model.py

'''
https://github.com/kuangliu/pytorch-cifar

ResNet in PyTorch.

For Pre-activation ResNet, see 'preact_resnet.py'.

Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
from torch import nn
from torch.nn import functional as F
from torch_lr_finder import LRFinder


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(
            in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                               stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
                               stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


def ResNet18():
    return ResNet(BasicBlock, [2, 2, 2, 2])

import torch.nn as nn
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt

from data_loader import CifarAlbumentationsDataset,\
    CIFAR_CLASS_LABELS, TRAIN_TRANSFORM, TEST_TRANSFORM
import model
from torch_lr_finder import LRFinder

import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_lightning import LightningModule
from torch.optim.lr_scheduler import OneCycleLR
from torchmetrics.functional import accuracy

class LitResnet(LightningModule):
    def __init__(self, lr=0.03, batch_size=512):
        super().__init__()

        self.save_hyperparameters()
        self.criterion = nn.CrossEntropyLoss()
        self.model = ResNet18()

    def forward(self, x):
        return self.model(x)

    def training_step(self, batch, batch_idx):
        x, y = batch
        output = self.forward(x)
        loss = self.criterion(output, y)
        self.log("train_loss", loss)
        acc = accuracy(torch.argmax(output, dim=1), 
                       y, 'multiclass', num_classes=10)
        self.log(f"train_acc", acc, prog_bar=True)
        return loss

    def evaluate(self, batch, stage=None):
        x, y = batch
        output = self.forward(x)
        loss = self.criterion(output, y)
        preds = torch.argmax(output, dim=1)
        acc = accuracy(preds, y, 'multiclass', num_classes=10)

        if stage:
            self.log(f"{stage}_loss", loss, prog_bar=True)
            self.log(f"{stage}_acc", acc, prog_bar=True)

    def validation_step(self, batch, batch_idx):
        self.evaluate(batch, "val")

    def test_step(self, batch, batch_idx):
        self.evaluate(batch, "test")

    # todo
    # change the default for num_iter
    def lr_finder(self, optimizer,  num_iter=200,):
        lr_finder = LRFinder(self, optimizer, self.criterion,
            device=self.device)
        lr_finder.range_test(
            self.train_dataloader(), end_lr=1,
            num_iter=num_iter, step_mode='exp',
            )
        ax, suggested_lr = lr_finder.plot(suggest_lr=True)
        # todo
        # how to log maplotlib images
        # self.logger.experiment.add_image('lr_finder', plt.gcf(), 0)
        lr_finder.reset() 
        return suggested_lr
    def configure_optimizers(self):
        optimizer = torch.optim.SGD(
            self.parameters(),
            lr=self.hparams.lr,
            momentum=0.9,
            weight_decay=5e-4,
        )
        suggested_lr = self.lr_finder(optimizer)
        steps_per_epoch = len(self.train_dataloader())
        scheduler_dict = {
            "scheduler":  OneCycleLR(
                optimizer, max_lr=suggested_lr,
                steps_per_epoch=steps_per_epoch,
                epochs=self.trainer.max_epochs, 
                pct_start=5/self.trainer.max_epochs,
                three_phase=False,
                div_factor=100,
                final_div_factor=100,
                anneal_strategy='linear',
            ),
            "interval": "step",
        }
        return {"optimizer": optimizer, "lr_scheduler": scheduler_dict}
    ####################
    # DATA RELATED HOOKS
    ####################

    def prepare_data(self, data_path='../data'):
        CifarAlbumentationsDataset(
                data_path, train=True, download=True)
        CifarAlbumentationsDataset(
                data_path, train=False, download=True)

    def setup(self, stage=None, data_dir='../data'):

        if stage == "fit" or stage is None:
            self.train_dataset = CifarAlbumentationsDataset(data_dir, train=True, transform=TRAIN_TRANSFORM)
            self.test_dataset = CifarAlbumentationsDataset(data_dir, train=False, transform=TEST_TRANSFORM)


    def train_dataloader(self):
        return DataLoader(self.train_dataset, batch_size=self.hparams.batch_size,
        shuffle=True, pin_memory=True) #num_workers=4, 

    def val_dataloader(self):
        return DataLoader(self.test_dataset, batch_size=self.hparams.batch_size,
        shuffle=False, pin_memory=True)