change logging to logger

dygraph/core/infer.py

@@ -21,7 +21,7 @@ import cv2
 import tqdm
 
 from dygraph import utils
-import dygraph.utils.logging as logging
+import dygraph.utils.logger as logger
 
 
 def mkdir(path):
@@ -39,7 +39,7 @@ def infer(model, test_dataset=None, model_dir=None, save_dir='output'):
     added_saved_dir = os.path.join(save_dir, 'added')
     pred_saved_dir = os.path.join(save_dir, 'prediction')
 
-    logging.info("Start to predict...")
+    logger.info("Start to predict...")
     for im, im_info, im_path in tqdm.tqdm(test_dataset):
         im = to_variable(im)
         pred, _ = model(im)

dygraph/core/train.py

@@ -19,7 +19,7 @@ from paddle.fluid.dygraph.parallel import ParallelEnv
 from paddle.fluid.io import DataLoader
 from paddle.incubate.hapi.distributed import DistributedBatchSampler
 
-import dygraph.utils.logging as logging
+import dygraph.utils.logger as logger
 from dygraph.utils import load_pretrained_model
 from dygraph.utils import resume
 from dygraph.utils import Timer, calculate_eta
@@ -111,7 +111,7 @@ def train(model,
                 train_batch_cost = 0.0
                 remain_steps = total_steps - num_steps
                 eta = calculate_eta(remain_steps, avg_train_batch_cost)
-                logging.info(
+                logger.info(
                     "[TRAIN] Epoch={}/{}, Step={}/{}, loss={:.4f}, lr={:.6f}, batch_cost={:.4f}, reader_cost={:.4f} | ETA {}"
                     .format(epoch + 1, num_epochs, step + 1, steps_per_epoch,
                             avg_loss * nranks, lr, avg_train_batch_cost,
@@ -152,7 +152,7 @@ def train(model,
                     best_model_dir = os.path.join(save_dir, "best_model")
                     fluid.save_dygraph(model.state_dict(),
                                        os.path.join(best_model_dir, 'model'))
-                logging.info(
+                logger.info(
                     'Current evaluated best model in eval_dataset is epoch_{}, miou={:4f}'
                     .format(best_model_epoch, best_mean_iou))
 

dygraph/core/val.py

@@ -20,7 +20,7 @@ import cv2
 from paddle.fluid.dygraph.base import to_variable
 import paddle.fluid as fluid
 
-import dygraph.utils.logging as logging
+import dygraph.utils.logger as logger
 from dygraph.utils import ConfusionMatrix
 from dygraph.utils import Timer, calculate_eta
 
@@ -39,7 +39,7 @@ def evaluate(model,
     total_steps = len(eval_dataset)
     conf_mat = ConfusionMatrix(num_classes, streaming=True)
 
-    logging.info(
+    logger.info(
         "Start to evaluating(total_samples={}, total_steps={})...".format(
             len(eval_dataset), total_steps))
     timer = Timer()
@@ -69,7 +69,7 @@ def evaluate(model,
 
         time_step = timer.elapsed_time()
         remain_step = total_steps - step - 1
-        logging.debug(
+        logger.debug(
             "[EVAL] Epoch={}, Step={}/{}, iou={:4f}, sec/step={:.4f} | ETA {}".
             format(epoch_id, step + 1, total_steps, iou, time_step,
                    calculate_eta(remain_step, time_step)))
@@ -77,9 +77,9 @@ def evaluate(model,
 
     category_iou, miou = conf_mat.mean_iou()
     category_acc, macc = conf_mat.accuracy()
-    logging.info("[EVAL] #Images={} mAcc={:.4f} mIoU={:.4f}".format(
+    logger.info("[EVAL] #Images={} mAcc={:.4f} mIoU={:.4f}".format(
         len(eval_dataset), macc, miou))
-    logging.info("[EVAL] Category IoU: " + str(category_iou))
-    logging.info("[EVAL] Category Acc: " + str(category_acc))
-    logging.info("[EVAL] Kappa:{:.4f} ".format(conf_mat.kappa()))
+    logger.info("[EVAL] Category IoU: " + str(category_iou))
+    logger.info("[EVAL] Category Acc: " + str(category_acc))
+    logger.info("[EVAL] Kappa:{:.4f} ".format(conf_mat.kappa()))
     return miou, macc

dygraph/utils/__init__.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from . import logging
+from . import logger
 from . import download
 from .metrics import ConfusionMatrix
 from .utils import *

dygraph/utils/utils.py

@@ -18,7 +18,7 @@ import math
 import cv2
 import paddle.fluid as fluid
 
-from . import logging
+from . import logger
 
 
 def seconds_to_hms(seconds):
@@ -49,7 +49,7 @@ def get_environ_info():
 
 def load_pretrained_model(model, pretrained_model):
     if pretrained_model is not None:
-        logging.info('Load pretrained model from {}'.format(pretrained_model))
+        logger.info('Load pretrained model from {}'.format(pretrained_model))
         if os.path.exists(pretrained_model):
             ckpt_path = os.path.join(pretrained_model, 'model')
             try:
@@ -62,10 +62,10 @@ def load_pretrained_model(model, pretrained_model):
             num_params_loaded = 0
             for k in keys:
                 if k not in para_state_dict:
-                    logging.warning("{} is not in pretrained model".format(k))
+                    logger.warning("{} is not in pretrained model".format(k))
                 elif list(para_state_dict[k].shape) != list(
                         model_state_dict[k].shape):
-                    logging.warning(
+                    logger.warning(
                         "[SKIP] Shape of pretrained params {} doesn't match.(Pretrained: {}, Actual: {})"
                         .format(k, para_state_dict[k].shape,
                                 model_state_dict[k].shape))
@@ -73,7 +73,7 @@ def load_pretrained_model(model, pretrained_model):
                     model_state_dict[k] = para_state_dict[k]
                     num_params_loaded += 1
             model.set_dict(model_state_dict)
-            logging.info("There are {}/{} varaibles are loaded.".format(
+            logger.info("There are {}/{} varaibles are loaded.".format(
                 num_params_loaded, len(model_state_dict)))
 
         else:
@@ -81,12 +81,12 @@ def load_pretrained_model(model, pretrained_model):
                 'The pretrained model directory is not Found: {}'.format(
                     pretrained_model))
     else:
-        logging.info('No pretrained model to load, train from scratch')
+        logger.info('No pretrained model to load, train from scratch')
 
 
 def resume(model, optimizer, resume_model):
     if resume_model is not None:
-        logging.info('Resume model from {}'.format(resume_model))
+        logger.info('Resume model from {}'.format(resume_model))
         if os.path.exists(resume_model):
             resume_model = os.path.normpath(resume_model)
             ckpt_path = os.path.join(resume_model, 'model')
@@ -102,7 +102,7 @@ def resume(model, optimizer, resume_model):
                 'The resume model directory is not Found: {}'.format(
                     resume_model))
     else:
-        logging.info('No model need to resume')
+        logger.info('No model need to resume')
 
 
 def visualize(image, result, save_dir=None, weight=0.6):