301 строка
13 KiB
Python
301 строка
13 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. 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,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""
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Inception V3, suitable for images with around 299 x 299
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Reference:
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Szegedy, Christian, et al. "Rethinking the Inception Architecture for Computer Vision."
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arXiv preprint arXiv:1512.00567 (2015).
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Adopted from https://github.com/apache/incubator-mxnet/blob/
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master/example/image-classification/symbols/inception-v3.py
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"""
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# pylint: disable=invalid-name,missing-docstring,unused-argument
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from tvm import relay
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from .init import create_workload
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from . import layers
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def Conv(data, num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name=None, suffix=''):
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conv = layers.conv2d(
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data=data,
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channels=int(num_filter),
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kernel_size=kernel,
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strides=stride,
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padding=pad,
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name='%s%s_conv1' % (name, suffix))
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bn = layers.batch_norm_infer(data=conv, epsilon=2e-5, scale=False,
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name='%s%s_bn' % (name, suffix))
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act = relay.nn.relu(data=bn)
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return act
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def Pooling(data, kernel, stride, pad, pool_type, name):
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if pool_type == 'max':
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return relay.nn.max_pool2d(data=data, pool_size=kernel, strides=stride, padding=pad)
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if pool_type == 'avg':
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return relay.nn.avg_pool2d(data=data, pool_size=kernel, strides=stride, padding=pad,
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count_include_pad=True)
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raise ValueError("Invalid pooling type: " + pool_type)
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def Inception7A(data,
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num_1x1,
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num_3x3_red, num_3x3_1, num_3x3_2,
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num_5x5_red, num_5x5,
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pool, proj,
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name):
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tower_1x1 = Conv(data, num_1x1, name=('%s_conv' % name))
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tower_5x5 = Conv(data, num_5x5_red, name=('%s_tower' % name), suffix='_conv')
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tower_5x5 = Conv(tower_5x5, num_5x5, kernel=(5, 5), pad=(2, 2), name=('%s_tower' % name),
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suffix='_conv_1')
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tower_3x3 = Conv(data, num_3x3_red, name=('%s_tower_1' % name), suffix='_conv')
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tower_3x3 = Conv(tower_3x3, num_3x3_1, kernel=(3, 3), pad=(1, 1), name=('%s_tower_1' % name),
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suffix='_conv_1')
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tower_3x3 = Conv(tower_3x3, num_3x3_2, kernel=(3, 3), pad=(1, 1), name=('%s_tower_1' % name),
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suffix='_conv_2')
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pooling = Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool,
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name=('%s_pool_%s_pool' % (pool, name)))
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cproj = Conv(pooling, proj, name=('%s_tower_2' % name), suffix='_conv')
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concat = relay.concatenate((tower_1x1, tower_5x5, tower_3x3, cproj), axis=1)
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return concat
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# First Downsample
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def Inception7B(data,
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num_3x3,
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num_d3x3_red, num_d3x3_1, num_d3x3_2,
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pool,
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name):
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tower_3x3 = Conv(data, num_3x3, kernel=(3, 3), pad=(0, 0), stride=(2, 2),
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name=('%s_conv' % name))
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tower_d3x3 = Conv(data, num_d3x3_red, name=('%s_tower' % name), suffix='_conv')
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tower_d3x3 = Conv(tower_d3x3, num_d3x3_1, kernel=(3, 3), pad=(1, 1), stride=(1, 1),
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name=('%s_tower' % name), suffix='_conv_1')
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tower_d3x3 = Conv(tower_d3x3, num_d3x3_2, kernel=(3, 3), pad=(0, 0), stride=(2, 2),
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name=('%s_tower' % name), suffix='_conv_2')
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pooling = Pooling(data=data, kernel=(3, 3), stride=(2, 2), pad=(0, 0), pool_type="max",
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name=('max_pool_%s_pool' % name))
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concat = relay.concatenate((tower_3x3, tower_d3x3, pooling), axis=1)
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return concat
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def Inception7C(data,
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num_1x1,
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num_d7_red, num_d7_1, num_d7_2,
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num_q7_red, num_q7_1, num_q7_2, num_q7_3, num_q7_4,
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pool, proj,
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name):
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tower_1x1 = Conv(data=data, num_filter=num_1x1, kernel=(1, 1), name=('%s_conv' % name))
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tower_d7 = Conv(data=data, num_filter=num_d7_red, name=('%s_tower' % name), suffix='_conv')
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tower_d7 = Conv(data=tower_d7, num_filter=num_d7_1, kernel=(1, 7), pad=(0, 3),
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name=('%s_tower' % name), suffix='_conv_1')
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tower_d7 = Conv(data=tower_d7, num_filter=num_d7_2, kernel=(7, 1), pad=(3, 0),
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name=('%s_tower' % name), suffix='_conv_2')
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tower_q7 = Conv(data=data, num_filter=num_q7_red, name=('%s_tower_1' % name), suffix='_conv')
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tower_q7 = Conv(data=tower_q7, num_filter=num_q7_1, kernel=(7, 1), pad=(3, 0),
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name=('%s_tower_1' % name), suffix='_conv_1')
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tower_q7 = Conv(data=tower_q7, num_filter=num_q7_2, kernel=(1, 7), pad=(0, 3),
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name=('%s_tower_1' % name), suffix='_conv_2')
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tower_q7 = Conv(data=tower_q7, num_filter=num_q7_3, kernel=(7, 1), pad=(3, 0),
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name=('%s_tower_1' % name), suffix='_conv_3')
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tower_q7 = Conv(data=tower_q7, num_filter=num_q7_4, kernel=(1, 7), pad=(0, 3),
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name=('%s_tower_1' % name), suffix='_conv_4')
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pooling = Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool,
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name=('%s_pool_%s_pool' % (pool, name)))
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cproj = Conv(data=pooling, num_filter=proj, kernel=(1, 1),
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name=('%s_tower_2' % name), suffix='_conv')
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# concat
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concat = relay.concatenate((tower_1x1, tower_d7, tower_q7, cproj), axis=1)
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return concat
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def Inception7D(data,
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num_3x3_red, num_3x3,
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num_d7_3x3_red, num_d7_1, num_d7_2, num_d7_3x3,
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pool,
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name):
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tower_3x3 = Conv(data=data, num_filter=num_3x3_red, name=('%s_tower' % name),
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suffix='_conv')
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tower_3x3 = Conv(data=tower_3x3, num_filter=num_3x3, kernel=(3, 3), pad=(0, 0), stride=(2, 2),
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name=('%s_tower' % name), suffix='_conv_1')
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tower_d7_3x3 = Conv(data=data, num_filter=num_d7_3x3_red, name=('%s_tower_1' % name),
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suffix='_conv')
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tower_d7_3x3 = Conv(data=tower_d7_3x3, num_filter=num_d7_1, kernel=(1, 7), pad=(0, 3),
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name=('%s_tower_1' % name), suffix='_conv_1')
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tower_d7_3x3 = Conv(data=tower_d7_3x3, num_filter=num_d7_2, kernel=(7, 1), pad=(3, 0),
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name=('%s_tower_1' % name), suffix='_conv_2')
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tower_d7_3x3 = Conv(data=tower_d7_3x3, num_filter=num_d7_3x3, kernel=(3, 3), stride=(2, 2),
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name=('%s_tower_1' % name), suffix='_conv_3')
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pooling = Pooling(data=data, kernel=(3, 3), stride=(2, 2), pool_type=pool, pad=(0, 0),
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name=('%s_pool_%s_pool' % (pool, name)))
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# concat
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concat = relay.concatenate((tower_3x3, tower_d7_3x3, pooling), axis=1)
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return concat
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def Inception7E(data,
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num_1x1,
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num_d3_red, num_d3_1, num_d3_2,
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num_3x3_d3_red, num_3x3, num_3x3_d3_1, num_3x3_d3_2,
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pool, proj,
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name):
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tower_1x1 = Conv(data=data, num_filter=num_1x1, kernel=(1, 1), name=('%s_conv' % name))
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tower_d3 = Conv(data=data, num_filter=num_d3_red, name=('%s_tower' % name), suffix='_conv')
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tower_d3_a = Conv(data=tower_d3, num_filter=num_d3_1, kernel=(1, 3), pad=(0, 1),
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name=('%s_tower' % name), suffix='_mixed_conv')
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tower_d3_b = Conv(data=tower_d3, num_filter=num_d3_2, kernel=(3, 1), pad=(1, 0),
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name=('%s_tower' % name), suffix='_mixed_conv_1')
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tower_3x3_d3 = Conv(data=data, num_filter=num_3x3_d3_red, name=('%s_tower_1' % name),
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suffix='_conv')
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tower_3x3_d3 = Conv(data=tower_3x3_d3, num_filter=num_3x3, kernel=(3, 3), pad=(1, 1),
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name=('%s_tower_1' % name), suffix='_conv_1')
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tower_3x3_d3_a = Conv(data=tower_3x3_d3, num_filter=num_3x3_d3_1, kernel=(1, 3), pad=(0, 1),
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name=('%s_tower_1' % name), suffix='_mixed_conv')
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tower_3x3_d3_b = Conv(data=tower_3x3_d3, num_filter=num_3x3_d3_2, kernel=(3, 1), pad=(1, 0),
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name=('%s_tower_1' % name), suffix='_mixed_conv_1')
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pooling = Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool,
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name=('%s_pool_%s_pool' % (pool, name)))
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cproj = Conv(data=pooling, num_filter=proj, kernel=(1, 1), name=('%s_tower_2' % name),
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suffix='_conv')
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# concat
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concat = relay.concatenate(
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(tower_1x1, tower_d3_a, tower_d3_b, tower_3x3_d3_a, tower_3x3_d3_b, cproj), axis=1)
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return concat
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def get_net(batch_size,
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num_classes,
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image_shape,
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dtype):
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"""Get network a Inception v3 network.
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batch_size : int
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The batch size used in the model
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num_classes : int, optional
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Number of claseses
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image_shape : tuple, optional
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The input image shape
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dtype : str, optional
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The data type
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Returns
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-------
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net : relay.Function
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The dataflow.
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"""
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data_shape = (batch_size,) + image_shape
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data = relay.var("data",
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shape=data_shape,
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dtype=dtype)
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# stage 1
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conv = Conv(data, 32, kernel=(3, 3), stride=(2, 2), name="conv")
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conv_1 = Conv(conv, 32, kernel=(3, 3), name="conv_1")
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conv_2 = Conv(conv_1, 64, kernel=(3, 3), pad=(1, 1), name="conv_2")
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pool = Pooling(data=conv_2, kernel=(3, 3), stride=(2, 2), pool_type="max", pad=(0, 0),
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name="pool")
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# stage 2
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conv_3 = Conv(pool, 80, kernel=(1, 1), name="conv_3")
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conv_4 = Conv(conv_3, 192, kernel=(3, 3), name="conv_4")
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pool1 = Pooling(data=conv_4, kernel=(3, 3), stride=(2, 2), pool_type="max", pad=(0, 0),
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name="pool1")
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# stage 3
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in3a = Inception7A(pool1, 64,
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64, 96, 96,
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48, 64,
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"avg", 32, "mixed")
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in3b = Inception7A(in3a, 64,
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64, 96, 96,
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48, 64,
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"avg", 64, "mixed_1")
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in3c = Inception7A(in3b, 64,
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64, 96, 96,
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48, 64,
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"avg", 64, "mixed_2")
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in3d = Inception7B(in3c, 384,
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64, 96, 96,
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"max", "mixed_3")
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# stage 4
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in4a = Inception7C(in3d, 192,
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128, 128, 192,
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128, 128, 128, 128, 192,
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"avg", 192, "mixed_4")
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in4b = Inception7C(in4a, 192,
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160, 160, 192,
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160, 160, 160, 160, 192,
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"avg", 192, "mixed_5")
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in4c = Inception7C(in4b, 192,
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160, 160, 192,
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160, 160, 160, 160, 192,
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"avg", 192, "mixed_6")
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in4d = Inception7C(in4c, 192,
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192, 192, 192,
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192, 192, 192, 192, 192,
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"avg", 192, "mixed_7")
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in4e = Inception7D(in4d, 192, 320,
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192, 192, 192, 192,
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"max", "mixed_8")
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# stage 5
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in5a = Inception7E(in4e, 320,
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384, 384, 384,
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448, 384, 384, 384,
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"avg", 192, "mixed_9")
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in5b = Inception7E(in5a, 320,
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384, 384, 384,
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448, 384, 384, 384,
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"max", 192, "mixed_10")
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# pool
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pool = Pooling(data=in5b, kernel=(8, 8), stride=(1, 1), pool_type="avg", pad=(0, 0),
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name="global_pool")
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flatten = relay.nn.batch_flatten(pool)
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fc1 = relay.nn.dense(flatten, relay.var("fc1_weight"), units=num_classes)
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fc1 = relay.nn.bias_add(fc1, relay.var("fc2_bias"), axis=-1)
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inception_v3 = relay.nn.softmax(data=fc1)
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args = relay.analysis.free_vars(inception_v3)
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return relay.Function(args, inception_v3)
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def get_workload(batch_size=1, num_classes=1000,
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image_shape=(3, 299, 299), dtype="float32"):
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"""Get benchmark workload for InceptionV3
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Parameters
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----------
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batch_size : int
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The batch size used in the model
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num_classes : int, optional
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Number of classes
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image_shape : tuple, optional
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The input image shape
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dtype : str, optional
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The data type
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Returns
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-------
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mod : tvm.relay.Module
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The relay module that contains an Inception V3 network.
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params : dict of str to NDArray
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The parameters.
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"""
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net = get_net(batch_size, num_classes, image_shape, dtype)
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return create_workload(net)
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