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#####################################################################################
# The MIT License (MIT)
#
# Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#####################################################################################
import migraphx
def test_conv_relu():
p = migraphx.parse_onnx("conv_relu_maxpool_test.onnx")
print(p)
print("Compiling ...")
p.compile(migraphx.get_target("gpu"), offload_copy=False)
print(p)
params = {}
for key, value in p.get_parameter_shapes().items():
print("Parameter {} -> {}".format(key, value))
params[key] = migraphx.to_gpu(migraphx.generate_argument(value))
r = migraphx.from_gpu(p.run(params)[-1])
print(r)
# TODO: placeholder until tuple shapes and arguments exposed
#def test_dyn_batch():
# a = migraphx.shape.dynamic_dimension(1, 4, {2, 4})
# b = migraphx.shape.dynamic_dimension(3, 3)
# c = migraphx.shape.dynamic_dimension(32, 32)
# dd_map = {"0": [a, b, c, c]}
# p = migraphx.parse_onnx("conv_relu_maxpool_test.onnx",
# map_dyn_input_dims=dd_map)
# print(p)
# print("Compiling ...")
# p.compile(migraphx.get_target("gpu"), offload_copy=False)
#
# print(p)
#
# def run_prog(batch_size):
# params = {}
# for key, value in p.get_parameter_shapes().items():
# print("Parameter {} -> {}".format(key, value))
# params[key] = migraphx.to_gpu(
# migraphx.generate_argument(value.to_static(batch_size)))
#
# print("before_output")
# outputs = p.run(params)
# print(outputs)
# r = migraphx.from_gpu(p.run(params)[-1])
# print(r)
#
# run_prog(1)
# run_prog(2)
# run_prog(3)
# run_prog(4)
test_conv_relu()
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