Merge pull request #160 from LielinJiang/fix_bug_from_qa

Fix some typos

slim/distillation/README.md

@@ -49,7 +49,7 @@ print(teacher_vars)
 
 ```bash
 # student model
-bilinear_interp_1.tmp_0
+bilinear_interp_0.tmp_0
 # teacher model
 bilinear_interp_2.tmp_0
 ```
@@ -58,7 +58,7 @@ bilinear_interp_2.tmp_0
 它们形状两两相同，且分别处于两个网络的输出部分。所以，我们用`l2_loss`对这几个特征图两两对应添加蒸馏loss。需要注意的是，teacher的Variable在merge过程中被自动添加了一个`name_prefix`，所以这里也需要加上这个前缀`"teacher_"`，merge过程请参考[蒸馏API文档](https://paddlepaddle.github.io/PaddleSlim/api/single_distiller_api/#merge)
 
 ```python
-distill_loss = l2_loss('teacher_bilinear_interp_2.tmp_0', 'bilinear_interp_1.tmp_0')
+distill_loss = l2_loss('teacher_bilinear_interp_2.tmp_0', 'bilinear_interp_0.tmp_0')
 ```
 
 我们也可以根据上述操作为蒸馏策略选择其他loss，PaddleSlim支持的有`FSP_loss`, `L2_loss`, `softmax_with_cross_entropy_loss` 以及自定义的任何loss。
@@ -71,10 +71,10 @@ distill_loss = l2_loss('teacher_bilinear_interp_2.tmp_0', 'bilinear_interp_1.tmp
 ### 执行示例
 如下命令启动训练，每间隔```cfg.TRAIN.SNAPSHOT_EPOCH```会进行一次评估。
 ```shell
-CUDA_VISIBLE_DEVICES=0,1
-python -m paddle.distributed.launch ./slim/distill/train.py \
---log_steps 10 --cfg ./slim/distill/cityscape_fast_scnn.yaml \
---teacher_cfg ./slim/distill/cityscape_teacher.yaml \
+CUDA_VISIBLE_DEVICES=0,1 
+python -m paddle.distributed.launch ./slim/distillation/train_distill.py \
+--log_steps 10 --cfg ./slim/distillation/cityscape.yaml \
+--teacher_cfg ./slim/distillation/cityscape_teacher.yaml \
 --use_gpu \
 --use_mpio \
 --do_eval

slim/distillation/model_builder.py

@@ -157,7 +157,7 @@ def export_preprocess(image):
 
 
 def build_model(main_prog=None, start_prog=None, phase=ModelPhase.TRAIN, **kwargs):
-    print('debugggggggggg')
+
     if not ModelPhase.is_valid_phase(phase):
         raise ValueError("ModelPhase {} is not valid!".format(phase))
     if ModelPhase.is_train(phase):
@@ -176,7 +176,6 @@ def build_model(main_prog=None, start_prog=None, phase=ModelPhase.TRAIN, **kwarg
     # 在导出模型的时候，增加图像标准化预处理,减小预测部署时图像的处理流程
     # 预测部署时只须对输入图像增加batch_size维度即可
     if cfg.SLIM.KNOWLEDGE_DISTILL_IS_TEACHER:
-        print('teacher input:')
         image = main_prog.global_block()._clone_variable(kwargs['image'],
                                                          force_persistable=False)
         label = main_prog.global_block()._clone_variable(kwargs['label'],

slim/distillation/train_distill.py

@@ -278,8 +278,6 @@ def train(cfg):
                 label=grts, mask=masks)
 
     exe.run(teacher_startup_program)
-    # assert FLAGS.teacher_pretrained, "teacher_pretrained should be set"
-    # checkpoint.load_params(exe, teacher_program, FLAGS.teacher_pretrained)
 
     teacher_program = teacher_program.clone(for_test=True)
     ckpt_dir = cfg.SLIM.KNOWLEDGE_DISTILL_TEACHER_MODEL_DIR
@@ -295,14 +293,14 @@ def train(cfg):
         'mask': 'mask',
     }
     merge(teacher_program, fluid.default_main_program(), data_name_map, place)
-    distill_pairs = [['teacher_bilinear_interp_2.tmp_0', 'bilinear_interp_1.tmp_0']]
+    distill_pairs = [['teacher_bilinear_interp_2.tmp_0', 'bilinear_interp_0.tmp_0']]
 
     def distill(pairs, weight):
         """
         Add 3 pairs of distillation losses, each pair of feature maps is the
         input of teacher and student's yolov3_loss respectively
         """
-        loss = l2_loss(pairs[0][0], pairs[0][1], masks)
+        loss = l2_loss(pairs[0][0], pairs[0][1])
         weighted_loss = loss * weight
         return weighted_loss
 

slim/nas/README.md

@@ -46,7 +46,7 @@ SLIM:
 ## 训练与评估
 执行以下命令，边训练边评估
 ```shell
-python -u ./slim/nas/train.py --log_steps 10 --cfg configs/cityscape.yaml --use_gpu --use_mpio \
+python -u ./slim/nas/train_nas.py --log_steps 10 --cfg configs/deeplabv3p_mobilenetv2_cityscapes.yaml --use_gpu --use_mpio \
 SLIM.NAS_PORT 23333 \
 SLIM.NAS_ADDRESS "" \
 SLIM.NAS_SEARCH_STEPS 2 \