def evaluate(model,
eval_dataset,
aug_eval=False,
scales=1.0,
flip_horizontal=True,
flip_vertical=False,
is_slide=False,
stride=None,
crop_size=None,
num_workers=0,
print_detail=True):
"""
Launch evalution.
Args:
model（nn.Layer): A sementic segmentation model.
eval_dataset (paddle.io.Dataset): Used to read and process validation datasets.
aug_eval (bool, optional): Whether to use mulit-scales and flip augment for evaluation. Default: False.
scales (list|float, optional): Scales for augment. It is valid when `aug_eval` is True. Default: 1.0.
flip_horizontal (bool, optional): Whether to use flip horizontally augment. It is valid when `aug_eval` is True. Default: True.
flip_vertical (bool, optional): Whether to use flip vertically augment. It is valid when `aug_eval` is True. Default: False.
is_slide (bool, optional): Whether to evaluate by sliding window. Default: False.
stride (tuple|list, optional): The stride of sliding window, the first is width and the second is height.
It should be provided when `is_slide` is True.
crop_size (tuple|list, optional): The crop size of sliding window, the first is width and the second is height.
It should be provided when `is_slide` is True.
num_workers (int, optional): Num workers for data loader. Default: 0.
print_detail (bool, optional): Whether to print detailed information about the evaluation process. Default: True.
Returns:
float: The mIoU of validation datasets.
float: The accuracy of validation datasets.
"""
model.eval()
nranks = paddle.distributed.ParallelEnv().nranks
local_rank = paddle.distributed.ParallelEnv().local_rank
if nranks > 1:
# Initialize parallel environment if not done.
if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized(
):
paddle.distributed.init_parallel_env()
batch_sampler = paddle.io.DistributedBatchSampler(eval_dataset,
batch_size=1,
shuffle=False,
drop_last=False)
loader = paddle.io.DataLoader(
eval_dataset,
batch_sampler=batch_sampler,
num_workers=num_workers,
return_list=True,
)
total_iters = len(loader)
intersect_area_all = 0
pred_area_all = 0
label_area_all = 0
if print_detail:
logger.info(
"Start evaluating (total_samples={}, total_iters={})...".format(
len(eval_dataset), total_iters))
progbar_val = progbar.Progbar(target=total_iters, verbose=1)
reader_cost_averager = TimeAverager()
batch_cost_averager = TimeAverager()
batch_start = time.time()
with paddle.no_grad():
for iter, (im, label) in enumerate(loader):
reader_cost_averager.record(time.time() - batch_start)
label = label.astype('int64')
ori_shape = label.shape[-2:]
if aug_eval:
pred = infer.aug_inference(
model,
im,
ori_shape=ori_shape,
transforms=eval_dataset.transforms.transforms,
scales=scales,
flip_horizontal=flip_horizontal,
flip_vertical=flip_vertical,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
else:
pred = infer.inference(
model,
im,
ori_shape=ori_shape,
transforms=eval_dataset.transforms.transforms,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
intersect_area, pred_area, label_area = metrics.calculate_area(
pred,
label,
eval_dataset.num_classes,
ignore_index=eval_dataset.ignore_index)
# Gather from all ranks
if nranks > 1:
intersect_area_list = []
pred_area_list = []
label_area_list = []
paddle.distributed.all_gather(intersect_area_list,
intersect_area)
paddle.distributed.all_gather(pred_area_list, pred_area)
paddle.distributed.all_gather(label_area_list, label_area)
# Some image has been evaluated and should be eliminated in last iter
if (iter + 1) * nranks > len(eval_dataset):
valid = len(eval_dataset) - iter * nranks
intersect_area_list = intersect_area_list[:valid]
pred_area_list = pred_area_list[:valid]
label_area_list = label_area_list[:valid]
for i in range(len(intersect_area_list)):
intersect_area_all = intersect_area_all + intersect_area_list[
i]
pred_area_all = pred_area_all + pred_area_list[i]
label_area_all = label_area_all + label_area_list[i]
else:
intersect_area_all = intersect_area_all + intersect_area
pred_area_all = pred_area_all + pred_area
label_area_all = label_area_all + label_area
batch_cost_averager.record(time.time() - batch_start,
num_samples=len(label))
batch_cost = batch_cost_averager.get_average()
reader_cost = reader_cost_averager.get_average()
if local_rank == 0 and print_detail:
progbar_val.update(iter + 1, [('batch_cost', batch_cost),
('reader cost', reader_cost)])
reader_cost_averager.reset()
batch_cost_averager.reset()
batch_start = time.time()
class_iou, miou = metrics.mean_iou(intersect_area_all, pred_area_all,
label_area_all)
class_acc, acc = metrics.accuracy(intersect_area_all, pred_area_all)
kappa = metrics.kappa(intersect_area_all, pred_area_all, label_area_all)
if print_detail:
logger.info(
"[EVAL] #Images={} mIoU={:.4f} Acc={:.4f} Kappa={:.4f} ".format(
len(eval_dataset), miou, acc, kappa))
logger.info("[EVAL] Class IoU: \n" + str(np.round(class_iou, 4)))
logger.info("[EVAL] Class Acc: \n" + str(np.round(class_acc, 4)))
return miou, acc
def predict(model,
model_path,
transforms,
image_list,
image_dir=None,
save_dir='output',
aug_pred=False,
scales=1.0,
flip_horizontal=True,
flip_vertical=False,
is_slide=False,
stride=None,
crop_size=None):
"""
predict and visualize the image_list.
Args:
model (nn.Layer): Used to predict for input image.
model_path (str): The path of pretrained model.
transforms (transform.Compose): Preprocess for input image.
image_list (list): A list of images to be predicted.
image_dir (str): The directory of the images to be predicted. Default: None.
save_dir (str): The directory to save the visualized results. Default: 'output'.
"""
para_state_dict = paddle.load(model_path)
model.set_dict(para_state_dict)
model.eval()
added_saved_dir = os.path.join(save_dir, 'added_prediction')
pred_saved_dir = os.path.join(save_dir, 'pseudo_color_prediction')
logger.info("Start to predict...")
progbar_pred = progbar.Progbar(target=len(image_list), verbose=1)
for i, im_path in enumerate(image_list):
im = cv2.imread(im_path)
ori_shape = im.shape[:2]
im, _ = transforms(im)
im = im[np.newaxis, ...]
im = paddle.to_tensor(im)
if aug_pred:
pred = infer.aug_inference(model,
im,
ori_shape=ori_shape,
transforms=transforms.transforms,
scales=scales,
flip_horizontal=flip_horizontal,
flip_vertical=flip_vertical,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
else:
pred = infer.inference(model,
im,
ori_shape=ori_shape,
transforms=transforms.transforms,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
pred = paddle.squeeze(pred)
pred = pred.numpy().astype('uint8')
# get the saved name
if image_dir is not None:
im_file = im_path.replace(image_dir, '')
else:
im_file = os.path.basename(im_path)
if im_file[0] == '/':
im_file = im_file[1:]
# save added image
added_image = utils.visualize.visualize(im_path, pred, weight=0.6)
added_image_path = os.path.join(added_saved_dir, im_file)
mkdir(added_image_path)
cv2.imwrite(added_image_path, added_image)
# save pseudo color prediction
pred_mask = utils.visualize.get_pseudo_color_map(pred)
pred_saved_path = os.path.join(pred_saved_dir,
im_file.rsplit(".")[0] + ".png")
mkdir(pred_saved_path)
pred_mask.save(pred_saved_path)
# pred_im = utils.visualize(im_path, pred, weight=0.0)
# pred_saved_path = os.path.join(pred_saved_dir, im_file)
# mkdir(pred_saved_path)
# cv2.imwrite(pred_saved_path, pred_im)
progbar_pred.update(i + 1)
def predict(model,
model_path,
transforms,
image_list,
image_dir=None,
save_dir='output',
aug_pred=False,
scales=1.0,
flip_horizontal=True,
flip_vertical=False,
is_slide=False,
stride=None,
crop_size=None,
custom_color=None):
"""
predict and visualize the image_list.
Args:
model (nn.Layer): Used to predict for input image.
model_path (str): The path of pretrained model.
transforms (transform.Compose): Preprocess for input image.
image_list (list): A list of image path to be predicted.
image_dir (str, optional): The root directory of the images predicted. Default: None.
save_dir (str, optional): The directory to save the visualized results. Default: 'output'.
aug_pred (bool, optional): Whether to use mulit-scales and flip augment for predition. Default: False.
scales (list|float, optional): Scales for augment. It is valid when `aug_pred` is True. Default: 1.0.
flip_horizontal (bool, optional): Whether to use flip horizontally augment. It is valid when `aug_pred` is True. Default: True.
flip_vertical (bool, optional): Whether to use flip vertically augment. It is valid when `aug_pred` is True. Default: False.
is_slide (bool, optional): Whether to predict by sliding window. Default: False.
stride (tuple|list, optional): The stride of sliding window, the first is width and the second is height.
It should be provided when `is_slide` is True.
crop_size (tuple|list, optional): The crop size of sliding window, the first is width and the second is height.
It should be provided when `is_slide` is True.
custom_color (list, optional): Save images with a custom color map. Default: None, use paddleseg's default color map.
"""
utils.utils.load_entire_model(model, model_path)
model.eval()
nranks = paddle.distributed.get_world_size()
local_rank = paddle.distributed.get_rank()
if nranks > 1:
img_lists = partition_list(image_list, nranks)
else:
img_lists = [image_list]
added_saved_dir = os.path.join(save_dir, 'added_prediction')
pred_saved_dir = os.path.join(save_dir, 'pseudo_color_prediction')
logger.info("Start to predict...")
progbar_pred = progbar.Progbar(target=len(img_lists[0]), verbose=1)
color_map = visualize.get_color_map_list(256, custom_color=custom_color)
with paddle.no_grad():
for i, im_path in enumerate(img_lists[local_rank]):
data = preprocess(im_path, transforms)
if aug_pred:
pred, _ = infer.aug_inference(model,
data['img'],
trans_info=data['trans_info'],
scales=scales,
flip_horizontal=flip_horizontal,
flip_vertical=flip_vertical,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
else:
pred, _ = infer.inference(model,
data['img'],
trans_info=data['trans_info'],
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
pred = paddle.squeeze(pred)
pred = pred.numpy().astype('uint8')
# get the saved name
if image_dir is not None:
im_file = im_path.replace(image_dir, '')
else:
im_file = os.path.basename(im_path)
if im_file[0] == '/' or im_file[0] == '\\':
im_file = im_file[1:]
# save added image
added_image = utils.visualize.visualize(im_path,
pred,
color_map,
weight=0.6)
added_image_path = os.path.join(added_saved_dir, im_file)
mkdir(added_image_path)
cv2.imwrite(added_image_path, added_image)
# save pseudo color prediction
pred_mask = utils.visualize.get_pseudo_color_map(pred, color_map)
pred_saved_path = os.path.join(
pred_saved_dir,
os.path.splitext(im_file)[0] + ".png")
mkdir(pred_saved_path)
pred_mask.save(pred_saved_path)
progbar_pred.update(i + 1)
def evaluate(model,
eval_dataset,
aug_eval=False,
scales=1.0,
flip_horizontal=True,
flip_vertical=False,
is_slide=False,
stride=None,
crop_size=None,
num_workers=0):
model.eval()
nranks = paddle.distributed.ParallelEnv().nranks
local_rank = paddle.distributed.ParallelEnv().local_rank
if nranks > 1:
# Initialize parallel environment if not done.
if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized(
):
paddle.distributed.init_parallel_env()
batch_sampler = paddle.io.DistributedBatchSampler(
eval_dataset, batch_size=1, shuffle=False, drop_last=False)
loader = paddle.io.DataLoader(
eval_dataset,
batch_sampler=batch_sampler,
num_workers=num_workers,
return_list=True,
)
total_iters = len(loader)
intersect_area_all = 0
pred_area_all = 0
label_area_all = 0
logger.info("Start evaluating (total_samples={}, total_iters={})...".format(
len(eval_dataset), total_iters))
progbar_val = progbar.Progbar(target=total_iters, verbose=1)
timer = Timer()
for iter, (im, label) in enumerate(loader):
reader_cost = timer.elapsed_time()
label = label.astype('int64')
ori_shape = label.shape[-2:]
if aug_eval:
pred = infer.aug_inference(
model,
im,
ori_shape=ori_shape,
transforms=eval_dataset.transforms.transforms,
scales=scales,
flip_horizontal=flip_horizontal,
flip_vertical=flip_vertical,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
else:
pred = infer.inference(
model,
im,
ori_shape=ori_shape,
transforms=eval_dataset.transforms.transforms,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
intersect_area, pred_area, label_area = metrics.calculate_area(
pred,
label,
eval_dataset.num_classes,
ignore_index=eval_dataset.ignore_index)
# Gather from all ranks
if nranks > 1:
intersect_area_list = []
pred_area_list = []
label_area_list = []
paddle.distributed.all_gather(intersect_area_list, intersect_area)
paddle.distributed.all_gather(pred_area_list, pred_area)
paddle.distributed.all_gather(label_area_list, label_area)
# Some image has been evaluated and should be eliminated in last iter
if (iter + 1) * nranks > len(eval_dataset):
valid = len(eval_dataset) - iter * nranks
intersect_area_list = intersect_area_list[:valid]
pred_area_list = pred_area_list[:valid]
label_area_list = label_area_list[:valid]
for i in range(len(intersect_area_list)):
intersect_area_all = intersect_area_all + intersect_area_list[i]
pred_area_all = pred_area_all + pred_area_list[i]
label_area_all = label_area_all + label_area_list[i]
else:
intersect_area_all = intersect_area_all + intersect_area
pred_area_all = pred_area_all + pred_area
label_area_all = label_area_all + label_area
batch_cost = timer.elapsed_time()
timer.restart()
if local_rank == 0:
progbar_val.update(iter + 1, [('batch_cost', batch_cost),
('reader cost', reader_cost)])
class_iou, miou = metrics.mean_iou(intersect_area_all, pred_area_all,
label_area_all)
class_acc, acc = metrics.accuracy(intersect_area_all, pred_area_all)
kappa = metrics.kappa(intersect_area_all, pred_area_all, label_area_all)
logger.info("[EVAL] #Images={} mIoU={:.4f} Acc={:.4f} Kappa={:.4f} ".format(
len(eval_dataset), miou, acc, kappa))
logger.info("[EVAL] Class IoU: \n" + str(np.round(class_iou, 4)))
logger.info("[EVAL] Class Acc: \n" + str(np.round(class_acc, 4)))
return miou, acc
