def __init__(self, model_path):
self.model = handpose_model()
if torch.cuda.is_available():
self.model = self.model.cuda()
model_dict = util.transfer(self.model, torch.load(model_path))
self.model.load_state_dict(model_dict)
self.model.eval()
from model import handpose_model
import torch
from tqdm import tqdm
import json
model = handpose_model()
size = {}
for i in tqdm(range(10, 1000)):
data = torch.randn(1, 3, i, i)
if torch.cuda.is_available():
data = data.cuda()
size[i] = model(data).size(2)
with open('hand_model_output_size.json') as f:
json.dump(size, f)
