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test_RGBD_ms_sc.py
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test_RGBD_ms_sc.py
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##########################
# Test normal estimation
# RGBD input
# coupled with train_RGBD_ms.py
# Jin Zeng, 20181109
#########################
import sys, os
import torch
import argparse
import timeit
import numpy as np
import scipy.misc as misc
import scipy.io as sio
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from os.path import join as pjoin
import scipy.io as io
from torch.autograd import Variable
from torch.utils import data
from tqdm import tqdm
from models import get_model, get_lossfun
from loader import get_data_path, get_loader
from pre_trained import get_premodel
from utils import norm_imsave
from models.eval import eval_normal_pixel, eval_normal_detail, eval_print, eval_mask_resize
from loader.loader_utils import png_reader_32bit, png_reader_uint8
# from sync_batchnorm import DataParallelWithCallback
def test(args):
# Setup Model
model_name_F = args.arch_F
model_F = get_model(model_name_F,True) # concat and output
model_F = torch.nn.DataParallel(model_F, device_ids=range(torch.cuda.device_count()))
if args.arch_map == 'map_conv' or args.arch_map == 'hybrid':
model_name_map = 'map_conv'
model_map = get_model(model_name_map,True) # concat and output
model_map = torch.nn.DataParallel(model_map, device_ids=range(torch.cuda.device_count()))
if args.model_full_name != '':
# Use the full name of model to load
print("Load training model: " + args.model_full_name)
checkpoint = torch.load(pjoin(args.model_savepath, args.model_full_name))
model_F.load_state_dict(checkpoint['model_F_state'])
model_map.load_state_dict(checkpoint["model_map_state"])
# Setup image
if args.imgset:
print("Test on dataset: {}".format(args.dataset))
data_loader = get_loader(args.dataset)
data_path = get_data_path(args.dataset)
v_loader = data_loader(data_path, split=args.test_split, img_size=(args.img_rows,args.img_cols), img_norm=args.img_norm,mode='seg')
evalloader = data.DataLoader(v_loader, batch_size=1)
print("Finish Loader Setup")
model_F.cuda()
model_F.eval()
if args.arch_map == 'map_conv' or args.arch_map == 'hybrid':
model_map.cuda()
model_map.eval()
sum_mean, sum_median, sum_small, sum_mid, sum_large, sum_num = [], [], [], [], [], []
sum_mean_b, sum_median_b, sum_small_b, sum_mid_b, sum_large_b, sum_num_b = [], [], [], [], [], []
sum_mean_s, sum_median_s, sum_small_s, sum_mid_s, sum_large_s, sum_num_s = [], [], [], [], [], []
sum_mean_c, sum_median_c, sum_small_c, sum_mid_c, sum_large_c, sum_num_c = [], [], [], [], [], []
evalcount = 0
with torch.no_grad():
for i_val, (images_val, labels_val, masks_val, valids_val, depthes_val, segment_val) in tqdm(enumerate(evalloader)):
# if i_val>10:
# break
images_val = Variable(images_val.contiguous().cuda())
labels_val = Variable(labels_val.contiguous().cuda())
masks_val = Variable(masks_val.contiguous().cuda())
valids_val = Variable(valids_val.contiguous().cuda())
depthes_val = Variable(depthes_val.contiguous().cuda())
segment_val = Variable(segment_val.contiguous().cuda())
# Bed:11 1191 494 786 1349
# Sofa:6 1313
# Chair:2 10 23 74 885 1184 1291 1338
segment_bed = torch.eq(segment_val,11)+torch.eq(segment_val,1191)+torch.eq(segment_val,494)+torch.eq(segment_val,786)+torch.eq(segment_val,1349)
segment_sofa = torch.eq(segment_val,6)+torch.eq(segment_val,1313)
segment_chair = torch.eq(segment_val,2)+torch.eq(segment_val,10)+torch.eq(segment_val,23)+torch.eq(segment_val,74)+torch.eq(segment_val,885)+torch.eq(segment_val,1184)+torch.eq(segment_val,1291)+torch.eq(segment_val,1338)
if segment_val.shape != masks_val.shape:
segment_bed = eval_mask_resize(segment_bed, args.img_rows, args.img_cols)
segment_sofa = eval_mask_resize(segment_sofa, args.img_rows, args.img_cols)
segment_chair = eval_mask_resize(segment_chair, args.img_rows, args.img_cols)
if args.arch_map == 'map_conv' or args.arch_map == 'hybrid':
outputs_valid = model_map(torch.cat((depthes_val, valids_val[:,np.newaxis,:,:]), dim=1))
outputs, outputs1, outputs2, outputs3,output_d = model_F(images_val, depthes_val, outputs_valid.squeeze(1))
else:
outputs, outputs1, outputs2, outputs3,output_d = model_F(images_val, depthes_val, valids_val)
outputs_n, pixelnum, mean_i, median_i, small_i, mid_i, large_i = eval_normal_pixel(outputs, labels_val, masks_val)
masks_bed_val = segment_bed.to(torch.float64)*masks_val
masks_sofa_val = segment_sofa.to(torch.float64)*masks_val
masks_chair_val = segment_chair.to(torch.float64)*masks_val
_, pixelnum_b, mean_i_b, median_i_b, small_i_b, mid_i_b, large_i_b = eval_normal_pixel(outputs, labels_val, masks_bed_val)
_, pixelnum_s, mean_i_s, median_i_s, small_i_s, mid_i_s, large_i_s = eval_normal_pixel(outputs, labels_val, masks_sofa_val)
_, pixelnum_c, mean_i_c, median_i_c, small_i_c, mid_i_c, large_i_c = eval_normal_pixel(outputs, labels_val, masks_chair_val)
# outputs_norm = np.squeeze(outputs_n.data.cpu().numpy(), axis=0)
# labels_val_norm = np.squeeze(labels_val.data.cpu().numpy(), axis=0)
# images_val = np.squeeze(images_val.data.cpu().numpy(), axis=0)
# images_val = images_val+0.5
# images_val = images_val.transpose(1, 2, 0)
# depthes_val = np.squeeze(depthes_val.data.cpu().numpy(), axis=0)
# depthes_val = np.transpose(depthes_val, [1,2,0])
# depthes_val = np.repeat(depthes_val, 3, axis = 2)
# masks_bed_val = np.squeeze(masks_bed_val.data.cpu().numpy(), axis=0)
# masks_sofa_val = np.squeeze(masks_sofa_val.data.cpu().numpy(), axis=0)
# masks_chair_val = np.squeeze(masks_chair_val.data.cpu().numpy(), axis=0)
# outputs_valid = np.squeeze(outputs_valid.data.cpu().numpy(), axis=0)
# outputs_valid = np.transpose(outputs_valid, [1,2,0])
# outputs_valid = np.repeat(outputs_valid, 3, axis = 2)
# if (i_val+1)%1 == 0:
# misc.imsave(pjoin(args.testset_out_path, "{}_fms_hybrid2.png".format(i_val+1)), outputs_norm)
# misc.imsave(pjoin(args.testset_out_path, "{}_fms1_l1.png".format(i_val+1)), outputs_norm1)
# misc.imsave(pjoin(args.testset_out_path, "{}_fms2_l1.png".format(i_val+1)), outputs_norm2)
# misc.imsave(pjoin(args.testset_out_path, "{}_fms3_l1.png".format(i_val+1)), outputs_norm3)
# misc.imsave(pjoin(args.testset_out_path, "{}_fmsd_l1.png".format(i_val+1)), outputs_normd)
# misc.imsave(pjoin(args.testset_out_path, "{}_d_l1_imgout.png".format(i_val+1)), outputs_norm)
# misc.imsave(pjoin(args.testset_out_path, "{}_gt.png".format(i_val+1)), labels_val_norm)
# misc.imsave(pjoin(args.testset_out_path, "{}_in.jpg".format(i_val+1)), images_val)
# # misc.imsave(pjoin(args.testset_out_path, "{}_depth.png".format(i_val+1)), depthes_val)
# misc.imsave(pjoin(args.testset_out_path, "{}_bed.png".format(i_val+1)), masks_bed_val)
# misc.imsave(pjoin(args.testset_out_path, "{}_sofa.png".format(i_val+1)), masks_sofa_val)
# misc.imsave(pjoin(args.testset_out_path, "{}_chair.png".format(i_val+1)), masks_chair_val)
# misc.imsave(pjoin(args.testset_out_path, "{}_ms_conf.png".format(i_val+1)), outputs_valid)
# if i_val == 0:
# outputs_mat = outputs_n.data.cpu().numpy()
# else:
# outputs_mat = np.concatenate((outputs_mat,outputs_n.data.cpu().numpy()), axis=0)
# accumulate the metrics in matrix
if ((np.isnan(mean_i))|(np.isinf(mean_i)) == False):
sum_mean.append(mean_i)
sum_median.append(median_i)
sum_small.append(small_i)
sum_mid.append(mid_i)
sum_large.append(large_i)
sum_num.append(pixelnum)
sum_mean_b.append(mean_i_b)
sum_median_b.append(median_i_b)
sum_small_b.append(small_i_b)
sum_mid_b.append(mid_i_b)
sum_large_b.append(large_i_b)
sum_num_b.append(pixelnum_b)
sum_mean_s.append(mean_i_s)
sum_median_s.append(median_i_s)
sum_small_s.append(small_i_s)
sum_mid_s.append(mid_i_s)
sum_large_s.append(large_i_s)
sum_num_s.append(pixelnum_s)
sum_mean_c.append(mean_i_c)
sum_median_c.append(median_i_c)
sum_small_c.append(small_i_c)
sum_mid_c.append(mid_i_c)
sum_large_c.append(large_i_c)
sum_num_c.append(pixelnum_c)
evalcount+=1
if (i_val+1) % 10 == 0:
print("Iteration %d Evaluation Loss: mean %.4f, median %.4f, 11.25 %.4f, 22.5 %.4f, 30 %.4f" % (i_val+1,
mean_i, median_i, small_i, mid_i, large_i))
# Summarize the result
eval_print(sum_mean, sum_median, sum_small, sum_mid, sum_large, sum_num, item='Pixel-Level')
eval_print(sum_mean_b, sum_median_b, sum_small_b, sum_mid_b, sum_large_b, sum_num_b, item='Bed-pixel')
eval_print(sum_mean_s, sum_median_s, sum_small_s, sum_mid_s, sum_large_s, sum_num_s, item='Sofa-pixel')
eval_print(sum_mean_c, sum_median_c, sum_small_c, sum_mid_c, sum_large_c, sum_num_c, item='Chair-pixel')
avg_mean = sum(sum_mean)/evalcount
sum_mean.append(avg_mean)
avg_median = sum(sum_median)/evalcount
sum_median.append(avg_median)
avg_small = sum(sum_small)/evalcount
sum_small.append(avg_small)
avg_mid = sum(sum_mid)/evalcount
sum_mid.append(avg_mid)
avg_large = sum(sum_large)/evalcount
sum_large.append(avg_large)
print("evalnum is %d, Evaluation Image-Level Mean Loss: mean %.4f, median %.4f, 11.25 %.4f, 22.5 %.4f, 30 %.4f" % (evalcount,
avg_mean, avg_median, avg_small, avg_mid, avg_large))
sum_matrix = np.transpose([sum_mean,sum_median,sum_small,sum_mid,sum_large])
if args.model_full_name != '':
sum_file = args.model_full_name[:-4] + '.csv'
np.savetxt(pjoin(args.model_savepath,sum_file), sum_matrix, fmt='%.6f', delimiter=',')
print("Saving to %s" % (sum_file))
# save normal output
# sio.savemat('./result/scannet/RGBD_map.mat', {'outputs_mat':outputs_mat})
# end of dataset test
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Params')
parser.add_argument('--arch_RGB', nargs='?', type=str, default='vgg_16_in',
help='Architecture for RGB to use [\'vgg_16,vgg_16_in etc\']')
parser.add_argument('--arch_D', nargs='?', type=str, default='unet_3_mask_in',
help='Architecture for Depth to use [\'unet_3, unet_3_mask, unet_3_mask_in etc\']')
parser.add_argument('--arch_F', nargs='?', type=str, default='fconv_ms',
help='Architecture for Fusion to use [\'fconv,fconv_in, fconv_ms etc\']')
parser.add_argument('--arch_map', nargs='?', type=str, default='hybrid',
help='Architecture for confidence map to use [\'mask, map_conv, hybrid etc\']')
parser.add_argument('--model_savepath', nargs='?', type=str, default='./checkpoint/FCONV_MS',
help='Path for model saving [\'checkpoint etc\']')
parser.add_argument('--model_full_name', nargs='?', type=str, default='',
help='The full name of the model to be tested.')
parser.add_argument('--dataset', nargs='?', type=str, default='scannet',
help='Dataset to use [\'nyuv2, matterport, scannet, etc\']')
parser.add_argument('--test_split', nargs='?', type=str, default='', help='The split of dataset in testing')
parser.add_argument('--loss', nargs='?', type=str, default='l1',
help='Loss type: cosine, l1')
parser.add_argument('--model_num', nargs='?', type=str, default='1',
help='Checkpoint index [\'1,2,3, etc\']')
parser.add_argument('--img_rows', nargs='?', type=int, default=240,
help='Height of the input image, 256(mt), 240(nyu)')
parser.add_argument('--img_cols', nargs='?', type=int, default=320,
help='Width of the input image, 320(yinda and nyu)')
parser.add_argument('--testset', dest='imgset', action='store_true',
help='Test on set from dataloader, decided by --dataset | True by default')
parser.add_argument('--no_testset', dest='imgset', action='store_false',
help='Test on single image | True by default')
parser.set_defaults(imgset=True)
parser.add_argument('--testset_out_path', nargs='?', type=str, default='./result/sc_small',
help='Path of the output normal')
parser.add_argument('--img_path', nargs='?', type=str, default='../Depth2Normal/Dataset/normal/',
help='Path of the input image')
parser.add_argument('--depth_path', nargs='?', type=str, default='../Depth2Normal/Dataset/normal/',
help='Path of the input image, mt_data_clean!!!!!!!!!')
parser.add_argument('--ir_path', nargs='?', type=str, default='../Depth2Normal/Dataset/ir_mask/',
help='Path of the input image, mt_data_clean!!!!!!!!!')
parser.add_argument('--out_path', nargs='?', type=str, default='../Depth2Normal/Dataset/normal/',
help='Path of the output normal')
parser.add_argument('--img_norm', dest='img_norm', action='store_true',
help='Enable input image scales normalization [0, 1] | True by default')
parser.add_argument('--no-img_norm', dest='img_norm', action='store_false',
help='Disable input image scales normalization [0, 1] | True by default')
parser.set_defaults(img_norm=True)
parser.add_argument('--img_rotate', dest='img_rot', action='store_true',
help='Enable input image transpose | False by default')
parser.add_argument('--no-img_rotate', dest='img_rot', action='store_false',
help='Disable input image transpose | False by default')
parser.set_defaults(img_rot=True)
args = parser.parse_args()
test(args)