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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import lightning as pl \n",
"from src.datamodule import CIFAR10DataModule\n",
"from src.vit import ViTLightning"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"GPU available: True (cuda), used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"HPU available: False, using: 0 HPUs\n"
]
}
],
"source": [
"trainer = pl.Trainer(max_epochs=15,accelerator='auto',reload_dataloaders_every_n_epochs=2)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"model = ViTLightning()\n",
"dm = CIFAR10DataModule()\n",
"dm.setup()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"You are using a CUDA device ('NVIDIA GeForce RTX 4050 Laptop GPU') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"Loading `train_dataloader` to estimate number of stepping batches.\n",
"\n",
" | Name | Type | Params | Mode \n",
"---------------------------------------------------------\n",
"0 | vit | ViT | 154 K | train\n",
"1 | train_acc | MulticlassAccuracy | 0 | train\n",
"2 | val_acc | MulticlassAccuracy | 0 | train\n",
"3 | test_acc | MulticlassAccuracy | 0 | train\n",
"---------------------------------------------------------\n",
"154 K Trainable params\n",
"0 Non-trainable params\n",
"154 K Total params\n",
"0.616 Total estimated model params size (MB)\n",
"37 Modules in train mode\n",
"0 Modules in eval mode\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 14: 100%|██████████| 1407/1407 [00:28<00:00, 49.90it/s, v_num=0, train_loss=0.518, train_acc=0.875, val_loss=0.996, val_acc=0.644]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"`Trainer.fit` stopped: `max_epochs=15` reached.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 14: 100%|██████████| 1407/1407 [00:28<00:00, 49.87it/s, v_num=0, train_loss=0.518, train_acc=0.875, val_loss=0.996, val_acc=0.644]\n"
]
}
],
"source": [
"trainer.fit(datamodule=dm,model=model)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation DataLoader 0: 100%|██████████| 157/157 [00:00<00:00, 163.27it/s]\n"
]
},
{
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃<span style=\"font-weight: bold\"> Validate metric </span>┃<span style=\"font-weight: bold\"> DataLoader 0 </span>┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│<span style=\"color: #008080; text-decoration-color: #008080\"> val_acc </span>│<span style=\"color: #800080; text-decoration-color: #800080\"> 0.6284000277519226 </span>│\n",
"│<span style=\"color: #008080; text-decoration-color: #008080\"> val_loss </span>│<span style=\"color: #800080; text-decoration-color: #800080\"> 1.0169780254364014 </span>│\n",
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│\u001b[36m \u001b[0m\u001b[36m val_acc \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.6284000277519226 \u001b[0m\u001b[35m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36m val_loss \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 1.0169780254364014 \u001b[0m\u001b[35m \u001b[0m│\n",
"└───────────────────────────┴───────────────────────────┘\n"
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"data": {
"text/plain": [
"[{'val_loss': 1.0169780254364014, 'val_acc': 0.6284000277519226}]"
]
},
"execution_count": 6,
"metadata": {},
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],
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"trainer.validate(model,dm)"
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