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tqdm"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')\n","\n","# Define your model save directory\n","save_path = \"/content/drive/My Drive/CLIP_Project/Model_Files\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KL33U5DwXv1y","executionInfo":{"status":"ok","timestamp":1748703606748,"user_tz":-300,"elapsed":38276,"user":{"displayName":"Ramzan0001","userId":"17484931948124609414"}},"outputId":"79942def-6472-45cb-a5ff-0cd5cd65aa2a"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import os\n","import pandas as pd\n","\n","# Paths\n","CSV_PATH = '/content/drive/My Drive/CLIP_Project/Data/results.csv'\n","IMG_DIR = '/content/drive/My Drive/CLIP_Project/Data/Fine_Tuning_Images'\n","\n","# Step 1: Read CSV correctly\n","df = pd.read_csv(CSV_PATH, sep='|', engine='python', skip_blank_lines=True)\n","df.columns = [col.strip() for col in df.columns]  # Clean column names\n","\n","# Step 2: Clean up data\n","df['image_name'] = df['image_name'].astype(str).str.strip()\n","df['comment'] = df['comment'].astype(str).str.strip()\n","df['comment_number'] = pd.to_numeric(df['comment_number'], errors='coerce')\n","\n","# Step 3: Filter for first caption per image\n","df = df[df['comment_number'] == 0]\n","\n","# Step 4: Create full file paths and filter only existing images\n","df['filepath'] = df['image_name'].apply(lambda x: os.path.join(IMG_DIR, x))\n","df = df[df['filepath'].apply(os.path.exists)].reset_index(drop=True)\n","\n","# Step 5 (Optional): Limit to 5000 samples max\n","df = df.sample(n=min(5000, len(df)), random_state=42).reset_index(drop=True)\n","\n","# Final check\n","print(f\"Total valid image-caption pairs: {len(df)}\")\n","print(\"Sample Data:\\n\", df.head())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"U_DXo5gkYYkX","executionInfo":{"status":"ok","timestamp":1748703951547,"user_tz":-300,"elapsed":5300,"user":{"displayName":"Ramzan0001","userId":"17484931948124609414"}},"outputId":"5af08e28-36c2-4b25-dbd5-fe294b4614fd"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-4-73c8d02e31e2>:21: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  df['filepath'] = df['image_name'].apply(lambda x: os.path.join(IMG_DIR, x))\n"]},{"output_type":"stream","name":"stdout","text":["Total valid image-caption pairs: 5000\n","Sample Data:\n","       image_name  comment_number  \\\n","0  223299142.jpg             0.0   \n","1  309687244.jpg             0.0   \n","2  316266025.jpg             0.0   \n","3  185075930.jpg             0.0   \n","4  153963868.jpg             0.0   \n","\n","                                             comment  \\\n","0  The grass has ice on it and there is a mountai...   \n","1  A man dressed in gray climbing a large brown r...   \n","2  A man wearing a tan sweater is sitting on the ...   \n","3  A blond boy smiles with his eyes closed , buri...   \n","4  An operator in a gold hard hat and orange vest...   \n","\n","                                            filepath  \n","0  /content/drive/My Drive/CLIP_Project/Data/Fine...  \n","1  /content/drive/My Drive/CLIP_Project/Data/Fine...  \n","2  /content/drive/My Drive/CLIP_Project/Data/Fine...  \n","3  /content/drive/My Drive/CLIP_Project/Data/Fine...  \n","4  /content/drive/My Drive/CLIP_Project/Data/Fine...  \n"]}]},{"cell_type":"code","source":["from PIL import Image\n","from torch.utils.data import Dataset, DataLoader\n","from transformers import CLIPProcessor, CLIPModel\n","import torch"],"metadata":{"id":"s0MXU2FQYeAQ","executionInfo":{"status":"ok","timestamp":1748703996982,"user_tz":-300,"elapsed":33361,"user":{"displayName":"Ramzan0001","userId":"17484931948124609414"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["device = \"cuda\" if torch.cuda.is_available() else \"cpu\""],"metadata":{"id":"H3egvczAYCmm","executionInfo":{"status":"ok","timestamp":1748704006767,"user_tz":-300,"elapsed":38,"user":{"displayName":"Ramzan0001","userId":"17484931948124609414"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\").to(device)\n","processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")"],"metadata":{"id":"qrXgy1RAYyaQ","colab":{"base_uri":"https://localhost:8080/","height":453,"referenced_widgets":["daaa8c18609c4e5692bb2f3d5c2dd39e","c76e597ac7404afbaf2cc27943a11ea7","e7164e23d85c4149a66035ffc3977d0e","7e20ed768f404d1bba0a0cda7266ff36","1ead78f36fff432f9d966b602a91d483","55b124d0eee845f3bd3d41d6350fdff0","71330ecf507541d58f776bac0d6427a9","0bd36fadc9f046908cf8e12efb9ee7af","ffec20549c234c35877a623c04cb2727","5bedb06d00db4c11822cccf6a37bbd68","2d001a21e5b3439d8ec2f7ebcb34a734","1a8cecaf71714cd9a7d589d8f68c545b","02ee62ab49254a8f873ef2b852e9ad3e","16b503e8aaa6451d92fed764c0e07e6a","a7e12e5fd7a14ad19a0cabe12ea84946","807a4f7ce46a46f0956232391f0928e5","327ef716ce1e4535bb7208eb7763702f","6cc3086d103d4da3943bbae1aae31863","8defe1c117f64c5a8e923cdcc9bbe484","c4b1d5c796e44059a0bea782b48f175e","8ee67427c71b41a98fa36bb33f7255d6","ebc6eeeed44741ab9317f4294fd67f55","9434871c66b74ff2bd367ec82bca3da6","94967fa2f310488c8b50b113a75a0e5d","392b2cc806b6444c97360bdb5d903d7c","6e33a52fc2624b1dbfc646e1aacb5de6","28ae51f90af84ecf86f854d56d2db531","5f60eea8bde148ec9271735eadf7e1e8","4b4ad12130694011a55c2a36e280b2b4","dfa6825737be401cb6eed473c7cf0b51","eb8fc9194f1f49c7afc9b78f89724c51","82cf97a023864fccae1c62bbff7d555f","484d81f2c42d449990cb8a291e939f4b","d95d175315cb4ffcad7e6dab55425ce8","ba81066de60c43a9b0b63c703264ff16","c8687f02e4544a9a8e3e7a507bcea544","4d2923e5213d4361acde79185b04433a","031bdce6b2314500977a95d1b1f31dcb","b9287af72c884bc7a0823d1b35da7db5","958140ee381041d0820a16598590ae60","a64ab080e5d5456a8584313465bbef4c","5b0a166da3ee44499109781a15388917","87be267c5e8a443db7ecff6a841b1469","bcbc2405a41e4bb79e925055ea60779d","c69405bf499a4f53b5a8e35420f2d5a9","6288708a768d4b45aed5be2bdea4e4ad","9adb2d05d30a4da1a5d60ff307913f38","9f3d1db93ed647f6a4dcaf9b5f73200d","992b16f5d6434c3fa129deb2d2c27440","27c363b960454eabb99f3810403c9453","39ff1f6ecdf74ed7849c59f9af1a6052","968b486c482249c090da3d6b91a00840","197d3a50b2ef4931977841f18c50e618","513a5bb595a6456c8ffd90fde728a7f5","0a3444b0306241b2a00a81e53c9ebc25","8e30fb57ffcc4d38904e5f6665b514b3","afc5c50f187d4a0eacf3c7300fb98bcb","addcb46423534905887e8887ae548b20","7538a6f88ce94f2da30fc090fc57d22d","e54e2cc91b3e4502baf49051cfc5bc7f","3ebd9c0f52a54db195916b5d5e0db07a","0425f1fe3a74453fb39457b63e456f8b","726e39ae350845509386c0c362626205","37306dbd88774d5da5f615520b71500a","2ccf4d992e2242c48d9425d8c0c4dab1","9c0f9a54144d4153977d632448ae5f7b","6c9bca981b7d4ddf811b763a0579e0f1","155f6cf9283b4b4d8922f22dbcaf7a51","827c138f60ea42688223e53939e86151","59b19c65009e44598d2071228800575d","f562dfcb15be41c58b305890ed6e210c","222d874fc5394076b0aea93247c5a196","708fe3f1ffc944129d441d0bdcb67aaf","8b0014015c4c4b0888cd401ffe9d6530","84bec341a8c040de89e9193d54f8f11c","3c2e94e30c364b77a03635efbbea507a","b3d1d41d2de14eb4a8d914b59a5a73c7","5ae42e187ce14ec8af1d7c83c670a912","52cf8391eb8f4c57aab6f1858d4220ae","85cc6ff765564e4c878d233d352f8c11","e560064743db4d5eb2c857e4464a222c","e39761510dd24c91adb074d06e29ca22","43a045b6b7954f6f8b0501fb4e0fe796","203144caba9c473b9a91c59e319c1385","a62d653b89294b8fa45132fc81f5cbed","a90a71c0f6b1458a8274ab3295d28c84","77c03236e41743989e719a4ca514bae6","ccc950674b5c4b7b98fcd8bdafdbffba","b52a126def7c4e98accef3d3fe88785b","190f1b1b2bbd4b5f8044411951f24e37","92909adcb6494bf5a54e0a4f1316ffbb","88b0028ae8904923b335d19330057ef7","3c966e1dccb34e5f9d3a78fa836d71cf","9aeae0482b6e4924939fa132c83e2e8a","1a05c170478c4bfe8fa0a3573c1df03b","8a460b85260943b1bdaeff7f5a10ac8d","39849086e1044d44add258ef58587766","33a58409fdb94483b9d8211b6574f2cf","2c39c20d8abb4e8da59c1e223f4c2c47"]},"executionInfo":{"status":"ok","timestamp":1748704019065,"user_tz":-300,"elapsed":10268,"user":{"displayName":"Ramzan0001","userId":"17484931948124609414"}},"outputId":"f9301b2b-0dbb-4a28-f8a1-29d49e6cd03e"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/4.19k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"daaa8c18609c4e5692bb2f3d5c2dd39e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["pytorch_model.bin:   0%|          | 0.00/605M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1a8cecaf71714cd9a7d589d8f68c545b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/605M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9434871c66b74ff2bd367ec82bca3da6"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n"]},{"output_type":"display_data","data":{"text/plain":["preprocessor_config.json:   0%|          | 0.00/316 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d95d175315cb4ffcad7e6dab55425ce8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/592 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c69405bf499a4f53b5a8e35420f2d5a9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json:   0%|          | 0.00/862k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8e30fb57ffcc4d38904e5f6665b514b3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt:   0%|          | 0.00/525k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6c9bca981b7d4ddf811b763a0579e0f1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/2.22M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5ae42e187ce14ec8af1d7c83c670a912"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/389 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b52a126def7c4e98accef3d3fe88785b"}},"metadata":{}}]},{"cell_type":"code","source":["# Dataset Class\n","class FlickrDataset(Dataset):\n","    def __init__(self, dataframe):\n","        self.data = dataframe\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, idx):\n","        image_path = self.data.iloc[idx]['filepath']\n","        caption = self.data.iloc[idx]['comment']\n","        image = Image.open(image_path).convert(\"RGB\")\n","        return {'image': image, 'text': caption}  # raw text and PIL image\n","\n","# Collate Function\n","def collate_fn(batch):\n","    texts = [item['text'] for item in batch]\n","    images = [item['image'] for item in batch]\n","\n","    # Use processor to pad text & process images\n","    inputs = processor(\n","        text=texts,\n","        images=images,\n","        return_tensors=\"pt\",\n","        padding=True,\n","        truncation=True\n","    )\n","    return inputs\n","\n","# DataLoader\n","dataset = FlickrDataset(df)\n","dataloader = DataLoader(dataset, batch_size=8, shuffle=True, collate_fn=collate_fn)"],"metadata":{"id":"UcTMGtiakbtK","executionInfo":{"status":"ok","timestamp":1748704212000,"user_tz":-300,"elapsed":43,"user":{"displayName":"Ramzan0001","userId":"17484931948124609414"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["# Training\n","import torch.nn.functional as F\n","\n","# Contrastive loss function\n","loss_fn = CrossEntropyLoss()\n","optimizer = torch.optim.Adam(model.parameters(), lr=5e-6)\n","model.train()\n","\n","for epoch in range(1):  # 1 epoch for Colab\n","    for batch in dataloader:\n","        for k in batch:\n","            batch[k] = batch[k].to(device)\n","\n","        # Forward pass\n","        outputs = model(**batch)\n","        image_embeds = outputs.image_embeds  # [batch_size, hidden_dim]\n","        text_embeds = outputs.text_embeds    # [batch_size, hidden_dim]\n","\n","        # Normalize embeddings\n","        image_embeds = F.normalize(image_embeds, p=2, dim=1)\n","        text_embeds = F.normalize(text_embeds, p=2, dim=1)\n","\n","        # Compute cosine similarity\n","        logits_per_image = image_embeds @ text_embeds.t()  # [batch, batch]\n","        logits_per_text = text_embeds @ image_embeds.t()   # [batch, batch]\n","\n","        # Labels: diagonal matches\n","        labels = torch.arange(image_embeds.size(0), device=device)\n","\n","        # Compute loss in both directions\n","        loss_i = loss_fn(logits_per_image, labels)\n","        loss_t = loss_fn(logits_per_text, labels)\n","        loss = (loss_i + loss_t) / 2\n","\n","        # Backpropagation\n","        loss.backward()\n","        optimizer.step()\n","        optimizer.zero_grad()\n","\n","    print(f\"Epoch {epoch+1} completed. Loss: {loss.item():.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iZItAujCknvj","executionInfo":{"status":"ok","timestamp":1748705809981,"user_tz":-300,"elapsed":1431090,"user":{"displayName":"Ramzan0001","userId":"17484931948124609414"}},"outputId":"fc96fd89-7953-4bd5-f050-b96d12455d4d"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1 completed. Loss: 1.5125\n"]}]},{"cell_type":"code","source":["model.save_pretrained(save_path)\n","processor.save_pretrained(save_path)\n","\n","print(\">>>Model and Processor saved successfully<<<\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V8QA2TfFkwsM","executionInfo":{"status":"ok","timestamp":1748705901389,"user_tz":-300,"elapsed":8150,"user":{"displayName":"Ramzan0001","userId":"17484931948124609414"}},"outputId":"eb9cb948-96c9-4d4f-a0ed-8e9e793dc735"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":[">>>Model and Processor saved successfully<<<\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"Nq7hFTAx_lnx"},"execution_count":null,"outputs":[]}]}