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{
"cells": [
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Srujan Jujare\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer\n",
"import torch\n",
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"VisionEncoderDecoderModel(\n",
" (encoder): ViTModel(\n",
" (embeddings): ViTEmbeddings(\n",
" (patch_embeddings): ViTPatchEmbeddings(\n",
" (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
" )\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (encoder): ViTEncoder(\n",
" (layer): ModuleList(\n",
" (0-11): 12 x ViTLayer(\n",
" (attention): ViTAttention(\n",
" (attention): ViTSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): ViTSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): ViTIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" (intermediate_act_fn): GELUActivation()\n",
" )\n",
" (output): ViTOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" )\n",
" )\n",
" )\n",
" (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (pooler): ViTPooler(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (activation): Tanh()\n",
" )\n",
" )\n",
" (decoder): GPT2LMHeadModel(\n",
" (transformer): GPT2Model(\n",
" (wte): Embedding(50257, 768)\n",
" (wpe): Embedding(1024, 768)\n",
" (drop): Dropout(p=0.1, inplace=False)\n",
" (h): ModuleList(\n",
" (0-11): 12 x GPT2Block(\n",
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (crossattention): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (q_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_cross_attn): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n",
" )\n",
")"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = VisionEncoderDecoderModel.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")\n",
"feature_extractor = ViTImageProcessor.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")\n",
"tokenizer = AutoTokenizer.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"model.to(device)\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"max_length = 16\n",
"num_beams = 4\n",
"gen_kwargs = {\"max_length\": max_length, \"num_beams\": num_beams}\n",
"def predict_step(image_paths):\n",
" images = []\n",
" for image_path in image_paths:\n",
" i_image = Image.open(image_path)\n",
" if i_image.mode != \"RGB\":\n",
" i_image = i_image.convert(mode=\"RGB\")\n",
"\n",
" images.append(i_image)\n",
"\n",
" pixel_values = feature_extractor(images=images, return_tensors=\"pt\").pixel_values\n",
" pixel_values = pixel_values.to(device)\n",
"\n",
" output_ids = model.generate(pixel_values, **gen_kwargs)\n",
"\n",
" preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)\n",
" preds = [pred.strip() for pred in preds]\n",
" return preds"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n",
"You may ignore this warning if your `pad_token_id` (50256) is identical to the `bos_token_id` (50256), `eos_token_id` (50256), or the `sep_token_id` (None), and your input is not padded.\n"
]
},
{
"data": {
"text/plain": [
"['a clock on a dashboard of a car']"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predict_step(['D:\\\\Validation\\\\Class 2\\\\i17.jpg'])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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