replace warning function >> flow.image.Resize() with new api

Classification/cnns/ofrecord_util.py

@@ -92,11 +92,10 @@ def load_imagenet_for_training(args):
                                                     color_space=color_space)
     label = flow.data.OFRecordRawDecoder(
         ofrecord, "class/label", shape=(), dtype=flow.int32)
-    rsz = flow.image.Resize(image, resize_x=args.image_size, resize_y=args.image_size,
-                            color_space=color_space)
 
+    rsz = flow.image.Resize(image, target_size=[args.image_size, args.image_size]) 
     rng = flow.random.CoinFlip(batch_size=train_batch_size)  # , seed=seed)
-    normal = flow.image.CropMirrorNormalize(rsz, mirror_blob=rng, color_space=color_space,
+    normal = flow.image.CropMirrorNormalize(rsz[0], mirror_blob=rng, color_space=color_space,
                                             mean=args.rgb_mean, std=args.rgb_std, output_dtype=flow.float)
     return label, normal
 
@@ -115,10 +114,13 @@ def load_imagenet_for_validation(args):
         ofrecord, "encoded", color_space=color_space)
     label = flow.data.OFRecordRawDecoder(
         ofrecord, "class/label", shape=(), dtype=flow.int32)
+
     rsz = flow.image.Resize(
-        image, resize_shorter=args.resize_shorter, color_space=color_space)
+        image, resize_side="shorter",
+        keep_aspect_ratio=True,
+        target_size=args.resize_shorter)
 
-    normal = flow.image.CropMirrorNormalize(rsz, color_space=color_space,
+    normal = flow.image.CropMirrorNormalize(rsz[0], color_space=color_space,
                                             crop_h=args.image_size, crop_w=args.image_size, crop_pos_y=0.5, crop_pos_x=0.5,
                                             mean=args.rgb_mean, std=args.rgb_std, output_dtype=flow.float)
     return label, normal

Classification/cnns/train.sh

-#!/bin/sh
+#!/bin/bash
 rm -rf core.* 
 rm -rf ./output/snapshots/*
 
@@ -17,6 +17,29 @@ python3 of_cnn_train_val.py \
     --model="resnet50"
 
 
+
+# # training with mini-imagenet
+# DATA_ROOT=data/mini-imagenet/ofrecord
+# python3 of_cnn_train_val.py \
+#     --train_data_dir=$DATA_ROOT/train \
+#     --num_examples=50 \
+#     --train_data_part_num=1 \
+#     --val_data_dir=$DATA_ROOT/validation \
+#     --num_val_examples=50 \
+#     --val_data_part_num=1 \
+#     --num_nodes=1 \
+#     --gpu_num_per_node=1 \
+#     --model_update="momentum" \
+#     --learning_rate=0.001 \
+#     --loss_print_every_n_iter=1 \
+#     --batch_size_per_device=16 \
+#     --val_batch_size_per_device=10 \
+#     --num_epoch=10 \
+#     --model="resnet50"
+
+
+
+
 # # training with imagenet
 # DATA_ROOT=/datasets/ImageNet/ofrecord
 # LOG_FOLDER=../logs