Spaces:
Sleeping
Sleeping
File size: 7,941 Bytes
7b58366 7e7d672 0da089d 7e7d672 0da089d 7b58366 7e7d672 7b58366 7e7d672 7b58366 7e7d672 7b58366 7e7d672 7b58366 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 |
{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Train the Model"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"source": [
"import torch\n",
"from train_model import train_model\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.optim import lr_scheduler\n",
"from model import build_hybrid_model\n",
"import pennylane as qml\n",
"\n",
"STEP = 0.0004 # Learning rate\n",
"BATCH_SIZE = 4 # Number of samples for each training step\n",
"N_QUBITS = 4\n",
"NUM_EPOCHS = 3 # Number of training epochs\n",
"GAMMA_LR_SCHEDULER = 0.1 # Learning rate reduction applied every 10 epochs.\n",
"PARAMETER_FILEPATH = \"state.pt\" # \n",
"IF_INITIAL_TRAIN = False # Whether this is the first time training the model\n",
"\n",
"# Initialize Pennylane backend\n",
"dev = qml.device(\"default.qubit\", wires=N_QUBITS)\n",
"\n",
"# Initialize PyTorch config\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"# Load Model\n",
"model = build_hybrid_model(\n",
" pennylane_dev=dev,\n",
" device=device,\n",
" n_qubits=N_QUBITS,\n",
" q_depth=6,\n",
" q_delta=0.01\n",
")\n",
"\n",
"if not IF_INITIAL_TRAIN:\n",
" # Load Model Weights from Disk\n",
" print(\"Loading Model Weights from Disk...\")\n",
" model.load_state_dict(torch.load(PARAMETER_FILEPATH))\n",
"\n",
"# Define Loss for Training\n",
"criterion = nn.CrossEntropyLoss()\n",
"optimizer_hybrid = optim.Adam(model.fc.parameters(), lr=STEP)\n",
"exp_lr_scheduler = lr_scheduler.StepLR(\n",
" optimizer_hybrid, step_size=10, gamma=GAMMA_LR_SCHEDULER\n",
")\n",
"\n",
"# Train the Model\n",
"trained_model = train_model(\n",
" model=model, \n",
" criterion=criterion, \n",
" optimizer=optimizer_hybrid, \n",
" scheduler=exp_lr_scheduler, \n",
" num_epochs=NUM_EPOCHS,\n",
" batch_size=BATCH_SIZE,\n",
" device=device\n",
")\n",
"\n",
"# Save Model Parameters to Disk\n",
"print(\"Saving Model Weights to Disk...\")\n",
"torch.save(trained_model.state_dict(), PARAMETER_FILEPATH)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-09-11T22:38:45.705122Z",
"start_time": "2024-09-11T22:16:02.896239Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MaxHu\\anaconda3\\envs\\quantum-env\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
" warnings.warn(\n",
"C:\\Users\\MaxHu\\anaconda3\\envs\\quantum-env\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
" warnings.warn(msg)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading Model Weights from Disk...\n",
"Training started:\n",
"Data Loading Completed in 0m 0s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\MaxHu\\AppData\\Local\\Temp\\ipykernel_12896\\449253314.py:35: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
" model.load_state_dict(torch.load(PARAMETER_FILEPATH))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Phase: train Epoch: 1/3 Loss: 0.8986 Acc: 0.5877 33\n",
"Phase: validation Epoch: 1/3 Loss: 1.9974 Acc: 0.3929 \n",
"Phase: train Epoch: 2/3 Loss: 0.8994 Acc: 0.5849 76\n",
"Phase: validation Epoch: 2/3 Loss: 1.9718 Acc: 0.3929 \n",
"Phase: train Epoch: 3/3 Loss: 0.8977 Acc: 0.5860 76\n",
"Phase: validation Epoch: 3/3 Loss: 2.0105 Acc: 0.3750 \n",
"Training Completed in 22m 34s\n",
"Best test loss: 1.9718 | Best test accuracy: 0.3929\n",
"Saving Model Weights to Disk...\n"
]
}
],
"execution_count": 1
},
{
"cell_type": "markdown",
"source": [
"# Visualize a Batch of Predictions"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"source": [
"from helpers import visualize_model\n",
"import torch\n",
"from train_model import train_model\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.optim import lr_scheduler\n",
"from model import build_hybrid_model\n",
"import pennylane as qml\n",
"\n",
"BATCH_SIZE = 4\n",
"N_QUBITS = 4\n",
"PARAMETER_FILEPATH = \"state.pt\"\n",
"\n",
"# Initialize Pennylane backend\n",
"dev = qml.device(\"default.qubit\", wires=N_QUBITS)\n",
"\n",
"# Initialize PyTorch config\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"# Load Model\n",
"model = build_hybrid_model(\n",
" pennylane_dev=dev,\n",
" device=device,\n",
" n_qubits=N_QUBITS,\n",
" q_depth=6,\n",
" q_delta=0.01\n",
")\n",
"\n",
"# Load Model Weights from Disk\n",
"print(\"Loading Model Weights from Disk...\")\n",
"model.load_state_dict(torch.load(PARAMETER_FILEPATH))\n",
"\n",
"visualize_model(\n",
" model=model,\n",
" device=device,\n",
" batch_size=BATCH_SIZE\n",
")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"# Prediction Test"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"source": [
"from generate_prediction import generate_prediction\n",
"pred = generate_prediction(\"./image.jpeg\")\n",
"print(\"PREDICTION:\",pred)"
],
"metadata": {
"collapsed": false
},
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"source": "from app import *",
"metadata": {
"collapsed": false
},
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "quantum-env",
"language": "python",
"name": "quantum-env"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|