Table Of Contents
Table Of Contents

Source code for gluoncv.nn.predictor

# pylint: disable=unused-argument,arguments-differ
"""Predictor for classification/box prediction."""
from __future__ import absolute_import
import mxnet as mx
from mxnet.gluon import HybridBlock
from mxnet.gluon import nn


[docs]class ConvPredictor(HybridBlock): """Convolutional predictor. Convolutional predictor is widely used in object-detection. It can be used to predict classification scores (1 channel per class) or box predictor, which is usually 4 channels per box. The output is of shape (N, num_channel, H, W). Parameters ---------- num_channel : int Number of conv channels. kernel : tuple of (int, int), default (3, 3) Conv kernel size as (H, W). pad : tuple of (int, int), default (1, 1) Conv padding size as (H, W). stride : tuple of (int, int), default (1, 1) Conv stride size as (H, W). activation : str, optional Optional activation after conv, e.g. 'relu'. use_bias : bool Use bias in convolution. It is not necessary if BatchNorm is followed. """ def __init__(self, num_channel, kernel=(3, 3), pad=(1, 1), stride=(1, 1), activation=None, use_bias=True, **kwargs): super(ConvPredictor, self).__init__(**kwargs) with self.name_scope(): self.predictor = nn.Conv2D( num_channel, kernel, strides=stride, padding=pad, activation=activation, use_bias=use_bias, weight_initializer=mx.init.Xavier(magnitude=2), bias_initializer='zeros')
[docs] def hybrid_forward(self, F, x): return self.predictor(x)
[docs]class FCPredictor(HybridBlock): """Fully connected predictor. Fully connected predictor is used to ignore spatial information and will output fixed-sized predictions. Parameters ---------- num_output : int Number of fully connected outputs. activation : str, optional Optional activation after conv, e.g. 'relu'. use_bias : bool Use bias in convolution. It is not necessary if BatchNorm is followed. """ def __init__(self, num_output, activation=None, use_bias=True, **kwargs): super(FCPredictor, self).__init__(**kwargs) with self.name_scope(): self.predictor = nn.Dense( num_output, activation=activation, use_bias=use_bias)
[docs] def hybrid_forward(self, F, x): return self.predictor(x)