tensorflow: Standalone graph trained using on NCHW data_format giving errors on CPU when testing (running forward pass)

System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes,
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 14.04
• TensorFlow installed from (source or binary): Binary (.whl file)
• TensorFlow version (use command below): 1.2.0-rc1
• Python version: 2.7
• Bazel version (if compiling from source): N/A
• CUDA/cuDNN version: CUDA 8.0.61 CuDNN 5.1.10
• GPU model and memory: GeForce GTX 1080
• Exact command to reproduce:

Describe the problem

I’ve a standalone graph create by freezing my model/net which has a few slim.conv2d, slim.max_pool2d operations defined with NCHW data format. I’ve used freeze_graph.py utility to create the stand alone graph using a trained checkpoint (trained using NCHW format). When I tested the graph (ran forward pass) on a Ubuntu machine with GPU, it was running very well. But when I ran the same code to test (forward pass) with the same stand alone graph a Ubuntu machine that has no GPU but CPU, I got the following errors:

2017-09-18 17:57:48.890761: E tensorflow/core/common_runtime/executor.cc:644] Executor failed to create kernel. Invalid argument: Default MaxPoolingOp only supports NHWC.
         [[Node: text_box_300/pool1/MaxPool = MaxPool[T=DT_FLOAT, data_format="NCHW", ksize=[1, 1, 2, 2], padding="SAME", strides=[1, 1, 2, 2], _device="/job:localhost/replica:0/task:0/cpu:0"](text_box_300/conv1/conv1_2/Relu)]]
2017-09-18 17:57:48.892768: E main_textd_test.cc:457] Running model failed: Invalid argument: Default MaxPoolingOp only supports NHWC.
         [[Node: text_box_300/pool1/MaxPool = MaxPool[T=DT_FLOAT, data_format="NCHW", ksize=[1, 1, 2, 2], padding="SAME", strides=[1, 1, 2, 2], _device="/job:localhost/replica:0/task:0/cpu:0"](text_box_300/conv1/conv1_2/Relu)]]

I believe the errors are related to https://github.com/tensorflow/tensorflow/issues/2660 Is it possible to modify the stand alone graph that is running well on GPU to make it run successfully on CPU machine ? Is it possible to avoid retraining the model using NHWC format and recreate the standalone model ?

Source code / logs

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