modify

cnn_e2e/ofrecord_util.py

@@ -34,8 +34,8 @@ def load_imagenet(args, batch_size, data_dir, data_part_num, codec):
         batch_size=batch_size,
         data_part_num=data_part_num,
         part_name_suffix_length=5,
-        shuffle = True,
-        buffer_size=32768,
+        #shuffle = True,
+        #buffer_size=32768,
         name="decode",
     )
 
@@ -45,7 +45,7 @@ def load_imagenet_for_training(args):
     train_batch_size = total_device_num * args.batch_size_per_device
     codec=flow.data.ImageCodec([
         #flow.data.ImagePreprocessor('bgr2rgb'),
-        flow.data.ImageCropWithRandomSizePreprocessor(area=(0.08, 1)),
+        #flow.data.ImageCropWithRandomSizePreprocessor(area=(0.08, 1)),
         flow.data.ImageResizePreprocessor(args.image_size, args.image_size),
         flow.data.ImagePreprocessor('mirror'),
     ])
@@ -101,13 +101,10 @@ def load_imagenet_for_training2(args):
         rsz = flow.image.Resize(image, resize_x=float(args.image_size),
                                 resize_y=float(args.image_size),
                                 color_space=color_space)
-        print(rsz.shape)
-        print(label.shape)
 
         rng = flow.image.CoinFlip(batch_size=train_batch_size, seed=seed)
         normal = flow.image.CropMirrorNormalize(rsz, mirror_blob=rng, color_space=color_space,
             mean=args.rgb_mean, std=args.rgb_std, output_dtype = flow.float)
-        print(normal.shape)
         return label, normal
 
 
@@ -132,8 +129,7 @@ if __name__ == "__main__":
         else:
             print("Loading synthetic data.")
             (labels, images) = load_synthetic(args)
-        predictions = labels
-        outputs = {"predictions":predictions, "labels": labels}
+        outputs = {"images":images, "labels": labels}
         return outputs
 
     total_device_num = args.num_nodes * args.gpu_num_per_node
@@ -141,6 +137,6 @@ if __name__ == "__main__":
     summary = Summary(args.log_dir, args, filename='io_test.csv')
     metric = Metric(desc='io_test', calculate_batches=args.loss_print_every_n_iter,
                     summary=summary, save_summary_steps=args.loss_print_every_n_iter,
-                    batch_size=train_batch_size)
+                    batch_size=train_batch_size, prediction_key=None)
     for i in range(1000):
         IOTest().async_get(metric.metric_cb(0, i))