[NNVM][TENSORFLOW] Mobilenet support. #1335
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srkreddy1238
commented
Jun 25, 2018
srkreddy1238
commented
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@tqchen @Huyuwei Please review. |
* reshape_like and Shape operators support. * fix in FusedBatchNorm * mobilenet frontend testcase updated.
| # Rearrange inputs from | ||
| # (data, gamma, beta, moving_mean, moving_variance) | ||
| # to | ||
| # (data, gamma, beta, moving_mean, moving_varience) |
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new_inputs is the same as inputs?
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Yes, FusedBatchNorm has same template as NNVM.
updated this unnecessary assignment.
nnvm/python/nnvm/testing/tf.py
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| @@ -227,3 +227,31 @@ def get_workload_inception_v1(): | |||
| graph_def.ParseFromString(f.read()) | |||
| graph = tf.import_graph_def(graph_def, name='') | |||
| return (image_data, tvm_data, graph_def) | |||
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| def get_workload_mobilenet(): | |||
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get_workload_inception_v1() get_workload_inception_v3() get_workload_mobilenet()
these functions can be merged into a unified one that takes url and returns a tensorflow model
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Unified the model import across all these.
tqchen
added
status: review in progress
status: need update
| graph_def.ParseFromString(f.read()) | ||
| graph = tf.import_graph_def(graph_def, name='') | ||
| return graph_def | ||
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| def get_workload_inception_v3(): |
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get_workload_inception_v3() and get_workload_inception_v1() should also have only one line code as get_workload_mobilenet()
No need to prepare inpur data here, use random input for all tests
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InceptionV1 and V3 has some challenges using random data now.
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Pool operators has difference in implementation with SAME padding. we use pad operator to meet 4 sides padding but the pad_value is 0 or passed value. In fact we should not consider the padded value in operation (avg/ max). At least maxpool I could workaround by pad_value = -(FLOAT_MAX) but avg pool is a tricky now.
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Actual input for V1 is jpeg encoded stream. TF use decode_jpeg operator for this. NNVM we bypass it and feed decoded frame directly. I could try this by taking random_data, encoding and giving to tensorflow once the pool operators perform consistently.
I will revisit these test_cases once I get the pool ops working in sync with tensorflow.
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I see. Since now count_include_pad is introduced to AvgPool: #1163, can you use it to handle same padding?
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Great. In fact the pool operator test_cases I kept on hold on these reasons.
I will revisit the pool ops now. Thanks.
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Taking this discussion to https://discuss.tvm.ai/t/avgpool-same-padding/314 to resolve avg_pool issue.
tqchen
added
status: accepted
and removed
status: need update
