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val.py
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import torch
import numpy as np
import argparse
from tqdm.autonotebook import tqdm
import os
from utils import smp_metrics
from utils.utils import ConfusionMatrix, postprocess, scale_coords, process_batch, ap_per_class, fitness, \
save_checkpoint, DataLoaderX, BBoxTransform, ClipBoxes, boolean_string, Params
from backbone import HybridNetsBackbone
from hybridnets.dataset import BddDataset
from hybridnets.custom_dataset import CustomDataset
from torchvision import transforms
import torch.nn.functional as F
from hybridnets.model import ModelWithLoss
from utils.constants import *
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
@torch.no_grad()
def val(model, val_generator, params, opt, seg_mode, is_training, **kwargs):
model.eval()
optimizer = kwargs.get('optimizer', None)
scaler = kwargs.get('scaler', None)
writer = kwargs.get('writer', None)
epoch = kwargs.get('epoch', 0)
step = kwargs.get('step', 0)
best_fitness = kwargs.get('best_fitness', 0)
best_loss = kwargs.get('best_loss', 0)
best_epoch = kwargs.get('best_epoch', 0)
loss_regression_ls = []
loss_classification_ls = []
loss_segmentation_ls = []
stats, ap, ap_class = [], [], []
iou_thresholds = torch.linspace(0.5, 0.95, 10).cuda() # iou vector for [email protected]:0.95
num_thresholds = iou_thresholds.numel()
names = {i: v for i, v in enumerate(params.obj_list)}
nc = len(names)
ncs = 1 if seg_mode == BINARY_MODE else len(params.seg_list) + 1
seen = 0
confusion_matrix = ConfusionMatrix(nc=nc)
s_seg = ' ' * (15 + 11 * 8)
s = ('%-15s' + '%-11s' * 8) % ('Class', 'Images', 'Labels', 'P', 'R', '[email protected]', '[email protected]:.95', 'mIoU', 'mAcc')
for i in range(len(params.seg_list)):
s_seg += '%-33s' % params.seg_list[i]
s += ('%-11s' * 3) % ('mIoU', 'IoU', 'Acc')
p, r, f1, mp, mr, map50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
iou_ls = [[] for _ in range(ncs)]
acc_ls = [[] for _ in range(ncs)]
regressBoxes = BBoxTransform()
clipBoxes = ClipBoxes()
val_loader = tqdm(val_generator, ascii=True)
for iter, data in enumerate(val_loader):
imgs = data['img']
annot = data['annot']
seg_annot = data['segmentation']
filenames = data['filenames']
shapes = data['shapes']
if opt.num_gpus == 1:
imgs = imgs.cuda()
annot = annot.cuda()
seg_annot = seg_annot.cuda()
cls_loss, reg_loss, seg_loss, regression, classification, anchors, segmentation = model(imgs, annot,
seg_annot,
obj_list=params.obj_list)
cls_loss = cls_loss.mean()
reg_loss = reg_loss.mean()
seg_loss = seg_loss.mean()
if opt.cal_map:
out = postprocess(imgs.detach(),
torch.stack([anchors[0]] * imgs.shape[0], 0).detach(), regression.detach(),
classification.detach(),
regressBoxes, clipBoxes,
opt.conf_thres, opt.iou_thres) # 0.5, 0.3
for i in range(annot.size(0)):
seen += 1
labels = annot[i]
labels = labels[labels[:, 4] != -1]
ou = out[i]
nl = len(labels)
pred = np.column_stack([ou['rois'], ou['scores']])
pred = np.column_stack([pred, ou['class_ids']])
pred = torch.from_numpy(pred).cuda()
target_class = labels[:, 4].tolist() if nl else [] # target class
if len(pred) == 0:
if nl:
stats.append((torch.zeros(0, num_thresholds, dtype=torch.bool),
torch.Tensor(), torch.Tensor(), target_class))
# print("here")
continue
if nl:
pred[:, :4] = scale_coords(imgs[i][1:], pred[:, :4], shapes[i][0], shapes[i][1])
labels = scale_coords(imgs[i][1:], labels, shapes[i][0], shapes[i][1])
# ori_img = cv2.imread('datasets/bdd100k_effdet/val/' + filenames[i],
# cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION | cv2.IMREAD_UNCHANGED)
# for label in labels:
# x1, y1, x2, y2 = [int(x) for x in label[:4]]
# ori_img = cv2.rectangle(ori_img, (x1, y1), (x2, y2), (255, 0, 0), 1)
# for pre in pred:
# x1, y1, x2, y2 = [int(x) for x in pre[:4]]
# # ori_img = cv2.putText(ori_img, str(pre[4].cpu().numpy()), (x1 - 10, y1 - 10),
# # cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA)
# ori_img = cv2.rectangle(ori_img, (x1, y1), (x2, y2), (0, 255, 0), 1)
# cv2.imwrite('pre+label-{}.jpg'.format(filenames[i]), ori_img)
correct = process_batch(pred, labels, iou_thresholds)
if opt.plots:
confusion_matrix.process_batch(pred, labels)
else:
correct = torch.zeros(pred.shape[0], num_thresholds, dtype=torch.bool)
stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), target_class))
# print(stats)
# Visualization
# seg_0 = segmentation[i]
# # print('bbb', seg_0.shape)
# seg_0 = torch.argmax(seg_0, dim = 0)
# # print('before', seg_0.shape)
# seg_0 = seg_0.cpu().numpy()
# #.transpose(1, 2, 0)
# # print(seg_0.shape)
# anh = np.zeros((384,640,3))
# anh[seg_0 == 0] = (255,0,0)
# anh[seg_0 == 1] = (0,255,0)
# anh[seg_0 == 2] = (0,0,255)
# anh = np.uint8(anh)
# cv2.imwrite('segmentation-{}.jpg'.format(filenames[i]),anh)
if seg_mode == MULTICLASS_MODE:
segmentation = segmentation.log_softmax(dim=1).exp()
_, segmentation = torch.max(segmentation, 1) # (bs, C, H, W) -> (bs, H, W)
else:
segmentation = F.logsigmoid(segmentation).exp()
tp_seg, fp_seg, fn_seg, tn_seg = smp_metrics.get_stats(segmentation, seg_annot, mode=seg_mode,
threshold=0.5 if seg_mode != MULTICLASS_MODE else None,
num_classes=ncs if seg_mode == MULTICLASS_MODE else None)
iou = smp_metrics.iou_score(tp_seg, fp_seg, fn_seg, tn_seg, reduction='none')
# print(iou)
acc = smp_metrics.balanced_accuracy(tp_seg, fp_seg, fn_seg, tn_seg, reduction='none')
for i in range(ncs):
iou_ls[i].append(iou.T[i].detach().cpu().numpy())
acc_ls[i].append(acc.T[i].detach().cpu().numpy())
loss = cls_loss + reg_loss + seg_loss
if loss == 0 or not torch.isfinite(loss):
continue
loss_classification_ls.append(cls_loss.item())
loss_regression_ls.append(reg_loss.item())
loss_segmentation_ls.append(seg_loss.item())
cls_loss = np.mean(loss_classification_ls)
reg_loss = np.mean(loss_regression_ls)
seg_loss = np.mean(loss_segmentation_ls)
loss = cls_loss + reg_loss + seg_loss
print(
'Val. Epoch: {}/{}. Classification loss: {:1.5f}. Regression loss: {:1.5f}. Segmentation loss: {:1.5f}. Total loss: {:1.5f}'.format(
epoch, opt.num_epochs if is_training else 0, cls_loss, reg_loss, seg_loss, loss))
if is_training:
writer.add_scalars('Loss', {'val': loss}, step)
writer.add_scalars('Regression_loss', {'val': reg_loss}, step)
writer.add_scalars('Classfication_loss', {'val': cls_loss}, step)
writer.add_scalars('Segmentation_loss', {'val': seg_loss}, step)
if opt.cal_map:
for i in range(ncs):
iou_ls[i] = np.concatenate(iou_ls[i])
acc_ls[i] = np.concatenate(acc_ls[i])
# print(len(iou_ls[0]))
iou_score = np.mean(iou_ls)
# print(iou_score)
acc_score = np.mean(acc_ls)
miou_ls = []
for i in range(len(params.seg_list)):
if seg_mode == BINARY_MODE:
# typically this runs once with i == 0
miou_ls.append(np.mean(iou_ls[i]))
else:
miou_ls.append(np.mean( (iou_ls[0] + iou_ls[i+1]) / 2))
for i in range(ncs):
iou_ls[i] = np.mean(iou_ls[i])
acc_ls[i] = np.mean(acc_ls[i])
# Compute statistics
stats = [np.concatenate(x, 0) for x in zip(*stats)]
# print(stats[3])
# Count detected boxes per class
# boxes_per_class = np.bincount(stats[2].astype(np.int64), minlength=1)
ap50 = None
save_dir = 'plots'
os.makedirs(save_dir, exist_ok=True)
# Compute metrics
if len(stats) and stats[0].any():
p, r, f1, ap, ap_class = ap_per_class(*stats, plot=opt.plots, save_dir=save_dir, names=names)
ap50, ap = ap[:, 0], ap.mean(1) # [email protected], [email protected]:0.95
mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
nt = np.bincount(stats[3].astype(np.int64), minlength=1) # number of targets per class
else:
nt = torch.zeros(1)
# Print results
print(s_seg)
print(s)
pf = ('%-15s' + '%-11i' * 2 + '%-11.3g' * 6) % ('all', seen, nt.sum(), mp, mr, map50, map, iou_score, acc_score)
for i in range(len(params.seg_list)):
tmp = i+1 if seg_mode != BINARY_MODE else i
pf += ('%-11.3g' * 3) % (miou_ls[i], iou_ls[tmp], acc_ls[tmp])
print(pf)
# Print results per class
if opt.verbose and nc > 1 and len(stats):
pf = '%-15s' + '%-11i' * 2 + '%-11.3g' * 4
for i, c in enumerate(ap_class):
print("print class-----\n")
print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
# Plots
if opt.plots:
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
confusion_matrix.tp_fp()
results = (mp, mr, map50, map, iou_score, acc_score, loss)
fi = fitness(
np.array(results).reshape(1, -1)) # weighted combination of [P, R, [email protected], [email protected], iou, acc, loss ]
# if calculating map, save by best fitness
if is_training and fi > best_fitness:
best_fitness = fi
ckpt = {'epoch': epoch,
'step': step,
'best_fitness': best_fitness,
'model': model.model.state_dict(),
'optimizer': optimizer.state_dict(),
'scaler': scaler.state_dict()}
print("Saving checkpoint with best fitness", fi[0])
save_checkpoint(ckpt, opt.saved_path, f'hybridnets-d{opt.compound_coef}_{epoch}_{step}_best.pth')
else:
# if not calculating map, save by best loss
if is_training and loss + opt.es_min_delta < best_loss:
best_loss = loss
best_epoch = epoch
save_checkpoint(model, opt.saved_path, f'hybridnets-d{opt.compound_coef}_{epoch}_{step}_best.pth')
# Early stopping
if is_training and epoch - best_epoch > opt.es_patience > 0:
print('[Info] Stop training at epoch {}. The lowest loss achieved is {}'.format(epoch, best_loss))
exit(0)
model.train()
return (best_fitness, best_loss, best_epoch) if is_training else 0
if __name__ == "__main__":
ap = argparse.ArgumentParser()
ap.add_argument('-p', '--project', type=str, default='bdd100k', help='Project file that contains parameters')
ap.add_argument('-bb', '--backbone', type=str,
help='Use timm to create another backbone replacing efficientnet. '
'https://github.com/rwightman/pytorch-image-models')
ap.add_argument('-c', '--compound_coef', type=int, default=3, help='Coefficients of efficientnet backbone')
ap.add_argument('-w', '--weights', type=str, default='weights/hybridnets.pth', help='/path/to/weights')
ap.add_argument('-n', '--num_workers', type=int, default=28, help='Num_workers of dataloader')
ap.add_argument('--batch_size', type=int, default=12, help='The number of images per batch among all devices')
ap.add_argument('-v', '--verbose', type=boolean_string, default=True,
help='Whether to print results per class when valing')
ap.add_argument('--cal_map', type=boolean_string, default=True,
help='Calculate mAP in validation')
ap.add_argument('--plots', type=boolean_string, default=True,
help='Whether to plot confusion matrix when valing')
ap.add_argument('--num_gpus', type=int, default=1,
help='Number of GPUs to be used (0 to use CPU)')
ap.add_argument('--conf_thres', type=float, default=0.1,
help='Confidence threshold in NMS')
ap.add_argument('--iou_thres', type=float, default=0.6,
help='IoU threshold in NMS')
args = ap.parse_args()
compound_coef = args.compound_coef
project_name = args.project
weights_path = f'weights/hybridnets-d{compound_coef}.pth' if args.weights is None else args.weights
params = Params(f'projects/{project_name}.yml')
obj_list = params.obj_list
seg_mode = MULTILABEL_MODE if params.seg_multilabel else MULTICLASS_MODE if len(params.seg_list) > 1 else BINARY_MODE
valid_dataset = BddDataset(
params=params,
is_train=False,
inputsize=params.model['image_size'],
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=params.mean, std=params.std
)
]),
seg_mode=seg_mode
)
val_generator = DataLoaderX(
valid_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=params.pin_memory,
collate_fn=BddDataset.collate_fn
)
model = HybridNetsBackbone(compound_coef=compound_coef, num_classes=len(params.obj_list),
ratios=eval(params.anchors_ratios), scales=eval(params.anchors_scales),
seg_classes=len(params.seg_list), backbone_name=args.backbone,
seg_mode=seg_mode)
try:
model.load_state_dict(torch.load(weights_path))
except:
model.load_state_dict(torch.load(weights_path)['model'])
model = ModelWithLoss(model, debug=False)
model.requires_grad_(False)
if args.num_gpus > 0:
model.cuda()
val(model, val_generator, params, args, seg_mode, is_training=False)