def test(dataloader, model, log):
model.eval()
Fs = AverageMeter()
Js = AverageMeter()
n_b = len(dataloader)
log.info("Start testing.")
for b_i, (images_rgb, annotations) in enumerate(dataloader):
images_rgb = [r.cuda() for r in images_rgb]
annotations = [q.cuda() for q in annotations]
N = len(images_rgb)
for i in range(N - 1):
rgb_0 = images_rgb[i]
rgb_1 = images_rgb[i + 1]
if i == 0:
anno_0 = annotations[i]
else:
anno_0 = output
anno_1 = annotations[i + 1]
_, _, h, w = anno_0.size()
with torch.no_grad():
output = model(rgb_0, anno_0, rgb_1)
output = F.interpolate(output, (h, w), mode='bilinear')
output = torch.argmax(output, 1, keepdim=True).float()
max_class = anno_1.max()
js, fs = [], []
for classid in range(1, max_class + 1):
obj_true = (anno_1 == classid).cpu().numpy()[0, 0]
obj_pred = (output == classid).cpu().numpy()[0, 0]
f = db_eval_boundary(obj_true, obj_pred)
j = db_eval_iou(obj_true, obj_pred)
fs.append(f)
js.append(j)
f = np.mean(fs)
j = np.mean(js)
Fs.update(f)
Js.update(j)
info = '\t'.join(
['Js: ({:.3f}). Fs: ({:.3f}).'.format(Js.avg, Fs.avg)])
log.info('[{}/{}] {}'.format(b_i, n_b, info))
def test(dataloader, model, log):
model.eval()
torch.backends.cudnn.benchmark = True
Fs = AverageMeter()
Js = AverageMeter()
n_b = len(dataloader)
log.info("Start testing.")
for b_i, (images_rgb, annotations) in enumerate(dataloader):
fb = AverageMeter(); jb = AverageMeter()
images_rgb = [r.cuda() for r in images_rgb]
annotations = [q.cuda() for q in annotations]
N = len(images_rgb)
outputs = [annotations[0].contiguous()]
for i in range(N-1):
mem_gap = 2
# ref_index = [i]
if args.ref == 0:
ref_index = list(filter(lambda x: x <= i, [0, 5])) + list(filter(lambda x:x>0,range(i,i-mem_gap*3,-mem_gap)))[::-1]
ref_index = sorted(list(set(ref_index)))
elif args.ref == 1:
ref_index = [0] + list(filter(lambda x: x > 0, range(i, i - mem_gap * 3, -mem_gap)))[::-1]
elif args.ref == 2:
ref_index = [i]
else:
raise NotImplementedError
rgb_0 = [images_rgb[ind] for ind in ref_index]
rgb_1 = images_rgb[i+1]
anno_0 = [outputs[ind] for ind in ref_index]
anno_1 = annotations[i+1]
_, _, h, w = anno_0[0].size()
max_class = anno_1.max()
with torch.no_grad():
_output = model(rgb_0, anno_0, rgb_1, ref_index, i+1)
_output = F.interpolate(_output, (h,w), mode='bilinear')
output = torch.argmax(_output, 1, keepdim=True).float()
outputs.append(output)
js, fs = [], []
for classid in range(1, max_class + 1):
obj_true = (anno_1 == classid).cpu().numpy()[0, 0]
obj_pred = (output == classid).cpu().numpy()[0, 0]
f = db_eval_boundary(obj_true, obj_pred)
j = db_eval_iou(obj_true, obj_pred)
fs.append(f); js.append(j)
Fs.update(f); Js.update(j)
###
folder = os.path.join(args.savepath,'benchmark')
if not os.path.exists(folder): os.mkdir(folder)
output_folder = os.path.join(folder, D.catnames[b_i].strip())
if not os.path.exists(output_folder):
os.mkdir(output_folder)
pad = ((0,0), (0,0))
if i == 0:
# output first mask
output_file = os.path.join(output_folder, '%s.png' % str(0).zfill(5))
out_img = anno_0[0][0, 0].cpu().numpy().astype(np.uint8)
out_img = np.pad(out_img, pad, 'edge').astype(np.uint8)
imwrite_indexed(output_file, out_img )
output_file = os.path.join(output_folder, '%s.png' % str(i + 1).zfill(5))
out_img = output[0, 0].cpu().numpy().astype(np.uint8)
out_img = np.pad(out_img, pad, 'edge').astype(np.uint8)
imwrite_indexed(output_file, out_img)
info = '\t'.join(['Js: ({:.3f}). Fs: ({:.3f}).'
.format(Js.avg, Fs.avg)])
log.info('[{}/{}] {}'.format( b_i, n_b, info ))
return Js.avg
def test(dataloader, model, log):
model.eval()
Fs = AverageMeter()
Js = AverageMeter()
n_b = len(dataloader)
log.info("Start testing.")
for b_i, (images_rgb, annotations) in enumerate(dataloader):
images_rgb = [r.cuda() for r in images_rgb]
annotations = [q.cuda() for q in annotations]
N = len(images_rgb)
for i in range(N - 1):
rgb_0 = images_rgb[i]
rgb_1 = images_rgb[i + 1]
if i == 0:
anno_0 = annotations[i]
else:
anno_0 = output
anno_1 = annotations[i + 1]
_, _, h, w = anno_0.size()
with torch.no_grad():
output = model(rgb_0, anno_0, rgb_1)
output = F.interpolate(output, (h, w), mode='bilinear')
output = torch.argmax(output, 1, keepdim=True).float()
max_class = anno_1.max()
js, fs = [], []
for classid in range(1, max_class + 1):
obj_true = (anno_1 == classid).cpu().numpy()[0, 0]
obj_pred = (output == classid).cpu().numpy()[0, 0]
f = db_eval_boundary(obj_true, obj_pred)
j = db_eval_iou(obj_true, obj_pred)
fs.append(f)
js.append(j)
###
folder = os.path.join(args.savepath, 'benchmark')
if not os.path.exists(folder): os.mkdir(folder)
output_folder = os.path.join(folder, D.catnames[b_i].strip())
if not os.path.exists(output_folder):
os.mkdir(output_folder)
pad = ((0, 0), (3, 3)) if anno_0.size(3) < 1152 else ((0, 0), (0,
0))
if i == 0:
# output first mask
output_file = os.path.join(output_folder,
'%s.png' % str(0).zfill(5))
out_img = anno_0[0, 0].cpu().numpy().astype(np.uint8)
out_img = np.pad(out_img, pad, 'edge').astype(np.uint8)
imwrite_indexed(output_file, out_img)
output_file = os.path.join(output_folder,
'%s.png' % str(i + 1).zfill(5))
out_img = output[0, 0].cpu().numpy().astype(np.uint8)
out_img = np.pad(out_img, pad, 'edge').astype(np.uint8)
imwrite_indexed(output_file, out_img)
f = np.mean(fs)
j = np.mean(js)
Fs.update(f)
Js.update(j)
info = '\t'.join(
['Js: ({:.3f}). Fs: ({:.3f}).'.format(Js.avg, Fs.avg)])
log.info('[{}/{}] {}'.format(b_i, n_b, info))
def test(dataloader, model, log):
model.eval()
torch.backends.cudnn.benchmark = True
Fs = AverageMeter()
Js = AverageMeter()
n_b = len(dataloader)
log.info("Start testing.")
for b_i, (images_rgb, annotations) in enumerate(dataloader):
# if b_i<=27: continue
fb = AverageMeter()
jb = AverageMeter()
images_rgb = [r.cuda() for r in images_rgb]
annotations = [q.cuda() for q in annotations]
N = len(images_rgb)
outputs = [annotations[0].contiguous()]
for i in range(N - 1):
mem_gap = 2
# ref_index = [i]
if args.ref == 0:
ref_index = list(filter(lambda x: x <= i, [0, 5])) + list(
filter(lambda x: x > 0, range(i, i - mem_gap * 3,
-mem_gap)))[::-1]
ref_index = sorted(list(set(ref_index)))
elif args.ref == 1:
ref_index = [0] + list(
filter(lambda x: x > 0, range(i, i - mem_gap * 3,
-mem_gap)))[::-1]
elif args.ref == 2:
ref_index = [i]
else:
raise NotImplementedError
rgb_0 = [images_rgb[ind] for ind in ref_index]
rgb_1 = images_rgb[i + 1]
anno_0 = [outputs[ind] for ind in ref_index]
anno_1 = annotations[i + 1]
_, _, h, w = anno_0[0].size()
max_class = anno_1.max()
with torch.no_grad():
_output, _ = model(rgb_0, anno_0, rgb_1, ref_index, i + 1)
# _output, _ = model(rgb_0, anno_0, rgb_0[0], ref_index, i+1)
# rgb_0: list(5),[1,3,480,910]
# anno_0: list(5),[1,1,480,910]
# rgb_1: tensor, [1,3,480,910]
# warp_grid = F.interpolate(_.permute(0,3,1,2), (h,w), mode='bilinear')
# _output = F.grid_sample(torch.cat(anno_0,0).float(), warp_grid.permute(0,2,3,1), mode='bilinear')
# output = F.interpolate(torch.mean(_output,0,keepdim=True), (h,w), mode='bilinear')
# outputs.append(torch.round(output).long())
_output = F.interpolate(_output, (h, w), mode='bilinear')
output = torch.argmax(_output, 1, keepdim=True).float()
outputs.append(output)
if i > 15:
# draw flow from grid with cv2.line
from functional.data import dataset
from functional.utils import util
import cv2
warp_grid = F.interpolate(_, (h, w), mode='bilinear')
# warp_grid = F.interpolate(_.permute(0,3,1,2), (h,w), mode='bilinear')
# draw gate
# util.save_image_tensor2cv2(warp_grid[0,...].unsqueeze(0), 'test.png', cv2.COLOR_GRAY2RGB)
# draw grid or flow
grid_x = warp_grid.permute(0, 2, 3, 1)[..., [0]]
grid_y = warp_grid.permute(0, 2, 3, 1)[..., [1]]
b = warp_grid.shape[0]
# regular grid / [-1,1] normalized
grid_X, grid_Y = np.meshgrid(np.linspace(
-1, 1, w), np.linspace(
-1, 1,
h)) # grid_X & grid_Y : feature_H x feature_W
grid_X = torch.tensor(grid_X,
dtype=torch.float,
requires_grad=False,
device=warp_grid.device)
grid_Y = torch.tensor(grid_Y,
dtype=torch.float,
requires_grad=False,
device=warp_grid.device)
grid_X = grid_X.expand(
b, h, w) # x coordinates of a regular grid
grid_X = grid_X.unsqueeze(1) # b x 1 x h x w
grid_Y = grid_Y.expand(
b, h, w) # y coordinates of a regular grid
grid_Y = grid_Y.unsqueeze(1)
from functional.utils.track_vis import draw_track_trace, draw_trace_from_flow
# draw pic from grid
# source = torch.cat(((grid_Y.permute(0,2,3,1)+1)/2*h,(grid_X.permute(0,2,3,1)+1)/2*w),3).cpu()
# target = torch.cat(((grid_y+1)/2*h,(grid_x+1)/2*w),3).cpu()
# frame = (torch.cat(rgb_0,0)+1)/2*255
# imgs_dbg = draw_track_trace(np.array(source),np.array(target),np.array(frame.permute(0,2,3,1).cpu()))
# util.save_image_tensor2cv2(torch.tensor(imgs_dbg[0]/255).permute(2,0,1).unsqueeze(0), 'test1.png', cv2.COLOR_Lab2BGR)
# util.save_image_tensor2cv2((rgb_1+1)/2, 'test2.png', cv2.COLOR_Lab2BGR)
# draw pic from flow
source = torch.cat(
((grid_Y + 1) / 2 * h, (grid_X + 1) / 2 * w),
1).permute(0, 2, 3, 1).cpu()
target = torch.cat(
((warp_grid[:, 1, ...].unsqueeze(1) + grid_Y + 1) / 2 *
h, (warp_grid[:, 0, ...].unsqueeze(1) + grid_X + 1) /
2 * w), 1).cpu()
target = target.permute(0, 2, 3, 1)
# frame = torch.zeros((b,3,h,w))
frame = (torch.cat(rgb_0, 0) + 1) / 2 * 255
imgs_dbg = draw_track_trace(
np.array(source), np.array(target),
np.array(frame.permute(0, 2, 3, 1).cpu()))
util.save_image_tensor2cv2(
torch.tensor(imgs_dbg[0] / 255).permute(
2, 0, 1).unsqueeze(0), 'test.png')
# draw dense flow map
from functional.utils.flow_vis import flow_to_color
tmp = flow_to_color(
np.array((source[0, ...] - target[0, ...]).cpu()))
util.save_image_tensor2cv2(
torch.tensor(tmp).permute(2, 0, 1).unsqueeze(0),
'test.png')
# warp_grid = F.interpolate(_.permute(0,3,1,2), (h,w), mode='bilinear')
# pred_anno_1 = F.grid_sample(torch.cat(rgb_0,0), warp_grid.permute(0,2,3,1), mode='bilinear')
# util.save_image_tensor2cv2((rgb_0[0]+1)/2, 'test.png', cv2.COLOR_LAB2BGR)
# util.save_image_tensor2cv2((_output+1)/2, 'test.png', cv2.COLOR_LAB2BGR)
# util.save_image_tensor2cv2(torch.sum(anno_0[0], 1,keepdim=True), 'test.png', cv2.COLOR_GRAY2RGB)
# util.save_image_tensor2cv2(anno_1[:,1,...].unsqueeze(1), 'test.png', cv2.COLOR_GRAY2RGB)
# util.save_image_tensor2cv2(_[:,0,...].unsqueeze(1), 'test.png', cv2.COLOR_GRAY2RGB)
# util.save_image_tensor2cv2(torch.tensor(_).permute(2,0,1).unsqueeze(0), 'test.png', cv2.COLOR_BGR2RGB)
# util.save_image_tensor2cv2(torch.tensor((frame[0]/frame[0].max()).transpose(0,3,1,2)), 'test.png', cv2.COLOR_GRAY2RGB)
# util.save_image_tensor2cv2(torch.tensor((frame/frame[0].max()).transpose(0,3,1,2)), 'test.png', cv2.COLOR_GRAY2RGB)
# util.save_image_tensor2cv2(torch.tensor((mask/mask[0].max()).transpose(0,3,1,2)), 'test.png', cv2.COLOR_GRAY2RGB)
# util.save_image_tensor2cv2(torch.tensor(tmp).permute(2,0,1).unsqueeze(0), 'test.png')
# util.save_image_tensor2cv2(_[1,...].unsqueeze(0), 'test.png', cv2.COLOR_GRAY2RGB)
# util.save_image_tensor2cv2((pred_anno_1[0,...].unsqueeze(0)+1)/2, 'test.png', cv2.COLOR_Lab2BGR)
# util.save_image_tensor2cv2(dataset.UnNormalize()(rgb_0[0]), 'test.png', cv2.COLOR_Lab2BGR)
# util.save_image_tensor2cv2((rgb_1+1)/2, 'test.png', cv2.COLOR_Lab2BGR)
js, fs = [], []
for classid in range(1, max_class + 1):
obj_true = (anno_1 == classid).cpu().numpy()[0, 0]
obj_pred = (output == classid).cpu().numpy()[0, 0]
f = db_eval_boundary(obj_true, obj_pred)
j = db_eval_iou(obj_true, obj_pred)
fs.append(f)
js.append(j)
Fs.update(f)
Js.update(j)
###
folder = os.path.join(args.savepath, 'benchmark')
if not os.path.exists(folder): os.mkdir(folder)
output_folder = os.path.join(folder, D.catnames[b_i].strip())
if not os.path.exists(output_folder):
os.mkdir(output_folder)
pad = ((0, 0), (0, 0))
if i == 0:
# output first mask
output_file = os.path.join(output_folder,
'%s.png' % str(0).zfill(5))
out_img = anno_0[0][0, 0].cpu().numpy().astype(np.uint8)
out_img = np.pad(out_img, pad, 'edge').astype(np.uint8)
imwrite_indexed(output_file, out_img)
output_file = os.path.join(output_folder,
'%s.png' % str(i + 1).zfill(5))
out_img = output[0, 0].cpu().numpy().astype(np.uint8)
out_img = np.pad(out_img, pad, 'edge').astype(np.uint8)
imwrite_indexed(output_file, out_img)
info = '\t'.join(
['Js: ({:.3f}). Fs: ({:.3f}).'.format(Js.avg, Fs.avg)])
log.info('[{}/{}] {}'.format(b_i, n_b, info))
return Js.avg