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" test_loss += loss_fn(pred, label).asnumpy()\n", + " correct += (pred.argmax(axis=1) == label).asnumpy().sum()\n", + " test_loss /= num_batches\n", + " correct /= total\n", + " print(f\"Test: \\n Accuracy: {(100 * correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")\n", + "\n", + "# 训练与评估\n", + "print(\"============== Starting Training ==============\")\n", + "epochs = config.epochs\n", + "for t in range(epochs):\n", + " print(f\"Epoch {t + 1}\\n-------------------------------\")\n", + " train_loop(network, train_dataset, loss, opt)\n", + " save_checkpoint(network, os.path.join(CKPT_PATH, \"save_mobilenetV2_model.ckpt\"))\n", + " test_loop(network, eval_dataset, loss)\n", + "print(\"Done!\")" + ] + }, + { + "cell_type": "markdown", + "id": "7630eb4e", + "metadata": {}, + "source": [ + "## 模型预测" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ae0e051", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "from PIL import Image\n", + "from mindspore import Tensor, load_checkpoint\n", + "from sklearn.metrics import confusion_matrix, roc_curve, auc\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def image_process(image):\n", + " \"\"\"Process one image at a time.\n", + "\n", + " Args:\n", + " image: shape (H, W, C)\n", + " \"\"\"\n", + " mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]\n", + " std = [0.229 * 255, 0.224 * 255, 0.225 * 255]\n", + " image = (np.array(image) - mean) / std\n", + " image = image.transpose((2, 0, 1)) # Change to (C, H, W) format\n", + " img_tensor = Tensor(np.array([image], np.float32)) # Add batch dimension\n", + " return img_tensor\n", + "\n", + "def infer_one(network, image_path):\n", + " \"\"\"Infer a single image and return predicted label.\"\"\"\n", + " image = Image.open(image_path).resize((config.image_height, config.image_width))\n", + " logits = network(image_process(image))\n", + " pred = np.argmax(logits.asnumpy(), axis=1)[0]\n", + " return pred\n", + "\n", + "def infer_folder(network, folder_path, label_map):\n", + " \"\"\"Infer all images in a folder.\"\"\"\n", + " true_labels = []\n", + " pred_labels = []\n", + " \n", + " for class_name, label in label_map.items():\n", + " class_folder = os.path.join(folder_path, class_name)\n", + " if not os.path.exists(class_folder):\n", + " continue\n", + " \n", + " for image_name in os.listdir(class_folder):\n", + " image_path = os.path.join(class_folder, image_name)\n", + " true_labels.append(label)\n", + " pred_labels.append(infer_one(network, image_path))\n", + " \n", + " return true_labels, pred_labels\n", + "\n", + "def plot_metrics(true_labels, pred_labels, label_map):\n", + " \"\"\"Plot confusion matrix and ROC curve.\"\"\"\n", + " # Confusion Matrix\n", + " cm = confusion_matrix(true_labels, pred_labels)\n", + " print(\"Confusion Matrix:\\n\", cm)\n", + " \n", + " plt.figure(figsize=(10, 5))\n", + " plt.subplot(1, 2, 1)\n", + " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", + " plt.title(\"Confusion Matrix\")\n", + " plt.colorbar()\n", + " plt.xticks(ticks=np.arange(len(label_map)), labels=label_map.keys(), rotation=45)\n", + " plt.yticks(ticks=np.arange(len(label_map)), labels=label_map.keys())\n", + " plt.ylabel(\"True Label\")\n", + " plt.xlabel(\"Predicted Label\")\n", + " \n", + " # ROC Curve\n", + " n_classes = len(label_map)\n", + " y_true = np.eye(n_classes)[true_labels] # One-hot encode true labels\n", + " y_pred_proba = np.eye(n_classes)[pred_labels] # Simulated probabilities for simplicity\n", + "\n", + " plt.subplot(1, 2, 2)\n", + " for i, label in enumerate(label_map.keys()):\n", + " fpr, tpr, _ = roc_curve(y_true[:, i], y_pred_proba[:, i])\n", + " roc_auc = auc(fpr, tpr)\n", + " plt.plot(fpr, tpr, label=f\"Class {label} (AUC = {roc_auc:.2f})\")\n", + "\n", + " plt.plot([0, 1], [0, 1], \"k--\")\n", + " plt.title(\"ROC Curve\")\n", + " plt.xlabel(\"False Positive Rate\")\n", + " plt.ylabel(\"True Positive Rate\")\n", + " plt.legend(loc=\"lower right\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "def infer_sleep(folder_path):\n", + " # Load network\n", + " backbone = MobileNetV2Backbone(last_channel=config.backbone_out_channels)\n", + " head = MobileNetV2Head(input_channel=backbone.out_channels, num_classes=config.num_classes)\n", + " network = mobilenet_v2(backbone, head)\n", + " load_checkpoint(os.path.join(config.save_ckpt_path, CKPT), network)\n", + " \n", + " # Define label mapping (adjust according to your dataset)\n", + " label_map = {\"NS\": 0, \"S\": 1} # Example class mapping\n", + " \n", + " # Folder containing subfolders for each class\n", + " #folder_path = \"./For_test2/\" +file_name\n", + " \n", + " # Perform inference and compute metrics\n", + " true_labels, pred_labels = infer_folder(network, folder_path, label_map)\n", + " S_num = sum(pred_labels)\n", + " print(\"Sleep_time: %.2f min\" %(S_num/2))\n", + " return S_num/2\n", + " #plot_metrics(true_labels, pred_labels, label_map)\n", + "\n", + "def infer_sas(folder_path):\n", + " # Load network\n", + " backbone = MobileNetV2Backbone(last_channel=config.backbone_out_channels)\n", + " head = MobileNetV2Head(input_channel=backbone.out_channels, num_classes=config.num_classes)\n", + " network = mobilenet_v2(backbone, head)\n", + " load_checkpoint(os.path.join(config.save_ckpt_path, CKPT), network)\n", + " \n", + " # Define label mapping (adjust according to your dataset)\n", + " label_map = {\"N\": 0, \"A\": 1} # Example class mapping\n", + " \n", + " # Perform inference and compute metrics\n", + " true_labels, pred_labels = infer_folder(network, folder_path, label_map)\n", + " A_num = sum(pred_labels)\n", + " print(\"A_num: %d\" %(A_num))\n", + " return A_num\n", + " \n", + "# Run inference\n", + "mother_name = './0-AP'\n", + "folder_path = mother_name + \"/For_test_s\"\n", + "config.save_ckpt_path = \"./ckpt2\"\n", + "CKPT = \"save_mobilenetV2_SLEEP_model.ckpt\"\n", + "\n", + "# 获取当前路径下的文件名,返回List\n", + "fileNames = os.listdir(folder_path) \n", + "for file in fileNames:\n", + " # 将文件命加入到当前文件路径后面\n", + " newDir = os.path.join(folder_path,file)\n", + " time_60s = infer_sleep(newDir)\n", + " print(\"%s 睡眠时长= %.2f min\" %(file, time_60s))\n", + "\n", + "\n", + "\n", + "\n", + "# folder_path = mother_name + \"./For_test/\" +file_name\n", + "# config.save_ckpt_path = \"./ckpt\"\n", + "# CKPT = \"save_mobilenetV2_model.ckpt\"\n", + "# sas_num = infer_sas(folder_path)\n", + "\n", + "# print(\"AHI = %.2f\" %(sas_num*120/time_30s))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-mindspore_py37]", + "language": "python", + "name": "conda-env-.conda-mindspore_py37-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} -- Gitee