{ "cells": [ { "cell_type": "markdown", "id": "84ce039f", "metadata": { "papermill": { "duration": 0.011271, "end_time": "2024-08-02T10:54:52.690831", "exception": false, "start_time": "2024-08-02T10:54:52.679560", "status": "completed" }, "tags": [] }, "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": null, "id": "06d892d8", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2024-08-02T10:54:52.713748Z", "iopub.status.busy": "2024-08-02T10:54:52.713036Z", "iopub.status.idle": "2024-08-02T10:55:02.203716Z", "shell.execute_reply": "2024-08-02T10:55:02.202749Z" }, "papermill": { "duration": 9.504801, "end_time": "2024-08-02T10:55:02.206195", "exception": false, "start_time": "2024-08-02T10:54:52.701394", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from torch.utils.data import Dataset,DataLoader\n", "from torch.cuda.amp import GradScaler, autocast\n", "from timm import create_model\n", "from transformers import GPT2LMHeadModel,GPT2TokenizerFast\n", "from torchinfo import summary\n", "\n", "import albumentations as A\n", "from albumentations.pytorch import ToTensorV2\n", "\n", "from PIL import Image\n", "from pathlib import Path\n", "from sklearn.model_selection import train_test_split\n", "import matplotlib.pyplot as plt\n", "from tqdm.notebook import tqdm\n", "import gc\n", "import json\n", "from types import SimpleNamespace" ] }, { "cell_type": "code", "execution_count": null, "id": "17209e71", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:02.228945Z", "iopub.status.busy": "2024-08-02T10:55:02.228460Z", "iopub.status.idle": "2024-08-02T10:55:02.233783Z", "shell.execute_reply": "2024-08-02T10:55:02.232945Z" }, "papermill": { "duration": 0.01897, "end_time": "2024-08-02T10:55:02.235978", "exception": false, "start_time": "2024-08-02T10:55:02.217008", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "%env TOKENIZERS_PARALLELISM = false" ] }, { "cell_type": "markdown", "id": "1d5a7e2c", "metadata": { "papermill": { "duration": 0.011411, "end_time": "2024-08-02T10:55:02.257808", "exception": false, "start_time": "2024-08-02T10:55:02.246397", "status": "completed" }, "tags": [] }, "source": [ "# Explore and format Dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "7b19bb8e", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:02.279872Z", "iopub.status.busy": "2024-08-02T10:55:02.279615Z", "iopub.status.idle": "2024-08-02T10:55:02.286862Z", "shell.execute_reply": "2024-08-02T10:55:02.285956Z" }, "papermill": { "duration": 0.02032, "end_time": "2024-08-02T10:55:02.288716", "exception": false, "start_time": "2024-08-02T10:55:02.268396", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "sample_tfms = [\n", " A.HorizontalFlip(),\n", " A.RandomBrightnessContrast(),\n", " A.ColorJitter(),\n", " A.ShiftScaleRotate(shift_limit=0.1,scale_limit=0.3,always_apply =True),\n", " A.HueSaturationValue(p=0.3)\n", "]\n", "\n", "train_tfms = A.Compose([\n", " *sample_tfms,\n", " A.Resize(224,224),\n", " A.Normalize(mean=[0.5,0.5,0.5],std=[0.5,0.5,0.5],always_apply = True),\n", " ToTensorV2()\n", "])\n", "\n", "valid_tfms = A.Compose([\n", " A.Resize(224,224),\n", " A.Normalize(mean=[0.5,0.5,0.5],std=[0.5,0.5,0.5],always_apply = True),\n", " ToTensorV2()\n", "])" ] }, { "cell_type": "code", "execution_count": null, "id": "4fe47077", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:02.311109Z", "iopub.status.busy": "2024-08-02T10:55:02.310820Z", "iopub.status.idle": "2024-08-02T10:55:04.221059Z", "shell.execute_reply": "2024-08-02T10:55:04.219972Z" }, "papermill": { "duration": 1.923725, "end_time": "2024-08-02T10:55:04.223265", "exception": false, "start_time": "2024-08-02T10:55:02.299540", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')\n", "tokenizer.pad_token = tokenizer.eos_token\n", "print(tokenizer.pad_token)" ] }, { "cell_type": "code", "execution_count": null, "id": "6de7d70d", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:04.248366Z", "iopub.status.busy": "2024-08-02T10:55:04.247689Z", "iopub.status.idle": "2024-08-02T10:55:04.254576Z", "shell.execute_reply": "2024-08-02T10:55:04.253637Z" }, "papermill": { "duration": 0.021386, "end_time": "2024-08-02T10:55:04.256505", "exception": false, "start_time": "2024-08-02T10:55:04.235119", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "print(tokenizer.encode_plus(\"Hi hello testing caption\"))" ] }, { "cell_type": "code", "execution_count": null, "id": "2f71ea59", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:04.280969Z", "iopub.status.busy": "2024-08-02T10:55:04.280712Z", "iopub.status.idle": "2024-08-02T10:55:04.288313Z", "shell.execute_reply": "2024-08-02T10:55:04.287602Z" }, "papermill": { "duration": 0.022027, "end_time": "2024-08-02T10:55:04.290228", "exception": false, "start_time": "2024-08-02T10:55:04.268201", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "class CustomDS(Dataset):\n", " def __init__(self,df,tfms):\n", " super(CustomDS,self).__init__()\n", " self.df = df.copy()\n", " self.tfms = tfms\n", " \n", " def __len__(self):\n", " return len(self.df)\n", " \n", " def __getitem__(self,idx):\n", " sample = self.df.iloc[idx,:]\n", " image = sample['image']\n", " caption = sample['caption']\n", " image = Image.open(image).convert('RGB')\n", " image = np.array(image)\n", " augs = self.tfms(image = image)\n", " image = augs['image']\n", " caption = f\"{caption}<|endoftext|>\"\n", " input_ids = tokenizer(caption,truncation = True)['input_ids']\n", " labels = input_ids.copy()\n", " labels[:-1] = input_ids[1:]\n", " \n", " return image,input_ids,labels" ] }, { "cell_type": "markdown", "id": "9582ee79", "metadata": { "papermill": { "duration": 0.01171, "end_time": "2024-08-02T10:55:04.313690", "exception": false, "start_time": "2024-08-02T10:55:04.301980", "status": "completed" }, "tags": [] }, "source": [ "### Flickr8k" ] }, { "cell_type": "code", "execution_count": null, "id": "7764c7d0", "metadata": { "collapsed": true, "execution": { "iopub.execute_input": "2024-08-02T10:55:04.338174Z", "iopub.status.busy": "2024-08-02T10:55:04.337868Z", "iopub.status.idle": "2024-08-02T10:55:04.341771Z", "shell.execute_reply": "2024-08-02T10:55:04.340936Z" }, "jupyter": { "outputs_hidden": true }, "papermill": { "duration": 0.018195, "end_time": "2024-08-02T10:55:04.343736", "exception": false, "start_time": "2024-08-02T10:55:04.325541", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "\n", "# base_path = Path(\"/kaggle/input/flickr8k/Images\")\n", "# df = pd.read_csv(\"/kaggle/input/flickr8k/captions.txt\")\n", "# df[\"image\"] = df[\"image\"].map(lambda x:base_path/x.strip())\n", "# df['caption'] = df['caption'].map(lambda x:x.strip().lower())\n", "# df.head()" ] }, { "cell_type": "markdown", "id": "1355c931", "metadata": { "papermill": { "duration": 0.011107, "end_time": "2024-08-02T10:55:04.366241", "exception": false, "start_time": "2024-08-02T10:55:04.355134", "status": "completed" }, "tags": [] }, "source": [ "### Flickr30k" ] }, { "cell_type": "code", "execution_count": null, "id": "895c1f8c", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:04.390200Z", "iopub.status.busy": "2024-08-02T10:55:04.389937Z", "iopub.status.idle": "2024-08-02T10:55:06.295115Z", "shell.execute_reply": "2024-08-02T10:55:06.294154Z" }, "papermill": { "duration": 1.919753, "end_time": "2024-08-02T10:55:06.297359", "exception": false, "start_time": "2024-08-02T10:55:04.377606", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "base_path = Path(\"/kaggle/input/flickr30k/flickr30k_images\")\n", "df = pd.read_csv(\"/kaggle/input/flickr30k/captions.txt\",)\n", "df.drop([\"comment_number\"],axis=1,inplace = True)\n", "df.rename({'image_name':'image','comment': 'caption'},inplace=True,axis=1)\n", "df[\"image\"] = df[\"image\"].map(lambda x:base_path/x.strip())\n", "df['caption'] = df['caption'].map(lambda x:x.strip().lower())\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "id": "cda4aa1c", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:06.322588Z", "iopub.status.busy": "2024-08-02T10:55:06.322283Z", "iopub.status.idle": "2024-08-02T10:55:07.703974Z", "shell.execute_reply": "2024-08-02T10:55:07.703163Z" }, "papermill": { "duration": 1.406637, "end_time": "2024-08-02T10:55:07.715994", "exception": false, "start_time": "2024-08-02T10:55:06.309357", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "sampled_df = df.sample(n=10)\n", "fig,axs = plt.subplots(5,2,figsize = (20,10))\n", "\n", "for i,row in enumerate(sampled_df.iterrows()):\n", " ax = axs[i//2,i%2]\n", " image_path = row[1]['image']\n", " caption = row[1]['caption']\n", " image = Image.open(image_path)\n", " ax.imshow(image)\n", " ax.axis('off')\n", " ax.set_title(caption)\n", " \n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "c2dfdb54", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:07.764122Z", "iopub.status.busy": "2024-08-02T10:55:07.763820Z", "iopub.status.idle": "2024-08-02T10:55:07.791493Z", "shell.execute_reply": "2024-08-02T10:55:07.790457Z" }, "papermill": { "duration": 0.053732, "end_time": "2024-08-02T10:55:07.793414", "exception": false, "start_time": "2024-08-02T10:55:07.739682", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "train_df,val_df = train_test_split(df,test_size=0.1)\n", "train_df.reset_index(drop=True,inplace = True)\n", "val_df.reset_index(drop = True,inplace = True)\n", "print(len(train_df),len(val_df))" ] }, { "cell_type": "code", "execution_count": null, "id": "2ae3d72a", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:07.840856Z", "iopub.status.busy": "2024-08-02T10:55:07.840277Z", "iopub.status.idle": "2024-08-02T10:55:07.866763Z", "shell.execute_reply": "2024-08-02T10:55:07.865806Z" }, "papermill": { "duration": 0.052543, "end_time": "2024-08-02T10:55:07.868969", "exception": false, "start_time": "2024-08-02T10:55:07.816426", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "train_ds = CustomDS(train_df,train_tfms)\n", "val_ds = CustomDS(val_df,valid_tfms)" ] }, { "cell_type": "code", "execution_count": null, "id": "c2e897b5", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:07.916512Z", "iopub.status.busy": "2024-08-02T10:55:07.916175Z", "iopub.status.idle": "2024-08-02T10:55:07.923198Z", "shell.execute_reply": "2024-08-02T10:55:07.922370Z" }, "papermill": { "duration": 0.03285, "end_time": "2024-08-02T10:55:07.925183", "exception": false, "start_time": "2024-08-02T10:55:07.892333", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def collate_fn(batch):\n", " image = [i[0] for i in batch]\n", " input_ids = [i[1] for i in batch]\n", " labels = [i[2] for i in batch]\n", " \n", " image = torch.stack(image,dim=0)\n", " input_ids = tokenizer.pad(\n", " {'input_ids':input_ids},\n", " padding = 'longest',\n", " return_attention_mask = False,\n", " return_tensors = 'pt'\n", " )['input_ids']\n", " \n", " labels = tokenizer.pad(\n", " {'input_ids':labels},\n", " padding = 'longest',\n", " return_attention_mask = False,\n", " return_tensors = 'pt'\n", " )['input_ids']\n", " \n", " mask = (input_ids!=tokenizer.pad_token_id).long()\n", " labels[mask==0] = -100\n", " return image,input_ids,labels" ] }, { "cell_type": "code", "execution_count": null, "id": "6e2daa80", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:07.975501Z", "iopub.status.busy": "2024-08-02T10:55:07.974822Z", "iopub.status.idle": "2024-08-02T10:55:08.155078Z", "shell.execute_reply": "2024-08-02T10:55:08.154046Z" }, "papermill": { "duration": 0.207898, "end_time": "2024-08-02T10:55:08.157080", "exception": false, "start_time": "2024-08-02T10:55:07.949182", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "dl = DataLoader(train_ds,shuffle = True,batch_size =2,collate_fn=collate_fn)\n", "_,c,l = next(iter(dl))\n", "print(c[0])\n", "print(l[0])" ] }, { "cell_type": "markdown", "id": "8e7f3048", "metadata": { "papermill": { "duration": 0.023207, "end_time": "2024-08-02T10:55:08.203713", "exception": false, "start_time": "2024-08-02T10:55:08.180506", "status": "completed" }, "tags": [] }, "source": [ "# Models" ] }, { "cell_type": "markdown", "id": "067730df", "metadata": { "papermill": { "duration": 0.022902, "end_time": "2024-08-02T10:55:08.249841", "exception": false, "start_time": "2024-08-02T10:55:08.226939", "status": "completed" }, "tags": [] }, "source": [ "## Causal Attention Block" ] }, { "cell_type": "code", "execution_count": null, "id": "547b8694", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:08.297736Z", "iopub.status.busy": "2024-08-02T10:55:08.297402Z", "iopub.status.idle": "2024-08-02T10:55:08.309620Z", "shell.execute_reply": "2024-08-02T10:55:08.308879Z" }, "papermill": { "duration": 0.038513, "end_time": "2024-08-02T10:55:08.311451", "exception": false, "start_time": "2024-08-02T10:55:08.272938", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "class GPT2Attention(nn.Module):\n", " def __init__(self,config):\n", " super(GPT2Attention,self).__init__()\n", " self.embed_dim = config.embed_dim\n", " self.n_heads = config.num_heads\n", " assert self.embed_dim %self.n_heads == 0, \"embedding dim must be divisible by num heads\"\n", " self.head_size = self.embed_dim // self.n_heads\n", " self.seq_len = config.seq_len\n", " self.c_attn = nn.Linear(self.embed_dim,self.embed_dim*3)\n", " self.scale = self.head_size ** -0.5\n", " \n", " self.register_buffer('mask',torch.tril(torch.ones(1,1,self.seq_len,self.seq_len)))\n", " self.c_proj = nn.Linear(self.embed_dim,self.embed_dim)\n", " self.attn_dropout = nn.Dropout(config.attention_dropout)\n", " self.resid_dropout = nn.Dropout(config.residual_dropout)\n", " \n", " def forward(self,x):\n", " b,t,c = x.shape\n", " \n", " q,k,v = self.c_attn(x).chunk(3,dim=-1)\n", " q = q.view(b,t,self.n_heads,self.head_size).permute(0,2,1,3)\n", " k = k.view(b,t,self.n_heads,self.head_size).permute(0,2,1,3)\n", " v = v.view(b,t,self.n_heads,self.head_size).permute(0,2,1,3)\n", " \n", " qk_t = (q@k.transpose(-2,-1))*self.scale\n", " qk_t = qk_t.masked_fill(self.mask[:,:,:t,:t]==0,float('-inf'))\n", " qk_t = F.softmax(qk_t,dim=-1)\n", " weights = self.attn_dropout(qk_t)\n", " \n", " attention = weights@v\n", " attention = attention.permute(0,2,1,3).contiguous().view(b,t,c)\n", " \n", " out = self.c_proj(attention)\n", " return self.resid_dropout(out)" ] }, { "cell_type": "markdown", "id": "df9347c2", "metadata": { "papermill": { "duration": 0.0228, "end_time": "2024-08-02T10:55:08.357237", "exception": false, "start_time": "2024-08-02T10:55:08.334437", "status": "completed" }, "tags": [] }, "source": [ "## Cross Attention Block" ] }, { "cell_type": "code", "execution_count": null, "id": "fcec486a", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:08.405176Z", "iopub.status.busy": "2024-08-02T10:55:08.404841Z", "iopub.status.idle": "2024-08-02T10:55:08.418957Z", "shell.execute_reply": "2024-08-02T10:55:08.418070Z" }, "papermill": { "duration": 0.040451, "end_time": "2024-08-02T10:55:08.420783", "exception": false, "start_time": "2024-08-02T10:55:08.380332", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "class GPT2CrossAttention(nn.Module):\n", " def __init__(self,config):\n", " super(GPT2CrossAttention,self).__init__()\n", " self.embed_dim = config.embed_dim\n", " self.n_heads = config.num_heads\n", " assert self.embed_dim %self.n_heads == 0, \"embedding dim must be divisible by num heads\"\n", " self.head_size = self.embed_dim // self.n_heads\n", " self.seq_len = config.seq_len\n", " \n", " self.q = nn.Linear(self.embed_dim,self.embed_dim)\n", " self.k = nn.Linear(self.embed_dim,self.embed_dim)\n", " self.v = nn.Linear(self.embed_dim,self.embed_dim)\n", " self.scale = self.head_size ** -0.5\n", " \n", " self.c_proj = nn.Linear(self.embed_dim,self.embed_dim)\n", " self.attn_dropout = nn.Dropout(config.attention_dropout)\n", " self.resid_dropout = nn.Dropout(config.residual_dropout)\n", " self.apply(self._init_weights)\n", " \n", " def _init_weights(self,module):\n", " if isinstance(module,nn.Linear):\n", " nn.init.normal_(module.weight,mean=0.0,std=0.02)\n", " \n", " if module.bias is not None:\n", " nn.init.zeros_(module.bias)\n", " \n", " def forward(self,q,k,v):\n", " b,t,c = q.shape\n", " \n", " q,k,v = self.q(q),self.k(k),self.v(v)\n", " \n", " q = q.view(b,q.size(1),self.n_heads,self.head_size).permute(0,2,1,3)\n", " k = k.view(b,k.size(1),self.n_heads,self.head_size).permute(0,2,1,3)\n", " v = v.view(b,v.size(1),self.n_heads,self.head_size).permute(0,2,1,3)\n", " \n", " qk_t = (q@k.transpose(-2,-1))*self.scale\n", " qk_t = F.softmax(qk_t,dim=-1)\n", " weights = self.attn_dropout(qk_t)\n", " \n", " attention = weights@v\n", " attention = attention.permute(0,2,1,3).contiguous().view(b,t,c)\n", " \n", " out = self.c_proj(attention)\n", " return self.resid_dropout(out)" ] }, { "cell_type": "markdown", "id": "cead665a", "metadata": { "papermill": { "duration": 0.022769, "end_time": "2024-08-02T10:55:08.466744", "exception": false, "start_time": "2024-08-02T10:55:08.443975", "status": "completed" }, "tags": [] }, "source": [ "## Feed Forward Block" ] }, { "cell_type": "code", "execution_count": null, "id": "f9d51c73", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:08.513787Z", "iopub.status.busy": "2024-08-02T10:55:08.513439Z", "iopub.status.idle": "2024-08-02T10:55:08.520296Z", "shell.execute_reply": "2024-08-02T10:55:08.519451Z" }, "papermill": { "duration": 0.032691, "end_time": "2024-08-02T10:55:08.522238", "exception": false, "start_time": "2024-08-02T10:55:08.489547", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "class GPT2MLP(nn.Module):\n", " def __init__(self,config):\n", " super().__init__()\n", " self.embed_dim = config.embed_dim\n", " self.mlp_ratio = config.mlp_ratio\n", " self.mlp_dropout = config.mlp_dropout\n", " self.c_fc = nn.Linear(self.embed_dim,self.embed_dim*self.mlp_ratio)\n", " self.c_proj = nn.Linear(self.embed_dim*self.mlp_ratio,self.embed_dim)\n", " self.act = nn.GELU()\n", " self.dropout = nn.Dropout(self.mlp_dropout)\n", " \n", " def forward(self,x):\n", " x = self.c_fc(x)\n", " x = self.act(x)\n", " x = self.c_proj(x)\n", " return self.dropout(x)" ] }, { "cell_type": "markdown", "id": "9bc60d18", "metadata": { "papermill": { "duration": 0.02271, "end_time": "2024-08-02T10:55:08.568015", "exception": false, "start_time": "2024-08-02T10:55:08.545305", "status": "completed" }, "tags": [] }, "source": [ "## Decoder Block" ] }, { "cell_type": "code", "execution_count": null, "id": "212c84bc", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:08.615234Z", "iopub.status.busy": "2024-08-02T10:55:08.614968Z", "iopub.status.idle": "2024-08-02T10:55:08.621789Z", "shell.execute_reply": "2024-08-02T10:55:08.620936Z" }, "papermill": { "duration": 0.032766, "end_time": "2024-08-02T10:55:08.623685", "exception": false, "start_time": "2024-08-02T10:55:08.590919", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "class GPT2Block(nn.Module):\n", " def __init__(self,config):\n", " super(GPT2Block,self).__init__()\n", " self.embed_dim = config.embed_dim\n", " self.ln_1 = nn.LayerNorm(self.embed_dim)\n", " self.attn = GPT2Attention(config)\n", " self.ln_2 = nn.LayerNorm(self.embed_dim)\n", " self.mlp = GPT2MLP(config)\n", " self.ln_3 = nn.LayerNorm(self.embed_dim)\n", " self.cross_attn = GPT2CrossAttention(config)\n", " \n", " def forward(self,x,enc_out):\n", " x = x+self.attn(self.ln_1(x))\n", " x = x+self.cross_attn(self.ln_2(x),enc_out,enc_out)\n", " x = x+self.mlp(self.ln_3(x))\n", " return x" ] }, { "cell_type": "markdown", "id": "ffece89b", "metadata": { "papermill": { "duration": 0.022566, "end_time": "2024-08-02T10:55:08.669101", "exception": false, "start_time": "2024-08-02T10:55:08.646535", "status": "completed" }, "tags": [] }, "source": [ "## Main Model" ] }, { "cell_type": "code", "execution_count": null, "id": "3e49751f", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:08.716213Z", "iopub.status.busy": "2024-08-02T10:55:08.715937Z", "iopub.status.idle": "2024-08-02T10:55:08.744694Z", "shell.execute_reply": "2024-08-02T10:55:08.743846Z" }, "papermill": { "duration": 0.054675, "end_time": "2024-08-02T10:55:08.746677", "exception": false, "start_time": "2024-08-02T10:55:08.692002", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "class VisionGPT2Model(nn.Module):\n", " def __init__(self,config):\n", " super(VisionGPT2Model,self).__init__()\n", " self.config = config\n", " vit = create_model('vit_base_patch16_224',pretrained=True,num_classes=0)\n", " self.patch_embed = vit.patch_embed\n", " num_patches = self.patch_embed.num_patches\n", " self.cls_token = vit.cls_token\n", " embed_len = num_patches + vit.num_prefix_tokens\n", " self.pos_embed = vit.pos_embed\n", " self.blocks = nn.ModuleList([vit.blocks[i] for i in range(config.depth)])\n", " self.transformer = nn.ModuleDict(dict(\n", " wte = nn.Embedding(config.vocab_size,config.embed_dim),\n", " wpe = nn.Embedding(config.seq_len,config.embed_dim),\n", " drop = nn.Dropout(config.emb_dropout),\n", " h = nn.ModuleList([GPT2Block(config) for _ in range(config.depth)]),\n", " ln_f = nn.LayerNorm(config.embed_dim),\n", " ))\n", " self.lm_head = nn.Linear(config.embed_dim,config.vocab_size,bias= False)\n", " self.transformer.wte.weight = self.lm_head.weight\n", " \n", " def _pos_embed(self,x):\n", " pos_embed = self.pos_embed\n", " x = torch.cat((self.cls_token.expand(x.shape[0],-1,-1),x),dim =1)\n", " x = x+pos_embed\n", " return x\n", " \n", " def pretrained_layers_trainable(self,t = False):\n", " layers =[\n", " self.cls_token,self.patch_embed,self.pos_embed,self.blocks,\n", " self.transformer.wte,self.transformer.wpe,\n", " self.transformer.ln_f,self.lm_head\n", " ]\n", " gpt_layers = [[\n", " self.transformer.h[i].ln_1,self.transformer.h[i].ln_2,\n", " self.transformer.h[i].attn,self.transformer.h[i].mlp\n", " ]for i in range(self.config.depth)]\n", " \n", " for l in gpt_layers:\n", " layers.extend(l)\n", " \n", " for layer in layers:\n", " if not isinstance(layer,nn.Parameter):\n", " for p in layer.parameters():\n", " p.requires_grad = t\n", " else:\n", " layer.requires_grad = t\n", " \n", " total_frozen_params = sum([p.numel() for p in self.parameters() if not p.requires_grad])\n", " print(f\"{total_frozen_params =}\")\n", " \n", " def unfreeze_gpt_layers(self):\n", " gpt_layers = [[\n", " self.transformer.h[i].ln_1,self.transformer.h[i].ln_2,\n", " self.transformer.h[i].attn,self.transformer.h[i].mlp\n", " ]for i in range(self.config.depth)]\n", " \n", " flatten = []\n", " \n", " for l in gpt_layers:\n", " flatten.extend(l)\n", " \n", " for layer in flatten:\n", " if not isinstance(layer,nn.Parameter):\n", " for p in layer.parameters():\n", " p.requires_grad = True\n", " else:\n", " layer.requires_grad = True\n", " \n", " @classmethod\n", " def from_pretrained(self,config):\n", " model = VisionGPT2Model(config)\n", " sd = model.state_dict()\n", " keys = sd.keys()\n", " ignore_matches = ['blocks.','cross_attn.','ln_3','cls_token',\n", " 'pos_embed','patch_embed.','.attn.mask']\n", " vit_keys = [key for key in keys if any(match in key for match in ignore_matches)]\n", " gpt_keys = [key for key in keys if key not in vit_keys]\n", " gpt2_small = GPT2LMHeadModel.from_pretrained('gpt2')\n", " sd_hf = gpt2_small.state_dict()\n", " hf_keys = sd_hf.keys()\n", " hf_keys = [k for k in hf_keys if not k.endswith('.attn.masked_bias')]\n", " hf_keys = [k for k in hf_keys if not k.endswith('.attn.bias')]\n", " transposed = ['attn.c_attn.weight','attn.c_proj.weight',\n", " 'mlp.c_fc.weight','mlp.c_proj.weight']\n", " \n", " for k in hf_keys:\n", " if any(match in k for match in ignore_matches):\n", " continue\n", " if any(k.endswith(w) for w in transposed):\n", " assert sd_hf[k].shape[::-1] == sd[k].shape\n", " with torch.no_grad():\n", " sd[k].copy_(sd_hf[k].t())\n", " else:\n", " assert sd_hf[k].shape == sd[k].shape\n", " with torch.no_grad():\n", " sd[k].copy_(sd_hf[k])\n", " \n", " model.load_state_dict(sd)\n", " return model\n", "\n", " def forward(self,image,input_ids,labels=None):\n", " image = self.patch_embed(image)\n", " image = self._pos_embed(image)\n", " token_embeddings = self.transformer.wte(input_ids)\n", " pos_embs = torch.arange(0,input_ids.size(1)).to(input_ids.device)\n", " positional_embeddings = self.transformer.wpe(pos_embs)\n", " input_ids = self.transformer.drop(token_embeddings+positional_embeddings)\n", " \n", " for i in range(self.config.depth):\n", " image = self.blocks[i](image)\n", " input_ids = self.transformer.h[i](input_ids,image)\n", " input_ids = self.transformer.ln_f(input_ids)\n", " \n", " if labels is not None:\n", " lm_logits = self.lm_head(input_ids)\n", " loss = F.cross_entropy(lm_logits.view(-1,lm_logits.shape[-1]),labels.view(-1))\n", " return loss\n", " lm_logits = self.lm_head(input_ids[:,[-1],:])\n", " return lm_logits\n", " \n", " def generate(self,image,sequence,max_tokens=50,temp=1.0,deter=False):\n", " for _ in range(max_tokens):\n", " out = self(image,sequence)\n", " out = out[:,-1,:]/temp\n", " probs = F.softmax(out,dim=-1)\n", " if deter:\n", " next_token = torch.argmax(probs,dim=-1,keepdim=True)\n", " else:\n", " next_token = torch.multinomial(probs,num_samples=1)\n", " \n", " sequence = torch.cat([sequence,next_token],dim=1)\n", " if next_token.item() == tokenizer.eos_token_id:\n", " break\n", " return sequence.cpu().flatten()\n", " " ] }, { "cell_type": "markdown", "id": "ca94df09", "metadata": { "papermill": { "duration": 0.022389, "end_time": "2024-08-02T10:55:08.791948", "exception": false, "start_time": "2024-08-02T10:55:08.769559", "status": "completed" }, "tags": [] }, "source": [ "# Trainer" ] }, { "cell_type": "code", "execution_count": null, "id": "9eeba757", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:08.838754Z", "iopub.status.busy": "2024-08-02T10:55:08.838479Z", "iopub.status.idle": "2024-08-02T10:55:08.863253Z", "shell.execute_reply": "2024-08-02T10:55:08.862453Z" }, "papermill": { "duration": 0.050405, "end_time": "2024-08-02T10:55:08.865056", "exception": false, "start_time": "2024-08-02T10:55:08.814651", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "class Trainer:\n", " def __init__(self,model_config,train_config,dls):\n", " self.train_config = train_config\n", " self.model_config = model_config\n", " self.device = self.train_config.device\n", " self.model = VisionGPT2Model.from_pretrained(model_config).to(self.device)\n", " self.model.pretrained_layers_trainable(False)\n", " self.tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')\n", " self.tokenizer.pad_token = self.tokenizer.eos_token\n", " self.scaler = GradScaler()\n", " self.train_dl,self.val_dl = dls\n", " total_steps = len(self.train_dl)\n", " self.optim = torch.optim.Adam(self.model.parameters(),lr = self.train_config.lr/25.)\n", " self.sched = torch.optim.lr_scheduler.OneCycleLR(\n", " self.optim,\n", " max_lr = self.train_config.lr,\n", " epochs = self.train_config.epochs,\n", " steps_per_epoch = total_steps\n", " )\n", " self.metrics = pd.DataFrame()\n", " self.metrics[['train_loss','train_perplexity',\"val_loss\",'val_perplexity']] = None\n", " self.gen_tfms = A.Compose([\n", " A.Resize(224,224),\n", " A.Normalize(mean = [0.5,0.5,0.5],std = [0.5,0.5,0.5],always_apply = True),\n", " ToTensorV2()\n", " ])\n", " \n", " def save_model(self):\n", " self.train_config.model_path.mkdir(exist_ok=True)\n", " sd = self.model.state_dict()\n", " torch.save(sd,self.train_config.model_path/'captioner.pt')\n", " \n", " def load_best_model(self):\n", "# sd = torch.load(self.train_config.model_path/'captioner.pt')\n", " sd = torch.load('/kaggle/input/imagecaptioning/captioner/captioner.pt')\n", " self.model.load_state_dict(sd)\n", " \n", " def train_one_epoch(self,epoch):\n", " prog = tqdm(self.train_dl,total = len(self.train_dl))\n", " running_loss = 0.\n", " for image,input_ids,labels in prog:\n", " with autocast():\n", " image = image.to(self.device)\n", " input_ids = input_ids.to(self.device)\n", " labels = labels.to(self.device)\n", " loss = self.model(image,input_ids,labels)\n", " \n", " self.scaler.scale(loss).backward()\n", " self.scaler.step(self.optim)\n", " self.scaler.update()\n", " self.sched.step()\n", " self.optim.zero_grad()\n", " \n", " running_loss += loss.item()\n", " prog.set_description(f\"train loss: {loss.item():.3f}\")\n", " train_loss = running_loss/len(self.train_dl)\n", " train_pxp = np.exp(train_loss)\n", " \n", " del image,input_ids,labels,loss\n", " \n", " self.metrics.loc[epoch,['train_loss','train_perplexity']] = (train_loss,train_pxp)\n", " \n", " @torch.no_grad()\n", " def valid_one_epoch(self,epoch):\n", " prog = tqdm(self.train_dl,total = len(self.train_dl))\n", " running_loss = 0.\n", " for image,input_ids,labels in prog:\n", " with autocast():\n", " image = image.to(self.device)\n", " input_ids = input_ids.to(self.device)\n", " labels = labels.to(self.device)\n", " loss = self.model(image,input_ids,labels)\n", " \n", " running_loss += loss.item()\n", " prog.set_description(f\"valid loss: {loss.item():.3f}\")\n", " val_loss = running_loss/len(self.train_dl)\n", " val_pxp = np.exp(val_loss)\n", " \n", " del image,input_ids,labels,loss\n", " \n", " self.metrics.loc[epoch,['val_loss','val_perplexity']] = (val_loss,val_pxp)\n", " \n", " return val_pxp\n", " \n", " def clean(self):\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", " \n", " def fit(self):\n", " best_pxp = 1e9\n", " best_epoch = -1\n", " prog = tqdm(range(self.train_config.epochs))\n", " \n", " for epoch in prog:\n", " if epoch == self.train_config.freeze_epochs_gpt:\n", " self.model.unfreeze_gpt_layers()\n", " print('unfreezing GPT2 entirely...')\n", " if epoch == self.train_config.freeze_epochs_all:\n", " self.model.pretrained_layers_trainable(True)\n", " \n", " self.model.train()\n", " prog.set_description('Training')\n", " self.train_one_epoch(epoch)\n", " self.clean()\n", " \n", " self.model.eval()\n", " prog.set_description('Validating')\n", " pxp = self.valid_one_epoch(epoch)\n", " self.clean()\n", " \n", " print(self.metrics.tail(1))\n", " \n", " if pxp < best_pxp:\n", " best_pxp = pxp\n", " best_epoch = epoch\n", " print('saving best model....')\n", " self.save_model()\n", " \n", " return {\n", " 'best_perplexity': best_pxp,\n", " 'best_epoch': best_epoch\n", " }\n", " \n", " @torch.no_grad()\n", " def generate_caption(self,image,max_tokens = 50,temperature = 1.,deterministic = False):\n", " self.model.eval()\n", " \n", " image = Image.open(image).convert('RGB')\n", " image = np.array(image)\n", " image = self.gen_tfms(image=image)['image']\n", " image = image.unsqueeze(0).to(self.device)\n", " sequence = torch.ones(1,1).to(self.device).long()*self.tokenizer.bos_token_id\n", " \n", " caption = self.model.generate(\n", " image,\n", " sequence,\n", " max_tokens,\n", " temperature,\n", " deterministic\n", " )\n", " \n", " caption = self.tokenizer.decode(caption.numpy(),skip_special_tokens = True)\n", " \n", " return caption" ] }, { "cell_type": "code", "execution_count": null, "id": "45ff3d5f", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:08.912043Z", "iopub.status.busy": "2024-08-02T10:55:08.911782Z", "iopub.status.idle": "2024-08-02T10:55:08.965310Z", "shell.execute_reply": "2024-08-02T10:55:08.964438Z" }, "papermill": { "duration": 0.079583, "end_time": "2024-08-02T10:55:08.967275", "exception": false, "start_time": "2024-08-02T10:55:08.887692", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "model_config = SimpleNamespace(\n", " vocab_size = 50257, # GPT2 vocb size\n", " embed_dim = 768, # dim same for both VIT and GPT2\n", " num_heads = 12,\n", " seq_len = 1024,\n", " depth = 12,\n", " attention_dropout = 0.1,\n", " residual_dropout = 0.1,\n", " mlp_ratio = 4,\n", " mlp_dropout = 0.1,\n", " emb_dropout = 0.1,\n", ")\n", "\n", "train_config = SimpleNamespace(\n", " epochs = 8,\n", " freeze_epochs_gpt = 2,\n", " freeze_epochs_all = 3,\n", " lr = 1e-4,\n", " device = 'cuda' if torch.cuda.is_available() else 'cpu',\n", " model_path = Path('captioner'),\n", " batch_size = 32\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "0a68efe0", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:09.014815Z", "iopub.status.busy": "2024-08-02T10:55:09.014519Z", "iopub.status.idle": "2024-08-02T10:55:09.019338Z", "shell.execute_reply": "2024-08-02T10:55:09.018524Z" }, "papermill": { "duration": 0.030734, "end_time": "2024-08-02T10:55:09.021167", "exception": false, "start_time": "2024-08-02T10:55:08.990433", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "train_dl = DataLoader(train_ds,batch_size = train_config.batch_size,shuffle = True,pin_memory = True,num_workers = 2,persistent_workers = True,collate_fn = collate_fn)\n", "val_dl = DataLoader(val_ds,batch_size = train_config.batch_size,pin_memory = True,num_workers = 2,persistent_workers = True,collate_fn = collate_fn)" ] }, { "cell_type": "code", "execution_count": null, "id": "bc58ac95", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:09.069770Z", "iopub.status.busy": "2024-08-02T10:55:09.069439Z", "iopub.status.idle": "2024-08-02T10:55:18.101291Z", "shell.execute_reply": "2024-08-02T10:55:18.099441Z" }, "papermill": { "duration": 9.060547, "end_time": "2024-08-02T10:55:18.105464", "exception": false, "start_time": "2024-08-02T10:55:09.044917", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "trainer = Trainer(model_config,train_config,(train_dl,val_dl))" ] }, { "cell_type": "code", "execution_count": null, "id": "3766d89c", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:18.168631Z", "iopub.status.busy": "2024-08-02T10:55:18.167987Z", "iopub.status.idle": "2024-08-02T10:55:18.176779Z", "shell.execute_reply": "2024-08-02T10:55:18.175907Z" }, "papermill": { "duration": 0.039526, "end_time": "2024-08-02T10:55:18.178825", "exception": false, "start_time": "2024-08-02T10:55:18.139299", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "x0 = torch.rand(1,3,224,224).to(train_config.device)\n", "x1 = torch.randint(0,50000,(1,16)).to(train_config.device)" ] }, { "cell_type": "code", "execution_count": null, "id": "6aac8725", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:18.226850Z", "iopub.status.busy": "2024-08-02T10:55:18.226577Z", "iopub.status.idle": "2024-08-02T10:55:19.062222Z", "shell.execute_reply": "2024-08-02T10:55:19.061318Z" }, "papermill": { "duration": 0.862892, "end_time": "2024-08-02T10:55:19.065354", "exception": false, "start_time": "2024-08-02T10:55:18.202462", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "print(summary(trainer.model,input_data=[x0,x1],depth = 3,col_names = [\"input_size\",\"output_size\"]))" ] }, { "cell_type": "code", "execution_count": null, "id": "762c6bdf", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:55:19.163188Z", "iopub.status.busy": "2024-08-02T10:55:19.162238Z", "iopub.status.idle": "2024-08-02T20:22:09.514258Z", "shell.execute_reply": "2024-08-02T20:22:09.513128Z" }, "papermill": { "duration": 34010.423576, "end_time": "2024-08-02T20:22:09.516684", "exception": false, "start_time": "2024-08-02T10:55:19.093108", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "trainer.fit()" ] }, { "cell_type": "code", "execution_count": null, "id": "6d59564f", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T20:22:09.584585Z", "iopub.status.busy": "2024-08-02T20:22:09.584166Z", "iopub.status.idle": "2024-08-02T20:22:09.596369Z", "shell.execute_reply": "2024-08-02T20:22:09.595461Z" }, "papermill": { "duration": 0.048488, "end_time": "2024-08-02T20:22:09.598505", "exception": false, "start_time": "2024-08-02T20:22:09.550017", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "met = trainer.metrics\n", "met" ] }, { "cell_type": "code", "execution_count": null, "id": "e71fc180", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T20:22:09.663201Z", "iopub.status.busy": "2024-08-02T20:22:09.662848Z", "iopub.status.idle": "2024-08-02T20:22:09.997897Z", "shell.execute_reply": "2024-08-02T20:22:09.996939Z" }, "papermill": { "duration": 0.369761, "end_time": "2024-08-02T20:22:10.000188", "exception": false, "start_time": "2024-08-02T20:22:09.630427", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "plt.plot(met['train_loss'],color = 'red',label = \"train loss\")\n", "plt.plot(met['val_loss'],color = 'orange',label = \"val loss\")\n", "plt.title('loss {lower = better}')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "8d230328", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T20:22:10.067207Z", "iopub.status.busy": "2024-08-02T20:22:10.066333Z", "iopub.status.idle": "2024-08-02T20:22:10.367302Z", "shell.execute_reply": "2024-08-02T20:22:10.366349Z" }, "papermill": { "duration": 0.336779, "end_time": "2024-08-02T20:22:10.369479", "exception": false, "start_time": "2024-08-02T20:22:10.032700", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "plt.plot(met['train_perplexity'],color = 'red',label = \"train perplexity\")\n", "plt.plot(met['val_perplexity'],color = 'orange',label = \"val perplexity\")\n", "plt.title('perplexity {lower = better}')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "c38bbecf", "metadata": { "papermill": { "duration": 0.033431, "end_time": "2024-08-02T20:22:10.435827", "exception": false, "start_time": "2024-08-02T20:22:10.402396", "status": "completed" }, "tags": [] }, "source": [ "# Predictions" ] }, { "cell_type": "code", "execution_count": null, "id": "a43b3168", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T20:22:10.504210Z", "iopub.status.busy": "2024-08-02T20:22:10.503358Z", "iopub.status.idle": "2024-08-02T20:22:11.329299Z", "shell.execute_reply": "2024-08-02T20:22:11.328007Z" }, "papermill": { "duration": 0.862068, "end_time": "2024-08-02T20:22:11.331035", "exception": true, "start_time": "2024-08-02T20:22:10.468967", "status": "failed" }, "tags": [] }, "outputs": [], "source": [ "trainer.load_best_model()" ] }, { "cell_type": "code", "execution_count": null, "id": "8d476a5d", "metadata": { "execution": { "iopub.execute_input": "2024-08-02T10:31:19.656513Z", "iopub.status.busy": "2024-08-02T10:31:19.656132Z" }, "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "outputs": [], "source": [ "ex = 40\n", "for i in range(ex):\n", " det = False\n", " test = val_df.sample(1).values[0]\n", " test_img,test_caption = test[0],test[1]\n", " plt.imshow(Image.open(test_img).convert('RGB'))\n", " t = np.random.uniform(0.5,1.5)\n", " if i>ex//2:\n", " det = True\n", " gen_caption = trainer.generate_caption(test_img,temperature = t,deterministic = det)\n", " plt.title(f\"actual:{test_caption}\\nmodel:{gen_caption}\\ntemp:{t:.4} deterministic: {det}\")\n", " plt.axis('off')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "68522e82", "metadata": { "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kaggle": { "accelerator": "gpu", "dataSources": [ { "datasetId": 623289, "sourceId": 1111676, "sourceType": "datasetVersion" }, { "datasetId": 2808179, "sourceId": 4845244, "sourceType": "datasetVersion" } ], "dockerImageVersionId": 30746, "isGpuEnabled": true, "isInternetEnabled": true, "language": "python", "sourceType": "notebook" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": 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