def collect_printer_info(self):
pdb = PrinterModelDB(self.cls_name)
database = []
modeldb = LineModModelDB()
for k in range(pdb.image_num):
data = {}
data['rgb_pth'] = pdb.image_pattern.format(k + 1)
data['dpt_pth'] = pdb.mask_pattern.format(k + 1)
data['RT'] = pdb.aligned_poses[k]
data['K'] = pdb.K[self.cls_name]
data['cls_typ'] = self.cls_name
data['rnd_typ'] = 'printer'
data['corners'] = Projector.project_K(
modeldb.get_corners_3d(self.cls_name), data['RT'],
pdb.K[self.cls_name])
data['farthest'] = Projector.project_K(
modeldb.get_farthest_3d(self.cls_name), data['RT'],
pdb.K[self.cls_name])
for num in [4, 12, 16, 20]:
data['farthest{}'.format(num)] = Projector.project_K(
modeldb.get_farthest_3d(self.cls_name, num), data['RT'],
pdb.K[self.cls_name])
data['center'] = Projector.project_K(
modeldb.get_centers_3d(self.cls_name)[None, :], data['RT'],
pdb.K[self.cls_name])
database.append(data)
save_pickle(database, self.printer_pkl)
return database
def collect_truncated_set_info(self):
database=[]
projector=Projector()
modeldb=LineModModelDB()
img_num=len(os.listdir(os.path.join(self.linemod_dir,self.cls_name,'JPEGImages')))
for k in range(img_num):
data={}
data['rgb_pth']=os.path.join('truncated',self.cls_name,'{:06}_rgb.jpg'.format(k))
data['dpt_pth']=os.path.join('truncated',self.cls_name,'{:04}_msk.png'.format(k))
pose,K=read_pickle(os.path.join(self.linemod_dir,'truncated',self.cls_name,'{:06}_info.pkl'.format(k)))
data['RT']=pose
data['cls_typ']=self.cls_name
data['rnd_typ']='truncated'
data['corners']=projector.project_K(modeldb.get_corners_3d(self.cls_name),data['RT'],K)
data['farthest']=projector.project_K(modeldb.get_farthest_3d(self.cls_name),data['RT'],K)
for num in [4,12,16,20]:
data['farthest{}'.format(num)]=projector.project_K(modeldb.get_farthest_3d(self.cls_name,num),data['RT'],K)
data['small_bbox'] = projector.project_K(modeldb.get_small_bbox(self.cls_name), data['RT'], K)
data['center']=projector.project_K(modeldb.get_centers_3d(self.cls_name)[None,:],data['RT'],K)
# axis_direct=np.concatenate([np.identity(3), np.zeros([3, 1])], 1).astype(np.float32)
# data['van_pts']=projector.project_h(axis_direct, data['RT'], K)
data['K']=K
database.append(data)
save_pickle(database,self.pkl)
return database
def collect_real_set_info(self):
database=[]
projector=Projector()
modeldb=LineModModelDB()
transformer=PoseTransformer(class_type=self.cls_name)
img_num=len(os.listdir(os.path.join(self.linemod_dir,self.rgb_dir)))
print(img_num)
for k in range(img_num):
data={}
data['rgb_pth']=os.path.join(self.rgb_dir,'color_{:05}.png'.format(k))
data['dpt_pth']=os.path.join(self.mask_dir,'{}.png'.format(k))
pose=self.read_pose(os.path.join(self.rt_dir,'info_{:05}.txt'.format(k)))
if len(pose)==0:
# os.system('cp {} ./{:05}.png'.format(os.path.join(cfg.OCCLUSION_LINEMOD,data['rgb_pth']),k))
continue
data['RT']=transformer.occlusion_pose_to_blender_pose(pose)
data['cls_typ']=self.cls_name
data['rnd_typ']='real'
data['corners']=projector.project(modeldb.get_corners_3d(self.cls_name),data['RT'],'linemod')
data['farthest']=projector.project(modeldb.get_farthest_3d(self.cls_name),data['RT'],'linemod')
for num in [4,12,16,20]:
data['farthest{}'.format(num)]=projector.project(modeldb.get_farthest_3d(self.cls_name,num),data['RT'],'linemod')
data['center']=projector.project(modeldb.get_centers_3d(self.cls_name)[None,:],data['RT'],'linemod')
data['small_bbox'] = projector.project(modeldb.get_small_bbox(self.cls_name), data['RT'], 'linemod')
axis_direct=np.concatenate([np.identity(3), np.zeros([3, 1])], 1).astype(np.float32)
data['van_pts']=projector.project_h(axis_direct, data['RT'], 'blender')
database.append(data)
save_pickle(database,self.real_pkl)
return database
def collect_ms_info(self):
database=[]
projector=Projector()
model_db=LineModModelDB()
for k in range(self.ms_num):
data=dict()
data['rgb_pth']=os.path.join(self.ms_dir, '{}.jpg'.format(k))
data['dpt_pth']=os.path.join(self.ms_dir, '{}_{}_mask.png'.format(k,self.cls_name))
# if too few foreground pts then continue
mask=imread(os.path.join(self.linemod_dir,data['dpt_pth']))
if np.sum(mask)<5: continue
data['RT'] = read_pickle(os.path.join(self.linemod_dir, self.ms_dir, '{}_{}_RT.pkl'.format(self.cls_name,k)))['RT']
data['cls_typ']=self.cls_name
data['rnd_typ']='render_multi'
data['corners']=projector.project(model_db.get_corners_3d(self.cls_name),data['RT'],'blender')
data['farthest']=projector.project(model_db.get_farthest_3d(self.cls_name),data['RT'],'blender')
for num in [4,12,16,20]:
data['farthest{}'.format(num)]=projector.project(modeldb.get_farthest_3d(self.cls_name,num),data['RT'],'blender')
data['center']=projector.project(model_db.get_centers_3d(self.cls_name)[None,:],data['RT'],'blender')
data['small_bbox'] = projector.project(modeldb.get_small_bbox(self.cls_name), data['RT'], 'blender')
axis_direct=np.concatenate([np.identity(3), np.zeros([3, 1])], 1).astype(np.float32)
data['van_pts']=projector.project_h(axis_direct, data['RT'], 'blender')
database.append(data)
save_pickle(database,self.ms_pkl)
return database
def collect_fuse_info(self):
database=[]
modeldb=LineModModelDB()
projector=Projector()
for k in range(self.fuse_num):
data=dict()
data['rgb_pth']=os.path.join(self.fuse_dir, '{}_rgb.jpg'.format(k))
data['dpt_pth']=os.path.join(self.fuse_dir, '{}_mask.png'.format(k))
# if too few foreground pts then continue
mask=imread(os.path.join(self.linemod_dir,data['dpt_pth']))
if np.sum(mask==(cfg.linemod_cls_names.index(self.cls_name)+1))<400: continue
data['cls_typ']=self.cls_name
data['rnd_typ']='fuse'
begins,poses=read_pickle(os.path.join(self.linemod_dir,self.fuse_dir,'{}_info.pkl'.format(k)))
data['RT'] = poses[self.cls_idx]
K=projector.intrinsic_matrix['linemod'].copy()
K[0,2]+=begins[self.cls_idx,1]
K[1,2]+=begins[self.cls_idx,0]
data['K']=K
data['corners']=projector.project_K(modeldb.get_corners_3d(self.cls_name),data['RT'],K)
data['center']=projector.project_K(modeldb.get_centers_3d(self.cls_name),data['RT'],K)
data['farthest']=projector.project_K(modeldb.get_farthest_3d(self.cls_name),data['RT'],K)
for num in [4,12,16,20]:
data['farthest{}'.format(num)]=projector.project_K(modeldb.get_farthest_3d(self.cls_name,num),data['RT'],K)
data['small_bbox'] = projector.project_K(modeldb.get_small_bbox(self.cls_name), data['RT'], K)
database.append(data)
save_pickle(database,self.fuse_pkl)
return database
def collect_real_set_info(self): # linemod standard
database=[]
projector=Projector()
modeldb=LineModModelDB()
img_num=len(os.listdir(os.path.join(self.linemod_dir,self.rgb_dir)))
for k in range(img_num):
data={}
data['rgb_pth']=os.path.join(self.rgb_dir, '{:06}.jpg'.format(k))
data['dpt_pth']=os.path.join(self.mask_dir, '{:04}.png'.format(k))
pose=read_pose(os.path.join(self.rt_dir, 'rot{}.rot'.format(k)),
os.path.join(self.rt_dir, 'tra{}.tra'.format(k)))
pose_transformer = PoseTransformer(class_type=self.cls_name)
data['RT'] = pose_transformer.orig_pose_to_blender_pose(pose).astype(np.float32)
data['cls_typ']=self.cls_name
data['rnd_typ']='real'
data['corners']=projector.project(modeldb.get_corners_3d(self.cls_name),data['RT'],'linemod')
data['farthest']=projector.project(modeldb.get_farthest_3d(self.cls_name),data['RT'],'linemod')
for num in [4,12,16,20]:
data['farthest{}'.format(num)]=projector.project(modeldb.get_farthest_3d(self.cls_name,num),data['RT'],'linemod')
data['center']=projector.project(modeldb.get_centers_3d(self.cls_name)[None, :],data['RT'],'linemod')
data['small_bbox'] = projector.project(modeldb.get_small_bbox(self.cls_name), data['RT'], 'linemod')
axis_direct=np.concatenate([np.identity(3), np.zeros([3, 1])], 1).astype(np.float32)
data['van_pts']=projector.project_h(axis_direct, data['RT'], 'linemod')
database.append(data)
save_pickle(database,self.set_pkl)
return database
def make_truncated_linemod_dataset():
for cls_name in cfg.linemod_cls_names:
print(cls_name)
linemod_dir = cfg.LINEMOD
rgb_dir = '{}/JPEGImages'.format(cls_name)
mask_dir = '{}/mask'.format(cls_name)
rt_dir = os.path.join(cfg.DATA_DIR, 'LINEMOD_ORIG', cls_name,
'data')
if not os.path.exists(
os.path.join(linemod_dir, 'truncated', cls_name)):
os.mkdir(os.path.join(linemod_dir, 'truncated', cls_name))
projector = Projector()
img_num = len(os.listdir(os.path.join(linemod_dir, rgb_dir)))
print(img_num)
for k in range(img_num):
rgb = imread(
os.path.join(linemod_dir, rgb_dir, '{:06}.jpg'.format(k)))
msk = imread(
os.path.join(linemod_dir, mask_dir, '{:04}.png'.format(k)))
msk = (np.sum(msk, 2) > 0).astype(np.uint8)
before = np.sum(msk)
count = 0
while True:
rgb_new, msk_new, hbeg, wbeg = LineModImageDB.crop_instance(
rgb, msk, 256)
after = np.sum(msk_new)
count += 1
if after / before >= 0.2 or count > 50:
rgb, msk = rgb_new, msk_new
break
imsave(
os.path.join(linemod_dir, 'truncated', cls_name,
'{:06}_rgb.jpg'.format(k)), rgb)
imsave(
os.path.join(linemod_dir, 'truncated', cls_name,
'{:04}_msk.png'.format(k)), msk)
pose = read_pose(os.path.join(rt_dir, 'rot{}.rot'.format(k)),
os.path.join(rt_dir, 'tra{}.tra'.format(k)))
pose_transformer = PoseTransformer(class_type=cls_name)
pose = pose_transformer.orig_pose_to_blender_pose(pose).astype(
np.float32)
K = projector.intrinsic_matrix['linemod'].copy()
K[0, 2] += wbeg
K[1, 2] += hbeg
save_pickle([pose, K],
os.path.join(linemod_dir, 'truncated', cls_name,
'{:06}_info.pkl'.format(k)))
if k % 500 == 0: print(k)
def fuse():
W, H = 720, 540
noofobjects = 30
if not os.path.exists(output_rgb_dir):
os.makedirs(output_rgb_dir)
if not os.path.exists(output_mask_dir):
os.makedirs(output_mask_dir)
noofimages = {
i + 1: len(glob.glob(os.path.join(tless_dir, str(i + 1), '*.pkl')))
for i in range(noofobjects)
}
obj_info = []
for i in tqdm.tqdm(range(num_train_imgs)):
train_img = np.zeros((H, W, 3), dtype=np.uint8)
train_mask = np.zeros((H, W), dtype=np.uint8)
instance_id = 0
ann = []
for k in range(max_objects_in_scene):
obj_id = np.random.randint(0, noofobjects) + 1
img_id = np.random.randint(0, noofimages[obj_id])
img_path = os.path.join(tless_dir, str(obj_id),
str(img_id) + '.png')
dpt_path = os.path.join(tless_dir, str(obj_id),
str(img_id) + '_depth.png')
rand_img = cv2.imread(img_path)
dpt = np.array(Image.open(dpt_path))
mask = (dpt != 65535).astype(np.uint8)
instance_id += 1
cut_and_paste(rand_img, mask, train_img, train_mask, instance_id)
ann.append([obj_id, instance_id])
new_img = cv2.resize(
cv2.imread(bg_paths[np.random.randint(0, len(bg_paths))]), (W, H))
fg_mask = train_mask != 0
new_img[fg_mask] = train_img[fg_mask]
img_path = os.path.join(output_rgb_dir, str(i) + '.png')
mask_path = os.path.join(output_mask_dir, str(i) + '.png')
obj_info.append({
'img_path': img_path,
'mask_path': mask_path,
'ann': ann
})
cv2.imwrite(img_path, new_img)
cv2.imwrite(mask_path, train_mask)
base_utils.save_pickle(obj_info, os.path.join(output_dir, 'obj_info.pkl'))
def _handle_render_train_symmetry_pose(obj_id):
data_root = 'data/tless'
render_dir = os.path.join(data_root, 'renders', str(obj_id))
ann_num = len(glob.glob(os.path.join(render_dir, '*.pkl')))
for ind in tqdm.tqdm(range(ann_num)):
pkl_path = os.path.join(render_dir, '{}_RT.pkl'.format(ind))
K_P = read_pickle(pkl_path)
pose = K_P['RT']
symmetry_R = symmetry_utils.TLESS_rectify(obj_id, pose[:, :3])
K_P['s_RT'] = np.concatenate([symmetry_R, pose[:, 3:]], axis=1)
save_pickle(K_P, pkl_path)
def collect_real_set_info(self):
'''
pvnet 的数据集linemod有做更改， cat.ply 与原始的linemod中的cat.ply,有模型偏移和旋转，
所以 原始数据集中的pose真值，需要 简单变换，就是这边数据集的 pose
会这样问了，既然图片数据集 这是没有改变的，怎么RT改变呢
因为 这边提的3d特征是 新的model上取的，所以计算RT的时候， RT要变的
pose_real.pkl
'''
database = []
projector = Projector()
modeldb = LineModModelDB()
img_num = len(os.listdir(os.path.join(self.linemod_dir, self.rgb_dir)))
for k in range(img_num):
data = {}
data['rgb_pth'] = os.path.join(self.rgb_dir, '{:06}.jpg'.format(k))
data['dpt_pth'] = os.path.join(self.mask_dir,
'{:04}.png'.format(k))
pose = read_pose(os.path.join(self.rt_dir, 'rot{}.rot'.format(k)),
os.path.join(self.rt_dir, 'tra{}.tra'.format(k)))
pose_transformer = PoseTransformer(class_type=self.cls_name)
data['RT'] = pose_transformer.orig_pose_to_blender_pose(
pose).astype(np.float32)
data['cls_typ'] = self.cls_name
data['rnd_typ'] = 'real'
data['corners'] = projector.project(
modeldb.get_corners_3d(self.cls_name), data['RT'], 'linemod')
data['farthest'] = projector.project(
modeldb.get_farthest_3d(self.cls_name), data['RT'], 'linemod')
for num in [4, 12, 16, 20]:
data['farthest{}'.format(num)] = projector.project(
modeldb.get_farthest_3d(self.cls_name, num), data['RT'],
'linemod')
data['center'] = projector.project(
modeldb.get_centers_3d(self.cls_name)[None, :], data['RT'],
'linemod')
data['small_bbox'] = projector.project(
modeldb.get_small_bbox(self.cls_name), data['RT'], 'linemod')
axis_direct = np.concatenate(
[np.identity(3), np.zeros([3, 1])], 1).astype(np.float32)
data['van_pts'] = projector.project_h(axis_direct, data['RT'],
'linemod')
database.append(data)
save_pickle(database, self.real_pkl)
return database
def get_plane_height(self):
if os.path.exists(self.plane_height_path):
plane_height = read_pickle(self.plane_height_path)
else:
plane_height = {}
if self.class_type in plane_height:
return plane_height[self.class_type]
else:
pose_transformer = PoseTransformer(self.class_type)
model = pose_transformer.get_blender_model()
height = np.min(model[:, -1])
plane_height[self.class_type] = height
save_pickle(plane_height, self.plane_height_path)
return height
def get_rendering_model(data_root):
rendering_model_path = os.path.join(data_root, 'rendering_model.pkl')
if os.path.exists(rendering_model_path):
return base_utils.read_pickle(rendering_model_path)
cad_path_pattern = os.path.join(data_root, 'obj_{:03}.ply')
obj_ids = [i + 1 for i in range(30)]
models = {}
for obj_id in tqdm.tqdm(obj_ids):
cad_path = cad_path_pattern.format(obj_id)
model = opengl_renderer.load_ply(cad_path)
models.update({obj_id: model})
base_utils.save_pickle(models, rendering_model_path)
return models
def get_mask():
data_root = 'data/tless/test_primesense'
scene_ids = [i + 1 for i in range(20)]
models = get_rendering_model('data/tless/models_cad')
for scene_id in tqdm.tqdm(scene_ids):
scene_dir = os.path.join(data_root, '{:02}'.format(scene_id))
rgb_dir = os.path.join(scene_dir, 'rgb')
mask_dir = os.path.join(scene_dir, 'mask')
os.system('mkdir -p {}'.format(mask_dir))
rgb_paths = glob.glob(os.path.join(rgb_dir, '*.png'))
gt = yaml.load(open(os.path.join(scene_dir, 'gt.yml')))
K_info = yaml.load(open(os.path.join(scene_dir, 'info.yml')))
a_pixel_num_dict = {}
for rgb_path in tqdm.tqdm(rgb_paths):
img_id = int(os.path.basename(rgb_path).replace('.png', ''))
gt_ = gt[img_id]
w, h = Image.open(rgb_path).size
K = K_info[img_id]['cam_K']
K = np.array(K).reshape(3, 3)
mask_map = np.zeros(shape=[h, w], dtype=np.int16)
depth_map = 10 * np.ones(shape=[h, w], dtype=np.float32)
a_pixel_nums = []
for instance_id, instance_gt in enumerate(gt_):
R = np.array(instance_gt['cam_R_m2c']).reshape(3, 3)
t = np.array(instance_gt['cam_t_m2c']) * 0.001
pose = np.concatenate([R, t[:, None]], axis=1)
obj_id = instance_gt['obj_id']
depth = opengl_renderer.render(models[obj_id], pose, K, w, h)
update_mask(depth, w, mask_map, depth_map, obj_id, instance_id)
a_pixel_nums.append(np.sum(depth != 0))
mask_path = rgb_path.replace('rgb', 'mask')
Image.fromarray(mask_map).save(mask_path, 'PNG')
a_pixel_num_dict.update({img_id: a_pixel_nums})
base_utils.save_pickle(a_pixel_num_dict,
os.path.join(scene_dir, 'pixel_num.pkl'))
def collect_render_set_info(self,pkl_file,render_dir,format='jpg'):
database=[] # blender standard
projector=Projector()
modeldb=LineModModelDB()
for k in range(self.render_num):
data={}
data['rgb_pth']=os.path.join(render_dir,'{}.{}'.format(k,format))
data['RT']=read_pickle(os.path.join(self.linemod_dir,render_dir,'{}_RT.pkl'.format(k)))['RT']
data['cls_typ']=self.cls_name
data['rnd_typ']='render'
data['corners']=projector.project(modeldb.get_corners_3d(self.cls_name),data['RT'],'blender')
data['farthest']=projector.project(modeldb.get_farthest_3d(self.cls_name),data['RT'],'blender')
data['center']=projector.project(modeldb.get_centers_3d(self.cls_name)[None,:],data['RT'],'blender')
for num in [4,12,16,20]:
data['farthest{}'.format(num)]=projector.project(modeldb.get_farthest_3d(self.cls_name,num),data['RT'],'blender')
data['small_bbox'] = projector.project(modeldb.get_small_bbox(self.cls_name), data['RT'], 'blender')
axis_direct=np.concatenate([np.identity(3), np.zeros([3, 1])], 1).astype(np.float32)
data['van_pts']=projector.project_h(axis_direct, data['RT'], 'blender')
database.append(data)
save_pickle(database,pkl_file)
return database
def collect_val_render(self, pkl_file, render_dir, format='jpg'):
database = []
projector = Projector()
modeldb = HomemadeModelDB()
for k in range(3482, 3499):
data = {}
print(os.path.join(self.render_val_dir, '{}.{}'.format(k, format)))
data['rgb_pth'] = os.path.join(self.render_val_dir,
'{}.{}'.format(k, format))
data['dpt_pth'] = os.path.join(self.render_val_dir,
'{}_depth.png'.format(k))
data['RT'] = read_pickle(
os.path.join(self.homemade_dir, self.render_val_dir,
'{}_RT.pkl'.format(k)))['RT']
data['cls_typ'] = self.cls_name
data['rnd_typ'] = 'render'
data['corners'] = projector.project(
modeldb.get_corners_3d(self.cls_name), data['RT'], 'blender')
data['farthest'] = projector.project(
modeldb.get_farthest_3d(self.cls_name), data['RT'], 'blender')
data['center'] = projector.project(
modeldb.get_centers_3d(self.cls_name)[None, :], data['RT'],
'blender')
for num in [4, 12, 16, 20]:
data['farthest{}'.format(num)] = projector.project(
modeldb.get_farthest_3d(self.cls_name, num), data['RT'],
'blender')
data['small_bbox'] = projector.project(
modeldb.get_small_bbox(self.cls_name), data['RT'], 'blender')
axis_direct = np.concatenate(
[np.identity(3), np.zeros([3, 1])], 1).astype(np.float32)
data['van_pts'] = projector.project_h(axis_direct, data['RT'],
'blender')
database.append(data)
print("collectval successful?: length = ", len(database))
save_pickle(database, pkl_file)
return database
def val(net,
dataloader,
epoch,
val_prefix='val',
use_camera_intrinsic=False,
use_motion=False):
for rec in recs:
rec.reset()
test_begin = time.time()
evaluator = Evaluator()
eval_net = DataParallel(
EvalWrapper().cuda()) if not use_motion else DataParallel(
MotionEvalWrapper().cuda())
uncertain_eval_net = DataParallel(UncertaintyEvalWrapper().cuda())
net.eval()
for idx, data in enumerate(dataloader):
if use_camera_intrinsic:
image, mask, vertex, vertex_weights, pose, corner_target, Ks = [
d.cuda() for d in data
]
else:
image, mask, vertex, vertex_weights, pose, corner_target = [
d.cuda() for d in data
]
with torch.no_grad():
seg_pred, vertex_pred, loss_seg, loss_vertex, precision, recall = net(
image, mask, vertex, vertex_weights)
loss_seg, loss_vertex, precision, recall = [
torch.mean(val)
for val in (loss_seg, loss_vertex, precision, recall)
]
if (train_cfg['eval_epoch']
and epoch % train_cfg['eval_inter'] == 0 and
epoch >= train_cfg['eval_epoch_begin']) or args.test_model:
if args.use_uncertainty_pnp:
mean, cov_inv = uncertain_eval_net(seg_pred, vertex_pred)
mean = mean.cpu().numpy()
cov_inv = cov_inv.cpu().numpy()
else:
corner_pred = eval_net(seg_pred,
vertex_pred).cpu().detach().numpy()
pose = pose.cpu().numpy()
b = pose.shape[0]
pose_preds = []
for bi in range(b):
intri_type = 'use_intrinsic' if use_camera_intrinsic else 'linemod'
K = Ks[bi].cpu().numpy() if use_camera_intrinsic else None
if args.use_uncertainty_pnp:
pose_preds.append(
evaluator.evaluate_uncertainty(mean[bi],
cov_inv[bi],
pose[bi],
args.linemod_cls,
intri_type,
vote_type,
intri_matrix=K))
else:
pose_preds.append(
evaluator.evaluate(corner_pred[bi],
pose[bi],
args.linemod_cls,
intri_type,
vote_type,
intri_matrix=K))
if args.save_inter_result:
mask_pr = torch.argmax(seg_pred, 1).cpu().detach().numpy()
mask_gt = mask.cpu().detach().numpy()
# assume batch size = 1
imsave(
os.path.join(args.save_inter_dir,
'{}_mask_pr.png'.format(idx)), mask_pr[0])
imsave(
os.path.join(args.save_inter_dir,
'{}_mask_gt.png'.format(idx)), mask_gt[0])
imsave(
os.path.join(args.save_inter_dir,
'{}_rgb.png'.format(idx)),
imagenet_to_uint8(image.cpu().detach().numpy()[0]))
save_pickle([pose_preds[0], pose[0]],
os.path.join(args.save_inter_dir,
'{}_pose.pkl'.format(idx)))
vals = [loss_seg, loss_vertex, precision, recall]
for rec, val in zip(recs, vals):
rec.update(val)
with torch.no_grad():
batch_size = image.shape[0]
nrow = 5 if batch_size > 5 else batch_size
recorder.rec_segmentation(F.softmax(seg_pred, dim=1),
num_classes=2,
nrow=nrow,
step=epoch,
name='{}/image/seg'.format(val_prefix))
recorder.rec_vertex(vertex_pred,
vertex_weights,
nrow=4,
step=epoch,
name='{}/image/ver'.format(val_prefix))
losses_batch = OrderedDict()
for name, rec in zip(recs_names, recs):
losses_batch['{}/'.format(val_prefix) + name] = rec.avg
if (train_cfg['eval_epoch'] and epoch % train_cfg['eval_inter'] == 0
and epoch >= train_cfg['eval_epoch_begin']) or args.test_model:
proj_err, add, cm = evaluator.average_precision(False)
losses_batch['{}/scalar/projection_error'.format(
val_prefix)] = proj_err
losses_batch['{}/scalar/add'.format(val_prefix)] = add
losses_batch['{}/scalar/cm'.format(val_prefix)] = cm
recorder.rec_loss_batch(losses_batch, epoch, epoch, val_prefix)
for rec in recs:
rec.reset()
print('epoch {} {} cost {} s'.format(epoch, val_prefix,
time.time() - test_begin))
