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run.py
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run.py
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from lib.config import cfg, args
import numpy as np
import os
import requests
from tqdm import tqdm
data_root = args.path
def download_file_with_progressbar(url, output_path):
os.makedirs(os.path.dirname(output_path), exist_ok=True)
response = requests.get(url, stream=True)
total_size = int(response.headers.get('content-length', 0))
block_size = 1024 # 1 KB
with open(output_path, 'wb') as file, tqdm(
desc=f"Downloading {url}",
total=total_size,
unit="KB",
unit_scale=True,
unit_divisor=block_size,
) as bar:
for data in response.iter_content(block_size):
bar.update(len(data))
file.write(data)
def run_rgb():
import glob
from scipy.misc import imread
import matplotlib.pyplot as plt
syn_ids = sorted(os.listdir('data/ShapeNet/renders/02958343/'))[-10:]
for syn_id in syn_ids:
pkl_paths = glob.glob('data/ShapeNet/renders/02958343/{}/*.pkl'.format(syn_id))
np.random.shuffle(pkl_paths)
for pkl_path in pkl_paths:
img_path = pkl_path.replace('_RT.pkl', '.png')
img = imread(img_path)
plt.imshow(img)
plt.show()
def run_dataset():
from lib.datasets import make_data_loader
import tqdm
cfg.train.num_workers = 0
data_loader = make_data_loader(cfg, is_train=False)
for batch in tqdm.tqdm(data_loader):
pass
def run_network():
from lib.networks import make_network
from lib.datasets import make_data_loader
from lib.utils.net_utils import load_network
import tqdm
import torch
import time
network = make_network(cfg).cuda()
load_network(network, cfg.model_dir, epoch=cfg.test.epoch)
network.eval()
data_loader = make_data_loader(cfg, is_train=False)
total_time = 0
for batch in tqdm.tqdm(data_loader):
for k in batch:
if k != 'meta':
batch[k] = batch[k].cuda()
with torch.no_grad():
torch.cuda.synchronize()
start = time.time()
network(batch['inp'], batch)
torch.cuda.synchronize()
total_time += time.time() - start
print(total_time / len(data_loader))
def run_evaluate():
from lib.datasets import make_data_loader
from lib.evaluators import make_evaluator
import tqdm
import torch
from lib.networks import make_network
from lib.utils.net_utils import load_network
if args.type == 'l':
destination_path = os.path.join("/home/clean-pvnet/data/model/pvnet/LND", '239.pth')
download_file_with_progressbar("https://storage.googleapis.com/acmond.com/surgripe/lnd/239.pth", destination_path)
elif args.type == 'm':
destination_path = os.path.join("/home/clean-pvnet/data/model/pvnet/MBF", '239.pth')
download_file_with_progressbar("https://storage.googleapis.com/acmond.com/surgripe/mbf/239.pth", destination_path)
else:
print('Error: Model dowload fail')
os.system('rm data/custom')
cmd = 'ln -s {} data/custom'.format(args.path)
os.system(cmd)
os.system('rm data/model/pvnet/custom')
if args.type == 'l':
os.system('ln -s LND data/model/pvnet/custom')
elif args.type == 'm':
os.system('ln -s MBF data/model/pvnet/custom')
torch.manual_seed(0)
network = make_network(cfg).cuda()
load_network(network, cfg.model_dir, epoch=cfg.test.epoch)
network.eval()
data_loader = make_data_loader(cfg, is_train=False)
evaluator = make_evaluator(cfg)
for batch in tqdm.tqdm(data_loader):
inp = batch['inp'].cuda()
with torch.no_grad():
output = network(inp)
evaluator.evaluate(output, batch)
evaluator.summarize()
def run_visualize():
from lib.networks import make_network
from lib.datasets import make_data_loader
from lib.utils.net_utils import load_network
import tqdm
import torch
from lib.visualizers import make_visualizer
network = make_network(cfg).cuda()
load_network(network, cfg.model_dir, resume=cfg.resume, epoch=cfg.test.epoch)
network.eval()
data_loader = make_data_loader(cfg, is_train=False)
visualizer = make_visualizer(cfg)
for batch in tqdm.tqdm(data_loader):
for k in batch:
if k != 'meta':
batch[k] = batch[k].cuda()
with torch.no_grad():
output = network(batch['inp'], batch)
visualizer.visualize(output, batch)
def run_visualize_train():
from lib.networks import make_network
from lib.datasets import make_data_loader
from lib.utils.net_utils import load_network
import tqdm
import torch
from lib.visualizers import make_visualizer
network = make_network(cfg).cuda()
load_network(network, cfg.model_dir, resume=cfg.resume, epoch=cfg.test.epoch)
network.eval()
data_loader = make_data_loader(cfg, is_train=True)
visualizer = make_visualizer(cfg, 'train')
for batch in tqdm.tqdm(data_loader):
for k in batch:
if k != 'meta':
batch[k] = batch[k].cuda()
with torch.no_grad():
output = network(batch['inp'], batch)
visualizer.visualize_train(output, batch)
def run_analyze():
from lib.networks import make_network
from lib.datasets import make_data_loader
from lib.utils.net_utils import load_network
import tqdm
import torch
from lib.analyzers import make_analyzer
network = make_network(cfg).cuda()
load_network(network, cfg.model_dir, epoch=cfg.test.epoch)
network.eval()
cfg.train.num_workers = 0
data_loader = make_data_loader(cfg, is_train=False)
analyzer = make_analyzer(cfg)
for batch in tqdm.tqdm(data_loader):
for k in batch:
if k != 'meta':
batch[k] = batch[k].cuda()
with torch.no_grad():
output = network(batch['inp'], batch)
analyzer.analyze(output, batch)
def run_net_utils():
from lib.utils import net_utils
import torch
import os
model_path = 'data/model/rcnn_snake/rcnn/139.pth'
pretrained_model = torch.load(model_path)
net = pretrained_model['net']
net = net_utils.remove_net_prefix(net, 'dla.')
net = net_utils.remove_net_prefix(net, 'cp.')
pretrained_model['net'] = net
model_path = 'data/model/rcnn_snake/rcnn/139.pth'
os.system('mkdir -p {}'.format(os.path.dirname(model_path)))
torch.save(pretrained_model, model_path)
def run_linemod():
from lib.datasets.linemod import linemod_to_coco
linemod_to_coco.linemod_to_coco(cfg)
def run_tless():
from lib.datasets.tless import handle_rendering_data, fuse, handle_test_data, handle_ag_data, tless_to_coco
# handle_rendering_data.render()
# handle_rendering_data.render_to_coco()
# handle_rendering_data.prepare_asset()
# fuse.fuse()
# handle_test_data.get_mask()
# handle_test_data.test_to_coco()
handle_test_data.test_pose_to_coco()
# handle_ag_data.ag_to_coco()
# handle_ag_data.get_ag_mask()
# handle_ag_data.prepare_asset()
# tless_to_coco.handle_train_symmetry_pose()
# tless_to_coco.tless_train_to_coco()
def run_ycb():
from lib.datasets.ycb import handle_ycb
handle_ycb.collect_ycb()
def run_render():
from lib.utils.renderer import opengl_utils
from lib.utils.vsd import inout
from lib.utils.linemod import linemod_config
import matplotlib.pyplot as plt
obj_path = 'data/linemod/cat/cat.ply'
model = inout.load_ply(obj_path)
model['pts'] = model['pts'] * 1000.
im_size = (640, 300)
opengl = opengl_utils.NormalRender(model, im_size)
K = linemod_config.linemod_K
pose = np.load('data/linemod/cat/pose/pose0.npy')
depth = opengl.render(im_size, 100, 10000, K, pose[:, :3], pose[:, 3:] * 1000)
plt.imshow(depth)
plt.show()
def run_preprocess():
from tools import handle_custom_dataset
data_root = args.path
data_root = args.path
if args.type == 'l':
download_file_with_progressbar("https://storage.googleapis.com/acmond.com/surgripe/lnd/joint.ply", os.path.join(data_root, 'joint.ply'))
download_file_with_progressbar("https://storage.googleapis.com/acmond.com/surgripe/lnd/camera.txt", os.path.join(data_root, 'camera.txt'))
download_file_with_progressbar("https://storage.googleapis.com/acmond.com/surgripe/lnd/diameter.txt", os.path.join(data_root, 'diameter.txt'))
elif args.type == 'm':
# 下载文件到指定的data_root目录
download_file_with_progressbar("https://storage.googleapis.com/acmond.com/surgripe/mbf/joint.ply", os.path.join(data_root, 'joint.ply'))
download_file_with_progressbar("https://storage.googleapis.com/acmond.com/surgripe/mbf/camera.txt", os.path.join(data_root, 'camera.txt'))
download_file_with_progressbar("https://storage.googleapis.com/acmond.com/surgripe/mbf/diameter.txt", os.path.join(data_root, 'diameter.txt'))
handle_custom_dataset.sample_fps_points(data_root)
handle_custom_dataset.custom_to_coco(data_root)
def run_detector_pvnet():
from lib.networks import make_network
from lib.datasets import make_data_loader
from lib.utils.net_utils import load_network
import tqdm
import torch
from lib.visualizers import make_visualizer
network = make_network(cfg).cuda()
network.eval()
data_loader = make_data_loader(cfg, is_train=False)
visualizer = make_visualizer(cfg)
for batch in tqdm.tqdm(data_loader):
for k in batch:
if k != 'meta':
batch[k] = batch[k].cuda()
with torch.no_grad():
output = network(batch['inp'], batch)
visualizer.visualize(output, batch)
def run_demo():
from lib.datasets import make_data_loader
from lib.visualizers import make_visualizer
import tqdm
import torch
from lib.networks import make_network
from lib.utils.net_utils import load_network
import glob
from PIL import Image
torch.manual_seed(0)
meta = np.load(os.path.join(cfg.demo_path, 'meta.npy'), allow_pickle=True).item()
demo_images = glob.glob(cfg.demo_path + '/*jpg')
network = make_network(cfg).cuda()
load_network(network, cfg.model_dir, epoch=cfg.test.epoch)
network.eval()
visualizer = make_visualizer(cfg)
mean, std = np.array([0.485, 0.456, 0.406]), np.array([0.229, 0.224, 0.225])
for demo_image in demo_images:
demo_image = np.array(Image.open(demo_image)).astype(np.float32)
inp = (((demo_image/255.)-mean)/std).transpose(2, 0, 1).astype(np.float32)
inp = torch.Tensor(inp[None]).cuda()
with torch.no_grad():
output = network(inp)
visualizer.visualize_demo(output, inp, meta)
if __name__ == '__main__':
globals()['run_'+args.func]()