(l, ch_out, stride)
| 105 | |
| 106 | |
| 107 | def resnext32x4d_bottleneck(l, ch_out, stride): |
| 108 | shortcut = l |
| 109 | l = Conv2D('conv1', l, ch_out * 2, 1, strides=1, activation=BNReLU) |
| 110 | l = Conv2D('conv2', l, ch_out * 2, 3, strides=stride, activation=BNReLU, split=32) |
| 111 | l = Conv2D('conv3', l, ch_out * 4, 1, activation=get_bn(zero_init=True)) |
| 112 | out = l + resnet_shortcut(shortcut, ch_out * 4, stride, activation=get_bn(zero_init=False)) |
| 113 | return tf.nn.relu(out) |
| 114 | |
| 115 | |
| 116 | def resnet_group(name, l, block_func, features, count, stride): |
nothing calls this directly
no test coverage detected