def infer(self, model, pred_dir, patch_size, overlap, ext='_mask', file_ext='png', visualize=False,
densecrf=False, crf_params=None):
if isinstance(model, list) or isinstance(model, tuple):
lbl_margin = model[0].lbl_margin
else:
lbl_margin = model.lbl_margin
if crf_params is None and densecrf:
crf_params = {'sxy': 3, 'srgb': 3, 'compat': 5}
misc_utils.make_dir_if_not_exist(pred_dir)
pbar = tqdm(self.rgb_files)
for rgb_file in pbar:
file_name = os.path.splitext(os.path.basename(rgb_file))[0].split('_')[0]
pbar.set_description('Inferring {}'.format(file_name))
# read data
rgb = misc_utils.load_file(rgb_file)[:, :, :3]
# evaluate on tiles
tile_dim = rgb.shape[:2]
tile_dim_pad = [tile_dim[0] + 2 * lbl_margin, tile_dim[1] + 2 * lbl_margin]
grid_list = patch_extractor.make_grid(tile_dim_pad, patch_size, overlap)
if isinstance(model, list) or isinstance(model, tuple):
tile_preds = 0
for m in model:
tile_preds = tile_preds + self.infer_tile(m, rgb, grid_list, patch_size, tile_dim, tile_dim_pad,
lbl_margin)
else:
tile_preds = self.infer_tile(model, rgb, grid_list, patch_size, tile_dim, tile_dim_pad, lbl_margin)
if densecrf:
d = dcrf.DenseCRF2D(*tile_preds.shape)
U = unary_from_softmax(np.ascontiguousarray(
data_utils.change_channel_order(tile_preds, False)))
d.setUnaryEnergy(U)
d.addPairwiseBilateral(rgbim=rgb, **crf_params)
Q = d.inference(5)
tile_preds = np.argmax(Q, axis=0).reshape(*tile_preds.shape[:2])
else:
tile_preds = np.argmax(tile_preds, -1)
if self.encode_func:
pred_img = self.encode_func(tile_preds)
else:
pred_img = tile_preds
if visualize:
vis_utils.compare_figures([rgb, pred_img], (1, 2), fig_size=(12, 5))
misc_utils.save_file(os.path.join(pred_dir, '{}{}.{}'.format(file_name, ext, file_ext)), pred_img)
def evaluate(self,
model,
patch_size,
overlap,
pred_dir=None,
report_dir=None,
save_conf=False):
iou_a, iou_b = 0, 0
report = []
if pred_dir:
misc_utils.make_dir_if_not_exist(pred_dir)
for lbl_file in self.lbl_files:
# only do NZ image
if 'NZ' not in lbl_file:
continue
# parse ground truth
file_name = os.path.splitext(os.path.basename(lbl_file))[0]
boxes, lines = data_utils.csv_to_annotation(lbl_file)
tile_id = int(''.join([a for a in file_name if a.isdigit()]))
if tile_id > 3:
continue
# read rgb image
rgb_file = os.path.join(self.image_dir, '{}.tif'.format(file_name))
rgb = misc_utils.load_file(rgb_file)[..., :3]
# make line map
lbl, _ = data_utils.render_line_graph(rgb.shape[:2], boxes, lines)
# evaluate on tiles
tile_dim = rgb.shape[:2]
grid_list = patch_extractor.make_grid(tile_dim, patch_size,
overlap)
tile_preds = []
for patch in patch_extractor.patch_block(rgb, 0, grid_list,
patch_size, False):
for tsfm in self.tsfm:
tsfm_image = tsfm(image=patch)
patch = tsfm_image['image']
patch = torch.unsqueeze(patch, 0).to(self.device)
pred = model.inference(patch).detach().cpu().numpy()
tile_preds.append(change_channel_order(pred, True)[0, :, :, :])
# stitch back to tiles
tile_preds = patch_extractor.unpatch_block(np.array(tile_preds),
tile_dim,
patch_size,
tile_dim,
patch_size,
overlap=0)
if save_conf:
misc_utils.save_file(
os.path.join(pred_dir, '{}.npy'.format(file_name)),
tile_preds)
tile_preds = np.argmax(tile_preds, -1)
a, b = metric_utils.iou_metric(lbl, tile_preds)
file_name = os.path.splitext(os.path.basename(lbl_file))[0]
print('{}: IoU={:.2f}'.format(file_name, a / b * 100))
report.append('{},{},{},{}\n'.format(file_name, a, b, a / b * 100))
iou_a += a
iou_b += b
if pred_dir:
misc_utils.save_file(
os.path.join(pred_dir, '{}.png'.format(file_name)),
tile_preds)
print('Overall: IoU={:.2f}'.format(iou_a / iou_b * 100))
report.append('Overall,{},{},{}\n'.format(iou_a, iou_b,
iou_a / iou_b * 100))
if report_dir:
misc_utils.make_dir_if_not_exist(report_dir)
misc_utils.save_file(os.path.join(report_dir, 'result.txt'),
report)
def evaluate(self,
model,
patch_size,
overlap,
pred_dir=None,
report_dir=None,
save_conf=False,
delta=1e-6,
eval_class=(1, ),
visualize=False,
densecrf=False,
crf_params=None,
verbose=True):
if isinstance(model, list) or isinstance(model, tuple):
lbl_margin = model[0].lbl_margin
else:
lbl_margin = model.lbl_margin
if crf_params is None and densecrf:
crf_params = {'sxy': 3, 'srgb': 3, 'compat': 5}
iou_a, iou_b = np.zeros(len(eval_class)), np.zeros(len(eval_class))
report = []
if pred_dir:
misc_utils.make_dir_if_not_exist(pred_dir)
for rgb_file, lbl_file in zip(self.rgb_files, self.lbl_files):
file_name = os.path.splitext(os.path.basename(lbl_file))[0]
# read data
rgb = misc_utils.load_file(rgb_file)[:, :, :3]
lbl = misc_utils.load_file(lbl_file)
if self.decode_func:
lbl = self.decode_func(lbl)
# evaluate on tiles
tile_dim = rgb.shape[:2]
tile_dim_pad = [
tile_dim[0] + 2 * lbl_margin, tile_dim[1] + 2 * lbl_margin
]
grid_list = patch_extractor.make_grid(tile_dim_pad, patch_size,
overlap)
if isinstance(model, list) or isinstance(model, tuple):
tile_preds = 0
for m in model:
tile_preds = tile_preds + self.infer_tile(
m, rgb, grid_list, patch_size, tile_dim, tile_dim_pad,
lbl_margin)
else:
tile_preds = self.infer_tile(model, rgb, grid_list, patch_size,
tile_dim, tile_dim_pad,
lbl_margin)
if save_conf:
misc_utils.save_file(
os.path.join(pred_dir, '{}.npy'.format(file_name)),
scipy.special.softmax(tile_preds, axis=-1)[:, :, 1])
if densecrf:
d = dcrf.DenseCRF2D(*tile_preds.shape)
U = unary_from_softmax(
np.ascontiguousarray(
data_utils.change_channel_order(
scipy.special.softmax(tile_preds, axis=-1),
False)))
d.setUnaryEnergy(U)
d.addPairwiseBilateral(rgbim=rgb, **crf_params)
Q = d.inference(5)
tile_preds = np.argmax(Q,
axis=0).reshape(*tile_preds.shape[:2])
else:
tile_preds = np.argmax(tile_preds, -1)
iou_score = metric_utils.iou_metric(lbl / self.truth_val,
tile_preds,
eval_class=eval_class)
pstr, rstr = self.get_result_strings(file_name, iou_score, delta)
tm.misc_utils.verb_print(pstr, verbose)
report.append(rstr)
iou_a += iou_score[0, :]
iou_b += iou_score[1, :]
if visualize:
if self.encode_func:
vis_utils.compare_figures([
rgb,
self.encode_func(lbl),
self.encode_func(tile_preds)
], (1, 3),
fig_size=(15, 5))
else:
vis_utils.compare_figures([rgb, lbl, tile_preds], (1, 3),
fig_size=(15, 5))
if pred_dir:
if self.encode_func:
misc_utils.save_file(
os.path.join(pred_dir, '{}.png'.format(file_name)),
self.encode_func(tile_preds))
else:
misc_utils.save_file(
os.path.join(pred_dir, '{}.png'.format(file_name)),
tile_preds)
pstr, rstr = self.get_result_strings('Overall',
np.stack([iou_a, iou_b], axis=0),
delta)
tm.misc_utils.verb_print(pstr, verbose)
report.append(rstr)
if report_dir:
misc_utils.make_dir_if_not_exist(report_dir)
misc_utils.save_file(os.path.join(report_dir, 'result.txt'),
report)
return np.mean(iou_a / (iou_b + delta)) * 100