83 lines
3.2 KiB
Python
83 lines
3.2 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. 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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from model.layers import downsample_block, upsample_block, output_layer, input_block
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class Builder:
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def __init__(self, n_classes, mode, normalization='none'):
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self._n_classes = n_classes
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self._mode = mode
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self._normalization = normalization
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def __call__(self, features):
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skip_128 = input_block(x=features,
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out_channels=32,
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normalization=self._normalization,
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mode=self._mode)
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skip_64 = downsample_block(x=skip_128,
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out_channels=64,
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normalization=self._normalization,
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mode=self._mode)
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skip_32 = downsample_block(x=skip_64,
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out_channels=128,
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normalization=self._normalization,
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mode=self._mode)
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skip_16 = downsample_block(x=skip_32,
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out_channels=256,
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normalization=self._normalization,
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mode=self._mode)
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skip_8 = downsample_block(x=skip_16,
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out_channels=320,
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normalization=self._normalization,
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mode=self._mode)
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x = downsample_block(x=skip_8,
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out_channels=320,
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normalization=self._normalization,
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mode=self._mode)
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x = upsample_block(x, skip_8,
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out_channels=320,
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normalization=self._normalization,
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mode=self._mode)
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x = upsample_block(x, skip_16,
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out_channels=256,
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normalization=self._normalization,
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mode=self._mode)
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x = upsample_block(x, skip_32,
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out_channels=128,
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normalization=self._normalization,
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mode=self._mode)
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x = upsample_block(x, skip_64,
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out_channels=64,
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normalization=self._normalization,
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mode=self._mode)
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x = upsample_block(x, skip_128,
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out_channels=32,
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normalization=self._normalization,
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mode=self._mode)
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return output_layer(x=x,
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out_channels=self._n_classes,
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activation='softmax')
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