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@UmarZein
Created December 12, 2025 02:45
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "2157fc69-c6c1-4d8b-86e2-ff05f15516dd",
"metadata": {},
"outputs": [],
"source": [
"from torchvision.datasets import ImageFolder\n",
"from torchvision.transforms import v2\n",
"from torch.utils.data import DataLoader, WeightedRandomSampler, random_split, Subset\n",
"import os\n",
"import torch\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "80add420-6fcc-4652-89a4-0a00d66636cd",
"metadata": {},
"outputs": [],
"source": [
"mean_rgb=torch.tensor([0.2373, 0.2999, 0.3003])\n",
"std_rgb=torch.tensor([0.1740, 0.1648, 0.1348])\n",
"train_transform=v2.Compose([\n",
" v2.ToImage(), \n",
" v2.ToDtype(torch.float32, scale=True),\n",
" v2.RandomResizedCrop(size = (224, 224), scale = (0.8, 1.0)),\n",
" v2.ColorJitter(0.2, 0.2, 0.2, 0.05),\n",
" v2.GaussianBlur(kernel_size=(9, 9), sigma=(0.1,5)),\n",
" v2.RandomHorizontalFlip(0.5),\n",
" v2.RandomRotation(180),\n",
" v2.Normalize(\n",
" mean=mean_rgb,\n",
" std=std_rgb\n",
" ),\n",
"])\n",
"val_transform=v2.Compose([\n",
" v2.ToImage(), \n",
" v2.ToDtype(torch.float32, scale=True),\n",
" v2.RandomResizedCrop(size = (224, 224), scale = (0.8, 1.0)),\n",
" v2.Normalize(\n",
" mean=mean_rgb,\n",
" std=std_rgb\n",
" ),\n",
"])\n",
"\n",
"full_dataset=ImageFolder(\n",
" \"./combined_data/\",\n",
" transform=train_transform\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ab4aee75-378c-4dbc-bef6-1759d14e31cb",
"metadata": {},
"outputs": [],
"source": [
"n_total = len(full_dataset)\n",
"\n",
"train_size = int(0.8 * n_total)\n",
"val_size = n_total - train_size\n",
"\n",
"generator = torch.Generator()#.manual_seed(42) \n",
"train_indices, val_indices = random_split(range(n_total), [train_size, val_size], generator=generator)\n",
"\n",
"train_dataset_full = ImageFolder(\"./combined_data/\", transform=train_transform)\n",
"val_dataset_full = ImageFolder(\"./combined_data/\", transform=val_transform)\n",
"\n",
"train_subset = Subset(train_dataset_full, train_indices.indices)\n",
"val_subset = Subset(val_dataset_full, val_indices.indices)\n",
"\n",
"train_loader = DataLoader(train_subset, batch_size=32, shuffle=True)\n",
"val_loader = DataLoader(val_subset, batch_size=32, shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e38f0466-02b2-4d53-b5dd-886b71a60559",
"metadata": {},
"outputs": [],
"source": [
"data_unlabeled=ImageFolder(\"./unlabeled_400m_rgb/\", transform=val_transform)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "28e4825b-1f08-4ad3-9250-d7892d7aa946",
"metadata": {},
"outputs": [],
"source": [
"idx,counts = torch.unique(torch.tensor(full_dataset.targets), return_counts=True)\n",
"class_weights = counts.sum()/counts\n",
"class_weights = class_weights/class_weights.sum()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "06449130-dfc6-4975-9e5a-c06096e82ccb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([0.1479, 0.7397, 0.1123])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_weights"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "ff7e92fe-a9e6-46ee-9d3a-680ca7ca75e0",
"metadata": {},
"outputs": [],
"source": [
"from torchvision.models.resnet import resnet50\n",
"from torchvision.models.resnet import ResNet50_Weights\n",
"from torch import nn\n",
"import pytorch_lightning as pl\n",
"from torchmetrics.classification import MulticlassF1Score\n",
"from torchmetrics import Accuracy, Precision, Recall, F1Score\n",
"import torch\n",
"\n",
"class ResNet50(pl.LightningModule):\n",
" def __init__(self, lr=1e-3):\n",
" super().__init__()\n",
" self.lr=lr\n",
" self.num_classes=len(class_weights)\n",
" self.inner = resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)\n",
" self.inner.fc=nn.Sequential(\n",
" nn.Dropout(p=0.5),\n",
" nn.Linear(self.inner.fc.in_features, self.num_classes),\n",
" )\n",
"\n",
" \n",
" self.train_acc = Accuracy(task='multiclass', num_classes=self.num_classes)\n",
" self.train_f1 = F1Score(task='multiclass', num_classes=self.num_classes, average='macro')\n",
" \n",
" \n",
" #self.train_precision = Precision(task='multiclass', num_classes=self.num_classes, average='macro')\n",
" #self.val_precision = Precision(task='multiclass', num_classes=self.num_classes, average='macro')\n",
" self.val_acc = Accuracy(task='multiclass', num_classes=self.num_classes)\n",
" self.val_f1 = F1Score(task='multiclass', num_classes=self.num_classes, average='macro')\n",
" \n",
" self.test_acc = Accuracy(task='multiclass', num_classes=self.num_classes)\n",
" self.test_f1 = F1Score(task='multiclass', num_classes=self.num_classes, average='macro')\n",
" \n",
" #self.train_recall = Recall(task='multiclass', num_classes=self.num_classes, average='macro')\n",
" #self.val_recall = Recall(task='multiclass', num_classes=self.num_classes, average='macro')\n",
" \n",
" #self.automatic_optimization = False\n",
" \n",
" def configure_optimizers(self):\n",
" # Differential Learning Rate\n",
" optimizer = torch.optim.AdamW([\n",
" # 1. Head (FC) -> Learning Rate Normal\n",
" {'params': self.inner.fc.parameters(), 'lr': self.lr}, \n",
" \n",
" # 2. Backbone (Layer 4) -> Learning Rate Kecil (10x lebih kecil)\n",
" {'params': self.inner.layer4.parameters(), 'lr': self.lr * 0.1},\n",
" ], weight_decay=1e-4)\n",
" \n",
" scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
" optimizer,\n",
" mode='max', \n",
" factor=0.5, \n",
" patience=3, \n",
" min_lr=1e-7,\n",
" )\n",
" \n",
" return {\n",
" \"optimizer\": optimizer,\n",
" \"lr_scheduler\": {\n",
" \"scheduler\": scheduler,\n",
" \"monitor\": \"val_f1\",\n",
" \"interval\": \"epoch\",\n",
" \"frequency\": 1\n",
" }\n",
" }\n",
"\n",
" def forward(self, image):\n",
" if type(image)==tuple:\n",
" image, target = image\n",
" x=self.inner(image).softmax(-1)[:,0]\n",
" return x\n",
"\n",
" def forward_logit_garimpo(self, image):\n",
" x=self.inner(image)\n",
" return x[:,0]\n",
" \n",
" def training_step(self, batch, batch_idx):\n",
" image, target = batch\n",
" pred = self.inner(image) # logits (N, C)\n",
" weights = class_weights.to(image.device)\n",
"\n",
" # Update metric\n",
" preds = torch.argmax(pred, dim=1)\n",
" loss = nn.functional.cross_entropy(pred, target, weight=weights)#, reduction='none')\n",
" \n",
" preds = torch.argmax(pred, dim=1)\n",
" \n",
" self.train_acc(preds, target)\n",
" self.train_f1(preds, target)\n",
" self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True)\n",
" self.log('train_acc', self.train_acc, on_step=False, on_epoch=True, prog_bar=True)\n",
" self.log('train_f1', self.train_f1, on_step=False, on_epoch=True, prog_bar=True)\n",
" return loss\n",
"\n",
" def validation_step(self, batch, batch_idx):\n",
" image, target = batch\n",
" pred = self.inner(image) # logits (N, C)\n",
"\n",
" weights = class_weights.to(image.device)\n",
"\n",
" # Update metric\n",
" preds = torch.argmax(pred, dim=1)\n",
" loss = nn.functional.cross_entropy(pred, target, weight=weights)#, reduction='none')\n",
" \n",
" self.val_acc(preds, target)\n",
" self.val_f1(preds, target)\n",
" self.log('val_loss', loss, on_step=False, on_epoch=True, prog_bar=True)\n",
" self.log('val_acc', self.val_acc, on_step=False, on_epoch=True, prog_bar=True)\n",
" self.log('val_f1', self.val_f1, on_step=False, on_epoch=True, prog_bar=True)\n",
" return loss\n",
"\n",
" def test_step(self, batch, batch_idx):\n",
" image, target = batch\n",
" pred = self.inner(image) # logits (N, C)\n",
" weights = class_weights.to(image.device)\n",
"\n",
" # Update metric\n",
" preds = torch.argmax(pred, dim=1)\n",
" loss = nn.functional.cross_entropy(pred, target, weight=weights)#, reduction='none')\n",
" #loss = (loss*class_weights.to(target.device)[target]).sum()\n",
" \n",
" self.test_acc(preds, target)\n",
" self.test_f1(preds, target)\n",
" self.log('test_loss', loss, on_step=False, on_epoch=True, prog_bar=True)\n",
" self.log('test_acc', self.test_acc, on_step=False, on_epoch=True, prog_bar=True)\n",
" self.log('test_f1', self.test_f1, on_step=False, on_epoch=True, prog_bar=True)\n",
" return loss\n",
"\n",
" def on_validation_epoch_end(self):\n",
" # reset metric agar epoch berikutnya bersi h\n",
" self.val_acc.reset()\n",
" self.val_f1.reset()\n",
" \n",
" def on_train_epoch_end(self):\n",
" # reset metric agar epoch berikutnya bersi h\n",
" self.train_acc.reset()\n",
" self.train_f1.reset()\n",
" \n",
" def on_test_epoch_end(self):\n",
" # reset metric agar epoch berikutnya bersi h\n",
" self.test_acc.reset()\n",
" self.test_f1.reset()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "455e50a8-5978-4240-8098-bf48d7001589",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"float(np.array([[1]]).squeeze())"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "a56d2712-2afe-4b7c-ad0b-f1cd2dd1068e",
"metadata": {},
"outputs": [],
"source": [
"with torch.no_grad():\n",
" example_inputs = (torch.randn(1, 3, 224, 224))\n",
" onnx_program = torch.onnx.export(model.cpu(), example_inputs, \"model3.onnx\")#torch.onnx.export(model, example_inputs, dynamo=True)\n",
" #out=model.forward_logit_garimpo(input_sample.cpu())"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "80e45b27-6b3f-46f3-8a80-34a604b09aaa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([0.5418], grad_fn=<SelectBackward0>)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model(example_inputs)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "d064cea7-e6b3-4d43-9fc3-ff873df427a6",
"metadata": {},
"outputs": [],
"source": [
"#model=ResNet50(model)# <- inisialisasi pakai ini\n",
"model=ResNet50.load_from_checkpoint(\"checkpoints/ResNet50-v1.ckpt\")#(2e-4)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "295f718e-3416-40dd-84e8-4a61c5b6980c",
"metadata": {},
"outputs": [],
"source": [
"udl=DataLoader(data_unlabeled,batch_size=8,shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "fc6580cf-bed4-4c20-8bc9-ebdf38231d0b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([0.0490, 0.8896, 0.8837, 0.3256, 0.7802, 0.0937], device='cuda:0',\n",
" grad_fn=<SelectBackward0>)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model(b.cuda())"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b8248b3d-7a78-44b6-9488-b8f13b33afaf",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e3348b5d5081433aa782cd51f795b817",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/273 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from tqdm.auto import tqdm\n",
"results=[]\n",
"model.eval()\n",
"with torch.no_grad():\n",
" for b,_ in tqdm(udl):\n",
" out=model.forward_logit_garimpo(b.cuda())\n",
" #out: N,3\n",
" for i in range(out.shape[0]):\n",
" results.append(tuple(out[i].cpu()))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "62f46449-9e9d-475a-a391-d11221f373fb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([2182, 3])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results_t=torch.tensor(results)\n",
"results_t.shape"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "c9e8d862-9caa-4ac0-9a87-e2683bb97bd1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[ 1.9109, -1.4354, -0.6787],\n",
" [-0.9991, -1.0525, 1.8996],\n",
" [-0.3347, -2.2388, 2.0659],\n",
" ...,\n",
" [-1.2573, -0.9666, 1.7693],\n",
" [ 0.9282, -1.7666, 0.4683],\n",
" [-1.2549, -0.4471, 1.6032]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results_t#.softmax(-1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "16979925-499f-4af0-a4f2-d3fc95006d45",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 63,
"id": "3b95b84a-ff58-4adf-980b-3741ff84a3b0",
"metadata": {},
"outputs": [],
"source": [
"from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping, LearningRateMonitor\n",
"\n",
"checkpoint_callback = ModelCheckpoint(\n",
" dirpath='checkpoints',\n",
" filename='ResNet50',\n",
" monitor='val_f1',\n",
" mode='max',\n",
" save_top_k=1,\n",
" verbose=True\n",
")\n",
"\n",
"early_stop_callback = EarlyStopping(\n",
" monitor='val_f1',\n",
" patience=6,\n",
" mode='max',\n",
" verbose=True\n",
")\n",
"\n",
"lr_monitor = LearningRateMonitor(logging_interval='epoch')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "6e4f0af6-3091-436e-9318-119b3a21d0a7",
"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": [
"from pytorch_lightning.loggers import CSVLogger\n",
"\n",
"logger = CSVLogger(\"logs\", name=\"resnet50_pretrained_with_correct_normalization\")\n",
"\n",
"trainer=pl.Trainer(logger=logger, max_epochs=10,\n",
" callbacks=[checkpoint_callback, early_stop_callback, lr_monitor],\n",
" accelerator='auto',\n",
" devices=1,\n",
" log_every_n_steps=5,\n",
" #detect_anomaly=True,\n",
" #deterministic=True,\n",
" precision='32', \n",
" gradient_clip_val=0.5,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "c0527cb7-e4d7-4e15-9c19-a370cdd54a46",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\umarzein\\micromamba\\envs\\textmining\\lib\\site-packages\\pytorch_lightning\\callbacks\\model_checkpoint.py:751: Checkpoint directory C:\\Users\\umarzein\\Desktop\\tnks\\example\\checkpoints exists and is not empty.\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"\n",
" | Name | Type | Params | Mode \n",
"---------------------------------------------------------\n",
"0 | inner | ResNet | 23.5 M | train\n",
"1 | train_acc | MulticlassAccuracy | 0 | train\n",
"2 | train_f1 | MulticlassF1Score | 0 | train\n",
"3 | val_acc | MulticlassAccuracy | 0 | train\n",
"4 | val_f1 | MulticlassF1Score | 0 | train\n",
"5 | test_acc | MulticlassAccuracy | 0 | train\n",
"6 | test_f1 | MulticlassF1Score | 0 | train\n",
"---------------------------------------------------------\n",
"23.5 M Trainable params\n",
"0 Non-trainable params\n",
"23.5 M Total params\n",
"94.057 Total estimated model params size (MB)\n",
"159 Modules in train mode\n",
"0 Modules in eval mode\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Sanity Checking: | | 0/? [00:00<…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\umarzein\\micromamba\\envs\\textmining\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:433: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n",
"C:\\Users\\umarzein\\micromamba\\envs\\textmining\\lib\\site-packages\\torchmetrics\\utilities\\prints.py:43: UserWarning: The ``compute`` method of metric MulticlassAccuracy was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n",
" warnings.warn(*args, **kwargs)\n",
"C:\\Users\\umarzein\\micromamba\\envs\\textmining\\lib\\site-packages\\torchmetrics\\utilities\\prints.py:43: UserWarning: The ``compute`` method of metric MulticlassF1Score was called before the ``update`` method which may lead to errors, as metric states have not yet been updated.\n",
" warnings.warn(*args, **kwargs)\n",
"C:\\Users\\umarzein\\micromamba\\envs\\textmining\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:433: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "861715d3925b457fa470b86ebb8fa91f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Training: | | 0/? [00:00<…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Metric val_f1 improved. New best score: 0.876\n",
"Epoch 0, global step 17: 'val_f1' reached 0.87562 (best 0.87562), saving model to 'C:\\\\Users\\\\umarzein\\\\Desktop\\\\tnks\\\\example\\\\checkpoints\\\\ResNet50-v1.ckpt' as top 1\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Metric val_f1 improved by 0.033 >= min_delta = 0.0. New best score: 0.908\n",
"Epoch 1, global step 34: 'val_f1' reached 0.90812 (best 0.90812), saving model to 'C:\\\\Users\\\\umarzein\\\\Desktop\\\\tnks\\\\example\\\\checkpoints\\\\ResNet50-v1.ckpt' as top 1\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Metric val_f1 improved by 0.014 >= min_delta = 0.0. New best score: 0.922\n",
"Epoch 2, global step 51: 'val_f1' reached 0.92191 (best 0.92191), saving model to 'C:\\\\Users\\\\umarzein\\\\Desktop\\\\tnks\\\\example\\\\checkpoints\\\\ResNet50-v1.ckpt' as top 1\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Metric val_f1 improved by 0.039 >= min_delta = 0.0. New best score: 0.961\n",
"Epoch 3, global step 68: 'val_f1' reached 0.96051 (best 0.96051), saving model to 'C:\\\\Users\\\\umarzein\\\\Desktop\\\\tnks\\\\example\\\\checkpoints\\\\ResNet50-v1.ckpt' as top 1\n"
]
},
{
"data": {
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"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Metric val_f1 improved by 0.003 >= min_delta = 0.0. New best score: 0.963\n",
"Epoch 4, global step 85: 'val_f1' reached 0.96312 (best 0.96312), saving model to 'C:\\\\Users\\\\umarzein\\\\Desktop\\\\tnks\\\\example\\\\checkpoints\\\\ResNet50-v1.ckpt' as top 1\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 5, global step 102: 'val_f1' was not in top 1\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Metric val_f1 improved by 0.003 >= min_delta = 0.0. New best score: 0.966\n",
"Epoch 6, global step 119: 'val_f1' reached 0.96610 (best 0.96610), saving model to 'C:\\\\Users\\\\umarzein\\\\Desktop\\\\tnks\\\\example\\\\checkpoints\\\\ResNet50-v1.ckpt' as top 1\n"
]
},
{
"data": {
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"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Metric val_f1 improved by 0.011 >= min_delta = 0.0. New best score: 0.977\n",
"Epoch 7, global step 136: 'val_f1' reached 0.97732 (best 0.97732), saving model to 'C:\\\\Users\\\\umarzein\\\\Desktop\\\\tnks\\\\example\\\\checkpoints\\\\ResNet50-v1.ckpt' as top 1\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Metric val_f1 improved by 0.011 >= min_delta = 0.0. New best score: 0.989\n",
"Epoch 8, global step 153: 'val_f1' reached 0.98861 (best 0.98861), saving model to 'C:\\\\Users\\\\umarzein\\\\Desktop\\\\tnks\\\\example\\\\checkpoints\\\\ResNet50-v1.ckpt' as top 1\n"
]
},
{
"data": {
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"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Validation: | | 0/? [00:00<…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 9, global step 170: 'val_f1' was not in top 1\n",
"`Trainer.fit` stopped: `max_epochs=10` reached.\n"
]
}
],
"source": [
"trainer.fit(model, train_loader, val_loader)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"id": "0304c01e-99c9-4a09-bd33-6063c29ca9d1",
"metadata": {},
"outputs": [],
"source": [
"model.eval()\n",
"input_sample = torch.randn((1, 3, 224, 224)).cuda()\n",
"#model.to_onnx(\"model2.onnx\", input_sample, export_params=True)"
]
},
{
"cell_type": "code",
"execution_count": 102,
"id": "7985d8d8-c1bf-4e0c-82a2-cb374941aecd",
"metadata": {},
"outputs": [],
"source": [
"with torch.no_grad():\n",
" example_inputs = (torch.randn(1, 3, 224, 224))\n",
" onnx_program = torch.onnx.export(model, example_inputs, \"model2.onnx\")#torch.onnx.export(model, example_inputs, dynamo=True)\n",
" #out=model.forward_logit_garimpo(input_sample.cpu())"
]
},
{
"cell_type": "code",
"execution_count": 100,
"id": "9d558516-6944-4df5-8d5f-a44b65535b86",
"metadata": {},
"outputs": [],
"source": [
"model.forward=model.forward_logit_garimpo"
]
},
{
"cell_type": "code",
"execution_count": 80,
"id": "1e47bd78-6bea-4363-ae00-e5141fd85599",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x270ff9cde40>"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow((train_dataset_full[400][0].permute(1,2,0)*std_rgb+mean_rgb))"
]
},
{
"cell_type": "code",
"execution_count": 85,
"id": "079631ec-f14a-48a5-baef-563df27d8b8a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1])"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.forward_logit_garimpo(train_dataset_full[400][0].unsqueeze(0).cuda())"
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "4d98589d-c0e9-4511-bea7-eddc9ac89597",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 3, 224, 224])"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_dataset_full[400][0].unsqueeze(0).shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af8cdc13-6808-436b-ab61-9ba22ad78e53",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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