From 168af40fe9a3cc81c6ee16b3e81f154780c36bdb Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 15:03:27 +0800
Subject: [PATCH] up new v4
---
lib/detecter_tools/darknet/crnn_layer.c | 766 +++++++++++++++++++++++++++++-----------------------------
1 files changed, 383 insertions(+), 383 deletions(-)
diff --git a/lib/detecter_tools/darknet/crnn_layer.c b/lib/detecter_tools/darknet/crnn_layer.c
index 1691a5f..84646b4 100644
--- a/lib/detecter_tools/darknet/crnn_layer.c
+++ b/lib/detecter_tools/darknet/crnn_layer.c
@@ -1,383 +1,383 @@
-#include "crnn_layer.h"
-#include "convolutional_layer.h"
-#include "utils.h"
-#include "dark_cuda.h"
-#include "blas.h"
-#include "gemm.h"
-
-#include <math.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-
-static void increment_layer(layer *l, int steps)
-{
- int num = l->outputs*l->batch*steps;
- l->output += num;
- l->delta += num;
- l->x += num;
- l->x_norm += num;
-
-#ifdef GPU
- l->output_gpu += num;
- l->delta_gpu += num;
- l->x_gpu += num;
- l->x_norm_gpu += num;
-#endif
-}
-
-layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int xnor, int train)
-{
- fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters);
- batch = batch / steps;
- layer l = { (LAYER_TYPE)0 };
- l.train = train;
- l.batch = batch;
- l.type = CRNN;
- l.steps = steps;
- l.size = size;
- l.stride = stride;
- l.dilation = dilation;
- l.pad = pad;
- l.h = h;
- l.w = w;
- l.c = c;
- l.groups = groups;
- l.out_c = output_filters;
- l.inputs = h * w * c;
- l.hidden = h * w * hidden_filters;
- l.xnor = xnor;
-
- l.state = (float*)xcalloc(l.hidden * l.batch * (l.steps + 1), sizeof(float));
-
- l.input_layer = (layer*)xcalloc(1, sizeof(layer));
- *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train);
- l.input_layer->batch = batch;
- if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size;
-
- l.self_layer = (layer*)xcalloc(1, sizeof(layer));
- *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train);
- l.self_layer->batch = batch;
- if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size;
-
- l.output_layer = (layer*)xcalloc(1, sizeof(layer));
- *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train);
- l.output_layer->batch = batch;
- if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size;
-
- l.out_h = l.output_layer->out_h;
- l.out_w = l.output_layer->out_w;
- l.outputs = l.output_layer->outputs;
-
- assert(l.input_layer->outputs == l.self_layer->outputs);
- assert(l.input_layer->outputs == l.output_layer->inputs);
-
- l.output = l.output_layer->output;
- l.delta = l.output_layer->delta;
-
- l.forward = forward_crnn_layer;
- l.backward = backward_crnn_layer;
- l.update = update_crnn_layer;
-
-#ifdef GPU
- l.forward_gpu = forward_crnn_layer_gpu;
- l.backward_gpu = backward_crnn_layer_gpu;
- l.update_gpu = update_crnn_layer_gpu;
- l.state_gpu = cuda_make_array(l.state, l.batch*l.hidden*(l.steps + 1));
- l.output_gpu = l.output_layer->output_gpu;
- l.delta_gpu = l.output_layer->delta_gpu;
-#endif
-
- l.bflops = l.input_layer->bflops + l.self_layer->bflops + l.output_layer->bflops;
-
- return l;
-}
-
-void resize_crnn_layer(layer *l, int w, int h)
-{
- resize_convolutional_layer(l->input_layer, w, h);
- if (l->workspace_size < l->input_layer->workspace_size) l->workspace_size = l->input_layer->workspace_size;
-
- resize_convolutional_layer(l->self_layer, w, h);
- if (l->workspace_size < l->self_layer->workspace_size) l->workspace_size = l->self_layer->workspace_size;
-
- resize_convolutional_layer(l->output_layer, w, h);
- if (l->workspace_size < l->output_layer->workspace_size) l->workspace_size = l->output_layer->workspace_size;
-
- l->output = l->output_layer->output;
- l->delta = l->output_layer->delta;
-
- int hidden_filters = l->self_layer->c;
- l->w = w;
- l->h = h;
- l->inputs = h * w * l->c;
- l->hidden = h * w * hidden_filters;
-
- l->out_h = l->output_layer->out_h;
- l->out_w = l->output_layer->out_w;
- l->outputs = l->output_layer->outputs;
-
- assert(l->input_layer->inputs == l->inputs);
- assert(l->self_layer->inputs == l->hidden);
- assert(l->input_layer->outputs == l->self_layer->outputs);
- assert(l->input_layer->outputs == l->output_layer->inputs);
-
- l->state = (float*)xrealloc(l->state, l->batch*l->hidden*(l->steps + 1)*sizeof(float));
-
-#ifdef GPU
- if (l->state_gpu) cudaFree(l->state_gpu);
- l->state_gpu = cuda_make_array(l->state, l->batch*l->hidden*(l->steps + 1));
-
- l->output_gpu = l->output_layer->output_gpu;
- l->delta_gpu = l->output_layer->delta_gpu;
-#endif
-}
-
-void free_state_crnn(layer l)
-{
- int i;
- for (i = 0; i < l.outputs * l.batch; ++i) l.self_layer->output[i] = rand_uniform(-1, 1);
-
-#ifdef GPU
- cuda_push_array(l.self_layer->output_gpu, l.self_layer->output, l.outputs * l.batch);
-#endif // GPU
-}
-
-void update_crnn_layer(layer l, int batch, float learning_rate, float momentum, float decay)
-{
- update_convolutional_layer(*(l.input_layer), batch, learning_rate, momentum, decay);
- update_convolutional_layer(*(l.self_layer), batch, learning_rate, momentum, decay);
- update_convolutional_layer(*(l.output_layer), batch, learning_rate, momentum, decay);
-}
-
-void forward_crnn_layer(layer l, network_state state)
-{
- network_state s = {0};
- s.train = state.train;
- s.workspace = state.workspace;
- s.net = state.net;
- //s.index = state.index;
- int i;
- layer input_layer = *(l.input_layer);
- layer self_layer = *(l.self_layer);
- layer output_layer = *(l.output_layer);
-
- if (state.train) {
- fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1);
- fill_cpu(l.hidden * l.batch * l.steps, 0, self_layer.delta, 1);
- fill_cpu(l.hidden * l.batch * l.steps, 0, input_layer.delta, 1);
- fill_cpu(l.hidden * l.batch, 0, l.state, 1);
- }
-
- for (i = 0; i < l.steps; ++i) {
- s.input = state.input;
- forward_convolutional_layer(input_layer, s);
-
- s.input = l.state;
- forward_convolutional_layer(self_layer, s);
-
- float *old_state = l.state;
- if(state.train) l.state += l.hidden*l.batch;
- if(l.shortcut){
- copy_cpu(l.hidden * l.batch, old_state, 1, l.state, 1);
- }else{
- fill_cpu(l.hidden * l.batch, 0, l.state, 1);
- }
- axpy_cpu(l.hidden * l.batch, 1, input_layer.output, 1, l.state, 1);
- axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1);
-
- s.input = l.state;
- forward_convolutional_layer(output_layer, s);
-
- state.input += l.inputs*l.batch;
- increment_layer(&input_layer, 1);
- increment_layer(&self_layer, 1);
- increment_layer(&output_layer, 1);
- }
-}
-
-void backward_crnn_layer(layer l, network_state state)
-{
- network_state s = {0};
- s.train = state.train;
- s.workspace = state.workspace;
- s.net = state.net;
- //s.index = state.index;
- int i;
- layer input_layer = *(l.input_layer);
- layer self_layer = *(l.self_layer);
- layer output_layer = *(l.output_layer);
-
- increment_layer(&input_layer, l.steps-1);
- increment_layer(&self_layer, l.steps-1);
- increment_layer(&output_layer, l.steps-1);
-
- l.state += l.hidden*l.batch*l.steps;
- for (i = l.steps-1; i >= 0; --i) {
- copy_cpu(l.hidden * l.batch, input_layer.output, 1, l.state, 1);
- axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1);
-
- s.input = l.state;
- s.delta = self_layer.delta;
- backward_convolutional_layer(output_layer, s);
-
- l.state -= l.hidden*l.batch;
- /*
- if(i > 0){
- copy_cpu(l.hidden * l.batch, input_layer.output - l.hidden*l.batch, 1, l.state, 1);
- axpy_cpu(l.hidden * l.batch, 1, self_layer.output - l.hidden*l.batch, 1, l.state, 1);
- }else{
- fill_cpu(l.hidden * l.batch, 0, l.state, 1);
- }
- */
-
- s.input = l.state;
- s.delta = self_layer.delta - l.hidden*l.batch;
- if (i == 0) s.delta = 0;
- backward_convolutional_layer(self_layer, s);
-
- copy_cpu(l.hidden*l.batch, self_layer.delta, 1, input_layer.delta, 1);
- if (i > 0 && l.shortcut) axpy_cpu(l.hidden*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.hidden*l.batch, 1);
- s.input = state.input + i*l.inputs*l.batch;
- if(state.delta) s.delta = state.delta + i*l.inputs*l.batch;
- else s.delta = 0;
- backward_convolutional_layer(input_layer, s);
-
- increment_layer(&input_layer, -1);
- increment_layer(&self_layer, -1);
- increment_layer(&output_layer, -1);
- }
-}
-
-#ifdef GPU
-
-void pull_crnn_layer(layer l)
-{
- pull_convolutional_layer(*(l.input_layer));
- pull_convolutional_layer(*(l.self_layer));
- pull_convolutional_layer(*(l.output_layer));
-}
-
-void push_crnn_layer(layer l)
-{
- push_convolutional_layer(*(l.input_layer));
- push_convolutional_layer(*(l.self_layer));
- push_convolutional_layer(*(l.output_layer));
-}
-
-void update_crnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale)
-{
- update_convolutional_layer_gpu(*(l.input_layer), batch, learning_rate, momentum, decay, loss_scale);
- update_convolutional_layer_gpu(*(l.self_layer), batch, learning_rate, momentum, decay, loss_scale);
- update_convolutional_layer_gpu(*(l.output_layer), batch, learning_rate, momentum, decay, loss_scale);
-}
-
-void forward_crnn_layer_gpu(layer l, network_state state)
-{
- network_state s = {0};
- s.train = state.train;
- s.workspace = state.workspace;
- s.net = state.net;
- if(!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() )
- int i;
- layer input_layer = *(l.input_layer);
- layer self_layer = *(l.self_layer);
- layer output_layer = *(l.output_layer);
-
-/*
-#ifdef CUDNN_HALF // slow and bad for training
- if (!state.train && state.net.cudnn_half) {
- s.index = state.index;
- cuda_convert_f32_to_f16(input_layer.weights_gpu, input_layer.c*input_layer.n*input_layer.size*input_layer.size, input_layer.weights_gpu16);
- cuda_convert_f32_to_f16(self_layer.weights_gpu, self_layer.c*self_layer.n*self_layer.size*self_layer.size, self_layer.weights_gpu16);
- cuda_convert_f32_to_f16(output_layer.weights_gpu, output_layer.c*output_layer.n*output_layer.size*output_layer.size, output_layer.weights_gpu16);
- }
-#endif //CUDNN_HALF
-*/
-
- if (state.train) {
- fill_ongpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1);
- fill_ongpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1);
- fill_ongpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1);
- fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1);
- }
-
- for (i = 0; i < l.steps; ++i) {
- s.input = state.input;
- forward_convolutional_layer_gpu(input_layer, s);
-
- s.input = l.state_gpu;
- forward_convolutional_layer_gpu(self_layer, s);
-
- float *old_state = l.state_gpu;
- if(state.train) l.state_gpu += l.hidden*l.batch;
- if(l.shortcut){
- copy_ongpu(l.hidden * l.batch, old_state, 1, l.state_gpu, 1);
- }else{
- fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1);
- }
- axpy_ongpu(l.hidden * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1);
- axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1);
-
- s.input = l.state_gpu;
- forward_convolutional_layer_gpu(output_layer, s);
-
- state.input += l.inputs*l.batch;
- increment_layer(&input_layer, 1);
- increment_layer(&self_layer, 1);
- increment_layer(&output_layer, 1);
- }
-}
-
-void backward_crnn_layer_gpu(layer l, network_state state)
-{
- network_state s = {0};
- s.train = state.train;
- s.workspace = state.workspace;
- s.net = state.net;
- //s.index = state.index;
- int i;
- layer input_layer = *(l.input_layer);
- layer self_layer = *(l.self_layer);
- layer output_layer = *(l.output_layer);
- increment_layer(&input_layer, l.steps - 1);
- increment_layer(&self_layer, l.steps - 1);
- increment_layer(&output_layer, l.steps - 1);
- float *init_state_gpu = l.state_gpu;
- l.state_gpu += l.hidden*l.batch*l.steps;
- for (i = l.steps-1; i >= 0; --i) {
- //copy_ongpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1); // commented in RNN
- //axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); // commented in RNN
-
- s.input = l.state_gpu;
- s.delta = self_layer.delta_gpu;
- backward_convolutional_layer_gpu(output_layer, s);
-
- l.state_gpu -= l.hidden*l.batch;
-
- copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1);
-
- s.input = l.state_gpu;
- s.delta = self_layer.delta_gpu - l.hidden*l.batch;
- if (i == 0) s.delta = 0;
- backward_convolutional_layer_gpu(self_layer, s);
-
- if (i > 0 && l.shortcut) axpy_ongpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1);
- s.input = state.input + i*l.inputs*l.batch;
- if(state.delta) s.delta = state.delta + i*l.inputs*l.batch;
- else s.delta = 0;
- backward_convolutional_layer_gpu(input_layer, s);
-
- if (state.net.try_fix_nan) {
- fix_nan_and_inf(output_layer.delta_gpu, output_layer.inputs * output_layer.batch);
- fix_nan_and_inf(self_layer.delta_gpu, self_layer.inputs * self_layer.batch);
- fix_nan_and_inf(input_layer.delta_gpu, input_layer.inputs * input_layer.batch);
- }
-
- increment_layer(&input_layer, -1);
- increment_layer(&self_layer, -1);
- increment_layer(&output_layer, -1);
- }
- fill_ongpu(l.hidden * l.batch, 0, init_state_gpu, 1); //clean l.state_gpu
-}
-#endif
+#include "crnn_layer.h"
+#include "convolutional_layer.h"
+#include "utils.h"
+#include "dark_cuda.h"
+#include "blas.h"
+#include "gemm.h"
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+static void increment_layer(layer *l, int steps)
+{
+ int num = l->outputs*l->batch*steps;
+ l->output += num;
+ l->delta += num;
+ l->x += num;
+ l->x_norm += num;
+
+#ifdef GPU
+ l->output_gpu += num;
+ l->delta_gpu += num;
+ l->x_gpu += num;
+ l->x_norm_gpu += num;
+#endif
+}
+
+layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int xnor, int train)
+{
+ fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters);
+ batch = batch / steps;
+ layer l = { (LAYER_TYPE)0 };
+ l.train = train;
+ l.batch = batch;
+ l.type = CRNN;
+ l.steps = steps;
+ l.size = size;
+ l.stride = stride;
+ l.dilation = dilation;
+ l.pad = pad;
+ l.h = h;
+ l.w = w;
+ l.c = c;
+ l.groups = groups;
+ l.out_c = output_filters;
+ l.inputs = h * w * c;
+ l.hidden = h * w * hidden_filters;
+ l.xnor = xnor;
+
+ l.state = (float*)xcalloc(l.hidden * l.batch * (l.steps + 1), sizeof(float));
+
+ l.input_layer = (layer*)xcalloc(1, sizeof(layer));
+ *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train);
+ l.input_layer->batch = batch;
+ if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size;
+
+ l.self_layer = (layer*)xcalloc(1, sizeof(layer));
+ *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train);
+ l.self_layer->batch = batch;
+ if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size;
+
+ l.output_layer = (layer*)xcalloc(1, sizeof(layer));
+ *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train);
+ l.output_layer->batch = batch;
+ if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size;
+
+ l.out_h = l.output_layer->out_h;
+ l.out_w = l.output_layer->out_w;
+ l.outputs = l.output_layer->outputs;
+
+ assert(l.input_layer->outputs == l.self_layer->outputs);
+ assert(l.input_layer->outputs == l.output_layer->inputs);
+
+ l.output = l.output_layer->output;
+ l.delta = l.output_layer->delta;
+
+ l.forward = forward_crnn_layer;
+ l.backward = backward_crnn_layer;
+ l.update = update_crnn_layer;
+
+#ifdef GPU
+ l.forward_gpu = forward_crnn_layer_gpu;
+ l.backward_gpu = backward_crnn_layer_gpu;
+ l.update_gpu = update_crnn_layer_gpu;
+ l.state_gpu = cuda_make_array(l.state, l.batch*l.hidden*(l.steps + 1));
+ l.output_gpu = l.output_layer->output_gpu;
+ l.delta_gpu = l.output_layer->delta_gpu;
+#endif
+
+ l.bflops = l.input_layer->bflops + l.self_layer->bflops + l.output_layer->bflops;
+
+ return l;
+}
+
+void resize_crnn_layer(layer *l, int w, int h)
+{
+ resize_convolutional_layer(l->input_layer, w, h);
+ if (l->workspace_size < l->input_layer->workspace_size) l->workspace_size = l->input_layer->workspace_size;
+
+ resize_convolutional_layer(l->self_layer, w, h);
+ if (l->workspace_size < l->self_layer->workspace_size) l->workspace_size = l->self_layer->workspace_size;
+
+ resize_convolutional_layer(l->output_layer, w, h);
+ if (l->workspace_size < l->output_layer->workspace_size) l->workspace_size = l->output_layer->workspace_size;
+
+ l->output = l->output_layer->output;
+ l->delta = l->output_layer->delta;
+
+ int hidden_filters = l->self_layer->c;
+ l->w = w;
+ l->h = h;
+ l->inputs = h * w * l->c;
+ l->hidden = h * w * hidden_filters;
+
+ l->out_h = l->output_layer->out_h;
+ l->out_w = l->output_layer->out_w;
+ l->outputs = l->output_layer->outputs;
+
+ assert(l->input_layer->inputs == l->inputs);
+ assert(l->self_layer->inputs == l->hidden);
+ assert(l->input_layer->outputs == l->self_layer->outputs);
+ assert(l->input_layer->outputs == l->output_layer->inputs);
+
+ l->state = (float*)xrealloc(l->state, l->batch*l->hidden*(l->steps + 1)*sizeof(float));
+
+#ifdef GPU
+ if (l->state_gpu) cudaFree(l->state_gpu);
+ l->state_gpu = cuda_make_array(l->state, l->batch*l->hidden*(l->steps + 1));
+
+ l->output_gpu = l->output_layer->output_gpu;
+ l->delta_gpu = l->output_layer->delta_gpu;
+#endif
+}
+
+void free_state_crnn(layer l)
+{
+ int i;
+ for (i = 0; i < l.outputs * l.batch; ++i) l.self_layer->output[i] = rand_uniform(-1, 1);
+
+#ifdef GPU
+ cuda_push_array(l.self_layer->output_gpu, l.self_layer->output, l.outputs * l.batch);
+#endif // GPU
+}
+
+void update_crnn_layer(layer l, int batch, float learning_rate, float momentum, float decay)
+{
+ update_convolutional_layer(*(l.input_layer), batch, learning_rate, momentum, decay);
+ update_convolutional_layer(*(l.self_layer), batch, learning_rate, momentum, decay);
+ update_convolutional_layer(*(l.output_layer), batch, learning_rate, momentum, decay);
+}
+
+void forward_crnn_layer(layer l, network_state state)
+{
+ network_state s = {0};
+ s.train = state.train;
+ s.workspace = state.workspace;
+ s.net = state.net;
+ //s.index = state.index;
+ int i;
+ layer input_layer = *(l.input_layer);
+ layer self_layer = *(l.self_layer);
+ layer output_layer = *(l.output_layer);
+
+ if (state.train) {
+ fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1);
+ fill_cpu(l.hidden * l.batch * l.steps, 0, self_layer.delta, 1);
+ fill_cpu(l.hidden * l.batch * l.steps, 0, input_layer.delta, 1);
+ fill_cpu(l.hidden * l.batch, 0, l.state, 1);
+ }
+
+ for (i = 0; i < l.steps; ++i) {
+ s.input = state.input;
+ forward_convolutional_layer(input_layer, s);
+
+ s.input = l.state;
+ forward_convolutional_layer(self_layer, s);
+
+ float *old_state = l.state;
+ if(state.train) l.state += l.hidden*l.batch;
+ if(l.shortcut){
+ copy_cpu(l.hidden * l.batch, old_state, 1, l.state, 1);
+ }else{
+ fill_cpu(l.hidden * l.batch, 0, l.state, 1);
+ }
+ axpy_cpu(l.hidden * l.batch, 1, input_layer.output, 1, l.state, 1);
+ axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1);
+
+ s.input = l.state;
+ forward_convolutional_layer(output_layer, s);
+
+ state.input += l.inputs*l.batch;
+ increment_layer(&input_layer, 1);
+ increment_layer(&self_layer, 1);
+ increment_layer(&output_layer, 1);
+ }
+}
+
+void backward_crnn_layer(layer l, network_state state)
+{
+ network_state s = {0};
+ s.train = state.train;
+ s.workspace = state.workspace;
+ s.net = state.net;
+ //s.index = state.index;
+ int i;
+ layer input_layer = *(l.input_layer);
+ layer self_layer = *(l.self_layer);
+ layer output_layer = *(l.output_layer);
+
+ increment_layer(&input_layer, l.steps-1);
+ increment_layer(&self_layer, l.steps-1);
+ increment_layer(&output_layer, l.steps-1);
+
+ l.state += l.hidden*l.batch*l.steps;
+ for (i = l.steps-1; i >= 0; --i) {
+ copy_cpu(l.hidden * l.batch, input_layer.output, 1, l.state, 1);
+ axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1);
+
+ s.input = l.state;
+ s.delta = self_layer.delta;
+ backward_convolutional_layer(output_layer, s);
+
+ l.state -= l.hidden*l.batch;
+ /*
+ if(i > 0){
+ copy_cpu(l.hidden * l.batch, input_layer.output - l.hidden*l.batch, 1, l.state, 1);
+ axpy_cpu(l.hidden * l.batch, 1, self_layer.output - l.hidden*l.batch, 1, l.state, 1);
+ }else{
+ fill_cpu(l.hidden * l.batch, 0, l.state, 1);
+ }
+ */
+
+ s.input = l.state;
+ s.delta = self_layer.delta - l.hidden*l.batch;
+ if (i == 0) s.delta = 0;
+ backward_convolutional_layer(self_layer, s);
+
+ copy_cpu(l.hidden*l.batch, self_layer.delta, 1, input_layer.delta, 1);
+ if (i > 0 && l.shortcut) axpy_cpu(l.hidden*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.hidden*l.batch, 1);
+ s.input = state.input + i*l.inputs*l.batch;
+ if(state.delta) s.delta = state.delta + i*l.inputs*l.batch;
+ else s.delta = 0;
+ backward_convolutional_layer(input_layer, s);
+
+ increment_layer(&input_layer, -1);
+ increment_layer(&self_layer, -1);
+ increment_layer(&output_layer, -1);
+ }
+}
+
+#ifdef GPU
+
+void pull_crnn_layer(layer l)
+{
+ pull_convolutional_layer(*(l.input_layer));
+ pull_convolutional_layer(*(l.self_layer));
+ pull_convolutional_layer(*(l.output_layer));
+}
+
+void push_crnn_layer(layer l)
+{
+ push_convolutional_layer(*(l.input_layer));
+ push_convolutional_layer(*(l.self_layer));
+ push_convolutional_layer(*(l.output_layer));
+}
+
+void update_crnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale)
+{
+ update_convolutional_layer_gpu(*(l.input_layer), batch, learning_rate, momentum, decay, loss_scale);
+ update_convolutional_layer_gpu(*(l.self_layer), batch, learning_rate, momentum, decay, loss_scale);
+ update_convolutional_layer_gpu(*(l.output_layer), batch, learning_rate, momentum, decay, loss_scale);
+}
+
+void forward_crnn_layer_gpu(layer l, network_state state)
+{
+ network_state s = {0};
+ s.train = state.train;
+ s.workspace = state.workspace;
+ s.net = state.net;
+ if(!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() )
+ int i;
+ layer input_layer = *(l.input_layer);
+ layer self_layer = *(l.self_layer);
+ layer output_layer = *(l.output_layer);
+
+/*
+#ifdef CUDNN_HALF // slow and bad for training
+ if (!state.train && state.net.cudnn_half) {
+ s.index = state.index;
+ cuda_convert_f32_to_f16(input_layer.weights_gpu, input_layer.c*input_layer.n*input_layer.size*input_layer.size, input_layer.weights_gpu16);
+ cuda_convert_f32_to_f16(self_layer.weights_gpu, self_layer.c*self_layer.n*self_layer.size*self_layer.size, self_layer.weights_gpu16);
+ cuda_convert_f32_to_f16(output_layer.weights_gpu, output_layer.c*output_layer.n*output_layer.size*output_layer.size, output_layer.weights_gpu16);
+ }
+#endif //CUDNN_HALF
+*/
+
+ if (state.train) {
+ fill_ongpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1);
+ fill_ongpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1);
+ fill_ongpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1);
+ fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1);
+ }
+
+ for (i = 0; i < l.steps; ++i) {
+ s.input = state.input;
+ forward_convolutional_layer_gpu(input_layer, s);
+
+ s.input = l.state_gpu;
+ forward_convolutional_layer_gpu(self_layer, s);
+
+ float *old_state = l.state_gpu;
+ if(state.train) l.state_gpu += l.hidden*l.batch;
+ if(l.shortcut){
+ copy_ongpu(l.hidden * l.batch, old_state, 1, l.state_gpu, 1);
+ }else{
+ fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1);
+ }
+ axpy_ongpu(l.hidden * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1);
+ axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1);
+
+ s.input = l.state_gpu;
+ forward_convolutional_layer_gpu(output_layer, s);
+
+ state.input += l.inputs*l.batch;
+ increment_layer(&input_layer, 1);
+ increment_layer(&self_layer, 1);
+ increment_layer(&output_layer, 1);
+ }
+}
+
+void backward_crnn_layer_gpu(layer l, network_state state)
+{
+ network_state s = {0};
+ s.train = state.train;
+ s.workspace = state.workspace;
+ s.net = state.net;
+ //s.index = state.index;
+ int i;
+ layer input_layer = *(l.input_layer);
+ layer self_layer = *(l.self_layer);
+ layer output_layer = *(l.output_layer);
+ increment_layer(&input_layer, l.steps - 1);
+ increment_layer(&self_layer, l.steps - 1);
+ increment_layer(&output_layer, l.steps - 1);
+ float *init_state_gpu = l.state_gpu;
+ l.state_gpu += l.hidden*l.batch*l.steps;
+ for (i = l.steps-1; i >= 0; --i) {
+ //copy_ongpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1); // commented in RNN
+ //axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); // commented in RNN
+
+ s.input = l.state_gpu;
+ s.delta = self_layer.delta_gpu;
+ backward_convolutional_layer_gpu(output_layer, s);
+
+ l.state_gpu -= l.hidden*l.batch;
+
+ copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1);
+
+ s.input = l.state_gpu;
+ s.delta = self_layer.delta_gpu - l.hidden*l.batch;
+ if (i == 0) s.delta = 0;
+ backward_convolutional_layer_gpu(self_layer, s);
+
+ if (i > 0 && l.shortcut) axpy_ongpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1);
+ s.input = state.input + i*l.inputs*l.batch;
+ if(state.delta) s.delta = state.delta + i*l.inputs*l.batch;
+ else s.delta = 0;
+ backward_convolutional_layer_gpu(input_layer, s);
+
+ if (state.net.try_fix_nan) {
+ fix_nan_and_inf(output_layer.delta_gpu, output_layer.inputs * output_layer.batch);
+ fix_nan_and_inf(self_layer.delta_gpu, self_layer.inputs * self_layer.batch);
+ fix_nan_and_inf(input_layer.delta_gpu, input_layer.inputs * input_layer.batch);
+ }
+
+ increment_layer(&input_layer, -1);
+ increment_layer(&self_layer, -1);
+ increment_layer(&output_layer, -1);
+ }
+ fill_ongpu(l.hidden * l.batch, 0, init_state_gpu, 1); //clean l.state_gpu
+}
+#endif
--
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