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train_deform.py
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import os
import time
import math
import random
import numpy as np
import cv2
import h5py
from glob import glob
from scipy.ndimage.filters import gaussian_filter
from scipy.ndimage import sobel
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.autograd import Variable
from utils import *
from models import *
from kornia.geometry import get_affine_matrix3d, warp_affine3d
import ChamferDistancePytorch.chamfer3D.dist_chamfer_3D
chamLoss = ChamferDistancePytorch.chamfer3D.dist_chamfer_3D.chamfer_3DDist()
class MODEL_DEFORM(object):
def __init__(self, config):
self.real_size = 256
#self.real_size = 128
self.mask_margin = 8
self.g_dim = config.g_dim
self.z_dim = 3
self.input_size = config.input_size # 32
self.output_size = config.output_size # 128
self.patch_size = 18
self.stride = 4
self.pt_num = 512
self.csize = config.csize
self.c_range = config.c_range
self.max_sample_num = config.max_sample_num
# self.upsample_rate = self.output_size//self.input_size
self.upsample_rate = 1
self.asymmetry = True
self.save_epoch = 5
self.eval_epoch = 2
self.start_epoch = 0
self.mode = config.mode
self.gua_filter = True
self.small_dataset = config.small_dataset
self.predict_dis = True
self.pred_coarse = True
self.dump_deform = config.dump_deform
self.w_dis = config.w_dis
self.top_num = 8
self.pre_fix = ''
self.sampling_threshold = 0.3
self.render_view_id = 0
self.voxel_renderer = voxel_renderer(self.real_size)
self.checkpoint_dir = config.checkpoint_dir
self.data_dir = config.data_dir
self.dump_deform_path = config.dump_deform_path
self.data_content = config.data_content # content_chair
self.log_path = os.path.join(config.log_dir, self.model_dir, config.model_name)
self.sample_dir = os.path.join(config.sample_dir, self.model_dir, config.model_name)
if not os.path.exists(self.log_path):
os.makedirs(self.log_path)
if not os.path.exists(self.sample_dir):
os.makedirs(self.sample_dir)
self.log_fout = open(os.path.join(self.log_path, 'log_train.txt'), 'a')
self.log_fout.write(str(config)+'\n')
if torch.cuda.is_available():
self.device = torch.device('cuda')
torch.backends.cudnn.benchmark = True
else:
self.device = torch.device('cpu')
x_ = range(self.patch_size)
y_ = range(self.patch_size)
z_ = range(self.patch_size)
yy, xx, zz = np.meshgrid(x_,y_,z_)
self.patch_coord = np.concatenate((np.expand_dims(xx, -1), np.expand_dims(yy, -1),np.expand_dims(zz, -1)), -1)
#load data
print("preprocessing - start")
vox_name = "/model.binvox"
self.imgout_0 = np.full([self.real_size*4, self.real_size*4*3], 255, np.uint8)
if os.path.exists("splits/"+self.data_content+"_train.txt"):
#load data
fin = open("splits/"+self.data_content+"_%s.txt"%(self.mode))
self.dataset_names = [name.strip() for name in fin.readlines()]
fin.close()
self.dataset_len = len(self.dataset_names)
self.dataset_len = 1000
if self.mode=='test':
self.dataset_len = 200
if self.dump_deform and self.mode=='train':
fin2 = open("splits/"+self.data_content+"_test.txt")
test_names = [name.strip() for name in fin2.readlines()]
fin2.close()
self.dataset_names = self.dataset_names + test_names[:200]
self.dataset_len += 200
if self.small_dataset:
self.dataset_len = 4
self.mask_content = []
self.input_content = []
self.partial_content = []
self.partial_mask = []
self.gt_content = []
self.pos_content = []
self.gt_pc = []
self.re_ids = []
self.re_dis = []
self.re_dis_c = []
self.re_trans = []
self.re_trans_c = []
self.coarse_dis = []
self.partial_dis = []
self.shape_names = []
self.names = range(self.dataset_len)
if config.train_deform:
for i in range(self.dataset_len):
print("preprocessing content - "+str(i+1)+"/"+str(self.dataset_len))
dump_files = glob(os.path.join(self.dump_deform_path, self.dataset_names[self.names[i]], 'dump_deform_wc_top*.npz'))
if len(dump_files)==0:
print('not exist', os.path.join(self.dump_deform_path, self.dataset_names[self.names[i]]))
self.dataset_len = self.dataset_len -1
continue
else:
dump_f = dump_files[-1]
tmp_raw = get_vox_from_binvox_1over2(os.path.join(self.data_dir,self.dataset_names[self.names[i]]+vox_name)).astype(np.uint8)
xmin,xmax,ymin,ymax,zmin,zmax = self.get_voxel_bbox(tmp_raw)
xmin2,xmax2,ymin2,ymax2,zmin2,zmax2 = xmin,xmax,ymin,ymax,zmin,zmax
a = np.load(dump_f, allow_pickle=True)
re_ids = a['re_ids']
re_dis = a['re_dis']*10.0
re_trans = a['re_trans']
re_dis_c = a['re_dis_c']*10.0
re_trans_c = a['re_trans_c']
crop_locs = a['crop_locs']
tmp = self.crop_voxel(tmp_raw,xmin,xmax,ymin,ymax,zmin,zmax)
gt_voxel = tmp
tmp_input, tmp_mask = self.get_voxel_input_Dmask_mask(tmp)
partial_shape, partial_mask = self.random_crop(tmp, c_locs=crop_locs)
partial_mask = tmp_mask
if self.gua_filter:
tmp_input = gaussian_filter(tmp_input.astype(np.float32), sigma=1)
partial_shape = gaussian_filter(partial_shape.astype(np.float32), sigma=1)
self.input_content.append(tmp_input)
self.gt_content.append(gt_voxel)
self.mask_content.append(tmp_mask)
self.partial_content.append(partial_shape)
self.partial_mask.append(partial_mask)
self.pos_content.append([xmin,xmax,ymin,ymax,zmin,zmax])
self.re_dis.append(re_dis)
self.re_ids.append(re_ids)
self.re_dis_c.append(re_dis_c)
self.re_trans.append(re_trans)
self.re_trans_c.append(re_trans_c)
self.shape_names.append(self.dataset_names[self.names[i]])
img_y = i//4
img_x = (i%4)*3+2
if img_y<4:
# xmin,xmax,ymin,ymax,zmin,zmax = self.pos_content[i]
tmpvox = self.recover_voxel(gt_voxel,xmin,xmax,ymin,ymax,zmin,zmax)
self.imgout_0[img_y*self.real_size:(img_y+1)*self.real_size,img_x*self.real_size:(img_x+1)*self.real_size] = self.voxel_renderer.render_img(tmpvox, self.sampling_threshold, self.render_view_id)
img_y = i//4
img_x = (i%4)*3
if img_y<4:
tmpvox = self.recover_voxel(tmp_input,xmin,xmax,ymin,ymax,zmin,zmax)
self.imgout_0[img_y*self.real_size:(img_y+1)*self.real_size,img_x*self.real_size:(img_x+1)*self.real_size] = self.voxel_renderer.render_img(tmpvox, self.sampling_threshold, self.render_view_id)
img_y = i//4
img_x = (i%4)*3 + 1
if img_y<4:
tmpvox = self.recover_voxel(partial_shape,xmin,xmax,ymin,ymax,zmin,zmax)
self.imgout_0[img_y*self.real_size:(img_y+1)*self.real_size,img_x*self.real_size:(img_x+1)*self.real_size] = self.voxel_renderer.render_img(tmpvox, self.sampling_threshold, self.render_view_id)
cv2.imwrite(self.sample_dir+"/a_content_%s.png"%(self.mode), self.imgout_0)
else:
print("ERROR: cannot load dataset txt: "+"splits/"+self.data_content+"_train.txt")
exit(-1)
self.model = PatchDeformer(self.g_dim, self.z_dim, self.predict_dis,) # 3: translate only
self.model.to(self.device)
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=config.lr)
self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, 100, gamma=0.99)
if self.pred_coarse:
print('coarse predictor', self.data_content)
if self.data_content =='content_chair':
checkpoint = torch.load('checkpoint_complete/content_chair_complete/coarse_comp/checkpoint-90.pth')
elif self.data_content=='content_boat':
checkpoint = torch.load('checkpoint_complete/content_boat_complete/coarse_comp/checkpoint-90.pth')
elif self.data_content=='content_lamp':
checkpoint = torch.load('checkpoint_complete/content_lamp_complete/coarse_comp/checkpoint-90.pth')
elif self.data_content=='content_couch':
checkpoint = torch.load('checkpoint_complete/content_couch_complete/coarse_comp/checkpoint-150.pth')
elif self.data_content=='content_cabinet':
checkpoint = torch.load('checkpoint_complete/content_cabinet_complete/coarse_comp/checkpoint-90.pth')
elif self.data_content=='content_plane':
checkpoint = torch.load('checkpoint_complete/content_plane_complete/coarse_comp/checkpoint-90.pth')
elif self.data_content=='content_car':
checkpoint = torch.load('checkpoint_complete/content_car_complete/coarse_comp/checkpoint-90.pth') #
elif self.data_content=='content_table':
checkpoint = torch.load('checkpoint_complete/content_table_complete/coarse_comp/checkpoint-90.pth')
self.coarse_predictor = CoarseCompletor_skip(32)
self.coarse_predictor.to(self.device)
self.coarse_predictor.load_state_dict(checkpoint['generator'])
self.coarse_predictor.eval()
self.max_to_keep = 2
self.checkpoint_path = os.path.join(self.checkpoint_dir, self.model_dir, config.model_name)
self.checkpoint_name='checkpoint'
self.checkpoint_manager_list = [None] * self.max_to_keep
self.checkpoint_manager_pointer = 0
def log_string(self, out_str, ):
self.log_fout.write(out_str+'\n')
self.log_fout.flush()
print(out_str)
def get_voxel_input_Dmask_mask(self,vox):
vox_tensor = torch.from_numpy(vox).to(self.device).unsqueeze(0).unsqueeze(0).float()
#input
down_rate = self.output_size//self.input_size
if down_rate==2:
smallmaskx_tensor = F.max_pool3d(vox_tensor, kernel_size = down_rate, stride = down_rate, padding = 0)
input_vox = F.interpolate(smallmaskx_tensor, scale_factor = down_rate, mode='nearest')
else:
input_vox = F.max_pool3d(vox_tensor, kernel_size = down_rate, stride = 1, padding = down_rate//2)
input_vox = input_vox[:,:,:-1,:-1,:-1]
#Dmask
# smallmask_tensor = vox_tensor[:,:,crop_margin:-crop_margin,crop_margin:-crop_margin,crop_margin:-crop_margin]
smallmask_tensor = F.max_pool3d(input_vox, kernel_size = self.patch_size, stride = self.stride, padding = 0)
#to numpy
input_in = input_vox.detach().cpu().numpy()[0,0]
input_in = np.round(input_in).astype(np.uint8)
mask_in = smallmask_tensor.detach().cpu().numpy()[0,0]
mask_in = np.round(mask_in).astype(np.uint8)
return input_in, mask_in
def random_crop(self, vox, crop_size=20, c_range=20, prob=None, c_locs=None):
vx, vy, vz = vox.shape
edges = sobel(vox)
edges = edges.astype(np.float32)/255.0
csize = crop_size + (np.random.rand(3)-0.5)*c_range
csize = csize.astype(np.int32)
csize[0] = min(vx-16, csize[0])
csize[1] = min(vy-16, csize[1])
csize[2] = min(vz-16, csize[2])
loc_starts = np.zeros(3,np.int32)
new_vox = np.zeros(vox.shape)
new_vox[:,:,:] = vox[:,:,:].copy()
loc_starts[0] = np.random.randint(0, vx-csize[0])
loc_starts[1] = np.random.randint(0, vy-csize[1])
loc_starts[2] = np.random.randint(0, vz-csize[2])
if np.array(edges>0.4,np.int32).sum()<100:
p = 0
else:
if prob is None:
p = np.random.random()
else:
p = prob
if (p<=0.4 or not self.data_content=='content_chair') and c_locs is None:
while(vox[loc_starts[0]:loc_starts[0]+csize[0], loc_starts[1]:loc_starts[1]+csize[1],\
loc_starts[2]:loc_starts[2]+csize[2],].sum()<30):
csize = crop_size + (np.random.rand(3)-0.5)*c_range
csize = csize.astype(np.int32)
loc_starts[0] = np.random.randint(0, vx-csize[0])
loc_starts[1] = np.random.randint(0, vy-csize[1])
loc_starts[2] = np.random.randint(0, vz-csize[2])
elif c_locs is None:
cnt=0
while(vox[loc_starts[0]:loc_starts[0]+csize[0], loc_starts[1]:loc_starts[1]+csize[1],\
loc_starts[2]:loc_starts[2]+csize[2],].sum()<50 or loc_starts[1]<vy/2):
csize = crop_size + (np.random.rand(3)-0.5)*c_range
csize[0] = min(vx-16, csize[0])
csize[1] = min(vy-16, csize[1])
csize[2] = min(vz-16, csize[2])
csize = csize.astype(np.int32)
loc_starts[0] = np.random.randint(0, vx-csize[0])
loc_starts[1] = np.random.randint(0, vy-csize[1])
loc_starts[2] = np.random.randint(0, vz-csize[2])
cnt+=1
if cnt>10:
break
else:
loc_starts = c_locs[0:3]
csize = c_locs[3:] - loc_starts
new_vox[loc_starts[0]:loc_starts[0]+csize[0], loc_starts[1]:loc_starts[1]+csize[1],\
loc_starts[2]:loc_starts[2]+csize[2],] = 0
mask_in = np.zeros(vox.shape)
mask_in[loc_starts[0]:loc_starts[0]+csize[0], loc_starts[1]:loc_starts[1]+csize[1],\
loc_starts[2]:loc_starts[2]+csize[2],] = 1.0
return new_vox, mask_in
def point_cloud_to_volume(self, points, vsize=256, radius=0.5):
""" input is Nx3 points.
output is vsize*vsize*vsize
assumes points are in range [-radius, radius]
"""
vol = np.zeros((vsize,vsize,vsize))
voxel = 2*radius/float(vsize)
locations = (points + radius)/voxel
locations = np.clip(locations, 0, vsize-1e-4)
locations = locations.astype(int)
vol[locations[:,0],locations[:,1],locations[:,2]] = 1.0
return vol
def get_voxel_bbox(self,vox):
#minimap
vox_tensor = torch.from_numpy(vox).to(self.device).unsqueeze(0).unsqueeze(0).float()
#smallmaskx_tensor = F.max_pool3d(vox_tensor, kernel_size = 2, stride = 2, padding = 0)
#smallmaskx_tensor = F.interpolate(smallmaskx_tensor, scale_factor = 2, mode='nearest')
smallmaskx_tensor = F.max_pool3d(vox_tensor, kernel_size=self.upsample_rate, stride=self.upsample_rate, padding=0)
smallmaskx_tensor = F.interpolate(smallmaskx_tensor, scale_factor = self.upsample_rate, mode='nearest')
smallmaskx = smallmaskx_tensor.detach().cpu().numpy()[0,0]
smallmaskx = np.round(smallmaskx).astype(np.uint8)
smallx,smally,smallz = smallmaskx.shape
#x
ray = np.max(smallmaskx,(1,2))
xmin = 0
xmax = 0
for i in range(smallx):
if ray[i]>0:
if xmin==0:
xmin = i
xmax = i
#y
ray = np.max(smallmaskx,(0,2))
ymin = 0
ymax = 0
for i in range(smally):
if ray[i]>0:
if ymin==0:
ymin = i
ymax = i
#z
ray = np.max(smallmaskx,(0,1))
if self.asymmetry:
zmin = 0
zmax = 0
for i in range(smallz):
if ray[i]>0:
if zmin==0:
zmin = i
zmax = i
else:
zmin = smallz//2
zmax = 0
for i in range(zmin,smallz):
if ray[i]>0:
zmax = i
return xmin,xmax+1,ymin,ymax+1,zmin,zmax+1
def get_voxel_mask_exact(self,vox):
#256 -maxpoolk4s4- 64 -upsample- 256
vox_tensor = torch.from_numpy(vox).to(self.device).unsqueeze(0).unsqueeze(0).float()
#input
smallmaskx_tensor = F.max_pool3d(vox_tensor, kernel_size=self.upsample_rate, stride=self.upsample_rate, padding=0)
#mask
smallmask_tensor = F.interpolate(smallmaskx_tensor, scale_factor = self.upsample_rate, mode='nearest')
#to numpy
smallmask = smallmask_tensor.detach().cpu().numpy()[0,0]
smallmask = np.round(smallmask).astype(np.uint8)
return smallmask
def crop_voxel(self,vox,xmin,xmax,ymin,ymax,zmin,zmax):
xspan = xmax-xmin
yspan = ymax-ymin
zspan = zmax-zmin
tmp = np.zeros([xspan*self.upsample_rate+self.mask_margin*2,yspan*self.upsample_rate+self.mask_margin*2,zspan*self.upsample_rate+self.mask_margin*2], np.uint8)
if self.asymmetry:
tmp[self.mask_margin:-self.mask_margin,self.mask_margin:-self.mask_margin,self.mask_margin:-self.mask_margin] = vox[xmin*self.upsample_rate:xmax*self.upsample_rate,ymin*self.upsample_rate:ymax*self.upsample_rate,zmin*self.upsample_rate:zmax*self.upsample_rate]
else:
#note z is special: only get half of the shape in z: 0 0.5-----1
tmp[self.mask_margin:-self.mask_margin,self.mask_margin:-self.mask_margin,:-self.mask_margin] = vox[xmin*self.upsample_rate:xmax*self.upsample_rate,ymin*self.upsample_rate:ymax*self.upsample_rate,zmin*self.upsample_rate-self.mask_margin:zmax*self.upsample_rate]
return tmp
def recover_voxel(self,vox,xmin,xmax,ymin,ymax,zmin,zmax):
tmpvox = np.zeros([self.real_size,self.real_size,self.real_size], np.float32)
xmin_,ymin_,zmin_ = (0,0,0)
xmax_,ymax_,zmax_ = vox.shape
xmin = xmin*self.upsample_rate-self.mask_margin
xmax = xmax*self.upsample_rate+self.mask_margin
ymin = ymin*self.upsample_rate-self.mask_margin
ymax = ymax*self.upsample_rate+self.mask_margin
if self.asymmetry:
zmin = zmin*self.upsample_rate-self.mask_margin
else:
zmin = zmin*self.upsample_rate
zmin_ = self.mask_margin
zmax = zmax*self.upsample_rate+self.mask_margin
if xmin<0:
xmin_ = -xmin
xmin = 0
if xmax>self.real_size:
xmax_ = xmax_+self.real_size-xmax
xmax = self.real_size
if ymin<0:
ymin_ = -ymin
ymin = 0
if ymax>self.real_size:
ymax_ = ymax_+self.real_size-ymax
ymax = self.real_size
if zmin<0:
zmin_ = -zmin
zmin = 0
if zmax>self.real_size:
zmax_ = zmax_+self.real_size-zmax
zmax = self.real_size
if self.asymmetry:
tmpvox[xmin:xmax,ymin:ymax,zmin:zmax] = vox[xmin_:xmax_,ymin_:ymax_,zmin_:zmax_]
else:
tmpvox[xmin:xmax,ymin:ymax,zmin:zmax] = vox[xmin_:xmax_,ymin_:ymax_,zmin_:zmax_]
if zmin*2-zmax-1<0:
tmpvox[xmin:xmax,ymin:ymax,zmin-1::-1] = vox[xmin_:xmax_,ymin_:ymax_,zmin_:zmax_]
else:
tmpvox[xmin:xmax,ymin:ymax,zmin-1:zmin*2-zmax-1:-1] = vox[xmin_:xmax_,ymin_:ymax_,zmin_:zmax_]
return tmpvox
def pad_to_same_size(self, v1, v2):
_,_,x1,y1,z1 = v1.shape
_,_,x2,y2,z2 = v2.shape
x = max(x1, x2)
y = max(y1, y2)
z = max(z1, z2)
new_v1 = F.pad(v1, (int((z-z1)/2), z-z1-int((z-z1)/2), int((y-y1)/2), y-y1-int((y-y1)/2), int((x-x1)/2), x-x1-int((x-x1)/2)))
new_v2 = F.pad(v2, (int((z-z2)/2), z-z2-int((z-z2)/2), int((y-y2)/2), y-y2-int((y-y2)/2), int((x-x2)/2), x-x2-int((x-x2)/2)))
return new_v1, new_v2
def reshape_to_patch_size(self, v1, v2):
x1,y1,z1 = v1.shape
x,y,z = v2.shape
new_v1 = v1
padding = np.zeros(6)
if z1 < z:
padding[0], padding[1] = int((z-z1)/2), z-z1-int((z-z1)/2)
else:
new_v1 = new_v1[:, :,int((z1-z)/2):int((z1-z)/2)+z ]
if y1 < y:
padding[2], padding[3] = int((y-y1)/2), y-y1-int((y-y1)/2)
else:
new_v1 = new_v1[:, int((y1-y)/2):int((y1-y)/2)+y,: ]
if x1 < x:
padding[4], padding[5] = int((x-x1)/2), x-x1-int((x-x1)/2)
else:
new_v1 = new_v1[int((x1-x)/2):int((x1-x)/2)+x,:,: ]
new_v1 = torch.from_numpy(new_v1,).unsqueeze(0).unsqueeze(0)
new_v1 = F.pad(new_v1, tuple(padding.astype(np.int8)))
return new_v1.numpy()[0,0]
def reshape_to_size_torch(self, v1, v2_shape):
_,_,x1,y1,z1 = v1.shape
x,y,z = v2_shape
new_v1 = v1
padding = np.zeros(6)
if z1 < z:
padding[0], padding[1] = int((z-z1)/2), z-z1-int((z-z1)/2)
else:
new_v1 = new_v1[:,:,:, :,int((z1-z)/2):int((z1-z)/2)+z ]
if y1 < y:
padding[2], padding[3] = int((y-y1)/2), y-y1-int((y-y1)/2)
else:
new_v1 = new_v1[:,:,:, int((y1-y)/2):int((y1-y)/2)+y,: ]
if x1 < x:
padding[4], padding[5] = int((x-x1)/2), x-x1-int((x-x1)/2)
else:
new_v1 = new_v1[:,:,int((x1-x)/2):int((x1-x)/2)+x,:,: ]
new_v1 = F.pad(new_v1, tuple(padding.astype(np.int8)))
return new_v1
def load(self):
#load previous checkpoint
checkpoint_txt = os.path.join(self.checkpoint_path, "checkpoint")
if os.path.exists(checkpoint_txt):
fin = open(checkpoint_txt)
model_dir = fin.readline().strip()
print('loading from', model_dir)
fin.close()
checkpoint = torch.load(model_dir)
self.model.load_state_dict(checkpoint['model'])
if 'optimizer' in checkpoint.keys():
self.optimizer.load_state_dict(checkpoint['optimizer'])
if 'scheduler' in checkpoint.keys():
self.scheduler.load_state_dict(checkpoint['scheduler'])
self.start_epoch = checkpoint['epoch'] + 1
print(" [*] Load SUCCESS")
return True
else:
print(" [!] Load failed...")
return False
def save(self,epoch):
if not os.path.exists(self.checkpoint_path):
os.makedirs(self.checkpoint_path)
save_dir = os.path.join(self.checkpoint_path,self.checkpoint_name+"-"+str(epoch)+".pth")
self.checkpoint_manager_pointer = (self.checkpoint_manager_pointer+1)%self.max_to_keep
#delete checkpoint
if self.checkpoint_manager_list[self.checkpoint_manager_pointer] is not None:
if os.path.exists(self.checkpoint_manager_list[self.checkpoint_manager_pointer]):
os.remove(self.checkpoint_manager_list[self.checkpoint_manager_pointer])
#save checkpoint
torch.save({
'model': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'scheduler': self.scheduler.state_dict(),
'epoch': epoch
}, save_dir)
# update checkpoint manager
self.checkpoint_manager_list[self.checkpoint_manager_pointer] = save_dir
# write file
checkpoint_txt = os.path.join(self.checkpoint_path, "checkpoint")
fout = open(checkpoint_txt, 'w')
for i in range(self.max_to_keep):
pointer = (self.checkpoint_manager_pointer+self.max_to_keep-i)%self.max_to_keep
if self.checkpoint_manager_list[pointer] is not None:
fout.write(self.checkpoint_manager_list[pointer]+"\n")
fout.close()
def filter_locs(self, c_mask, d_mask):
x_size, y_size, z_size = c_mask.shape
x_ = range(x_size)
y_ = range(y_size)
z_ = range(z_size)
yy, xx, zz = np.meshgrid(y_,x_,z_)
feat_coord = np.concatenate((np.expand_dims(xx, -1), np.expand_dims(yy, -1),np.expand_dims(zz, -1)), -1)
c_coord = feat_coord[c_mask>0]
x_size, y_size, z_size = d_mask.shape
x_ = range(x_size)
y_ = range(y_size)
z_ = range(z_size)
yy, xx, zz = np.meshgrid(y_,x_,z_)
feat_coord = np.concatenate((np.expand_dims(xx, -1), np.expand_dims(yy, -1),np.expand_dims(zz, -1)), -1)
d_coord = feat_coord[d_mask>0]
return c_coord, d_coord
def get_locs(self, re_ids, c_locs, d_locs):
out_locs = np.zeros((re_ids.shape[0], re_ids.shape[1], 3))
out_locs[:, 0, :] = c_locs[re_ids[:,0]]
for i in range(out_locs.shape[1]-1):
out_locs[:, i+1, :] = d_locs[re_ids[:,i+1]]
return out_locs.astype(np.int32)
def get_ids_with_gt(self, re_pairs, total_d_locs):
c_num = re_pairs.shape[0]
valid_ids = []
for i in range(c_num):
dif = np.abs(total_d_locs - re_pairs[i, 0,]).sum(1).min()
if dif==0:
valid_ids.append(i)
return valid_ids
def get_dis_by_locs(self, dis_dict, locs, return_mask=False):
out_dis = np.zeros(locs.shape[0])
dis_dict = dis_dict.tolist()
mask = torch.ones(locs.shape[0]).to(self.device)
for i in range(locs.shape[0]):
if not tuple(locs[i]) in dis_dict.keys():
mask[i] = 0.0
out_dis[i] = 1.0
continue
out_dis[i] = 10.0*dis_dict[tuple(locs[i])]
if return_mask:
return out_dis, mask
return out_dis
def get_exact_partial_mask(self, partial_in, gt_in, p_mask_in):
partial_in = (partial_in>0.3).float()
vox_dif = torch.abs(gt_in - partial_in)
patch_sum = F.avg_pool3d(partial_in, kernel_size = self.patch_size, stride = self.stride, padding = 0)
patch_sum = patch_sum*float(self.patch_size**3)
patch_dif = F.avg_pool3d(vox_dif, kernel_size = self.patch_size, stride = self.stride, padding = 0)
patch_dif = patch_dif*float(self.patch_size**3)
# patch_dif = (patch_dif<220).float()
patch_dif = ((patch_dif/(patch_sum+1e-3))<0.8).float()
mask_final = p_mask_in*patch_dif
return mask_final
def distance_retrieve(self, re_ids, re_locs, re_dis, re_trans, max_sample_num=300,sigma=1.0):
"""
re_locs: [nc, (1+k), 3]
re_dis: [nc, k]
flip_list: [nc, k]
return:
c_ids, d_ids, distance matrix
"""
c_num = min(re_locs.shape[0], max_sample_num)
c_ids = np.array(sorted(np.random.randint(0, re_locs.shape[0], c_num)))
K = re_locs.shape[1] -1
dis_prob = re_dis / sigma
dis_prob = np.exp(-(dis_prob - np.expand_dims(np.max(dis_prob, axis=1), -1)))
dis_prob = dis_prob/(np.expand_dims(dis_prob.sum(1), axis=1)+1e-7)
out_locs = np.zeros((c_num, 2, 3), np.int32)
out_locs[:, 0,] = re_locs[c_ids, 0]
# out_ids = np.zeros(c_num, np.int32)
out_d_ids = np.zeros(c_num, np.int32)
out_c_ids = re_ids[c_ids, 0,]
out_flip = np.zeros(c_num, np.int32)
out_trans = np.zeros((c_num, 3))
out_dis = np.zeros(c_num)
for i in range(c_num):
idx = c_ids[i]
#if dis_prob[idx].sum()<1e-5 or not dis_prob[idx].sum()<1000:
pid = np.random.randint(0, K,)
out_locs[i, 1] = re_locs[idx, 1+pid,]
out_flip[i] = re_trans[idx, pid, 3]
out_trans[i] = re_trans[idx, pid, 0:3]
out_d_ids[i] = re_ids[idx, 1+pid]
out_dis[i] = re_dis[idx, pid]
return c_ids, out_c_ids, out_d_ids, out_locs, out_dis, out_trans, out_flip
def regression_loss(self, embedding_distance, actual_distance, mask=None,obj_sigmas=1.0):
if mask is not None:
loss = torch.sum(torch.square(mask*(embedding_distance-actual_distance)),)/mask.sum()
else:
loss = torch.mean(torch.square(embedding_distance-actual_distance),)
return loss
def compute_iou(self, recons, gt, thresh=0.3):
x = (recons>thresh).astype(np.float32)
y = (gt>thresh).astype(np.float32)
intersection = np.sum(x*y)
union = np.sum(x) + np.sum(y) - np.sum(x*y)
iou = intersection/(union+1e-5)
return iou
@property
def model_dir(self):
return "{}_retriv".format(self.data_content)
def train(self, config):
if self.dump_deform:
self.mode='test'
if config.continue_train or self.mode=='test':
self.load()
start_time = time.time()
training_epoch = config.epoch
if self.mode=='test':
training_epoch += 1
self.dataset_len = len(self.input_content)
batch_index_list = np.arange(self.dataset_len)
for epoch in range(self.start_epoch, training_epoch):
if self.mode!='test':
np.random.shuffle(batch_index_list)
total_loss = 0.0
total_iou = 0.0
total_c_iou = 0.0
total_coa_iou = 0.0
self.model.train()
if self.mode=='test':
self.model.eval()
for idx in range(self.dataset_len):
dxb = batch_index_list[idx]
if not self.pred_coarse:
input_in = torch.from_numpy(self.input_content[dxb]).to(self.device).unsqueeze(0).unsqueeze(0).float()
mask_in = torch.from_numpy(self.mask_content[dxb]).to(self.device).unsqueeze(0).unsqueeze(0).float()
partial_in = torch.from_numpy(self.partial_content[dxb]).to(self.device).unsqueeze(0).unsqueeze(0).float()
mask_partial_in = torch.from_numpy(self.partial_mask[dxb]).to(self.device).unsqueeze(0).unsqueeze(0).float()
gt_in = torch.from_numpy(self.gt_content[dxb]).to(self.device).unsqueeze(0).unsqueeze(0).float()
if self.pred_coarse:
mask_partial_in = F.max_pool3d((partial_in>self.sampling_threshold).float(), kernel_size=self.patch_size, \
stride = self.stride, padding = 0)
partial_full = self.reshape_to_size_torch(partial_in, (self.output_size, self.output_size, self.output_size))
coarse_out = self.coarse_predictor(partial_full)
coarse_out = coarse_out.detach()
input_in = self.reshape_to_size_torch(coarse_out, self.input_content[dxb].shape)
mask_in = F.max_pool3d((input_in>self.sampling_threshold).float(), kernel_size = self.patch_size, stride = self.stride, padding = 0)
# re_pairs = self.re_pairs[dxb]
re_ids = self.re_ids[dxb].astype(np.int32)
re_dis = self.re_dis[dxb]
re_dis_c = self.re_dis_c[dxb]
re_trans = self.re_trans[dxb]
re_trans_c = self.re_trans_c[dxb]
# total_c_locs, total_d_locs = self.filter_locs(self.mask_content[dxb], self.partial_mask[dxb])
total_c_locs, total_d_locs = self.filter_locs(mask_in.cpu().numpy()[0][0], mask_partial_in.cpu().numpy()[0][0])
try:
re_pairs = self.get_locs(re_ids, total_c_locs, total_d_locs)
re_pairs_or = re_pairs
except:
print(idx,'get locs error')
continue
if self.dump_deform and (epoch%20==0 or epoch == self.start_epoch):
total_pred_trans, total_pred_dis, total_init_trans = self.retrieve_to_full_shape_fromdis(\
self.gt_content[dxb], partial_in.cpu().numpy()[0][0], \
input_in.cpu().numpy()[0][0], re_pairs_or, \
self.re_dis[dxb], self.re_trans_c[dxb], align=True, dump_deform=True)
np.savez(os.path.join(self.dump_deform_path, self.shape_names[dxb], "pred_deform_%d.npz"%(epoch)), \
pred_trans=total_pred_trans, pred_dis=total_pred_dis, init_trans=total_init_trans)
if self.mode=='test' and (epoch%20==0 or epoch==self.start_epoch) and dxb<20:
xmin,xmax,ymin,ymax,zmin,zmax = self.pos_content[dxb]
imgout_0 = np.full([self.real_size, self.real_size*6], 255, np.uint8)
pred_shape, retrieved_patches, aligned_patches, = self.retrieve_to_full_shape_fromdis(\
self.gt_content[dxb], partial_in.cpu().numpy()[0][0], input_in.cpu().numpy()[0][0], \
re_pairs_or, self.re_dis[dxb], self.re_trans_c[dxb], align=True,)
iou = self.compute_iou(pred_shape, self.gt_content[dxb],thresh=self.sampling_threshold)
total_iou += iou
print(idx ,iou, total_iou/(idx+1))
iou = self.compute_iou(input_in.cpu().numpy()[0,0], self.gt_content[dxb],thresh=self.sampling_threshold)
total_coa_iou += iou
print('coa iou', idx ,iou, total_coa_iou/(idx+1))
# imgout_0 = np.full([self.real_size, self.real_size*6], 255, np.uint8)
xmin,xmax,ymin,ymax,zmin,zmax = self.pos_content[dxb]
tmpvox = self.recover_voxel(input_in.cpu().numpy()[0][0],xmin,xmax,ymin,ymax,zmin,zmax)
imgout_0[:,0:self.real_size] = self.voxel_renderer.render_img(tmpvox, self.sampling_threshold, \
self.render_view_id)
tmpvox = self.reshape_to_patch_size(partial_in.cpu().numpy()[0][0], np.zeros((256,256,256)))
# tmpvox = self.recover_voxel(self.partial_content[dxb],xmin,xmax,ymin,ymax,zmin,zmax)
imgout_0[:,self.real_size:self.real_size*2] = self.voxel_renderer.render_img(tmpvox, \
self.sampling_threshold, self.render_view_id)
tmpvox = self.recover_voxel(self.gt_content[dxb],xmin,xmax,ymin,ymax,zmin,zmax)
imgout_0[:,5*self.real_size:self.real_size*6] = self.voxel_renderer.render_img(tmpvox, \
self.sampling_threshold, self.render_view_id)
tmpvox = self.recover_voxel(pred_shape,xmin,xmax,ymin,ymax,zmin,zmax)
imgout_0[:,4*self.real_size:5*self.real_size] = self.voxel_renderer.render_img(tmpvox, \
self.sampling_threshold, self.render_view_id)
tmpvox = self.recover_voxel(retrieved_patches,xmin,xmax,ymin,ymax,zmin,zmax)
imgout_0[:,2*self.real_size:3*self.real_size] = self.voxel_renderer.render_img(tmpvox, \
self.sampling_threshold, self.render_view_id)
tmpvox = self.recover_voxel(aligned_patches,xmin,xmax,ymin,ymax,zmin,zmax)
imgout_0[:,3*self.real_size:4*self.real_size] = self.voxel_renderer.render_img(tmpvox, \
self.sampling_threshold, self.render_view_id)
if self.dump_deform:
cv2.imwrite(self.dump_deform_path+"/preddeform_%s_%s_%d_%d.png"%(self.mode, self.pre_fix, epoch, \
self.names[dxb]), imgout_0)
else:
cv2.imwrite(self.sample_dir+"/%s_%s_%d_%d.png"%(self.mode, self.pre_fix, epoch, \
self.names[dxb]), imgout_0)
if self.mode=='test':
continue
self.optimizer.zero_grad()
if re_ids.shape[1]>2:
sample_ids, re_c_ids, re_d_ids, out_locs, out_dis, out_trans, out_flip = self.distance_retrieve(\
re_ids, re_pairs, re_dis, re_trans_c, self.max_sample_num, sigma=1.0)
else:
c_num = min(re_pairs.shape[0], self.max_sample_num)
c_ids = np.array(sorted(np.random.randint(0, re_pairs.shape[0], c_num)))
re_c_ids = re_ids[c_ids,0]
re_d_ids = re_ids[c_ids, 1]
out_locs = re_pairs[c_ids, :, :]
out_trans = re_trans_c[c_ids, 0,0:3]
out_flip = re_trans_c[c_ids, 0, 3]
out_dis = re_dis[c_ids, ]
c_patches, d_patches, gt_patches = self.get_patch_pairs(self.gt_content[dxb], \
self.partial_content[dxb], input_in.cpu().numpy()[0][0], out_locs[:,0], \
out_locs[:,1], out_trans, out_flip,)
c_patches_in = torch.from_numpy(c_patches).to(self.device).unsqueeze(1).float()
d_patches_in = torch.from_numpy(d_patches).to(self.device).unsqueeze(1).float()
gt_patches_in = torch.from_numpy(gt_patches).to(self.device).unsqueeze(1).float()
gt_dis = torch.from_numpy(out_dis).to(self.device).float()
d_patches_in_or = d_patches_in
batch_size = c_patches_in.shape[0]
pred_deform, pred_dis = self.model(c_patches_in, d_patches_in)
center = torch.Tensor([self.patch_size,self.patch_size,self.patch_size,]).float().cuda().view(1,3).repeat(batch_size, 1)/2.0 -0.5
scale = torch.Tensor([1.0]).view(1,1).cuda().repeat(batch_size, 1)
angle = torch.Tensor([0, 0, 0]).float().view(1,3).cuda().repeat(batch_size, 1)
aff_matrix = get_affine_matrix3d(pred_deform[:,0:3]*(self.patch_size/2),center, scale, angle)
out_vox = warp_affine3d(d_patches_in_or, aff_matrix[:,:3,:], (self.patch_size,self.patch_size,\
self.patch_size,),)
loss_de = torch.sum(torch.square(out_vox - gt_patches_in),(1,2,3,4))/(torch.sum(gt_patches_in, (1,2,3,4))\
+ out_vox.sum((1,2,3,4)) - (out_vox*gt_patches_in).sum((1,2,3,4))+1e-5)
loss_de = torch.mean(loss_de)
iou_pred = (out_vox*gt_patches_in).sum((1,2,3,4))/(out_vox.sum((1,2,3,4)) \
+ gt_patches_in.sum((1,2,3,4)) - (out_vox*gt_patches_in).sum((1,2,3,4)) + 1e-5)
pred_dis = pred_dis[:, 0]
loss_dis = torch.mean(torch.square(pred_dis - gt_dis))
loss = loss_de + self.w_dis * loss_dis
loss.backward()
self.optimizer.step()
if self.mode=='test':
return
self.log_string("Epoch: [%d/%d] time: %.0f, loss_recons: %.5f, loss_dis: %.4f, iou: %.5f," % \
(epoch, training_epoch, time.time() - start_time, loss_de.item(), loss_dis.item(), \
torch.mean(iou_pred).item()))
if epoch%10==0 and self.mode=='train':
pred_shape, retrieved_patches, aligned_patches, = self.retrieve_to_full_shape_fromdis(\
self.gt_content[dxb], self.partial_content[dxb], input_in.cpu().numpy()[0][0], \
re_pairs_or, self.re_dis[dxb], self.re_trans_c[dxb], align=True,)
imgout_0 = np.full([self.real_size, self.real_size*6], 255, np.uint8)
xmin,xmax,ymin,ymax,zmin,zmax = self.pos_content[dxb]
tmpvox = self.recover_voxel(input_in.cpu().numpy()[0][0],xmin,xmax,ymin,ymax,zmin,zmax)
imgout_0[:,0:self.real_size] = self.voxel_renderer.render_img(tmpvox, self.sampling_threshold, \
self.render_view_id)
tmpvox = self.recover_voxel(self.partial_content[dxb],xmin,xmax,ymin,ymax,zmin,zmax)
imgout_0[:,self.real_size:self.real_size*2] = self.voxel_renderer.render_img(tmpvox, \
self.sampling_threshold, self.render_view_id)
tmpvox = self.recover_voxel(self.gt_content[dxb],xmin,xmax,ymin,ymax,zmin,zmax)
imgout_0[:,5*self.real_size:self.real_size*6] = self.voxel_renderer.render_img(tmpvox, \
self.sampling_threshold, self.render_view_id)
tmpvox = self.recover_voxel(pred_shape,xmin,xmax,ymin,ymax,zmin,zmax)
imgout_0[:,4*self.real_size:5*self.real_size] = self.voxel_renderer.render_img(tmpvox, \
self.sampling_threshold, self.render_view_id)
tmpvox = self.recover_voxel(retrieved_patches,xmin,xmax,ymin,ymax,zmin,zmax)
imgout_0[:,2*self.real_size:3*self.real_size] = self.voxel_renderer.render_img(tmpvox, \
self.sampling_threshold, self.render_view_id)
tmpvox = self.recover_voxel(aligned_patches,xmin,xmax,ymin,ymax,zmin,zmax)
imgout_0[:,3*self.real_size:4*self.real_size] = self.voxel_renderer.render_img(tmpvox, \
self.sampling_threshold, self.render_view_id)
cv2.imwrite(self.sample_dir+"/%s_%d.png"%(self.mode, epoch,), imgout_0)
if epoch%self.save_epoch==0 and self.mode=='train':
# if epoch%self.save_epoch==0 and (self.mode=='train' or config.continue_train):
self.save(epoch)
self.scheduler.step()
if self.mode=='train':
self.save(epoch)
def get_patch_pairs(self, gt_np, partial_np, coarse_np, c_locs, d_locs,re_trans, flip_list, align=True):
c_starts = c_locs*self.stride
d_starts = d_locs*self.stride
or_c_vox = np.zeros((c_starts.shape[0], self.patch_size, self.patch_size, self.patch_size))
or_d_vox = np.zeros((c_starts.shape[0], self.patch_size, self.patch_size, self.patch_size))
gt_vox = np.zeros((c_starts.shape[0], self.patch_size, self.patch_size, self.patch_size))
for i in range(c_starts.shape[0]):
gt_vox[i] = gt_np[c_starts[i, 0]:c_starts[i, 0]+self.patch_size, \
c_starts[i, 1]:c_starts[i, 1]+self.patch_size, c_starts[i, 2]:c_starts[i, 2]+self.patch_size]
or_c_vox[i] = coarse_np[c_starts[i, 0]:c_starts[i, 0]+self.patch_size, \
c_starts[i, 1]:c_starts[i, 1] + self.patch_size, c_starts[i, 2]:c_starts[i, 2]+self.patch_size]
d_vox = partial_np[d_starts[i, 0]:d_starts[i, 0]+self.patch_size, \
d_starts[i, 1]:d_starts[i, 1]+self.patch_size, d_starts[i, 2]:d_starts[i, 2]+self.patch_size]
if flip_list[i]==1:
d_vox = np.flip(d_vox, 2)
if align:
# d_vox = self.move_vox(d_vox, np.around(re_trans[i]))
mask_s = np.array(d_vox>0.3, np.float32)
mask_t = np.array(or_c_vox[i]>0.3, np.float32)
c_s = np.sum(self.patch_coord*np.expand_dims(mask_s, -1), axis=(0,1,2))/ (np.sum(mask_s, )+1e-5)
c_t = np.sum(self.patch_coord*np.expand_dims(mask_t, -1), axis=(0,1,2))/ (np.sum(mask_t, )+1e-5)
d_vox = self.move_vox(d_vox, np.around(c_t - c_s).astype(np.int32))
or_d_vox[i] = d_vox
return or_c_vox, or_d_vox, gt_vox
def retrieve_to_full_shape_fromdis(self, gt_np, partial_np, coarse_np, re_pairs, re_dis, re_trans, \
align=True, mask_in=None, dump_deform=False):
print('retrieve to full shape...')
# self.model.eval()
c_locs = re_pairs[:, 0]
# c_locs_mask = (c_locs % (self.patch_size//self.stride)).sum(1)
c_locs_mask = (c_locs % 4).sum(1)
c_locs_mask2 = (c_locs[:,0]!=c_locs[:,0].max())*(c_locs[:,1]!=c_locs[:,1].max())*(c_locs[:,2]!=c_locs[:,2].max())
c_locs_mask = c_locs_mask2.astype(np.int32)*c_locs_mask
c_ids = np.where(c_locs_mask==0)[0]
if dump_deform:
c_ids = range(c_locs.shape[0])
c_locs_final = c_locs[c_ids, :].astype(np.int32)
re_pairs = re_pairs[c_ids, :, :].astype(np.int32)
re_dis = re_dis[c_ids, :]
flip_list = re_trans[c_ids, :, 3].astype(np.int32)