Utilities/model.py

"""
Implementation of YOLOv3 architecture
"""

import random
from typing import Any, Optional

import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.optim as optim
from pytorch_lightning.utilities.types import STEP_OUTPUT
from torch.optim.lr_scheduler import OneCycleLR

from . import config
from .loss import YoloLoss

""" 
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
"""

model_config = [
    (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(pl.LightningModule):
    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(pl.LightningModule):
    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(pl.LightningModule):
    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(pl.LightningModule):
    def __init__(self, in_channels=3, num_classes=20):
        super().__init__()
        self.num_classes = num_classes
        self.in_channels = in_channels
        self.layers = self._create_conv_layers()

        self.scaled_anchors = (
            torch.tensor(config.ANCHORS)
            * torch.tensor(config.S).unsqueeze(1).unsqueeze(1).repeat(1, 3, 2)
        ).to(config.DEVICE)

        self.learning_rate = config.LEARNING_RATE
        self.weight_decay = config.WEIGHT_DECAY
        self.best_lr = 1e-3

    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 model_config:
            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

    def yololoss(self):
        return YoloLoss()

    def training_step(self, batch, batch_idx):
        x, y = batch
        y0, y1, y2 = y[0], y[1], y[2]
        out = self(x)
        # print(out[0].shape, y0.shape)
        loss = (
            self.yololoss()(out[0], y0, self.scaled_anchors[0])
            + self.yololoss()(out[1], y1, self.scaled_anchors[1])
            + self.yololoss()(out[2], y2, self.scaled_anchors[2])
        )

        self.log(
            "train_loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True
        )

        # config.IMAGE_SIZE = 416 if random.random() < 0.5 else 544
        # config.S = [
        #     config.IMAGE_SIZE // 32,
        #     config.IMAGE_SIZE // 16,
        #     config.IMAGE_SIZE // 8,
        # ]
        # print(f"{self.trainer.datamodule.train_dataset.S=}")
        # self.trainer.datamodule.train_dataset.S = [
        #     config.IMAGE_SIZE // 32,
        #     config.IMAGE_SIZE // 16,
        #     config.IMAGE_SIZE // 8,
        # ]
        # self.trainer.datamodule.train_dataset.image_size = config.IMAGE_SIZE
        # self.scaled_anchors = (
        #     torch.tensor(config.ANCHORS)
        #     * torch.tensor(config.S).unsqueeze(1).unsqueeze(1).repeat(1, 3, 2)
        # ).to(config.DEVICE)
        return loss

    def on_train_epoch_end(self) -> None:
        print(
            f"EPOCH: {self.current_epoch}, Loss: {self.trainer.callback_metrics['train_loss_epoch']}"
        )

    def configure_optimizers(self) -> Any:
        optimizer = optim.Adam(
            self.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay
        )
        scheduler = OneCycleLR(
            optimizer,
            max_lr=self.best_lr,
            steps_per_epoch=len(self.trainer.datamodule.train_dataloader()),
            epochs=config.NUM_EPOCHS,
            pct_start=8 / config.NUM_EPOCHS,
            div_factor=100,
            three_phase=False,
            final_div_factor=100,
            anneal_strategy="linear",
        )

        return [optimizer], [
            {"scheduler": scheduler, "interval": "step", "frequency": 1}
        ]

    def on_train_end(self) -> None:
        torch.save(self.state_dict(), config.MODEL_STATE_DICT_PATH)


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!")