Datasets:
jblitzar
/
github-python-corpus

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interp_target = nn.Upsample(size=(args.rcrop[1], args.rcrop[0]), mode='bilinear', align_corners=True)
# labels for adversarial training
source_label = 0
target_label = 1
# set up tensor board
if args.tensorboard and gpu == 0:
writer = SummaryWriter(args.snapshot_dir)
validate(model_B2, model_B, head, classifier, seg_loss, gpu, logger if gpu == 0 else None, datasets.target_train_loader, args.output_folder)
# exit()
def validate(model_B2, model_B, head, classifier, seg_loss, gpu, logger, testloader, output_folder):
if gpu == 0:
logger.info("Start Evaluation")
# evaluate
loss_meter = AverageMeter()
intersection_meter = AverageMeter()
union_meter = AverageMeter()
model_B2.eval()
model_B.eval()
head.eval()
classifier.eval()
with torch.no_grad():
for i, batch in enumerate(testloader):
images = batch["img_full"].cuda()
labels = batch["lbl_full"].cuda()
img_paths = batch['img_path']
pred = model_B(model_B2(images))
pred = classifier(head(pred))
output = F.interpolate(pred, size=labels.size()[-2:], mode='bilinear', align_corners=True)
loss = seg_loss(output, labels)
output = F.softmax(output, 1)
output_np = pred.detach().cpu().numpy().squeeze()
logits, output = output.max(1)
for b in range(output_np.shape[0]):
mask_filename = img_paths[b].split("/")[-1].split(".")[0]
np.save(os.path.join(output_folder, mask_filename+".npy"), output_np[b])
intersection, union, _ = intersectionAndUnionGPU(output, labels, args.num_classes, args.ignore_label)
dist.all_reduce(intersection), dist.all_reduce(union)
intersection, union = intersection.cpu().numpy(), union.cpu().numpy()
intersection_meter.update(intersection), union_meter.update(union)
loss_meter.update(loss.item(), images.size(0))
if gpu == 0 and i % 50 == 0 and i != 0:
logger.info("Evaluation iter = {0:5d}/{1:5d}, loss_eval = {2:.3f}".format(
i, len(testloader), loss_meter.val
))
iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
miou = np.mean(iou_class)
if gpu == 0:
logger.info("Val result: mIoU = {:.3f}".format(miou))
for i in range(args.num_classes):
logger.info("Class_{} Result: iou = {:.3f}".format(i, iou_class[i]))
logger.info("End Evaluation")
return miou, loss_meter.avg
def find_free_port():
import socket
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# Binding to port 0 will cause the OS to find an available port for us
sock.bind(("", 0))
port = sock.getsockname()[1]
sock.close()
# NOTE: there is still a chance the port could be taken by other processes.
return port
if __name__ == '__main__':
args.gpus = [int(x) for x in args.gpus.split(",")]
args.world_size = len(args.gpus)
os.makedirs(args.output_folder, exist_ok=True)
if args.dist:
port = find_free_port()
args.dist_url = f"tcp://127.0.0.1:{port}"
mp.spawn(main_worker, nprocs=args.world_size, args=(args.world_size, args.dist_url))
else:
main_worker(args.train_gpu, args.world_size, args)
# <FILESEP>
import os
import lightly.loss as loss
import lightly.models as models
import pytorch_lightning as pl
import torch
import torchvision
from PIL import ImageFile