SDFT2.1 finetune code

.gitattributes

@@ -423,3 +423,5 @@ Vaani/Img_Audio_Alignment/checkpoints/csip/probs/probs_epoch_9_loss_4.1521.png f
 Vaani/Img_Audio_Alignment/available_img_audios_TRAIN3.csv filter=lfs diff=lfs merge=lfs -text
 Vaani/Img_Audio_Alignment/dog[[:space:]]and[[:space:]]cat.png filter=lfs diff=lfs merge=lfs -text
 Vaani/SDFT/SD2_1_Audio/dog[[:space:]]and[[:space:]]cat.png filter=lfs diff=lfs merge=lfs -text
+Vaani/SDFT/SD2_1_Audio/dog[[:space:]]and[[:space:]]cat.mp4 filter=lfs diff=lfs merge=lfs -text
+Vaani/vaani_scripts/__pycache__/CSIP.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text

Vaani/Img_Audio_Alignment/_2.1.2_OpenCLIP_Image_Features.ipynb

@@ -7128,14 +7128,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Processing Images]:  91%|\u001b[34m█████████ \u001b[0m| 444/490 [21:48<02:16,  2.96s/it]\u001b[0m"
+      "[Processing Images]: 100%|\u001b[34m██████████\u001b[0m| 490/490 [24:02<00:00,  2.94s/it]\u001b[0m\n"
      ]
     }
    ],
@@ -7179,14 +7179,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Processing Images]: 100%|\u001b[34m██████████\u001b[0m| 173/173 [05:03<00:00,  1.75s/it]\u001b[0m\n"
+      "[Processing Images]: 100%|\u001b[34m████████████\u001b[0m| 87/87 [04:22<00:00,  3.02s/it]\u001b[0m\n"
      ]
     }
    ],
@@ -7223,7 +7223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -7232,7 +7232,7 @@
        "73755"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7246,7 +7246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -7270,14 +7270,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Processing Audios]: 100%|\u001b[34m██████████\u001b[0m| 980/980 [03:59<00:00,  4.09it/s]\u001b[0m\n"
+      "[Processing Audios]: 100%|\u001b[34m██████████\u001b[0m| 490/490 [04:26<00:00,  1.84it/s]\u001b[0m\n"
      ]
     }
    ],
@@ -7337,14 +7337,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Processing Audios]: 100%|\u001b[34m██████████\u001b[0m| 173/173 [01:02<00:00,  2.75it/s]\u001b[0m\n"
+      "[Processing Audios]: 100%|\u001b[34m████████████\u001b[0m| 87/87 [00:55<00:00,  1.58it/s]\u001b[0m\n"
      ]
     }
    ],

Vaani/Img_Audio_Alignment/_2.1.2_Train_OpenCLIP.py

@@ -6912,7 +6912,8 @@ class CSIP(nn.Module):
         self.audio_proj = nn.Linear(dim_audio, dim_emb)
 
         # Learnable temperature parameter
-        self.log_temp = nn.Parameter(torch.tensor(0.07).log())
+        # self.log_temp = nn.Parameter(torch.tensor(1/0.07).log())
+        self.log_temp = torch.nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
 
     def forward(self, images, audios):
         
@@ -6967,7 +6968,7 @@ class VaaniImageAudioDataset(torch.utils.data.Dataset):
         }
     
 
-train_df = pd.read_csv("/home/IITB/ai-at-ieor/23m1521/ashish/MTP/Vaani/Img_Audio_Alignment/available_img_audios_TRAIN2.csv")
+train_df = pd.read_csv("/home/IITB/ai-at-ieor/23m1521/ashish/MTP/Vaani/Img_Audio_Alignment/available_img_audios_TRAIN3.csv")
 test_df = pd.read_csv("/home/IITB/ai-at-ieor/23m1521/ashish/MTP/Vaani/Img_Audio_Alignment/available_img_audios_TEST2.csv")
 image_features_savedir = '/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaani/Hindi_Image_features/'
 audio_tensors_savedir = '/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaani/Hindi_Audio_tensors/'
@@ -7014,14 +7015,18 @@ csip_model = CSIP(open_clip_model.visual, peft_clap_audio_encoder).to(device)
 
 from torchinfo import summary
 import subprocess
+
+for param in csip_model.audio_encoder.model.projection.parameters():
+    param.requires_grad = True
+    
 summary(model=csip_model,
         input_data=((image_features_batch.to(device)), (audio_tensor_batch.to(device))),
         # input_size = (1, 3, config.IMAGE_SIZE, config.IMAGE_SIZE),
         dtypes=[torch.long],
-        col_names = ["input_size", "output_size", "num_params", "trainable", "params_percent"],
+        col_names = ["trainable", "params_percent", "input_size", "output_size", "num_params"],
         col_width=20,
         row_settings=["var_names"],
-        depth = 2,
+        depth = 4,
         # verbose=2,
         # device=device
 )
@@ -7041,7 +7046,8 @@ def train_batch(model, images, audio, optimizer):
 @torch.no_grad()
 def evaluate_batch(model, images, audio):
     model.eval()
-    loss, loss_i, loss_a, logits, probs, avg_similarity = model(images, audio)
+    with torch.inference_mode():
+        loss, loss_i, loss_a, logits, probs, avg_similarity = model(images, audio)
     return loss.item(), loss_i.item(), loss_a.item(), logits, probs, avg_similarity.item()
 
 
@@ -7123,9 +7129,10 @@ def load_checkpoint(checkpoint_dir, model, optimizer, scheduler, load_best):
     else:
         # Load current training state
         model.load_state_dict(checkpoint['current']['model_state'])
-        optimizer.load_state_dict(checkpoint['current']['optimizer_state'])
+        # optimizer.load_state_dict(checkpoint['current']['optimizer_state'])
         if scheduler and checkpoint['current']['scheduler_state'] is not None:
-            scheduler.load_state_dict(checkpoint['current']['scheduler_state'])
+            # scheduler.load_state_dict(checkpoint['current']['scheduler_state'])
+            pass
 
         print(f"Resumed training from epoch {checkpoint['epoch']+1},"
               f" best model was at epoch {checkpoint['best']['epoch']} with loss {checkpoint['best']['loss']:.4f}")
@@ -7134,7 +7141,7 @@ def load_checkpoint(checkpoint_dir, model, optimizer, scheduler, load_best):
             checkpoint['best']['loss'], 
             checkpoint['best']['model_state'], 
             checkpoint['best']['optimizer_state'], 
-            checkpoint['best']['scheduler_state']
+            # checkpoint['best']['scheduler_state']
         )
 
 
@@ -7196,26 +7203,34 @@ def train_model(model, train_loader, test_loader,
     best_scheduler_state = None
 
     
-    # if resume:
-    #     start_epoch, best_loss = load_checkpoint(checkpoint_dir, model, optimizer, scheduler)
     if resume:
         (
             start_epoch, 
             best_loss, 
             best_model_state, 
             best_optimizer_state, 
-            best_scheduler_state
+            # best_scheduler_state
 
         ) = load_checkpoint(checkpoint_dir, model, optimizer, scheduler, load_best=False)
 
-    # If resuming, don't overwrite the CSV
     if not (resume and os.path.exists(csv_path)):
         with open(csv_path, mode='w', newline='') as f:
             writer_csv = csv.writer(f)
             writer_csv.writerow(["Epoch", "Best Epoch", "Train Loss", "Test Loss", 
                                  "Best Loss", "Train Sim", "Test Sim", "Learning Rate",
                                  "Train I-Loss", "Test I-Loss", "Train A-Loss", "Test A-Loss"])
-
+    
+    
+    summary(model=model,
+            input_data=((image_features_batch.to(device)), (audio_tensor_batch.to(device))),
+            dtypes=[torch.long],
+            col_names = ["trainable", "params_percent", "input_size", "output_size", "num_params"],
+            col_width=20,
+            row_settings=["var_names"],
+            depth=4
+    )
+    current_lr = optimizer.param_groups[0]['lr']
+    print(f"Starting with learning rate: {current_lr:.2e}, ")
     for epoch in trange(start_epoch, epochs, colour='yellow', dynamic_ncols=True):
         train_losses = []
         test_losses = []
@@ -7277,23 +7292,6 @@ def train_model(model, train_loader, test_loader,
         )
 
 
-        # if avg_test_loss < best_loss:
-        #     save_similarity_heatmaps(logits, epoch, avg_test_loss, checkpoint_dir, writer)
-        #     best_loss = avg_test_loss
-        #     best_epoch = epoch + 1
-        #     save_checkpoint({
-        #         'epoch': epoch,
-        #         'model_state': model.state_dict(),
-        #         'optimizer_state': optimizer.state_dict(),
-        #         'best_loss': best_loss,
-        #         'train_loss': avg_train_loss,
-        #         'similarity': avg_test_sim,
-        #         'train_similarity': avg_train_sim,
-        #         'learning_rate': current_lr,
-        #         'scheduler_state': scheduler.state_dict() if scheduler else None
-        #     }, checkpoint_dir, epoch)
-        #     print(f">>> Saved new best model at epoch {epoch+1}")
-
         if avg_test_loss < best_loss:
             best_loss = avg_test_loss
             best_epoch = epoch + 1
@@ -7340,9 +7338,9 @@ def train_model(model, train_loader, test_loader,
 model_name = "csip_model_openClip_CLAP"
 epochs = 1000
 
-learning_rate = 1e-4
+learning_rate = 1e-10
 optimizer = torch.optim.AdamW(csip_model.parameters(), lr=learning_rate)
-scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-6)
+scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-10)
 
 # learning_rate = 1e-3
 # optimizer = torch.optim.AdamW(csip_model.parameters(), lr=learning_rate)

Vaani/Img_Audio_Alignment/_2_Image_data.ipynb

@@ -9017,6 +9017,202 @@
     "train_df.to_csv(\"available_img_audios_TRAIN3.csv\", index=False)\n",
     "test_df.to_csv(\"available_img_audios_TEST3.csv\", index=False)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Checking train test 3 for data leackage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "120"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "# import pandas as pd\n",
+    "import fireducks.pandas as pd\n",
+    "import polars as pl\n",
+    "from tqdm import tqdm, trange\n",
+    "\n",
+    "HINDI_AUDIO_DIR = r\"/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaani/Audios/Hindi\"\n",
+    "IMAGEDIR = r\"/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaani/Images/Folder3/\"\n",
+    "IMAGEAUDIOCSV = r\"/home/IITB/ai-at-ieor/23m1521/ashish/MTP/Vaani/Vaani-Audio-Image-Hindi3.csv\"\n",
+    "METADIR = r\"/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaani/MetaData\"\n",
+    "\n",
+    "shards = [METADIR+\"/\"+i for i in sorted(os.listdir(METADIR))]\n",
+    "len(shards)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((26810, 2), (11490, 2))"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_df2 = pd.read_csv(\"available_img_audios_TRAIN2.csv\")\n",
+    "test_df2 = pd.read_csv(\"available_img_audios_TEST2.csv\")\n",
+    "train_df2.shape, test_df2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(73755, 2)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "available_img_audios_df = pd.concat([pd.read_csv(\"available_img_audios_TRAIN3.csv\"), pd.read_csv(\"available_img_audios_TEST3.csv\")], \n",
+    "                                    ignore_index=True).sample(frac=1).reset_index(drop=True)\n",
+    "available_img_audios_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>image_path</th>\n",
+       "      <th>audio_path</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62260</th>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62261</th>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62262</th>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62263</th>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62264</th>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "      <td>/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaan...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>62265 rows x 2 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train_df3 = available_img_audios_df.merge(\n",
+    "    test_df2, on=['image_path', 'audio_path'], how='left', indicator=True\n",
+    ")\n",
+    "train_df3 = train_df3[train_df3['_merge'] == 'left_only'].drop(columns=['_merge']).reset_index(drop=True)\n",
+    "train_df3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df3.to_csv(\"available_img_audios_TRAIN3.csv\", index=False)"
+   ]
   }
  ],
  "metadata": {

Vaani/Img_Audio_Alignment/available_img_audios_TRAIN3.csv

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Vaani/Img_Audio_Alignment/csip_model_openClip_CLAP/checkpoints/csip/csip_epoch_950_best_epoch_-1.pt

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