From 22796c5a496a89e47dfafa68342e090d576c4866 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Sun, 10 Jun 2018 10:58:37 +0000
Subject: [PATCH] Minor fix
---
src/batchnorm_layer.c | 146 +++++++++++++++++++++++++++++++++++++-----------
1 files changed, 111 insertions(+), 35 deletions(-)
diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c
index 510f1b2..d35d9d2 100644
--- a/src/batchnorm_layer.c
+++ b/src/batchnorm_layer.c
@@ -52,6 +52,12 @@
layer.x_gpu = cuda_make_array(layer.output, layer.batch*layer.outputs);
layer.x_norm_gpu = cuda_make_array(layer.output, layer.batch*layer.outputs);
+#ifdef CUDNN
+ cudnnCreateTensorDescriptor(&layer.normTensorDesc);
+ cudnnCreateTensorDescriptor(&layer.normDstTensorDesc);
+ cudnnSetTensor4dDescriptor(layer.normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, layer.batch, layer.out_c, layer.out_h, layer.out_w);
+ cudnnSetTensor4dDescriptor(layer.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, layer.out_c, 1, 1);
+#endif
#endif
return layer;
}
@@ -127,17 +133,33 @@
l.out_h = l.out_w = 1;
}
if(state.train){
- mean_cpu(l.output, l.batch, l.out_c, l.out_h*l.out_w, l.mean);
- variance_cpu(l.output, l.mean, l.batch, l.out_c, l.out_h*l.out_w, l.variance);
+ mean_cpu(l.output, l.batch, l.out_c, l.out_h*l.out_w, l.mean);
+ variance_cpu(l.output, l.mean, l.batch, l.out_c, l.out_h*l.out_w, l.variance);
+
+ scal_cpu(l.out_c, .9, l.rolling_mean, 1);
+ axpy_cpu(l.out_c, .1, l.mean, 1, l.rolling_mean, 1);
+ scal_cpu(l.out_c, .9, l.rolling_variance, 1);
+ axpy_cpu(l.out_c, .1, l.variance, 1, l.rolling_variance, 1);
+
+ copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1);
normalize_cpu(l.output, l.mean, l.variance, l.batch, l.out_c, l.out_h*l.out_w);
+ copy_cpu(l.outputs*l.batch, l.output, 1, l.x_norm, 1);
} else {
normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.out_c, l.out_h*l.out_w);
}
scale_bias(l.output, l.scales, l.batch, l.out_c, l.out_h*l.out_w);
}
-void backward_batchnorm_layer(const layer layer, network_state state)
+void backward_batchnorm_layer(const layer l, network_state state)
{
+ backward_scale_cpu(l.x_norm, l.delta, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates);
+
+ scale_bias(l.delta, l.scales, l.batch, l.out_c, l.out_h*l.out_w);
+
+ mean_delta_cpu(l.delta, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta);
+ variance_delta_cpu(l.x, l.delta, l.mean, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta);
+ normalize_delta_cpu(l.x, l.mean, l.variance, l.mean_delta, l.variance_delta, l.batch, l.out_c, l.out_w*l.out_h, l.delta);
+ if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, l.delta, 1, state.delta, 1);
}
#ifdef GPU
@@ -157,39 +179,93 @@
void forward_batchnorm_layer_gpu(layer l, network_state state)
{
- if(l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1);
- if(l.type == CONNECTED){
- l.out_c = l.outputs;
- l.out_h = l.out_w = 1;
- }
- if (state.train) {
- fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu);
- fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu);
+ if (l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1);
+ copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1);
+ if (state.train) {
+#ifdef CUDNN
+ float one = 1;
+ float zero = 0;
+ cudnnBatchNormalizationForwardTraining(cudnn_handle(),
+ CUDNN_BATCHNORM_SPATIAL,
+ &one,
+ &zero,
+ l.normDstTensorDesc,
+ l.x_gpu, // input
+ l.normDstTensorDesc,
+ l.output_gpu, // output
+ l.normTensorDesc,
+ l.scales_gpu,
+ l.biases_gpu,
+ .01,
+ l.rolling_mean_gpu, // output (should be FP32)
+ l.rolling_variance_gpu, // output (should be FP32)
+ .00001,
+ l.mean_gpu, // output (should be FP32)
+ l.variance_gpu); // output (should be FP32)
+#else
+ fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu);
+ fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu);
- scal_ongpu(l.out_c, .95, l.rolling_mean_gpu, 1);
- axpy_ongpu(l.out_c, .05, l.mean_gpu, 1, l.rolling_mean_gpu, 1);
- scal_ongpu(l.out_c, .95, l.rolling_variance_gpu, 1);
- axpy_ongpu(l.out_c, .05, l.variance_gpu, 1, l.rolling_variance_gpu, 1);
+ scal_ongpu(l.out_c, .99, l.rolling_mean_gpu, 1);
+ axpy_ongpu(l.out_c, .01, l.mean_gpu, 1, l.rolling_mean_gpu, 1);
+ scal_ongpu(l.out_c, .99, l.rolling_variance_gpu, 1);
+ axpy_ongpu(l.out_c, .01, l.variance_gpu, 1, l.rolling_variance_gpu, 1);
- copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1);
- normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_h*l.out_w);
- copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1);
- } else {
- normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w);
- }
+ copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1);
+ normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_h*l.out_w);
+ copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1);
- scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w);
-}
-
-void backward_batchnorm_layer_gpu(const layer l, network_state state)
-{
- backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates_gpu);
-
- scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w);
-
- fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta_gpu);
- fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta_gpu);
- normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu);
- if(l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1);
-}
+ scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w);
+ add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h);
#endif
+ }
+ else {
+ normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w);
+ scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w);
+ add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h);
+ }
+
+}
+
+void backward_batchnorm_layer_gpu(layer l, network_state state)
+{
+ if (!state.train) {
+ l.mean_gpu = l.rolling_mean_gpu;
+ l.variance_gpu = l.rolling_variance_gpu;
+ }
+#ifdef CUDNN
+ float one = 1;
+ float zero = 0;
+ cudnnBatchNormalizationBackward(cudnn_handle(),
+ CUDNN_BATCHNORM_SPATIAL,
+ &one,
+ &zero,
+ &one,
+ &one,
+ l.normDstTensorDesc,
+ l.x_gpu, // input
+ l.normDstTensorDesc,
+ l.delta_gpu, // input
+ l.normDstTensorDesc,
+ l.x_norm_gpu, // output
+ l.normTensorDesc,
+ l.scales_gpu, // output (should be FP32)
+ l.scale_updates_gpu, // output (should be FP32)
+ l.bias_updates_gpu, // output (should be FP32)
+ .00001,
+ l.mean_gpu, // input (should be FP32)
+ l.variance_gpu); // input (should be FP32)
+ copy_ongpu(l.outputs*l.batch, l.x_norm_gpu, 1, l.delta_gpu, 1);
+#else
+ backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h);
+ backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates_gpu);
+
+ scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w);
+
+ fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta_gpu);
+ fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta_gpu);
+ normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu);
+#endif
+ if (l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1);
+}
+#endif
\ No newline at end of file
--
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