fix vd

configs/high_performance/dali_mix.yaml

+mode: 'train'
+ARCHITECTURE:
+    name: 'ResNet50_vd'
+
+pretrained_model: ""
+model_save_dir: "./output/"
+classes_num: 1000
+total_images: 1281167
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 200
+topk: 5
+image_shape: [3, 224, 224]
+
+use_dali: True
+use_mix: True
+ls_epsilon: 0.1
+
+LEARNING_RATE:
+    function: 'Cosine'
+    params:
+        lr: 0.1
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.000070
+
+TRAIN:
+    batch_size: 256
+    num_workers: 4
+    file_list: "./dataset/ILSVRC2012/train_list.txt"
+    data_dir: "./dataset/ILSVRC2012/"
+    shuffle_seed: 0
+
+VALID:
+    batch_size: 64
+    num_workers: 4
+    file_list: "./dataset/ILSVRC2012/val_list.txt"
+    data_dir: "./dataset/ILSVRC2012/"
+    shuffle_seed: 0

tools/dali.py

@@ -313,6 +313,7 @@ def normalize(feeds, config):
 
 
 def mix(feeds, config, is_train=True):
+    env = os.environ
     gpu_num = paddle.fluid.core.get_cuda_device_count() if (
         'PADDLE_TRAINERS_NUM') and (
             'PADDLE_TRAINER_ID'

tools/run_dali.sh

@@ -5,8 +5,15 @@ export FLAGS_fraction_of_gpu_memory_to_use=0.8
 
 
 python3 -m paddle.distributed.launch \
-    --selected_gpus="0,1" \
+    --selected_gpus="0,1,2,3" \
     tools/train.py \
-        -c ./configs/high_performance/dali.yaml \
+        -c ./configs/high_performance/dali_mix.yaml \
 	-o use_mix=True \
         -o use_dali=True
+
+#python3 -m paddle.distributed.launch \
+#    --selected_gpus="0,1,2,3" \
+#    tools/train.py \
+#        -c ./configs/high_performance/dali.yaml \
+#	 -o use_mix=True \
+#        -o use_dali=True