Save infer model when saving checkpoint

contrib/HumanSeg/models/humanseg.py

@@ -27,6 +27,7 @@ import cv2
 import yaml
 import shutil
 import paddleslim as slim
+import paddle
 
 import utils
 import utils.logging as logging
@@ -37,6 +38,15 @@ from nets import DeepLabv3p, ShuffleSeg, HRNet
 import transforms as T
 
 
+def save_infer_program(test_program, ckpt_dir):
+    _test_program = test_program.clone()
+    _test_program.desc.flush()
+    _test_program.desc._set_version()
+    paddle.fluid.core.save_op_compatible_info(_test_program.desc)
+    with open(os.path.join(ckpt_dir, 'model') + ".pdmodel", "wb") as f:
+        f.write(_test_program.desc.serialize_to_string())
+
+
 def dict2str(dict_input):
     out = ''
     for k, v in dict_input.items():
@@ -244,6 +254,7 @@ class SegModel(object):
 
         if self.status == 'Normal':
             fluid.save(self.train_prog, osp.join(save_dir, 'model'))
+            save_infer_program(self.test_prog, save_dir)
             model_info['status'] = 'Normal'
         elif self.status == 'Quant':
             fluid.save(self.test_prog, osp.join(save_dir, 'model'))

contrib/RemoteSensing/__init__.py

@@ -21,5 +21,3 @@ import readers
 from utils.utils import get_environ_info
 
 env_info = get_environ_info()
-
-log_level = 2

contrib/RemoteSensing/models/base.py

@@ -30,6 +30,16 @@ from utils.utils import seconds_to_hms, get_environ_info
 from utils.metrics import ConfusionMatrix
 import transforms.transforms as T
 import utils
+import paddle
+
+
+def save_infer_program(test_program, ckpt_dir):
+    _test_program = test_program.clone()
+    _test_program.desc.flush()
+    _test_program.desc._set_version()
+    paddle.fluid.core.save_op_compatible_info(_test_program.desc)
+    with open(os.path.join(ckpt_dir, 'model') + ".pdmodel", "wb") as f:
+        f.write(_test_program.desc.serialize_to_string())
 
 
 def dict2str(dict_input):
@@ -238,6 +248,7 @@ class BaseModel(object):
 
         if self.status == 'Normal':
             fluid.save(self.train_prog, osp.join(save_dir, 'model'))
+            save_infer_program(self.test_prog, save_dir)
 
         model_info['status'] = self.status
         with open(

contrib/RemoteSensing/utils/logging.py

@@ -16,7 +16,6 @@
 import time
 import os
 import sys
-import __init__
 
 levels = {0: 'ERROR', 1: 'WARNING', 2: 'INFO', 3: 'DEBUG'}
 
@@ -25,10 +24,9 @@ def log(level=2, message=""):
     current_time = time.time()
     time_array = time.localtime(current_time)
     current_time = time.strftime("%Y-%m-%d %H:%M:%S", time_array)
-    if __init__.log_level >= level:
-        print("{} [{}]\t{}".format(current_time, levels[level],
-                                   message).encode("utf-8").decode("latin1"))
-        sys.stdout.flush()
+    print("{} [{}]\t{}".format(current_time, levels[level],
+                               message).encode("utf-8").decode("latin1"))
+    sys.stdout.flush()
 
 
 def debug(message=""):

pdseg/train.py

@@ -27,6 +27,7 @@ import pprint
 import random
 import shutil
 
+import paddle
 import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid import profiler
@@ -158,6 +159,15 @@ def load_checkpoint(exe, program):
     return begin_epoch
 
 
+def save_infer_program(test_program, ckpt_dir):
+    _test_program = test_program.clone()
+    _test_program.desc.flush()
+    _test_program.desc._set_version()
+    paddle.fluid.core.save_op_compatible_info(_test_program.desc)
+    with open(os.path.join(ckpt_dir, 'model') + ".pdmodel", "wb") as f:
+        f.write(_test_program.desc.serialize_to_string())
+
+
 def update_best_model(ckpt_dir):
     best_model_dir = os.path.join(cfg.TRAIN.MODEL_SAVE_DIR, 'best_model')
     if os.path.exists(best_model_dir):
@@ -173,6 +183,7 @@ def print_info(*msg):
 def train(cfg):
     startup_prog = fluid.Program()
     train_prog = fluid.Program()
+    test_prog = fluid.Program()
     if args.enable_ce:
         startup_prog.random_seed = 1000
         train_prog.random_seed = 1000
@@ -224,6 +235,7 @@ def train(cfg):
 
     data_loader, avg_loss, lr, pred, grts, masks = build_model(
         train_prog, startup_prog, phase=ModelPhase.TRAIN)
+    build_model(test_prog, fluid.Program(), phase=ModelPhase.EVAL)
     data_loader.set_sample_generator(
         data_generator, batch_size=batch_size_per_dev, drop_last=drop_last)
 
@@ -387,6 +399,7 @@ def train(cfg):
         if (epoch % cfg.TRAIN.SNAPSHOT_EPOCH == 0
                 or epoch == cfg.SOLVER.NUM_EPOCHS) and cfg.TRAINER_ID == 0:
             ckpt_dir = save_checkpoint(train_prog, epoch)
+            save_infer_program(test_prog, ckpt_dir)
 
             if args.do_eval:
                 print("Evaluation start")
@@ -419,7 +432,8 @@ def train(cfg):
 
     # save final model
     if cfg.TRAINER_ID == 0:
-        save_checkpoint(train_prog, 'final')
+        ckpt_dir = save_checkpoint(train_prog, 'final')
+        save_infer_program(test_prog, ckpt_dir)
 
 
 def main(args):