refine scripts

Classification/cnns/evaluate.sh

@@ -2,7 +2,7 @@
 rm -rf core.* 
 
 # Set up  dataset root dir
-DATA_ROOT=/dataset/ImageNet/ofrecord
+DATA_ROOT=/datasets/ImageNet/ofrecord
 
 # Set up model path, e.g. :  vgg16_of_best_model_val_top1_721  alexnet_of_best_model_val_top1_54762 
 MODEL_LOAD_DIR="resnet_v15_of_best_model_val_top1_77318"
@@ -20,20 +20,19 @@ MODEL_LOAD_DIR="resnet_v15_of_best_model_val_top1_77318"
     --model="resnet50"
 
 
-# # # Validatation on imagenet
-# DATA_ROOT=/datasets/ImageNet/ofrecord
 
-# # Set up model path, e.g. : output/snapshots/model_save-20200831131226/snapshot_epoch_9
-# MODEL_LOAD_DIR="saved_models/snapshots/model_save-20200905093837/snapshot_epoch_89"
 
-#   python3  of_cnn_evaluate.py \
+# # Evaluate with mini-imagenet
+# DATA_ROOT=data/mini-imagenet/ofrecord
+# MODEL_LOAD_DIR="output/snapshots/model_save-20200907130848/snapshot_epoch_9"
+# python3  of_cnn_evaluate.py \
 #     --num_epochs=3 \
-#     --num_val_examples=50000 \
+#     --num_val_examples=50 \
 #     --model_load_dir=$MODEL_LOAD_DIR  \
 #     --val_data_dir=$DATA_ROOT/validation \
-#     --val_data_part_num=256 \
+#     --val_data_part_num=1 \
 #     --num_nodes=1 \
 #     --node_ips='127.0.0.1' \
-#     --gpu_num_per_node=8 \
-#     --val_batch_size_per_device=100 \
+#     --gpu_num_per_node=1 \
+#     --val_batch_size_per_device=10 \
 #     --model="resnet50"

Classification/cnns/train.sh

@@ -2,14 +2,14 @@
 rm -rf core.* 
 rm -rf ./output/snapshots/*
 
+
 # training with synthetic data
 python3 of_cnn_train_val.py \
     --num_examples=50 \
     --num_val_examples=50 \
     --num_nodes=1 \
     --gpu_num_per_node=1 \
-    --optimizer="sgd" \
-    --momentum=0.875 \
+    --optimizer="adam" \
     --learning_rate=0.001 \
     --loss_print_every_n_iter=1 \
     --batch_size_per_device=16 \
@@ -19,6 +19,7 @@ python3 of_cnn_train_val.py \
 
 
 
+
 # # training with mini-imagenet
 # DATA_ROOT=data/mini-imagenet/ofrecord
 # python3 of_cnn_train_val.py \
@@ -48,7 +49,7 @@ python3 of_cnn_train_val.py \
 #  mkdir -p $LOG_FOLDER
 #  LOGFILE=$LOG_FOLDER/resnet_training.log
 
-# python3 of_cnn_train_val_new.py \
+# python3 of_cnn_train_val.py \
 #      --train_data_dir=$DATA_ROOT/train \
 #      --train_data_part_num=256 \
 #      --val_data_dir=$DATA_ROOT/validation \

requirements.txt

@@ -1,2 +1,3 @@
 numpy>=1.17.2
-pandas>=1.0.4
+pandas>=1.0.4
+pillow>=7.2.0