.round(tf.math.sigmoid(pred))
+                    else:
+                        y_true = tf.concat((y_true, torch_labels), axis=0)
+                        y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
 
-        classifier_model = keras.models.load_model(os.path.join(output_dir, "best_classifier.pt"))
+                t2 = time.perf_counter()
 
-    if args.dataset == 'pnb':
-        epoch_loss = 0
+                y_true = tf.cast(y_true, tf.int32)
+                y_pred = tf.cast(y_pred, tf.int32)
 
-        y_true = None
-        y_pred = None
+                y_true_train = tf.cast(y_true_train, tf.int32)
+                y_pred_train = tf.cast(y_pred_train, tf.int32)
 
-        pred_dict = {}
-        gt_dict = {}
+                f1 = f1_score(y_true, y_pred, average='macro')
+                accuracy = accuracy_score(y_true, y_pred)
 
-        t1 = time.perf_counter()
-#         test_videos = [vid_f for labels, local_batch, vid_f in batch for batch in test_loader]
-        test_videos = [single_vid for labels, local_batch, vid_f in test_loader for single_vid in vid_f]
+                train_accuracy = accuracy_score(y_true_train, y_pred_train)
 
-        for k, v in tqdm(dataset.get_final_clips().items()):
-            if k not in test_videos:
-                continue
-            labels, local_batch, vid_f = v
-            images = local_batch.unsqueeze(0).permute(0, 2, 3, 4, 1).numpy()
-            labels = [labels]
+                print('epoch={}, i_fold={}, time={:.2f}, train_loss={:.2f}, test_loss={:.2f}, train_acc={:.2f}, test_f1={:.2f}, test_acc={:.2f}'.format(epoch, i_fold, t2-t1, epoch_loss, test_loss, train_accuracy, f1, accuracy))
+    #             print(epoch_loss)
+                if f1 >= best_so_far:
+                    print("found better model")
+                    # Save model parameters
+                    classifier_model.save(os.path.join(output_dir, "best_classifier_{}.pt".format(i_fold)))
+                    recon_model.save(os.path.join(output_dir, "best_sparse_model_{}.pt".format(i_fold)))
+                    pickle.dump(prediction_optimizer.get_weights(), open(os.path.join(output_dir, 'optimizer_{}.pt'.format(i_fold)), 'wb+'))
+                    best_so_far = f1
 
-            torch_labels = np.zeros(len(labels))
-            torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
-            torch_labels = np.expand_dims(torch_labels, axis=1)
+            classifier_model = keras.models.load_model(os.path.join(output_dir, "best_classifier_{}.pt".format(i_fold)))
+            recon_model = keras.models.load_model(os.path.join(output_dir, 'best_sparse_model_{}.pt'.format(i_fold)))
 
-            activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
+        if args.dataset == 'pnb':
+            epoch_loss = 0
+            
+            transform = torchvision.transforms.Compose(
+            [VideoGrayScaler(),
+             MinMaxScaler(0, 255),
+             torchvision.transforms.Resize((image_height, image_width))
+            ])
 
-            pred = classifier_model(activations)
+            y_true = None
+            y_pred = None
 
-            loss = criterion(torch_labels, pred)
-            epoch_loss += loss
+            pred_dict = {}
+            gt_dict = {}
 
-            final_pred = tf.math.round(tf.math.sigmoid(pred))
-            gt = torch_labels
+            t1 = time.perf_counter()
+    #         test_videos = [vid_f for labels, local_batch, vid_f in batch for batch in test_loader]
+            test_videos = set()
+            for labels, local_batch, vid_f in test_loader:
+                test_videos.update(vid_f)
+                
+            test_labels = [vid_f.split('/')[-3] for vid_f in test_videos]
+
+#             test_videos = glob.glob(pathname='/home/dwh48@drexel.edu/special_splits/test/*/*.mp4', recursive=True)
+#             test_labels = [f.split('/')[-2] for f in test_videos]
+#             print(test_videos)
+#             print(test_labels)
             
-            if final_pred != gt:
-                if final_pred == 0:
-                    fn_ids.append(k)
-                else:
-                    fp_ids.append(k)
-
-            overall_true.append(gt)
-            overall_pred.append(final_pred)
+            if args.frames_to_skip == 1:
+                y_pred, y_true, fn, fp = calculate_pnb_scores(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform)
+            else:
+                y_pred, y_true, fn, fp = calculate_pnb_scores_skipped_frames(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, args.frames_to_skip, image_width, image_height, transform)
 
-            if final_pred != gt:
-                if final_pred == 0:
-                    fn_ids.append(vid_f)
-                else:
-                    fp_ids.append(vid_f)
+            t2 = time.perf_counter()
 
-            if y_true is None:
-                y_true = torch_labels
-                y_pred = tf.math.round(tf.math.sigmoid(pred))
-            else:
-                y_true = tf.concat((y_true, torch_labels), axis=0)
-                y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+            print('i_fold={}, time={:.2f}'.format(i_fold, t2-t1))
 
-        t2 = time.perf_counter()
+            y_true = tf.cast(y_true, tf.int32)
+            y_pred = tf.cast(y_pred, tf.int32)
 
-        print('loss={:.2f}, time={:.2f}'.format(loss, t2-t1))
+            f1 = f1_score(y_true, y_pred, average='macro')
+            accuracy = accuracy_score(y_true, y_pred)
+            
+            fn_ids.extend(fn)
+            fp_ids.extend(fp)
+            
+            overall_true.extend(y_true)
+            overall_pred.extend(y_pred)
 
-        y_true = tf.cast(y_true, tf.int32)
-        y_pred = tf.cast(y_pred, tf.int32)
+            print("Test f1={:.2f}, vid_acc={:.2f}".format(f1, accuracy))
 
-        f1 = f1_score(y_true, y_pred, average='macro')
-        accuracy = accuracy_score(y_true, y_pred)
+            print(confusion_matrix(y_true, y_pred))
+        elif args.dataset == 'ptx':
+            epoch_loss = 0
 
-        print("Test f1={:.2f}, vid_acc={:.2f}".format(f1, accuracy))
+            y_true = None
+            y_pred = None
 
-        print(confusion_matrix(y_true, y_pred))
-    elif args.dataset == 'ptx':
-        epoch_loss = 0
+            pred_dict = {}
+            gt_dict = {}
 
-        y_true = None
-        y_pred = None
+            t1 = time.perf_counter()
+            for labels, local_batch, vid_f in test_loader:
+                images = local_batch.permute(0, 2, 3, 4, 1).numpy()
 
-        pred_dict = {}
-        gt_dict = {}
+                torch_labels = np.zeros(len(labels))
+                torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
+                torch_labels = np.expand_dims(torch_labels, axis=1)
 
-        t1 = time.perf_counter()
-        for labels, local_batch, vid_f in test_loader:
-            images = local_batch.permute(0, 2, 3, 4, 1).numpy()
+                activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
 
-            torch_labels = np.zeros(len(labels))
-            torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
-            torch_labels = np.expand_dims(torch_labels, axis=1)
+                pred = classifier_model(activations)
 
-            activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
+                loss = criterion(torch_labels, pred)
+                epoch_loss += loss * local_batch.size(0)
 
-            pred = classifier_model(activations)
+                for i, v_f in enumerate(vid_f):
+                    if v_f not in pred_dict:
+                        pred_dict[v_f] = tf.math.round(tf.math.sigmoid(pred[i]))
+                    else:
+                        pred_dict[v_f] = tf.concat((pred_dict[v_f], tf.math.round(tf.math.sigmoid(pred[i]))), axis=0)
 
-            loss = criterion(torch_labels, pred)
-            epoch_loss += loss * local_batch.size(0)
+                    if v_f not in gt_dict:
+                        gt_dict[v_f] = torch_labels[i]
+                    else:
+                        gt_dict[v_f] = tf.concat((gt_dict[v_f], torch_labels[i]), axis=0)
 
-            for i, v_f in enumerate(vid_f):
-                if v_f not in pred_dict:
-                    pred_dict[v_f] = tf.math.round(tf.math.sigmoid(pred[i]))
+                if y_true is None:
+                    y_true = torch_labels
+                    y_pred = tf.math.round(tf.math.sigmoid(pred))
                 else:
-                    pred_dict[v_f] = tf.concat((pred_dict[v_f], tf.math.round(tf.math.sigmoid(pred[i]))), axis=0)
+                    y_true = tf.concat((y_true, torch_labels), axis=0)
+                    y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
 
-                if v_f not in gt_dict:
-                    gt_dict[v_f] = torch_labels[i]
-                else:
-                    gt_dict[v_f] = tf.concat((gt_dict[v_f], torch_labels[i]), axis=0)
+            t2 = time.perf_counter()
 
-            if y_true is None:
-                y_true = torch_labels
-                y_pred = tf.math.round(tf.math.sigmoid(pred))
-            else:
-                y_true = tf.concat((y_true, torch_labels), axis=0)
-                y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
-
-        t2 = time.perf_counter()
-
-        vid_acc = []
-        for k in pred_dict.keys():
-            gt_tmp = torch.tensor(gt_dict[k].numpy())
-            pred_tmp = torch.tensor(pred_dict[k].numpy())
-
-            gt_mode = torch.mode(gt_tmp)[0].item()
-#             perm = torch.randperm(pred_tmp.size(0))
-#             cutoff = int(pred_tmp.size(0)/4)
-#             if cutoff < 3:
-#                 cutoff = 3
-#             idx = perm[:cutoff]
-#             samples = pred_tmp[idx]
-            pred_mode = torch.mode(pred_tmp)[0].item()
-            overall_true.append(gt_mode)
-            overall_pred.append(pred_mode)
-            if pred_mode == gt_mode:
-                vid_acc.append(1)
-            else:
-                vid_acc.append(0)
-                if pred_mode == 0:
-                    fn_ids.append(k)
+            vid_acc = []
+            for k in pred_dict.keys():
+                gt_tmp = torch.tensor(gt_dict[k].numpy())
+                pred_tmp = torch.tensor(pred_dict[k].numpy())
+
+                gt_mode = torch.mode(gt_tmp)[0].item()
+    #             perm = torch.randperm(pred_tmp.size(0))
+    #             cutoff = int(pred_tmp.size(0)/4)
+    #             if cutoff < 3:
+    #                 cutoff = 3
+    #             idx = perm[:cutoff]
+    #             samples = pred_tmp[idx]
+                pred_mode = torch.mode(pred_tmp)[0].item()
+                overall_true.append(gt_mode)
+                overall_pred.append(pred_mode)
+                if pred_mode == gt_mode:
+                    vid_acc.append(1)
                 else:
-                    fp_ids.append(k)
-
-        vid_acc = np.array(vid_acc)
-
-        print('----------------------------------------------------------------------------')
-        for k in pred_dict.keys():
-            print(k)
-            print('Predictions:')
-            print(pred_dict[k])
-            print('Ground Truth:')
-            print(gt_dict[k])
-            print('Overall Prediction:')
-            print(torch.mode(torch.tensor(pred_dict[k].numpy()))[0].item())
-            print('----------------------------------------------------------------------------')
+                    vid_acc.append(0)
+                    if pred_mode == 0:
+                        fn_ids.append(k)
+                    else:
+                        fp_ids.append(k)
+
+            vid_acc = np.array(vid_acc)
 
-        print('loss={:.2f}, time={:.2f}'.format(loss, t2-t1))
+            print('----------------------------------------------------------------------------')
+            for k in pred_dict.keys():
+                print(k)
+                print('Predictions:')
+                print(pred_dict[k])
+                print('Ground Truth:')
+                print(gt_dict[k])
+                print('Overall Prediction:')
+                print(torch.mode(torch.tensor(pred_dict[k].numpy()))[0].item())
+                print('----------------------------------------------------------------------------')
+
+            print('loss={:.2f}, time={:.2f}'.format(loss, t2-t1))
 
-        y_true = tf.cast(y_true, tf.int32)
-        y_pred = tf.cast(y_pred, tf.int32)
+            y_true = tf.cast(y_true, tf.int32)
+            y_pred = tf.cast(y_pred, tf.int32)
 
-        f1 = f1_score(y_true, y_pred, average='macro')
-        accuracy = accuracy_score(y_true, y_pred)
-        all_errors.append(np.sum(vid_acc) / len(vid_acc))
+            f1 = f1_score(y_true, y_pred, average='macro')
+            accuracy = accuracy_score(y_true, y_pred)
+            all_errors.append(np.sum(vid_acc) / len(vid_acc))
 
-        print("Test f1={:.2f}, clip_acc={:.2f}, vid_acc={:.2f}".format(f1, accuracy, np.sum(vid_acc) / len(vid_acc)))
+            print("Test f1={:.2f}, clip_acc={:.2f}, vid_acc={:.2f}".format(f1, accuracy, np.sum(vid_acc) / len(vid_acc)))
 
-        print(confusion_matrix(y_true, y_pred))
+            print(confusion_matrix(y_true, y_pred))
+            
+        i_fold += 1
 
     fp_fn_file = os.path.join(args.output_dir, 'fp_fn.txt')
     with open(fp_fn_file, 'w+') as in_f:
@@ -392,3 +429,14 @@ if __name__ == "__main__":
         in_f.write(str(fp_ids) + '\n\n')
         in_f.write('FN:\n')
         in_f.write(str(fn_ids) + '\n\n')
+        
+    overall_true = np.array(overall_true)
+    overall_pred = np.array(overall_pred)
+            
+    final_f1 = f1_score(overall_true, overall_pred, average='macro')
+    final_acc = accuracy_score(overall_true, overall_pred)
+    final_conf = confusion_matrix(overall_true, overall_pred)
+            
+    print("Final accuracy={:.2f}, f1={:.2f}".format(final_acc, final_f1))
+    print(final_conf)
+
diff --git a/sparse_coding_torch/train_classifier_needle.py b/sparse_coding_torch/train_classifier_needle.py
new file mode 100644
index 0000000000000000000000000000000000000000..79fa2494dcabd3049d7ecf67105bffaeef177ee2
--- /dev/null
+++ b/sparse_coding_torch/train_classifier_needle.py
@@ -0,0 +1,426 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+import argparse
+import os
+from sparse_coding_torch.load_data import load_yolo_clips, load_pnb_videos, SubsetWeightedRandomSampler, get_sample_weights
+from sparse_coding_torch.keras_model import SparseCode, PNBClassifier, PTXClassifier, ReconSparse, normalize_weights, normalize_weights_3d
+import time
+import numpy as np
+from sklearn.metrics import f1_score, accuracy_score, confusion_matrix
+import random
+import pickle
+import tensorflow.keras as keras
+import tensorflow as tf
+from sparse_coding_torch.train_sparse_model import sparse_loss
+from sparse_coding_torch.utils import calculate_pnb_scores
+from yolov4.get_bounding_boxes import YoloModel
+
+configproto = tf.compat.v1.ConfigProto()
+configproto.gpu_options.polling_inactive_delay_msecs = 5000
+configproto.gpu_options.allow_growth = True
+sess = tf.compat.v1.Session(config=configproto) 
+tf.compat.v1.keras.backend.set_session(sess)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch_size', default=32, type=int)
+    parser.add_argument('--kernel_size', default=5, type=int)
+    parser.add_argument('--kernel_depth', default=5, type=int)
+    parser.add_argument('--num_kernels', default=40, type=int)
+    parser.add_argument('--stride', default=1, type=int)
+    parser.add_argument('--max_activation_iter', default=150, type=int)
+    parser.add_argument('--activation_lr', default=1e-2, type=float)
+    parser.add_argument('--lr', default=5e-4, type=float)
+    parser.add_argument('--epochs', default=40, type=int)
+    parser.add_argument('--lam', default=0.05, type=float)
+    parser.add_argument('--output_dir', default='sparse_coding_torch/classifier_outputs/needle_model', type=str)
+    parser.add_argument('--needle_checkpoint', default='sparse_coding_torch/output/needle_codes/sparse_conv3d_model-best.pt/', type=str)
+    parser.add_argument('--non_needle_checkpoint', default='sparse_coding_torch/output/non_needle_codes/sparse_conv3d_model-best.pt/', type=str)
+    parser.add_argument('--checkpoint', default=None, type=str)
+    parser.add_argument('--splits', default='default', type=str, help='k_fold or leave_one_out or all_train')
+    parser.add_argument('--seed', default=26, type=int)
+    parser.add_argument('--train', action='store_true')
+    parser.add_argument('--num_positives', default=100, type=int)
+    parser.add_argument('--n_splits', default=1, type=int)
+    parser.add_argument('--save_train_test_splits', action='store_true')
+    parser.add_argument('--run_2d', action='store_true')
+    parser.add_argument('--balance_classes', action='store_true')
+    parser.add_argument('--dataset', default='pnb', type=str)
+    parser.add_argument('--train_sparse', action='store_true')
+    parser.add_argument('--mixing_ratio', type=float, default=1.0)
+    parser.add_argument('--sparse_lr', type=float, default=0.003)
+    parser.add_argument('--crop_height', type=int, default=285)
+    parser.add_argument('--crop_width', type=int, default=350)
+    parser.add_argument('--scale_factor', type=int, default=1)
+    parser.add_argument('--clip_depth', type=int, default=5)
+    parser.add_argument('--frames_to_skip', type=int, default=1)
+    
+    args = parser.parse_args()
+    
+    if args.dataset == 'pnb':
+        crop_height = args.crop_height
+        crop_width = args.crop_width
+
+        image_height = int(crop_height / args.scale_factor)
+        image_width = int(crop_width / args.scale_factor)
+        clip_depth = args.clip_depth
+    elif args.dataset == 'ptx':
+        image_height = 100
+        image_width = 200
+    else:
+        raise Exception('Invalid dataset')
+        
+    batch_size = args.batch_size
+    
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    
+    output_dir = args.output_dir
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+        
+    with open(os.path.join(output_dir, 'arguments.txt'), 'w+') as out_f:
+        out_f.write(str(args))
+    
+    yolo_model = YoloModel()
+
+    all_errors = []
+    
+    if args.run_2d:
+        inputs = keras.Input(shape=(image_height, image_width, clip_depth))
+    else:
+        inputs = keras.Input(shape=(clip_depth, image_height, image_width, 1))
+
+    filter_inputs = keras.Input(shape=(args.kernel_depth, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
+
+    output = SparseCode(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(inputs, filter_inputs)
+
+    sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)
+    
+    recon_inputs = keras.Input(shape=((clip_depth - args.kernel_depth) // 1 + 1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
+    
+    recon_outputs = ReconSparse(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(recon_inputs)
+    
+    recon_model = keras.Model(inputs=recon_inputs, outputs=recon_outputs)
+
+    if args.needle_checkpoint and args.non_needle_checkpoint:
+        weights = tf.concat([keras.models.load_model(args.needle_checkpoint).get_weights(), keras.models.load_model(args.non_needle_checkpoint).get_weights()], axis=5)
+        recon_model.set_weights(weights)
+        
+    positive_class = None
+    if args.dataset == 'pnb':
+        splits, dataset = load_pnb_videos(yolo_model, args.batch_size, input_size=(image_height, image_width, clip_depth), crop_size=(crop_height, crop_width, clip_depth), classify_mode=True, balance_classes=args.balance_classes, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, frames_to_skip=args.frames_to_skip)
+        positive_class = 'Positives'
+    elif args.dataset == 'ptx':
+        train_loader, test_loader, dataset = load_yolo_clips(args.batch_size, num_clips=1, num_positives=15, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, whole_video=False, positive_videos='positive_videos.json')
+        positive_class = 'No_Sliding'
+    else:
+        raise Exception('Invalid dataset')
+
+    overall_true = []
+    overall_pred = []
+    fn_ids = []
+    fp_ids = []
+    
+    i_fold = 0
+    for train_idx, test_idx in splits:
+        train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+        train_sampler = SubsetWeightedRandomSampler(get_sample_weights(train_idx, dataset), train_idx, replacement=True)
+        train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+                                               sampler=train_sampler)
+        
+        test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
+        test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+                                               sampler=test_sampler)
+        
+        with open(os.path.join(args.output_dir, 'test_videos_{}.txt'.format(i_fold)), 'w+') as test_videos_out:
+            test_set = set([x for tup in test_loader for x in tup[2]])
+            test_videos_out.writelines(test_set)
+        
+        if args.checkpoint:
+            classifier_model = keras.models.load_model(args.checkpoint)
+        else:
+            classifier_inputs = keras.Input(shape=((clip_depth - args.kernel_depth) // 1 + 1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
+
+            if args.dataset == 'pnb':
+                classifier_outputs = PNBClassifier()(classifier_inputs)
+            elif args.dataset == 'ptx':
+                classifier_outputs = PTXClassifier()(classifier_inputs)
+            else:
+                raise Exception('No classifier exists for that dataset')
+
+            classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
+
+        prediction_optimizer = keras.optimizers.Adam(learning_rate=args.lr)
+        filter_optimizer = tf.keras.optimizers.SGD(learning_rate=args.sparse_lr)
+
+        best_so_far = float('-inf')
+
+        criterion = keras.losses.BinaryCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
+
+        if args.train:
+            for epoch in range(args.epochs):
+                epoch_loss = 0
+                t1 = time.perf_counter()
+
+                y_true_train = None
+                y_pred_train = None
+
+                for labels, local_batch, vid_f in tqdm(train_loader):
+                    images = local_batch.permute(0, 2, 3, 4, 1).numpy()
+
+                    torch_labels = np.zeros(len(labels))
+                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
+                    torch_labels = np.expand_dims(torch_labels, axis=1)
+
+                    if args.train_sparse:
+                        with tf.GradientTape() as tape:
+                            activations = sparse_model([images, tf.expand_dims(recon_model.trainable_weights[0], axis=0)])
+                            pred = classifier_model(activations)
+                            loss = criterion(torch_labels, pred)
+
+                            print(loss)
+                    else:
+                        activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
+
+                        with tf.GradientTape() as tape:
+                            pred = classifier_model(activations)
+                            loss = criterion(torch_labels, pred)
+
+                    epoch_loss += loss * local_batch.size(0)
+
+                    if args.train_sparse:
+                        sparse_gradients, classifier_gradients = tape.gradient(loss, [recon_model.trainable_weights, classifier_model.trainable_weights])
+
+                        prediction_optimizer.apply_gradients(zip(classifier_gradients, classifier_model.trainable_weights))
+
+                        filter_optimizer.apply_gradients(zip(sparse_gradients, recon_model.trainable_weights))
+
+                        if args.run_2d:
+                            weights = normalize_weights(recon_model.get_weights(), args.num_kernels)
+                        else:
+                            weights = normalize_weights_3d(recon_model.get_weights(), args.num_kernels)
+                        recon_model.set_weights(weights)
+                    else:
+                        gradients = tape.gradient(loss, classifier_model.trainable_weights)
+
+                        prediction_optimizer.apply_gradients(zip(gradients, classifier_model.trainable_weights))
+
+
+                    if y_true_train is None:
+                        y_true_train = torch_labels
+                        y_pred_train = tf.math.round(tf.math.sigmoid(pred))
+                    else:
+                        y_true_train = tf.concat((y_true_train, torch_labels), axis=0)
+                        y_pred_train = tf.concat((y_pred_train, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+
+                t2 = time.perf_counter()
+
+                y_true = None
+                y_pred = None
+                test_loss = 0.0
+                for labels, local_batch, vid_f in tqdm(test_loader):
+                    images = local_batch.permute(0, 2, 3, 4, 1).numpy()
+
+                    torch_labels = np.zeros(len(labels))
+                    torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
+                    torch_labels = np.expand_dims(torch_labels, axis=1)
+
+                    activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
+
+                    pred = classifier_model(activations)
+                    loss = criterion(torch_labels, pred)
+
+                    test_loss += loss
+
+                    if y_true is None:
+                        y_true = torch_labels
+                        y_pred = tf.math.round(tf.math.sigmoid(pred))
+                    else:
+                        y_true = tf.concat((y_true, torch_labels), axis=0)
+                        y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+
+                t2 = time.perf_counter()
+
+                y_true = tf.cast(y_true, tf.int32)
+                y_pred = tf.cast(y_pred, tf.int32)
+
+                y_true_train = tf.cast(y_true_train, tf.int32)
+                y_pred_train = tf.cast(y_pred_train, tf.int32)
+
+                f1 = f1_score(y_true, y_pred, average='macro')
+                accuracy = accuracy_score(y_true, y_pred)
+
+                train_accuracy = accuracy_score(y_true_train, y_pred_train)
+
+                print('epoch={}, i_fold={}, time={:.2f}, train_loss={:.2f}, test_loss={:.2f}, train_acc={:.2f}, test_f1={:.2f}, test_acc={:.2f}'.format(epoch, i_fold, t2-t1, epoch_loss, test_loss, train_accuracy, f1, accuracy))
+    #             print(epoch_loss)
+                if f1 >= best_so_far:
+                    print("found better model")
+                    # Save model parameters
+                    classifier_model.save(os.path.join(output_dir, "best_classifier_{}.pt".format(i_fold)))
+#                     recon_model.save(os.path.join(output_dir, "best_sparse_model_{}.pt".format(i_fold)))
+                    pickle.dump(prediction_optimizer.get_weights(), open(os.path.join(output_dir, 'optimizer_{}.pt'.format(i_fold)), 'wb+'))
+                    best_so_far = f1
+
+            classifier_model = keras.models.load_model(os.path.join(output_dir, "best_classifier_{}.pt".format(i_fold)))
+#             recon_model = keras.models.load_model(os.path.join(output_dir, 'best_sparse_model_{}.pt'.format(i_fold)))
+
+        if args.dataset == 'pnb':
+            epoch_loss = 0
+            
+            transform = torchvision.transforms.Compose(
+            [VideoGrayScaler(),
+             MinMaxScaler(0, 255),
+             torchvision.transforms.Resize((image_height, image_width))
+            ])
+
+            y_true = None
+            y_pred = None
+
+            pred_dict = {}
+            gt_dict = {}
+
+            t1 = time.perf_counter()
+    #         test_videos = [vid_f for labels, local_batch, vid_f in batch for batch in test_loader]
+            test_videos = []
+            test_labels = []
+            for labels, local_batch, vid_f in test_loader:
+                test_videos.extend(vid_f)
+                test_labels.extend(labels)
+                
+            y_pred, y_true, fn, fp = calculate_pnb_scores(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, 30, image_width, image_height, transform)
+
+            t2 = time.perf_counter()
+
+            print('i_fold={}, time={:.2f}'.format(i_fold, t2-t1))
+
+            y_true = tf.cast(y_true, tf.int32)
+            y_pred = tf.cast(y_pred, tf.int32)
+
+            f1 = f1_score(y_true, y_pred, average='macro')
+            accuracy = accuracy_score(y_true, y_pred)
+            
+            fn_ids.extend(fn)
+            fp_idx.extend(fp)
+
+            print("Test f1={:.2f}, vid_acc={:.2f}".format(f1, accuracy))
+
+            print(confusion_matrix(y_true, y_pred))
+        elif args.dataset == 'ptx':
+            epoch_loss = 0
+
+            y_true = None
+            y_pred = None
+
+            pred_dict = {}
+            gt_dict = {}
+
+            t1 = time.perf_counter()
+            for labels, local_batch, vid_f in test_loader:
+                images = local_batch.permute(0, 2, 3, 4, 1).numpy()
+
+                torch_labels = np.zeros(len(labels))
+                torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
+                torch_labels = np.expand_dims(torch_labels, axis=1)
+
+                activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
+
+                pred = classifier_model(activations)
+
+                loss = criterion(torch_labels, pred)
+                epoch_loss += loss * local_batch.size(0)
+
+                for i, v_f in enumerate(vid_f):
+                    if v_f not in pred_dict:
+                        pred_dict[v_f] = tf.math.round(tf.math.sigmoid(pred[i]))
+                    else:
+                        pred_dict[v_f] = tf.concat((pred_dict[v_f], tf.math.round(tf.math.sigmoid(pred[i]))), axis=0)
+
+                    if v_f not in gt_dict:
+                        gt_dict[v_f] = torch_labels[i]
+                    else:
+                        gt_dict[v_f] = tf.concat((gt_dict[v_f], torch_labels[i]), axis=0)
+
+                if y_true is None:
+                    y_true = torch_labels
+                    y_pred = tf.math.round(tf.math.sigmoid(pred))
+                else:
+                    y_true = tf.concat((y_true, torch_labels), axis=0)
+                    y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+
+            t2 = time.perf_counter()
+
+            vid_acc = []
+            for k in pred_dict.keys():
+                gt_tmp = torch.tensor(gt_dict[k].numpy())
+                pred_tmp = torch.tensor(pred_dict[k].numpy())
+
+                gt_mode = torch.mode(gt_tmp)[0].item()
+    #             perm = torch.randperm(pred_tmp.size(0))
+    #             cutoff = int(pred_tmp.size(0)/4)
+    #             if cutoff < 3:
+    #                 cutoff = 3
+    #             idx = perm[:cutoff]
+    #             samples = pred_tmp[idx]
+                pred_mode = torch.mode(pred_tmp)[0].item()
+                overall_true.append(gt_mode)
+                overall_pred.append(pred_mode)
+                if pred_mode == gt_mode:
+                    vid_acc.append(1)
+                else:
+                    vid_acc.append(0)
+                    if pred_mode == 0:
+                        fn_ids.append(k)
+                    else:
+                        fp_ids.append(k)
+
+            vid_acc = np.array(vid_acc)
+
+            print('----------------------------------------------------------------------------')
+            for k in pred_dict.keys():
+                print(k)
+                print('Predictions:')
+                print(pred_dict[k])
+                print('Ground Truth:')
+                print(gt_dict[k])
+                print('Overall Prediction:')
+                print(torch.mode(torch.tensor(pred_dict[k].numpy()))[0].item())
+                print('----------------------------------------------------------------------------')
+
+            print('loss={:.2f}, time={:.2f}'.format(loss, t2-t1))
+
+            y_true = tf.cast(y_true, tf.int32)
+            y_pred = tf.cast(y_pred, tf.int32)
+
+            f1 = f1_score(y_true, y_pred, average='macro')
+            accuracy = accuracy_score(y_true, y_pred)
+            all_errors.append(np.sum(vid_acc) / len(vid_acc))
+
+            print("Test f1={:.2f}, clip_acc={:.2f}, vid_acc={:.2f}".format(f1, accuracy, np.sum(vid_acc) / len(vid_acc)))
+
+            print(confusion_matrix(y_true, y_pred))
+            
+        i_fold += 1
+
+    fp_fn_file = os.path.join(args.output_dir, 'fp_fn.txt')
+    with open(fp_fn_file, 'w+') as in_f:
+        in_f.write('FP:\n')
+        in_f.write(str(fp_ids) + '\n\n')
+        in_f.write('FN:\n')
+        in_f.write(str(fn_ids) + '\n\n')
+        
+    overall_true = np.array(overall_true)
+    overall_pred = np.array(overall_pred)
+            
+    final_f1 = f1_score(overall_true, overall_pred, average='macro')
+    final_acc = accuracy_score(overall_true, overall_pred)
+    final_conf = confusion_matrix(overall_true, overall_pred)
+            
+    print("Final accuracy={:.2f}, f1={:.2f}".format(final_acc, final_f1))
+    print(final_conf)
+
diff --git a/sparse_coding_torch/train_sparse_model.py b/sparse_coding_torch/train_sparse_model.py
index 72439f899751f59867a961c76b3b7e835e10be2a..8fc91ca5d995f1f68483d5c4bbf0497c742dafcd 100644
--- a/sparse_coding_torch/train_sparse_model.py
+++ b/sparse_coding_torch/train_sparse_model.py
@@ -7,7 +7,7 @@ from matplotlib.animation import FuncAnimation
 from tqdm import tqdm
 import argparse
 import os
-from sparse_coding_torch.load_data import load_yolo_clips, load_pnb_videos
+from sparse_coding_torch.load_data import load_yolo_clips, load_pnb_videos, load_needle_clips, load_onsd_videos
 import tensorflow.keras as keras
 import tensorflow as tf
 from sparse_coding_torch.keras_model import normalize_weights_3d, normalize_weights, SparseCode, load_pytorch_weights, ReconSparse
@@ -88,7 +88,7 @@ def plot_filters(filters):
 
     return FuncAnimation(plt.gcf(), update, interval=1000/20)
 
-def sparse_loss(recon, activations, batch_size, lam, stride):
+def sparse_loss(images, recon, activations, batch_size, lam, stride):
     loss = 0.5 * (1/batch_size) * tf.math.reduce_sum(tf.math.pow(images - recon, 2))
     loss += lam * tf.reduce_mean(tf.math.reduce_sum(tf.math.abs(tf.reshape(activations, (batch_size, -1))), axis=1))
     return loss
@@ -97,19 +97,25 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument('--batch_size', default=32, type=int)
     parser.add_argument('--kernel_size', default=15, type=int)
-    parser.add_argument('--num_kernels', default=16, type=int)
+    parser.add_argument('--kernel_depth', default=5, type=int)
+    parser.add_argument('--num_kernels', default=32, type=int)
     parser.add_argument('--stride', default=1, type=int)
-    parser.add_argument('--max_activation_iter', default=200, type=int)
+    parser.add_argument('--max_activation_iter', default=300, type=int)
     parser.add_argument('--activation_lr', default=1e-2, type=float)
     parser.add_argument('--lr', default=0.003, type=float)
-    parser.add_argument('--epochs', default=40, type=int)
+    parser.add_argument('--epochs', default=150, type=int)
     parser.add_argument('--lam', default=0.05, type=float)
     parser.add_argument('--output_dir', default='./output', type=str)
     parser.add_argument('--seed', default=42, type=int)
     parser.add_argument('--run_2d', action='store_true')
     parser.add_argument('--save_filters', action='store_true')
     parser.add_argument('--optimizer', default='sgd', type=str)
-    parser.add_argument('--dataset', default='pnb', type=str)
+    parser.add_argument('--dataset', default='onsd', type=str)
+    parser.add_argument('--crop_height', type=int, default=400)
+    parser.add_argument('--crop_width', type=int, default=400)
+    parser.add_argument('--scale_factor', type=int, default=1)
+    parser.add_argument('--clip_depth', type=int, default=5)
+    parser.add_argument('--frames_to_skip', type=int, default=1)
     
 
     args = parser.parse_args()
@@ -117,12 +123,13 @@ if __name__ == "__main__":
     random.seed(args.seed)
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
-    
-#     policy = keras.mixed_precision.Policy('mixed_float16')
-#     keras.mixed_precision.set_global_policy(policy)
 
-    image_height = 285
-    image_width = 235
+    crop_height = args.crop_height
+    crop_width = args.crop_width
+
+    image_height = int(crop_height / args.scale_factor)
+    image_width = int(crop_width / args.scale_factor)
+    clip_depth = args.clip_depth
 
     output_dir = args.output_dir
     if not os.path.exists(output_dir):
@@ -133,12 +140,22 @@ if __name__ == "__main__":
     with open(os.path.join(output_dir, 'arguments.txt'), 'w+') as out_f:
         out_f.write(str(args))
 
-    if args.dataset == 'pnb':
-        train_loader, test_loader, dataset = load_pnb_videos(args.batch_size, input_size=(image_height, image_width), classify_mode=False, balance_classes=False, mode='all_train')
+    if args.dataset == 'onsd':
+        splits, dataset = load_onsd_videos(args.batch_size, input_size=(image_height, image_width, clip_depth), mode='all_train')
+        train_idx, test_idx = splits[0]
+    elif args.dataset == 'pnb':
+        train_loader, test_loader, dataset = load_pnb_videos(args.batch_size, input_size=(image_height, image_width, clip_depth), crop_size=(crop_height, crop_width, clip_depth), classify_mode=False, balance_classes=False, mode='all_train', frames_to_skip=args.frames_to_skip)
     elif args.dataset == 'ptx':
         train_loader, _ = load_yolo_clips(args.batch_size, num_clips=1, num_positives=15, mode='all_train', device=device, n_splits=1, sparse_model=None, whole_video=False, positive_videos='../positive_videos.json')
+    elif args.dataset == 'needle':
+        train_loader, test_loader, dataset = load_needle_clips(args.batch_size, input_size=(image_height, image_width, clip_depth))
     else:
         raise Exception('Invalid dataset')
+    
+    train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+    train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size,
+                                           sampler=train_sampler)
+    
     print('Loaded', len(train_loader), 'train examples')
 
     example_data = next(iter(train_loader))
@@ -150,13 +167,13 @@ if __name__ == "__main__":
         
     filter_inputs = keras.Input(shape=(5, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
 
-    output = SparseCode(batch_size=args.batch_size, image_height=image_height, image_width=image_width, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(inputs, filter_inputs)
+    output = SparseCode(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(inputs, filter_inputs)
 
     sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)
     
     recon_inputs = keras.Input(shape=(1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
     
-    recon_outputs = ReconSparse(batch_size=args.batch_size, image_height=image_height, image_width=image_width, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(recon_inputs)
+    recon_outputs = ReconSparse(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(recon_inputs)
     
     recon_model = keras.Model(inputs=recon_inputs, outputs=recon_outputs)
     
@@ -195,7 +212,7 @@ if __name__ == "__main__":
             
             with tf.GradientTape() as tape:
                 recon = recon_model(activations)
-                loss = sparse_loss(recon, activations, args.batch_size, args.lam, args.stride)
+                loss = sparse_loss(images, recon, activations, args.batch_size, args.lam, args.stride)
 
             epoch_loss += loss * local_batch.size(0)
             running_loss += loss * local_batch.size(0)
@@ -209,16 +226,6 @@ if __name__ == "__main__":
             else:
                 weights = normalize_weights_3d(recon_model.get_weights(), args.num_kernels)
             recon_model.set_weights(weights)
-            
-#             if args.save_filters and num_iters % 25 == 0:
-#                 if args.run_2d:
-#                     filters = plot_filters(tf.stack(recon_model.get_weights(), axis=0))
-#                 else:
-#                     filters = plot_filters(recon_model.get_weights()[0])
-#                 filters.save(os.path.join(args.output_dir, 'filters_' + str(epoch) + '_' + str(num_iters) + '.mp4'))
-#                 loss_log.append(running_loss)
-#                 print(running_loss)
-#                 running_loss = 0.0
                 
             num_iters += 1
 
@@ -235,7 +242,7 @@ if __name__ == "__main__":
         if epoch_loss < best_so_far:
             print("found better model")
             # Save model parameters
-            recon_model.save(os.path.join(output_dir, "sparse_conv3d_model-best.pt"))
+            recon_model.save(os.path.join(output_dir, "best_sparse.pt"))
             best_so_far = epoch_loss
 
         loss_log.append(epoch_loss)
diff --git a/sparse_coding_torch/utils.py b/sparse_coding_torch/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..98c807bb4be5f285ed70a35fcab2bdfb1af5de6d
--- /dev/null
+++ b/sparse_coding_torch/utils.py
@@ -0,0 +1,113 @@
+import numpy as np
+from sparse_coding_torch.video_loader import get_yolo_regions, classify_nerve_is_right
+import torchvision as tv
+import torch
+import tensorflow as tf
+from tqdm import tqdm
+
+def calculate_pnb_scores(input_videos, labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform):
+    all_predictions = []
+    
+    numerical_labels = []
+    for label in labels:
+        if label == 'Positives':
+            numerical_labels.append(1.0)
+        else:
+            numerical_labels.append(0.0)
+
+    final_list = []
+    fp_ids = []
+    fn_ids = []
+    for v_idx, f in tqdm(enumerate(input_videos)):
+        vc = tv.io.read_video(f)[0].permute(3, 0, 1, 2)
+        is_right = classify_nerve_is_right(yolo_model, vc)
+        
+        all_preds = []
+        for j in range(vc.size(1) - 5, vc.size(1) - 25, -5):
+            if j-5 < 0:
+                break
+
+            vc_sub = vc[:, j-5:j, :, :]
+            
+            if vc_sub.size(1) < 5:
+                continue
+            
+            clip = get_yolo_regions(yolo_model, vc_sub, is_right, image_width, image_height)
+            
+            if not clip:
+                continue
+
+            clip = clip[0]
+            clip = transform(clip).to(torch.float32)
+            clip = tf.expand_dims(clip, axis=4) 
+
+            activations = tf.stop_gradient(sparse_model([clip, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
+
+            pred = tf.math.round(tf.math.sigmoid(classifier_model(activations)))
+
+            all_preds.append(pred)
+                
+        if all_preds:
+            final_pred = np.round(np.mean(np.array(all_preds)))
+        else:
+            final_pred = 1.0
+            
+        if final_pred != numerical_labels[v_idx]:
+            if final_pred == 0:
+                fn_ids.append(f)
+            else:
+                fp_ids.append(f)
+            
+        final_list.append(final_pred)
+        
+    return np.array(final_list), np.array(numerical_labels), fn_ids, fp_ids
+
+def calculate_pnb_scores_skipped_frames(input_videos, labels, yolo_model, sparse_model, recon_model, classifier_model, frames_to_skip, image_width, image_height, transform):
+    all_predictions = []
+    
+    numerical_labels = []
+    for label in labels:
+        if label == 'Positives':
+            numerical_labels.append(1.0)
+        else:
+            numerical_labels.append(0.0)
+
+    final_list = []
+    fp_ids = []
+    fn_ids = []
+    for v_idx, f in tqdm(enumerate(input_videos)):
+        vc = tv.io.read_video(f)[0].permute(3, 0, 1, 2)
+        is_right = classify_nerve_is_right(yolo_model, vc)
+        
+        all_preds = []
+        
+        frames = []
+        for k in range(vc.size(1) - 1, vc.size(1) - 5 * frames_to_skip, -frames_to_skip):
+            frames.append(vc[:, k, :, :])
+        vc_sub = torch.stack(frames, dim=1)
+            
+        if vc_sub.size(1) < 5:
+            continue
+
+        clip = get_yolo_regions(yolo_model, vc_sub, is_right, image_width, image_height)
+
+        if clip:
+            clip = clip[0]
+            clip = transform(clip).to(torch.float32)
+            clip = tf.expand_dims(clip, axis=4) 
+
+            activations = tf.stop_gradient(sparse_model([clip, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
+
+            pred = tf.math.round(tf.math.sigmoid(classifier_model(activations)))
+        else:
+            pred = 1.0
+            
+        if pred != numerical_labels[v_idx]:
+            if pred == 0:
+                fn_ids.append(f)
+            else:
+                fp_ids.append(f)
+            
+        final_list.append(pred)
+        
+    return np.array(final_list), np.array(numerical_labels), fn_ids, fp_ids
\ No newline at end of file
diff --git a/sparse_coding_torch/video_loader.py b/sparse_coding_torch/video_loader.py
index 9908d6e7ddbce0a413424a5b04bb4042d5d53b6a..fbe9e03b1fb93ef9ce670a8fecd8a8861101bd2f 100644
--- a/sparse_coding_torch/video_loader.py
+++ b/sparse_coding_torch/video_loader.py
@@ -36,7 +36,7 @@ def get_ptx_participants():
             
     return video_to_participant
 
-def get_pnb_participants(filenames):
+def get_participants(filenames):
     return [f.split('/')[-2] for f in filenames]
 
 class MinMaxScaler(object):
@@ -83,6 +83,9 @@ def get_yolo_regions(yolo_model, clip, is_right, crop_width, crop_height):
     bounding_boxes = bounding_boxes.squeeze(0)
     classes = classes.squeeze(0)
     
+    rotate_box = False
+    angle = 20
+    
     all_clips = []
     for bb, class_pred in zip(bounding_boxes, classes):
         if class_pred != 0:
@@ -103,16 +106,30 @@ def get_yolo_regions(yolo_model, clip, is_right, crop_width, crop_height):
         else:
             lower_x = center_x
             upper_x = center_x + crop_width
+            
+        if rotate_box:
+#             cv2.imwrite('test_1.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
+            if is_right:
+                clip = tv.transforms.functional.rotate(clip, angle=angle, center=[upper_x, upper_y])
+            else:
+#                 cv2.imwrite('test_1.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
+                clip = tv.transforms.functional.rotate(clip, angle=-angle, center=[lower_x, upper_y])
+#                 cv2.imwrite('test_2.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
 
         trimmed_clip = clip[:, :, lower_y:upper_y, lower_x:upper_x]
         
 #         if orig_width - center_x >= center_x:
 #         if not is_right:
-#         cv2.imwrite('test.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
-#         cv2.imwrite('test_yolo.png', trimmed_clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
-#         raise Exception
+# #         cv2.imwrite('test.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
+#             cv2.imwrite('test_3.png', trimmed_clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
+#             raise Exception
         
 #         print(trimmed_clip.size())
+
+        if not is_right:
+            trimmed_clip = tv.transforms.functional.hflip(trimmed_clip)
+#             cv2.imwrite('test_yolo.png', trimmed_clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
+#             raise Exception
         
         if trimmed_clip.shape[2] == 0 or trimmed_clip.shape[3] == 0:
             continue
@@ -167,28 +184,27 @@ def classify_nerve_is_right(yolo_model, video):
     
 class PNBLoader(Dataset):
     
-    def __init__(self, video_path, clip_width, clip_height, classify_mode=False, balance_classes=False, num_frames=5, frame_rate=20, frames_between_clips=None, transform=None, augmentation=None):
+    def __init__(self, yolo_model, video_path, clip_width, clip_height, clip_depth, classify_mode=False, balance_classes=False, num_frames=5, frames_to_skip=1, transform=None, augmentation=None):
         self.transform = transform
         self.augmentation = augmentation
         self.labels = [name for name in listdir(video_path) if isdir(join(video_path, name))]
         
-        clip_cache_file = 'clip_cache_pnb_{}_{}.pt'.format(clip_width, clip_height)
-        clip_cache_final_file = 'clip_cache_pnb_{}_{}_final.pt'.format(clip_width, clip_height)
+        if classify_mode:
+            clip_cache_file = 'clip_cache_pnb_{}_{}_{}_{}.pt'.format(clip_width, clip_height, clip_depth, frames_to_skip)
+            clip_cache_final_file = 'clip_cache_pnb_{}_{}_{}_{}_final.pt'.format(clip_width, clip_height, clip_depth, frames_to_skip)
+        else:
+            clip_cache_file = 'clip_cache_pnb_{}_{}_sparse.pt'.format(clip_width, clip_height)
+            clip_cache_final_file = 'clip_cache_pnb_{}_{}_final_sparse.pt'.format(clip_width, clip_height)
         
-        region_labels = load_pnb_region_labels(join(video_path, 'sme_region_labels.csv'))
-
-        yolo_model = YoloModel()
+        region_labels = load_pnb_region_labels('/shared_data/bamc_pnb_data/full_training_data/sme_region_labels.csv')
         
         self.videos = []
         for label in self.labels:
+#             self.videos.extend([(label, abspath(join(video_path, label, f)), f) for f in glob.glob(join(video_path, label, '*', '*.mp4'))])
             self.videos.extend([(label, abspath(join(video_path, label, f)), f) for f in glob.glob(join(video_path, label, '*', '*.mp4'))])
         
 #         self.videos = list(filter(lambda x: x[1].split('/')[-2] in ['67', '94', '134', '193', '222', '240'], self.videos))
 #         self.videos = list(filter(lambda x: x[1].split('/')[-2] in ['67'], self.videos))
-
-            
-        if not frames_between_clips:
-            frames_between_clips = num_frames
             
         self.clips = []
         self.final_clips = {}
@@ -203,9 +219,10 @@ class PNBLoader(Dataset):
                 is_right = classify_nerve_is_right(yolo_model, vc)
 
                 if classify_mode:
-                    person_idx = path.split('/')[-2]
+#                     person_idx = path.split('/')[-2]
+                    person_idx = path.split('/')[-1].split(' ')[1]
 
-                    if vc.size(1) < 5:
+                    if vc.size(1) < clip_depth:
                         continue
 
                     if label == 'Positives' and person_idx in region_labels:
@@ -213,34 +230,35 @@ class PNBLoader(Dataset):
                         for sub_region in negative_regions.split(','):
                             sub_region = sub_region.split('-')
                             start_loc = int(sub_region[0])
+#                             end_loc = int(sub_region[1]) - 50
                             end_loc = int(sub_region[1]) + 1
-                            for frame in range(start_loc, end_loc - 5, 5):
-                                vc_sub = vc[:, frame:frame+5, :, :]
-                                if vc_sub.size(1) < 5:
+                            for j in range(start_loc, end_loc - clip_depth * frames_to_skip, 5):
+                                frames = []
+                                for k in range(j, j + clip_depth * frames_to_skip, frames_to_skip):
+                                    frames.append(vc[:, k, :, :])
+                                vc_sub = torch.stack(frames, dim=1)
+
+                                if vc_sub.size(1) < clip_depth:
                                     continue
-                                    
-#                                 cv2.imwrite('test.png', vc_sub[0, 0, :, :].unsqueeze(2).numpy())
 
                                 for clip in get_yolo_regions(yolo_model, vc_sub, is_right, clip_width, clip_height):
                                     if self.transform:
                                         clip = self.transform(clip)
-                                        
-#                                     print(clip[0, 0, :, :].size())
-#                                     cv2.imwrite('test_yolo.png', clip[0, 0, :, :].unsqueeze(2).numpy())
-#                                     print(clip.shape)
-#                                     tv.io.write_video('test_yolo.mp4', clip.swapaxes(0,1).swapaxes(1,2).swapaxes(2,3).numpy(), fps=20)
-#                                     print(path)
-#                                     raise Exception
 
                                     self.clips.append(('Negatives', clip, self.videos[vid_idx][2]))
 
                         if positive_regions:
                             for sub_region in positive_regions.split(','):
                                 sub_region = sub_region.split('-')
+#                                 start_loc = int(sub_region[0]) + 15
                                 start_loc = int(sub_region[0])
-                                if len(sub_region) == 1:
-                                    vc_sub = vc[:, start_loc:start_loc+5, :, :]
-                                    if vc_sub.size(1) < 5:
+                                if len(sub_region) == 1 and vc.size(1) >= start_loc + clip_depth * frames_to_skip:
+                                    frames = []
+                                    for k in range(start_loc, start_loc + clip_depth * frames_to_skip, frames_to_skip):
+                                        frames.append(vc[:, k, :, :])
+                                    vc_sub = torch.stack(frames, dim=1)
+
+                                    if vc_sub.size(1) < clip_depth:
                                         continue
                                         
                                     for clip in get_yolo_regions(yolo_model, vc_sub, is_right, clip_width, clip_height):
@@ -248,28 +266,35 @@ class PNBLoader(Dataset):
                                             clip = self.transform(clip)
 
                                         self.clips.append(('Positives', clip, self.videos[vid_idx][2]))
-                                else:
+                                elif vc.size(1) >= start_loc + clip_depth * frames_to_skip:
                                     end_loc = sub_region[1]
                                     if end_loc.strip().lower() == 'end':
                                         end_loc = vc.size(1)
                                     else:
                                         end_loc = int(end_loc)
-                                    for frame in range(start_loc, end_loc - 5, 5):
-                                        vc_sub = vc[:, frame:frame+5, :, :]
-#                                         cv2.imwrite('test.png', vc_sub[0, 0, :, :].unsqueeze(2).numpy())
-                                        if vc_sub.size(1) < 5:
+                                    for j in range(start_loc, end_loc - clip_depth * frames_to_skip, 3):
+                                        frames = []
+                                        for k in range(j, j + clip_depth * frames_to_skip, frames_to_skip):
+                                            frames.append(vc[:, k, :, :])
+                                        vc_sub = torch.stack(frames, dim=1)
+
+                                        if vc_sub.size(1) < clip_depth:
                                             continue
                                         for clip in get_yolo_regions(yolo_model, vc_sub, is_right, clip_width, clip_height):
                                             if self.transform:
                                                 clip = self.transform(clip)
-                                                
-#                                             cv2.imwrite('test_yolo.png', clip[0, 0, :, :].unsqueeze(2).numpy())
-#                                             raise Exception
 
                                             self.clips.append(('Positives', clip, self.videos[vid_idx][2]))
+                                else:
+                                    continue
                     elif label == 'Positives':
-                        vc_sub = vc[:, -5:, :, :]
-                        if vc_sub.size(1) < 5:
+                        frames = []
+                        for k in range(j, -1 * clip_depth * frames_to_skip, frames_to_skip):
+                            frames.append(vc[:, k, :, :])
+                        if not frames:
+                            continue
+                        vc_sub = torch.stack(frames, dim=1)
+                        if vc_sub.size(1) < clip_depth:
                             continue
                         for clip in get_yolo_regions(yolo_model, vc_sub, is_right, clip_width, clip_height):
                             if self.transform:
@@ -277,9 +302,12 @@ class PNBLoader(Dataset):
 
                             self.clips.append((self.videos[vid_idx][0], clip, self.videos[vid_idx][2]))
                     elif label == 'Negatives':
-                        for j in range(0, vc.size(1) - 5, 5):
-                            vc_sub = vc[:, j:j+5, :, :]
-                            if vc_sub.size(1) < 5:
+                        for j in range(0, vc.size(1) - clip_depth * frames_to_skip, 10):
+                            frames = []
+                            for k in range(j, j + clip_depth * frames_to_skip, frames_to_skip):
+                                frames.append(vc[:, k, :, :])
+                            vc_sub = torch.stack(frames, dim=1)
+                            if vc_sub.size(1) < clip_depth:
                                 continue
                             for clip in get_yolo_regions(yolo_model, vc_sub, is_right, clip_width, clip_height):
                                 if self.transform:
@@ -289,15 +317,19 @@ class PNBLoader(Dataset):
                     else:
                         raise Exception('Invalid label')
                 else:
-                    for j in range(0, vc.size(1) - 5, 5):
-                        vc_sub = vc[:, j:j+5, :, :]
-                        if vc_sub.size(1) < 5:
+                    for j in range(0, vc.size(1) - clip_depth * frames_to_skip, 10):
+                        frames = []
+                        for k in range(j, j + clip_depth * frames_to_skip, frames_to_skip):
+                            frames.append(vc[:, k, :, :])
+                        vc_sub = torch.stack(frames, dim=1)
+                        
+                        if vc_sub.size(1) != clip_depth:
                             continue
-                        for clip in get_yolo_regions(yolo_model, vc_sub, is_right, clip_width, clip_height):
-                            if self.transform:
-                                clip = self.transform(clip)
+#                         for clip in get_yolo_regions(yolo_model, vc_sub, is_right, clip_width, clip_height):
+                        if self.transform:
+                            vc_sub = self.transform(vc_sub)
 
-                            self.clips.append((self.videos[vid_idx][0], clip, self.videos[vid_idx][2]))
+                        self.clips.append((self.videos[vid_idx][0], vc_sub, self.videos[vid_idx][2]))
 
                 self.final_clips[self.videos[vid_idx][2]] = self.clips[-1]
                 vid_idx += 1
@@ -351,6 +383,122 @@ class PNBLoader(Dataset):
     def __len__(self):
         return len(self.clips)
     
+class ONSDLoader(Dataset):
+    
+    def __init__(self, video_path, clip_width, clip_height, clip_depth, num_frames=5, transform=None, augmentation=None):
+        self.transform = transform
+        self.augmentation = augmentation
+        self.labels = [name for name in listdir(video_path) if isdir(join(video_path, name))]
+        
+        clip_cache_file = 'clip_cache_onsd_{}_{}_sparse.pt'.format(clip_width, clip_height)
+        clip_cache_final_file = 'clip_cache_onsd_{}_{}_final_sparse.pt'.format(clip_width, clip_height)
+        
+        self.videos = []
+        for label in self.labels:
+#             self.videos.extend([(label, abspath(join(video_path, label, f)), f) for f in glob.glob(join(video_path, label, '*', '*.mp4'))])
+            self.videos.extend([(label, abspath(join(video_path, label, f)), f) for f in glob.glob(join(video_path, label, '*', '*.mp4'))])
+        
+#         self.videos = list(filter(lambda x: x[1].split('/')[-2] in ['67', '94', '134', '193', '222', '240'], self.videos))
+#         self.videos = list(filter(lambda x: x[1].split('/')[-2] in ['67'], self.videos))
+            
+        self.clips = []
+        self.final_clips = {}
+        
+        if exists(clip_cache_file):
+            self.clips = torch.load(open(clip_cache_file, 'rb'))
+            self.final_clips = torch.load(open(clip_cache_final_file, 'rb'))
+        else:
+            vid_idx = 0
+            for label, path, _ in tqdm(self.videos):
+                vc = tv.io.read_video(path)[0].permute(3, 0, 1, 2)
+                
+                for j in range(0, vc.size(1) - clip_depth, 5):
+                    frames = []
+                    for k in range(j, j + clip_depth, 1):
+                        frames.append(vc[:, k, :, :])
+                    vc_sub = torch.stack(frames, dim=1)
+
+                    if vc_sub.size(1) != clip_depth:
+                        continue
+
+                    if self.transform:
+                        vc_sub = self.transform(vc_sub)
+
+                    self.clips.append((self.videos[vid_idx][0], vc_sub, self.videos[vid_idx][2]))
+
+                self.final_clips[self.videos[vid_idx][2]] = self.clips[-1]
+                vid_idx += 1
+                
+            torch.save(self.clips, open(clip_cache_file, 'wb+'))
+            torch.save(self.final_clips, open(clip_cache_final_file, 'wb+'))
+            
+        num_positive = len([clip[0] for clip in self.clips if clip[0] == 'Positives'])
+        num_negative = len([clip[0] for clip in self.clips if clip[0] == 'Negatives'])
+        
+        random.shuffle(self.clips)
+        
+        print('Loaded', num_positive, 'positive examples.')
+        print('Loaded', num_negative, 'negative examples.')
+        
+    def get_filenames(self):
+        return [self.clips[i][2] for i in range(len(self.clips))]
+        
+    def get_video_labels(self):
+        return [self.videos[i][0] for i in range(len(self.videos))]
+        
+    def get_labels(self):
+        return [self.clips[i][0] for i in range(len(self.clips))]
+    
+    def get_final_clips(self):
+        return self.final_clips
+    
+    def __getitem__(self, index):
+        label, clip, vid_f = self.clips[index]
+        if self.augmentation:
+            clip = clip.swapaxes(0, 1)
+            clip = self.augmentation(clip)
+            clip = clip.swapaxes(0, 1)
+        return (label, clip, vid_f)
+        
+    def __len__(self):
+        return len(self.clips)
+    
+class NeedleLoader(Dataset):
+    def __init__(self, video_path, transform=None, augmentation=None):
+        self.transform = transform
+        self.augmentation = augmentation
+
+        self.videos = [(abspath(join(video_path, f)), f) for f in glob.glob(join(video_path, '*.avi'))]
+            
+        self.clips = []
+
+        for path, vid_idx in tqdm(self.videos):
+            clip = tv.io.read_video(path)[0].permute(3, 0, 1, 2)
+            
+            if clip.size(1) != 5:
+                continue
+            
+            if self.transform:
+                clip = self.transform(clip)
+
+            self.clips.append((0, clip, vid_idx))
+        
+        random.shuffle(self.clips)
+        
+    def get_filenames(self):
+        return [self.clips[i][2] for i in range(len(self.clips))]
+    
+    def __getitem__(self, index):
+        label, clip, vid_f = self.clips[index]
+        if self.augmentation:
+            clip = clip.swapaxes(0, 1)
+            clip = self.augmentation(clip)
+            clip = clip.swapaxes(0, 1)
+        return (label, clip, vid_f)
+        
+    def __len__(self):
+        return len(self.clips)
+    
 class YoloClipLoader(Dataset):
     
     def __init__(self, yolo_output_path, num_frames=5, frames_between_clips=None,