model.py

"""
Implementation of YOLOv3 architecture
"""
import os
import pytorch_lightning as pl
import pandas as pd
import seaborn as sn
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from IPython.core.display import display
#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, ModelCheckpoint
from pytorch_lightning.callbacks.progress import TQDMProgressBar
from pytorch_lightning.loggers import CSVLogger
from torch.optim.lr_scheduler import OneCycleLR
from torchmetrics.functional import accuracy
import torch.cuda.amp as amp
from torch.utils.data import DataLoader
from loss import YoloLoss
from pytorch_lightning import LightningModule, Trainer
from torch.optim.lr_scheduler import OneCycleLR
from torch_lr_finder import LRFinder
import torch.nn as nn
from dataset import YOLODataset
import config
import torch
import torch.optim as optim
import os
#from model import YOLOv3
from tqdm import tqdm
from utils import (
    mean_average_precision,
    cells_to_bboxes,
    get_evaluation_bboxes,
    save_checkpoint,
    load_checkpoint,
    check_class_accuracy,
    get_loaders,
    plot_couple_examples
)
from loss import YoloLoss
import warnings
from pytorch_lightning import LightningModule
import torch
from loss import YoloLoss
import torch.nn as nn
import config
""" 
Information about architecture config:
Tuple is structured by (filters, kernel_size, stride) 
Every conv is a same convolution. 
List is structured by "B" indicating a residual block followed by the number of repeats
"S" is for scale prediction block and computing the yolo loss
"U" is for upsampling the feature map and concatenating with a previous layer
"""
config_1 = [
    (32, 3, 1),
    (64, 3, 2),
    ["B", 1],
    (128, 3, 2),
    ["B", 2],
    (256, 3, 2),
    ["B", 8],
    (512, 3, 2),
    ["B", 8],
    (1024, 3, 2),
    ["B", 4],  # To this point is Darknet-53
    (512, 1, 1),
    (1024, 3, 1),
    "S",
    (256, 1, 1),
    "U",
    (256, 1, 1),
    (512, 3, 1),
    "S",
    (128, 1, 1),
    "U",
    (128, 1, 1),
    (256, 3, 1),
    "S",
]


class CNNBlock(nn.Module):
    def __init__(self, in_channels, out_channels, bn_act=True, **kwargs):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, bias=not bn_act, **kwargs)
        self.bn = nn.BatchNorm2d(out_channels)
        self.leaky = nn.LeakyReLU(0.1)
        self.use_bn_act = bn_act

    def forward(self, x):
        if self.use_bn_act:
            return self.leaky(self.bn(self.conv(x)))
        else:
            return self.conv(x)


class ResidualBlock(nn.Module):
    def __init__(self, channels, use_residual=True, num_repeats=1):
        super().__init__()
        self.layers = nn.ModuleList()
        for repeat in range(num_repeats):
            self.layers += [
                nn.Sequential(
                    CNNBlock(channels, channels // 2, kernel_size=1),
                    CNNBlock(channels // 2, channels, kernel_size=3, padding=1),
                )
            ]

        self.use_residual = use_residual
        self.num_repeats = num_repeats

    def forward(self, x):
        for layer in self.layers:
            if self.use_residual:
                x = x + layer(x)
            else:
                x = layer(x)

        return x


class ScalePrediction(nn.Module):
    def __init__(self, in_channels, num_classes):
        super().__init__()
        self.pred = nn.Sequential(
            CNNBlock(in_channels, 2 * in_channels, kernel_size=3, padding=1),
            CNNBlock(
                2 * in_channels, (num_classes + 5) * 3, bn_act=False, kernel_size=1
            ),
        )
        self.num_classes = num_classes

    def forward(self, x):
        return (
            self.pred(x)
            .reshape(x.shape[0], 3, self.num_classes + 5, x.shape[2], x.shape[3])
            .permute(0, 1, 3, 4, 2)
        )


class YOLOv3(LightningModule):
    def __init__(self, in_channels=3, num_classes=80):
        super().__init__()
        self.num_classes = num_classes
        self.in_channels = in_channels
        self.layers = self._create_conv_layers()

    def forward(self, x):
        outputs = []  # for each scale
        route_connections = []
        for layer in self.layers:
            if isinstance(layer, ScalePrediction):
                outputs.append(layer(x))
                continue

            x = layer(x)

            if isinstance(layer, ResidualBlock) and layer.num_repeats == 8:
                route_connections.append(x)

            elif isinstance(layer, nn.Upsample):
                x = torch.cat([x, route_connections[-1]], dim=1)
                route_connections.pop()

        return outputs

    def _create_conv_layers(self):
        layers = nn.ModuleList()
        in_channels = self.in_channels

        for module in config_1:
            if isinstance(module, tuple):
                out_channels, kernel_size, stride = module
                layers.append(
                    CNNBlock(
                        in_channels,
                        out_channels,
                        kernel_size=kernel_size,
                        stride=stride,
                        padding=1 if kernel_size == 3 else 0,
                    )
                )
                in_channels = out_channels

            elif isinstance(module, list):
                num_repeats = module[1]
                layers.append(ResidualBlock(in_channels, num_repeats=num_repeats,))

            elif isinstance(module, str):
                if module == "S":
                    layers += [
                        ResidualBlock(in_channels, use_residual=False, num_repeats=1),
                        CNNBlock(in_channels, in_channels // 2, kernel_size=1),
                        ScalePrediction(in_channels // 2, num_classes=self.num_classes),
                    ]
                    in_channels = in_channels // 2

                elif module == "U":
                    layers.append(nn.Upsample(scale_factor=2),)
                    in_channels = in_channels * 3

        return layers

class YoloVersion3(LightningModule):
    def __init__(self):
        super(YoloVersion3, self).__init__( )
        self.save_hyperparameters()
       # Set our init args as class attributes
        self.learning_rate=config.LEARNING_RATE
        #self.config=config

        self.num_classes=config.NUM_CLASSES
        self.train_csv=config.DATASET + "/train.csv"
        self.test_csv=config.DATASET + "/test.csv"

        self.loss_fn= YoloLoss()
        self.scaler = amp.GradScaler()
        #self.train_transform_function= config.train_transforms
        #self.in_channels = 3
        self.model= YOLOv3(num_classes=config.NUM_CLASSES).to(config.DEVICE)
        self.scaled_anchors = (
                torch.tensor(config.ANCHORS) * torch.tensor(config.S).unsqueeze(1).unsqueeze(1).repeat(1, 3, 2)).to(config.DEVICE)
        #self.register_buffer("scaled_anchors", self.scaled_anchors)
        self.training_step_outputs = []

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

    def training_step(self, batch, batch_idx):
        x, y = batch
        y0, y1, y2 = (
            y[0],
            y[1],
            y[2],
        )
        out = self(x)
        loss = (
                self.loss_fn(out[0], y0, self.scaled_anchors[0])
                + self.loss_fn(out[1], y1, self.scaled_anchors[1])
                + self.loss_fn(out[2], y2, self.scaled_anchors[2])
        )
        self.log("train_loss", loss, on_epoch=True, prog_bar=True, logger=True)  # Logging the training loss for visualization
        self.training_step_outputs.append(loss)
        return loss

    def on_train_epoch_end(self):

        print(f"\nCurrently epoch {self.current_epoch}")
        train_epoch_average = torch.stack(self.training_step_outputs).mean()
        self.training_step_outputs.clear()
        print(f"Train loss {train_epoch_average}")
        print("On Train Eval loader:")
        print("On Train loader:")
        class_accuracy, no_obj_accuracy, obj_accuracy = check_class_accuracy(self.model, self.train_loader, threshold=config.CONF_THRESHOLD)
        self.log("class_accuracy", class_accuracy, on_epoch=True, prog_bar=True, logger=True)
        self.log("no_obj_accuracy", no_obj_accuracy, on_epoch=True, prog_bar=True, logger=True)
        self.log("obj_accuracy", obj_accuracy, on_epoch=True, prog_bar=True, logger=True)

        if (self.current_epoch>0) and ((self.current_epoch+1) % 6 == 0): # for every 10 epochs we are plotting
            plot_couple_examples(self.model, self.test_loader, 0.6, 0.5, self.scaled_anchors)

        if (self.current_epoch>0) and (self.current_epoch+1 == self.trainer.max_epochs ): #map calculation across last epoch
            check_class_accuracy(self.model, self.test_loader, threshold=config.CONF_THRESHOLD)
            pred_boxes, true_boxes = get_evaluation_bboxes(
                self.test_loader,
                self.model,
                iou_threshold=config.NMS_IOU_THRESH,
                anchors=config.ANCHORS,
                threshold=config.CONF_THRESHOLD,
            )
            mapval = mean_average_precision(
                pred_boxes,
                true_boxes,
                iou_threshold=config.MAP_IOU_THRESH,
                box_format="midpoint",
                num_classes=config.NUM_CLASSES,
            )
            print(f"MAP: {mapval.item()}")

            self.log("MAP", mapval.item(), on_epoch=True, prog_bar=True, logger=True)



    def configure_optimizers(self):
        optimizer = optim.Adam(
            self.parameters(),
            lr=config.LEARNING_RATE,
            weight_decay=config.WEIGHT_DECAY,
        )

        self.trainer.fit_loop.setup_data()
        dataloader = self.trainer.train_dataloader

        EPOCHS = config.NUM_EPOCHS # 40 % of number of epochs
        lr_scheduler = OneCycleLR(
            optimizer,
            max_lr=1E-3,
            steps_per_epoch=len(dataloader),
            epochs=EPOCHS,
            pct_start=5/EPOCHS,
            div_factor=100,
            three_phase=False,
            final_div_factor=100,
            anneal_strategy='linear'
        )

        scheduler = {"scheduler": lr_scheduler, "interval" : "step"}

        return [optimizer]

    def setup(self, stage=None):
        self.train_loader, self.test_loader, self.train_eval_loader = get_loaders(
            train_csv_path=self.train_csv,
            test_csv_path=self.test_csv,
        )

    def train_dataloader(self):
        return self.train_loader

    def val_dataloader(self):
        return self.train_eval_loader

    def test_dataloader(self):
        return self.test_loader
# if __name__ == "__main__":

#     model = YoloVersion3()

#     checkpoint = ModelCheckpoint(filename='last_epoch', save_last=True)
#     lr_rate_monitor = LearningRateMonitor(logging_interval="epoch")
#     trainer = pl.Trainer(
#                   max_epochs=config.NUM_EPOCHS,
#                   deterministic=True,
#                   logger=True,
#                   default_root_dir="/content/drive/MyDrive/sunandini/Checkpoint/",
#                   callbacks=[lr_rate_monitor],
#                   enable_model_summary=False,
#                   log_every_n_steps=1,
#                   precision="16-mixed"
#               )
#     print("---- Training Started ---- Sunandini ----")
#     trainer.fit(model)
#     torch.save(model.state_dict(), 'YOLOv3.pth')


if __name__ == "__main__":
    num_classes = 20
    IMAGE_SIZE = 416
    model = YOLOv3(num_classes=num_classes)
    x = torch.randn((2, 3, IMAGE_SIZE, IMAGE_SIZE))
    out = model(x)
    assert model(x)[0].shape == (2, 3, IMAGE_SIZE//32, IMAGE_SIZE//32, num_classes + 5)
    assert model(x)[1].shape == (2, 3, IMAGE_SIZE//16, IMAGE_SIZE//16, num_classes + 5)
    assert model(x)[2].shape == (2, 3, IMAGE_SIZE//8, IMAGE_SIZE//8, num_classes + 5)
    print("Success!")