212 lines
8.1 KiB
Python
212 lines
8.1 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Benchmark for Conv2D op."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import itertools
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import time
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from tensorflow.core.protobuf import config_pb2
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from tensorflow.core.protobuf import rewriter_config_pb2
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from tensorflow.python.client import session as session_lib
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import flags
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from tensorflow.python.platform import test
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FLAGS = flags.FLAGS
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flags.DEFINE_boolean(
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"enable_layout_optimizer", False,
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"If true, enables layout optimizer to update input data format for faster "
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"execution of convolution ops.")
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def build_graph(device, dtype, data_format, input_shape, filter_shape, strides,
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padding, num_iters, warmup_iters):
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"""builds a graph containing a sequence of conv2d operations.
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Args:
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device: String, the device to run on.
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dtype: Data type for the convolution.
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data_format: A string from: "NHWC" or "NCHW". Data format for input and
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output data.
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input_shape: Shape of the input tensor.
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filter_shape: Shape of the filter tensor.
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strides: A list of ints. 1-D of length 4. The stride of sliding
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window for each dimension of input.
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padding: A string from: "SAME", "VALID". The type of padding
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algorithm to use.
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num_iters: number of iterations to run conv2d.
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warmup_iters: number of iterations for warmup runs.
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Returns:
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An array of tensors to run()
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"""
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with ops.device("/%s:0" % device):
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inp = variables.VariableV1(
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random_ops.truncated_normal(input_shape, dtype=dtype))
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filt = variables.VariableV1(
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random_ops.truncated_normal(filter_shape, dtype=dtype))
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outputs = []
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conv2d_op = nn_ops.conv2d(
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inp, filt, strides, padding, data_format=data_format)
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outputs.append(conv2d_op)
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for _ in range(1, num_iters):
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with ops.control_dependencies([conv2d_op]):
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conv2d_op = nn_ops.conv2d(
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inp, filt, strides, padding, data_format=data_format)
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outputs.append(conv2d_op)
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warmup_groups = []
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warmup_conv2d_op = nn_ops.conv2d(
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inp, filt, strides, padding, data_format=data_format)
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warmup_groups.append(warmup_conv2d_op)
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for _ in range(1, warmup_iters):
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with ops.control_dependencies([warmup_conv2d_op]):
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warmup_conv2d_op = nn_ops.conv2d(
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inp, filt, strides, padding, data_format=data_format)
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warmup_groups.append(warmup_conv2d_op)
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return control_flow_ops.group(*warmup_groups), control_flow_ops.group(
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*outputs)
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class Conv2DBenchmark(test.Benchmark):
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"""Benchmark conv2d!"""
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def _run_graph(self, device, dtype, data_format, input_shape, filter_shape,
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strides, padding, num_iters, warmup_iters):
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"""runs the graph and print its execution time.
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Args:
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device: String, the device to run on.
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dtype: Data type for the convolution.
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data_format: A string from: "NHWC" or "NCHW". Data format for input and
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output data.
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input_shape: Shape of the input tensor.
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filter_shape: Shape of the filter tensor.
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strides: A list of ints. 1-D of length 4. The stride of sliding
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window for each dimension of input.
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padding: A string from: "SAME", "VALID". The type of padding
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algorithm to use. num_iters: Number of iterations to run the
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benchmark.
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num_iters: number of iterations to run conv2d.
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warmup_iters: number of iterations for warmup runs.
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Returns:
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The duration of the run in seconds.
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"""
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graph = ops.Graph()
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with graph.as_default():
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warmup_outputs, outputs = build_graph(device, dtype, data_format,
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input_shape, filter_shape, strides,
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padding, num_iters, warmup_iters)
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config = config_pb2.ConfigProto()
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config.graph_options.optimizer_options.opt_level = -1
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rewrite_options = config.graph_options.rewrite_options
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# Disable layout optimizer to not change input data_format.
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rewrite_options.layout_optimizer = (
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rewriter_config_pb2.RewriterConfig.ON if FLAGS.enable_layout_optimizer
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else rewriter_config_pb2.RewriterConfig.OFF)
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# Convolution ops are effectively noop in the test graph as we are not
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# fetching the convolution outputs. Disable dependency optimizer to not
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# remove the conv ops.
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rewrite_options.dependency_optimization = (
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rewriter_config_pb2.RewriterConfig.OFF)
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with session_lib.Session(graph=graph, config=config) as session:
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# TODO(hinsu): Use run_op_benchmark method from test.Benchmark to run
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# benchmark along with warmup.
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variables.global_variables_initializer().run()
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# warmup runs
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session.run(warmup_outputs)
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start_time = time.time()
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session.run(outputs)
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duration = (time.time() - start_time) / num_iters
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print("%s %s %s inputshape:%s filtershape:%s strides:%s padding:%s "
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"%d iters: %.8f sec" %
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(device, str(dtype), data_format, str(input_shape).replace(
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" ", ""), str(filter_shape).replace(" ", ""),
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str(strides).replace(" ", ""), padding, num_iters, duration))
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name_template = (
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"conv2d_{device}_{datatype}_{data_format}_input_shape_{inputshape}_"
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"filter_shape_{filtershape}_strides_{strides}_padding_{padding}")
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self.report_benchmark(
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name=name_template.format(
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device=device,
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datatype=str(dtype),
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data_format=str(data_format),
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inputshape=str(input_shape).replace(" ", ""),
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filtershape=str(filter_shape).replace(" ", ""),
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strides=str(strides).replace(" ", ""),
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padding=padding).replace(" ", ""),
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iters=num_iters,
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wall_time=duration)
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return duration
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def benchmark_conv2d(self):
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print("conv2d benchmark:")
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data_types = [dtypes.float32, dtypes.float16]
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data_formats = ["NHWC", "NCHW"]
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in_channels = list(range(1, 10)) + list(range(10, 20, 2)) + list(
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range(20, 33, 4))
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out_channels = [4, 16, 32]
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hw_strides = [[2, 2]]
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paddings = ["VALID", "SAME"]
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args_lists = [
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data_types, data_formats, in_channels, out_channels, hw_strides,
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paddings
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]
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for args in itertools.product(*args_lists):
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dtype, data_format, in_channel, out_channel, hw_stride, padding = args
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# Keep batch size same as out channels just to reduce the number of
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# different configurations to benchmark.
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batch_size = out_channel
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h, w, fh, fw = 500, 500, 3, 3
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if data_format == "NHWC":
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ishape = [batch_size, h, w, in_channel]
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stride = [1] + hw_stride + [1]
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elif data_format == "NCHW":
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ishape = [batch_size, in_channel, h, w]
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stride = [1, 1] + hw_stride
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else:
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raise ValueError("Unknown data_format: " + str(data_format))
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fshape = [fh, fw, in_channel, out_channel]
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num_iters = 80
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warmup_iters = 2
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self._run_graph("gpu", dtype, data_format, ishape, fshape, stride,
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padding, num_iters, warmup_iters)
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if __name__ == "__main__":
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test.main()
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