add hadnler

create_handler.ipynb

@@ -0,0 +1,152 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install transformers --upgrade"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Custom Handler for Inference Endpoints\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting handler.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile handler.py\n",
+    "from typing import Dict, List, Any\n",
+    "from transformers import DonutProcessor, VisionEncoderDecoderModel\n",
+    "import torch\n",
+    "\n",
+    "\n",
+    "# check for GPU\n",
+    "device = 0 if torch.cuda.is_available() else -1\n",
+    "\n",
+    "\n",
+    "class EndpointHandler:\n",
+    "    def __init__(self, path=\"\"):\n",
+    "        # load the model\n",
+    "        self.processor = DonutProcessor.from_pretrained(path)\n",
+    "        self.model = VisionEncoderDecoderModel.from_pretrained(path)\n",
+    "        # move model to device\n",
+    "        self.model.to(device)\n",
+    "        self.decoder_input_ids = self.processor.tokenizer(\n",
+    "            \"<s_cord-v2>\", add_special_tokens=False, return_tensors=\"pt\"\n",
+    "        ).input_ids\n",
+    "\n",
+    "    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:\n",
+    "\n",
+    "        inputs = data.pop(\"inputs\", data)\n",
+    "\n",
+    "\n",
+    "        # preprocess the input\n",
+    "        pixel_values = self.processor(inputs, return_tensors=\"pt\").pixel_values\n",
+    "\n",
+    "        # forward pass\n",
+    "        outputs = self.model.generate(\n",
+    "            pixel_values.to(device),\n",
+    "            decoder_input_ids=self.decoder_input_ids.to(device),\n",
+    "            max_length=self.model.decoder.config.max_position_embeddings,\n",
+    "            early_stopping=True,\n",
+    "            pad_token_id=self.processor.tokenizer.pad_token_id,\n",
+    "            eos_token_id=self.processor.tokenizer.eos_token_id,\n",
+    "            use_cache=True,\n",
+    "            num_beams=1,\n",
+    "            bad_words_ids=[[self.processor.tokenizer.unk_token_id]],\n",
+    "            return_dict_in_generate=True,\n",
+    "        )\n",
+    "        # process output\n",
+    "        prediction = self.processor.batch_decode(outputs.sequences)[0]\n",
+    "        prediction = self.processor.token2json(prediction)\n",
+    "\n",
+    "        return prediction\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "test custom pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from handler import EndpointHandler\n",
+    "\n",
+    "my_handler = EndpointHandler(\".\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "\n",
+    "payload = {\"inputs\": Image.open(\"sample.png\").convert(\"RGB\")}\n",
+    "\n",
+    "my_handler(payload)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.13 ('dev': conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "f6dd96c16031089903d5a31ec148b80aeb0d39c32affb1a1080393235fbfa2fc"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

handler.py

@@ -0,0 +1,46 @@
+from typing import Dict, List, Any
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+import torch
+
+
+# check for GPU
+device = 0 if torch.cuda.is_available() else -1
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load the model
+        self.processor = DonutProcessor.from_pretrained(path)
+        self.model = VisionEncoderDecoderModel.from_pretrained(path)
+        # move model to device
+        self.model.to(device)
+        self.decoder_input_ids = self.processor.tokenizer(
+            "<s_cord-v2>", add_special_tokens=False, return_tensors="pt"
+        ).input_ids
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+
+        inputs = data.pop("inputs", data)
+
+
+        # preprocess the input
+        pixel_values = self.processor(inputs, return_tensors="pt").pixel_values
+
+        # forward pass
+        outputs = self.model.generate(
+            pixel_values.to(device),
+            decoder_input_ids=self.decoder_input_ids.to(device),
+            max_length=self.model.decoder.config.max_position_embeddings,
+            early_stopping=True,
+            pad_token_id=self.processor.tokenizer.pad_token_id,
+            eos_token_id=self.processor.tokenizer.eos_token_id,
+            use_cache=True,
+            num_beams=1,
+            bad_words_ids=[[self.processor.tokenizer.unk_token_id]],
+            return_dict_in_generate=True,
+        )
+        # process output
+        prediction = self.processor.batch_decode(outputs.sequences)[0]
+        prediction = self.processor.token2json(prediction)
+
+        return prediction