fix plot sample :)

helper.py

@@ -52,6 +52,7 @@ def CreateDataFolder(raw_data_dir = 'raw_data', dataset_dir = 'dataset',
         splitFolder = ['train', 'val']
 
     if not os.path.isdir(dataset_dir) :
+        print(f'creating {dataset_dir} folder ...')
         os.makedirs(dataset_dir)
         for folder in splitFolder :
             os.makedirs(os.path.join(dataset_dir, folder))

predict.ipynb

@@ -1 +1 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOEfCfsksiRlW2gAwTV71yY"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"markdown","source":["### Change Runtime type to **GPU**"],"metadata":{"id":"PZ3OtlanAGYr"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"GsJjRNOV86Dc"},"outputs":[],"source":["#installation\n","from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","source":["### Change your **Google drive path** then just click run"],"metadata":{"id":"dfbScIVdAj08"}},{"cell_type":"code","source":["# CHANGE YOUR DRIVE PATH HERE\n","%cd /content/drive/MyDrive/YOUR_FOLDER_THAT_HAVE_RAW_DATA_FOLDER\n","!pip install -r requirement.txt"],"metadata":{"id":"doT_Sj4UAhLb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#import\n","import os\n","import torch\n","import numpy as np\n","import torchvision\n","import matplotlib.pyplot as plt\n","from torchvision import transforms as T\n","from helper import device, ToJPG, predict, ToTensor, ToTensorShowImage, plot_predict_most_confs, plot_predict"],"metadata":{"id":"uF_sI2Dk9p4C"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#load trained model\n","current_path = os.getcwd()\n","folder_name = current_path.split('/')[-1]\n","%cd ..\n","for file in os.listdir(folder_name) :\n","    if file.endswith('pt') :\n","        weight_path = file\n","os.chdir(folder_name)\n","class_names = [line.strip() for line in open('class_names.txt', 'r')]\n","\n","os.makedirs('predict_images', exist_ok=True)\n","trained_model = torchvision.models.efficientnet_b2().to(device)\n","trained_model.classifier = torch.nn.Sequential(\n","    torch.nn.Dropout(p=0.3, inplace=True), \n","    torch.nn.Linear(in_features=1408, \n","                    out_features=len(class_names), \n","                    bias=True)).to(device)\n","trained_model.load_state_dict(torch.load(f=weight_path, map_location=device))"],"metadata":{"id":"R4n_31lL9Hsf"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### upload predict images in **predict_images** folder\n","- !!! uploaded images before run the next cell"],"metadata":{"id":"3_yo5AXu-WSx"}},{"cell_type":"code","source":["# to .jpg format\n","for image_name in os.listdir('predict_images') :\n","    image_path = os.path.join('predict_images', image_name)\n","    ToJPG(image_path)"],"metadata":{"id":"mxyblz9q-RUu"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### predict a single image\n","- change image_path "],"metadata":{"id":"7FFp_4DEJLOm"}},{"cell_type":"code","source":["image_path = 'predict_images/YOUR_IMAGE_NAME.jpg' \n","most_like = 3 # find most like 3 classes\n","\n","pred_class, confs = predict(ToTensor(image_path), trained_model, class_names)\n","confs = confs*100\n","print(f'''\n","predict class : {pred_class}\n","confident : {np.max(confs):.2f}%\n","''')"],"metadata":{"id":"2FeGP8Bb-h56"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plot_predict_most_confs(image_path, most_like, class_names, trained_model)"],"metadata":{"id":"qyCjedA8Eunq"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### predict multiple images\n","- just run and see a result"],"metadata":{"id":"3SjaNoXQRXeU"}},{"cell_type":"code","source":["images_path = [os.path.join('predict_images', image_name) for image_name in os.listdir('predict_images') if not image_name.endswith('avif')]\n","size = int(len(images_path)**(1/2)) + 1\n","plt.figure(figsize=(4*size, 4*size))\n","for i, image_name in enumerate(images_path, start=1) : \n","    plt.subplot(size, size, i)\n","    image_fp = ToTensor(image_name)\n","    image_fs = ToTensorShowImage(image_name)\n","    plot_predict(image_fp, trained_model, image_fs, class_names, color='black')"],"metadata":{"id":"8LvSkzknQ0xp"},"execution_count":null,"outputs":[]}]}
+{"cells":[{"cell_type":"markdown","metadata":{"id":"PZ3OtlanAGYr"},"source":["### Change Runtime type to **GPU**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GsJjRNOV86Dc"},"outputs":[],"source":["#installation\n","from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","metadata":{"id":"dfbScIVdAj08"},"source":["### Change your **Google drive path** then just click run"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"doT_Sj4UAhLb"},"outputs":[],"source":["# CHANGE YOUR DRIVE PATH HERE\n","%cd /content/drive/MyDrive/YOUR_FOLDER_THAT_HAVE_RAW_DATA_FOLDER\n","!pip install -r requirement.txt"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uF_sI2Dk9p4C"},"outputs":[],"source":["#import\n","import os\n","import torch\n","import numpy as np\n","import torchvision\n","import matplotlib.pyplot as plt\n","from torchvision import transforms as T\n","from helper import device, ToJPG, predict, ToTensor, ToTensorShowImage, plot_predict_most_confs, plot_predict"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"R4n_31lL9Hsf"},"outputs":[],"source":["#load trained model\n","current_path = os.getcwd()\n","folder_name = current_path.split('/')[-1]\n","%cd ..\n","for file in os.listdir(folder_name) :\n","    if file.endswith('pt') :\n","        weight_path = file\n","os.chdir(folder_name)\n","class_names = [line.strip() for line in open('class_names.txt', 'r')]\n","\n","os.makedirs('predict_images', exist_ok=True)\n","trained_model = torchvision.models.efficientnet_b2().to(device)\n","trained_model.classifier = torch.nn.Sequential(\n","    torch.nn.Dropout(p=0.3, inplace=True), \n","    torch.nn.Linear(in_features=1408, \n","                    out_features=len(class_names), \n","                    bias=True)).to(device)\n","trained_model.load_state_dict(torch.load(f=weight_path, map_location=device))"]},{"cell_type":"markdown","metadata":{"id":"3_yo5AXu-WSx"},"source":["### upload predict images in **predict_images** folder\n","- !!! uploaded images before run the next cell"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mxyblz9q-RUu"},"outputs":[],"source":["# to .jpg format\n","for image_name in os.listdir('predict_images') :\n","    image_path = os.path.join('predict_images', image_name)\n","    ToJPG(image_path)"]},{"cell_type":"markdown","metadata":{"id":"7FFp_4DEJLOm"},"source":["### predict a single image\n","- change image_path "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2FeGP8Bb-h56"},"outputs":[],"source":["image_path = 'predict_images/YOUR_IMAGE_NAME.jpg' \n","most_like = 3 # find most like 3 classes\n","\n","pred_class, confs = predict(ToTensor(image_path), trained_model, class_names)\n","confs = confs*100\n","print(f'''\n","predict class : {pred_class}\n","confident : {np.max(confs):.2f}%\n","''')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qyCjedA8Eunq"},"outputs":[],"source":["plot_predict_most_confs(image_path, most_like, class_names, trained_model)"]},{"cell_type":"markdown","metadata":{"id":"3SjaNoXQRXeU"},"source":["### predict multiple images\n","- just run and see a result"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8LvSkzknQ0xp"},"outputs":[],"source":["images_path = [os.path.join('predict_images', image_name) for image_name in os.listdir('predict_images') if not image_name.endswith('avif')]\n","size = int(len(images_path)**(1/2)) + 1\n","plt.figure(figsize=(4*size, 4*size))\n","for i, image_name in enumerate(images_path, start=1) : \n","    plt.subplot(size, size, i)\n","    image_fp = ToTensor(image_name)\n","    image_fs = ToTensorShowImage(image_name)\n","    plot_predict(image_fp, trained_model, image_fs, class_names, color='black')"]}],"metadata":{"accelerator":"GPU","colab":{"authorship_tag":"ABX9TyOEfCfsksiRlW2gAwTV71yY","provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3.10.0 64-bit","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.0"},"vscode":{"interpreter":{"hash":"63e79917a05e390872358bfb73c58bc903ada01d2d04077091749088207d82cf"}}},"nbformat":4,"nbformat_minor":0}