When we use MNNConverter to convert other mobilenet model to MNN model, {Conv3x3Depthwise + BN + Relu + Conv1x1 + BN + Relu} will be converted and optimized to {Conv3x3Depthwise + Conv1x1}
| 17 | // {Conv3x3Depthwise + BN + Relu + Conv1x1 + BN + Relu} will be converted |
| 18 | // and optimized to {Conv3x3Depthwise + Conv1x1} |
| 19 | static VARP convBlock(VARP x, INTS channels, int stride) { |
| 20 | int inputChannel = channels[0], outputChannel = channels[1]; |
| 21 | int group = inputChannel; |
| 22 | x = _Conv(0.0f, 0.0f, x, {inputChannel, inputChannel}, {3, 3}, SAME, {stride, stride}, {1, 1}, group); |
| 23 | x = _Conv(0.0f, 0.0f, x, {inputChannel, outputChannel}, {1, 1}, SAME, {1, 1}, {1, 1}, 1); |
| 24 | return x; |
| 25 | } |
| 26 | |
| 27 | VARP mobileNetV1Expr(MobileNetWidthType alpha, MobileNetResolutionType beta, int numClass) { |
| 28 | int inputSize, poolSize; // MobileNet_224, MobileNet_192, MobileNet_160, MobileNet_128 |
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