import argparse
import csv
import os
import os.path as osp
import shutil
import cv2
import numpy as np
import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
from PIL import Image
from torch.optim import AdamW
from torchvision.datasets import VisionDataset
from torchvision.models.segmentation import deeplabv3_resnet50
from mmengine.dist import master_only
from mmengine.evaluator import BaseMetric
from mmengine.hooks import Hook
from mmengine.model import BaseModel
from mmengine.optim import AmpOptimWrapper
from mmengine.runner import Runner
def create_palette(csv_filepath):
color_to_class = {}
with open(csv_filepath, newline='') as csvfile:
reader = csv.DictReader(csvfile)
for idx, row in enumerate(reader):
r, g, b = int(row['r']), int(row['g']), int(row['b'])
color_to_class[(r, g, b)] = idx
return color_to_class
class CamVid(VisionDataset):
def __init__(self,
root,
img_folder,
mask_folder,
transform=None,
target_transform=None):
super().__init__(
root, transform=transform, target_transform=target_transform)
self.img_folder = img_folder
self.mask_folder = mask_folder
self.images = list(
sorted(os.listdir(os.path.join(self.root, img_folder))))
self.masks = list(
sorted(os.listdir(os.path.join(self.root, mask_folder))))
self.color_to_class = create_palette(
os.path.join(self.root, 'class_dict.csv'))
def __getitem__(self, index):
img_path = os.path.join(self.root, self.img_folder, self.images[index])
mask_path = os.path.join(self.root, self.mask_folder,
self.masks[index])
img = Image.open(img_path).convert('RGB')
mask = Image.open(mask_path).convert('RGB') # Convert to RGB
if self.transform is not None:
img = self.transform(img)
# Convert the RGB values to class indices
mask = np.array(mask)
mask = mask[:, :, 0] * 65536 + mask[:, :, 1] * 256 + mask[:, :, 2]
labels = np.zeros_like(mask, dtype=np.int64)
for color, class_index in self.color_to_class.items():
rgb = color[0] * 65536 + color[1] * 256 + color[2]
labels[mask == rgb] = class_index
if self.target_transform is not None:
labels = self.target_transform(labels)
data_samples = dict(
labels=labels, img_path=img_path, mask_path=mask_path)
return img, data_samples
def __len__(self):
return len(self.images)
class MMDeeplabV3(BaseModel):
def __init__(self, num_classes):
super().__init__()
self.deeplab = deeplabv3_resnet50(num_classes=num_classes)
def forward(self, imgs, data_samples=None, mode='tensor'):
x = self.deeplab(imgs)['out']
if mode == 'loss':
return {'loss': F.cross_entropy(x, data_samples['labels'])}
elif mode == 'predict':
return x, data_samples
class IoU(BaseMetric):
def process(self, data_batch, data_samples):
preds, labels = data_samples[0], data_samples[1]['labels']
preds = torch.argmax(preds, dim=1)
intersect = (labels == preds).sum()
union = (torch.logical_or(preds, labels)).sum()
iou = (intersect / union).cpu()
self.results.append(
dict(batch_size=len(labels), iou=iou * len(labels)))
def compute_metrics(self, results):
total_iou = sum(result['iou'] for result in self.results)
num_samples = sum(result['batch_size'] for result in self.results)
return dict(iou=total_iou / num_samples)
class SegVisHook(Hook):
def __init__(self, data_root, vis_num=1) -> None:
super().__init__()
self.vis_num = vis_num
self.palette = create_palette(osp.join(data_root, 'class_dict.csv'))
@master_only
def after_val_iter(self,
runner,
batch_idx: int,
data_batch=None,
outputs=None) -> None:
if batch_idx > self.vis_num:
return
preds, data_samples = outputs
img_paths = data_samples['img_path']
mask_paths = data_samples['mask_path']
_, C, H, W = preds.shape
preds = torch.argmax(preds, dim=1)
for idx, (pred, img_path,
mask_path) in enumerate(zip(preds, img_paths, mask_paths)):
pred_mask = np.zeros((H, W, 3), dtype=np.uint8)
runner.visualizer.set_image(pred_mask)
for color, class_id in self.palette.items():
runner.visualizer.draw_binary_masks(
pred == class_id,
colors=[color],
alphas=1.0,
)
# Convert RGB to BGR
pred_mask = runner.visualizer.get_image()[..., ::-1]
saved_dir = osp.join(runner.log_dir, 'vis_data', str(idx))
os.makedirs(saved_dir, exist_ok=True)
shutil.copyfile(img_path,
osp.join(saved_dir, osp.basename(img_path)))
shutil.copyfile(mask_path,
osp.join(saved_dir, osp.basename(mask_path)))
cv2.imwrite(
osp.join(saved_dir, f'pred_{osp.basename(img_path)}'),
pred_mask)
def parse_args():
parser = argparse.ArgumentParser(description='Distributed Training')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
return args
def main():
args = parse_args()
num_classes = 32 # Modify to actual number of categories.
norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(**norm_cfg)])
target_transform = transforms.Lambda(
lambda x: torch.tensor(np.array(x), dtype=torch.long))
train_set = CamVid(
'data/CamVid',
img_folder='train',
mask_folder='train_labels',
transform=transform,
target_transform=target_transform)
valid_set = CamVid(
'data/CamVid',
img_folder='val',
mask_folder='val_labels',
transform=transform,
target_transform=target_transform)
train_dataloader = dict(
batch_size=3,
dataset=train_set,
sampler=dict(type='DefaultSampler', shuffle=True),
collate_fn=dict(type='default_collate'))
val_dataloader = dict(
batch_size=3,
dataset=valid_set,
sampler=dict(type='DefaultSampler', shuffle=False),
collate_fn=dict(type='default_collate'))
runner = Runner(
model=MMDeeplabV3(num_classes),
work_dir='./work_dir',
train_dataloader=train_dataloader,
optim_wrapper=dict(
type=AmpOptimWrapper, optimizer=dict(type=AdamW, lr=2e-4)),
train_cfg=dict(by_epoch=True, max_epochs=10, val_interval=10),
val_dataloader=val_dataloader,
val_cfg=dict(),
val_evaluator=dict(type=IoU),
launcher=args.launcher,
custom_hooks=[SegVisHook('data/CamVid')],
default_hooks=dict(checkpoint=dict(type='CheckpointHook', interval=1)),
)
runner.train()
if __name__ == '__main__':
main()