""" Implementation of YOLOv3 architecture """ from typing import Any, Dict from lightning.pytorch.utilities.types import STEP_OUTPUT import torch import torch.nn as nn import lightning as L import config as config_ from utils.common import one_cycle_lr from utils.data import PascalDataModule from utils.loss import YoloLoss from utils.utils import ( mean_average_precision, cells_to_bboxes, get_evaluation_bboxes, save_checkpoint, load_checkpoint, check_class_accuracy, get_loaders, plot_couple_examples, ) """ 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 = [ (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(L.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(L.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(L.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(L.LightningModule): def __init__( self, in_channels=3, num_classes=80, epochs=40, loss_fn=YoloLoss, datamodule=PascalDataModule(), learning_rate=None, maxlr=None, scheduler_steps=None, device_count=2, ): super().__init__() self.num_classes = num_classes self.in_channels = in_channels self.epochs = epochs self.loss_fn = loss_fn() 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(self.device) self.datamodule = datamodule self.learning_rate = learning_rate self.maxlr = maxlr self.scheduler_steps = scheduler_steps self.device_count = device_count 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: 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 configure_optimizers(self) -> Dict: # effective_lr = self.learning_rate * self.device_count optimizer = torch.optim.Adam( self.parameters(), lr=self.learning_rate, weight_decay=config_.WEIGHT_DECAY ) scheduler = one_cycle_lr( optimizer=optimizer, maxlr=self.maxlr, steps=self.scheduler_steps, epochs=self.epochs, ) return { "optimizer": optimizer, "lr_scheduler": {"scheduler": scheduler, "interval": "step"}, } def _common_step(self, batch, batch_idx): self.scaled_anchors = self.scaled_anchors.to(self.device) 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]) ) return loss def training_step(self, batch, batch_idx): loss = self._common_step(batch, batch_idx) self.log(name="train_loss", value=loss, on_step=True, on_epoch=True, prog_bar=True) return loss def validation_step(self, batch, batch_idx): loss = self._common_step(batch, batch_idx) self.log(name="val_loss", value=loss, on_step=True, on_epoch=True, prog_bar=True) return loss def test_step(self, batch, batch_idx): class_acc, noobj_acc, obj_acc = check_class_accuracy( model=self, loader=self.datamodule.test_dataloader(), threshold=config_.CONF_THRESHOLD, ) self.log_dict( { "class_acc": class_acc, "noobj_acc": noobj_acc, "obj_acc": obj_acc, }, prog_bar=True, ) 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!")