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{
"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",
" 0%| | 0/48 [00:22<?, ?it/s]\n"
]
},
{
"ename": "AttributeError",
"evalue": "Caught AttributeError in replica 0 on device 0.\nOriginal Traceback (most recent call last):\n File \"/home/pma/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/parallel/parallel_apply.py\", line 83, in _worker\n output = module(*input, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/pma/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1553, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/pma/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1562, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/pma/projects/frbnn_narrow/CNN/resnet_model.py\", line 106, in forward\n return x, self.mask, self.value\n ^^^^^^^^^\n File \"/home/pma/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1729, in __getattr__\n raise AttributeError(f\"'{type(self).__name__}' object has no attribute '{name}'\")\nAttributeError: 'ResNet' object has no attribute 'mask'\n",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[1], line 50\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m images, labels \u001b[38;5;129;01min\u001b[39;00m tqdm(testloader):\n\u001b[1;32m 49\u001b[0m inputs, labels \u001b[38;5;241m=\u001b[39m images\u001b[38;5;241m.\u001b[39mto(device), labels\n\u001b[0;32m---> 50\u001b[0m outputs \u001b[38;5;241m=\u001b[39m model(inputs, return_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 51\u001b[0m _, predicted \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mmax(outputs, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 52\u001b[0m results[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutput\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mextend(outputs\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy()\u001b[38;5;241m.\u001b[39mtolist())\n",
"File \u001b[0;32m~/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[0;32m~/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m~/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/parallel/data_parallel.py:186\u001b[0m, in \u001b[0;36mDataParallel.forward\u001b[0;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodule(\u001b[38;5;241m*\u001b[39minputs[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodule_kwargs[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 185\u001b[0m replicas \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreplicate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodule, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice_ids[:\u001b[38;5;28mlen\u001b[39m(inputs)])\n\u001b[0;32m--> 186\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparallel_apply(replicas, inputs, module_kwargs)\n\u001b[1;32m 187\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgather(outputs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_device)\n",
"File \u001b[0;32m~/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/parallel/data_parallel.py:201\u001b[0m, in \u001b[0;36mDataParallel.parallel_apply\u001b[0;34m(self, replicas, inputs, kwargs)\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mparallel_apply\u001b[39m(\u001b[38;5;28mself\u001b[39m, replicas: Sequence[T], inputs: Sequence[Any], kwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[Any]:\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parallel_apply(replicas, inputs, kwargs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice_ids[:\u001b[38;5;28mlen\u001b[39m(replicas)])\n",
"File \u001b[0;32m~/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/parallel/parallel_apply.py:108\u001b[0m, in \u001b[0;36mparallel_apply\u001b[0;34m(modules, inputs, kwargs_tup, devices)\u001b[0m\n\u001b[1;32m 106\u001b[0m output \u001b[38;5;241m=\u001b[39m results[i]\n\u001b[1;32m 107\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, ExceptionWrapper):\n\u001b[0;32m--> 108\u001b[0m output\u001b[38;5;241m.\u001b[39mreraise()\n\u001b[1;32m 109\u001b[0m outputs\u001b[38;5;241m.\u001b[39mappend(output)\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outputs\n",
"File \u001b[0;32m~/.conda/envs/frbnn/lib/python3.11/site-packages/torch/_utils.py:706\u001b[0m, in \u001b[0;36mExceptionWrapper.reraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 702\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 703\u001b[0m \u001b[38;5;66;03m# If the exception takes multiple arguments, don't try to\u001b[39;00m\n\u001b[1;32m 704\u001b[0m \u001b[38;5;66;03m# instantiate since we don't know how to\u001b[39;00m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 706\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception\n",
"\u001b[0;31mAttributeError\u001b[0m: Caught AttributeError in replica 0 on device 0.\nOriginal Traceback (most recent call last):\n File \"/home/pma/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/parallel/parallel_apply.py\", line 83, in _worker\n output = module(*input, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/pma/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1553, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/pma/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1562, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/pma/projects/frbnn_narrow/CNN/resnet_model.py\", line 106, in forward\n return x, self.mask, self.value\n ^^^^^^^^^\n File \"/home/pma/.conda/envs/frbnn/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1729, in __getattr__\n raise AttributeError(f\"'{type(self).__name__}' object has no attribute '{name}'\")\nAttributeError: 'ResNet' object has no attribute 'mask'\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": null,
"id": "64733667-75c3-4fd3-ab9f-62b85c5e27e3",
"metadata": {},
"outputs": [],
"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": null,
"id": "fe74ada8-43e4-4c73-b772-0ef18983345d",
"metadata": {},
"outputs": [],
"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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