Victor Phan
441 lines
20 KiB
Plaintext
441 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "17da4353",
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"metadata": {},
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"outputs": [],
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"source": [
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"import importlib\n",
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"import new_import_ODC as odc_tools\n",
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"importlib.reload(odc_tools)\n",
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"from new_import_ODC import *\n",
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"print(\"✅ Import thành công\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9c063be3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Khởi tạo Dask + Datacube + S3\n",
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"cluster, client = initialize_dask(use_gateway=True)\n",
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"dc = datacube.Datacube()\n",
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"configure_s3_access(aws_unsigned=True)\n",
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"print(\"✅ Dask + Datacube + S3 sẵn sàng\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "83784d01",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Cấu hình vùng và thời gian\n",
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"date_range = (\"2022-09-01\", \"2023-10-01\")\n",
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"longtitude_range = (105.5, 106.4)\n",
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"latitude_range = (9.2, 10.0)\n",
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"\n",
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"data_sen2 = load_data(\n",
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" dc=dc,\n",
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" date_range=date_range,\n",
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" longtitude_range=longtitude_range,\n",
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" latitude_range=latitude_range,\n",
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")\n",
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"print(f\"✅ Sentinel-2 raw: {data_sen2.dims}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c3faed92",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Tiền xử lý Sentinel-2: cloud mask + NDVI + resampling\n",
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"data_clean = mask_clean(data_sen2)\n",
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"data_ndvi = calculate_indices(data_clean, index=\"NDVI\")\n",
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"data_fill = fill_nan(data_ndvi)\n",
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"data_sen2_monthly = data_fill.resample(time=\"1MS\").mean().compute()\n",
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"print(f\"✅ S2 monthly shape: {data_sen2_monthly.dims}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "569bfebb",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Tải Sentinel-1 (SAR VV/VH)\n",
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"data_sen1 = load_data_sen1(\n",
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" dc=dc,\n",
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" date_range=date_range,\n",
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" longtitude_range=longtitude_range,\n",
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" latitude_range=latitude_range,\n",
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")\n",
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"data_sen1_monthly = calculate_average(data_sen1, [\"VV\", \"VH\"], resample=\"1MS\").compute()\n",
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"print(f\"✅ S1 monthly shape: {data_sen1_monthly.dims}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ecc56c2f",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"# Ánh xạ nhãn lớp đất\n",
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"label_mapping = {\n",
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" \"Lua tom\": \"0\", \"Lua\": \"1\", \"CHN\": \"2\", \"CLN\": \"3\",\n",
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" \"TS\": \"4\", \"Song\": \"5\", \"Dat xay dung\": \"6\", \"Rung\": \"7\",\n",
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"}\n",
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"\n",
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"train_data = load_train_data(label_mapping=label_mapping)\n",
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"X, y = get_data_sen1_and_sen2(train_data, data_sen2_monthly, data_sen1_monthly)\n",
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"X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(X, y, test_size=0.2, val_size=0.1)\n",
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"\n",
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"X_train_np = np.array(X_train, dtype=np.float32)\n",
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"X_val_np = np.array(X_val, dtype=np.float32)\n",
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"X_test_np = np.array(X_test, dtype=np.float32)\n",
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"y_train_np = np.array(y_train, dtype=np.int64)\n",
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"y_val_np = np.array(y_val, dtype=np.int64)\n",
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"y_test_np = np.array(y_test, dtype=np.int64)\n",
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"\n",
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"n_features = X_train_np.shape[1]\n",
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"n_classes = len(np.unique(y_train_np))\n",
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"\n",
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"print(f\"✅ Train: {X_train_np.shape} Val: {X_val_np.shape} Test: {X_test_np.shape}\")\n",
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"print(f\" n_features={n_features} n_classes={n_classes}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5492528b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.optim as optim\n",
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"from torch.utils.data import TensorDataset, DataLoader\n",
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"\n",
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"# ── MobileNetV3 + LR-ASPP classifier ──────────────────────────────────────────\n",
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"class MobileNetLRASPPClassifier(nn.Module):\n",
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" \"\"\"\n",
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" MobileNetV3-inspired backbone with LR-ASPP (Lite Reduced ASPP) head\n",
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" for land-use classification on flat feature vectors.\n",
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" \"\"\"\n",
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" def __init__(self, n_features, n_classes):\n",
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" super().__init__()\n",
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" # Feature extraction backbone\n",
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" self.feature_extractor = nn.Sequential(\n",
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" nn.Linear(n_features, 128), nn.BatchNorm1d(128), nn.ReLU(inplace=True), nn.Dropout(0.2),\n",
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" nn.Linear(128, 256), nn.BatchNorm1d(256), nn.ReLU(inplace=True), nn.Dropout(0.3),\n",
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" nn.Linear(256, 512), nn.BatchNorm1d(512), nn.ReLU(inplace=True), nn.Dropout(0.3),\n",
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" )\n",
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" # LR-ASPP Branch 1: global pooling → 128\n",
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" self.global_pool = nn.AdaptiveAvgPool1d(1)\n",
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" self.global_conv = nn.Sequential(nn.Linear(512, 128), nn.ReLU(inplace=True))\n",
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" # LR-ASPP Branch 2: direct 1×1 → 128\n",
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" self.branch_conv = nn.Sequential(nn.Linear(512, 128), nn.BatchNorm1d(128), nn.ReLU(inplace=True))\n",
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" # Fusion → n_classes\n",
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" self.classifier = nn.Sequential(\n",
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" nn.Linear(256, 128), nn.BatchNorm1d(128), nn.ReLU(inplace=True), nn.Dropout(0.4),\n",
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" nn.Linear(128, n_classes),\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" feat = self.feature_extractor(x)\n",
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" global_feat = self.global_pool(feat.unsqueeze(-1)).squeeze(-1)\n",
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" global_feat = self.global_conv(global_feat)\n",
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" branch_feat = self.branch_conv(feat)\n",
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" fused = torch.cat([global_feat, branch_feat], dim=1)\n",
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" return self.classifier(fused)\n",
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"\n",
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
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"print(f\"✅ Device: {device}\")\n",
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"model = MobileNetLRASPPClassifier(n_features, n_classes).to(device)\n",
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"print(model)\n",
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"total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
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"print(f\" Trainable params: {total_params:,}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9da40f5a",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# ── Hyper-parameters ───────────────────────────────────────────────────────────\n",
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"LEARNING_RATE = 1e-3\n",
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"BATCH_SIZE = 64\n",
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"N_EPOCHS = 60\n",
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"PATIENCE = 10\n",
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"CONV_THRESHOLD = 1e-4 # cải thiện val_loss < THRESHOLD → đánh dấu hội tụ\n",
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"CONV_WINDOW = 3 # cần CONV_WINDOW bước liên tiếp thỏa mãn\n",
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"\n",
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"# ── Tensors & DataLoaders ──────────────────────────────────────────────────────\n",
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"X_tr_t = torch.FloatTensor(X_train_np)\n",
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"y_tr_t = torch.LongTensor(y_train_np)\n",
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"X_va_t = torch.FloatTensor(X_val_np)\n",
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"y_va_t = torch.LongTensor(y_val_np)\n",
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"\n",
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"train_loader = DataLoader(TensorDataset(X_tr_t, y_tr_t), batch_size=BATCH_SIZE, shuffle=True)\n",
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"val_loader = DataLoader(TensorDataset(X_va_t, y_va_t), batch_size=BATCH_SIZE, shuffle=False)\n",
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"\n",
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"# ── Class-weighted loss ────────────────────────────────────────────────────────\n",
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"class_counts = np.bincount(y_train_np)\n",
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"class_weights = 1.0 / (class_counts + 1e-6)\n",
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"class_weights = class_weights / class_weights.sum() * n_classes\n",
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"criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor(class_weights).to(device))\n",
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"\n",
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"optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=1e-4)\n",
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"scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode=\"min\", factor=0.5, patience=5)\n",
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"\n",
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"# ── Training loop ──────────────────────────────────────────────────────────────\n",
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"train_losses, val_losses, val_accs, lr_history = [], [], [], []\n",
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"best_val_loss = float(\"inf\")\n",
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"best_epoch = 1\n",
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"best_state_dict = None\n",
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"patience_counter = 0\n",
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"convergence_epoch = None # ← điểm hội tụ sẽ được ghi lại ở đây\n",
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"early_stop_epoch = None\n",
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"\n",
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"print(\"🚀 Training MobileNetV3 + LR-ASPP...\")\n",
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"print(f\" conv_threshold={CONV_THRESHOLD} conv_window={CONV_WINDOW} patience={PATIENCE}\")\n",
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"for epoch in range(1, N_EPOCHS + 1):\n",
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" # --- Train ---\n",
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" model.train()\n",
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" epoch_loss = 0.0\n",
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" for bx, by in train_loader:\n",
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" bx, by = bx.to(device), by.to(device)\n",
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" optimizer.zero_grad()\n",
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" loss = criterion(model(bx), by)\n",
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" loss.backward()\n",
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" torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
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" optimizer.step()\n",
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" epoch_loss += loss.item()\n",
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" avg_train_loss = epoch_loss / len(train_loader)\n",
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"\n",
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" # --- Validate ---\n",
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" model.eval()\n",
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" val_loss = 0.0; correct = 0; total = 0\n",
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" with torch.no_grad():\n",
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" for bx, by in val_loader:\n",
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" bx, by = bx.to(device), by.to(device)\n",
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" out = model(bx)\n",
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" val_loss += criterion(out, by).item()\n",
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" pred = out.argmax(1)\n",
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" correct += (pred == by).sum().item()\n",
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" total += by.size(0)\n",
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" avg_val_loss = val_loss / len(val_loader)\n",
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" val_acc = correct / total\n",
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"\n",
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" scheduler.step(avg_val_loss)\n",
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" lr = optimizer.param_groups[0][\"lr\"]\n",
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" lr_history.append(lr)\n",
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"\n",
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" train_losses.append(avg_train_loss)\n",
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" val_losses.append(avg_val_loss)\n",
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" val_accs.append(val_acc)\n",
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"\n",
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" # ── Phát hiện điểm hội tụ (sliding window trên val_loss) ──────────────\n",
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" if convergence_epoch is None and epoch >= CONV_WINDOW + 1:\n",
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" window = val_losses[-(CONV_WINDOW + 1):]\n",
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" improvements = [abs(window[i] - window[i - 1]) for i in range(1, len(window))]\n",
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" if all(imp < CONV_THRESHOLD for imp in improvements):\n",
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" convergence_epoch = epoch - CONV_WINDOW + 1\n",
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" print(f\" 📍 Hội tụ phát hiện tại epoch {convergence_epoch} \"\n",
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" f\"(val_loss={val_losses[convergence_epoch-1]:.4f})\")\n",
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"\n",
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" if epoch % 5 == 0 or epoch == 1:\n",
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" print(f\" Epoch {epoch:3d}/{N_EPOCHS} train={avg_train_loss:.4f} \"\n",
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" f\"val={avg_val_loss:.4f} acc={val_acc:.4f} lr={lr:.2e}\")\n",
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"\n",
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" # ── Early stopping ─────────────────────────────────────────────────────\n",
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" if avg_val_loss < best_val_loss:\n",
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" best_val_loss = avg_val_loss\n",
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" best_epoch = epoch\n",
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" best_state_dict = {k: v.clone() for k, v in model.state_dict().items()}\n",
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" patience_counter = 0\n",
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" else:\n",
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" patience_counter += 1\n",
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" if patience_counter >= PATIENCE:\n",
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" early_stop_epoch = epoch\n",
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" print(f\"⏹ Early stopping tại epoch {epoch} (patience={PATIENCE})\")\n",
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" break\n",
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"\n",
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"# Restore best weights\n",
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"model.load_state_dict(best_state_dict)\n",
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"total_epochs = len(train_losses)\n",
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"print(f\"\\n✅ Training hoàn tất! Best epoch={best_epoch} Best val_loss={best_val_loss:.4f}\")\n",
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"if convergence_epoch:\n",
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" print(f\" Điểm hội tụ : epoch {convergence_epoch}\")\n",
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"\n",
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"# ═══════════════════════════════════════════════════════════════════════════════\n",
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"# PHÂN TÍCH ĐIỂM HỘI TỤ — MobileNetV3 + LR-ASPP\n",
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"# EMA-smoothed curves + annotated convergence / best / early-stop markers\n",
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"# ═══════════════════════════════════════════════════════════════════════════════\n",
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"epochs_axis = list(range(1, total_epochs + 1))\n",
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"\n",
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"# Exponential Moving Average smoothing\n",
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"def ema(values, alpha=0.2):\n",
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" s = [values[0]]\n",
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" for v in values[1:]:\n",
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" s.append(alpha * v + (1 - alpha) * s[-1])\n",
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" return s\n",
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"\n",
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"val_losses_ema = ema(val_losses, alpha=0.3)\n",
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"train_losses_ema = ema(train_losses, alpha=0.3)\n",
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"val_accs_ema = ema(val_accs, alpha=0.3)\n",
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"\n",
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"# ── Tính ΔVal-loss per epoch ──────────────────────────────────────────────────\n",
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"delta_val = [abs(val_losses_ema[i] - val_losses_ema[i-1])\n",
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" for i in range(1, len(val_losses_ema))]\n",
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"\n",
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"fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n",
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"\n",
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"# --- Top-left: Loss curves ---\n",
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"ax = axes[0, 0]\n",
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"ax.plot(epochs_axis, train_losses, \"b-\", alpha=0.25, linewidth=0.8)\n",
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"ax.plot(epochs_axis, train_losses_ema, \"b-\", linewidth=2, label=\"Train loss (EMA)\")\n",
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"ax.plot(epochs_axis, val_losses, \"g-\", alpha=0.25, linewidth=0.8)\n",
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"ax.plot(epochs_axis, val_losses_ema, \"g-\", linewidth=2, label=\"Val loss (EMA)\")\n",
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"ax.axvline(x=best_epoch, color=\"red\", linestyle=\"--\", linewidth=1.5,\n",
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" label=f\"Best epoch={best_epoch}\")\n",
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"if convergence_epoch:\n",
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" ax.axvline(x=convergence_epoch, color=\"orange\", linestyle=\":\", linewidth=1.5,\n",
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" label=f\"HỘI TỤ epoch={convergence_epoch}\")\n",
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"if early_stop_epoch:\n",
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" ax.axvline(x=early_stop_epoch, color=\"gray\", linestyle=\"-.\", linewidth=1.5,\n",
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" label=f\"Early stop epoch={early_stop_epoch}\")\n",
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"ax.set_xlabel(\"Epoch\")\n",
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"ax.set_ylabel(\"Loss\")\n",
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"ax.set_title(\"Loss Curves (raw + EMA)\")\n",
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"ax.legend(fontsize=8)\n",
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"ax.grid(True, alpha=0.3)\n",
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"\n",
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"# --- Top-right: Val accuracy ---\n",
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"ax = axes[0, 1]\n",
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"ax.plot(epochs_axis, val_accs, \"g-\", alpha=0.3, linewidth=0.8)\n",
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"ax.plot(epochs_axis, val_accs_ema, \"g-\", linewidth=2, label=\"Val acc (EMA)\")\n",
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"ax.axvline(x=best_epoch, color=\"red\", linestyle=\"--\", linewidth=1.5,\n",
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" label=f\"Best epoch={best_epoch} ({val_accs[best_epoch-1]*100:.2f}%)\")\n",
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"if convergence_epoch:\n",
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" ax.axvline(x=convergence_epoch, color=\"orange\", linestyle=\":\", linewidth=1.5,\n",
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" label=f\"HỘI TỤ epoch={convergence_epoch} ({val_accs[convergence_epoch-1]*100:.2f}%)\")\n",
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"if early_stop_epoch:\n",
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" ax.axvline(x=early_stop_epoch, color=\"gray\", linestyle=\"-.\", linewidth=1.5,\n",
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" label=f\"Early stop\")\n",
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"ax.set_xlabel(\"Epoch\")\n",
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"ax.set_ylabel(\"Accuracy\")\n",
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"ax.set_title(\"Val Accuracy Curve\")\n",
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"ax.legend(fontsize=8)\n",
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"ax.grid(True, alpha=0.3)\n",
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"\n",
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"# --- Bottom-left: ΔVal-loss (marginal improvement) ---\n",
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"ax = axes[1, 0]\n",
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"ax.bar(epochs_axis[1:], delta_val,\n",
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" color=[\"green\" if d > CONV_THRESHOLD else \"salmon\" for d in delta_val],\n",
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" alpha=0.75)\n",
|
||
"ax.axhline(y=CONV_THRESHOLD, color=\"red\", linestyle=\"--\",\n",
|
||
" label=f\"Threshold = {CONV_THRESHOLD:.0e}\")\n",
|
||
"if convergence_epoch:\n",
|
||
" ax.axvline(x=convergence_epoch, color=\"orange\", linestyle=\":\", linewidth=1.5,\n",
|
||
" label=f\"HỘI TỤ epoch={convergence_epoch}\")\n",
|
||
"ax.set_xlabel(\"Epoch\")\n",
|
||
"ax.set_ylabel(\"|ΔVal Loss|\")\n",
|
||
"ax.set_title(\"Marginal Val-Loss Improvement per Epoch\")\n",
|
||
"ax.legend(fontsize=8)\n",
|
||
"ax.grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"# --- Bottom-right: Learning rate schedule ---\n",
|
||
"ax = axes[1, 1]\n",
|
||
"ax.semilogy(epochs_axis, lr_history, \"purple\", linewidth=2)\n",
|
||
"if convergence_epoch:\n",
|
||
" ax.axvline(x=convergence_epoch, color=\"orange\", linestyle=\":\", linewidth=1.5,\n",
|
||
" label=f\"HỘI TỤ epoch={convergence_epoch}\")\n",
|
||
"ax.axvline(x=best_epoch, color=\"red\", linestyle=\"--\", linewidth=1.5,\n",
|
||
" label=f\"Best epoch={best_epoch}\")\n",
|
||
"ax.set_xlabel(\"Epoch\")\n",
|
||
"ax.set_ylabel(\"Learning Rate (log)\")\n",
|
||
"ax.set_title(\"Learning Rate Schedule (ReduceLROnPlateau)\")\n",
|
||
"ax.legend(fontsize=8)\n",
|
||
"ax.grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"plt.suptitle(\"MobileNetV3 + LR-ASPP — Convergence Analysis\", fontsize=13, fontweight=\"bold\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# ── Tổng kết ──────────────────────────────────────────────────────────────────\n",
|
||
"print(f\"\\n{'═'*60}\")\n",
|
||
"print(f\" Tổng số epoch : {total_epochs}\")\n",
|
||
"print(f\" Best epoch : {best_epoch} (val_loss={best_val_loss:.4f})\")\n",
|
||
"print(f\" Best val accuracy : {val_accs[best_epoch-1]*100:.4f}%\")\n",
|
||
"if convergence_epoch:\n",
|
||
" print(f\" Điểm HỘI TỤ : epoch {convergence_epoch} \"\n",
|
||
" f\"(val_acc={val_accs[convergence_epoch-1]*100:.2f}%)\")\n",
|
||
" wasted = total_epochs - convergence_epoch\n",
|
||
" print(f\" Epochs sau hội tụ : {wasted} \"\n",
|
||
" f\"(có thể giảm N_EPOCHS không ảnh hưởng nhiều đến kết quả)\")\n",
|
||
"if early_stop_epoch:\n",
|
||
" print(f\" Early stop tại epoch: {early_stop_epoch}\")\n",
|
||
"print(f\"{'═'*60}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "bc690b92",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# ── Đánh giá trên tập test ─────────────────────────────────────────────────────\n",
|
||
"model.eval()\n",
|
||
"with torch.no_grad():\n",
|
||
" X_te_t = torch.FloatTensor(X_test_np).to(device)\n",
|
||
" logits = model(X_te_t)\n",
|
||
" y_pred = logits.argmax(1).cpu().numpy()\n",
|
||
"\n",
|
||
"acc = accuracy_score(y_test_np, y_pred)\n",
|
||
"print(f\"Test Accuracy : {acc:.4f} ({acc*100:.2f}%)\\n\")\n",
|
||
"print(classification_report(y_test_np, y_pred, digits=4))\n",
|
||
"\n",
|
||
"# ── Confusion matrix ────────────────────────────────────────────────────────────\n",
|
||
"class_names = list(label_mapping.keys())\n",
|
||
"cm = confusion_matrix(y_test_np, y_pred)\n",
|
||
"plt.figure(figsize=(9, 7))\n",
|
||
"sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Purples\",\n",
|
||
" xticklabels=class_names, yticklabels=class_names)\n",
|
||
"plt.xlabel(\"Predicted\")\n",
|
||
"plt.ylabel(\"Actual\")\n",
|
||
"plt.title(\"Confusion Matrix — MobileNetV3 + LR-ASPP\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"language_info": {
|
||
"name": "python"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|