Source code for objax.zoo.convnet

# Copyright 2020 Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     https://www.apache.org/licenses/LICENSE-2.0
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import functools

import objax
from objax.typing import JaxArray


[docs] class ConvNet(objax.nn.Sequential): """ConvNet implementation.""" @staticmethod def _mean_reduce(x: JaxArray) -> JaxArray: return x.mean((2, 3))
[docs] def __init__(self, nin, nclass, scales, filters, filters_max, pooling=objax.functional.max_pool_2d, **kwargs): """Creates ConvNet instance. Args: nin: number of channels in the input image. nclass: number of output classes. scales: number of pooling layers, each of which reduces spatial dimension by 2. filters: base number of convolution filters. Number of convolution filters is increased by 2 every scale until it reaches filters_max. filters_max: maximum number of filters. pooling: type of pooling layer. """ del kwargs def nf(scale): return min(filters_max, filters << scale) ops = [objax.nn.Conv2D(nin, nf(0), 3), objax.functional.leaky_relu] for i in range(scales): ops.extend([objax.nn.Conv2D(nf(i), nf(i), 3), objax.functional.leaky_relu, objax.nn.Conv2D(nf(i), nf(i + 1), 3), objax.functional.leaky_relu, functools.partial(pooling, size=2, strides=2)]) ops.extend([objax.nn.Conv2D(nf(scales), nclass, 3), self._mean_reduce]) super().__init__(ops)