From 29da85914ce9df6f83322b08ce0cdc801f82a521 Mon Sep 17 00:00:00 2001 From: ozeeeno Date: Wed, 25 Oct 2023 23:19:24 +0900 Subject: [PATCH 01/26] =?UTF-8?q?ml=20:=20OCR=20=EB=B0=8F=20=EB=A6=AC?= =?UTF-8?q?=EB=B7=B0=20=EB=B6=84=EB=A5=98=201=EC=B0=A8=20=EC=9E=91?= =?UTF-8?q?=EC=97=85/=20dataset=20=EA=B0=80=EA=B3=B5=20=EB=B0=8F=20?= =?UTF-8?q?=EC=BD=94=EB=93=9C=20=EC=88=98=EC=A0=95=20=EC=9A=94?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ml/.gitignore | 186 ++++++++++++++++++++++++ ml/ocr/assets/example.jpeg | Bin 0 -> 664175 bytes ml/ocr/execution.py | 116 +++++++++++++++ ml/requirements.txt | 21 +++ ml/review/execution.py | 45 ++++++ ml/review/model.py | 71 ++++++++++ ml/review/preprocessing.py | 153 ++++++++++++++++++++ ml/review/tokenization.py | 281 +++++++++++++++++++++++++++++++++++++ ml/review/train.py | 106 ++++++++++++++ 9 files changed, 979 insertions(+) create mode 100644 ml/.gitignore create mode 100644 ml/ocr/assets/example.jpeg create mode 100644 ml/ocr/execution.py create mode 100644 ml/requirements.txt create mode 100644 ml/review/execution.py create mode 100644 ml/review/model.py create mode 100644 ml/review/preprocessing.py create mode 100644 ml/review/tokenization.py create mode 100644 ml/review/train.py diff --git a/ml/.gitignore b/ml/.gitignore new file mode 100644 index 0000000..bb0600d --- /dev/null +++ b/ml/.gitignore @@ -0,0 +1,186 @@ +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +clova/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +### Python Patch ### +# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration +poetry.toml + +# ruff +.ruff_cache/ + +# LSP config files +pyrightconfig.json + +### venv ### +# Virtualenv +# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/ +[Bb]in +[Ii]nclude +[Ll]ib +[Ll]ib64 +[Ll]ocal +[Ss]cripts +pyvenv.cfg +pip-selfcheck.json + +# End of https://www.toptal.com/developers/gitignore/api/python,venv \ No newline at end of file diff --git a/ml/ocr/assets/example.jpeg b/ml/ocr/assets/example.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..fd05c1db2989023974ef86e3f573d74d61d0497f GIT binary patch literal 664175 zcmb@td03KL_cy+8KtUyE3kns5a?AtT04j!BPMNc1Wu@X!o-$Cg)CO>9FhX!B>zG41 z+oY$`W-t@AQcEjSD+)_ZtsJs6%lEd<>3Od2_5I`bd#`un_WrQ;+H0@1?mh3#Oy`UY zsJl72Ispg*zy1!l6Klh`9&!MvUwIJ#a{LcIwC!IEIu9@W 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zhuVM~iI|j0fKMGUj8sNF>UOX!az70IzlC2y?AT=iRAV{L57MLAwef+@59vS^LLW~r0OO1Rd(;i)#UiiX5(CfP!N(j{gzzbaiaF{wgU|s~uVHm2NMzs@JdU4A z0F!Gf`8hkl$k+(LJm8P5XdsPHAv*vW+Cjwkff@i5?SzI_La~E*BIq z^79bEQH-8WGtX}Ht1kquGse?_!N>HZFA92`4(E?FG^b!v%f2LUtaWzx`^lYmi7F7F?0|bmOG}N4gc1F;bf| zk^mV#f~%ZK=AEaOEs=x39An2y5MSy5X**?#(WDZV706v+g zaGU}OjFHLUU+0}79oz(pb`M%@S?y#5u1zv7lHMq3OLA6qbC4!k=B}a zvW}Fu>{l6%xFvM=OEB9@Qc9BsgF=91($4 zfQ^_g;sEMBe@d-F*RdgmE_}$dSZ=2 z<9^g70OVwjpkt3i#Uhx-OAr|GgWuEerzDZc5r;<~D8?!h3Z6b~ryS9!T(U+{S2%I) z#{)GPP*geRuh%%I@*~c8Bh>n0lW__FIAf2dF^V+{h+$8k0|Pn7OyF_(Q&m~lWO>ee zjNsy?$lR%nagIqm3Ieo1?QPw?I`pGZ(21dyeq~^+Ir;ed0qs?#g_c6Zf*1gMf_r`y zp_Ncn{qQ#9+;qomRDNs7zrID|1Y@bkKzm}1LqZHP$OME9qX3RDGt!?Nv6u4lH>u8g zWP5Q=X68<$jt5U#VUTCaDi6!S01SJ5DAZ)- 0) for i in token_ids] + + return mask + + +def tokenize_processed_data(tokenizer, processed_dataset): + labels = processed_dataset["label"].to_numpy() + + labels = labels.astype(np.int) + + tokenized_data = [ + data_to_token_ids(tokenizer, processed_data) + for processed_data in processed_dataset["sentence"] + ] + + attention_masks = [token_ids_to_mask(token_ids) for token_ids in tokenized_data] + + return tokenized_data, labels, attention_masks + + +def split_into_train_validation(whole_data, whole_label, whole_masks): + print("length of whole_data : " + str(len(whole_data))) + + train_inputs, validation_inputs, train_labels, validation_labels = train_test_split( + whole_data, whole_label, random_state=2022, test_size=0.1 + ) + train_masks, validation_masks, _, _ = train_test_split( + whole_masks, whole_data, random_state=2022, test_size=0.1 + ) + + print("length of train_data : " + str(len(train_inputs))) + + return ( + train_inputs, + validation_inputs, + train_labels, + validation_labels, + train_masks, + validation_masks, + ) + + +def data_to_tensor(inputs, labels, masks): + inputs_tensor = torch.tensor(inputs) + labels_tensor = torch.tensor(labels) + masks_tensor = torch.tensor(masks) + return inputs_tensor, labels_tensor, masks_tensor + + +def tensor_to_dataloader(inputs, labels, masks, mode): + from torch.utils.data import RandomSampler, SequentialSampler + + batch_size = 32 + data = TensorDataset(inputs, masks, labels) + + if mode == "train": + sampler = RandomSampler(data) + else: + sampler = SequentialSampler(data) + + dataloader = DataLoader(data, sampler=sampler, batch_size=batch_size) + + return dataloader + + +def preprocessing(tokenizer, whole_dataset): + processed_dataset = data_processing(whole_dataset) + + tokenized_dataset, labels, attention_masks = tokenize_processed_data( + tokenizer, processed_dataset + ) + + ( + train_inputs, + validation_inputs, + train_labels, + validation_labels, + train_masks, + validation_masks, + ) = split_into_train_validation(tokenized_dataset, labels, attention_masks) + + train_inputs, train_labels, train_masks = data_to_tensor( + train_inputs, train_labels, train_masks + ) + validation_inputs, validation_labels, validation_masks = data_to_tensor( + validation_inputs, validation_labels, validation_masks + ) + + train_dataloader = tensor_to_dataloader(train_inputs, train_labels, train_masks, "train") + validation_dataloader = tensor_to_dataloader( + validation_inputs, validation_labels, validation_masks, "validation" + ) + + return train_dataloader, validation_dataloader + + +def main(): + from tokenization import KoBertTokenizer + + # whole_dataset = + + tokenizer = KoBertTokenizer.from_pretrained("monologg/kobert") + + # train, valid = preprocessing(tokenizer, whole_dataset) + + +if __name__ == "__main__": + main() diff --git a/ml/review/tokenization.py b/ml/review/tokenization.py new file mode 100644 index 0000000..1fd4c38 --- /dev/null +++ b/ml/review/tokenization.py @@ -0,0 +1,281 @@ +# coding=utf-8 +# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team and Jangwon Park +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Tokenization classes for KoBERT model """ + + +import logging +import os +import unicodedata +from shutil import copyfile + +from transformers import PreTrainedTokenizer + +logger = logging.getLogger(__name__) + +VOCAB_FILES_NAMES = { + "vocab_file": "tokenizer_78b3253a26.model", + "vocab_txt": "vocab.txt", +} + +PRETRAINED_VOCAB_FILES_MAP = { + "vocab_file": { + "monologg/kobert": "https://s3.amazonaws.com/models.huggingface.co/bert/monologg/kobert/tokenizer_78b3253a26.model", + "monologg/kobert-lm": "https://s3.amazonaws.com/models.huggingface.co/bert/monologg/kobert-lm/tokenizer_78b3253a26.model", + "monologg/distilkobert": "https://s3.amazonaws.com/models.huggingface.co/bert/monologg/distilkobert/tokenizer_78b3253a26.model", + }, + "vocab_txt": { + "monologg/kobert": "https://s3.amazonaws.com/models.huggingface.co/bert/monologg/kobert/vocab.txt", + "monologg/kobert-lm": "https://s3.amazonaws.com/models.huggingface.co/bert/monologg/kobert-lm/vocab.txt", + "monologg/distilkobert": "https://s3.amazonaws.com/models.huggingface.co/bert/monologg/distilkobert/vocab.txt", + }, +} + +PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { + "monologg/kobert": 512, + "monologg/kobert-lm": 512, + "monologg/distilkobert": 512, +} + +PRETRAINED_INIT_CONFIGURATION = { + "monologg/kobert": {"do_lower_case": False}, + "monologg/kobert-lm": {"do_lower_case": False}, + "monologg/distilkobert": {"do_lower_case": False}, +} + +SPIECE_UNDERLINE = "▁" + + +class KoBertTokenizer(PreTrainedTokenizer): + """ + SentencePiece based tokenizer. Peculiarities: + - requires `SentencePiece `_ + """ + + vocab_files_names = VOCAB_FILES_NAMES + pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP + pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION + max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES + + def __init__( + self, + vocab_file, + vocab_txt, + do_lower_case=False, + remove_space=True, + keep_accents=False, + unk_token="[UNK]", + sep_token="[SEP]", + pad_token="[PAD]", + cls_token="[CLS]", + mask_token="[MASK]", + **kwargs, + ): + super().__init__( + unk_token=unk_token, + sep_token=sep_token, + pad_token=pad_token, + cls_token=cls_token, + mask_token=mask_token, + **kwargs, + ) + + # Build vocab + self.token2idx = dict() + self.idx2token = [] + with open(vocab_txt, "r", encoding="utf-8") as f: + for idx, token in enumerate(f): + token = token.strip() + self.token2idx[token] = idx + self.idx2token.append(token) + + try: + import sentencepiece as spm + except ImportError: + logger.warning( + "You need to install SentencePiece to use KoBertTokenizer: https://github.com/google/sentencepiece" + "pip install sentencepiece" + ) + + self.do_lower_case = do_lower_case + self.remove_space = remove_space + self.keep_accents = keep_accents + self.vocab_file = vocab_file + self.vocab_txt = vocab_txt + + self.sp_model = spm.SentencePieceProcessor() + self.sp_model.Load(vocab_file) + + @property + def vocab_size(self): + return len(self.idx2token) + + def get_vocab(self): + return dict(self.token2idx, **self.added_tokens_encoder) + + def __getstate__(self): + state = self.__dict__.copy() + state["sp_model"] = None + return state + + def __setstate__(self, d): + self.__dict__ = d + try: + import sentencepiece as spm + except ImportError: + logger.warning( + "You need to install SentencePiece to use KoBertTokenizer: https://github.com/google/sentencepiece" + "pip install sentencepiece" + ) + self.sp_model = spm.SentencePieceProcessor() + self.sp_model.Load(self.vocab_file) + + def preprocess_text(self, inputs): + if self.remove_space: + outputs = " ".join(inputs.strip().split()) + else: + outputs = inputs + outputs = outputs.replace("``", '"').replace("''", '"') + + if not self.keep_accents: + outputs = unicodedata.normalize("NFKD", outputs) + outputs = "".join([c for c in outputs if not unicodedata.combining(c)]) + if self.do_lower_case: + outputs = outputs.lower() + + return outputs + + def _tokenize(self, text): + """Tokenize a string.""" + text = self.preprocess_text(text) + pieces = self.sp_model.encode(text, out_type=str) + new_pieces = [] + for piece in pieces: + if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit(): + cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, "")) + if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: + if len(cur_pieces[0]) == 1: + cur_pieces = cur_pieces[1:] + else: + cur_pieces[0] = cur_pieces[0][1:] + cur_pieces.append(piece[-1]) + new_pieces.extend(cur_pieces) + else: + new_pieces.append(piece) + + return new_pieces + + def _convert_token_to_id(self, token): + """Converts a token (str/unicode) in an id using the vocab.""" + return self.token2idx.get(token, self.token2idx[self.unk_token]) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (string/unicode) using the vocab.""" + return self.idx2token[index] + + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (strings for sub-words) in a single string.""" + out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() + return out_string + + def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks + by concatenating and adding special tokens. + A KoBERT sequence has the following format: + single sequence: [CLS] X [SEP] + pair of sequences: [CLS] A [SEP] B [SEP] + """ + if token_ids_1 is None: + return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + cls = [self.cls_token_id] + sep = [self.sep_token_id] + return cls + token_ids_0 + sep + token_ids_1 + sep + + def get_special_tokens_mask( + self, token_ids_0, token_ids_1=None, already_has_special_tokens=False + ): + """ + Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods. + Args: + token_ids_0: list of ids (must not contain special tokens) + token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids + for sequence pairs + already_has_special_tokens: (default False) Set to True if the token list is already formated with + special tokens for the model + Returns: + A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token. + """ + + if already_has_special_tokens: + if token_ids_1 is not None: + raise ValueError( + "You should not supply a second sequence if the provided sequence of " + "ids is already formated with special tokens for the model." + ) + return list( + map( + lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, + token_ids_0, + ) + ) + + if token_ids_1 is not None: + return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + return [1] + ([0] * len(token_ids_0)) + [1] + + def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): + """ + Creates a mask from the two sequences passed to be used in a sequence-pair classification task. + A KoBERT sequence pair mask has the following format: + 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence + if token_ids_1 is None, only returns the first portion of the mask (0's). + """ + sep = [self.sep_token_id] + cls = [self.cls_token_id] + if token_ids_1 is None: + return len(cls + token_ids_0 + sep) * [0] + return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + + def save_vocabulary(self, save_directory): + """Save the sentencepiece vocabulary (copy original file) and special tokens file + to a directory. + """ + if not os.path.isdir(save_directory): + logger.error("Vocabulary path ({}) should be a directory".format(save_directory)) + return + + # 1. Save sentencepiece model + out_vocab_model = os.path.join(save_directory, VOCAB_FILES_NAMES["vocab_file"]) + + if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_model): + copyfile(self.vocab_file, out_vocab_model) + + # 2. Save vocab.txt + index = 0 + out_vocab_txt = os.path.join(save_directory, VOCAB_FILES_NAMES["vocab_txt"]) + with open(out_vocab_txt, "w", encoding="utf-8") as writer: + for token, token_index in sorted(self.token2idx.items(), key=lambda kv: kv[1]): + if index != token_index: + logger.warning( + "Saving vocabulary to {}: vocabulary indices are not consecutive." + " Please check that the vocabulary is not corrupted!".format(out_vocab_txt) + ) + index = token_index + writer.write(token + "\n") + index += 1 + + return out_vocab_model, out_vocab_txt diff --git a/ml/review/train.py b/ml/review/train.py new file mode 100644 index 0000000..ea95a1b --- /dev/null +++ b/ml/review/train.py @@ -0,0 +1,106 @@ +import random +import time + +import numpy as np +import pandas as pd +import torch +from model import BertModelInitialization, get_model, get_model_with_params +from preprocessing import preprocessing +from tqdm.notebook import tqdm + + +def accuracy(preds, labels): + f_pred = np.argmax(preds, axis=1).flatten() + f_labels = labels.flatten() + return np.sum(f_pred == f_labels) / len(f_labels) + + +seed_val = 2023 + +random.seed(seed_val) +np.random.seed(seed_val) +torch.manual_seed(seed_val) +torch.cuda.manual_seed_all(seed_val) + +from tokenization import KoBertTokenizer + +# whole_dataset = + +tokenizer = KoBertTokenizer.from_pretrained("monologg/kobert") + +train_dataloader, validation_dataloader = preprocessing(tokenizer, whole_dataset) + +# 초기 사용시에만 선언 +BertModelInitialization() + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +model, optimizer, scheduler, epochs, criterion = get_model_with_params( + len(train_dataloader), device, torch.cuda.is_available() +) + +model.zero_grad() + +for epoch_i in range(epochs): + print("") + print("========{:}번째 Epoch / 전체 {:}회 ========".format(epoch_i + 1, epochs)) + print("훈련 중") + + t0 = time.time() + total_loss = 0 + sum_loss = 0 + model.train() # 훈련모드 + + for step, batch in enumerate(tqdm(train_dataloader)): + if step % 50 == 0: + print("{}번째 까지의 평균 loss : {}".format(step, sum_loss / 50)) + sum_loss = 0 + + batch = tuple(t.to(device) for t in batch) + b_input_ids, b_input_mask, b_labels = batch + + outputs = model(b_input_ids, b_input_mask) + + loss = criterion(outputs, b_labels.long()) + total_loss += loss.item() + sum_loss += loss.item() + + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + optimizer.step() + scheduler.step() + model.zero_grad() + + avg_train_loss = total_loss / len(train_dataloader) + print("") + print(" Average training loss: {0:.2f}".format(avg_train_loss)) + + print("검증 중") + + t0 = time.time() + model.eval() + + eval_loss, eval_accuracy = 0, 0 + nb_eval_steps, nb_eval_examples = 0, 0 + + for batch in validation_dataloader: + batch = tuple(t.to(device) for t in batch) + + b_input_ids, b_input_mask, b_labels = batch + + with torch.no_grad(): + outputs = model(b_input_ids, attention_mask=b_input_mask) + + outputs = outputs.detach().cpu().numpy() + label_ids = b_labels.to("cpu").numpy() + + tmp_eval_accuracy = accuracy(outputs, label_ids) + eval_accuracy += tmp_eval_accuracy + nb_eval_steps += 1 + + print(" Accuracy: {0:.4f}".format(eval_accuracy / nb_eval_steps)) + +PATH = "model.pt" +torch.save(model.state_dict(), PATH) + +print("Training complete") From 3d9cbeba4a1f6f1ba9bf35b1782b9487400ae1d6 Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Thu, 26 Oct 2023 18:16:43 +0900 Subject: [PATCH 02/26] fastapi initialization --- backend/.gitignore | 3 ++- ml/Dockerfile | 17 +++++++++++++++++ ml/main.py | 30 ++++++++++++++++++++++++++++++ 3 files changed, 49 insertions(+), 1 deletion(-) create mode 100644 ml/Dockerfile create mode 100644 ml/main.py diff --git a/backend/.gitignore b/backend/.gitignore index eb9d755..6624a99 100644 --- a/backend/.gitignore +++ b/backend/.gitignore @@ -34,4 +34,5 @@ lerna-debug.log* !.vscode/launch.json !.vscode/extensions.json -.env.dev \ No newline at end of file +.env.dev +.env \ No newline at end of file diff --git a/ml/Dockerfile b/ml/Dockerfile new file mode 100644 index 0000000..31ba4bf --- /dev/null +++ b/ml/Dockerfile @@ -0,0 +1,17 @@ +FROM python:3.11 + +ENV PYTHONDONTWRITEBYTECODE 1 +ENV PYTHONUNBUFFERED 1 + +RUN apt-get update +RUN apt-get install -y cmake + +COPY . . +RUN pip install -r requirements.txt + +EXPOSE 81 + +ENTRYPOINT ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "81"] + + + diff --git a/ml/main.py b/ml/main.py new file mode 100644 index 0000000..a6524c9 --- /dev/null +++ b/ml/main.py @@ -0,0 +1,30 @@ +from io import BytesIO + +import requests +from PIL.Image import Image +from fastapi import FastAPI +from review.execution import predict_sentence + +app = FastAPI() + + +@app.get("/") +async def root(): + return {"message": "Hello World"} + + +@app.get("/ocr") +async def receipt_ocr(url: str): + image_res = requests.get(url) + image = Image.open(BytesIO(image_res.content)) + jpg_image = image.convert('RGB') + + return {"ocr": "통닭치킨"} + + +@app.get("/review") +async def review_classification(review_str: str): + res = predict_sentence(review_str) + return {"output": res} + + From a6b6295734c345e32f6295c27c785dda76b5e71e Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Thu, 26 Oct 2023 19:21:50 +0900 Subject: [PATCH 03/26] ml deploy test --- .github/workflows/publish-backend-docker.yml | 2 +- .github/workflows/publish-ml-docker.yml | 54 ++++++++++++++ ml/requirements.txt | 76 +++++++++++++++++++- ml/review/execution.py | 4 +- 4 files changed, 131 insertions(+), 5 deletions(-) create mode 100644 .github/workflows/publish-ml-docker.yml diff --git a/.github/workflows/publish-backend-docker.yml b/.github/workflows/publish-backend-docker.yml index c56fa90..b4d3b0d 100644 --- a/.github/workflows/publish-backend-docker.yml +++ b/.github/workflows/publish-backend-docker.yml @@ -25,7 +25,7 @@ jobs: uses: docker/login-action@v1 with: registry: ghcr.io - username: ${{ github.actor }} + username: eldpswp99 password: ${{ secrets.DOCKER_TOKEN }} - name: Build image diff --git a/.github/workflows/publish-ml-docker.yml b/.github/workflows/publish-ml-docker.yml new file mode 100644 index 0000000..d2ae65e --- /dev/null +++ b/.github/workflows/publish-ml-docker.yml @@ -0,0 +1,54 @@ +name: 배포 + +on: + push: + branches: + - ml-initialization + +jobs: + + push: + env: + working-directory: ./ml + runs-on: ubuntu-latest + if: github.event_name == 'push' + + + steps: + - uses: actions/checkout@v2 + + - name: create dotenv + working-directory: ${{env.working-directory}} + run: echo "${{ secrets.ENV }}" > .env + + - name: Log into GitHub Container Registry + uses: docker/login-action@v1 + with: + registry: ghcr.io + username: eldpswp99 + password: ${{ secrets.DOCKER_TOKEN }} + + - name: Build image + working-directory: ${{env.working-directory}} + run: | + docker build -f Dockerfile .\ + -t ghcr.io/snuhcs-course/swpp-2023-project-team-13/ml-image:${GITHUB_SHA}\ + -t ghcr.io/snuhcs-course/swpp-2023-project-team-13/ml-image:latest + - name: Push image + working-directory: ${{env.working-directory}} + run: | + docker push ghcr.io/snuhcs-course/swpp-2023-project-team-13/ml-image:${GITHUB_SHA} + docker push ghcr.io/snuhcs-course/swpp-2023-project-team-13/ml-image:latest + deploy: + needs: push + name: 배포 + runs-on: [ self-hosted ] + steps: + - name: Log into GitHub Container Registry + uses: docker/login-action@v1 + with: + registry: ghcr.io + username: eldpswp99 + password: ${{ secrets.DOCKER_TOKEN }} + - name: Docker compose up + run: SHA=${GITHUB_SHA} docker compose -f ~/docker-compose.yml up -d fooriend-ml \ No newline at end of file diff --git a/ml/requirements.txt b/ml/requirements.txt index 02b4c3d..f6b29e4 100644 --- a/ml/requirements.txt +++ b/ml/requirements.txt @@ -1,21 +1,93 @@ +annotated-types==0.6.0 +anyio==3.7.1 +appnope==0.1.3 +asttokens==2.4.0 +attrs==23.1.0 +backcall==0.2.0 +black==23.10.1 certifi==2023.7.22 charset-normalizer==3.3.1 +click==8.1.7 contourpy==1.1.1 cycler==0.12.1 +Cython==3.0.4 +decorator==5.1.1 +dynaconf==3.2.3 +executing==2.0.0 +fastapi==0.104.0 +fastjsonschema==2.18.1 +filelock==3.12.4 fonttools==4.43.1 +fsspec==2023.10.0 +gluonnlp==0.10.0 +graphviz==0.8.4 +h11==0.14.0 +huggingface-hub==0.17.3 idna==3.4 +import-ipynb==0.1.4 +ipython==8.16.1 +jedi==0.19.1 +Jinja2==3.1.2 +joblib==1.3.2 +jsonschema==4.19.1 +jsonschema-specifications==2023.7.1 +jupyter_core==5.4.0 +keras==2.14.0 kiwisolver==1.4.5 +MarkupSafe==2.1.3 matplotlib==3.8.0 +matplotlib-inline==0.1.6 +mpmath==1.3.0 +mxnet==1.6.0 +mypy-extensions==1.0.0 +nbformat==5.9.2 +networkx==3.2 numpy==1.26.1 opencv-contrib-python==4.8.1.78 opencv-python==4.8.1.78 packaging==23.2 +pandas==2.1.1 +parso==0.8.3 +pathspec==0.11.2 +pexpect==4.8.0 +pickleshare==0.7.5 Pillow==10.1.0 -pip==23.2.1 +platformdirs==3.11.0 +prompt-toolkit==3.0.39 +ptyprocess==0.7.0 +pure-eval==0.2.2 +pydantic==1.10.13 +pydantic_core==2.10.1 +Pygments==2.16.1 pyparsing==3.1.1 python-dateutil==2.8.2 python-dotenv==1.0.0 +pytz==2023.3.post1 +PyYAML==6.0.1 +referencing==0.30.2 +regex==2023.10.3 requests==2.31.0 -setuptools==68.0.0 +rpds-py==0.10.6 +safetensors==0.4.0 +scikit-learn==1.3.2 +scipy==1.11.3 six==1.16.0 +sniffio==1.3.0 +SQLAlchemy==1.4.49 +sqlalchemy2-stubs==0.0.2a35 +sqlmodel==0.0.9 +stack-data==0.6.3 +starlette==0.27.0 +sympy==1.12 +threadpoolctl==3.2.0 +tokenizers==0.14.1 +torch==2.1.0 +tqdm==4.66.1 +traitlets==5.12.0 +transformers==4.34.1 +typer==0.9.0 +typing_extensions==4.8.0 +tzdata==2023.3 urllib3==2.0.7 +uvicorn==0.23.2 +wcwidth==0.2.8 diff --git a/ml/review/execution.py b/ml/review/execution.py index 3644f49..b608774 100644 --- a/ml/review/execution.py +++ b/ml/review/execution.py @@ -1,7 +1,7 @@ import import_ipynb import torch -from model import get_model -from preprocessing import data_to_token_ids, token_ids_to_mask +from review.model import get_model +from review.preprocessing import data_to_token_ids, token_ids_to_mask from tokenization import KoBertTokenizer device = torch.device("cuda" if torch.cuda.is_available() else "cpu") From a0cd8249934d410320c04b1d719b8ea316c11102 Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Sun, 29 Oct 2023 19:03:34 +0900 Subject: [PATCH 04/26] deploy backend --- .github/workflows/publish-backend-docker.yml | 4 +- .github/workflows/publish-ml-docker.yml | 54 -------------------- 2 files changed, 2 insertions(+), 56 deletions(-) delete mode 100644 .github/workflows/publish-ml-docker.yml diff --git a/.github/workflows/publish-backend-docker.yml b/.github/workflows/publish-backend-docker.yml index b4d3b0d..6a4123d 100644 --- a/.github/workflows/publish-backend-docker.yml +++ b/.github/workflows/publish-backend-docker.yml @@ -3,7 +3,7 @@ name: 배포 on: push: branches: - - main + - ml-initialization jobs: @@ -51,4 +51,4 @@ jobs: username: eldpswp99 password: ${{ secrets.DOCKER_TOKEN }} - name: Docker compose up - run: SHA=${GITHUB_SHA} docker compose -f ~/docker-compose.yml up -d fooriend-backend \ No newline at end of file + run: SHA=${GITHUB_SHA} docker compose -f ~/docker-compose.yml up -d \ No newline at end of file diff --git a/.github/workflows/publish-ml-docker.yml b/.github/workflows/publish-ml-docker.yml deleted file mode 100644 index d2ae65e..0000000 --- a/.github/workflows/publish-ml-docker.yml +++ /dev/null @@ -1,54 +0,0 @@ -name: 배포 - -on: - push: - branches: - - ml-initialization - -jobs: - - push: - env: - working-directory: ./ml - runs-on: ubuntu-latest - if: github.event_name == 'push' - - - steps: - - uses: actions/checkout@v2 - - - name: create dotenv - working-directory: ${{env.working-directory}} - run: echo "${{ secrets.ENV }}" > .env - - - name: Log into GitHub Container Registry - uses: docker/login-action@v1 - with: - registry: ghcr.io - username: eldpswp99 - password: ${{ secrets.DOCKER_TOKEN }} - - - name: Build image - working-directory: ${{env.working-directory}} - run: | - docker build -f Dockerfile .\ - -t ghcr.io/snuhcs-course/swpp-2023-project-team-13/ml-image:${GITHUB_SHA}\ - -t ghcr.io/snuhcs-course/swpp-2023-project-team-13/ml-image:latest - - name: Push image - working-directory: ${{env.working-directory}} - run: | - docker push ghcr.io/snuhcs-course/swpp-2023-project-team-13/ml-image:${GITHUB_SHA} - docker push ghcr.io/snuhcs-course/swpp-2023-project-team-13/ml-image:latest - deploy: - needs: push - name: 배포 - runs-on: [ self-hosted ] - steps: - - name: Log into GitHub Container Registry - uses: docker/login-action@v1 - with: - registry: ghcr.io - username: eldpswp99 - password: ${{ secrets.DOCKER_TOKEN }} - - name: Docker compose up - run: SHA=${GITHUB_SHA} docker compose -f ~/docker-compose.yml up -d fooriend-ml \ No newline at end of file From f886e68f5411c01e14f6b1ac22455c95358bcae1 Mon Sep 17 00:00:00 2001 From: ozeeeno Date: Thu, 2 Nov 2023 16:57:04 +0900 Subject: [PATCH 05/26] =?UTF-8?q?ml=20:=20=EB=A6=AC=EB=B7=B0=EA=B8=8D?= =?UTF-8?q?=EB=B6=80=EC=A0=95=20=EB=B6=84=EB=A5=98=201=EC=B0=A8=20?= =?UTF-8?q?=EC=99=84=EC=84=B1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ml/review/execution.py | 45 ----- ml/review/model.py | 94 ++++----- ml/review/predict.py | 47 +++++ ml/review/preprocessing.py | 155 +-------------- ml/review/tokenization.py | 393 +++++++++++++------------------------ ml/review/train.py | 206 ++++++++++--------- 6 files changed, 339 insertions(+), 601 deletions(-) delete mode 100644 ml/review/execution.py create mode 100644 ml/review/predict.py diff --git a/ml/review/execution.py b/ml/review/execution.py deleted file mode 100644 index 3644f49..0000000 --- a/ml/review/execution.py +++ /dev/null @@ -1,45 +0,0 @@ -import import_ipynb -import torch -from model import get_model -from preprocessing import data_to_token_ids, token_ids_to_mask -from tokenization import KoBertTokenizer - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -model = get_model(device, torch.cuda.is_available()) - - -def predict_sentence(input_str): - tokenizer = KoBertTokenizer.from_pretrained("monologg/kobert") - tokenized_data = [data_to_token_ids(tokenizer, input_str)] - attention_masks = [token_ids_to_mask(token_ids) for token_ids in tokenized_data] - - inputs_tensor = torch.tensor(tokenized_data).to(device) - masks_tensor = torch.tensor(attention_masks).to(device) - - outputs = model(inputs_tensor, attention_mask=masks_tensor) - - softmax = torch.nn.Softmax() - output = softmax(outputs) - - return output - - -if __name__ == "__main__": - while True: - input_str = input() - if input_str == "q": - break - - tokenizer = KoBertTokenizer.from_pretrained("monologg/kobert") - tokenized_data = [data_to_token_ids(tokenizer, input_str)] - attention_masks = [token_ids_to_mask(token_ids) for token_ids in tokenized_data] - - inputs_tensor = torch.tensor(tokenized_data).to(device) - masks_tensor = torch.tensor(attention_masks).to(device) - - outputs = model(inputs_tensor, attention_mask=masks_tensor) - - softmax = torch.nn.Softmax() - output = softmax(outputs) - print(output) diff --git a/ml/review/model.py b/ml/review/model.py index 1e18624..f005e6e 100644 --- a/ml/review/model.py +++ b/ml/review/model.py @@ -1,71 +1,49 @@ +import numpy as np import torch from torch import nn -from transformers import ( - BertConfig, - BertForSequenceClassification, - BertModel, - get_linear_schedule_with_warmup, -) +from review.tokenization import BERTSentenceTransform -class BertClassifier(nn.Module): - def __init__(self, num_labels=7): - super(BertClassifier, self).__init__() - self.bert = BertModel.from_pretrained("monologg/kobert") - self.linear = nn.Linear(768, num_labels) +class BERTClassifier(nn.Module): + def __init__(self, bert, hidden_size=768, num_classes=2, dr_rate=None, params=None): + super(BERTClassifier, self).__init__() + self.bert = bert + self.dr_rate = dr_rate - def forward(self, input_ids, attention_mask): - _, pooled_output = self.bert(input_ids=input_ids, attention_mask=attention_mask) - linear_output = self.linear(pooled_output) + self.classifier = nn.Linear(hidden_size, num_classes) + if dr_rate: + self.dropout = nn.Dropout(p=dr_rate) - return linear_output + def gen_attention_mask(self, token_ids, valid_length): + attention_mask = torch.zeros_like(token_ids) + for i, v in enumerate(valid_length): + attention_mask[i][:v] = 1 + return attention_mask.float() + def forward(self, token_ids, valid_length, segment_ids): + attention_mask = self.gen_attention_mask(token_ids, valid_length) -def BertModelInitialization(): - PATH = "model.pt" - model = BertClassifier() + _, pooler = self.bert( + input_ids=token_ids, + token_type_ids=segment_ids.long(), + attention_mask=attention_mask.float().to(token_ids.device), + ) + if self.dr_rate: + out = self.dropout(pooler) + return self.classifier(out) - torch.save(model.state_dict(), PATH) +class BERTDataset(Dataset): + def __init__(self, dataset, sent_idx, label_idx, bert_tokenizer, vocab, max_len, pad, pair): + transform = BERTSentenceTransform( + bert_tokenizer, max_seq_length=max_len, vocab=vocab, pad=pad, pair=pair + ) + self.sentences = [transform([i[sent_idx]]) for i in dataset] + self.labels = [np.int32(i[label_idx]) for i in dataset] -def get_model(device, cuda_available): - PATH = "model.pt" + def __getitem__(self, i): + return self.sentences[i] + (self.labels[i],) - model = BertClassifier() - - if cuda_available: - model.load_state_dict(torch.load(PATH), strict=False) - - model = model.to(device) - else: - model.load_state_dict(torch.load(PATH, map_location=device), strict=False) - - return model - - -def get_model_with_params(num_data, device, cuda_available): - model = get_model(device, cuda_available) - - optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5, eps=1e-8) - epochs = 5 - - total_steps = num_data * epochs - - scheduler = get_linear_schedule_with_warmup( - optimizer, num_warmup_steps=0, num_training_steps=total_steps - ) - criterion = nn.CrossEntropyLoss() - - return model, optimizer, scheduler, epochs, criterion - - -def main(): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - BertModelInitialization() - print(get_model_with_params(34388, device, torch.cuda.is_available())) - - -if __name__ == "__main__": - main() + def __len__(self): + return len(self.labels) diff --git a/ml/review/predict.py b/ml/review/predict.py new file mode 100644 index 0000000..e653fe8 --- /dev/null +++ b/ml/review/predict.py @@ -0,0 +1,47 @@ +import gluonnlp as nlp +import numpy as np +import torch +from kobert_tokenizer import KoBERTTokenizer +from model import BERTDataset + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +PATH = "" + + +def predict(predict_sentence): + max_len = 64 + batch_size = 64 + data = [predict_sentence, "0"] + dataset_another = [data] + ko_tokenizer = KoBERTTokenizer.from_pretrained("skt/kobert-base-v1") + ko_vocab = nlp.vocab.BERTVocab.from_sentencepiece( + ko_tokenizer.vocab_file, padding_token="[PAD]" + ) + + another_test = BERTDataset(dataset_another, 0, 1, ko_tokenizer, ko_vocab, max_len, True, False) + test_dataloader = torch.utils.data.DataLoader( + another_test, batch_size=batch_size, num_workers=5 + ) + model = torch.load(PATH) + model.eval() + + for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(test_dataloader): + token_ids = token_ids.long().to(device) + segment_ids = segment_ids.long().to(device) + + valid_length = valid_length + label = label.long().to(device) + + out = model(token_ids, valid_length, segment_ids) + + test_eval = [] + for i in out: + logits = i + logits = logits.detach().cpu().numpy() + + if np.argmax(logits) == 0: + test_eval.append("부정") + elif np.argmax(logits) == 1: + test_eval.append("긍정") + + return ">> 입력하신 문장은 " + test_eval[0] + "적 리뷰입니다." diff --git a/ml/review/preprocessing.py b/ml/review/preprocessing.py index 6352c49..5a2ab03 100644 --- a/ml/review/preprocessing.py +++ b/ml/review/preprocessing.py @@ -1,153 +1,18 @@ -import traceback - -import gluonnlp as nlp -import numpy as np import pandas as pd -import torch -import torch.nn.functional as F -import torch.optim as optim -from keras.preprocessing.sequence import pad_sequences from sklearn.model_selection import train_test_split -from torch import nn -from torch.utils.data import DataLoader, TensorDataset -from tqdm import tqdm, tqdm_notebook - - -def data_processing(raw_data): - emotion_list = ["긍정", "부정"] # 감정 라벨링 추가 가능 - - for i in range(len(emotion_list)): - raw_data.loc[raw_data.Emotion == emotion_list[i], "Emotion"] = i - - processed_data = pd.concat([raw_data["Sentence"], raw_data["Emotion"]], axis=1) - - processed_data.columns = ["sentence", "label"] - - return processed_data - - -def data_to_token_ids(tokenizer, single_sentence): - special_token_added = "[CLS] " + str(single_sentence) + " [SEP]" - - tokenized_text = tokenizer.tokenize(special_token_added) - - token_ids = [tokenizer.convert_tokens_to_ids(tokenized_text)] - - MAX_LEN = 128 - token_ids_padded = pad_sequences( - token_ids, maxlen=MAX_LEN, dtype="long", truncating="post", padding="post" - ) - token_ids_flatten = token_ids_padded.flatten() - return token_ids_flatten +def preprcessing(data): + data_list = [] -def token_ids_to_mask(token_ids): - mask = [float(i > 0) for i in token_ids] + for q, label in zip(data["review"], data["y"]): + data = [] + data.append(q) + data.append(str(label)) - return mask + data_list.append(data) - -def tokenize_processed_data(tokenizer, processed_dataset): - labels = processed_dataset["label"].to_numpy() - - labels = labels.astype(np.int) - - tokenized_data = [ - data_to_token_ids(tokenizer, processed_data) - for processed_data in processed_dataset["sentence"] - ] - - attention_masks = [token_ids_to_mask(token_ids) for token_ids in tokenized_data] - - return tokenized_data, labels, attention_masks - - -def split_into_train_validation(whole_data, whole_label, whole_masks): - print("length of whole_data : " + str(len(whole_data))) - - train_inputs, validation_inputs, train_labels, validation_labels = train_test_split( - whole_data, whole_label, random_state=2022, test_size=0.1 - ) - train_masks, validation_masks, _, _ = train_test_split( - whole_masks, whole_data, random_state=2022, test_size=0.1 - ) - - print("length of train_data : " + str(len(train_inputs))) - - return ( - train_inputs, - validation_inputs, - train_labels, - validation_labels, - train_masks, - validation_masks, + dataset_train, dataset_test = train_test_split( + data_list, test_size=0.2, shuffle=True, random_state=23 ) - - -def data_to_tensor(inputs, labels, masks): - inputs_tensor = torch.tensor(inputs) - labels_tensor = torch.tensor(labels) - masks_tensor = torch.tensor(masks) - return inputs_tensor, labels_tensor, masks_tensor - - -def tensor_to_dataloader(inputs, labels, masks, mode): - from torch.utils.data import RandomSampler, SequentialSampler - - batch_size = 32 - data = TensorDataset(inputs, masks, labels) - - if mode == "train": - sampler = RandomSampler(data) - else: - sampler = SequentialSampler(data) - - dataloader = DataLoader(data, sampler=sampler, batch_size=batch_size) - - return dataloader - - -def preprocessing(tokenizer, whole_dataset): - processed_dataset = data_processing(whole_dataset) - - tokenized_dataset, labels, attention_masks = tokenize_processed_data( - tokenizer, processed_dataset - ) - - ( - train_inputs, - validation_inputs, - train_labels, - validation_labels, - train_masks, - validation_masks, - ) = split_into_train_validation(tokenized_dataset, labels, attention_masks) - - train_inputs, train_labels, train_masks = data_to_tensor( - train_inputs, train_labels, train_masks - ) - validation_inputs, validation_labels, validation_masks = data_to_tensor( - validation_inputs, validation_labels, validation_masks - ) - - train_dataloader = tensor_to_dataloader(train_inputs, train_labels, train_masks, "train") - validation_dataloader = tensor_to_dataloader( - validation_inputs, validation_labels, validation_masks, "validation" - ) - - return train_dataloader, validation_dataloader - - -def main(): - from tokenization import KoBertTokenizer - - # whole_dataset = - - tokenizer = KoBertTokenizer.from_pretrained("monologg/kobert") - - # train, valid = preprocessing(tokenizer, whole_dataset) - - -if __name__ == "__main__": - main() + return dataset_train, dataset_test diff --git a/ml/review/tokenization.py b/ml/review/tokenization.py index 1fd4c38..c18bee0 100644 --- a/ml/review/tokenization.py +++ b/ml/review/tokenization.py @@ -14,268 +14,147 @@ # limitations under the License. """ Tokenization classes for KoBERT model """ - -import logging -import os -import unicodedata -from shutil import copyfile - -from transformers import PreTrainedTokenizer - -logger = logging.getLogger(__name__) - -VOCAB_FILES_NAMES = { - "vocab_file": "tokenizer_78b3253a26.model", - "vocab_txt": "vocab.txt", -} - -PRETRAINED_VOCAB_FILES_MAP = { - "vocab_file": { - "monologg/kobert": "https://s3.amazonaws.com/models.huggingface.co/bert/monologg/kobert/tokenizer_78b3253a26.model", - "monologg/kobert-lm": "https://s3.amazonaws.com/models.huggingface.co/bert/monologg/kobert-lm/tokenizer_78b3253a26.model", - "monologg/distilkobert": "https://s3.amazonaws.com/models.huggingface.co/bert/monologg/distilkobert/tokenizer_78b3253a26.model", - }, - "vocab_txt": { - "monologg/kobert": "https://s3.amazonaws.com/models.huggingface.co/bert/monologg/kobert/vocab.txt", - "monologg/kobert-lm": "https://s3.amazonaws.com/models.huggingface.co/bert/monologg/kobert-lm/vocab.txt", - "monologg/distilkobert": "https://s3.amazonaws.com/models.huggingface.co/bert/monologg/distilkobert/vocab.txt", - }, -} - -PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { - "monologg/kobert": 512, - "monologg/kobert-lm": 512, - "monologg/distilkobert": 512, -} - -PRETRAINED_INIT_CONFIGURATION = { - "monologg/kobert": {"do_lower_case": False}, - "monologg/kobert-lm": {"do_lower_case": False}, - "monologg/distilkobert": {"do_lower_case": False}, -} - -SPIECE_UNDERLINE = "▁" - - -class KoBertTokenizer(PreTrainedTokenizer): - """ - SentencePiece based tokenizer. Peculiarities: - - requires `SentencePiece `_ +import gluonnlp as nlp +import numpy as np +from kobert_tokenizer import KoBERTTokenizer +from transformers import BertModel + +ko_tokenizer = KoBERTTokenizer.from_pretrained("skt/kobert-base-v1") +ko_bertmodel = BertModel.from_pretrained("skt/kobert-base-v1", return_dict=False) +ko_vocab = nlp.vocab.BERTVocab.from_sentencepiece(ko_tokenizer.vocab_file, padding_token="[PAD]") + + +class BERTSentenceTransform: + r"""BERT style data transformation. + + Parameters + ---------- + tokenizer : BERTTokenizer. + Tokenizer for the sentences. + max_seq_length : int. + Maximum sequence length of the sentences. + pad : bool, default True + Whether to pad the sentences to maximum length. + pair : bool, default True + Whether to transform sentences or sentence pairs. """ - vocab_files_names = VOCAB_FILES_NAMES - pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP - pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION - max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES + def __init__(self, tokenizer, max_seq_length, vocab, pad=True, pair=True): + self._tokenizer = tokenizer + self._max_seq_length = max_seq_length + self._pad = pad + self._pair = pair + self._vocab = vocab + + def __call__(self, line): + """Perform transformation for sequence pairs or single sequences. + + The transformation is processed in the following steps: + - tokenize the input sequences + - insert [CLS], [SEP] as necessary + - generate type ids to indicate whether a token belongs to the first + sequence or the second sequence. + - generate valid length + + For sequence pairs, the input is a tuple of 2 strings: + text_a, text_b. + + Inputs: + text_a: 'is this jacksonville ?' + text_b: 'no it is not' + Tokenization: + text_a: 'is this jack ##son ##ville ?' + text_b: 'no it is not .' + Processed: + tokens: '[CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]' + type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 + valid_length: 14 + + For single sequences, the input is a tuple of single string: + text_a. + + Inputs: + text_a: 'the dog is hairy .' + Tokenization: + text_a: 'the dog is hairy .' + Processed: + text_a: '[CLS] the dog is hairy . [SEP]' + type_ids: 0 0 0 0 0 0 0 + valid_length: 7 + + Parameters + ---------- + line: tuple of str + Input strings. For sequence pairs, the input is a tuple of 2 strings: + (text_a, text_b). For single sequences, the input is a tuple of single + string: (text_a,). + + Returns + ------- + np.array: input token ids in 'int32', shape (batch_size, seq_length) + np.array: valid length in 'int32', shape (batch_size,) + np.array: input token type ids in 'int32', shape (batch_size, seq_length) - def __init__( - self, - vocab_file, - vocab_txt, - do_lower_case=False, - remove_space=True, - keep_accents=False, - unk_token="[UNK]", - sep_token="[SEP]", - pad_token="[PAD]", - cls_token="[CLS]", - mask_token="[MASK]", - **kwargs, - ): - super().__init__( - unk_token=unk_token, - sep_token=sep_token, - pad_token=pad_token, - cls_token=cls_token, - mask_token=mask_token, - **kwargs, - ) - - # Build vocab - self.token2idx = dict() - self.idx2token = [] - with open(vocab_txt, "r", encoding="utf-8") as f: - for idx, token in enumerate(f): - token = token.strip() - self.token2idx[token] = idx - self.idx2token.append(token) - - try: - import sentencepiece as spm - except ImportError: - logger.warning( - "You need to install SentencePiece to use KoBertTokenizer: https://github.com/google/sentencepiece" - "pip install sentencepiece" - ) - - self.do_lower_case = do_lower_case - self.remove_space = remove_space - self.keep_accents = keep_accents - self.vocab_file = vocab_file - self.vocab_txt = vocab_txt - - self.sp_model = spm.SentencePieceProcessor() - self.sp_model.Load(vocab_file) - - @property - def vocab_size(self): - return len(self.idx2token) - - def get_vocab(self): - return dict(self.token2idx, **self.added_tokens_encoder) - - def __getstate__(self): - state = self.__dict__.copy() - state["sp_model"] = None - return state - - def __setstate__(self, d): - self.__dict__ = d - try: - import sentencepiece as spm - except ImportError: - logger.warning( - "You need to install SentencePiece to use KoBertTokenizer: https://github.com/google/sentencepiece" - "pip install sentencepiece" - ) - self.sp_model = spm.SentencePieceProcessor() - self.sp_model.Load(self.vocab_file) - - def preprocess_text(self, inputs): - if self.remove_space: - outputs = " ".join(inputs.strip().split()) - else: - outputs = inputs - outputs = outputs.replace("``", '"').replace("''", '"') - - if not self.keep_accents: - outputs = unicodedata.normalize("NFKD", outputs) - outputs = "".join([c for c in outputs if not unicodedata.combining(c)]) - if self.do_lower_case: - outputs = outputs.lower() - - return outputs - - def _tokenize(self, text): - """Tokenize a string.""" - text = self.preprocess_text(text) - pieces = self.sp_model.encode(text, out_type=str) - new_pieces = [] - for piece in pieces: - if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit(): - cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, "")) - if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: - if len(cur_pieces[0]) == 1: - cur_pieces = cur_pieces[1:] - else: - cur_pieces[0] = cur_pieces[0][1:] - cur_pieces.append(piece[-1]) - new_pieces.extend(cur_pieces) - else: - new_pieces.append(piece) - - return new_pieces - - def _convert_token_to_id(self, token): - """Converts a token (str/unicode) in an id using the vocab.""" - return self.token2idx.get(token, self.token2idx[self.unk_token]) - - def _convert_id_to_token(self, index): - """Converts an index (integer) in a token (string/unicode) using the vocab.""" - return self.idx2token[index] - - def convert_tokens_to_string(self, tokens): - """Converts a sequence of tokens (strings for sub-words) in a single string.""" - out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() - return out_string - - def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): - """ - Build model inputs from a sequence or a pair of sequence for sequence classification tasks - by concatenating and adding special tokens. - A KoBERT sequence has the following format: - single sequence: [CLS] X [SEP] - pair of sequences: [CLS] A [SEP] B [SEP] """ - if token_ids_1 is None: - return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] - cls = [self.cls_token_id] - sep = [self.sep_token_id] - return cls + token_ids_0 + sep + token_ids_1 + sep - def get_special_tokens_mask( - self, token_ids_0, token_ids_1=None, already_has_special_tokens=False - ): - """ - Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding - special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods. - Args: - token_ids_0: list of ids (must not contain special tokens) - token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids - for sequence pairs - already_has_special_tokens: (default False) Set to True if the token list is already formated with - special tokens for the model - Returns: - A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token. - """ + # convert to unicode + text_a = line[0] + if self._pair: + assert len(line) == 2 + text_b = line[1] - if already_has_special_tokens: - if token_ids_1 is not None: - raise ValueError( - "You should not supply a second sequence if the provided sequence of " - "ids is already formated with special tokens for the model." - ) - return list( - map( - lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, - token_ids_0, - ) - ) + tokens_a = self._tokenizer.tokenize(text_a) + tokens_b = None - if token_ids_1 is not None: - return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] - return [1] + ([0] * len(token_ids_0)) + [1] + if self._pair: + tokens_b = self._tokenizer(text_b) - def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): - """ - Creates a mask from the two sequences passed to be used in a sequence-pair classification task. - A KoBERT sequence pair mask has the following format: - 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 - | first sequence | second sequence - if token_ids_1 is None, only returns the first portion of the mask (0's). - """ - sep = [self.sep_token_id] - cls = [self.cls_token_id] - if token_ids_1 is None: - return len(cls + token_ids_0 + sep) * [0] - return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] - - def save_vocabulary(self, save_directory): - """Save the sentencepiece vocabulary (copy original file) and special tokens file - to a directory. - """ - if not os.path.isdir(save_directory): - logger.error("Vocabulary path ({}) should be a directory".format(save_directory)) - return - - # 1. Save sentencepiece model - out_vocab_model = os.path.join(save_directory, VOCAB_FILES_NAMES["vocab_file"]) - - if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_model): - copyfile(self.vocab_file, out_vocab_model) - - # 2. Save vocab.txt - index = 0 - out_vocab_txt = os.path.join(save_directory, VOCAB_FILES_NAMES["vocab_txt"]) - with open(out_vocab_txt, "w", encoding="utf-8") as writer: - for token, token_index in sorted(self.token2idx.items(), key=lambda kv: kv[1]): - if index != token_index: - logger.warning( - "Saving vocabulary to {}: vocabulary indices are not consecutive." - " Please check that the vocabulary is not corrupted!".format(out_vocab_txt) - ) - index = token_index - writer.write(token + "\n") - index += 1 - - return out_vocab_model, out_vocab_txt + if tokens_b: + # Modifies `tokens_a` and `tokens_b` in place so that the total + # length is less than the specified length. + # Account for [CLS], [SEP], [SEP] with "- 3" + self._truncate_seq_pair(tokens_a, tokens_b, self._max_seq_length - 3) + else: + # Account for [CLS] and [SEP] with "- 2" + if len(tokens_a) > self._max_seq_length - 2: + tokens_a = tokens_a[0 : (self._max_seq_length - 2)] + + # The embedding vectors for `type=0` and `type=1` were learned during + # pre-training and are added to the wordpiece embedding vector + # (and position vector). This is not *strictly* necessary since + # the [SEP] token unambiguously separates the sequences, but it makes + # it easier for the model to learn the concept of sequences. + + # For classification tasks, the first vector (corresponding to [CLS]) is + # used as as the "sentence vector". Note that this only makes sense because + # the entire model is fine-tuned. + # vocab = self._tokenizer.vocab + vocab = self._vocab + tokens = [] + tokens.append(vocab.cls_token) + tokens.extend(tokens_a) + tokens.append(vocab.sep_token) + segment_ids = [0] * len(tokens) + + if tokens_b: + tokens.extend(tokens_b) + tokens.append(vocab.sep_token) + segment_ids.extend([1] * (len(tokens) - len(segment_ids))) + + input_ids = self._tokenizer.convert_tokens_to_ids(tokens) + + # The valid length of sentences. Only real tokens are attended to. + valid_length = len(input_ids) + + if self._pad: + # Zero-pad up to the sequence length. + padding_length = self._max_seq_length - valid_length + # use padding tokens for the rest + input_ids.extend([vocab[vocab.padding_token]] * padding_length) + segment_ids.extend([0] * padding_length) + + return ( + np.array(input_ids, dtype="int32"), + np.array(valid_length, dtype="int32"), + np.array(segment_ids, dtype="int32"), + ) diff --git a/ml/review/train.py b/ml/review/train.py index ea95a1b..51399c2 100644 --- a/ml/review/train.py +++ b/ml/review/train.py @@ -1,106 +1,120 @@ -import random -import time - +import gluonnlp as nlp import numpy as np import pandas as pd import torch -from model import BertModelInitialization, get_model, get_model_with_params +from kobert_tokenizer import KoBERTTokenizer from preprocessing import preprocessing +from torch import nn from tqdm.notebook import tqdm +from transformers import AdamW, BertModel +from transformers.optimization import get_cosine_schedule_with_warmup +from review.model import BERTClassifier, BERTDataset -def accuracy(preds, labels): - f_pred = np.argmax(preds, axis=1).flatten() - f_labels = labels.flatten() - return np.sum(f_pred == f_labels) / len(f_labels) - - -seed_val = 2023 - -random.seed(seed_val) -np.random.seed(seed_val) -torch.manual_seed(seed_val) -torch.cuda.manual_seed_all(seed_val) - -from tokenization import KoBertTokenizer - -# whole_dataset = - -tokenizer = KoBertTokenizer.from_pretrained("monologg/kobert") - -train_dataloader, validation_dataloader = preprocessing(tokenizer, whole_dataset) - -# 초기 사용시에만 선언 -BertModelInitialization() +data = pd.read_csv("review_data.csv") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -model, optimizer, scheduler, epochs, criterion = get_model_with_params( - len(train_dataloader), device, torch.cuda.is_available() -) - -model.zero_grad() - -for epoch_i in range(epochs): - print("") - print("========{:}번째 Epoch / 전체 {:}회 ========".format(epoch_i + 1, epochs)) - print("훈련 중") - - t0 = time.time() - total_loss = 0 - sum_loss = 0 - model.train() # 훈련모드 - - for step, batch in enumerate(tqdm(train_dataloader)): - if step % 50 == 0: - print("{}번째 까지의 평균 loss : {}".format(step, sum_loss / 50)) - sum_loss = 0 - - batch = tuple(t.to(device) for t in batch) - b_input_ids, b_input_mask, b_labels = batch - - outputs = model(b_input_ids, b_input_mask) - - loss = criterion(outputs, b_labels.long()) - total_loss += loss.item() - sum_loss += loss.item() - - loss.backward() - torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) - optimizer.step() - scheduler.step() - model.zero_grad() - - avg_train_loss = total_loss / len(train_dataloader) - print("") - print(" Average training loss: {0:.2f}".format(avg_train_loss)) - - print("검증 중") - - t0 = time.time() - model.eval() - - eval_loss, eval_accuracy = 0, 0 - nb_eval_steps, nb_eval_examples = 0, 0 - - for batch in validation_dataloader: - batch = tuple(t.to(device) for t in batch) - - b_input_ids, b_input_mask, b_labels = batch - - with torch.no_grad(): - outputs = model(b_input_ids, attention_mask=b_input_mask) - - outputs = outputs.detach().cpu().numpy() - label_ids = b_labels.to("cpu").numpy() - - tmp_eval_accuracy = accuracy(outputs, label_ids) - eval_accuracy += tmp_eval_accuracy - nb_eval_steps += 1 - - print(" Accuracy: {0:.4f}".format(eval_accuracy / nb_eval_steps)) - -PATH = "model.pt" -torch.save(model.state_dict(), PATH) - -print("Training complete") +PATH = "" + + +def calc_accuracy(X, Y): + max_vals, max_indices = torch.max(X, 1) + train_acc = (max_indices == Y).sum().data.cpu().numpy() / max_indices.size()[0] + return train_acc + + +max_len = 64 +batch_size = 64 +warmup_ratio = 0.1 +num_epochs = 5 +max_grad_norm = 1 +log_interval = 200 +learning_rate = 5e-5 + +ko_tokenizer = KoBERTTokenizer.from_pretrained("skt/kobert-base-v1") +ko_bertmodel = BertModel.from_pretrained("skt/kobert-base-v1", return_dict=False) +ko_vocab = nlp.vocab.BERTVocab.from_sentencepiece(ko_tokenizer.vocab_file, padding_token="[PAD]") + + +def train(data): + dataset_train, dataset_test = preprocessing(data) + data_train = BERTDataset(dataset_train, 0, 1, ko_tokenizer, ko_vocab, max_len, True, False) + data_test = BERTDataset(dataset_test, 0, 1, ko_tokenizer, ko_vocab, max_len, True, False) + + train_dataloader = torch.utils.data.DataLoader( + data_train, batch_size=batch_size, num_workers=5 + ) + test_dataloader = torch.utils.data.DataLoader(data_test, batch_size=batch_size, num_workers=5) + + model = BERTClassifier(ko_bertmodel, dr_rate=0.5).to(device) + no_decay = ["bias", "LayerNorm.weight"] + optimizer_grouped_parameters = [ + { + "params": [ + p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay) + ], + "weight_decay": 0.01, + }, + { + "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], + "weight_decay": 0.0, + }, + ] + + optimizer = AdamW(optimizer_grouped_parameters, lr=learning_rate) + loss_fn = nn.CrossEntropyLoss() + + t_total = len(train_dataloader) * num_epochs + warmup_step = int(t_total * warmup_ratio) + + scheduler = get_cosine_schedule_with_warmup( + optimizer, num_warmup_steps=warmup_step, num_training_steps=t_total + ) + + train_history = [] + test_history = [] + loss_history = [] + + for e in range(num_epochs): + train_acc = 0.0 + test_acc = 0.0 + model.train() + for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate( + tqdm(train_dataloader) + ): + optimizer.zero_grad() + token_ids = token_ids.long().to(device) + segment_ids = segment_ids.long().to(device) + valid_length = valid_length + label = label.long().to(device) + out = model(token_ids, valid_length, segment_ids) + + loss = loss_fn(out, label) + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm) + optimizer.step() + scheduler.step() # Update learning rate schedule + train_acc += calc_accuracy(out, label) + if batch_id % log_interval == 0: + print( + "epoch {} batch id {} loss {} train acc {}".format( + e + 1, batch_id + 1, loss.data.cpu().numpy(), train_acc / (batch_id + 1) + ) + ) + train_history.append(train_acc / (batch_id + 1)) + loss_history.append(loss.data.cpu().numpy()) + print("epoch {} train acc {}".format(e + 1, train_acc / (batch_id + 1))) + model.eval() + for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate( + tqdm(test_dataloader) + ): + token_ids = token_ids.long().to(device) + segment_ids = segment_ids.long().to(device) + valid_length = valid_length + label = label.long().to(device) + out = model(token_ids, valid_length, segment_ids) + test_acc += calc_accuracy(out, label) + print("epoch {} test acc {}".format(e + 1, test_acc / (batch_id + 1))) + test_history.append(test_acc / (batch_id + 1)) + torch.save(model, PATH) From c7412995de3f755ee5ac249aa9b5dd983e5b4b3d Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Thu, 2 Nov 2023 17:09:44 +0900 Subject: [PATCH 06/26] server update --- ml/.gitignore | 4 +++- ml/main.py | 16 +++++++++++----- 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/ml/.gitignore b/ml/.gitignore index bb0600d..30f3b29 100644 --- a/ml/.gitignore +++ b/ml/.gitignore @@ -183,4 +183,6 @@ pyrightconfig.json pyvenv.cfg pip-selfcheck.json -# End of https://www.toptal.com/developers/gitignore/api/python,venv \ No newline at end of file +# End of https://www.toptal.com/developers/gitignore/api/python,venv + +pictures/* \ No newline at end of file diff --git a/ml/main.py b/ml/main.py index a6524c9..91d65bc 100644 --- a/ml/main.py +++ b/ml/main.py @@ -1,9 +1,9 @@ from io import BytesIO import requests -from PIL.Image import Image +import uuid +from PIL import Image from fastapi import FastAPI -from review.execution import predict_sentence app = FastAPI() @@ -14,17 +14,23 @@ async def root(): @app.get("/ocr") -async def receipt_ocr(url: str): +async def receipt_ocr(url_uuid: str): + url = "https://d2ghbk063u2bdn.cloudfront.net/" + url_uuid + print(url) + image_res = requests.get(url) image = Image.open(BytesIO(image_res.content)) jpg_image = image.convert('RGB') + uuid_string = uuid.uuid4() + jpg_image.save('pictures/' + str(uuid_string) + '.jpg') return {"ocr": "통닭치킨"} @app.get("/review") async def review_classification(review_str: str): - res = predict_sentence(review_str) - return {"output": res} + # res = predict_sentence(review_str) + # return {"output": res} + pass From e435c22dd9a093a470eccaa61586c6bde88cbe92 Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Thu, 2 Nov 2023 17:16:55 +0900 Subject: [PATCH 07/26] rollback workflow to main --- .github/workflows/publish-backend-docker.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/publish-backend-docker.yml b/.github/workflows/publish-backend-docker.yml index 6a4123d..05f485f 100644 --- a/.github/workflows/publish-backend-docker.yml +++ b/.github/workflows/publish-backend-docker.yml @@ -3,7 +3,7 @@ name: 배포 on: push: branches: - - ml-initialization + - main jobs: @@ -51,4 +51,4 @@ jobs: username: eldpswp99 password: ${{ secrets.DOCKER_TOKEN }} - name: Docker compose up - run: SHA=${GITHUB_SHA} docker compose -f ~/docker-compose.yml up -d \ No newline at end of file + run: SHA=${GITHUB_SHA} docker compose -f ~/docker-compose.yml up -d From ccfa22f8cf548a76d990ffe4cf8aa012a1e38e3b Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Thu, 2 Nov 2023 17:53:05 +0900 Subject: [PATCH 08/26] user predic --- ml/main.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/ml/main.py b/ml/main.py index 91d65bc..b2bebf5 100644 --- a/ml/main.py +++ b/ml/main.py @@ -4,6 +4,7 @@ import uuid from PIL import Image from fastapi import FastAPI +from review.predict import predict app = FastAPI() @@ -29,8 +30,6 @@ async def receipt_ocr(url_uuid: str): @app.get("/review") async def review_classification(review_str: str): - # res = predict_sentence(review_str) - # return {"output": res} - pass + return predict(review_str) From 09131bbcf9f389c971be4d28ae9a2492cfcf600f Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Thu, 2 Nov 2023 17:56:35 +0900 Subject: [PATCH 09/26] fix import --- ml/review/predict.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ml/review/predict.py b/ml/review/predict.py index e653fe8..a627e18 100644 --- a/ml/review/predict.py +++ b/ml/review/predict.py @@ -2,7 +2,7 @@ import numpy as np import torch from kobert_tokenizer import KoBERTTokenizer -from model import BERTDataset +from review.model import BERTDataset device = torch.device("cuda" if torch.cuda.is_available() else "cpu") PATH = "" From 81a64796b39453cb0574b9f641a6df6473641629 Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Thu, 2 Nov 2023 17:59:55 +0900 Subject: [PATCH 10/26] fix import --- ml/review/model.py | 1 + 1 file changed, 1 insertion(+) diff --git a/ml/review/model.py b/ml/review/model.py index f005e6e..9c68979 100644 --- a/ml/review/model.py +++ b/ml/review/model.py @@ -1,6 +1,7 @@ import numpy as np import torch from torch import nn +from torch.utils.data import Dataset from review.tokenization import BERTSentenceTransform From 21aef7c7346f170a26113b56ddffeaac00934835 Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Thu, 2 Nov 2023 18:34:21 +0900 Subject: [PATCH 11/26] train --- ml/dto.py | 8 ++++++++ ml/main.py | 17 ++++++++--------- ml/review/train.py | 4 ++-- 3 files changed, 18 insertions(+), 11 deletions(-) create mode 100644 ml/dto.py diff --git a/ml/dto.py b/ml/dto.py new file mode 100644 index 0000000..a414756 --- /dev/null +++ b/ml/dto.py @@ -0,0 +1,8 @@ +from pydantic import BaseModel + + +class OcrModel(BaseModel): + image_url: str + +class ReviewModel(BaseModel): + review: str diff --git a/ml/main.py b/ml/main.py index b2bebf5..1e32477 100644 --- a/ml/main.py +++ b/ml/main.py @@ -4,6 +4,8 @@ import uuid from PIL import Image from fastapi import FastAPI + +from dto import OcrModel, ReviewModel from review.predict import predict app = FastAPI() @@ -14,12 +16,9 @@ async def root(): return {"message": "Hello World"} -@app.get("/ocr") -async def receipt_ocr(url_uuid: str): - url = "https://d2ghbk063u2bdn.cloudfront.net/" + url_uuid - print(url) - - image_res = requests.get(url) +@app.post("/ocr") +async def receipt_ocr(data: OcrModel): + image_res = requests.get(data.image_url) image = Image.open(BytesIO(image_res.content)) jpg_image = image.convert('RGB') uuid_string = uuid.uuid4() @@ -28,8 +27,8 @@ async def receipt_ocr(url_uuid: str): return {"ocr": "통닭치킨"} -@app.get("/review") -async def review_classification(review_str: str): - return predict(review_str) +@app.post("/review") +async def review_classification(data: ReviewModel): + return predict(data.review) diff --git a/ml/review/train.py b/ml/review/train.py index 51399c2..e827fdb 100644 --- a/ml/review/train.py +++ b/ml/review/train.py @@ -3,7 +3,7 @@ import pandas as pd import torch from kobert_tokenizer import KoBERTTokenizer -from preprocessing import preprocessing +from review.preprocessing import preprocessing from torch import nn from tqdm.notebook import tqdm from transformers import AdamW, BertModel @@ -15,7 +15,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -PATH = "" +PATH = "../model" def calc_accuracy(X, Y): From 1abd048dd75881648f66364a485a1405e9c779ab Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Thu, 2 Nov 2023 18:38:46 +0900 Subject: [PATCH 12/26] fix typo --- ml/review/preprocessing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ml/review/preprocessing.py b/ml/review/preprocessing.py index 5a2ab03..028e847 100644 --- a/ml/review/preprocessing.py +++ b/ml/review/preprocessing.py @@ -2,7 +2,7 @@ from sklearn.model_selection import train_test_split -def preprcessing(data): +def preprocessing(data): data_list = [] for q, label in zip(data["review"], data["y"]): From f4d6a96e75f8360891d49a24e2ec60c4d3db1672 Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Thu, 2 Nov 2023 18:40:46 +0900 Subject: [PATCH 13/26] fix import --- ml/review/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ml/review/train.py b/ml/review/train.py index e827fdb..9c19bc4 100644 --- a/ml/review/train.py +++ b/ml/review/train.py @@ -3,7 +3,7 @@ import pandas as pd import torch from kobert_tokenizer import KoBERTTokenizer -from review.preprocessing import preprocessing +from preprocessing import preprocessing from torch import nn from tqdm.notebook import tqdm from transformers import AdamW, BertModel From 91f215a3b43f72c42c10c4c457dc80ac2301bcc8 Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Thu, 2 Nov 2023 18:42:38 +0900 Subject: [PATCH 14/26] fix import --- ml/review/model.py | 2 +- ml/review/predict.py | 4 ++-- ml/review/train.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/ml/review/model.py b/ml/review/model.py index 9c68979..62fefa7 100644 --- a/ml/review/model.py +++ b/ml/review/model.py @@ -3,7 +3,7 @@ from torch import nn from torch.utils.data import Dataset -from review.tokenization import BERTSentenceTransform +from tokenization import BERTSentenceTransform class BERTClassifier(nn.Module): diff --git a/ml/review/predict.py b/ml/review/predict.py index a627e18..674e6bf 100644 --- a/ml/review/predict.py +++ b/ml/review/predict.py @@ -2,10 +2,10 @@ import numpy as np import torch from kobert_tokenizer import KoBERTTokenizer -from review.model import BERTDataset +from model import BERTDataset device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -PATH = "" +PATH = "../model" def predict(predict_sentence): diff --git a/ml/review/train.py b/ml/review/train.py index 9c19bc4..0b8d486 100644 --- a/ml/review/train.py +++ b/ml/review/train.py @@ -9,7 +9,7 @@ from transformers import AdamW, BertModel from transformers.optimization import get_cosine_schedule_with_warmup -from review.model import BERTClassifier, BERTDataset +from model 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56638eb..95f12d5 100644 --- a/ml/ocr/execution.py +++ b/ml/ocr/execution.py @@ -1,6 +1,7 @@ import json import os import platform +import re import time import uuid @@ -17,100 +18,63 @@ secret_key = os.environ.get("secret_key") -def plt_imshow(title="image", img=None, figsize=(8, 5)): - plt.figure(figsize=figsize) +path = "assets/receipt.jpeg" - if type(img) == list: - if type(title) == list: - titles = title - else: - titles = [] - for i in range(len(img)): - titles.append(title) +def ocr_receipt(path): + files = [("file", open(path, "rb"))] - for i in range(len(img)): - if len(img[i].shape) <= 2: - rgbImg = cv2.cvtColor(img[i], cv2.COLOR_GRAY2RGB) - else: - rgbImg = cv2.cvtColor(img[i], cv2.COLOR_BGR2RGB) + request_json = { + "images": [{"format": "jpg", "name": "demo"}], + "requestId": str(uuid.uuid4()), + "version": "V2", + "timestamp": int(round(time.time() * 1000)), + } - plt.subplot(1, len(img), i + 1), plt.imshow(rgbImg) - plt.title(titles[i]) - plt.xticks([]), plt.yticks([]) + payload = {"message": json.dumps(request_json).encode("UTF-8")} - plt.show() - else: - if len(img.shape) < 3: - rgbImg = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) - else: - rgbImg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + headers = { + "X-OCR-SECRET": secret_key, + } - plt.imshow(rgbImg) - plt.title(title) - plt.xticks([]), plt.yticks([]) - plt.show() + response = requests.request("POST", api_url, headers=headers, data=payload, files=files) + response_body = json.loads(response.text) -def put_text(image, text, x, y, color=(0, 255, 0), font_size=22): - if type(image) == np.ndarray: - color_coverted = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - image = Image.fromarray(color_coverted) + images = response_body["images"] + images_receipt = images[0].get("receipt") - if platform.system() == "Darwin": - font = "AppleGothic.ttf" - elif platform.system() == "Windows": - font = "malgun.ttf" + receipt_title = images_receipt["result"]["storeInfo"]["name"]["text"] + receipt_address = images_receipt["result"]["storeInfo"]["addresses"][0]["text"] + receipt_date = images_receipt["result"]["paymentInfo"]["date"]["text"] - image_font = ImageFont.truetype(font, font_size) - font = ImageFont.load_default() - draw = ImageDraw.Draw(image) + sub_results = images_receipt["result"]["subResults"] + receipt_menu = [] + for sub_result in sub_results: + items = sub_result.get("items", []) + for item in items: + menu_name = item.get("name", {}).get("text", "") + receipt_menu.append(menu_name) - draw.text((x, y), text, font=image_font, fill=color) + receipt_price = int( + float( + re.sub( + r"[^\uAC00-\uD7A30-9a-zA-Z\s]", + "", + images_receipt["result"]["totalPrice"]["price"]["text"], + ) + ) + ) - numpy_image = np.array(image) - opencv_image = cv2.cvtColor(numpy_image, cv2.COLOR_RGB2BGR) + receipt_data = { + "title": receipt_title, + "address": receipt_address, + "date": receipt_date, + "menu": receipt_menu, + "price": receipt_price, + } - return opencv_image + return json.dumps(receipt_data, ensure_ascii=False) -path = "assets/example.jpeg" -files = [("file", open(path, "rb"))] - -request_json = { - "images": [{"format": "jpg", "name": "demo"}], - "requestId": str(uuid.uuid4()), - "version": "V2", - "timestamp": int(round(time.time() * 1000)), -} - -payload = {"message": json.dumps(request_json).encode("UTF-8")} - -headers = { - "X-OCR-SECRET": secret_key, -} - -response = requests.request("POST", api_url, headers=headers, data=payload, files=files) -result = response.json() - -img = cv2.imread(path) -roi_img = img.copy() - -for field in result["images"][0]["fields"]: - text = field["inferText"] - vertices_list = field["boundingPoly"]["vertices"] - pts = [tuple(vertice.values()) for vertice in vertices_list] - topLeft = [int(_) for _ in pts[0]] - topRight = [int(_) for _ in pts[1]] - bottomRight = [int(_) for _ in pts[2]] - bottomLeft = [int(_) for _ in pts[3]] - - cv2.line(roi_img, topLeft, topRight, (0, 255, 0), 2) - cv2.line(roi_img, topRight, bottomRight, (0, 255, 0), 2) - cv2.line(roi_img, bottomRight, bottomLeft, (0, 255, 0), 2) - cv2.line(roi_img, bottomLeft, topLeft, (0, 255, 0), 2) - roi_img = put_text(roi_img, text, topLeft[0], topLeft[1] - 10, font_size=30) - - print(text) - -plt_imshow(["Original", "ROI"], [img, roi_img], figsize=(16, 10)) +print(ocr_receipt(path)) From 7a3eb5173a9dbf537aa201c31377858a11983843 Mon Sep 17 00:00:00 2001 From: ozeeeno Date: Sat, 4 Nov 2023 19:32:00 +0900 Subject: [PATCH 16/26] resolve : review part resolve --- ml/review/model.py | 1 + ml/review/predict.py | 3 ++- ml/review/train.py | 2 +- 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/ml/review/model.py b/ml/review/model.py index f005e6e..9c68979 100644 --- a/ml/review/model.py +++ b/ml/review/model.py @@ -1,6 +1,7 @@ import numpy as np import torch from torch import nn +from torch.utils.data import Dataset from review.tokenization import BERTSentenceTransform diff --git a/ml/review/predict.py b/ml/review/predict.py index e653fe8..7727ac5 100644 --- a/ml/review/predict.py +++ b/ml/review/predict.py @@ -2,7 +2,8 @@ import numpy as np import torch from kobert_tokenizer import KoBERTTokenizer -from model import BERTDataset + +from review.model import BERTDataset device = torch.device("cuda" if torch.cuda.is_available() else "cpu") PATH = "" diff --git a/ml/review/train.py b/ml/review/train.py index 51399c2..e1d4b10 100644 --- a/ml/review/train.py +++ b/ml/review/train.py @@ -15,7 +15,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -PATH = "" +PATH = "/review" def calc_accuracy(X, Y): From 1385e3b03d84591be0518446b401ce297d2b806f Mon Sep 17 00:00:00 2001 From: ozeeeno Date: Sun, 5 Nov 2023 01:10:11 +0900 Subject: [PATCH 17/26] implement : for model execution --- ml/review/predict.py | 66 ++++++++++++++++++++------------------------ 1 file changed, 30 insertions(+), 36 deletions(-) diff --git a/ml/review/predict.py b/ml/review/predict.py index e95e854..7f8cf56 100644 --- a/ml/review/predict.py +++ b/ml/review/predict.py @@ -1,48 +1,42 @@ -import gluonnlp as nlp -import numpy as np import torch -from kobert_tokenizer import KoBERTTokenizer +from torch import nn +from transformers import AutoTokenizer, BertModel -from review.model import BERTDataset +tokenizer = AutoTokenizer.from_pretrained("WhitePeak/bert-base-cased-Korean-sentiment") -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -PATH = "../model" +device = "cuda" if torch.cuda.is_available() else "cpu" +model = torch.load("model.pt", map_location=device) -def predict(predict_sentence): - max_len = 64 - batch_size = 64 - data = [predict_sentence, "0"] - dataset_another = [data] - ko_tokenizer = KoBERTTokenizer.from_pretrained("skt/kobert-base-v1") - ko_vocab = nlp.vocab.BERTVocab.from_sentencepiece( - ko_tokenizer.vocab_file, padding_token="[PAD]" - ) +class CustomModel(nn.Module): + def __init__(self, num_classes=2): + super(CustomModel, self).__init__() + self.bert = BertModel.from_pretrained("bert-base-uncased") + self.dropout = nn.Dropout(0.1) + self.classifier = nn.Linear(768, num_classes) - another_test = BERTDataset(dataset_another, 0, 1, ko_tokenizer, ko_vocab, max_len, True, False) - test_dataloader = torch.utils.data.DataLoader( - another_test, batch_size=batch_size, num_workers=5 - ) - model = torch.load(PATH) - model.eval() + def forward(self, input_ids, attention_mask): + outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) + pooled_output = outputs.pooler_output + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + return logits - for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(test_dataloader): - token_ids = token_ids.long().to(device) - segment_ids = segment_ids.long().to(device) - valid_length = valid_length - label = label.long().to(device) +def predict_review(sentence): + inputs = tokenizer(sentence, return_tensors="pt") - out = model(token_ids, valid_length, segment_ids) + outputs = model(**inputs) - test_eval = [] - for i in out: - logits = i - logits = logits.detach().cpu().numpy() + probabilities = torch.nn.functional.softmax(outputs.logits, dim=1) - if np.argmax(logits) == 0: - test_eval.append("부정") - elif np.argmax(logits) == 1: - test_eval.append("긍정") + predicted_class = torch.argmax(probabilities, dim=1).item() - return ">> 입력하신 문장은 " + test_eval[0] + "적 리뷰입니다." + result = [] + + if predicted_class == 1: + result.append("긍정") + else: + result.append("부정") + + return result[0] From 25b950917b6e647c811927cbf8a3c4172a392969 Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Mon, 6 Nov 2023 14:15:13 +0900 Subject: [PATCH 18/26] preset ml server interaction --- backend/package-lock.json | 41 +++++++++++++++++++--- backend/package.json | 1 + backend/src/review/models/review.entity.ts | 3 ++ backend/src/review/review.service.ts | 13 +++++++ ml/.gitignore | 3 +- 5 files changed, 55 insertions(+), 6 deletions(-) diff --git a/backend/package-lock.json b/backend/package-lock.json index 0da5c5e..0b7c84c 100644 --- a/backend/package-lock.json +++ b/backend/package-lock.json @@ -21,6 +21,7 @@ "@types/bcryptjs": "^2.4.5", "@types/jsonwebtoken": "^9.0.3", "@types/multer": "^1.4.8", + "axios": "^1.6.0", "bcryptjs": "^2.4.3", "class-transformer": "^0.5.1", "class-validator": "^0.14.0", @@ -4323,8 +4324,17 @@ "node_modules/asynckit": { "version": "0.4.0", "resolved": "https://registry.npmjs.org/asynckit/-/asynckit-0.4.0.tgz", - "integrity": "sha512-Oei9OH4tRh0YqU3GxhX79dM/mwVgvbZJaSNaRk+bshkj0S5cfHcgYakreBjrHwatXKbz+IoIdYLxrKim2MjW0Q==", - "dev": true + "integrity": "sha512-Oei9OH4tRh0YqU3GxhX79dM/mwVgvbZJaSNaRk+bshkj0S5cfHcgYakreBjrHwatXKbz+IoIdYLxrKim2MjW0Q==" + }, + "node_modules/axios": { + "version": "1.6.0", + "resolved": "https://registry.npmjs.org/axios/-/axios-1.6.0.tgz", + "integrity": "sha512-EZ1DYihju9pwVB+jg67ogm+Tmqc6JmhamRN6I4Zt8DfZu5lbcQGw3ozH9lFejSJgs/ibaef3A9PMXPLeefFGJg==", + "dependencies": { + "follow-redirects": "^1.15.0", + "form-data": "^4.0.0", + "proxy-from-env": "^1.1.0" + } }, "node_modules/babel-jest": { "version": "29.7.0", @@ -5064,7 +5074,6 @@ "version": "1.0.8", "resolved": "https://registry.npmjs.org/combined-stream/-/combined-stream-1.0.8.tgz", "integrity": "sha512-FQN4MRfuJeHf7cBbBMJFXhKSDq+2kAArBlmRBvcvFE5BB1HZKXtSFASDhdlz9zOYwxh8lDdnvmMOe/+5cdoEdg==", - "dev": true, "dependencies": { "delayed-stream": "~1.0.0" }, @@ -5497,7 +5506,6 @@ "version": "1.0.0", "resolved": "https://registry.npmjs.org/delayed-stream/-/delayed-stream-1.0.0.tgz", "integrity": "sha512-ZySD7Nf91aLB0RxL4KGrKHBXl7Eds1DAmEdcoVawXnLD7SDhpNgtuII2aAkg7a7QS41jxPSZ17p4VdGnMHk3MQ==", - "dev": true, "engines": { "node": ">=0.4.0" } @@ -6355,6 +6363,25 @@ "integrity": "sha512-36yxDn5H7OFZQla0/jFJmbIKTdZAQHngCedGxiMmpNfEZM0sdEeT+WczLQrjK6D7o2aiyLYDnkw0R3JK0Qv1RQ==", "dev": true }, + "node_modules/follow-redirects": { + "version": "1.15.3", + "resolved": "https://registry.npmjs.org/follow-redirects/-/follow-redirects-1.15.3.tgz", + "integrity": "sha512-1VzOtuEM8pC9SFU1E+8KfTjZyMztRsgEfwQl44z8A25uy13jSzTj6dyK2Df52iV0vgHCfBwLhDWevLn95w5v6Q==", + "funding": [ + { + "type": "individual", + "url": "https://github.com/sponsors/RubenVerborgh" + } + ], + "engines": { + "node": ">=4.0" + }, + "peerDependenciesMeta": { + "debug": { + "optional": true + } + } + }, "node_modules/foreground-child": { "version": "3.1.1", "resolved": "https://registry.npmjs.org/foreground-child/-/foreground-child-3.1.1.tgz", @@ -6403,7 +6430,6 @@ "version": "4.0.0", "resolved": "https://registry.npmjs.org/form-data/-/form-data-4.0.0.tgz", "integrity": "sha512-ETEklSGi5t0QMZuiXoA/Q6vcnxcLQP5vdugSpuAyi6SVGi2clPPp+xgEhuMaHC+zGgn31Kd235W35f7Hykkaww==", - "dev": true, "dependencies": { "asynckit": "^0.4.0", "combined-stream": "^1.0.8", @@ -9090,6 +9116,11 @@ "node": ">= 0.10" } }, + "node_modules/proxy-from-env": { + "version": "1.1.0", + "resolved": "https://registry.npmjs.org/proxy-from-env/-/proxy-from-env-1.1.0.tgz", + "integrity": "sha512-D+zkORCbA9f1tdWRK0RaCR3GPv50cMxcrz4X8k5LTSUD1Dkw47mKJEZQNunItRTkWwgtaUSo1RVFRIG9ZXiFYg==" + }, "node_modules/pump": { "version": "3.0.0", "resolved": "https://registry.npmjs.org/pump/-/pump-3.0.0.tgz", diff --git a/backend/package.json b/backend/package.json index 01d6123..8d94ba3 100644 --- a/backend/package.json +++ b/backend/package.json @@ -32,6 +32,7 @@ "@types/bcryptjs": "^2.4.5", "@types/jsonwebtoken": "^9.0.3", "@types/multer": "^1.4.8", + "axios": "^1.6.0", "bcryptjs": "^2.4.3", "class-transformer": "^0.5.1", "class-validator": "^0.14.0", diff --git a/backend/src/review/models/review.entity.ts b/backend/src/review/models/review.entity.ts index 0b5100d..e125583 100644 --- a/backend/src/review/models/review.entity.ts +++ b/backend/src/review/models/review.entity.ts @@ -18,6 +18,9 @@ export class ReviewEntity extends IssuedAtMetaEntity { @Column({ type: 'varchar' }) content: string; + @Column({ type: 'boolean', default: false }) + isPositive: boolean; + get receiptImage() { return this.images.find((image) => image.isReceipt); } diff --git a/backend/src/review/review.service.ts b/backend/src/review/review.service.ts index 38695c3..01b1ff0 100644 --- a/backend/src/review/review.service.ts +++ b/backend/src/review/review.service.ts @@ -7,6 +7,7 @@ import { In } from 'typeorm'; import { ReviewEntity } from './models/review.entity'; import { ReviewAdjacentQueryDto } from './dtos/in-dtos/review-adjacent-query.dto'; import { getDistance } from 'geolib'; +// import axios from 'axios'; @Injectable() export class ReviewService { @@ -30,13 +31,25 @@ export class ReviewService { const receiptImage = images.find((image) => image.id === receiptImageId); if (receiptImage) { await receiptImage.markAsReceipt(); + // const receiptData = ( + // await axios.post(`${process.env.ML_URL}ocr`, { + // review: content, + // }) + // ).data; } + // const isPositive = ( + // await axios.post(`${process.env.ML_URL}review`, { + // review: content, + // }) + // ).data['result']; + return await ReviewEntity.create({ content, user, restaurant, images, + // isPositive, }).save(); } diff --git a/ml/.gitignore b/ml/.gitignore index 30f3b29..f9d46f4 100644 --- a/ml/.gitignore +++ b/ml/.gitignore @@ -185,4 +185,5 @@ pip-selfcheck.json # End of https://www.toptal.com/developers/gitignore/api/python,venv -pictures/* \ No newline at end of file +pictures/* +model/* \ No newline at end of file From 8f4895acdb12c8554d9cbcf79cb1d1151befb6cd Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Mon, 6 Nov 2023 14:16:38 +0900 Subject: [PATCH 19/26] gitignore model pt --- ml/.gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ml/.gitignore b/ml/.gitignore index f9d46f4..e3a0395 100644 --- a/ml/.gitignore +++ b/ml/.gitignore @@ -186,4 +186,5 @@ pip-selfcheck.json # End of https://www.toptal.com/developers/gitignore/api/python,venv pictures/* -model/* \ No newline at end of file +model/* +model.pt \ No newline at end of file From 508f509bd08f0749f66c8cac3d02506a2e346351 Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Mon, 6 Nov 2023 14:23:07 +0900 Subject: [PATCH 20/26] server use function in execution.pu --- ml/main.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/ml/main.py b/ml/main.py index 1e32477..d0ef5e7 100644 --- a/ml/main.py +++ b/ml/main.py @@ -6,7 +6,8 @@ from fastapi import FastAPI from dto import OcrModel, ReviewModel -from review.predict import predict +from ocr.execution import ocr_receipt +from review.predict import predict_review app = FastAPI() @@ -22,13 +23,15 @@ async def receipt_ocr(data: OcrModel): image = Image.open(BytesIO(image_res.content)) jpg_image = image.convert('RGB') uuid_string = uuid.uuid4() - jpg_image.save('pictures/' + str(uuid_string) + '.jpg') + receipt_path = 'pictures/' + str(uuid_string) + '.jpg' + jpg_image.save(receipt_path) + response = ocr_receipt(receipt_path) - return {"ocr": "통닭치킨"} + return response @app.post("/review") async def review_classification(data: ReviewModel): - return predict(data.review) + return predict_review(data.review) From 60550b44408f00ecd7975143b4307236b2bac41f Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Mon, 6 Nov 2023 14:25:20 +0900 Subject: [PATCH 21/26] remove print --- ml/ocr/execution.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ml/ocr/execution.py b/ml/ocr/execution.py index 95f12d5..770ae33 100644 --- a/ml/ocr/execution.py +++ b/ml/ocr/execution.py @@ -77,4 +77,4 @@ def ocr_receipt(path): return json.dumps(receipt_data, ensure_ascii=False) -print(ocr_receipt(path)) +# print(ocr_receipt(path)) From 2d39ea360175a4d9e702a8155df32c38e17f75b5 Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Mon, 6 Nov 2023 14:48:48 +0900 Subject: [PATCH 22/26] disable receipt price --- ml/ocr/execution.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/ml/ocr/execution.py b/ml/ocr/execution.py index 770ae33..3e9bdee 100644 --- a/ml/ocr/execution.py +++ b/ml/ocr/execution.py @@ -18,7 +18,7 @@ secret_key = os.environ.get("secret_key") -path = "assets/receipt.jpeg" +# path = "assets/receipt.jpeg" def ocr_receipt(path): @@ -56,22 +56,22 @@ def ocr_receipt(path): menu_name = item.get("name", {}).get("text", "") receipt_menu.append(menu_name) - receipt_price = int( - float( - re.sub( - r"[^\uAC00-\uD7A30-9a-zA-Z\s]", - "", - images_receipt["result"]["totalPrice"]["price"]["text"], - ) - ) - ) + # receipt_price = int( + # float( + # re.sub( + # r"[^\uAC00-\uD7A30-9a-zA-Z\s]", + # "", + # images_receipt["result"]["totalPrice"]["price"]["text"], + # ) + # ) + # ) receipt_data = { "title": receipt_title, "address": receipt_address, "date": receipt_date, "menu": receipt_menu, - "price": receipt_price, + # "price": receipt_price, } return json.dumps(receipt_data, ensure_ascii=False) From e7d697994b10cfe491565fe08862021b43714055 Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Mon, 6 Nov 2023 15:39:44 +0900 Subject: [PATCH 23/26] response type check --- ml/ocr/execution.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ml/ocr/execution.py b/ml/ocr/execution.py index 3e9bdee..a778952 100644 --- a/ml/ocr/execution.py +++ b/ml/ocr/execution.py @@ -74,7 +74,7 @@ def ocr_receipt(path): # "price": receipt_price, } - return json.dumps(receipt_data, ensure_ascii=False) + return receipt_data # print(ocr_receipt(path)) From 045610d953c2023af059c55ddaddb1b24342ed9a Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Tue, 7 Nov 2023 15:51:48 +0900 Subject: [PATCH 24/26] interaction with nest and ml server --- backend/src/review/ml-remote.ts | 19 +++ backend/src/review/models/review.entity.ts | 3 + backend/src/review/review.service.ts | 30 +++-- backend/src/test/review/create-review.spec.ts | 122 ++++++++++++++++-- 4 files changed, 150 insertions(+), 24 deletions(-) create mode 100644 backend/src/review/ml-remote.ts diff --git a/backend/src/review/ml-remote.ts b/backend/src/review/ml-remote.ts new file mode 100644 index 0000000..9f59b56 --- /dev/null +++ b/backend/src/review/ml-remote.ts @@ -0,0 +1,19 @@ +import axios from 'axios'; + +export async function getReceiptOcr(imageUrl: string) { + return ( + await axios.post(`${process.env.ML_URL}ocr`, { + image_url: imageUrl, + }) + ).data; +} + +export async function getReviewIsPositive(review: string) { + return ( + ( + await axios.post(`${process.env.ML_URL}review`, { + review, + }) + ).data['result'] === '긍정' + ); +} diff --git a/backend/src/review/models/review.entity.ts b/backend/src/review/models/review.entity.ts index e125583..856eab5 100644 --- a/backend/src/review/models/review.entity.ts +++ b/backend/src/review/models/review.entity.ts @@ -21,6 +21,9 @@ export class ReviewEntity extends IssuedAtMetaEntity { @Column({ type: 'boolean', default: false }) isPositive: boolean; + @Column('varchar', { array: true, default: [] }) + menu: string[]; + get receiptImage() { return this.images.find((image) => image.isReceipt); } diff --git a/backend/src/review/review.service.ts b/backend/src/review/review.service.ts index 01b1ff0..c6e39bc 100644 --- a/backend/src/review/review.service.ts +++ b/backend/src/review/review.service.ts @@ -1,4 +1,4 @@ -import { Injectable } from '@nestjs/common'; +import { BadRequestException, Injectable } from '@nestjs/common'; import { UserEntity } from '../user/models/user.entity'; import { CreateReviewDto } from './dtos/in-dtos/createReview.dto'; import { RestaurantRepository } from './repositories/restaurant.repository'; @@ -7,7 +7,7 @@ import { In } from 'typeorm'; import { ReviewEntity } from './models/review.entity'; import { ReviewAdjacentQueryDto } from './dtos/in-dtos/review-adjacent-query.dto'; import { getDistance } from 'geolib'; -// import axios from 'axios'; +import { getReceiptOcr, getReviewIsPositive } from './ml-remote'; @Injectable() export class ReviewService { @@ -29,27 +29,31 @@ export class ReviewService { id: In(imageIds.concat([receiptImageId ?? -1])), }); const receiptImage = images.find((image) => image.id === receiptImageId); + let menu = []; if (receiptImage) { await receiptImage.markAsReceipt(); - // const receiptData = ( - // await axios.post(`${process.env.ML_URL}ocr`, { - // review: content, - // }) - // ).data; + try { + const receiptData = await getReceiptOcr(receiptImage.url); + menu = receiptData['menu']; + } catch (e) { + throw new BadRequestException('잘못된 영수증입니다.'); + } } - // const isPositive = ( - // await axios.post(`${process.env.ML_URL}review`, { - // review: content, - // }) - // ).data['result']; + let isPositive = false; + try { + isPositive = await getReviewIsPositive(content); + } catch (e) { + throw new BadRequestException('리뷰 분석 중 오류가 발생했습니다.'); + } return await ReviewEntity.create({ content, user, restaurant, images, - // isPositive, + isPositive, + menu, }).save(); } diff --git a/backend/src/test/review/create-review.spec.ts b/backend/src/test/review/create-review.spec.ts index f7c514b..11f8d3c 100644 --- a/backend/src/test/review/create-review.spec.ts +++ b/backend/src/test/review/create-review.spec.ts @@ -11,6 +11,12 @@ import { validateDtoKeys } from '../utils'; import { RestaurantEntity } from '../../review/models/restaurant.entity'; import { RestaurantFixture } from '../fixture/restaurant.fixture'; import { ReviewEntity } from '../../review/models/review.entity'; +import axios from 'axios'; +import { getReceiptOcr, getReviewIsPositive } from '../../review/ml-remote'; +import { ImageFixture } from '../fixture/image.fixture'; + +// mock ml-remote.ts +jest.mock('../../review/ml-remote.ts'); describe('Create Review test', () => { let testServer: NestExpressApplication; @@ -49,6 +55,16 @@ describe('Create Review test', () => { .expect(HttpStatus.CREATED); accessToken = body.accessToken; + + jest.resetAllMocks(); + + (getReceiptOcr as jest.Mock).mockResolvedValue({ + title: '김태준의 탕탕집', + address: '서울 강남구 학동로4길 12. 1,2층(논현동)', + date: '2018/01/30', + menu: ['낙지탕탕이'], + }); + (getReviewIsPositive as jest.Mock).mockResolvedValue(true); }); it('unauthorized', async () => { @@ -100,9 +116,99 @@ describe('Create Review test', () => { expect(reviewEntity.user.id).toEqual(user.id); }); + it('긍정부정 / 영수증 반영', async () => { + const image = await ImageFixture.create({}); + + const receiptImageId = await ImageFixture.create({}); + + await supertest(testServer.getHttpServer()) + .post('/reviews') + .send({ + restaurant: { + googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', + latitude: 37.4224764, + longitude: -122.0842499, + }, + content: 'content', + imageIds: [image.id], + receiptImageId: receiptImageId.id, + }) + .set('Authorization', `Bearer ${accessToken}`) + .expect(HttpStatus.CREATED); + + const reviewEntity = await ReviewEntity.findOneOrFail({ + where: { + content: 'content', + }, + relations: { + restaurant: true, + images: true, + user: true, + }, + }); + + expect(reviewEntity.content).toEqual('content'); + expect(reviewEntity.restaurant.googleMapPlaceId).toEqual( + 'ChIJN1t_tDeuEmsRUsoyG83frY4', + ); + expect(reviewEntity.restaurant.latitude).toEqual(37.4224764); + expect(reviewEntity.restaurant.longitude).toEqual(-122.0842499); + expect(reviewEntity.user.id).toEqual(user.id); + expect(reviewEntity.isPositive).toEqual(true); + expect(reviewEntity.menu).toEqual(['낙지탕탕이']); + }); + + it('영수증 에러나면 400', async () => { + const image = await ImageFixture.create({}); + + const receiptImageId = await ImageFixture.create({}); + + jest.clearAllMocks(); + (getReceiptOcr as jest.Mock).mockRejectedValue(new Error('error')); + + await supertest(testServer.getHttpServer()) + .post('/reviews') + .send({ + restaurant: { + googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', + latitude: 37.4224764, + longitude: -122.0842499, + }, + content: 'content', + imageIds: [image.id], + receiptImageId: receiptImageId.id, + }) + .set('Authorization', `Bearer ${accessToken}`) + .expect(HttpStatus.BAD_REQUEST); + }); + + it('영수증 에러나면 400', async () => { + const image = await ImageFixture.create({}); + + const receiptImageId = await ImageFixture.create({}); + + jest.clearAllMocks(); + (getReviewIsPositive as jest.Mock).mockRejectedValue(new Error('error')); + + await supertest(testServer.getHttpServer()) + .post('/reviews') + .send({ + restaurant: { + googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', + latitude: 37.4224764, + longitude: -122.0842499, + }, + content: 'content', + imageIds: [image.id], + receiptImageId: receiptImageId.id, + }) + .set('Authorization', `Bearer ${accessToken}`) + .expect(HttpStatus.BAD_REQUEST); + }); + it('레스토랑 없었으면 생성한다.', async () => { const restaurantCount = await RestaurantEntity.count({}); - const { body } = await supertest(testServer.getHttpServer()) + await supertest(testServer.getHttpServer()) .post('/reviews') .send({ restaurant: { @@ -123,7 +229,7 @@ describe('Create Review test', () => { const restaurant = await RestaurantFixture.create({}); const restaurantCount = await RestaurantEntity.count({}); - const { body } = await supertest(testServer.getHttpServer()) + await supertest(testServer.getHttpServer()) .post('/reviews') .send({ restaurant: { @@ -166,18 +272,12 @@ describe('Create Review test', () => { content: 'content', }, ])('input validation', async (requestBody) => { - const { body } = await supertest(testServer.getHttpServer()) + await supertest(testServer.getHttpServer()) .post('/reviews') .send({ - restaurant: { - googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', - latitude: 37.4224764, - longitude: -122.0842499, - }, - content: 'content', - imageIds: [], + ...requestBody, }) .set('Authorization', `Bearer ${accessToken}`) - .expect(HttpStatus.CREATED); + .expect(HttpStatus.BAD_REQUEST); }); }); From 412a3f50074b9e0744f0c778053b8295491d7b08 Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Tue, 7 Nov 2023 16:47:54 +0900 Subject: [PATCH 25/26] test github workflow --- .github/workflows/test.yml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index ad9b490..641a9fb 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -2,9 +2,11 @@ name: 테스트 on: push: - branches: [ "main" ] + branches: + - "main" pull_request: - branches: [ "main" ] + branches: + - "main" jobs: test: From f4a5f5e33cd8cab2c2277af06bc59f655a424f5e Mon Sep 17 00:00:00 2001 From: eldpswp99 Date: Tue, 7 Nov 2023 17:16:55 +0900 Subject: [PATCH 26/26] add restaurant name --- .../src/review/dtos/in-dtos/restaurant.dto.ts | 3 +++ .../review/dtos/out-dtos/restaurant.dto.ts | 10 +++++++++- .../src/review/models/restaurant.entity.ts | 3 +++ .../repositories/restaurant.repository.ts | 3 ++- backend/src/test/review/create-review.spec.ts | 19 +++++++++++++++++++ backend/src/test/review/validateReviewList.ts | 8 +++++++- 6 files changed, 43 insertions(+), 3 deletions(-) diff --git a/backend/src/review/dtos/in-dtos/restaurant.dto.ts b/backend/src/review/dtos/in-dtos/restaurant.dto.ts index 95e9d18..b33bda7 100644 --- a/backend/src/review/dtos/in-dtos/restaurant.dto.ts +++ b/backend/src/review/dtos/in-dtos/restaurant.dto.ts @@ -4,6 +4,9 @@ export class RestaurantDto { @IsString() googleMapPlaceId: string; + @IsString() + name: string; + @IsNumber() latitude: number; diff --git a/backend/src/review/dtos/out-dtos/restaurant.dto.ts b/backend/src/review/dtos/out-dtos/restaurant.dto.ts index 0754756..eea0efa 100644 --- a/backend/src/review/dtos/out-dtos/restaurant.dto.ts +++ b/backend/src/review/dtos/out-dtos/restaurant.dto.ts @@ -5,11 +5,19 @@ export class RestaurantDto { private googleMapPlaceId: string; private longitude: number; private latitude: number; + private name: string; - constructor({ id, googleMapPlaceId, longitude, latitude }: RestaurantEntity) { + constructor({ + id, + googleMapPlaceId, + longitude, + latitude, + name, + }: RestaurantEntity) { this.id = id; this.googleMapPlaceId = googleMapPlaceId; this.longitude = longitude; this.latitude = latitude; + this.name = name; } } diff --git a/backend/src/review/models/restaurant.entity.ts b/backend/src/review/models/restaurant.entity.ts index e8c81eb..e454b7e 100644 --- a/backend/src/review/models/restaurant.entity.ts +++ b/backend/src/review/models/restaurant.entity.ts @@ -13,6 +13,9 @@ export class RestaurantEntity extends IssuedAtMetaEntity { @Column({ type: 'float8' }) longitude: number; + @Column({ type: 'varchar', default: '' }) + name: string; + @OneToMany(() => ReviewEntity, (review) => review.restaurant) reviews: ReviewEntity[]; } diff --git a/backend/src/review/repositories/restaurant.repository.ts b/backend/src/review/repositories/restaurant.repository.ts index d35c00a..edb71f9 100644 --- a/backend/src/review/repositories/restaurant.repository.ts +++ b/backend/src/review/repositories/restaurant.repository.ts @@ -6,7 +6,7 @@ import { RestaurantDto } from '../dtos/in-dtos/restaurant.dto'; @CustomRepository(RestaurantEntity) export class RestaurantRepository extends Repository { async findOrCreate(data: RestaurantDto) { - const { googleMapPlaceId, longitude, latitude } = data; + const { googleMapPlaceId, longitude, latitude, name } = data; const restaurant = await this.findOne({ where: { googleMapPlaceId }, }); @@ -17,6 +17,7 @@ export class RestaurantRepository extends Repository { googleMapPlaceId, longitude, latitude, + name, }).save(); } } diff --git a/backend/src/test/review/create-review.spec.ts b/backend/src/test/review/create-review.spec.ts index 11f8d3c..040833b 100644 --- a/backend/src/test/review/create-review.spec.ts +++ b/backend/src/test/review/create-review.spec.ts @@ -75,6 +75,7 @@ describe('Create Review test', () => { googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', latitude: 37.4224764, longitude: -122.0842499, + name: '두근두근쭈꾸미', }, content: 'content', }) @@ -89,6 +90,7 @@ describe('Create Review test', () => { googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', latitude: 37.4224764, longitude: -122.0842499, + name: '두근두근쭈꾸미', }, content: 'content', imageIds: [], @@ -128,6 +130,7 @@ describe('Create Review test', () => { googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', latitude: 37.4224764, longitude: -122.0842499, + name: '두근두근쭈꾸미', }, content: 'content', imageIds: [image.id], @@ -173,6 +176,7 @@ describe('Create Review test', () => { googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', latitude: 37.4224764, longitude: -122.0842499, + name: '두근두근쭈꾸미', }, content: 'content', imageIds: [image.id], @@ -197,6 +201,7 @@ describe('Create Review test', () => { googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', latitude: 37.4224764, longitude: -122.0842499, + name: '두근두근쭈꾸미', }, content: 'content', imageIds: [image.id], @@ -215,6 +220,7 @@ describe('Create Review test', () => { googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', latitude: 37.4224764, longitude: -122.0842499, + name: '두근두근쭈꾸미', }, content: 'content', imageIds: [], @@ -235,6 +241,7 @@ describe('Create Review test', () => { restaurant: { googleMapPlaceId: restaurant.googleMapPlaceId, latitude: restaurant.latitude, + name: '두근두근쭈꾸미', longitude: restaurant.longitude, }, content: 'content', @@ -250,6 +257,7 @@ describe('Create Review test', () => { { restaurant: { googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', + name: '두근두근쭈꾸미', latitude: 37.4224764, }, content: 'content', @@ -259,6 +267,7 @@ describe('Create Review test', () => { restaurant: { googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', latitude: 37.4224764, + name: '두근두근쭈꾸미', longitude: -122.0842499, }, imageIds: [], @@ -266,11 +275,21 @@ describe('Create Review test', () => { { restaurant: { googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', + name: '두근두근쭈꾸미', latitude: 37.4224764, longitude: -122.0842499, }, content: 'content', }, + { + restaurant: { + googleMapPlaceId: 'ChIJN1t_tDeuEmsRUsoyG83frY4', + latitude: 37.4224764, + longitude: -122.0842499, + }, + content: 'content', + imageIds: [1, 2, 3], + }, ])('input validation', async (requestBody) => { await supertest(testServer.getHttpServer()) .post('/reviews') diff --git a/backend/src/test/review/validateReviewList.ts b/backend/src/test/review/validateReviewList.ts index f50482c..195ece8 100644 --- a/backend/src/test/review/validateReviewList.ts +++ b/backend/src/test/review/validateReviewList.ts @@ -31,7 +31,13 @@ export function validateRestaurantList(body: any) { } export function validateRestaurant(body: any) { - validateDtoKeys(body, ['id', 'googleMapPlaceId', 'longitude', 'latitude']); + validateDtoKeys(body, [ + 'id', + 'googleMapPlaceId', + 'longitude', + 'latitude', + 'name', + ]); } export function validateUserSummary(body: any) {

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