sandeep-krishnamurthy commented on a change in pull request #15475: Add
transpose_conv, sorting and searching operator benchmarks to Opperf
URL: https://github.com/apache/incubator-mxnet/pull/15475#discussion_r304115134
##########
File path: benchmark/opperf/nd_operations/nn_conv_operators.py
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@@ -135,3 +135,50 @@ def run_convolution_operators_benchmarks(ctx=mx.cpu(),
dtype='float32', warmup=2
# Prepare combined results
mx_conv_op_results = merge_map_list(conv1d_benchmark_res +
conv2d_benchmark_res)
return mx_conv_op_results
+
+
+def run_transpose_convolution_operators_benchmarks(ctx=mx.cpu(),
dtype='float32', warmup=10, runs=50):
+ # Conv1DTranspose Benchmarks
+ conv1d_transpose_benchmark_res = []
+ for conv_data in [(32, 3, 256), (32, 3, 64)]:
+ conv1d_transpose_benchmark_res +=
run_performance_test([getattr(MX_OP_MODULE, "Deconvolution")],
+
run_backward=True,
+ dtype=dtype,
+ ctx=ctx,
+
inputs=[{"data": conv_data,
+
"weight": (3, 64, 3),
+
"bias": (64,),
+
"kernel": (3,),
+
"stride": (1,),
Review comment:
Agreed, for now, I used configuration used in standard architecture to get
started. I was planning to cover more variations in next phase with possible
automation around input generation. Is that okay?
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