{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98165c88-8ead-4fae-9ea8-6b2e82996fc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "num params encoder  50840\n",
      "num params  21496282\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                    | 0/48 [00:00<?, ?it/s]/home/pma/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/parallel/parallel_apply.py:79: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.device(device), torch.cuda.stream(stream), autocast(enabled=autocast_enabled):\n",
      "100%|███████████████████████████████████████████| 48/48 [00:46<00:00,  1.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========================\n",
      "accuracy:  98.815\n",
      "===========================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from utils import CustomDataset, transform, preproc, Convert_ONNX\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from utils import CustomDataset, TestingDataset, transform\n",
    "from tqdm import tqdm\n",
    "import torch\n",
    "import numpy as np\n",
    "from resnet_model import  ResidualBlock, ResNet\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from tqdm import tqdm \n",
    "import torch.nn.functional as F\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
    "import pickle\n",
    "\n",
    "torch.manual_seed(1)\n",
    "# torch.manual_seed(42)\n",
    "\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "num_gpus = torch.cuda.device_count()\n",
    "print(num_gpus)\n",
    "\n",
    "test_data_dir = '/mnt/buf1/pma/frbnn/test_ready'\n",
    "test_dataset = TestingDataset(test_data_dir, transform=transform)\n",
    "\n",
    "num_classes = 2\n",
    "testloader = DataLoader(test_dataset, batch_size=420, shuffle=True, num_workers=32)\n",
    "\n",
    "model = ResNet(24, ResidualBlock, [3, 4, 6, 3], num_classes=num_classes).to(device)\n",
    "model = nn.DataParallel(model)\n",
    "model = model.to(device)\n",
    "params = sum(p.numel() for p in model.parameters())\n",
    "print(\"num params \",params)\n",
    "\n",
    "model_1 = 'models/model-23-99.045.pt'\n",
    "# model_1 ='models/model-47-99.125.pt'\n",
    "model.load_state_dict(torch.load(model_1, weights_only=True))\n",
    "model = model.eval()\n",
    "\n",
    "# eval\n",
    "val_loss = 0.0\n",
    "correct_valid = 0\n",
    "total = 0\n",
    "results = {'output': [],'pred': [], 'true':[], 'freq':[], 'snr':[], 'dm':[], 'boxcar':[]}\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for images, labels in tqdm(testloader):\n",
    "        inputs, labels = images.to(device), labels\n",
    "        outputs = model(inputs)\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        results['output'].extend(outputs.cpu().numpy().tolist())\n",
    "        results['pred'].extend(predicted.cpu().numpy().tolist())\n",
    "        results['true'].extend(labels[0].cpu().numpy().tolist())\n",
    "        results['freq'].extend(labels[2].cpu().numpy().tolist())\n",
    "        results['dm'].extend(labels[1].cpu().numpy().tolist())\n",
    "        results['snr'].extend(labels[3].cpu().numpy().tolist())\n",
    "        results['boxcar'].extend(labels[4].cpu().numpy().tolist())\n",
    "        total += labels[0].size(0)\n",
    "        correct_valid += (predicted.cpu() == labels[0].cpu()).sum().item()\n",
    "# Calculate training accuracy after each epoch\n",
    "val_accuracy = correct_valid / total * 100.0\n",
    "print(\"===========================\")\n",
    "print('accuracy: ',  val_accuracy)\n",
    "print(\"===========================\")\n",
    "\n",
    "import pickle\n",
    "\n",
    "# Pickle the dictionary to a file\n",
    "with open('models/test_42.pkl', 'wb') as f:\n",
    "    pickle.dump(results, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "64733667-75c3-4fd3-ab9f-62b85c5e27e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "num params encoder  50840\n",
      "num params  21496282\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 48/48 [00:43<00:00,  1.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========================\n",
      "accuracy:  97.185\n",
      "===========================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from utils import CustomDataset, transform, preproc, Convert_ONNX\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from utils import CustomDataset, TestingDataset, transform\n",
    "from tqdm import tqdm\n",
    "import torch\n",
    "import numpy as np\n",
    "from resnet_model_mask import  ResidualBlock, ResNet\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from tqdm import tqdm \n",
    "import torch.nn.functional as F\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
    "import pickle\n",
    "\n",
    "torch.manual_seed(1)\n",
    "# torch.manual_seed(42)\n",
    "\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "num_gpus = torch.cuda.device_count()\n",
    "print(num_gpus)\n",
    "\n",
    "test_data_dir = '/mnt/buf1/pma/frbnn/test_ready'\n",
    "test_dataset = TestingDataset(test_data_dir, transform=transform)\n",
    "\n",
    "num_classes = 2\n",
    "testloader = DataLoader(test_dataset, batch_size=420, shuffle=True, num_workers=32)\n",
    "\n",
    "model = ResNet(24, ResidualBlock, [3, 4, 6, 3], num_classes=num_classes).to(device)\n",
    "model = nn.DataParallel(model)\n",
    "model = model.to(device)\n",
    "params = sum(p.numel() for p in model.parameters())\n",
    "print(\"num params \",params)\n",
    "\n",
    "\n",
    "model_1 = 'models/model-14-98.005.pt'\n",
    "# model_1 ='models/model-47-99.125.pt'\n",
    "model.load_state_dict(torch.load(model_1, weights_only=True))\n",
    "model = model.eval()\n",
    "\n",
    "# eval\n",
    "val_loss = 0.0\n",
    "correct_valid = 0\n",
    "total = 0\n",
    "results = {'output': [],'pred': [], 'true':[], 'freq':[], 'snr':[], 'dm':[], 'boxcar':[]}\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for images, labels in tqdm(testloader):\n",
    "        inputs, labels = images.to(device), labels\n",
    "        outputs = model(inputs)\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        results['output'].extend(outputs.cpu().numpy().tolist())\n",
    "        results['pred'].extend(predicted.cpu().numpy().tolist())\n",
    "        results['true'].extend(labels[0].cpu().numpy().tolist())\n",
    "        results['freq'].extend(labels[2].cpu().numpy().tolist())\n",
    "        results['dm'].extend(labels[1].cpu().numpy().tolist())\n",
    "        results['snr'].extend(labels[3].cpu().numpy().tolist())\n",
    "        results['boxcar'].extend(labels[4].cpu().numpy().tolist())\n",
    "        total += labels[0].size(0)\n",
    "        correct_valid += (predicted.cpu() == labels[0].cpu()).sum().item()\n",
    "    \n",
    "# Calculate training accuracy after each epoch\n",
    "val_accuracy = correct_valid / total * 100.0\n",
    "print(\"===========================\")\n",
    "print('accuracy: ',  val_accuracy)\n",
    "print(\"===========================\")\n",
    "\n",
    "import pickle\n",
    "\n",
    "# Pickle the dictionary to a file\n",
    "with open('models/test_1.pkl', 'wb') as f:\n",
    "    pickle.dump(results, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fe74ada8-43e4-4c73-b772-0ef18983345d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "num params encoder  50840\n",
      "num params  21496282\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 48/48 [00:44<00:00,  1.08it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========================\n",
      "accuracy:  98.455\n",
      "===========================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from utils import CustomDataset, transform, preproc, Convert_ONNX\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from utils import CustomDataset, TestingDataset, transform\n",
    "from tqdm import tqdm\n",
    "import torch\n",
    "import numpy as np\n",
    "from resnet_model_mask import  ResidualBlock, ResNet\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from tqdm import tqdm \n",
    "import torch.nn.functional as F\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
    "import pickle\n",
    "\n",
    "torch.manual_seed(1)\n",
    "# torch.manual_seed(42)\n",
    "\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "num_gpus = torch.cuda.device_count()\n",
    "print(num_gpus)\n",
    "\n",
    "test_data_dir = '/mnt/buf1/pma/frbnn/test_ready'\n",
    "test_dataset = TestingDataset(test_data_dir, transform=transform)\n",
    "\n",
    "num_classes = 2\n",
    "testloader = DataLoader(test_dataset, batch_size=420, shuffle=True, num_workers=32)\n",
    "\n",
    "model = ResNet(24, ResidualBlock, [3, 4, 6, 3], num_classes=num_classes).to(device)\n",
    "model = nn.DataParallel(model)\n",
    "model = model.to(device)\n",
    "params = sum(p.numel() for p in model.parameters())\n",
    "print(\"num params \",params)\n",
    "\n",
    "\n",
    "model_1 = 'models/model-28-98.955.pt'\n",
    "# model_1 ='models/model-47-99.125.pt'\n",
    "model.load_state_dict(torch.load(model_1, weights_only=True))\n",
    "model = model.eval()\n",
    "\n",
    "# eval\n",
    "val_loss = 0.0\n",
    "correct_valid = 0\n",
    "total = 0\n",
    "results = {'output': [],'pred': [], 'true':[], 'freq':[], 'snr':[], 'dm':[], 'boxcar':[]}\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for images, labels in tqdm(testloader):\n",
    "        inputs, labels = images.to(device), labels\n",
    "        outputs = model(inputs)\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        results['output'].extend(outputs.cpu().numpy().tolist())\n",
    "        results['pred'].extend(predicted.cpu().numpy().tolist())\n",
    "        results['true'].extend(labels[0].cpu().numpy().tolist())\n",
    "        results['freq'].extend(labels[2].cpu().numpy().tolist())\n",
    "        results['dm'].extend(labels[1].cpu().numpy().tolist())\n",
    "        results['snr'].extend(labels[3].cpu().numpy().tolist())\n",
    "        results['boxcar'].extend(labels[4].cpu().numpy().tolist())\n",
    "        total += labels[0].size(0)\n",
    "        correct_valid += (predicted.cpu() == labels[0].cpu()).sum().item()\n",
    "    \n",
    "# Calculate training accuracy after each epoch\n",
    "val_accuracy = correct_valid / total * 100.0\n",
    "print(\"===========================\")\n",
    "print('accuracy: ',  val_accuracy)\n",
    "print(\"===========================\")\n",
    "\n",
    "import pickle\n",
    "\n",
    "# Pickle the dictionary to a file\n",
    "with open('models/test_7109.pkl', 'wb') as f:\n",
    "    pickle.dump(results, f)"
   ]
  }
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