[GitHub] sandeep-krishnamurthy commented on a change in pull request #13930: Add batchify transformer to help end to end models

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sandeep-krishnamurthy commented on a change in pull request #13930: Add batchify transformer to help end to end models
URL: https://github.com/apache/incubator-mxnet/pull/13930#discussion_r249935599
 
 

 ##########
 File path: python/mxnet/gluon/data/vision/transforms.py
 ##########
 @@ -485,3 +485,52 @@ def __init__(self, alpha):
 
     def hybrid_forward(self, F, x):
         return F.image.random_lighting(x, self._alpha)
+
+
+class Batchify(HybridBlock):
+    """Joins a list of tensors of shape (C x H x W) into a single
+    tensor of shape (N x C x H x W) where N is the number of input
+    tensors.
+
+    If the input is a single tensor of shape (C x H x W) it is bathchified
+    to a tensor of shape (1 x C x H x W).
+
+    This transformer is useful when transformation pipeline is fused into
+    neural network graph resulting in single model/graph. When running
+    mini batch inference on such graph, raw input tensors, before transformations,
+    can be of different shapes, hence, cannot be batched into single tensor. Hence,
+    input to the graph will be list of Tensors that can be batched after Resize
+    transformation.
+
+    For example, a typical graph can look like below:
+
+    Imdecode -> Resize -> Batchify -> ToTensor -> Normalize -> Network
 
 Review comment:
   @zhreshold - Thanks for pointing it out. Functionally, input can be either HWC or CHW it gets converted to NHWC or NCHW respectively. Will update the code comment.

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