From e2f7998ee9ff14544ba6b7c272a7893567534c40 Mon Sep 17 00:00:00 2001 From: Kadir Date: Fri, 1 Mar 2024 16:42:48 +0100 Subject: [PATCH] merging the visual query answering with the crawler. Monorepo go! --- GUIDE => crawler/GUIDE | 0 Makefile => crawler/Makefile | 0 TASKS => crawler/TASKS | 0 {code => crawler/code}/image.py | 0 {code => crawler/code}/json/detail.json | 0 {code => crawler/code}/json/listings.json | 0 .../code}/json/routing_distancematrix.json | 0 .../code}/json/routing_routeapi.json | 0 .../code}/json/routing_routeapi_full.json | 0 .../routing_routeapi_with_alternative.json | 0 {code => crawler/code}/listings.py | 0 .../code}/routing_distancematrix.py | 0 {code => crawler/code}/routing_routing.py | 0 {code => crawler/code}/single-query.py | 0 {code => crawler/code}/utils.py | 0 poetry.lock => crawler/poetry.lock | 0 pyproject.toml => crawler/pyproject.toml | 0 {rec => crawler/rec}/__init__.py | 0 {rec => crawler/rec}/db.py | 0 {rec => crawler/rec}/query.py | 0 {rec => crawler/rec}/rightmove_parser.py | 0 testing.py => crawler/testing.py | 0 vqa/.gitignore | 3 + vqa/Untitled.ipynb | 300 ++ .../46001_32532509_FLP_00_0000.jpeg | Bin 0 -> 59984 bytes vqa/main.py | 24 + vqa/poetry.lock | 2984 +++++++++++++++++ vqa/pyproject.toml | 21 + vqa/questions.json | 8 + vqa/questions.py | 37 + vqa/util.py | 0 vqa/vqa.py | 72 + 32 files changed, 3449 insertions(+) rename GUIDE => crawler/GUIDE (100%) rename Makefile => crawler/Makefile (100%) rename TASKS => crawler/TASKS (100%) rename {code => crawler/code}/image.py (100%) rename {code => crawler/code}/json/detail.json (100%) rename {code => crawler/code}/json/listings.json (100%) rename {code => crawler/code}/json/routing_distancematrix.json (100%) rename {code => crawler/code}/json/routing_routeapi.json (100%) rename {code => crawler/code}/json/routing_routeapi_full.json (100%) rename {code => crawler/code}/json/routing_routeapi_with_alternative.json (100%) rename {code => crawler/code}/listings.py (100%) rename {code => crawler/code}/routing_distancematrix.py (100%) rename {code => crawler/code}/routing_routing.py (100%) rename {code => crawler/code}/single-query.py (100%) rename {code => crawler/code}/utils.py (100%) rename poetry.lock => crawler/poetry.lock (100%) rename pyproject.toml => crawler/pyproject.toml (100%) rename {rec => crawler/rec}/__init__.py (100%) rename {rec => crawler/rec}/db.py (100%) rename {rec => crawler/rec}/query.py (100%) rename {rec => crawler/rec}/rightmove_parser.py (100%) rename testing.py => crawler/testing.py (100%) create mode 100644 vqa/.gitignore create mode 100644 vqa/Untitled.ipynb create mode 100644 vqa/floorplans/46001_32532509_FLP_00_0000.jpeg create mode 100644 vqa/main.py create mode 100644 vqa/poetry.lock create mode 100644 vqa/pyproject.toml create mode 100644 vqa/questions.json create mode 100644 vqa/questions.py create mode 100644 vqa/util.py create mode 100644 vqa/vqa.py diff --git a/GUIDE b/crawler/GUIDE similarity index 100% rename from GUIDE rename to crawler/GUIDE diff --git a/Makefile b/crawler/Makefile similarity index 100% rename from Makefile rename to crawler/Makefile diff --git a/TASKS b/crawler/TASKS similarity index 100% rename from TASKS rename to crawler/TASKS diff --git a/code/image.py b/crawler/code/image.py similarity index 100% rename from code/image.py rename to crawler/code/image.py diff --git a/code/json/detail.json b/crawler/code/json/detail.json similarity index 100% rename from code/json/detail.json rename to crawler/code/json/detail.json diff --git a/code/json/listings.json b/crawler/code/json/listings.json similarity index 100% rename from code/json/listings.json rename to crawler/code/json/listings.json diff --git a/code/json/routing_distancematrix.json b/crawler/code/json/routing_distancematrix.json similarity index 100% rename from code/json/routing_distancematrix.json rename to crawler/code/json/routing_distancematrix.json diff --git a/code/json/routing_routeapi.json b/crawler/code/json/routing_routeapi.json similarity index 100% rename from code/json/routing_routeapi.json rename to crawler/code/json/routing_routeapi.json diff --git a/code/json/routing_routeapi_full.json b/crawler/code/json/routing_routeapi_full.json similarity index 100% rename from code/json/routing_routeapi_full.json rename to crawler/code/json/routing_routeapi_full.json diff --git a/code/json/routing_routeapi_with_alternative.json b/crawler/code/json/routing_routeapi_with_alternative.json similarity index 100% rename from code/json/routing_routeapi_with_alternative.json rename to crawler/code/json/routing_routeapi_with_alternative.json diff --git a/code/listings.py b/crawler/code/listings.py similarity index 100% rename from code/listings.py rename to crawler/code/listings.py diff --git a/code/routing_distancematrix.py b/crawler/code/routing_distancematrix.py similarity index 100% rename from code/routing_distancematrix.py rename to crawler/code/routing_distancematrix.py diff --git a/code/routing_routing.py b/crawler/code/routing_routing.py similarity index 100% rename from code/routing_routing.py rename to crawler/code/routing_routing.py diff --git a/code/single-query.py b/crawler/code/single-query.py similarity index 100% rename from code/single-query.py rename to crawler/code/single-query.py diff --git a/code/utils.py b/crawler/code/utils.py similarity index 100% rename from code/utils.py rename to crawler/code/utils.py diff --git a/poetry.lock b/crawler/poetry.lock similarity index 100% rename from poetry.lock rename to crawler/poetry.lock diff --git a/pyproject.toml b/crawler/pyproject.toml similarity index 100% rename from pyproject.toml rename to crawler/pyproject.toml diff --git a/rec/__init__.py b/crawler/rec/__init__.py similarity index 100% rename from rec/__init__.py rename to crawler/rec/__init__.py diff --git a/rec/db.py b/crawler/rec/db.py similarity index 100% rename from rec/db.py rename to crawler/rec/db.py diff --git a/rec/query.py b/crawler/rec/query.py similarity index 100% rename from rec/query.py rename to crawler/rec/query.py diff --git a/rec/rightmove_parser.py b/crawler/rec/rightmove_parser.py similarity index 100% rename from rec/rightmove_parser.py rename to crawler/rec/rightmove_parser.py diff --git a/testing.py b/crawler/testing.py similarity index 100% rename from testing.py rename to crawler/testing.py diff --git a/vqa/.gitignore b/vqa/.gitignore new file mode 100644 index 0000000..f0f79c9 --- /dev/null +++ b/vqa/.gitignore @@ -0,0 +1,3 @@ +venv/ +__pycache__/ +.ipynb_checkpoints/ diff --git a/vqa/Untitled.ipynb b/vqa/Untitled.ipynb new file mode 100644 index 0000000..a27b7d7 --- /dev/null +++ b/vqa/Untitled.ipynb @@ -0,0 +1,300 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "546cee84-249f-43ce-a1f9-8475682a833d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kadir/code/vqa/venv/lib/python3.12/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 Pix2StructProcessor, Pix2StructForConditionalGeneration\n", + "from PIL import Image\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "74e4a41f-2dfc-428e-8bca-4e9cc1c076c6", + "metadata": {}, + "outputs": [], + "source": [ + "image = Image.open(\"floorplans/46001_32532509_FLP_00_0000.jpeg\")\n", + "question = \"How many living rooms are displayed on this floor plan?\"" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c24b1560-563b-4ff2-8744-4591ac1cc57b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "processor = Pix2StructProcessor.from_pretrained('google/deplot')\n", + "model = Pix2StructForConditionalGeneration.from_pretrained('google/deplot')\n", + "\n", + "inputs = processor(images=image, text=question, return_tensors=\"pt\")\n", + "predictions = model.generate(**inputs, max_new_tokens=512)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ea990e1f-a660-4efb-be48-d095ab05b50d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'TITLE | <0x0A> Fifth Floor<0x0A>Floor Area 642 Sq Ft - 59.64 Sq M | Licence<0x0A>Remung<0x0A>Floor Area 642 Sq Ft - 59.64 Sq M <0x0A> Reception Room<0x0A>1210 x 12.10%<0x0A>39 x 39m | 1280 <0x0A> Balcony<0x0A>Reception Room<0x0A>1210 x 12.10%<0x0A>39 x 39m | 1280 <0x0A> Property<0x0A>Measurer | 1280 <0x0A> Ipaplus.com | 1280'" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = processor.decode(predictions[0], skip_special_tokens=True)\n", + "x" + ] + }, + { + "cell_type": "markdown", + "id": "e0d94c3e-e8de-452a-8556-12dd2e3e2351", + "metadata": {}, + "source": [ + "# google/pix2struct-ai2d-base" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "cd57d77b-3d70-46f0-97cf-ddd37cd61d23", + "metadata": {}, + "outputs": [], + "source": [ + "question = \"How many living rooms?\"\n", + "model = Pix2StructForConditionalGeneration.from_pretrained(\"google/pix2struct-ai2d-base\")\n", + "processor = Pix2StructProcessor.from_pretrained(\"google/pix2struct-ai2d-base\")\n", + "\n", + "inputs = processor(images=image, text=question, return_tensors=\"pt\")\n", + "predictions = model.generate(**inputs, max_new_tokens=10000)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "a507a4a4-33b6-4ca3-93dd-3aab489b5c24", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predictions 1\n" + ] + }, + { + "data": { + "text/plain": [ + "'floor plan is for illustrative purposes only and is not to scale. Every attempt has been made to ensure the accuracy of the floor plan shown, however all measurements, fixtures, fittings and data shown are an approximate interpretation for illustrative purposes only. 1 sq m + 10.76 sq feet. 7/8/2023>'" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = processor.decode(predictions[0], skip_special_tokens=True)\n", + "print(\"predictions: \", len(predictions))\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a13e12c9-0406-4568-9a36-e61d70e1e683", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "4865a480-230d-48bc-be05-ddc54d107c79", + "metadata": {}, + "source": [ + "# pix2struct-docvqa-large" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "cd209233-ad64-4af0-a8ce-f1d640458d02", + "metadata": {}, + "outputs": [], + "source": [ + "question = \"How many living rooms?\"\n", + "model = Pix2StructForConditionalGeneration.from_pretrained(\"google/pix2struct-docvqa-large\")\n", + "processor = Pix2StructProcessor.from_pretrained(\"google/pix2struct-docvqa-large\")\n", + "\n", + "inputs = processor(images=image, text=question, return_tensors=\"pt\")\n", + "predictions = model.generate(**inputs, max_new_tokens=10000)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "17378986-ae8b-4ab5-be40-837db0f7aaa6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predictions: 1\n" + ] + }, + { + "data": { + "text/plain": [ + "'2'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = processor.decode(predictions[0], skip_special_tokens=True)\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "2d3f5a65-34e8-4c2e-ab37-03e6da86fc1c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['what is the total area measured in square meters? 8. 5']\n" + ] + } + ], + "source": [ + "from transformers import AutoProcessor, AutoModelForCausalLM\n", + "from huggingface_hub import hf_hub_download\n", + "import torch\n", + "\n", + "processor = AutoProcessor.from_pretrained(\"microsoft/git-base-textvqa\")\n", + "model = AutoModelForCausalLM.from_pretrained(\"microsoft/git-base-textvqa\")\n", + "file_path = hf_hub_download(repo_id=\"nielsr/textvqa-sample\", filename=\"bus.png\", repo_type=\"dataset\")\n", + "\n", + "pixel_values = processor(images=image, return_tensors=\"pt\").pixel_values\n", + "question = \"What is the total area measured in square meters?\"\n", + "\n", + "input_ids = processor(text=question, add_special_tokens=False).input_ids\n", + "\n", + "input_ids = [processor.tokenizer.cls_token_id] + input_ids\n", + "\n", + "input_ids = torch.tensor(input_ids).unsqueeze(0)\n", + "\n", + "generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)\n", + "\n", + "print(processor.batch_decode(generated_ids, skip_special_tokens=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "254054f0-0102-4927-a93c-c6eba97b437c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['how many living rooms are displayed on this floor plan? 8']\n" + ] + } + ], + "source": [ + "from transformers import AutoProcessor, AutoModelForCausalLM\n", + "\n", + "from huggingface_hub import hf_hub_download\n", + "\n", + "from PIL import Image\n", + "\n", + "processor = AutoProcessor.from_pretrained(\"microsoft/git-base-textvqa\")\n", + "\n", + "model = AutoModelForCausalLM.from_pretrained(\"microsoft/git-base-textvqa\")\n", + "\n", + "file_path = hf_hub_download(repo_id=\"nielsr/textvqa-sample\", filename=\"bus.png\", repo_type=\"dataset\")\n", + "\n", + "pixel_values = processor(images=image, return_tensors=\"pt\").pixel_values\n", + "\n", + "input_ids = processor(text=question, add_special_tokens=False).input_ids\n", + "\n", + "input_ids = [processor.tokenizer.cls_token_id] + input_ids\n", + "\n", + "input_ids = torch.tensor(input_ids).unsqueeze(0)\n", + "\n", + "generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)\n", + "\n", + "print(processor.batch_decode(generated_ids, skip_special_tokens=True))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/vqa/floorplans/46001_32532509_FLP_00_0000.jpeg b/vqa/floorplans/46001_32532509_FLP_00_0000.jpeg new file mode 100644 index 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["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/vqa/questions.json b/vqa/questions.json new file mode 100644 index 0000000..d326197 --- /dev/null +++ b/vqa/questions.json @@ -0,0 +1,8 @@ +{ + "How many bedrooms?": "2", + "How many living rooms?":"1", + "How many reception rooms?":"1", + "How many bathrooms?": "1", + "What is the total square meter area?": "59.64", + "Which floor is it?": "5" +} diff --git a/vqa/questions.py b/vqa/questions.py new file mode 100644 index 0000000..609a398 --- /dev/null +++ b/vqa/questions.py @@ -0,0 +1,37 @@ +import json + + +def load_questions(bigquestion=False): + with open("questions.json") as f: + qs: dict = json.load(f) + + if bigquestion: + s = """ +Tell me from this floor plan information in a valid json format. The json format should have the following format. Only fill out the information if it is visible by the floor plan. Do not try to convert footage or meter into the each other or multiply width with height if area is not given. Instead use null whenever the data is not explicitly displayed. +``` +{ + "balcony": "boolean", + "terrace": "boolean", + "total_number_of_rooms": "number", + "number_of_bathrooms": "number", + "number_of_bedrooms": "number", + "total_square_meter": "number", // if described + "total_square_footage": "number", // if described + "kitchen_separate": "boolean", + "bedroom_faces_south_or_east": "boolean", + // Per room I also want following information + "rooms": { + "title": "e.g. bedroom 1" + "type": "bedroom|livingroom|bathroom", + "dimension_width_metric": "3", // in meters if exist + "dimension_height_metric": "4", // in meters if exist + "sqm" : "number", // if exists + "dimension_width_foot": "5.5", // in foot if exist + "dimension_height_foot": "7", // in foot if exist + } +} +``` + """.strip() + qs[s] = "" + + return qs diff --git a/vqa/util.py b/vqa/util.py new file mode 100644 index 0000000..e69de29 diff --git a/vqa/vqa.py b/vqa/vqa.py new file mode 100644 index 0000000..faad3c6 --- /dev/null +++ b/vqa/vqa.py @@ -0,0 +1,72 @@ +from transformers import BlipProcessor, BlipForQuestionAnswering +from transformers import ViltProcessor, ViltForQuestionAnswering +from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration +from transformers import GitVisionConfig, GitVisionModel, AutoProcessor, GitProcessor + +class VQA: + name = "Not defined" + def query(image, question: str) -> str: + pass + +class Blip(VQA): + name = "Blip" + def query(self, image, question): + processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-capfilt-large") + model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-capfilt-large") + inputs = processor(image, question, return_tensors="pt") + out = model.generate(max_new_tokens=50000, **inputs) + return processor.decode(out[0], skip_special_tokens=True) + + +class Vilt(VQA): + name = "Vilt" + def query(self, image, question): + processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa") + model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa") + + # prepare inputs + encoding = processor(image, question, return_tensors="pt") + + # forward pass + outputs = model(**encoding) + logits = outputs.logits + idx = logits.argmax(-1).item() + return model.config.id2label[idx] + + + +class Deplot(VQA): + name = "Deplot" + def query(self, image, question): + processor = Pix2StructProcessor.from_pretrained('google/deplot') + model = Pix2StructForConditionalGeneration.from_pretrained('google/deplot') + + inputs = processor(images=image, text=question, return_tensors="pt") + predictions = model.generate(**inputs, max_new_tokens=512) + return processor.decode(predictions[0], skip_special_tokens=True) + + +class PixStructDocVA(VQA): + name = "google/pix2struct-docvqa-large" + def query(self, image, question): + print(question) + model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-docvqa-large") + processor = Pix2StructProcessor.from_pretrained("google/pix2struct-docvqa-large") + + inputs = processor(images=image, text=question, return_tensors="pt") + predictions = model.generate(**inputs, max_new_tokens=10000) + answer = processor.decode(predictions[0], skip_special_tokens=True) + print(answer) + return answer + +class MicrosoftGIT(VQA): + name = "microsoft/git-base-textvqa" + def query(self, image, question): + processor = GitProcessor.from_pretrained("microsoft/git-base") + model = GitVisionModel.from_pretrained("microsoft/git-base") + inputs = processor(images=image, text=question, return_tensors="pt") + predictions = model.generate(**inputs, max_new_tokens=10000) + answer = processor.decode(predictions[0], skip_special_tokens=True) + + return answer + \ No newline at end of file