diff --git a/README.md b/README.md index 6849da5..df41bc7 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,20 @@ -WaseteNet -=============== +# WasteNet -深層学習でSfMLearnerのデータセット取捨選択を行ったプロジェクト \ No newline at end of file +深層学習で SfMLearner のデータセット取捨選択を行ったプロジェクト + +
+ +--- + +## 動作環境 + +python 3.8.12 +torch==1.10.0 + +## 準備 + +- 学習データセットは input ディレクトリにあるデータを参考にしてフォルダを配置する． + +## 稼働方法 + +`python src/train.py` diff --git a/input/bagging/corrects.csv b/input/bagging/corrects.csv new file mode 100644 index 0000000..ca6f201 --- /dev/null +++ b/input/bagging/corrects.csv @@ -0,0 +1,2577 @@ +D:\Deep_Learning\WasteNet\dataset\sequence0\00000002.png,D:\Deep_Learning\WasteNet\dataset\sequence0\00000003.png,D:\Deep_Learning\WasteNet\dataset\sequence0\00000004.png,True,999 +D:\Deep_Learning\WasteNet\dataset\sequence0\00000003.png,D:\Deep_Learning\WasteNet\dataset\sequence0\00000004.png,D:\Deep_Learning\WasteNet\dataset\sequence0\00000005.png,True,999 +D:\Deep_Learning\WasteNet\dataset\sequence0\00000004.png,D:\Deep_Learning\WasteNet\dataset\sequence0\00000005.png,D:\Deep_Learning\WasteNet\dataset\sequence0\00000006.png,True,999 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"execution_count": 13, + "id": "3f8c6593-fa93-44c3-bbc2-7191da00ed1f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cf716145-8bf5-41d5-a8dc-ecad0802ed28", + "metadata": {}, + "outputs": [], + "source": [ + "labels_df = pd.read_csv(\"train.csv\", header=None)\n", + "labels_df.columns = [\"path1\", \"path2\", \"path3\", \"is_collected\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "82b36668-d24c-42fb-a956-6db50cf93c5f", + "metadata": {}, + "outputs": [], + "source": [ + "items = []\n", + "for is_collected in labels_df[\"is_collected\"].values:\n", + " item = \"True\" if is_collected else \"False\"\n", + " items.append(item)\n", + "items = np.array(items)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1401cdb8-6fec-4635-b1f4-dd96becd81eb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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"cbadb302-2e35-4fcd-a6c9-f7826b4f935c", + "metadata": {}, + "outputs": [], + "source": [ + "incorrects.to_csv(\"../input/bagging/incorrects.csv\", header=None, index=False)\n", + "corrects.to_csv(\"../input/bagging/corrects.csv\", header=None, index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/ensemble.ipynb b/notebooks/ensemble.ipynb new file mode 100644 index 0000000..c78f7e5 --- /dev/null +++ b/notebooks/ensemble.ipynb @@ -0,0 +1,366 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "2a06e6dd-0d31-4d64-8498-051a26ffa896", + "metadata": {}, + "outputs": [], + "source": [ + "import os.path as osp\n", + "from glob import glob\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.metrics import confusion_matrix\n", + "import seaborn as sn\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ce10f877-908c-4f47-86b0-35a215ca0bee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sequence0\n", + "D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\0\\bagging0, sequence0.csv : 0.7677419354838709\n", + "D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\1\\bagging1, sequence0.csv : 0.7451612903225806\n", + "D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\2\\bagging2, sequence0.csv : 0.7645161290322581\n", + "D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\3\\bagging3, sequence0.csv : 0.7580645161290323\n", + "D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\4\\bagging4, sequence0.csv : 0.7516129032258064\n", + 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"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\9\\bagging9, sequence2.csv : 0.8968531468531469\n", + "(array([ 19, 45, 57, 168, 177, 219, 230, 289, 294, 303, 315, 325, 326,\n", + " 330, 347, 352, 367, 383, 388, 409, 413, 432, 434, 441, 459, 460,\n", + " 492, 499, 501, 518, 521, 527, 530, 531, 537, 541, 544, 563, 567,\n", + " 568], dtype=int64),)\n", + "ensemble : 0.9300699300699301\n", + "\n", + "sequence3\n", + "D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\0\\bagging0, sequence3.csv : 0.8541666666666666\n", + "D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\1\\bagging1, sequence3.csv : 0.8472222222222222\n", + "D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\2\\bagging2, sequence3.csv : 0.8038194444444444\n", + "D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\3\\bagging3, sequence3.csv : 0.7899305555555556\n", + "D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\4\\bagging4, sequence3.csv : 0.8246527777777778\n", + "D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\5\\bagging5, sequence3.csv : 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "base_path = r\"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\"\n", + "save_confusion_matrix = True\n", + "\n", + "for target_sequence in range(9):\n", + " print(f\"sequence{target_sequence}\")\n", + " target_csvs = glob(osp.join(base_path, \"*\", f\"bagging*, sequence{target_sequence}.csv\"))\n", + "\n", + " for i, target_csv in enumerate(target_csvs):\n", + " preds = pd.read_csv(target_csv, index_col=0)\n", + "\n", + " if i == 0:\n", + " out = preds[[\"pred0\", \"pred2\"]].values\n", + " else:\n", + " out = out + preds[[\"pred0\", \"pred2\"]].values\n", + "\n", + " label = preds[\"label\"].values\n", + " pred_index = np.argmax(preds[[\"pred0\", \"pred2\"]].values, axis=1)\n", + "\n", + " print(f\"{target_csv} : {np.sum(pred_index == label) / label.size}\")\n", + " ens_out = np.argmax(out, axis=1)\n", + " print(np.where(np.logical_not(ens_out == label)))\n", + " if save_confusion_matrix:\n", + " cf_matrix = confusion_matrix(list(label), list(ens_out))\n", + " df_cm = pd.DataFrame(cf_matrix, index=[\"bad data(label)\", \"good data(label)\"], columns=[\"bad data(predicted)\", \"good data(predicted)\"])\n", + " plt.figure(figsize=(12, 7))\n", + " show_fig = sn.heatmap(df_cm, annot=True).get_figure()\n", + " plt.savefig(f\"sequence{target_sequence}.png\")\n", + " print(f\"ensemble : {np.sum(ens_out == label) / label.size}\")\n", + " print(\"\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/make_dataset_csv.ipynb b/notebooks/make_dataset_csv.ipynb new file mode 100644 index 0000000..d4e91cb --- /dev/null +++ b/notebooks/make_dataset_csv.ipynb @@ -0,0 +1,112 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "id": "fae21126-1f50-4be3-9aa7-1e2e5ab682cc", + "metadata": {}, + "outputs": [], + "source": [ + "from glob import glob\n", + "import os.path as osp\n", + "import csv" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6b9888b7-fab6-4c90-9dc5-4fdd8f577486", + "metadata": {}, + "outputs": [], + "source": [ + "SFM_DATASETS_PATH = r\"D:\\Deep_Learning\\Endo-SfMLearner\\dataset_w_dpt\\datasets\"\n", + "ESO_DATASETS_PATH = r\"D:\\Deep_Learning\\WasteNet\\dataset\"\n", + "TARGET_IMGS_NUM = 3" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cf2acaf1-e06a-49c1-b63c-2117e9f5cc5b", + "metadata": {}, + "outputs": [], + "source": [ + "sfm_imgs_path = glob(osp.join(SFM_DATASETS_PATH, \"*\", \"*.*\"))\n", + "eso_imgs_path = glob(osp.join(ESO_DATASETS_PATH, \"*\", \"*.*\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4a478848-283f-4613-aa69-72140bf032ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['D:', 'Deep_Learning', 'WasteNet', 'dataset', 'sequence0', '00000000.png']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eso_imgs_path[0].split(\"\\\\\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a8e0dce0-b210-426c-8d7d-eefdfa4eede9", + "metadata": {}, + "outputs": [], + "source": [ + "out_list = []\n", + "for i in range(len(eso_imgs_path) - 2):\n", + " img_path1, img_path2, img_path3 = eso_imgs_path[i], eso_imgs_path[i + 1], eso_imgs_path[i + 2]\n", + " exists_list = []\n", + " for img_path in [img_path1, img_path2, img_path3]:\n", + " path_elements = img_path.split(\"\\\\\")\n", + " searched_path = osp.join(SFM_DATASETS_PATH, path_elements[-2], path_elements[-1])\n", + " exists_list.append(osp.exists(searched_path))\n", + " is_exists = all(exists_list)\n", + " out_list.append([img_path1, img_path2, img_path3, is_exists])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2eff971c-2b02-4536-8336-655e2faba252", + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"train.csv\", \"w\", newline=\"\") as f:\n", + " csv_writer = csv.writer(f)\n", + " csv_writer.writerows(out_list)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/pred_with_test.ipynb b/notebooks/pred_with_test.ipynb new file mode 100644 index 0000000..cb510c7 --- /dev/null +++ b/notebooks/pred_with_test.ipynb @@ -0,0 +1,295 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "211db4dd-7709-4bcc-a5ee-60fc64929069", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"../src\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fb9b463b-66d9-4ead-9f39-82e2ea1408c6", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "from glob import glob\n", + "import os\n", + "import os.path as osp\n", + "import random\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import torch\n", + "import torch.nn as nn\n", + "from tensorboardX import SummaryWriter\n", + "from sklearn.metrics import confusion_matrix\n", + "import seaborn as sn\n", + "from tqdm import tqdm\n", + "import argparse\n", + "from torchvision import transforms\n", + "from datasets import WasteNetDataset\n", + "from torch.utils.data import DataLoader\n", + "from utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86b21bc3-62b3-4a11-88fe-1c091a00f5b8", + "metadata": {}, + "outputs": [], + "source": [ + "# import pickle\n", + "# for i in range(9):\n", + "# data_table = pd.read_csv(\"../input/train.csv\", header=None).values.tolist()\n", + "# _, test_dset = train_test_split_by_sequence(data_table, i, True)\n", + "# with open(f\"test_dset{i}.pkl\", \"wb\") as f:\n", + "# pickle.dump(test_dset, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "59770128-4137-463b-af68-ca02463e97b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Use device: cuda\n" + ] + } + ], + "source": [ + "# 固定値系\n", + "IMG_WIDTH = 384\n", + "IMG_HEIGHT = 288\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "out_path = f\"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\"\n", + "print(f\"Use device: {device}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "91c591eb-a592-436c-9289-e98f2841789d", + "metadata": {}, + "outputs": [], + "source": [ + "# パラメータ系\n", + "base_path = r\"D:\\Deep_Learning\\WasteNet\\logs\\undersample+bagging\\effnetv2_m_7\"\n", + "bagging_num = 7\n", + "model = load_effnetv2().to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fb8d7305-3a0b-4902-b4ff-6cfbe857dbbd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 11%|██████████████████████▎ | 1/9 [00:12<01:39, 12.41s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[acc] 0.7290322580645161\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 22%|████████████████████████████████████████████▋ | 2/9 [00:56<03:35, 30.76s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[acc] 0.7259036144578314\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 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"text": [ + "[acc] 0.8296703296703297\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [03:09<00:00, 21.00s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[acc] 0.7298850574712644\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "for seq_num in tqdm(range(9)):\n", + " os.makedirs(osp.join(out_path, str(bagging_num)), exist_ok=True)\n", + "\n", + " pth_path = glob(osp.join(base_path, f\"sequence{seq_num}\", \"*.pth\"))[0]\n", + " model.load_state_dict(torch.load(pth_path, map_location=device), strict=False)\n", + " model.eval()\n", + "\n", + " transform = transforms.Compose([\n", + " transforms.ToTensor(),\n", + " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n", + " ])\n", + "\n", + " with open(f\"test_dset{seq_num}.pkl\", \"rb\") as f:\n", + " test_dset = pickle.load(f)\n", + " test_datasets = WasteNetDataset(test_dset, IMG_WIDTH, IMG_HEIGHT, transform, True)\n", + " test_loader = DataLoader(test_datasets, batch_size=4, num_workers=0)\n", + "\n", + " out_df = []\n", + " y_pred, y_true = [], []\n", + " correct_num = 0\n", + " pred_target_num = 0\n", + " for img, labels, path1, path2, path3 in test_loader:\n", + " img, labels = img.to(device), labels.to(device)\n", + " out = nn.functional.softmax(model(img), dim=1)\n", + " for batch_i in range(img.shape[0]):\n", + " out_df_row = [path1[batch_i], path2[batch_i], path3[batch_i]]\n", + " out_df_row.extend([float(out[batch_i][0]), float(out[batch_i][1]), int(labels[batch_i])])\n", + " out_df.append(out_df_row)\n", + " pred_target_num += out.shape[0]\n", + " pred_index = torch.argmax(out, dim=1)\n", + " correct_num += torch.sum(pred_index == labels).item()\n", + " \n", + " y_pred.extend(pred_index.data.cpu().numpy())\n", + " y_true.extend(labels.data.cpu().numpy())\n", + " print(f\"[acc] {correct_num / pred_target_num}\")\n", + " out_df = pd.DataFrame(out_df)\n", + " out_df.columns = [\"path1\", \"path2\", \"path3\", \"pred0\", \"pred2\", \"label\"]\n", + " out_df.to_csv(osp.join(out_path, str(bagging_num), f\"bagging{bagging_num}, sequence{seq_num}.csv\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/reconst_train_csv_with_split.ipynb b/notebooks/reconst_train_csv_with_split.ipynb new file mode 100644 index 0000000..735951f --- /dev/null +++ b/notebooks/reconst_train_csv_with_split.ipynb @@ -0,0 +1,818 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "0bbb5e31-7578-40b5-b687-80b81064a550", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"../src\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b833f6d0-1c26-451a-b951-8dcdfba882c1", + "metadata": {}, + "outputs": [], + "source": [ + "SPLIT_NUM = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bfdf015a-d937-4805-9f65-a8fc74df7b46", + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import os.path as osp\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6d676ad4-8863-4d4e-88dd-8966dbfe6b41", + "metadata": {}, + "outputs": [], + "source": [ + "src = pd.read_csv(\"../input/train.csv\", header=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5b6595d5-05e0-4a2e-b932-a2b61f97da05", + "metadata": {}, + "outputs": [], + "source": [ + "total_data_num = src.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "24a9b217-fa3d-42f9-b2d9-c344889d22f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32373" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_data_num" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2c255a0c-0649-48af-970e-919045c5bf18", + "metadata": {}, + "outputs": [], + "source": [ + "splited_data_num = 32373 // SPLIT_NUM" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "46cb1fd3-5498-4c98-bbd7-d4c0e3f30bb2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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True " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "src.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "537dae35-c4a4-49f6-ab4d-69044d9999ed", + "metadata": {}, + "outputs": [], + "source": [ + "corrects = src[src[3] == True]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "56969660-1a2b-4ae8-9a13-3c8e75411e16", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " corrects[\"split\"] = 999\n" + ] + } + ], + "source": [ + "corrects[\"split\"] = 999" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "fbb77cb4-0c87-4892-9d06-09678bd7ac6f", + "metadata": {}, + "outputs": [], + "source": [ + "corrects.to_csv(\"../input/bagging/corrects.csv\", header=None, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "389b6072-5e35-474b-85e0-475287ba633f", + "metadata": {}, + "outputs": [], + "source": [ + "target = src[src[3] == False]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "0dca8960-7590-4e30-867a-c3504511e2e6", + "metadata": {}, + "outputs": [], + "source": [ + "seq_col = []\n", + "for _, row in target.iterrows():\n", + " sample = row[0]\n", + " seq_name = osp.basename(osp.dirname(sample))\n", + " seq_col.append(int(seq_name[-1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "d2e221ab-587c-4bab-8595-0e3b126f010a", + "metadata": {}, + "outputs": [], + "source": [ + "tmp_target = target[target[\"seq\"] == 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "d4541d6a-6a90-41e3-b5d9-529efab3b569", + "metadata": {}, + "outputs": [], + "source": [ + "import itertools" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "cf940f00-6106-4614-8ca2-6af12d216b61", + "metadata": {}, + "outputs": [], + "source": [ + "tmp_split_num = []\n", + "for i in itertools.cycle(range(10)):\n", + " tmp_split_num.append(i)\n", + " if len(tmp_split_num) == tmp_target.shape[0]:\n", + " break\n", + "random.shuffle(tmp_split_num)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "a42ce653-fb5a-4fe1-be52-66e7dd35d15c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":10: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp_target[\"split\"] = tmp_split_num\n", + ":10: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp_target[\"split\"] = tmp_split_num\n", + ":10: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp_target[\"split\"] = tmp_split_num\n", + ":10: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp_target[\"split\"] = tmp_split_num\n", + ":10: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp_target[\"split\"] = tmp_split_num\n", + ":10: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp_target[\"split\"] = tmp_split_num\n", + ":10: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp_target[\"split\"] = tmp_split_num\n", + ":10: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp_target[\"split\"] = tmp_split_num\n", + ":10: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " tmp_target[\"split\"] = tmp_split_num\n" + ] + } + ], + "source": [ + "out_df = None\n", + "for seq_num in range(9):\n", + " tmp_target = target[target[\"seq\"] == seq_num]\n", + " tmp_split_num = []\n", + " for split_num in itertools.cycle(range(10)):\n", + " tmp_split_num.append(split_num)\n", + " if len(tmp_split_num) == tmp_target.shape[0]:\n", + " break\n", + " random.shuffle(tmp_split_num)\n", + " tmp_target[\"split\"] = tmp_split_num\n", + " \n", + " if seq_num == 0:\n", + " out_df = tmp_target\n", + " else:\n", + " out_df = pd.concat([out_df, tmp_target], axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "8906a080-d7b8-48a8-b247-57fe9ebedeb7", + "metadata": {}, + "outputs": [], + "source": [ + "tmp = out_df[out_df[\"seq\"] == 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": 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False 8 5 \n", + "\n", + "[24029 rows x 6 columns]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_df" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "dc50ec1e-40f6-4251-b6f6-654614168942", + "metadata": {}, + "outputs": [], + "source": [ + "out_df.drop(\"seq\", axis=1).to_csv(\"../input/bagging/incorrects.csv\", header=None, index=False)" + ] +<<<<<<< HEAD + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fcff95b-80b3-4bb9-abc6-850e6e614962", + "metadata": {}, + "outputs": [], + "source": [] +======= +>>>>>>> with_ssh + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/test_module.ipynb b/notebooks/test_module.ipynb new file mode 100644 index 0000000..e0feef5 --- /dev/null +++ b/notebooks/test_module.ipynb @@ -0,0 +1,260 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "35be6f81-5857-411d-b142-2a0155da87ee", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"../src\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0b3d8d4d-3e99-452c-bb77-973bd4f6b392", + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import os.path as osp\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import torch\n", + "import torch.nn as nn\n", + "from tensorboardX import SummaryWriter\n", + "from sklearn.metrics import confusion_matrix\n", + "import seaborn as sn\n", + "from tqdm import tqdm\n", + "from torchvision import transforms\n", + "from datasets import WasteNetDataset\n", + "from sklearn.model_selection import train_test_split\n", + "from torch.utils.data import DataLoader\n", + "from efficientnet_pytorch import EfficientNet\n", + "from efficientnet_pytorch.utils import Conv2dStaticSamePadding\n", + "\n", + "BATCH_SIZE = 8\n", + "IMG_WIDTH = 384\n", + "IMG_HEIGHT = 288\n", + "SHOW_INFO_FREQ = 100\n", + "VALID_SEQUENCE_NUM = 0\n", + "\n", + "# 学習パラメータ系\n", + "EPOCH_NUM = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "27806384-7a64-405a-8201-32124538d176", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random Seed: 999\n" + ] + } + ], + "source": [ + "# 再現性のためのrandom seedを設定する\n", + "manual_seed = 999\n", + "print(\"Random Seed: \", manual_seed)\n", + "random.seed(manual_seed)\n", + "torch.manual_seed(manual_seed)\n", + "\n", + "# データ選ぶ部\n", + "data_table = pd.read_csv(\"../input/train.csv\", header=None).values.tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d5975228-1443-4041-aec7-22532417a96e", + "metadata": {}, + "outputs": [], + "source": [ + "valid_sequence_num = VALID_SEQUENCE_NUM" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4e2c6727-7801-432a-95a9-6c702e50eb14", + "metadata": {}, + "outputs": [], + "source": [ + "train_sets, test_sets = [], []\n", + "for row in data_table:\n", + " sample_file_path = row[0]\n", + " sequence_name = osp.basename(osp.dirname(sample_file_path))\n", + " if sequence_name == f\"sequence{valid_sequence_num}\":\n", + " test_sets.append(row)\n", + " else:\n", + " train_sets.append(row)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6ae69ff1-9c7d-43a7-be73-557aa55f9eef", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "31821" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(train_sets)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0c28cd05-fa33-4876-b5cb-ef5d10ef6a0e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "552" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(test_sets)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "e4ae69ca-05c9-4c7b-b6b1-63aa71145927", + "metadata": {}, + "outputs": [], + "source": [ + "true_label_num = sum([1 for row in test_sets if row[-1]])\n", + "false_label_num = sum([1 for row in test_sets if not(row[-1])])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "ec747707-582c-47a9-9551-f8e7b979c298", + "metadata": {}, + "outputs": [], + "source": [ + "true_test_sets = [row for row in test_sets if row[-1]]\n", + "false_test_sets = [row for row in test_sets if not(row[-1])]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "ff41beaf-e378-4ecc-8438-18ad08d7c15f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "155" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(true_test_sets)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "1e5b2719-72a6-4994-a4cf-b46fd00bd640", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "397" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "false_label_num" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "cd28b3fa-6779-4d49-9183-654779f0b933", + "metadata": {}, + "outputs": [], + "source": [ + "min_data_num = min(len(true_test_sets), len(false_test_sets))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "838b0106-e58d-4ac2-8188-de23a738a7ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "155" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random.shuffle()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/coatnet.py b/src/coatnet.py new file mode 100644 index 0000000..ebbed77 --- /dev/null +++ b/src/coatnet.py @@ -0,0 +1,298 @@ +import torch +import torch.nn as nn + +from einops import rearrange +from einops.layers.torch import Rearrange + + +def conv_3x3_bn(inp, oup, image_size, downsample=False): + stride = 1 if downsample == False else 2 + return nn.Sequential( + nn.Conv2d(inp, oup, 3, stride, 1, bias=False), + nn.BatchNorm2d(oup), + nn.GELU() + ) + + +class PreNorm(nn.Module): + def __init__(self, dim, fn, norm): + super().__init__() + self.norm = norm(dim) + self.fn = fn + + def forward(self, x, **kwargs): + return self.fn(self.norm(x), **kwargs) + + +class SE(nn.Module): + def __init__(self, inp, oup, expansion=0.25): + super().__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(oup, int(inp * expansion), bias=False), + nn.GELU(), + nn.Linear(int(inp * expansion), oup, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + + +class FeedForward(nn.Module): + def __init__(self, dim, hidden_dim, dropout=0.): + super().__init__() + self.net = nn.Sequential( + nn.Linear(dim, hidden_dim), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim, dim), + nn.Dropout(dropout) + ) + + def forward(self, x): + return self.net(x) + + +class MBConv(nn.Module): + def __init__(self, inp, oup, image_size, downsample=False, expansion=4): + super().__init__() + self.downsample = downsample + stride = 1 if self.downsample == False else 2 + hidden_dim = int(inp * expansion) + + if self.downsample: + self.pool = nn.MaxPool2d(3, 2, 1) + self.proj = nn.Conv2d(inp, oup, 1, 1, 0, bias=False) + + if expansion == 1: + self.conv = nn.Sequential( + # dw + nn.Conv2d(hidden_dim, hidden_dim, 3, stride, + 1, groups=hidden_dim, bias=False), + nn.BatchNorm2d(hidden_dim), + nn.GELU(), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + ) + else: + self.conv = nn.Sequential( + # pw + # down-sample in the first conv + nn.Conv2d(inp, hidden_dim, 1, stride, 0, bias=False), + nn.BatchNorm2d(hidden_dim), + nn.GELU(), + # dw + nn.Conv2d(hidden_dim, hidden_dim, 3, 1, 1, + groups=hidden_dim, bias=False), + nn.BatchNorm2d(hidden_dim), + nn.GELU(), + SE(inp, hidden_dim), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + ) + + self.conv = PreNorm(inp, self.conv, nn.BatchNorm2d) + + def forward(self, x): + if self.downsample: + return self.proj(self.pool(x)) + self.conv(x) + else: + return x + self.conv(x) + + +class Attention(nn.Module): + def __init__(self, inp, oup, image_size, heads=8, dim_head=32, dropout=0.): + super().__init__() + inner_dim = dim_head * heads + project_out = not (heads == 1 and dim_head == inp) + + self.ih, self.iw = image_size + + self.heads = heads + self.scale = dim_head ** -0.5 + + # parameter table of relative position bias + self.relative_bias_table = nn.Parameter( + torch.zeros((2 * self.ih - 1) * (2 * self.iw - 1), heads)) + + coords = torch.meshgrid((torch.arange(self.ih), torch.arange(self.iw))) + coords = torch.flatten(torch.stack(coords), 1) + relative_coords = coords[:, :, None] - coords[:, None, :] + + relative_coords[0] += self.ih - 1 + relative_coords[1] += self.iw - 1 + relative_coords[0] *= 2 * self.iw - 1 + relative_coords = rearrange(relative_coords, 'c h w -> h w c') + relative_index = relative_coords.sum(-1).flatten().unsqueeze(1) + self.register_buffer("relative_index", relative_index) + + self.attend = nn.Softmax(dim=-1) + self.to_qkv = nn.Linear(inp, inner_dim * 3, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, oup), + nn.Dropout(dropout) + ) if project_out else nn.Identity() + + def forward(self, x): + qkv = self.to_qkv(x).chunk(3, dim=-1) + q, k, v = map(lambda t: rearrange( + t, 'b n (h d) -> b h n d', h=self.heads), qkv) + + dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale + + # Use "gather" for more efficiency on GPUs + relative_bias = self.relative_bias_table.gather( + 0, self.relative_index.repeat(1, self.heads)) + relative_bias = rearrange( + relative_bias, '(h w) c -> 1 c h w', h=self.ih * self.iw, w=self.ih * self.iw) + dots = dots + relative_bias + + attn = self.attend(dots) + out = torch.matmul(attn, v) + out = rearrange(out, 'b h n d -> b n (h d)') + out = self.to_out(out) + return out + + +class Transformer(nn.Module): + def __init__(self, inp, oup, image_size, heads=8, dim_head=32, downsample=False, dropout=0.): + super().__init__() + hidden_dim = int(inp * 4) + + self.ih, self.iw = image_size + self.downsample = downsample + + if self.downsample: + self.pool1 = nn.MaxPool2d(3, 2, 1) + self.pool2 = nn.MaxPool2d(3, 2, 1) + self.proj = nn.Conv2d(inp, oup, 1, 1, 0, bias=False) + + self.attn = Attention(inp, oup, image_size, heads, dim_head, dropout) + self.ff = FeedForward(oup, hidden_dim, dropout) + + self.attn = nn.Sequential( + Rearrange('b c ih iw -> b (ih iw) c'), + PreNorm(inp, self.attn, nn.LayerNorm), + Rearrange('b (ih iw) c -> b c ih iw', ih=self.ih, iw=self.iw) + ) + + self.ff = nn.Sequential( + Rearrange('b c ih iw -> b (ih iw) c'), + PreNorm(oup, self.ff, nn.LayerNorm), + Rearrange('b (ih iw) c -> b c ih iw', ih=self.ih, iw=self.iw) + ) + + def forward(self, x): + if self.downsample: + x = self.proj(self.pool1(x)) + self.attn(self.pool2(x)) + else: + x = x + self.attn(x) + x = x + self.ff(x) + return x + + +class CoAtNet(nn.Module): + def __init__(self, image_size, in_channels, num_blocks, channels, num_classes=1000, + block_types=['C', 'C', 'T', 'T']): + super().__init__() + ih, iw = image_size + block = {'C': MBConv, 'T': Transformer} + + self.s0 = self._make_layer( + conv_3x3_bn, in_channels, channels[0], num_blocks[0], (ih // 2, iw // 2)) + self.s1 = self._make_layer( + block[block_types[0]], channels[0], channels[1], num_blocks[1], (ih // 4, iw // 4)) + self.s2 = self._make_layer( + block[block_types[1]], channels[1], channels[2], num_blocks[2], (ih // 8, iw // 8)) + self.s3 = self._make_layer( + block[block_types[2]], channels[2], channels[3], num_blocks[3], (ih // 16, iw // 16)) + self.s4 = self._make_layer( + block[block_types[3]], channels[3], channels[4], num_blocks[4], (ih // 32, iw // 32)) + + self.pool = nn.AvgPool2d(ih // 32, 1) + self.fc = nn.Linear(channels[-1], num_classes, bias=False) + + def forward(self, x): + x = self.s0(x) + x = self.s1(x) + x = self.s2(x) + x = self.s3(x) + x = self.s4(x) + + x = self.pool(x).view(-1, x.shape[1]) + x = self.fc(x) + return x + + def _make_layer(self, block, inp, oup, depth, image_size): + layers = nn.ModuleList([]) + for i in range(depth): + if i == 0: + layers.append(block(inp, oup, image_size, downsample=True)) + else: + layers.append(block(oup, oup, image_size)) + return nn.Sequential(*layers) + + +def coatnet_0(): + num_blocks = [2, 2, 3, 5, 2] # L + channels = [64, 96, 192, 384, 768] # D + return CoAtNet((224, 224), 3, num_blocks, channels, num_classes=1000) + + +def coatnet_1(): + num_blocks = [2, 2, 6, 14, 2] # L + channels = [64, 96, 192, 384, 768] # D + return CoAtNet((224, 224), 3, num_blocks, channels, num_classes=1000) + + +def coatnet_2(): + num_blocks = [2, 2, 6, 14, 2] # L + channels = [128, 128, 256, 512, 1026] # D + return CoAtNet((224, 224), 3, num_blocks, channels, num_classes=1000) + + +def coatnet_3(): + num_blocks = [2, 2, 6, 14, 2] # L + channels = [192, 192, 384, 768, 1536] # D + return CoAtNet((224, 224), 3, num_blocks, channels, num_classes=1000) + + +def coatnet_4(): + num_blocks = [2, 2, 12, 28, 2] # L + channels = [192, 192, 384, 768, 1536] # D + return CoAtNet((224, 224), 3, num_blocks, channels, num_classes=1000) + + +def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + +if __name__ == '__main__': + img = torch.randn(1, 3, 224, 224) + + net = coatnet_0() + out = net(img) + print(out.shape, count_parameters(net)) + + net = coatnet_1() + out = net(img) + print(out.shape, count_parameters(net)) + + net = coatnet_2() + out = net(img) + print(out.shape, count_parameters(net)) + + net = coatnet_3() + out = net(img) + print(out.shape, count_parameters(net)) + + net = coatnet_4() + out = net(img) + print(out.shape, count_parameters(net)) \ No newline at end of file diff --git a/src/datasets.py b/src/datasets.py new file mode 100644 index 0000000..5a6cbf1 --- /dev/null +++ b/src/datasets.py @@ -0,0 +1,36 @@ +import torch +import torch.nn.functional as F +from PIL import Image +from torch.utils.data import Dataset +from torchvision.transforms import transforms + + +class WasteNetDataset(Dataset): + def __init__(self, datasets_table, img_width, img_height, transform=None, return_path=False): + self.transform = transform + self.img_width = img_width + self.img_height = img_height + # 以下のテーブルは 列１：1枚目画像へのpath, 列２：2枚目画像へのpath，列３：3枚目画像へのpath + # 列４：以上3枚がデータセットとして採択されるか + self.datasets_table = datasets_table + self.return_path = return_path + + def __len__(self): + return len(self.datasets_table) + + def __getitem__(self, idx): + target_row = self.datasets_table[idx] # [path1, path2, path3, label] + imgs = [Image.open(img_path) for img_path in target_row[:3]] + imgs = [img.resize((self.img_width, self.img_height), Image.BILINEAR).convert("RGB") for img in imgs] + if self.transform: + imgs = [self.transform(img) for img in imgs] + else: + imgs = [transforms.ToTensor()(img) for img in imgs] + + imgs = torch.cat(imgs, dim=0) + label = 1 if target_row[3] else 0 + + if self.return_path: + return imgs, label, *target_row[:3] + else: + return imgs, label diff --git a/src/effnetv2.py b/src/effnetv2.py new file mode 100644 index 0000000..be3f4bd --- /dev/null +++ b/src/effnetv2.py @@ -0,0 +1,231 @@ +""" +Creates a EfficientNetV2 Model as defined in: +Mingxing Tan, Quoc V. Le. (2021). +EfficientNetV2: Smaller Models and Faster Training +arXiv preprint arXiv:2104.00298. +import from https://github.com/d-li14/mobilenetv2.pytorch +""" + +import torch +import torch.nn as nn +import math + +__all__ = ['effnetv2_s', 'effnetv2_m', 'effnetv2_l', 'effnetv2_xl'] + + +def _make_divisible(v, divisor, min_value=None): + """ + This function is taken from the original tf repo. + It ensures that all layers have a channel number that is divisible by 8 + It can be seen here: + https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py + :param v: + :param divisor: + :param min_value: + :return: + """ + if min_value is None: + min_value = divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < 0.9 * v: + new_v += divisor + return new_v + + +# SiLU (Swish) activation function +if hasattr(nn, 'SiLU'): + SiLU = nn.SiLU +else: + # For compatibility with old PyTorch versions + class SiLU(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + + +class SELayer(nn.Module): + def __init__(self, inp, oup, reduction=4): + super(SELayer, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(oup, _make_divisible(inp // reduction, 8)), + SiLU(), + nn.Linear(_make_divisible(inp // reduction, 8), oup), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + + +def conv_3x3_bn(inp, oup, stride): + return nn.Sequential( + nn.Conv2d(inp, oup, 3, stride, 1, bias=False), + nn.BatchNorm2d(oup), + SiLU() + ) + + +def conv_1x1_bn(inp, oup): + return nn.Sequential( + nn.Conv2d(inp, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + SiLU() + ) + + +class MBConv(nn.Module): + def __init__(self, inp, oup, stride, expand_ratio, use_se): + super(MBConv, self).__init__() + assert stride in [1, 2] + + hidden_dim = round(inp * expand_ratio) + self.identity = stride == 1 and inp == oup + if use_se: + self.conv = nn.Sequential( + # pw + nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), + nn.BatchNorm2d(hidden_dim), + SiLU(), + # dw + nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False), + nn.BatchNorm2d(hidden_dim), + SiLU(), + SELayer(inp, hidden_dim), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + ) + else: + self.conv = nn.Sequential( + # fused + nn.Conv2d(inp, hidden_dim, 3, stride, 1, bias=False), + nn.BatchNorm2d(hidden_dim), + SiLU(), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + ) + + def forward(self, x): + if self.identity: + return x + self.conv(x) + else: + return self.conv(x) + + +class EffNetV2(nn.Module): + def __init__(self, cfgs, num_classes=1000, width_mult=1.): + super(EffNetV2, self).__init__() + self.cfgs = cfgs + + # building first layer + input_channel = _make_divisible(24 * width_mult, 8) + layers = [conv_3x3_bn(9, input_channel, 2)] + # building inverted residual blocks + block = MBConv + for t, c, n, s, use_se in self.cfgs: + output_channel = _make_divisible(c * width_mult, 8) + for i in range(n): + layers.append(block(input_channel, output_channel, s if i == 0 else 1, t, use_se)) + input_channel = output_channel + self.features = nn.Sequential(*layers) + # building last several layers + output_channel = _make_divisible(1792 * width_mult, 8) if width_mult > 1.0 else 1792 + self.conv = conv_1x1_bn(input_channel, output_channel) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.classifier = nn.Linear(output_channel, num_classes) + + self._initialize_weights() + + def forward(self, x): + x = self.features(x) + x = self.conv(x) + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.classifier(x) + return x + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + m.weight.data.normal_(0, 0.001) + m.bias.data.zero_() + + +def effnetv2_s(**kwargs): + """ + Constructs a EfficientNetV2-S model + """ + cfgs = [ + # t, c, n, s, SE + [1, 24, 2, 1, 0], + [4, 48, 4, 2, 0], + [4, 64, 4, 2, 0], + [4, 128, 6, 2, 1], + [6, 160, 9, 1, 1], + [6, 256, 15, 2, 1], + ] + return EffNetV2(cfgs, **kwargs) + + +def effnetv2_m(**kwargs): + """ + Constructs a EfficientNetV2-M model + """ + cfgs = [ + # t, c, n, s, SE + [1, 24, 3, 1, 0], + [4, 48, 5, 2, 0], + [4, 80, 5, 2, 0], + [4, 160, 7, 2, 1], + [6, 176, 14, 1, 1], + [6, 304, 18, 2, 1], + [6, 512, 5, 1, 1], + ] + return EffNetV2(cfgs, **kwargs) + + +def effnetv2_l(**kwargs): + """ + Constructs a EfficientNetV2-L model + """ + cfgs = [ + # t, c, n, s, SE + [1, 32, 4, 1, 0], + [4, 64, 7, 2, 0], + [4, 96, 7, 2, 0], + [4, 192, 10, 2, 1], + [6, 224, 19, 1, 1], + [6, 384, 25, 2, 1], + [6, 640, 7, 1, 1], + ] + return EffNetV2(cfgs, **kwargs) + + +def effnetv2_xl(**kwargs): + """ + Constructs a EfficientNetV2-XL model + """ + cfgs = [ + # t, c, n, s, SE + [1, 32, 4, 1, 0], + [4, 64, 8, 2, 0], + [4, 96, 8, 2, 0], + [4, 192, 16, 2, 1], + [6, 256, 24, 1, 1], + [6, 512, 32, 2, 1], + [6, 640, 8, 1, 1], + ] + return EffNetV2(cfgs, **kwargs) diff --git a/src/train.py b/src/train.py new file mode 100644 index 0000000..b5be26a --- /dev/null +++ b/src/train.py @@ -0,0 +1,196 @@ +import os +import os.path as osp +import random +import matplotlib.pyplot as plt +import pandas as pd +import torch +import torch.nn as nn +from tensorboardX import SummaryWriter +from sklearn.metrics import confusion_matrix +import seaborn as sn +from tqdm import tqdm +import argparse +from torchvision import transforms +from datasets import WasteNetDataset +from torch.utils.data import DataLoader + +# 自前の関数群 +from apex import amp, optimizers +from utils import * + +parser = argparse.ArgumentParser() + +parser.add_argument("model_name", type=str) #ex) "efficient_net_b5_1" +parser.add_argument("split_num", type=int) + +args = parser.parse_args() + +MODEL_NAME = args.model_name +LOGGING_DIR = "undersample+bagging" +SPLIT_NUM = args.split_num +START_SEQUENCE = 0 + +BATCH_SIZE = 4 +IMG_WIDTH = 384 +IMG_HEIGHT = 288 +SHOW_INFO_FREQ = 100 +ALL_SEQUENCE_NUM = 9 + +# 学習パラメータ系 +EPOCH_NUM = 10 + +# apex関連 +# opt_level = 'O1' + +if __name__ == "__main__": + for sequence_num in range(START_SEQUENCE, ALL_SEQUENCE_NUM): + torch.backends.cudnn.benchmark = True + + # 再現性のためのrandom seedを設定する + manual_seed = 999 + print("Random Seed: ", manual_seed) + random.seed(manual_seed) + torch.manual_seed(manual_seed) + + # どのデバイスで実行するかをきめる + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Use device: {device}") + + # データ選ぶ部 + # data_table = pd.read_csv("../input/train.csv", header=None).values.tolist() + # train_dset, test_dset = train_test_split_by_sequence(data_table, test_sequence_num=sequence_num, balanced_test=True) + train_dset, test_dset = load_dsets_for_bagging(sequence_num, SPLIT_NUM, balanced_test=True) + + if MODEL_NAME.startswith("efficient"): + model = load_efficientnet("efficientnet-b5", IMG_HEIGHT, IMG_WIDTH) + elif MODEL_NAME.startswith("resnet"): + model = load_resnet50() + elif MODEL_NAME.startswith("densenet"): + model = load_densenet161() + elif MODEL_NAME.startswith("deit"): + model = load_deit(IMG_HEIGHT, IMG_WIDTH) + elif MODEL_NAME.startswith("coatnet"): + model = load_CoAtNet() + elif MODEL_NAME.startswith("effnetv2"): + model = load_effnetv2() + elif MODEL_NAME.startswith("mobile"): + model = load_mobilenet_v2() + else: + raise NotImplementedError() + + model.to(device) + optimizer = torch.optim.Adam(model.parameters()) + # model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level) + + # データローダー部 + + if MODEL_NAME.startswith("coatnet"): + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + transforms.Resize((224, 224)) + ]) + else: + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + + train_datasets = WasteNetDataset(train_dset, IMG_WIDTH, IMG_HEIGHT, transform) + test_datasets = WasteNetDataset(test_dset, IMG_WIDTH, IMG_HEIGHT, transform) + train_loader = DataLoader(train_datasets, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True) + test_loader = DataLoader(test_datasets, batch_size=BATCH_SIZE, num_workers=0, pin_memory=True) + + # 損失関数 + criterion = nn.CrossEntropyLoss() + + # tensorboardX + os.makedirs(osp.join(f"../logs", LOGGING_DIR), exist_ok=True) + os.makedirs(osp.join(f"../logs", LOGGING_DIR, MODEL_NAME), exist_ok=True) + + os.makedirs(osp.join("../logs", LOGGING_DIR, MODEL_NAME, f"sequence{sequence_num}"), exist_ok=True) + writer = SummaryWriter(osp.join("../logs", LOGGING_DIR, MODEL_NAME, f"sequence{sequence_num}")) + classes = ("bad data", "good data") + + for epoch in range(1, EPOCH_NUM + 1): + + # train phase + model.train() + running_loss = 0.0 + correct_num = 0 + pred_target_num = 0 + + for i, (imgs, labels) in enumerate(tqdm(train_loader)): + model.zero_grad(set_to_none=True) + + imgs = imgs.to(device) + labels = labels.to(device) + + pred = model(imgs) + loss = criterion(pred, labels) + running_loss += loss.item() + + pred_target_num += pred.shape[0] + correct_num += torch.sum(torch.argmax(pred, dim=1) == labels).item() + + loss.backward() + # with amp.scale_loss(loss, optimizer) as scaled_loss: + # scaled_loss.backward() + + optimizer.step() + + if i % SHOW_INFO_FREQ == (SHOW_INFO_FREQ - 1): + print("train") + print(f"[sequence{sequence_num} {epoch}, {i}] {running_loss}") + print(f"[avg] {correct_num / pred_target_num}") + print() + + writer.add_scalar("train_loss", running_loss, train_loader.__len__() * (epoch - 1) + i) + writer.add_scalar("train_acc", correct_num / pred_target_num, train_loader.__len__() * (epoch - 1) + i) + + running_loss = 0.0 + correct_num = 0.0 + pred_target_num = 0.0 + + # test phase + with torch.no_grad(): + model.eval() + running_loss = 0.0 + correct_num = 0 + pred_target_num = 0 + + y_pred = [] + y_true = [] + for i, (imgs, labels) in enumerate(tqdm(test_loader)): + imgs = imgs.to(device) + labels = labels.to(device) + + pred = model(imgs) + loss = criterion(pred, labels) + running_loss += loss.item() + + pred_target_num += pred.shape[0] + pred_index = torch.argmax(pred, dim=1) + correct_num += torch.sum(pred_index == labels).item() + + y_pred.extend(pred_index.data.cpu().numpy()) + y_true.extend(labels.data.cpu().numpy()) + + print("test") + print(f"[sequence{sequence_num} {epoch}] {running_loss}") + print(f"[avg] {correct_num / pred_target_num}") + print() + + writer.add_scalar("test_loss", running_loss, train_loader.__len__() * (epoch - 1)) + writer.add_scalar("test_acc", correct_num / pred_target_num, train_loader.__len__() * (epoch - 1)) + torch.save(model.state_dict(), osp.join("../logs", LOGGING_DIR, MODEL_NAME, f"sequence{sequence_num}", f"test{epoch}_acc{(correct_num / pred_target_num):.2f}_weights.pth")) + + # 混同行列の保存 + cf_matrix = confusion_matrix(y_true, y_pred) + df_cm = pd.DataFrame(cf_matrix, index=[f"{i}(label)" for i in classes], columns=[f"{i}(predicted)" for i in classes]) + plt.figure(figsize=(12, 7)) + show_fig = sn.heatmap(df_cm, annot=True).get_figure() + writer.add_figure("Confusion matrix", show_fig, epoch) + del model + torch.cuda.empty_cache() + \ No newline at end of file diff --git a/src/utils.py b/src/utils.py new file mode 100644 index 0000000..93bb954 --- /dev/null +++ b/src/utils.py @@ -0,0 +1,116 @@ +import os.path as osp +import random +import torch +import torch.nn as nn +import pandas as pd +import torchvision +from efficientnet_pytorch import EfficientNet +from efficientnet_pytorch.utils import Conv2dStaticSamePadding +from transformers import DeiTModel, DeiTConfig +from coatnet import * +from effnetv2 import * + + +NO_SPLITTED = 999 + + +def train_test_split_by_sequence(data, test_sequence_num, balanced_test=False, random_seed=999): + train_sets, test_sets = [], [] + for row in data: + sample_file_path = row[0] + sequence_name = osp.basename(osp.dirname(sample_file_path)) + if sequence_name == f"sequence{test_sequence_num}": + test_sets.append(row) + else: + train_sets.append(row) + + if balanced_test: + random.seed(random_seed) + true_test_sets = [row for row in test_sets if row[-1]] + false_test_sets = [row for row in test_sets if not(row[-1])] + random.shuffle(true_test_sets) + random.shuffle(false_test_sets) + + min_data_num = min(len(true_test_sets), len(false_test_sets)) + test_sets = true_test_sets[:min_data_num] + false_test_sets[:min_data_num] + + return train_sets, test_sets + + +def load_dsets_for_bagging(test_sequence_num, split_num, balanced_test=False): + corrects = pd.read_csv(r"../input/bagging/corrects.csv", header=None).values.tolist() + incorrects = pd.read_csv(r"../input/bagging/incorrects.csv", header=None).values.tolist() + all_data = corrects + incorrects + + train_sets, test_sets = [], [] + for row in all_data: + sample_file_path = row[0] + sequence_name = osp.basename(osp.dirname(sample_file_path)) + if sequence_name == f"sequence{test_sequence_num}": + test_sets.append(row[:4]) + else: + if (row[-1] == split_num) or (row[-1] == NO_SPLITTED): + train_sets.append(row[:4]) + + if balanced_test: + true_test_sets = [row for row in test_sets if row[-1]] + false_test_sets = [row for row in test_sets if not(row[-1])] + random.shuffle(true_test_sets) + random.shuffle(false_test_sets) + + min_data_num = min(len(true_test_sets), len(false_test_sets)) + test_sets = true_test_sets[:min_data_num] + false_test_sets[:min_data_num] + + return train_sets, test_sets + + +def load_efficientnet(model_name, image_height, image_width): + model = EfficientNet.from_pretrained(model_name, advprop=True, num_classes=2) + model._conv_stem = Conv2dStaticSamePadding(9, 48, (3, 3), (2, 2), image_size=(image_height, image_width), bias=False) + + return model + + +def load_resnet50(): + model = torchvision.models.resnet50(pretrained=True) + model.conv1 = nn.Conv2d(9, 64, kernel_size=7, stride=2, padding=3, bias=False) + model.fc.out_features = 2 + return model + + +def load_densenet161(): + model = torchvision.models.densenet161(pretrained=True) + model.features[0] = nn.Conv2d(9, 96, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) + # model.classifier = nn.Linear(in_features=2208, out_features=2, bias=True) + model.classifier.out_features = 2 + + return model + + +def load_deit(image_height, image_width): + configuration = DeiTConfig(size=384, num_channels=9, ) + model = DeiTModel(configuration) + + return model + + +def load_CoAtNet(): + model = coatnet_2() + model.s0[0] = nn.Conv2d(9, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) + model.fc = nn.Linear(in_features=1026, out_features=2, bias=True) + + return model + + +def load_effnetv2(): + model = effnetv2_m(num_classes=2) + + return model + + +def load_mobilenet_v2(): + model = torchvision.models.mobilenet_v2(pretrained=True) + model.features[0][0] = nn.Conv2d(9, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) + model.classifier[1] = nn.Linear(1280, 2, bias=True) + + return model