test data loader

cnn_e2e/test_data_loader.py

+import config as configs
+parser = configs.get_parser()
+args = parser.parse_args()
+configs.print_args(args)
+
+from dali_util import get_rec_iter
+import numpy as np
+
+import ofrecord_util
+from job_function_util import get_val_config
+import oneflow as flow
+
+from PIL import Image
+
+flow.config.gpu_device_num(args.gpu_num_per_node)
+flow.config.enable_debug_mode(True)
+@flow.function(get_val_config(args))
+def InferenceNet():
+    (labels, images) = ofrecord_util.load_imagenet_for_validation(args)
+    return images, labels
+
+def save_bmp(array, filepath):
+    print(array.dtype)
+    im = Image.fromarray(array)
+    im.save(filepath)
+
+if __name__ == '__main__':
+    train_data_iter, val_data_iter = get_rec_iter(args, True)
+    #train_data_iter.reset()
+    for i, batches in enumerate(val_data_iter):
+        images, labels = batches
+        print(labels)
+        np.save('output/dali_val_data.npy', images)
+        save_bmp(images[0], 'output/dali_val.bmp')
+
+
+    for i, batches in enumerate(train_data_iter):
+        images, labels = batches
+        print(labels)
+        np.save('output/dali_train_data.npy', images)
+
+    images, labels = InferenceNet().get()
+    images = images.ndarray().astype(np.uint8)
+    np.save('output/of_val_data.npy', images)
+    save_bmp(images[0], 'output/of_val.bmp')
+    
\ No newline at end of file

test.sh

@@ -2,7 +2,7 @@ rm -rf core.*
 #gdb --args \
 #DATA_ROOT=/dataset/imagenet_10pics
 DATA_ROOT=/dataset/imagenet_1pic
-python cnn_e2e/test_dali.py \
+python cnn_e2e/test_data_loader.py \
   --data_train=$DATA_ROOT/mxnet/train.rec \
   --data_train_idx=$DATA_ROOT/mxnet/train.idx \
   --data_val=$DATA_ROOT/mxnet/train.rec \