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_r249938673
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File path: python/mxnet/gluon/data/vision/transforms.py
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@@ -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:
@szha - The way I am thinking is - this transformer is used when user is
exporting a model for inference and he wants to fuse transformations as part of
the network. This transformer is not applicable for training / validation which
all works as existing today.
So to summarize, the idea here is:
1. Training does not changing anything from what it is today - dataset and
dataloader
2. When user wants to export a model transformations (Resize, Batchify,
ToTensor, Normalize) is concatenated to network block and exported.
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