Merge pull request #1810 from shippingwang/fix_run_script_bug

fix eval bug and refine run script

fluid/PaddleCV/image_classification/README.md

@@ -81,7 +81,7 @@ python train.py \
 * **lr**: initialized learning rate. Default: 0.1.
 * **pretrained_model**: model path for pretraining. Default: None.
 * **checkpoint**: the checkpoint path to resume. Default: None.
-* **model_category**: the category of models, ("models"|"models_name"). Default: "models".
+* **model_category**: the category of models, ("models"|"models_name"). Default: "models_name".
 
 Or can start the training step by running the ```run.sh```.
 
@@ -221,6 +221,8 @@ Models are trained by starting with learning rate ```0.1``` and decaying it by `
 
 - Released models: not specify parameter names
 
+**NOTE: These are trained by using model_category=models**
+
 |model | top-1/top-5 accuracy(PIL)| top-1/top-5 accuracy(CV2) |
 |- |:-: |:-:|
 |[ResNet152](http://paddle-imagenet-models.bj.bcebos.com/ResNet152_pretrained.zip) | 78.18%/93.93% | 78.11%/94.04% |

fluid/PaddleCV/image_classification/README_cn.md

@@ -79,7 +79,7 @@ python train.py \
 * **lr**: initialized learning rate. Default: 0.1.
 * **pretrained_model**: model path for pretraining. Default: None.
 * **checkpoint**: the checkpoint path to resume. Default: None.
-* **model_category**: the category of models, ("models"|"models_name"). Default:"models".
+* **model_category**: the category of models, ("models"|"models_name"). Default:"models_name".
 
 **数据读取器说明：** 数据读取器定义在```reader.py```和```reader_cv2.py```中, 一般, CV2 reader可以提高数据读取速度, reader(PIL)可以得到相对更高的精度, 在[训练阶段](#training-a-model), 默认采用的增广方式是随机裁剪与水平翻转, 而在[评估](#inference)与[推断](#inference)阶段用的默认方式是中心裁剪。当前支持的数据增广方式有：
 * 旋转
@@ -213,6 +213,8 @@ Models包括两种模型：带有参数名字的模型，和不带有参数名
 
 - Released models: not specify parameter names
 
+**注意：这是model_category = models 的预训练模型**
+
 |model | top-1/top-5 accuracy(PIL)| top-1/top-5 accuracy(CV2) |
 |- |:-: |:-:|
 |[ResNet152](http://paddle-imagenet-models.bj.bcebos.com/ResNet152_pretrained.zip) | 78.18%/93.93% | 78.11%/94.04% |

fluid/PaddleCV/image_classification/eval.py

@@ -7,8 +7,6 @@ import time
 import sys
 import paddle
 import paddle.fluid as fluid
-#import models
-import models_name as models
 #import reader_cv2 as reader
 import reader as reader
 import argparse
@@ -27,9 +25,20 @@ add_arg('image_shape',      str,  "3,224,224",         "Input image size")
 add_arg('with_mem_opt',     bool, True,                "Whether to use memory optimization or not.")
 add_arg('pretrained_model', str,  None,                "Whether to use pretrained model.")
 add_arg('model',            str,  "SE_ResNeXt50_32x4d", "Set the network to use.")
+add_arg('model_category',   str,  "models_name",            "Whether to use models_name or not, valid value:'models','models_name'." )
+
 # yapf: enable
 
-model_list = [m for m in dir(models) if "__" not in m]
+
+def set_models(model_category):
+    global models
+    assert model_category in ["models", "models_name"
+                              ], "{} is not in lists: {}".format(
+                                  model_category, ["models", "models_name"])
+    if model_category == "models_name":
+        import models_name as models
+    else:
+        import models as models
 
 
 def eval(args):
@@ -40,6 +49,7 @@ def eval(args):
     with_memory_optimization = args.with_mem_opt
     image_shape = [int(m) for m in args.image_shape.split(",")]
 
+    model_list = [m for m in dir(models) if "__" not in m]
     assert model_name in model_list, "{} is not in lists: {}".format(args.model,
                                                                      model_list)
 
@@ -63,11 +73,11 @@ def eval(args):
         acc_top5 = fluid.layers.accuracy(input=out0, label=label, k=5)
     else:
         out = model.net(input=image, class_dim=class_dim)
-        cost = fluid.layers.cross_entropy(input=out, label=label)
-
+        cost, pred = fluid.layers.softmax_with_cross_entropy(
+            out, label, return_softmax=True)
         avg_cost = fluid.layers.mean(x=cost)
-        acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
-        acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+        acc_top1 = fluid.layers.accuracy(input=pred, label=label, k=1)
+        acc_top5 = fluid.layers.accuracy(input=pred, label=label, k=5)
 
     test_program = fluid.default_main_program().clone(for_test=True)
 
@@ -125,6 +135,7 @@ def eval(args):
 def main():
     args = parser.parse_args()
     print_arguments(args)
+    set_models(args.model_category)
     eval(args)
 
 

fluid/PaddleCV/image_classification/infer.py

@@ -7,7 +7,6 @@ import time
 import sys
 import paddle
 import paddle.fluid as fluid
-import models
 import reader
 import argparse
 import functools
@@ -23,9 +22,19 @@ add_arg('image_shape',      str,  "3,224,224",          "Input image size")
 add_arg('with_mem_opt',     bool, True,                 "Whether to use memory optimization or not.")
 add_arg('pretrained_model', str,  None,                 "Whether to use pretrained model.")
 add_arg('model',            str,  "SE_ResNeXt50_32x4d", "Set the network to use.")
+add_arg('model_category',   str,  "models_name",        "Whether to use models_name or not, valid value:'models','models_name'." )
 # yapf: enable
 
-model_list = [m for m in dir(models) if "__" not in m]
+
+def set_models(model_category):
+    global models
+    assert model_category in ["models", "models_name"
+                              ], "{} is not in lists: {}".format(
+                                  model_category, ["models", "models_name"])
+    if model_category == "models_name":
+        import models_name as models
+    else:
+        import models as models
 
 
 def infer(args):
@@ -35,7 +44,7 @@ def infer(args):
     pretrained_model = args.pretrained_model
     with_memory_optimization = args.with_mem_opt
     image_shape = [int(m) for m in args.image_shape.split(",")]
-
+    model_list = [m for m in dir(models) if "__" not in m]
     assert model_name in model_list, "{} is not in lists: {}".format(args.model,
                                                                      model_list)
 
@@ -85,6 +94,7 @@ def infer(args):
 def main():
     args = parser.parse_args()
     print_arguments(args)
+    set_models(args.model_category)
     infer(args)
 
 

fluid/PaddleCV/image_classification/run.sh

 #Hyperparameters config
+#Example: SE_ResNext50_32x4d
 python train.py \
        --model=SE_ResNeXt50_32x4d \
-       --batch_size=32 \
+       --batch_size=400 \
        --total_images=1281167 \
        --class_dim=1000 \
        --image_shape=3,224,224 \
        --model_save_dir=output/ \
        --with_mem_opt=True \
-       --lr_strategy=piecewise_decay \
-       --lr=0.1
+       --lr_strategy=cosine_decay \
+       --lr=0.1 \
+       --num_epochs=200 \
+       --l2_decay=1.2e-4 \
+       --model_category=models_name \
 #      >log_SE_ResNeXt50_32x4d.txt 2>&1 &
-
 #AlexNet:
 #python train.py \
 #       --model=AlexNet \
@@ -20,23 +23,11 @@ python train.py \
 #       --image_shape=3,224,224 \
 #       --model_save_dir=output/ \
 #       --with_mem_opt=True \
+#       --model_category=models_name \
 #       --lr_strategy=piecewise_decay \
 #	--num_epochs=120 \
-#       --lr=0.01
-
-#VGG11:
-#python train.py \
-#       --model=VGG11 \
-#       --batch_size=512 \
-#       --total_images=1281167 \
-#       --class_dim=1000 \
-#       --image_shape=3,224,224 \
-#       --model_save_dir=output/ \
-#       --with_mem_opt=True \
-#       --lr_strategy=piecewise_decay \
-#       --num_epochs=120 \
-#       --lr=0.1
-
+#       --lr=0.01 \
+#       --l2_decay=1e-4
 
 #MobileNet v1:
 #python train.py \
@@ -47,9 +38,11 @@ python train.py \
 #       --image_shape=3,224,224 \
 #       --model_save_dir=output/ \
 #       --with_mem_opt=True \
+#       --model_category=models_name \
 #       --lr_strategy=piecewise_decay \
 #	--num_epochs=120 \
-#       --lr=0.1
+#       --lr=0.1 \
+#       --l2_decay=3e-5
 
 #python train.py \
 #	--model=MobileNetV2 \
@@ -58,10 +51,12 @@ python train.py \
 #	--class_dim=1000 \
 #	--image_shape=3,224,224 \
 #	--model_save_dir=output/ \
+#       --model_category=models_name \
 #	--with_mem_opt=True \
 #	--lr_strategy=cosine_decay \
-#	--num_epochs=200 \
-#	--lr=0.1
+#	--num_epochs=240 \
+#	--lr=0.1 \
+#       --l2_decay=4e-5
 #ResNet50:
 #python train.py \
 #       --model=ResNet50 \
@@ -71,9 +66,11 @@ python train.py \
 #       --image_shape=3,224,224 \
 #       --model_save_dir=output/ \
 #       --with_mem_opt=True \
+#       --model_category=models_name \
 #       --lr_strategy=piecewise_decay \
 #	--num_epochs=120 \
-#       --lr=0.1
+#       --lr=0.1 \
+#       --l2_decay=1e-4
 
 #ResNet101:
 #python train.py \
@@ -83,44 +80,58 @@ python train.py \
 #       --class_dim=1000 \
 #       --image_shape=3,224,224 \
 #       --model_save_dir=output/ \
-#       --with_mem_opt=False \
+#       --model_category=models_name \
+#       --with_mem_opt=True \
 #       --lr_strategy=piecewise_decay \
 #	--num_epochs=120 \
-#       --lr=0.1
+#       --lr=0.1 \
+#       --l2_decay=1e-4
 
 #ResNet152:
 #python train.py \
 #       --model=ResNet152 \
 #       --batch_size=256 \
 #       --total_images=1281167 \
+#       --class_dim=1000 \
 #       --image_shape=3,224,224 \
+#       --model_save_dir=output/ \
 #       --lr_strategy=piecewise_decay \
+#       --model_category=models_name \
+#       --with_mem_opt=True \
 #       --lr=0.1 \
 #       --num_epochs=120 \
 #       --l2_decay=1e-4
 
 
-#SE_ResNeXt50:
+#SE_ResNeXt50_32x4d:
 #python train.py \
-#       --model=SE_ResNeXt50 \
+#       --model=SE_ResNeXt50_32x4d \
 #       --batch_size=400 \
 #       --total_images=1281167 \
+#	--class_dim=1000 \
 #       --image_shape=3,224,224 \
 #       --lr_strategy=cosine_decay \
+#	--model_category=models_name \
+#       --model_save_dir=output/ \
 #       --lr=0.1 \
 #       --num_epochs=200 \
-#       --l2_decay=12e-5
+#       --with_mem_opt=True \
+#       --l2_decay=1.2e-4
 
-#SE_ResNeXt101:
+#SE_ResNeXt101_32x4d:
 #python train.py \
-#        --model=SE_ResNeXt101 \
+#        --model=SE_ResNeXt101_32x4d \
 #        --batch_size=400 \
 #        --total_images=1281167 \
+#        --class_dim=1000 \
 #        --image_shape=3,224,224 \
 #        --lr_strategy=cosine_decay \
+#        --model_category=models_name \
+#        --model_save_dir=output/ \
 #        --lr=0.1 \
 #        --num_epochs=200 \
-#        --l2_decay=15e-5
+#        --with_mem_opt=True \
+#        --l2_decay=1.5e-5
 
 #VGG11:
 #python train.py \
@@ -129,8 +140,12 @@ python train.py \
 #        --total_images=1281167 \
 #        --image_shape=3,224,224 \
 #        --lr_strategy=cosine_decay \
+#        --class_dim=1000 \
+#        --model_category=models_name \
+#        --model_save_dir=output/ \
 #        --lr=0.1 \
 #        --num_epochs=90 \
+#        --with_mem_opt=True \
 #        --l2_decay=2e-4
 
 #VGG13:
@@ -138,8 +153,42 @@ python train.py \
 #        --model=VGG13 \          
 #        --batch_size=256 \
 #        --total_images=1281167 \
+#        --class_dim=1000 \
 #        --image_shape=3,224,224 \
 #        --lr_strategy=cosine_decay \
 #        --lr=0.01 \
 #        --num_epochs=90 \
+#        --model_category=models_name \
+#        --model_save_dir=output/ \
+#        --with_mem_opt=True \
+#        --l2_decay=3e-4
+
+#VGG16:
+#python train.py
+#	--model=VGG16 \
+#	--batch_size=256 \
+#	--total_images=1281167 \
+#       --class_dim=1000 \
+#	--lr_strategy=cosine_decay \
+#	--image_shape=3,224,224 \
+#	--model_category=models_name \
+#       --model_save_dir=output/ \
+#	--lr=0.01 \
+#	--num_epochs=90 \
+#       --with_mem_opt=True \
+#	--l2_decay=3e-4
+
+#VGG19:
+#python train.py
+#	--model=VGG19 \
+#	--batch_size=256 \
+#	--total_images=1281167 \
+#	--class_dim=1000 \
+#	--image_shape=3,224,224 \
+#	--lr_strategy=cosine_decay \
+#	--lr=0.01 \
+#	--num_epochs=90 \
+#       --with_mem_opt=True \
+#	--model_category=models_name \
+#       --model_save_dir=output/ \
 #	--l2_decay=3e-4

fluid/PaddleCV/image_classification/train.py

@@ -39,7 +39,7 @@ add_arg('lr_strategy',      str,   "piecewise_decay",    "Set the learning rate
 add_arg('model',            str,   "SE_ResNeXt50_32x4d", "Set the network to use.")
 add_arg('enable_ce',        bool,  False,                "If set True, enable continuous evaluation job.")
 add_arg('data_dir',         str,   "./data/ILSVRC2012",  "The ImageNet dataset root dir.")
-add_arg('model_category',   str,   "models",        "Whether to use models_name or not, valid value:'models','models_name'." )
+add_arg('model_category',   str,   "models_name",        "Whether to use models_name or not, valid value:'models','models_name'." )
 add_arg('fp16',             bool,  False,                "Enable half precision training with fp16." )
 add_arg('scale_loss',       float, 1.0,                  "Scale loss for fp16." )
 add_arg('l2_decay',         float, 1e-4,                 "L2_decay parameter.")