def main():
torch.manual_seed(args.seed)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
sys.stdout = Logger(osp.join(args.save_dir, 'log_test.txt'))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(args.seed)
else:
print("Currently using CPU (GPU is highly recommended)")
print('Initializing image data manager')
dm = DataManager(args, use_gpu)
trainloader, testloader = dm.return_dataloaders()
model = Model(scale_cls=args.scale_cls, num_classes=args.num_classes)
# load the model
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
print("Loaded checkpoint from '{}'".format(args.resume))
if use_gpu:
model = model.cuda()
test(model, testloader, use_gpu)
num_workers=1,
shuffle=True,
pin_memory=True)
device = torch.device('cuda:0')
model = Model()
model = model.to(device)
imsize = 256, 256
print(f"TestSize = {len(test)}")
for epoch in range(122):
# TEST
if epoch % 10 == 0 and epoch > 1:
print(f"------ EPOCH {epoch} ------")
model.load_state_dict(
torch.load(models_path / f"model{epoch}.pth", map_location=device))
model.eval()
test_losses_AEE = []
test_losses_percent_AEE = []
with torch.no_grad():
for i_batch, sample_batched in tqdm(enumerate(test_loader)):
event_images, gt_flow = sample_batched
event_mask = torch.sum(event_images[:, :2, ...], dim=1)
event_images = event_images.to(device)
flow = model.forward(event_images)
flow = flow.cpu()
from PIL import Image
from torchvision import transforms
from torch.nn import functional as F
import numpy as np
import cv2
import torch
from net import Model
model = Model(scale_cls=7, num_classes=8)
resume = '/home/lemon/few-shot/fewshot-CAN/ChengR/CAN_ResNet_5_5/temp_Gobal4/model_best.pth.tar'
checkpoint = torch.load(resume)
model.load_state_dict(checkpoint['state_dict'])
# final_convname = 'clasifier'
features_blobs = []
def hook_feature(module, input, output):
features_blobs.append(output.data.cpu().numpy())
# model._modules.get(final_convname).register_forward_hook(hook_feature)
# print(model.state_dict())
# get the softmax weight
params = list(model.parameters())
weight_softmax = np.squeeze(params[-2].data.numpy())
def returnCAM(feature_conv, weight_softmax, class_idx):
outputfile = open(
'/home/xulzee/Documents/IQA/output/TID2013/vr_jpeg_result.txt',
'a+')
outputfile.write(
('{} {:.7f} {:.7f}'.format(i, output_txt[0], label_txt[0])) +
'\r\n')
outputfile.close()
use_gpu = torch.cuda.is_available()
model = Model()
print('Model structure:', model)
if use_gpu:
model = model.cuda()
model_weights_file = '/home/xulzee/Documents/IQA/output/TID2013/79-0.0015128param.pth'
model.load_state_dict(torch.load(model_weights_file))
print('load weights from', model_weights_file)
test_dataset = MyDataset(
data_file='/home/xulzee/Documents/IQA/vr_jpeg.h5') # test datasets
test_dataloader = DataLoader(dataset=test_dataset,
batch_size=1,
shuffle=False,
num_workers=0)
if __name__ == '__main__':
test()
