├── packages.txt ├── photo2geo ├── __init__.py ├── data.py ├── model.py └── transform.py ├── doc └── result.gif ├── .gitattributes ├── notebook ├── predict.ipynb ├── create_visualization.ipynb └── description_wih_gpt_vision.ipynb ├── results └── class7_resnet50_batch30_lr1e-05_commit6241653_202112070034 │ ├── model_epoch199.pth │ └── params.json ├── Dockerfile ├── pyproject.toml ├── scripts ├── predict.py ├── web.py ├── create_dataset.py ├── helper.py └── train.py ├── README.md ├── .gitignore └── poetry.lock /packages.txt: -------------------------------------------------------------------------------- 1 | libgl1-mesa-glx 2 | libglib2.0-0 3 | -------------------------------------------------------------------------------- /photo2geo/__init__.py: -------------------------------------------------------------------------------- 1 | from . import data, model, transform -------------------------------------------------------------------------------- /doc/result.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Hi-king/tokyo_photo2geo/HEAD/doc/result.gif -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | *.pth filter=lfs diff=lfs merge=lfs -text 2 | *.ipynb filter=lfs diff=lfs merge=lfs -text 3 | -------------------------------------------------------------------------------- /notebook/predict.ipynb: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:464414bf99b8946ba4ca2b40fce643b87fac943c4cf3719ff7b766c01d5babfe 3 | size 2624506 4 | -------------------------------------------------------------------------------- /notebook/create_visualization.ipynb: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:78d36c491d818861db06085dde4881d91155f6d6d456cb7f596508c39a78e374 3 | size 39734411 4 | -------------------------------------------------------------------------------- /notebook/description_wih_gpt_vision.ipynb: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:05e7ff492117e6359d8b21fa6a904cb3a0a01b56494b351ab5f53e637823f1cc 3 | size 7231 4 | -------------------------------------------------------------------------------- /results/class7_resnet50_batch30_lr1e-05_commit6241653_202112070034/model_epoch199.pth: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:791ef2be88981d1537fdd73ef6c480eb6c2dcbdf6bf51c5e3ffa8cf460d36c36 3 | size 94401793 4 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM python:3.9 2 | 3 | ADD pyproject.toml . 4 | ADD poetry.lock . 5 | RUN pip install -U pip &&\ 6 | pip install poetry &&\ 7 | poetry config virtualenvs.create false --local &&\ 8 | poetry install &&\ 9 | rm -rf ~/.cache 10 | 11 | ADD photo2geo photo2geo/ 12 | RUN poetry install 13 | ADD scripts scripts/ 14 | 15 | CMD ["python", "-c", "print('hello')"] 16 | -------------------------------------------------------------------------------- /photo2geo/data.py: -------------------------------------------------------------------------------- 1 | class DatasetWithIndex: 2 | ''' 3 | :see: https://zenn.dev/hidetoshi/articles/20210619_pytorch-dataset-with-index 4 | ''' 5 | def __init__(self, dataset): 6 | self.dataset = dataset 7 | 8 | def __getitem__(self, index): 9 | data, label = self.dataset[index] 10 | return data, label, index 11 | 12 | def __len__(self): 13 | return len(self.dataset) 14 | 15 | @property 16 | def classes(self): 17 | return self.dataset.classes 18 | -------------------------------------------------------------------------------- /photo2geo/model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torchvision 3 | 4 | def load_model(model: str, classnum: int) -> torchvision.models.ResNet: 5 | # model setup 6 | if model == "resnet18": 7 | model_impl = torchvision.models.resnet18(pretrained=True) 8 | model_impl.fc = torch.nn.Linear(512, classnum) 9 | elif model == "resnet50": 10 | model_impl = torchvision.models.resnet50(pretrained=True) 11 | model_impl.fc = torch.nn.Linear(2048, classnum) 12 | else: 13 | raise Exception() 14 | return model_impl 15 | -------------------------------------------------------------------------------- /results/class7_resnet50_batch30_lr1e-05_commit6241653_202112070034/params.json: -------------------------------------------------------------------------------- 1 | { 2 | "classes": [ 3 | "\u5317\u6d77\u9053\u65ed\u5ddd\u5e02", 4 | "\u6771\u4eac\u90fd\u4e2d\u592e\u533a", 5 | "\u6771\u4eac\u90fd\u5343\u4ee3\u7530\u533a", 6 | "\u6771\u4eac\u90fd\u65b0\u5bbf\u533a", 7 | "\u6771\u4eac\u90fd\u6c5f\u6771\u533a", 8 | "\u6771\u4eac\u90fd\u6e0b\u8c37\u533a", 9 | "\u6771\u4eac\u90fd\u6e2f\u533a" 10 | ], 11 | "batch_size": 30, 12 | "train_ratio": 0.9, 13 | "lr": 1e-05, 14 | "weight_decay": 0, 15 | "epoch": 200, 16 | "model": "resnet50", 17 | "num_workers": 6, 18 | "data_sizes": { 19 | "train": 26432, 20 | "val": 2937 21 | }, 22 | "git_commit_id": "6241653", 23 | "use_gpu": true 24 | } -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "photo2geo" 3 | version = "0.1.0" 4 | description = "" 5 | authors = ["Hi-king "] 6 | 7 | [tool.poetry.dependencies] 8 | python = ">=3.8,<3.11" 9 | requests = "^2.26.0" 10 | pillow = ">=10.0.0" 11 | numpy = "^1.21.4" 12 | pandas = "*" 13 | fire = "^0.4.0" 14 | torchvision = ">=0.11.1" 15 | tqdm = "^4.62.3" 16 | scikit-learn = "^1.0.1" 17 | google-cloud-logging = "^2.7.0" 18 | streamlit = "^1.3.0" 19 | grad-cam = "^1.3.7" 20 | torch = ">=1.13.1" 21 | openai = ">=1.3.5" 22 | python-dotenv = "*" 23 | 24 | 25 | [tool.poetry.dev-dependencies] 26 | 27 | [build-system] 28 | requires = ["poetry>=0.12"] 29 | build-backend = "poetry.masonry.api" 30 | 31 | [tool.yapf] 32 | based_on_style = "pep8" 33 | column_limit = 160 34 | -------------------------------------------------------------------------------- /scripts/predict.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import json 3 | import pathlib 4 | import photo2geo 5 | import fire 6 | import helper 7 | 8 | def predict( 9 | path: str, 10 | result_path: str = (pathlib.Path(__file__).parent.parent 11 | / "results/class7_resnet50_batch30_lr1e-05_commit6241653_202112070034").as_posix(), 12 | ): 13 | params = json.load((pathlib.Path(result_path) / "params.json").open()) 14 | print(params) 15 | models = list(pathlib.Path(result_path).glob("model*")) 16 | last_model_path = sorted(models)[-1] 17 | 18 | model = photo2geo.model.load_model( 19 | model=params["model"], classnum=len(params["classes"]) 20 | ) 21 | model.load_state_dict(torch.load(last_model_path)) 22 | model = model.eval() 23 | print(f'{path} -> {helper.pred(path, model, params)[0]}') 24 | 25 | 26 | if __name__ == "__main__": 27 | fire.Fire(predict) 28 | -------------------------------------------------------------------------------- /photo2geo/transform.py: -------------------------------------------------------------------------------- 1 | from torchvision import transforms 2 | 3 | transform_dict = { 4 | "train": transforms.Compose( 5 | [ 6 | # transforms.Resize((256, 256)), 7 | transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.7, 1.2)), 8 | transforms.RandomResizedCrop((256, 256), scale=(0.5, 1.0)), 9 | transforms.ColorJitter(brightness=0.5, contrast=0.2, saturation=0.1, hue=0.1), 10 | transforms.RandomPerspective(distortion_scale=0.3, p=0.5), 11 | transforms.RandomHorizontalFlip(), 12 | transforms.ToTensor(), 13 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), 14 | ] 15 | ), 16 | "test": transforms.Compose( 17 | [ 18 | transforms.Resize((256, 256)), 19 | transforms.ToTensor(), 20 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), 21 | ] 22 | ), 23 | } 24 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/hi-king/tokyo_photo2geo/main/scripts/web.py) 2 | 3 | # tokyo_photo2geo 4 | 5 | Predict where the photo was taken from single shot image 6 | 7 | ![](./doc/result.gif) 8 | 9 | # Predict your photo 10 | 11 | ``` 12 | poetry install 13 | poetry run python scripts/predict.py ${your_image_path} 14 | ``` 15 | 16 | or 17 | 18 | notebooks/predict.ipynb 19 | 20 | # Create your own model 21 | 22 | ```python 23 | poetry install 24 | KEY=XXXXXX poetry run python scripts/create_dataset.py 25 | poetry run python scripts/train.py \ 26 | --model resnet50 \ 27 | --batch_size 30 \ 28 | --lr 0.00001 \ 29 | --weight_decay 0 \ 30 | --epoch 200 31 | ``` 32 | 33 | # References 34 | 35 | - [機械学習で逆ストリートビューを作り、写真から港区らしさを判定する](https://www.m3tech.blog/entry/photo2geo) 36 | - [画像から撮影場所を当てる話 ~ 理論的背景 & どこが〇〇区らしいか ~](https://speakerdeck.com/hiking/hua-xiang-karacuo-ying-chang-suo-wodang-teruhua-li-lun-de-bei-jing-and-dokoga00qu-rasiika) 37 | -------------------------------------------------------------------------------- /scripts/web.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | import torch 3 | import json 4 | import pathlib 5 | import photo2geo 6 | import PIL.Image 7 | import torchvision 8 | import numpy as np 9 | import helper 10 | 11 | RESULT_PATH = (pathlib.Path(__file__).parent.parent / "results/class7_resnet50_batch30_lr1e-05_commit6241653_202112070034").as_posix() 12 | 13 | 14 | def myapp(): 15 | st.write("hello") 16 | 17 | file_up = st.file_uploader("Upload an image", type=['png','jpg']) 18 | 19 | if file_up is not None: 20 | model, params = helper.load_params(RESULT_PATH) 21 | image = PIL.Image.open(file_up) 22 | st.image(image, caption='Uploaded Image.', use_column_width=True) 23 | 24 | predicted, scores = helper.pred(file_up, model, params) 25 | st.text(f'予測: {predicted}') 26 | 27 | TOPK=3 28 | cols = st.columns(TOPK) 29 | for col, (key, score) in zip(cols, list(sorted(scores.items(), key=lambda x: -x[1]))[:TOPK]): 30 | with col: 31 | cam_image = helper.show_cam(file_up, model, params, key) 32 | st.image(cam_image, caption=f'GradCAM {key} [{score:.2f}]', use_column_width=True) 33 | 34 | if __name__ == "__main__": 35 | myapp() 36 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.ipynb 2 | *.png 3 | .DS_Store 4 | private 5 | k8s 6 | results 7 | .vscode 8 | notebook 9 | 10 | ### https://raw.github.com/github/gitignore/499ae899e7b54e701e878759f73d9092302fd07a/Python.gitignore 11 | 12 | # Byte-compiled / optimized / DLL files 13 | __pycache__/ 14 | *.py[cod] 15 | *$py.class 16 | 17 | # C extensions 18 | *.so 19 | 20 | # Distribution / packaging 21 | .Python 22 | build/ 23 | develop-eggs/ 24 | dist/ 25 | downloads/ 26 | eggs/ 27 | .eggs/ 28 | lib/ 29 | lib64/ 30 | parts/ 31 | sdist/ 32 | var/ 33 | wheels/ 34 | pip-wheel-metadata/ 35 | share/python-wheels/ 36 | *.egg-info/ 37 | .installed.cfg 38 | *.egg 39 | MANIFEST 40 | 41 | # PyInstaller 42 | # Usually these files are written by a python script from a template 43 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 44 | *.manifest 45 | *.spec 46 | 47 | # Installer logs 48 | pip-log.txt 49 | pip-delete-this-directory.txt 50 | 51 | # Unit test / coverage reports 52 | htmlcov/ 53 | .tox/ 54 | .nox/ 55 | .coverage 56 | .coverage.* 57 | .cache 58 | nosetests.xml 59 | coverage.xml 60 | *.cover 61 | .hypothesis/ 62 | .pytest_cache/ 63 | 64 | # Translations 65 | *.mo 66 | *.pot 67 | 68 | # Django stuff: 69 | *.log 70 | local_settings.py 71 | db.sqlite3 72 | db.sqlite3-journal 73 | 74 | # Flask stuff: 75 | instance/ 76 | .webassets-cache 77 | 78 | # Scrapy stuff: 79 | .scrapy 80 | 81 | # Sphinx documentation 82 | docs/_build/ 83 | 84 | # PyBuilder 85 | target/ 86 | 87 | # Jupyter Notebook 88 | .ipynb_checkpoints 89 | 90 | # IPython 91 | profile_default/ 92 | ipython_config.py 93 | 94 | # pyenv 95 | .python-version 96 | 97 | # pipenv 98 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 99 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 100 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 101 | # install all needed dependencies. 102 | #Pipfile.lock 103 | 104 | # celery beat schedule file 105 | celerybeat-schedule 106 | 107 | # SageMath parsed files 108 | *.sage.py 109 | 110 | # Environments 111 | .env 112 | .venv 113 | env/ 114 | venv/ 115 | ENV/ 116 | env.bak/ 117 | venv.bak/ 118 | 119 | # Spyder project settings 120 | .spyderproject 121 | .spyproject 122 | 123 | # Rope project settings 124 | .ropeproject 125 | 126 | # mkdocs documentation 127 | /site 128 | 129 | # mypy 130 | .mypy_cache/ 131 | .dmypy.json 132 | dmypy.json 133 | 134 | # Pyre type checker 135 | .pyre/ 136 | 137 | 138 | -------------------------------------------------------------------------------- /scripts/create_dataset.py: -------------------------------------------------------------------------------- 1 | import hashlib 2 | import io 3 | import os 4 | 5 | import fire 6 | import numpy as np 7 | import PIL.Image 8 | import requests 9 | import pathlib 10 | import pandas as pd 11 | import sklearn.metrics 12 | 13 | KEY = os.environ.get('KEY') 14 | assert KEY is not None 15 | 16 | 17 | def get_image(latitude, longitude, heading=0): 18 | url = f'https://maps.googleapis.com/maps/api/streetview?location={latitude},{longitude}&size=456x456&key={KEY}&heading={heading}' 19 | d = requests.get(url) 20 | if d.status_code == 200: 21 | return PIL.Image.open(io.BytesIO(d.content)) 22 | else: 23 | print(d.status_code) 24 | return None 25 | 26 | 27 | def uniform_random_noise(n): 28 | for _ in range(n): 29 | length = np.sqrt(np.random.uniform(0, 1)) 30 | angle = np.pi * np.random.uniform(0, 2) 31 | x = length * np.cos(angle) 32 | y = length * np.sin(angle) 33 | yield (x, y) 34 | 35 | 36 | def get_town(district, area): 37 | d = requests.get(f'https://geolonia.github.io/japanese-addresses/api/ja/{district}/{area}.json') 38 | df = pd.DataFrame(d.json()) 39 | return df 40 | 41 | 42 | AREAS = { 43 | ('東京都', '中央区'): 50, 44 | ('東京都', '港区'): 50, 45 | ('北海道', '旭川市'): 2, 46 | ('東京都', '新宿区'): 15, 47 | ('東京都', '渋谷区'): 30, 48 | ('東京都', '江東区'): 15, 49 | ('東京都', '千代田区'): 20, 50 | } 51 | 52 | def main(): 53 | basedir = pathlib.Path(__file__).parent.parent / 'data' 54 | for (district, area), sample_per_town in AREAS.items(): 55 | towns: pd.DataFrame = get_town(district, area) 56 | towns = towns[towns.lng.notna()] 57 | darray = sklearn.metrics.pairwise_distances(towns[['lat', 'lng']], towns[['lat', 'lng']]) 58 | r = darray[darray > 0.0001].min() 59 | 60 | for _, row in towns.iterrows(): 61 | print(row.town) 62 | seed = int(hashlib.sha1(row.town.encode("UTF-8")).hexdigest(), 16) % 2**32 63 | np.random.seed(seed) 64 | target_dir = basedir / (district + area) / row.town 65 | target_dir.mkdir(exist_ok=True, parents=True) 66 | center_lat, center_lng = row.lat, row.lng 67 | for x, y in uniform_random_noise(sample_per_town): 68 | lat = center_lat + x * r 69 | lng = center_lng + y * r 70 | heading = np.random.randint(180) 71 | path: pathlib.Path = target_dir / f'{lat}{lng}{heading}.png' 72 | if not path.exists(): 73 | img = get_image(lat, lng, heading=heading) 74 | if img is not None: 75 | img.save(path) 76 | 77 | 78 | if __name__ == '__main__': 79 | fire.Fire(main) 80 | -------------------------------------------------------------------------------- /scripts/helper.py: -------------------------------------------------------------------------------- 1 | import json 2 | import pathlib 3 | import torch 4 | import photo2geo 5 | import PIL.Image 6 | import torchvision 7 | import numpy as np 8 | import pytorch_grad_cam 9 | import pytorch_grad_cam.utils 10 | import pytorch_grad_cam.utils.image 11 | 12 | def load_small_image(path): 13 | i = PIL.Image.open(path).convert("RGB") 14 | i = torchvision.transforms.Resize((456, 456))( 15 | torchvision.transforms.CenterCrop(i.width)(i) 16 | ) 17 | return i 18 | 19 | def pred(path, model, params): 20 | def softmax(x): 21 | """Compute softmax values for each sets of scores in x.""" 22 | e_x = np.exp(x - np.max(x)) 23 | return e_x / e_x.sum(axis=0) # only difference 24 | i = load_small_image(path) 25 | with torch.inference_mode(): 26 | predict = model(photo2geo.transform.transform_dict["test"](i).unsqueeze(0))[0].numpy() 27 | scores = softmax(predict) 28 | return params["classes"][predict.argmax()], {label: score for label, score in zip(params["classes"], scores)} 29 | 30 | def load_params( 31 | result_path: str = (pathlib.Path(__file__).parent.parent 32 | / "results/class7_resnet50_batch30_lr1e-05_commit6241653_202112070034").as_posix(), 33 | 34 | ): 35 | params = json.load((pathlib.Path(result_path) / "params.json").open()) 36 | print(params) 37 | models = list(pathlib.Path(result_path).glob("model*")) 38 | last_model_path = sorted(models)[-1] 39 | 40 | model = photo2geo.model.load_model( 41 | model=params["model"], classnum=len(params["classes"]) 42 | ) 43 | model.load_state_dict(torch.load(last_model_path)) 44 | model = model.eval() 45 | return model, params 46 | 47 | 48 | def show_cam(path, model, params, target=None): 49 | def image2tensor(image): 50 | return photo2geo.transform.transform_dict["test"](image).unsqueeze(0) 51 | target_layers = [model.layer4[-1]] 52 | cam = pytorch_grad_cam.GradCAM(model=model, target_layers=target_layers, use_cuda=False) 53 | input_Image = load_small_image(path) 54 | if target is not None: 55 | target_class_index = np.argwhere(np.array(params['classes']) == target).flatten()[0] 56 | grayscale_cam = cam( 57 | input_tensor=image2tensor(input_Image), 58 | targets=[ 59 | pytorch_grad_cam.utils.model_targets.ClassifierOutputTarget(target_class_index) # type: ignore 60 | ] 61 | ) 62 | else: 63 | grayscale_cam = cam( 64 | input_tensor=image2tensor(input_Image) 65 | ) 66 | 67 | grayscale_cam = grayscale_cam[0, :] 68 | cam_image = pytorch_grad_cam.utils.image.show_cam_on_image( 69 | np.asarray(input_Image.resize((256, 256))).astype(np.float32) / 255, 70 | grayscale_cam, use_rgb=True 71 | ) 72 | return PIL.Image.fromarray(cam_image) 73 | -------------------------------------------------------------------------------- /scripts/train.py: -------------------------------------------------------------------------------- 1 | from numpy.core.numeric import indices 2 | import photo2geo 3 | import fire 4 | import pathlib 5 | import torch 6 | import torchvision.datasets 7 | import torchvision.models 8 | import time 9 | import tqdm 10 | import copy 11 | from torchvision import transforms 12 | import datetime 13 | import pandas as pd 14 | import sklearn.metrics 15 | import os 16 | import numpy as np 17 | import json 18 | import inspect 19 | import subprocess 20 | 21 | 22 | def train_model( 23 | model, 24 | criterion, 25 | optimizer, 26 | dataloaders, 27 | data_sizes, 28 | results_dir: pathlib.Path, 29 | num_epochs=25, 30 | ): 31 | use_gpu = torch.cuda.is_available() 32 | since = time.time() 33 | best_model_wts = copy.deepcopy(model.state_dict()) 34 | best_acc = 0.0 35 | 36 | loss_dict = {"train": [], "val": []} 37 | acc_dict = {"train": [], "val": []} 38 | 39 | for epoch in range(num_epochs): 40 | if (epoch + 1) % 5 == 0: # ５回に１回エポックを表示します。 41 | print("Epoch {}/{}".format(epoch, num_epochs - 1)) 42 | print("-" * 10) 43 | 44 | for phase in ["train", "val"]: 45 | if phase == "train": 46 | model.train() # 学習モード。dropoutなどを行う。 47 | else: 48 | model.eval() # 推論モード。dropoutなどを行わない。 49 | 50 | running_loss = 0.0 51 | epoch_predicts = [] 52 | epoch_scores = None 53 | epoch_labels = [] 54 | epoch_indices = [] 55 | data_loader = dataloaders[phase] 56 | data_size = data_sizes[phase] 57 | classes = data_loader.dataset.dataset.dataset.classes 58 | 59 | for data in tqdm.tqdm(data_loader): 60 | inputs, labels, indices = data 61 | 62 | # debug augmentation 63 | # import PIL.Image 64 | # print(inputs.shape) 65 | # for image, index in zip(inputs, indices): 66 | # MEANS = [0, 0, 0] 67 | # print(image.max(), image.min()) 68 | # img = (image.to('cpu').detach().numpy().transpose(1, 2, 0)*256 + (MEANS[2], MEANS[1], MEANS[0])).astype(np.uint8).copy() 69 | # print(data_loader.dataset.dataset.dataset.imgs[index]) 70 | # PIL.Image.open(data_loader.dataset.dataset.dataset.imgs[index][0]).save(results_dir / f'{phase}_{index}_original.png') 71 | # PIL.Image.fromarray(img).save(results_dir / f'{phase}_{index}.png') 72 | 73 | if use_gpu: 74 | inputs = inputs.cuda() 75 | labels = labels.cuda() 76 | 77 | if phase == "train": 78 | outputs = model(inputs) 79 | else: 80 | with torch.inference_mode(): 81 | outputs = model(inputs) 82 | 83 | _, preds = torch.max(outputs.data, 1) 84 | loss = criterion(outputs, labels) 85 | 86 | if phase == "train": 87 | optimizer.zero_grad() 88 | loss.backward() 89 | optimizer.step() 90 | 91 | running_loss += loss.item() * inputs.size(0) 92 | epoch_scores = ( 93 | outputs.cpu().data.numpy() 94 | if epoch_scores is None 95 | else np.concatenate( 96 | [epoch_scores, outputs.cpu().data.numpy()], axis=0 97 | ) 98 | ) 99 | epoch_predicts += preds.tolist() 100 | epoch_labels += labels.tolist() 101 | epoch_indices += indices.tolist() 102 | 103 | epoch_loss = running_loss / data_size 104 | 105 | # Result 106 | epoch_acc = sklearn.metrics.accuracy_score(epoch_labels, epoch_predicts) 107 | print( 108 | f"[{phase}{len(loss_dict[phase])}] loss {epoch_loss}, acc {epoch_acc}" 109 | ) 110 | sub_classes = [classes[i] for i in list(set(epoch_labels))] 111 | confusion_matrix = pd.DataFrame( 112 | sklearn.metrics.confusion_matrix(epoch_labels, epoch_predicts), 113 | index=sub_classes, 114 | columns=sub_classes, 115 | ) 116 | print(confusion_matrix) 117 | result_df = pd.DataFrame( 118 | dict( 119 | predict=epoch_predicts, 120 | label=epoch_labels, 121 | file_index=epoch_indices, 122 | **{ 123 | f"score[{classes[i]}]": epoch_scores[:, i] 124 | for i in range(epoch_scores.shape[1]) 125 | }, 126 | ) 127 | ).assign( 128 | file=lambda df: df.file_index.map( 129 | lambda i: data_loader.dataset.dataset.dataset.imgs[i][0] 130 | ), 131 | labels_file=lambda df: df.file_index.map( 132 | lambda i: data_loader.dataset.dataset.dataset.imgs[i][1] 133 | ), 134 | ) 135 | result_df.to_csv(results_dir / f"result_{phase}_epoch{epoch}.csv") 136 | 137 | loss_dict[phase].append(epoch_loss) 138 | acc_dict[phase].append(epoch_acc) 139 | 140 | # save 141 | torch.save( 142 | copy.deepcopy(model).to("cpu").state_dict(), 143 | results_dir / f"model_epoch{epoch}.pth", 144 | ) 145 | if phase == "val" and epoch_acc > best_acc: 146 | best_acc = epoch_acc 147 | best_model_wts = copy.deepcopy(model.state_dict()) 148 | 149 | time_elapsed = time.time() - since 150 | print( 151 | "Training complete in {:.0f}m {:.0f}s".format( 152 | time_elapsed // 60, time_elapsed % 60 153 | ) 154 | ) 155 | print("Best val acc: {:.4f}".format(best_acc)) 156 | 157 | model.load_state_dict(best_model_wts) 158 | return model, loss_dict, acc_dict 159 | 160 | 161 | transform_dict = { 162 | "train": transforms.Compose( 163 | [ 164 | # transforms.Resize((256, 256)), 165 | transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.7, 1.2)), 166 | transforms.RandomResizedCrop((256, 256), scale=(0.5, 1.0)), 167 | transforms.ColorJitter(brightness=0.5, contrast=0.2, saturation=0.1, hue=0.1), 168 | transforms.RandomPerspective(distortion_scale=0.3, p=0.5), 169 | transforms.RandomHorizontalFlip(), 170 | transforms.ToTensor(), 171 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), 172 | ] 173 | ), 174 | "test": transforms.Compose( 175 | [ 176 | transforms.Resize((256, 256)), 177 | transforms.ToTensor(), 178 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), 179 | ] 180 | ), 181 | } 182 | 183 | 184 | def main( 185 | batch_size=10, 186 | train_ratio=0.9, 187 | lr=1e-4, 188 | weight_decay=1e-5, 189 | epoch=40, 190 | model="resnet18", 191 | num_workers=os.cpu_count(), 192 | ): 193 | use_gpu = torch.cuda.is_available() 194 | basedir = pathlib.Path(__file__).parent.parent / "data" 195 | dataset = photo2geo.data.DatasetWithIndex( 196 | torchvision.datasets.ImageFolder(root=basedir) 197 | ) 198 | classnum = len(dataset.classes) 199 | print(dataset.classes) 200 | git_commit_id = ( 201 | subprocess.check_output(["git", "rev-parse", "--short", "HEAD"]) 202 | .decode("ascii") 203 | .strip() 204 | ) 205 | resultdir = ( 206 | pathlib.Path(__file__).parent.parent 207 | / "results" 208 | / f'class{classnum}_{model}_batch{batch_size}_lr{lr}_commit{git_commit_id}_{datetime.datetime.now().strftime("%Y%m%d%H%M")}' 209 | ) 210 | resultdir.mkdir(parents=True, exist_ok=True) 211 | 212 | train_size = int(train_ratio * len(dataset)) 213 | val_size = len(dataset) - train_size 214 | data_sizes = {"train": train_size, "val": val_size} 215 | data_train, data_val = torch.utils.data.random_split( 216 | dataset, [train_size, val_size] 217 | ) 218 | 219 | # set train/test augmentation 220 | data_train.dataset.dataset.transform = transform_dict["train"] 221 | data_val.dataset = copy.deepcopy(data_val.dataset) 222 | data_val.dataset.dataset.transform = transform_dict["test"] 223 | 224 | # save params 225 | all_params = locals() 226 | params = dict( 227 | classes=dataset.classes, 228 | **{ 229 | key: all_params[key] 230 | for key in (inspect.getfullargspec(main).args + ["data_sizes", "git_commit_id", "use_gpu"]) 231 | }, 232 | ) 233 | print(params) 234 | json.dump(params, (resultdir / "params.json").open("w+"), indent=2) 235 | 236 | # save data split 237 | filename_df = pd.DataFrame(dataset.dataset.imgs).assign(trainval="None") 238 | filename_df.loc[data_train.indices, "trainval"] = "train" 239 | filename_df.loc[data_val.indices, "trainval"] = "val" 240 | filename_df.to_csv(resultdir / "datasplit.csv") 241 | 242 | train_loader = torch.utils.data.DataLoader( 243 | data_train, batch_size=batch_size, shuffle=True, num_workers=num_workers 244 | ) 245 | val_loader = torch.utils.data.DataLoader( 246 | data_val, batch_size=batch_size, shuffle=False, num_workers=num_workers 247 | ) 248 | dataloaders = {"train": train_loader, "val": val_loader} 249 | 250 | model = photo2geo.model.load_model(model, classnum) 251 | # # model setup 252 | # if model == "resnet18": 253 | # model = torchvision.models.resnet18(pretrained=True) 254 | # model.fc = torch.nn.Linear(512, classnum) 255 | # elif model == "resnet50": 256 | # model = torchvision.models.resnet50(pretrained=True) 257 | # model.fc = torch.nn.Linear(2048, classnum) 258 | # else: 259 | # raise Exception() 260 | 261 | if use_gpu: 262 | model = model.cuda() 263 | optim = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay) 264 | if use_gpu: 265 | criterion = 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