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import os
import torch
import torchvision
from torchvision import datasets, transforms, utils
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.transforms.dataset_normalizations import cifar10_normalization
from pytorch_lightning import LightningModule, Trainer, seed_everything
from pytorch_lightning.callbacks import LearningRateMonitor
from pytorch_lightning.callbacks.progress import TQDMProgressBar
from pytorch_lightning.loggers import CSVLogger
from torch.optim.lr_scheduler import OneCycleLR
from torch.optim.swa_utils import AveragedModel, update_bn
from torchmetrics.functional import accuracy
import pandas as pd
import torch.nn as nn
import torch.nn.functional as F
# from IPython.core.display import display
import misclas_helper
import gradcam_helper
import lightningmodel
from misclas_helper import display_cifar_misclassified_data
from gradcam_helper import display_gradcam_output
from misclas_helper import get_misclassified_data2
from misclas_helper import classify_images
from lightningmodel import LitResnet
#ref : https://pytorch-lightning.readthedocs.io/en/1.2.10/common/weights_loading.html
from pytorch_lightning.callbacks import ModelCheckpoint
classes = ('plane', 'car', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck', 'NotApplicable')
inv_normalize = transforms.Normalize(
mean=[-0.50/0.23, -0.50/0.23, -0.50/0.23],
std=[1/0.23, 1/0.23, 1/0.23]
)
def ts_lt( # Train and Save Vs Load and Test
save1_or_load0, # decision maker for training Vs testing
Epochs = 1, # argument for training
wt_fname = "/content/weights.ckpt" # argument for testing
):
checkpoint_callback = ModelCheckpoint(
monitor='val_acc',
dirpath='/content/',
filename='weights_{epoch:02d}_{val_acc:.2f}',
save_top_k=3,
mode='max',
)
trainer = Trainer(
max_epochs=Epochs, #26
accelerator="auto",
devices=1 if torch.cuda.is_available() else None, # limiting got iPython runs
logger=CSVLogger(save_dir="logs/"),
callbacks=[LearningRateMonitor(logging_interval="step"), TQDMProgressBar(refresh_rate=10), checkpoint_callback],
)
PATH_DATASETS = os.environ.get("PATH_DATASETS", ".")
BATCH_SIZE = 256 if torch.cuda.is_available() else 64
NUM_WORKERS = int(os.cpu_count() / 2)
train_transforms = torchvision.transforms.Compose(
[
torchvision.transforms.RandomCrop(32, padding=4),
torchvision.transforms.RandomHorizontalFlip(),
torchvision.transforms.ToTensor(),
cifar10_normalization(),
]
)
test_transforms = torchvision.transforms.Compose(
[
torchvision.transforms.ToTensor(),
cifar10_normalization(),
]
)
cifar10_dm = CIFAR10DataModule(
data_dir=PATH_DATASETS,
batch_size=BATCH_SIZE,
num_workers=NUM_WORKERS,
train_transforms=train_transforms,
test_transforms=test_transforms,
val_transforms=test_transforms,
)
if save1_or_load0 == True:
model = LitResnet(lr=0.05)
checkpoint_callback = ModelCheckpoint(
monitor='val_acc',
dirpath='/content/',
filename='weights_{epoch:02d}_{val_acc:.2f}',
save_top_k=3,
mode='max',
)
trainer.fit(model, cifar10_dm)
else:
model = LitResnet(lr=0.05).load_from_checkpoint(wt_fname)
trainer.test(model, datamodule=cifar10_dm)
return model, trainer |