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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "851f001a-3882-42cf-8e45-1bb7c4193d20",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6\n",
"num params encoder 50840\n",
"num params 21496282\n"
]
}
],
"source": [
"from utils import CustomDataset, transform, preproc, Convert_ONNX\n",
"from torch.utils.data import Dataset, DataLoader\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",
"# Create custom dataset instance\n",
"# Create custom dataset instance\n",
"data_dir = '/mnt/buf0/pma/frbnn/train_ready'\n",
"dataset = CustomDataset(data_dir, transform=transform)\n",
"valid_data_dir = '/mnt/buf0/pma/frbnn/valid_ready'\n",
"valid_dataset = CustomDataset(valid_data_dir, transform=transform)\n",
"\n",
"\n",
"num_classes = 2\n",
"trainloader = DataLoader(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"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "676a6ffa-5bed-403d-ba03-627f14b36de2",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/477 [00:00<?, ?batch/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%|██████████████████████████████████████| 477/477 [08:57<00:00, 1.13s/batch]\n"
]
},
{
"ename": "NameError",
"evalue": "name 'validloader' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 29\u001b[0m\n\u001b[1;32m 27\u001b[0m model\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[0;32m---> 29\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m images, labels \u001b[38;5;129;01min\u001b[39;00m validloader:\n\u001b[1;32m 30\u001b[0m inputs, labels \u001b[38;5;241m=\u001b[39m images\u001b[38;5;241m.\u001b[39mto(device), labels\u001b[38;5;241m.\u001b[39mto(device)\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[1;32m 31\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n",
"\u001b[0;31mNameError\u001b[0m: name 'validloader' is not defined"
]
}
],
"source": [
"criterion = nn.CrossEntropyLoss(weight = torch.tensor([1,1]).to(device))\n",
"optimizer = optim.Adam(model.parameters(), lr=0.0001)\n",
"scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=10)\n",
"\n",
"for epoch in range(5):\n",
" running_loss = 0.0\n",
" correct_train = 0\n",
" total_train = 0\n",
" with tqdm(trainloader, unit=\"batch\") as tepoch:\n",
" model.train()\n",
" for i, (images, labels) in enumerate(tepoch):\n",
" inputs, labels = images.to(device), labels.to(device).float()\n",
" optimizer.zero_grad()\n",
" outputs = model(inputs, return_mask=False).to(device)\n",
" new_label = F.one_hot(labels.type(torch.int64),num_classes=2).type(torch.float32).to(device)\n",
" loss = criterion(outputs, new_label)\n",
" loss.backward()\n",
" optimizer.step()\n",
" running_loss += loss.item()\n",
" # Calculate training accuracy\n",
" _, predicted = torch.max(outputs.data, 1)\n",
" total_train += labels.size(0)\n",
" correct_train += (predicted == labels).sum().item() \n",
" val_loss = 0.0\n",
" correct_valid = 0\n",
" total = 0\n",
" model.eval()\n",
" with torch.no_grad():\n",
" for images, labels in validloader:\n",
" inputs, labels = images.to(device), labels.to(device).float()\n",
" optimizer.zero_grad()\n",
" outputs = model(inputs, return_mask=False)\n",
" new_label = F.one_hot(labels.type(torch.int64),num_classes=2).type(torch.float32)\n",
" loss = criterion(outputs, new_label)\n",
" val_loss += loss.item()\n",
" _, predicted = torch.max(outputs, 1)\n",
" total += labels.size(0)\n",
" correct_valid += (predicted == labels).sum().item()\n",
" scheduler.step(val_loss)\n",
" # Calculate training accuracy after each epoch\n",
" train_accuracy = 100 * correct_train / total_train\n",
" val_accuracy = correct_valid / total * 100.0\n",
"\n",
"\n",
" print(\"===========================\")\n",
" print('accuracy: ', epoch, train_accuracy, val_accuracy)\n",
" print('learning rate: ', scheduler.get_last_lr())\n",
" print(\"===========================\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3faa4a11-89fb-4556-ae87-3645a47fa00d",
"metadata": {},
"outputs": [],
"source": [
"train_accuracy = 100 * correct_train / total_train\n",
"print('accuracy: ', epoch, train_accuracy)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e586c4d2-a7f4-4f14-81fc-4f84ffac52b3",
"metadata": {},
"outputs": [],
"source": [
"import sigpyproc.readers as r\n",
"import cv2\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from scipy.special import softmax\n",
"%matplotlib inline\n",
"path = '/mnt/primary/ata/projects/p051/fil_60565_59210_9756774_J0534+2200_0001/LoB.C0928/fil_60565_59210_9756774_J0534+2200_0001-beam0000.fil'\n",
"# path = '/mnt/primary/ata/projects/p051/fil_60564_62428_4679748_J0332+5434_0001/LoB.C0928/fil_60564_62428_4679748_J0332+5434_0001-beam0000.fil'\n",
"\n",
"# Get some metadata\n",
"\n",
"# Open the filterbank file\n",
"fil = r.FilReader(path)\n",
"header = fil.header\n",
"print(\"Header:\", header)\n",
"n=100\n",
"li = [ 7257608, 7324207, 10393163, 10641071, 11130537, 11085081,\n",
" 11419145, 11964112, 12329364, 13047181]\n",
"for el in li:\n",
" data = torch.tensor(fil.read_block(el-1024, 2048)).cuda()\n",
" print(data.shape)\n",
" out = model(transform(torch.tensor(data).cuda())[None])\n",
" print(softmax(out.detach().cpu().numpy(), axis=1))\n",
" plt.figure(figsize=(10,10))\n",
" plt.imshow(data.cpu().numpy(), aspect = 10)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "609e5564-f14f-4bd1-b604-68e7e7d42834",
"metadata": {},
"outputs": [],
"source": [
"triggers = []\n",
"counter = 0\n",
"with torch.no_grad():\n",
" for i in range(2048,10201921, 2048 ):\n",
" data = torch.tensor(fil.read_block(i-1024, 2048)).cuda()\n",
" # Shuffle the tensor using the random indices\n",
" out = model(transform(torch.tensor(data).cuda())[None])\n",
" triggers.append(softmax(out.detach().cpu().numpy(), axis=1))\n",
" counter += 1\n",
" if counter > 1000:\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08ee6dcf-cb30-4490-8624-4e52552fdf39",
"metadata": {},
"outputs": [],
"source": [
"print(triggers[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8c56c6f5-5a0b-4854-8a94-066a9baf4cfc",
"metadata": {},
"outputs": [],
"source": [
"stack = np.stack(triggers)\n",
"positives = stack[:,0,1]\n",
"num_pos = np.where(positives > 0.5)[0].shape[0]\n",
"print(num_pos)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eb1d1591-8855-4989-bf12-c8a9cdbf2a4d",
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"# Path to your pickle file\n",
"file_path = \"../dataset_generator/dir.pkl\"\n",
"\n",
"# Open and load the pickle file\n",
"with open(file_path, \"rb\") as file: # Use \"rb\" mode for reading binary files\n",
" data = pickle.load(file)\n",
"\n",
"# Print the contents of the file\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "46f61d7e-55fa-44fe-be94-d4ddb3c576f9",
"metadata": {},
"outputs": [],
"source": [
"import sigpyproc.readers as r\n",
"import cv2\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from scipy.special import softmax\n",
"%matplotlib inline\n",
"path = data[0]\n",
"model.eval()\n",
"\n",
"fil = r.FilReader(path)\n",
"header = fil.header\n",
"print(\"Header:\", header)\n",
"n=100\n",
"\n",
"\n",
"triggers = []\n",
"counter = 0\n",
"for i in range(2048,10201921, 2048):\n",
" data = torch.tensor(fil.read_block(i-1024, 2048)).cuda()\n",
" # Shuffle the tensor using the random indices\n",
" out = model(transform(torch.tensor(data).cuda())[None])\n",
" triggers.append(softmax(out.detach().cpu().numpy(), axis=1))\n",
" counter += 1\n",
" if counter > 1000:\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "413d402e-2ce3-49fc-bbd4-a3cf1cc92388",
"metadata": {},
"outputs": [],
"source": [
"print(triggers[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5c039dee-1b9b-4664-b42a-a79d780f37f1",
"metadata": {},
"outputs": [],
"source": [
"stack = np.stack(triggers)\n",
"positives = stack[:,0,1]\n",
"num_pos = np.where(positives > 0.5)[0].shape[0]\n",
"print(num_pos)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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