from inference import Infer
from models.AlbuNet.AlbuNet import AlbuNet
import segmentation_models_pytorch as smp
inferer = Infer(threshold=0.75)
model = smp.Unet("se_resnext50_32x4d", classes=6)
model.cuda()
inferer.inference(model)
video_transforms,
is_train=False)
testloader = torch.utils.data.DataLoader(
testdataset,
batch_size=cfg.TRAIN.ST_BATCH_SIZE * num_gpu,
drop_last=True,
shuffle=False,
num_workers=int(cfg.WORKERS))
if args.eval_fid:
algo = Infer(output_dir, 1.0)
algo.eval_fid2(testloader, video_transforms, image_transforms)
elif args.eval_fvd:
algo = Infer(output_dir, 1.0)
algo.eval_fvd(imageloader, storyloader, testloader, cfg.STAGE)
elif args.load_ckpt != None:
# For inference training result
algo = Infer(output_dir, 1.0, args.load_ckpt)
algo.inference(imageloader, storyloader, testloader, cfg.STAGE)
else:
# For training model
algo = GANTrainer(output_dir, args, ratio=1.0)
algo.train(imageloader, storyloader, testloader, cfg.STAGE)
else:
datapath = '%s/test/val_captions.t7' % (cfg.DATA_DIR)
algo = GANTrainer(output_dir)
algo.sample(datapath, cfg.STAGE)
from inference import Infer
from datetime import datetime
import os
import segmentation_models_pytorch as smp
inferer1 = Infer(
rez_dir="inferred",
image_folder="inferred2/overlay",
batch_size=3,
num_batches=2,
batch_id=0,
)
inferer2 = Infer(
rez_dir="inferred",
image_folder="inferred2/overlay",
batch_size=3,
num_batches=2,
batch_id=1,
)
model = smp.Unet("se_resnext50_32x4d")
model.cuda()
inferer1.inference(model)
inferer2.inference(model)