( 참고 : 패스트 캠퍼스 , 한번에 끝내는 컴퓨터비전 초격차 패키지 )
08.Image Feature Analysis
1. Load Model
transformer = build_transformer()
dataset = Face_Dataset(data_dir, transformer=transformer)
dataloader = DataLoader(dataset, shuffle=False, batch_size=1)
ckpt_path = "./trained_model/model_76.pth"
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = load_model(ckpt_path, DEVICE)
2. Visualizing Latent Vector with Tensorboard
from torchvision.utils import make_grid
from tensorboardX import SummaryWriter
writer = SummaryWriter('runs/face_features')
print(os.listdir())
print(os.listdir('runs'))
print(os.listdir('runs/face_features'))
['trained_model', 'SOLUTION.ipynb', 'runs']
['face_features']
['events.out.tfevents.1659588627.iseunghan-ui-MacBookPro.local']
- all the results/objects will be saved here!
How to write data/results to TensorBoard?
all_images = []
all_labels = []
all_embeds = []
for idx, sample in enumerate(dataloader):
image = sample[0]
label = sample[1]
with torch.no_grad():
embed = model(image.to(DEVICE))
embed = embed.detach().cpu().numpy()
image = make_grid(image, normalize=True).permute(1,2,0)
image = cv2.resize(np.array(image), dsize=(80, 80), interpolation=cv2.INTER_NEAREST)
all_images.append(image)
all_labels.append(label)
all_embeds.append(embed)
all_images = torch.Tensor(np.moveaxis(np.stack(all_images, axis=0), 3, 1))
all_embeds = torch.Tensor(np.stack(all_embeds, axis=0).squeeze(1))
all_labels = np.concatenate(all_labels).tolist()
writer.add_embedding(all_embeds,
label_img=all_images,
metadata=all_labels)
writer.close()
open terminal…
$ tensorboard.exe --logdir runs/
- connect to localhost
