from model.fcos import FCOSDetector
import torch
from dataloader.VOC_dataset import VOCDataset
import math, time
from torch.utils.tensorboard import SummaryWriter
train_dataset = VOCDataset(
"D:\\Research\\My_tamper_detect_dataset_train_val_test\\voc_dataset_tmp",
resize_size=[512, 800],
split='train')
val_dataset = VOCDataset(
"D:\\Research\\My_tamper_detect_dataset_train_val_test\\voc_dataset_tmp",
resize_size=[512, 800],
split='val')
model = FCOSDetector(mode="training").cuda()
# model.load_state_dict(torch.load("./checkpoints/voc_512x800_loss2.0635.pth"))
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
BATCH_SIZE = 3
EPOCHS = 25
WARMPUP_STEPS_RATIO = 0.12
# train_loader=torch.utils.data.DataLoader(train_dataset,batch_size=BATCH_SIZE,shuffle=True,collate_fn=train_dataset.collate_fn)
# val_loader=torch.utils.data.DataLoader(val_dataset,batch_size=BATCH_SIZE,shuffle=True,collate_fn=val_dataset.collate_fn)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
collate_fn=train_dataset.collate_fn)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
val_loader = torch.utils.data.DataLoader(Dataset(images_root,
val_path,
img_size=512,
transform=transform,
train=False),
batch_size=64,
shuffle=False)
draw = False
if draw:
writer = SummaryWriter(comment='voc_mAP')
model = FCOSDetector(mode="inference")
# model.load_state_dict(torch.load("/mnt/hdd1/benkebishe01/FCOS/80epoch/fcos_voc_3/voc_epoch80_loss0.8893.pth"))
model = model.to(device).eval()
print("===>success loading model")
names = os.listdir(model_root)
names = sorted(names, key=functools.cmp_to_key(compare))
for name in names:
# print(name)
model.load_state_dict(torch.load(os.path.join(model_root + "/" +
name)))
gt_boxes = []
gt_classes = []
pred_boxes = []
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor",
)
model = FCOSDetector(mode="inference", config=Config)
# model = torch.nn.DataParallel(model)
ckpt = torch.load('/mnt/hdd1/benkebishe01/FCOS/fcos_fusion/voc2012_512x512_epoch71_loss0.7739.pth')
# ckpt = torch.load('/mnt/hdd1/benkebishe01/fcos_anchor/voc2012_512x512_epoch68_loss0.7201.pth')
model.load_state_dict(ckpt)
model.to(device).eval()
print('loading weights successfully...')
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
def preprocess_img(img, img_size=416):
# img = np.array(img) # h w
train_dataset = COCODataset(
"/HDD/jaeha/dataset/COCO/2017/train2017",
'/HDD/jaeha/dataset/COCO/2017/annotations/changed500_instances_train2017.json',
transform=transform,
resize_size=resize_)
val_dataset = COCOGenerator(
"/HDD/jaeha/dataset/COCO/2017/val2017",
'/HDD/jaeha/dataset/COCO/2017/annotations/changed500_instances_val2017.json',
resize_size=resize_)
# changed_val_dataset=COCOGenerator("/HDD/jaeha/dataset/COCO/2017/val2017",
# '/HDD/jaeha/dataset/COCO/2017/annotations/changed_instances_train2014.json',resize_size=resize_)
# train_dataset=COCODataset("/HDD/jaeha/dataset/COCO/2017/val2017",
# '/HDD/jaeha/dataset/COCO/2017/annotations/instances_val2017.json',transform=transform,resize_size=[600,600])
model = FCOSDetector(mode="training").cuda()
print(model)
model = torch.nn.DataParallel(model)
# model.load_state_dict(torch.load("./checkpoint/model_{}.pth".format(21), map_location=torch.device('cuda:0')))
BATCH_SIZE = opt.batch_size
EPOCHS = opt.epochs
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
collate_fn=train_dataset.collate_fn,
num_workers=opt.n_cpu,
worker_init_fn=np.random.seed(0))
steps_per_epoch = len(train_dataset) // BATCH_SIZE
TOTAL_STEPS = steps_per_epoch * EPOCHS
