adding the finetuning code

VQA_FineTuning_Fashion_Datasets.ipynb

@@ -0,0 +1,1647 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rAmJ-WxxjSC6"
+      },
+      "source": [
+        "# **BLIP model finetuing**\n",
+        "\n",
+        "**Datasets used**\n",
+        "\n",
+        "\n",
+        "\n",
+        "*   [Control Net Deep Fashion](https://huggingface.co/datasets/ldhnam/deepfashion_controlnet)\n",
+        "*   [Deep Fashion with masks](https://huggingface.co/datasets/SaffalPoosh/deepFashion-with-masks)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1loyolj_jI_p"
+      },
+      "source": [
+        "# Install Dependences\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import notebook_login\n",
+        "notebook_login()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 145,
+          "referenced_widgets": [
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+        },
+        "id": "IF2wU1arJRPk",
+        "outputId": "a507c47f-46d9-40eb-87c2-fe4a95b789f1"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "fd2d3aac84e747ef9fea550ec9a7386d"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "n4UvOghEh7IZ",
+        "outputId": "49734576-beed-4d19-e837-b445fba4861c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
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+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install transformers\n",
+        "!pip install datasets\n",
+        "!pip install 'transformers[torch]'\n",
+        "!pip install 'transformers[tf-cpu]'\n",
+        "!pip install 'transformers[flax]'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LxSR-PEwDLkE"
+      },
+      "source": [
+        "**Testing the imports**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Nt1jz4F-knoM"
+      },
+      "source": [
+        "**Loading the datasets**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RJDHNaqZkEqY"
+      },
+      "outputs": [],
+      "source": [
+        "from datasets import load_dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "89tsRz5pj2fh"
+      },
+      "outputs": [],
+      "source": [
+        "saffal_possh_df = load_dataset(\"SaffalPoosh/deepFashion-with-masks\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cP4KZxObk0Zk"
+      },
+      "outputs": [],
+      "source": [
+        "# Checking a simple sample from the dataset\n",
+        "for data in saffal_possh_df.items():\n",
+        "  print(data[1][\"gender\"])\n",
+        "  print(data[1][\"cloth_type\"])\n",
+        "  print(data[1][\"caption\"])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7euwilYIkWl3"
+      },
+      "outputs": [],
+      "source": [
+        "control_net_deep_fashion = load_dataset(\"ldhnam/deepfashion_controlnet\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "wTHcR1LYk7Wq"
+      },
+      "outputs": [],
+      "source": [
+        "# Checking a simple sample from the dataset\n",
+        "for data in control_net_deep_fashion.items():\n",
+        "  print(data[1][\"caption\"])\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qf8n3B5e2Hnb"
+      },
+      "source": [
+        "# **Pseudo-label and Pseudo-Questions**\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hjwwKCCiQLlZ"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
+        "\n",
+        "\n",
+        "tokenizer = AutoTokenizer.from_pretrained(\"potsawee/t5-large-generation-squad-QuestionAnswer\")\n",
+        "model = AutoModelForSeq2SeqLM.from_pretrained(\"potsawee/t5-large-generation-squad-QuestionAnswer\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bygZiKk22T1V"
+      },
+      "outputs": [],
+      "source": [
+        "from tqdm import tqdm\n",
+        "import numpy as np\n",
+        "\n",
+        "action_key_words = [\"in\", \"wearing\", \"standing\", \"is wearing\",\n",
+        "                      \"posing\", \"sitting\", \"walking\", \"carrying\",\n",
+        "                      \"leaning\"]\n",
+        "\n",
+        "# creating pseudo questions\n",
+        "def create_pseudo_questions_for_saffal_possh(data, size=200):\n",
+        "  dataset_selection = data[\"train\"][0: size]\n",
+        "  questions = []\n",
+        "  answers = []\n",
+        "  images = []\n",
+        "  input_ids = []\n",
+        "\n",
+        "  print(\"Loading the dataset..\")\n",
+        "\n",
+        "  sample_id = 0\n",
+        "\n",
+        "  for key, sample in dataset_selection.items():\n",
+        "      if key == \"caption\":\n",
+        "        for caption in tqdm(sample):\n",
+        "          caption_tokens = caption.split(\" \")\n",
+        "          if caption_tokens[2] in action_key_words:\n",
+        "\n",
+        "            inputs = tokenizer(caption, return_tensors=\"pt\")\n",
+        "            outputs = model.generate(**inputs, max_length=100)\n",
+        "            question_answer = tokenizer.decode(outputs[0], skip_special_tokens=False)\n",
+        "            question_answer = question_answer.replace(tokenizer.pad_token, \"\").replace(tokenizer.eos_token, \"\")\n",
+        "            question, answer = question_answer.split(tokenizer.sep_token)\n",
+        "\n",
+        "            questions.append(question)\n",
+        "            answers.append(answer)\n",
+        "            input_ids.append(sample_id)\n",
+        "          else:\n",
+        "            questions.append(\"Is there a person in the image?\")\n",
+        "            answers.append(\"Yes, there it is\")\n",
+        "            input_ids.append(sample_id)\n",
+        "          sample_id += 1\n",
+        "\n",
+        "  dataset_selection[\"questions\"] = questions\n",
+        "  dataset_selection[\"answers\"] = answers\n",
+        "  dataset_selection[\"input_ids\"] = input_ids\n",
+        "\n",
+        "\n",
+        "  return dataset_selection\n",
+        "\n",
+        "saffal_possh_df_processed = create_pseudo_questions_for_saffal_possh(saffal_possh_df)\n",
+        "print(saffal_possh_df_processed)\n",
+        "\n",
+        "control_net_deep_fashion_processed = create_pseudo_questions_for_saffal_possh(control_net_deep_fashion)\n",
+        "print(control_net_deep_fashion_processed)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "P3dxkODty-Vl"
+      },
+      "outputs": [],
+      "source": [
+        "from datasets import Dataset\n",
+        "\n",
+        "saffal_dataset = Dataset.from_dict(saffal_possh_df_processed)\n",
+        "control_net_dataset = Dataset.from_dict(control_net_deep_fashion_processed)\n",
+        "\n",
+        "print(saffal_dataset)\n",
+        "print(control_net_dataset)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ak_doWyi8cZs"
+      },
+      "outputs": [],
+      "source": [
+        "saffal_dataset = saffal_dataset.remove_columns([\"gender\", \"pose\", \"cloth_type\", \"pid\", \"mask\", \"mask_overlay\", \"caption\"])\n",
+        "control_net_dataset = control_net_dataset.remove_columns([\"openpose\", \"cloth\", \"caption\"])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0tn4Sb0Z80Iz"
+      },
+      "outputs": [],
+      "source": [
+        "print(saffal_dataset)\n",
+        "print(control_net_dataset)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ao9xvSJM9PW6"
+      },
+      "outputs": [],
+      "source": [
+        "from PIL import Image\n",
+        "\n",
+        "image = saffal_dataset['images'][0]\n",
+        "image"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "x0jzKEAZ9T4b"
+      },
+      "outputs": [],
+      "source": [
+        "image = control_net_dataset['image'][0]\n",
+        "image"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NY64HtXa3xgz"
+      },
+      "source": [
+        "**Structuring the dataset for Pytorch model train**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "\n",
+        "# creating the dataset structure and model train based in\n",
+        "# https://github.com/dino-chiio/blip-vqa-finetune/blob/main/finetuning.py\n",
+        "\n",
+        "class GenericFashionDataset(torch.utils.data.Dataset):\n",
+        "    \"\"\"VQA (v2) dataset.\"\"\"\n",
+        "\n",
+        "    def __init__(self, dataset, processor):\n",
+        "        self.dataset = dataset\n",
+        "        self.processor = processor\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return len(self.dataset)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        question = self.dataset['questions'][idx]\n",
+        "        answer = self.dataset['answers'][idx]\n",
+        "        image_id = self.dataset['input_ids'][idx]\n",
+        "        try:\n",
+        "          image = self.dataset['images'][idx]\n",
+        "        except:\n",
+        "          image = self.dataset['image'][idx]\n",
+        "        text = question\n",
+        "\n",
+        "        encoding = self.processor(image, text, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
+        "        labels = self.processor.tokenizer.encode(\n",
+        "            answer, max_length= 8, pad_to_max_length=True, return_tensors='pt'\n",
+        "        )\n",
+        "        encoding[\"labels\"] = labels\n",
+        "\n",
+        "        for k,v in encoding.items():  encoding[k] = v.squeeze()\n",
+        "        return encoding"
+      ],
+      "metadata": {
+        "id": "3LEmVCwdrC_Q"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from transformers import BlipProcessor, BlipForQuestionAnswering\n",
+        "\n",
+        "model = BlipForQuestionAnswering.from_pretrained(\"Salesforce/blip-vqa-base\")\n",
+        "processor = BlipProcessor.from_pretrained(\"Salesforce/blip-vqa-base\")\n",
+        "\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "model.to(device)"
+      ],
+      "metadata": {
+        "id": "_rusAJj3q7T9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "saffal_train_dataset = GenericFashionDataset(dataset=saffal_dataset, processor=processor)\n",
+        "control_net_train_dataset = GenericFashionDataset(dataset=control_net_dataset, processor=processor)"
+      ],
+      "metadata": {
+        "id": "s9NCVsHlWSi0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from torch.utils.data import DataLoader\n",
+        "\n",
+        "batch_size = 2\n",
+        "\n",
+        "saffal_train_dataloader = DataLoader(saffal_train_dataset, batch_size=batch_size, shuffle=False, pin_memory=True)\n",
+        "control_net_train_dataloader = DataLoader(control_net_train_dataset, batch_size=batch_size, shuffle=False, pin_memory=True)"
+      ],
+      "metadata": {
+        "id": "iET_OYWQWqsR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lsL4fxFknYwG"
+      },
+      "source": [
+        "# **Model Train**\n",
+        "\n",
+        "\n",
+        "deepFashion-with-masks**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def train_model(data_loader, num_epochs=50, patience=5):\n",
+        "  optimizer = torch.optim.AdamW(model.parameters(), lr=4e-5)\n",
+        "  scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9, last_epoch=-1, verbose=False)\n",
+        "\n",
+        "  information = []\n",
+        "  scaler = torch.cuda.amp.GradScaler()\n",
+        "\n",
+        "  for epoch in range(num_epochs):\n",
+        "      epoch_loss = 0\n",
+        "      model.train()\n",
+        "      for idx, batch in zip(tqdm(range(len(data_loader)), desc='Training batch: ...'), data_loader):\n",
+        "          input_ids = batch.pop('input_ids').to(device)\n",
+        "          pixel_values = batch.pop('pixel_values').to(device)\n",
+        "          labels = batch.pop('labels').to(device)\n",
+        "\n",
+        "          with torch.amp.autocast(device_type='cuda', dtype=torch.float16):\n",
+        "              outputs = model(input_ids=input_ids,\n",
+        "                          pixel_values=pixel_values,\n",
+        "                          labels=labels)\n",
+        "\n",
+        "          loss = outputs.loss\n",
+        "          epoch_loss += loss.item()\n",
+        "          optimizer.zero_grad()\n",
+        "\n",
+        "          scaler.scale(loss).backward()\n",
+        "          scaler.step(optimizer)\n",
+        "          scaler.update()\n",
+        "\n",
+        "      information.append((epoch_loss/len(saffal_train_dataloader), optimizer.param_groups[0][\"lr\"]))\n",
+        "      print(\"Epoch: {} - Training loss: {} - LR: {}\".format(epoch+1, epoch_loss/len(saffal_train_dataloader), optimizer.param_groups[0][\"lr\"]))\n",
+        "      scheduler.step()\n",
+        "  return model, information\n"
+      ],
+      "metadata": {
+        "id": "Xllczl5lYAPk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Training a model for Saffal Dataset**"
+      ],
+      "metadata": {
+        "id": "VgNBleRlZlKi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model, information = train_model(saffal_train_dataloader, num_epochs=1)"
+      ],
+      "metadata": {
+        "id": "eePu8scwZHn6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pickle as pk\n",
+        "\n",
+        "model_path = \"/content/drive/MyDrive/Hvar/saffal_fashion_model.pt\"\n",
+        "model.save_pretrained(model_path, from_pt=True) #saving in the drive\n",
+        "\n",
+        "results_path = \"/content/drive/MyDrive/Hvar/saffal_fashion_model_train.pkl\"\n",
+        "pk.dump(information, open(results_path, \"wb\"))"
+      ],
+      "metadata": {
+        "id": "PdbvNJ8CXXIJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Pusing model to hugging face**"
+      ],
+      "metadata": {
+        "id": "IXNkTzVfaHZC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_repo_name = \"wiusdy/blip_pretrained_saffal_fashion_finetuning\"\n",
+        "model.push_to_hub(model_repo_name)"
+      ],
+      "metadata": {
+        "id": "eZ6_yM02aKoO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Training a dataset for Control Net dataset**"
+      ],
+      "metadata": {
+        "id": "vvsoYZdIZoj-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model, information = train_model(control_net_train_dataloader, num_epochs=1)"
+      ],
+      "metadata": {
+        "id": "gNuRYjwyZr40"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_path = \"/content/drive/MyDrive/Hvar/control_net_fashion_model.pt\"\n",
+        "model.save_pretrained(model_path, from_pt=True) #saving in the drive\n",
+        "\n",
+        "results_path = \"/content/drive/MyDrive/Hvar/control_net_fashion_model_train.pkl\"\n",
+        "pk.dump(information, open(results_path, \"wb\"))"
+      ],
+      "metadata": {
+        "id": "858Qld6fZt3U"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Pushing model to hugging face**"
+      ],
+      "metadata": {
+        "id": "xyUZlTRmaMhl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_repo_name = \"wiusdy/blip_pretrained_control_net_fashion_finetuning\"\n",
+        "model.push_to_hub(model_repo_name)"
+      ],
+      "metadata": {
+        "id": "TFRuT5V8aOvm"
+      },
+      "execution_count": null,
+      "outputs": []
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