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test_RGBD_ms.py
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test_RGBD_ms.py
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##########################
# Test normal estimation
# RGBD input
# coupled with train_RGBD_ms.py
# Jin Zeng, 20181031
#########################
import sys, os
import torch
import argparse
import timeit
import numpy as np
import scipy.misc as misc
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, change_channel
from models.eval import eval_normal_pixel, eval_print
from loader.loader_utils import png_reader_32bit, png_reader_uint8
def test(args):
# Setup Model
# Setup the fusion model (RGB+Depth)
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()))
# Setup the map model
if args.arch_map == 'map_conv':
model_name_map = args.arch_map
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)
evalloader = data.DataLoader(v_loader, batch_size=1)
print("Finish Loader Setup")
model_F.cuda()
model_F.eval()
if args.arch_map == 'map_conv':
model_map.cuda()
model_map.eval()
sum_mean, sum_median, sum_small, sum_mid, sum_large, sum_num = [], [], [], [], [], []
evalcount = 0
with torch.no_grad():
for i_val, (images_val, labels_val, masks_val, valids_val, depthes_val, meshdepthes_val) in tqdm(
enumerate(evalloader)):
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())
if args.arch_map == 'map_conv':
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)
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)
outputs_norm = change_channel(outputs_norm)
labels_val_norm = (labels_val_norm + 1) / 2
labels_val_norm = change_channel(labels_val_norm)
# if (i_val+1)%10 == 0:
misc.imsave(pjoin(args.testset_out_path, "{}_MS_hyb.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)
# 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)
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')
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))
# end of dataset test
else:
if os.path.isdir(args.out_path) == False:
os.mkdir(args.out_path)
print("Read Input Image from : {}".format(args.img_path))
for i in os.listdir(args.img_path):
if not i.endswith('.jpg'):
continue
print i
input_f = args.img_path + i
depth_f = args.depth_path + i[:-4] + '.png'
output_f = args.out_path + i[:-4] + '_rgbd.png'
img = misc.imread(input_f)
orig_size = img.shape[:-1]
if args.img_rot:
img = np.transpose(img, (1, 0, 2))
img = np.flipud(img)
img = misc.imresize(img, (args.img_cols, args.img_rows)) # Need resize the image to model inputsize
else:
img = misc.imresize(img, (args.img_rows, args.img_cols)) # Need resize the image to model inputsize
img = img.astype(np.float)
if args.img_norm:
img = (img - 128) / 255
# NHWC -> NCHW
img = img.transpose(2, 0, 1)
img = np.expand_dims(img, 0)
img = torch.from_numpy(img).float()
if args.img_rot:
depth = png_reader_32bit(depth_f, (args.img_rows, args.img_cols))
depth = np.transpose(depth, (1, 0))
depth = np.flipud(depth)
# valid = png_reader_uint8(mask_f, (args.img_rows,args.img_cols))
# valid = np.transpose(valid, (1,0))
# valid = np.flipud(valid)
else:
depth = png_reader_32bit(depth_f, (args.img_rows, args.img_cols))
# valid = png_reader_uint8(mask_f, (args.img_rows,args.img_cols))
depth = depth.astype(float)
# Please change to the scale so that scaled_depth=1 corresponding to real 10m depth
# matterpot depth=depth/40000 scannet depth=depth/10000
depth = depth / (args.d_scale)
if depth.ndim == 3: # to dim 2
depth = depth[:, :, 0]
# if valid.ndim == 3: #to dim 2
# valid = valid[:,:,0]
# valid = 1-depth
# valid[valid>1] = 1
valid = (depth > 0.0001).astype(float)
# valid = depth.astype(float)
depth = depth[np.newaxis, :, :]
depth = np.expand_dims(depth, 0)
valid = np.expand_dims(valid, 0)
depth = torch.from_numpy(depth).float()
valid = torch.from_numpy(valid).float()
if torch.cuda.is_available():
model_F.cuda()
model_F.eval()
if args.arch_map == 'map_conv':
model_map.cuda()
model_map.eval()
images = Variable(img.contiguous().cuda())
depth = Variable(depth.contiguous().cuda())
valid = Variable(valid.contiguous().cuda())
else:
images = Variable(img)
depth = Variable(depth)
valid = Variable(valid)
with torch.no_grad():
if args.arch_map == 'map_conv':
outputs_valid = model_map(torch.cat((depth, valid[:, np.newaxis, :, :]), dim=1))
outputs, outputs1, outputs2, outputs3, output_d = model_F(images, depth,
outputs_valid.squeeze(1))
else:
outputs, outputs1, outputs2, outputs3, output_d = model_F(images, depth, outputs_valid)
outputs_norm = norm_imsave(outputs)
outputs_norm = np.squeeze(outputs_norm.data.cpu().numpy(), axis=0)
# outputs_norm = misc.imresize(outputs_norm, orig_size)
outputs_norm = change_channel(outputs_norm)
misc.imsave(output_f, outputs_norm)
print("Complete")
# end of test on no dataset images
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='map_conv',
help='Architecture for confidence map to use [\'mask, map_conv 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='matterport',
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='2',
help='Checkpoint index [\'1,2,3, etc\']')
parser.add_argument('--img_rows', nargs='?', type=int, default=256,
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/mt_clean_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('--d_scale', nargs='?', type=int, default=40000,
help='Depth scale for depth input. Set the scale to make the 1 in scaled depth equal to 10m.\
Only valid testing using image folder')
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=False)
args = parser.parse_args()
test(args)