fix py3 bugs (#2437)

PaddleCV/image_classification/README.md

@@ -167,13 +167,13 @@ Available top-1/top-5 validation accuracy on ImageNet 2012 are listed in table.
 |[ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar) | 79.44%/94.47% |
 |[ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_pretrained.tar) | 78.26%/93.96% |
 |[ResNet152_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_vd_pretrained.tar) | 80.59%/95.30% |
-|[ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_vd_pretrained.tar) | 80.93%/95.33% |
+|[ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet200_vd_pretrained.tar) | 80.93%/95.33% |
 |[ResNeXt101_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_64x4d_pretrained.tar) | 79.35%/94.52% |
 |[ResNeXt101_vd_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar) | 80.78%/95.20% |
 |[SE_ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar) | 78.44%/93.96% |
 |[SE_ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) | 79.12%/94.20% |
-|[SE154_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SE154_vd_pretrained.tar) | 81.45%/95.49% |
+|[SE154_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SE154_vd_pretrained.tar) | 81.40%/95.48% |
 |[GoogleNet](https://paddle-imagenet-models-name.bj.bcebos.com/GoogleNet_pretrained.tar) | 70.70%/89.66% |
 |[ShuffleNetV2](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_pretrained.tar) | 70.03%/89.17% |
-|[InceptionV4](https://paddle-imagenet-models-name.bj.bcebos.com/InceptionV4_pretrained.tar) | 80.88%/95.28% |
+|[InceptionV4](https://paddle-imagenet-models-name.bj.bcebos.com/InceptionV4_pretrained.tar) | 80.77%/95.26% |
 

PaddleCV/image_classification/README_cn.md

@@ -163,12 +163,12 @@ python infer.py \
 |[ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar) | 79.44%/94.47% |
 |[ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_pretrained.tar) | 78.26%/93.96% |
 |[ResNet152_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_vd_pretrained.tar) | 80.59%/95.30% |
-|[ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_vd_pretrained.tar) | 80.93%/95.33% |
+|[ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet200_vd_pretrained.tar) | 80.93%/95.33% |
 |[ResNeXt101_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_64x4d_pretrained.tar) | 79.35%/94.52% |
 |[ResNeXt101_vd_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar) | 80.78%/95.20% |
 |[SE_ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar) | 78.44%/93.96% |
 |[SE_ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) | 79.12%/94.20% |
-|[SE154_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SE154_vd_pretrained.tar) | 81.45%/95.49% |
+|[SE154_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SE154_vd_pretrained.tar) | 81.40%/95.48% |
 |[GoogleNet](https://paddle-imagenet-models-name.bj.bcebos.com/GoogleNet_pretrained.tar) | 70.70%/89.66% |
 |[ShuffleNetV2](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_pretrained.tar) | 70.03%/89.17% |
-|[InceptionV4](https://paddle-imagenet-models-name.bj.bcebos.com/InceptionV4_pretrained.tar) | 80.88%/95.28% |
+|[InceptionV4](https://paddle-imagenet-models-name.bj.bcebos.com/InceptionV4_pretrained.tar) | 80.77%/95.26% |

PaddleCV/image_classification/eval.py

@@ -77,12 +77,8 @@ def eval(args):
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
-    if pretrained_model:
 
-        def if_exist(var):
-            return os.path.exists(os.path.join(pretrained_model, var.name))
-
-        fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
+    fluid.io.load_persistables(exe, pretrained_model)
 
     val_reader = paddle.batch(reader.val(settings=args), batch_size=args.batch_size)
     feeder = fluid.DataFeeder(place=place, feed_list=[image, label])

PaddleCV/image_classification/infer.py

@@ -61,12 +61,7 @@ def infer(args):
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
-    if pretrained_model:
-
-        def if_exist(var):
-            return os.path.exists(os.path.join(pretrained_model, var.name))
-
-        fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
+    fluid.io.load_persistables(exe, pretrained_model)
     if save_inference:
         fluid.io.save_inference_model(
                 dirname=model_name,

PaddleCV/image_classification/models/se_resnext_vd.py

@@ -147,7 +147,7 @@ class SE_ResNeXt():
             act='relu',
             name='conv'+name+'_x2')
         if cardinality == 64:
-            num_filters = num_filters / 2
+            num_filters = num_filters // 2
         conv2 = self.conv_bn_layer(
             input=conv1, num_filters=num_filters * 2, filter_size=1, act=None, name='conv'+name+'_x3')
         scale = self.squeeze_excitation(
@@ -224,7 +224,7 @@ class SE_ResNeXt():
             input=input, pool_size=0, pool_type='avg', global_pooling=True)
         stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
         squeeze = fluid.layers.fc(input=pool,
-                                  size=num_channels / reduction_ratio,
+                                  size=num_channels // reduction_ratio,
                                   act='relu',
                                   param_attr=fluid.param_attr.ParamAttr(
                                       initializer=fluid.initializer.Uniform(

PaddleCV/image_classification/reader_cv2.py

@@ -81,8 +81,8 @@ def crop_image(img, target_size, center):
     height, width = img.shape[:2]
     size = target_size
     if center == True:
-        w_start = (width - size) / 2
-        h_start = (height - size) / 2
+        w_start = (width - size) // 2
+        h_start = (height - size) // 2
     else:
         w_start = np.random.randint(0, width - size + 1)
         h_start = np.random.randint(0, height - size + 1)

PaddleCV/image_classification/run.sh

@@ -15,143 +15,141 @@ python train.py \
 #      >log_SE_ResNeXt50_32x4d.txt 2>&1 &
 
 #SE_154
-"""
-python train.py \
-            --model=SE_154_vd \
-            --batch_size=256 \
-            --total_images=1281167 \
-            --image_shape=3,224,224 \
-            --input_dtype=float32 \
-            --class_dim=1000 \
-            --lr_strategy=cosine_decay \
-            --lr=0.1 \
-            --num_epochs=200 \
-            --with_mem_opt=True \
-            --model_save_dir=output/ \
-            --l2_decay=1e-4 \
-            --use_mixup=True \
-            --use_label_smoothing=True \
-            --label_smoothing_epsilon=0.1 \
+#python train.py \
+#            --model=SE_154_vd \
+#            --batch_size=256 \
+#            --total_images=1281167 \
+#            --image_shape=3,224,224 \
+#            --input_dtype=float32 \
+#            --class_dim=1000 \
+#            --lr_strategy=cosine_decay \
+#            --lr=0.1 \
+#            --num_epochs=200 \
+#            --with_mem_opt=True \
+#            --model_save_dir=output/ \
+#            --l2_decay=1e-4 \
+#            --use_mixup=True \
+#            --use_label_smoothing=True \
+#            --label_smoothing_epsilon=0.1 \
 
 #ResNeXt101_64x4d
-python train.py \
-	--model=ResNeXt101_64x4d \
-            --batch_size=256 \
-            --total_images=1281167 \
-            --image_shape=3,224,224 \
-            --input_dtype=float32 \
-            --class_dim=1000 \
-            --lr_strategy=piecewise_decay \
-            --lr=0.1 \
-            --num_epochs=120 \
-            --with_mem_opt=True \
-            --model_save_dir=output/ \
-            --l2_decay=15e-5
+#python train.py \
+#	--model=ResNeXt101_64x4d \
+#            --batch_size=256 \
+#            --total_images=1281167 \
+#            --image_shape=3,224,224 \
+#            --input_dtype=float32 \
+#            --class_dim=1000 \
+#            --lr_strategy=piecewise_decay \
+#            --lr=0.1 \
+#            --num_epochs=120 \
+#            --with_mem_opt=True \
+#            --model_save_dir=output/ \
+#            --l2_decay=15e-5
 
-python train.py \
+#python train.py \
 #ResNeXt101_vd_64x4d
-	--model=ResNeXt101_vd_64x4d \
-            --batch_size=256 \
-            --total_images=1281167 \
-            --image_shape=3,224,224 \
-            --input_dtype=float32 \
-            --class_dim=1000 \
-            --lr_strategy=cosine_decay \
-            --lr=0.1 \
-            --num_epochs=200 \
-            --with_mem_opt=True \
-            --model_save_dir=output/ \
-            --l2_decay=1e-4 \
-            --use_mixup=True \
-            --use_label_smoothing=True \
-            --label_smoothing_epsilon=0.1
+#	--model=ResNeXt101_vd_64x4d \
+#            --batch_size=256 \
+#            --total_images=1281167 \
+#            --image_shape=3,224,224 \
+#            --input_dtype=float32 \
+#            --class_dim=1000 \
+#            --lr_strategy=cosine_decay \
+#            --lr=0.1 \
+#            --num_epochs=200 \
+#            --with_mem_opt=True \
+#            --model_save_dir=output/ \
+#            --l2_decay=1e-4 \
+#            --use_mixup=True \
+#            --use_label_smoothing=True \
+#            --label_smoothing_epsilon=0.1
 
 #InceptionV4
-python train.py
-	    --model=InceptionV4 \
-            --batch_size=256 \
-            --total_images=1281167 \
-            --image_shape=3,299,299 \
-            --input_dtype=float32 \
-            --class_dim=1000 \
-            --lr_strategy=cosine_decay \
-            --lr=0.045 \
-            --num_epochs=200 \
-            --with_mem_opt=True \
-            --model_save_dir=output/ \
-            --l2_decay=1e-4 \
-            --use_mixup=True \
-            --resize_short_size=320 \
-            --use_label_smoothing=True \
-            --label_smoothing_epsilon=0.1 \
+#python train.py
+#	    --model=InceptionV4 \
+#            --batch_size=256 \
+#            --total_images=1281167 \
+#            --image_shape=3,299,299 \
+#            --input_dtype=float32 \
+#            --class_dim=1000 \
+#            --lr_strategy=cosine_decay \
+#            --lr=0.045 \
+#            --num_epochs=200 \
+#            --with_mem_opt=True \
+#            --model_save_dir=output/ \
+#            --l2_decay=1e-4 \
+#            --use_mixup=True \
+#            --resize_short_size=320 \
+#            --use_label_smoothing=True \
+#            --label_smoothing_epsilon=0.1 \
 #ResNet152_vd
-python train.py
-            --model=ResNet152_vd \
-            --batch_size=256 \
-            --total_images=1281167 \
-            --image_shape=3,224,224 \
-            --input_dtype=float32 \
-            --class_dim=1000 \
-            --lr_strategy=cosine_decay \
-            --lr=0.1 \
-            --num_epochs=200 \
-            --with_mem_opt=True \
-            --model_save_dir=output/ \
-            --l2_decay=1e-4 \
-            --use_mixup=True \
-            --use_label_smoothing=True \
-            --label_smoothing_epsilon=0.1
+#python train.py
+#            --model=ResNet152_vd \
+#            --batch_size=256 \
+#            --total_images=1281167 \
+#            --image_shape=3,224,224 \
+#            --input_dtype=float32 \
+#            --class_dim=1000 \
+#            --lr_strategy=cosine_decay \
+#            --lr=0.1 \
+#            --num_epochs=200 \
+#            --with_mem_opt=True \
+#            --model_save_dir=output/ \
+#            --l2_decay=1e-4 \
+#            --use_mixup=True \
+#            --use_label_smoothing=True \
+#            --label_smoothing_epsilon=0.1
 
 #ResNet200_vd
-python train.py
-            --model=ResNet200_vd \
-            --batch_size=256 \
-            --total_images=1281167 \
-            --image_shape=3,224,224 \
-            --input_dtype=float32 \
-            --class_dim=1000 \
-            --lr_strategy=cosine_decay \
-            --lr=0.1 \
-            --num_epochs=200 \
-            --with_mem_opt=True \
-            --model_save_dir=output/ \
-            --l2_decay=1e-4 \
-            --use_mixup=True \
-            --use_label_smoothing=True \
-            --label_smoothing_epsilon=0.1
+#python train.py
+#            --model=ResNet200_vd \
+#            --batch_size=256 \
+#            --total_images=1281167 \
+#            --image_shape=3,224,224 \
+#            --input_dtype=float32 \
+#            --class_dim=1000 \
+#            --lr_strategy=cosine_decay \
+#            --lr=0.1 \
+#            --num_epochs=200 \
+#            --with_mem_opt=True \
+#            --model_save_dir=output/ \
+#            --l2_decay=1e-4 \
+#            --use_mixup=True \
+#            --use_label_smoothing=True \
+#            --label_smoothing_epsilon=0.1
 
 #ResNet50_vd
-python train.py
-            --model=ResNet50_vd \
-            --batch_size=256 \
-            --total_images=1281167 \
-            --image_shape=3,224,224 \
-            --input_dtype=float32 \
-            --class_dim=1000 \
-            --lr_strategy=cosine_decay \
-            --lr=0.1 \
-            --num_epochs=200 \
-            --with_mem_opt=True \
-            --model_save_dir=output/ \
-            --l2_decay=7e-5 \
-            --use_mixup=True \
-            --use_label_smoothing=True \
-            --label_smoothing_epsilon=0.1
+#python train.py
+#            --model=ResNet50_vd \
+#            --batch_size=256 \
+#            --total_images=1281167 \
+#            --image_shape=3,224,224 \
+#            --input_dtype=float32 \
+#            --class_dim=1000 \
+#            --lr_strategy=cosine_decay \
+#            --lr=0.1 \
+#            --num_epochs=200 \
+#            --with_mem_opt=True \
+#            --model_save_dir=output/ \
+#            --l2_decay=7e-5 \
+#            --use_mixup=True \
+#            --use_label_smoothing=True \
+#            --label_smoothing_epsilon=0.1
 #ResNet50_vc
-python train.py
-  	    --model=ResNet50_vc \
-	    --batch_size=256 \
-            --total_images=1281167 \
-            --image_shape=3,224,224 \
-            --input_dtype=float32 \
-            --class_dim=1000 \
-            --lr_strategy=cosine_decay \
-            --lr=0.1 \
-            --num_epochs=200 \
-            --with_mem_opt=True \
-            --model_save_dir=output/ \
-            --l2_decay=1e-4 \
-"""
+#python train.py
+#  	    --model=ResNet50_vc \
+#	    --batch_size=256 \
+#            --total_images=1281167 \
+#            --image_shape=3,224,224 \
+#            --input_dtype=float32 \
+#            --class_dim=1000 \
+#            --lr_strategy=cosine_decay \
+#            --lr=0.1 \
+#            --num_epochs=200 \
+#            --with_mem_opt=True \
+#            --model_save_dir=output/ \
+#            --l2_decay=1e-4 \
 
 #AlexNet:
 #python train.py \