Package ai.djl.basicmodelzoo.cv.classification

Class ResNetV1

  • public final class ResNetV1
extends java.lang.Object
    ResNetV1 contains a generic implementation of ResNet adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py (Original author Wei Wu) by Antti-Pekka Hynninen.

    Implementing the original resnet ILSVRC 2015 winning network from Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition"

    See Also:
    The D2L chapter on ResNet

    • Nested Class Summary

      Nested Classes 
      Modifier and Type Class Description
      static class  ResNetV1.Builder
      The Builder to construct a ResNetV1 object.

    • Method Summary

      All Methods Static Methods Concrete Methods 
      Modifier and Type Method Description
      static ResNetV1.Builder builder()
      Creates a builder to build a ResNetV1.
      static ai.djl.nn.Block residualUnit​(int numFilters, ai.djl.ndarray.types.Shape stride, boolean dimMatch, boolean bottleneck, float batchNormMomentum)
      Builds a Block that represents a residual unit used in the implementation of the Resnet model.
      static ai.djl.nn.SequentialBlock resnet​(ResNetV1.Builder builder)
      Creates a new Block of ResNetV1 with the arguments from the given ResNetV1.Builder.

      • Methods inherited from class java.lang.Object

        clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    • Method Detail

      • residualUnit

        public static ai.djl.nn.Block residualUnit​(int numFilters,
                                           ai.djl.ndarray.types.Shape stride,
                                           boolean dimMatch,
                                           boolean bottleneck,
                                           float batchNormMomentum)
        Builds a Block that represents a residual unit used in the implementation of the Resnet model.
        Parameters:
        numFilters - the number of output channels
        stride - the stride of the convolution in each dimension
        dimMatch - whether the number of channels between input and output has to remain the same
        bottleneck - whether to use bottleneck architecture
        batchNormMomentum - the momentum to be used for BatchNorm
        Returns:
        a Block that represents a residual unit

      • resnet

        public static ai.djl.nn.SequentialBlock resnet​(ResNetV1.Builder builder)
        Creates a new Block of ResNetV1 with the arguments from the given ResNetV1.Builder.
        Parameters:
        builder - the ResNetV1.Builder with the necessary arguments
        Returns:
        a Block that represents the required ResNet model