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"""
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!")
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