From 352ae7e65b6a74bcd768aa88b866a44c713284c8 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 26 Oct 2016 15:35:44 +0000
Subject: [PATCH] ADAM
---
src/region_layer.c | 309 +++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 309 insertions(+), 0 deletions(-)
diff --git a/src/region_layer.c b/src/region_layer.c
new file mode 100644
index 0000000..5f8b3cc
--- /dev/null
+++ b/src/region_layer.c
@@ -0,0 +1,309 @@
+#include "region_layer.h"
+#include "activations.h"
+#include "blas.h"
+#include "box.h"
+#include "cuda.h"
+#include "utils.h"
+#include <stdio.h>
+#include <assert.h>
+#include <string.h>
+#include <stdlib.h>
+
+region_layer make_region_layer(int batch, int w, int h, int n, int classes, int coords)
+{
+ region_layer l = {0};
+ l.type = REGION;
+
+ l.n = n;
+ l.batch = batch;
+ l.h = h;
+ l.w = w;
+ l.classes = classes;
+ l.coords = coords;
+ l.cost = calloc(1, sizeof(float));
+ l.biases = calloc(n*2, sizeof(float));
+ l.bias_updates = calloc(n*2, sizeof(float));
+ l.outputs = h*w*n*(classes + coords + 1);
+ l.inputs = l.outputs;
+ l.truths = 30*(5);
+ l.delta = calloc(batch*l.outputs, sizeof(float));
+ l.output = calloc(batch*l.outputs, sizeof(float));
+ int i;
+ for(i = 0; i < n*2; ++i){
+ l.biases[i] = .5;
+ }
+
+ l.forward = forward_region_layer;
+ l.backward = backward_region_layer;
+#ifdef GPU
+ l.forward_gpu = forward_region_layer_gpu;
+ l.backward_gpu = backward_region_layer_gpu;
+ l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
+ l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
+#endif
+
+ fprintf(stderr, "Region Layer\n");
+ srand(0);
+
+ return l;
+}
+
+box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h)
+{
+ box b;
+ b.x = (i + .5)/w + x[index + 0] * biases[2*n];
+ b.y = (j + .5)/h + x[index + 1] * biases[2*n + 1];
+ b.w = exp(x[index + 2]) * biases[2*n];
+ b.h = exp(x[index + 3]) * biases[2*n+1];
+ return b;
+}
+
+float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale)
+{
+ box pred = get_region_box(x, biases, n, index, i, j, w, h);
+ float iou = box_iou(pred, truth);
+
+ float tx = (truth.x - (i + .5)/w) / biases[2*n];
+ float ty = (truth.y - (j + .5)/h) / biases[2*n + 1];
+ float tw = log(truth.w / biases[2*n]);
+ float th = log(truth.h / biases[2*n + 1]);
+
+ delta[index + 0] = scale * (tx - x[index + 0]);
+ delta[index + 1] = scale * (ty - x[index + 1]);
+ delta[index + 2] = scale * (tw - x[index + 2]);
+ delta[index + 3] = scale * (th - x[index + 3]);
+ return iou;
+}
+
+float logit(float x)
+{
+ return log(x/(1.-x));
+}
+
+float tisnan(float x)
+{
+ return (x != x);
+}
+
+#define LOG 0
+
+void forward_region_layer(const region_layer l, network_state state)
+{
+ int i,j,b,t,n;
+ int size = l.coords + l.classes + 1;
+ memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
+ reorg(l.output, l.w*l.h, size*l.n, l.batch, 1);
+ for (b = 0; b < l.batch; ++b){
+ for(i = 0; i < l.h*l.w*l.n; ++i){
+ int index = size*i + b*l.outputs;
+ l.output[index + 4] = logistic_activate(l.output[index + 4]);
+ if(l.softmax){
+ softmax(l.output + index + 5, l.classes, 1, l.output + index + 5);
+ }
+ }
+ }
+ if(!state.train) return;
+ memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
+ float avg_iou = 0;
+ float recall = 0;
+ float avg_cat = 0;
+ float avg_obj = 0;
+ float avg_anyobj = 0;
+ int count = 0;
+ *(l.cost) = 0;
+ for (b = 0; b < l.batch; ++b) {
+ for (j = 0; j < l.h; ++j) {
+ for (i = 0; i < l.w; ++i) {
+ for (n = 0; n < l.n; ++n) {
+ int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
+ box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
+ float best_iou = 0;
+ for(t = 0; t < 30; ++t){
+ box truth = float_to_box(state.truth + t*5 + b*l.truths);
+ if(!truth.x) break;
+ float iou = box_iou(pred, truth);
+ if (iou > best_iou) best_iou = iou;
+ }
+ avg_anyobj += l.output[index + 4];
+ l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
+ if(best_iou > .5) l.delta[index + 4] = 0;
+
+ if(*(state.net.seen) < 6400){
+ box truth = {0};
+ truth.x = (i + .5)/l.w;
+ truth.y = (j + .5)/l.h;
+ truth.w = .5;
+ truth.h = .5;
+ delta_region_box(truth, l.output, l.biases, n, index, i, j, l.w, l.h, l.delta, .01);
+ //l.delta[index + 0] = .1 * (0 - l.output[index + 0]);
+ //l.delta[index + 1] = .1 * (0 - l.output[index + 1]);
+ //l.delta[index + 2] = .1 * (0 - l.output[index + 2]);
+ //l.delta[index + 3] = .1 * (0 - l.output[index + 3]);
+ }
+ }
+ }
+ }
+ for(t = 0; t < 30; ++t){
+ box truth = float_to_box(state.truth + t*5 + b*l.truths);
+ int class = state.truth[t*5 + b*l.truths + 4];
+ if(!truth.x) break;
+ float best_iou = 0;
+ int best_index = 0;
+ int best_n = 0;
+ i = (truth.x * l.w);
+ j = (truth.y * l.h);
+ //printf("%d %f %d %f\n", i, truth.x*l.w, j, truth.y*l.h);
+ box truth_shift = truth;
+ truth_shift.x = 0;
+ truth_shift.y = 0;
+ printf("index %d %d\n",i, j);
+ for(n = 0; n < l.n; ++n){
+ int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
+ box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
+ printf("pred: (%f, %f) %f x %f\n", pred.x*l.w - i - .5, pred.y * l.h - j - .5, pred.w, pred.h);
+ pred.x = 0;
+ pred.y = 0;
+ float iou = box_iou(pred, truth_shift);
+ if (iou > best_iou){
+ best_index = index;
+ best_iou = iou;
+ best_n = n;
+ }
+ }
+ printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x * l.w - i - .5, truth.y*l.h - j - .5, truth.w, truth.h);
+
+ float iou = delta_region_box(truth, l.output, l.biases, best_n, best_index, i, j, l.w, l.h, l.delta, l.coord_scale);
+ if(iou > .5) recall += 1;
+ avg_iou += iou;
+
+ //l.delta[best_index + 4] = iou - l.output[best_index + 4];
+ avg_obj += l.output[best_index + 4];
+ l.delta[best_index + 4] = l.object_scale * (1 - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
+ if (l.rescore) {
+ l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
+ }
+ //printf("%f\n", l.delta[best_index+1]);
+ /*
+ if(isnan(l.delta[best_index+1])){
+ printf("%f\n", true_scale);
+ printf("%f\n", l.output[best_index + 1]);
+ printf("%f\n", truth.w);
+ printf("%f\n", truth.h);
+ error("bad");
+ }
+ */
+ for(n = 0; n < l.classes; ++n){
+ l.delta[best_index + 5 + n] = l.class_scale * (((n == class)?1 : 0) - l.output[best_index + 5 + n]);
+ if(n == class) avg_cat += l.output[best_index + 5 + n];
+ }
+ /*
+ if(0){
+ printf("truth: %f %f %f %f\n", truth.x, truth.y, truth.w, truth.h);
+ printf("pred: %f %f %f %f\n\n", pred.x, pred.y, pred.w, pred.h);
+ float aspect = exp(true_aspect);
+ float scale = logistic_activate(true_scale);
+ float move_x = true_dx;
+ float move_y = true_dy;
+
+ box b;
+ b.w = sqrt(scale * aspect);
+ b.h = b.w * 1./aspect;
+ b.x = move_x * b.w + (i + .5)/l.w;
+ b.y = move_y * b.h + (j + .5)/l.h;
+ printf("%f %f\n", b.x, truth.x);
+ printf("%f %f\n", b.y, truth.y);
+ printf("%f %f\n", b.w, truth.w);
+ printf("%f %f\n", b.h, truth.h);
+ //printf("%f\n", box_iou(b, truth));
+ }
+ */
+ ++count;
+ }
+ }
+ printf("\n");
+ reorg(l.delta, l.w*l.h, size*l.n, l.batch, 0);
+ *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
+ printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count);
+}
+
+void backward_region_layer(const region_layer l, network_state state)
+{
+ axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
+}
+
+void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
+{
+ int i,j,n;
+ float *predictions = l.output;
+ //int per_cell = 5*num+classes;
+ for (i = 0; i < l.w*l.h; ++i){
+ int row = i / l.w;
+ int col = i % l.w;
+ for(n = 0; n < l.n; ++n){
+ int index = i*l.n + n;
+ int p_index = index * (l.classes + 5) + 4;
+ float scale = predictions[p_index];
+ int box_index = index * (l.classes + 5);
+ boxes[index].x = (predictions[box_index + 0] + col + .5) / l.w * w;
+ boxes[index].y = (predictions[box_index + 1] + row + .5) / l.h * h;
+ if(0){
+ boxes[index].x = (logistic_activate(predictions[box_index + 0]) + col) / l.w * w;
+ boxes[index].y = (logistic_activate(predictions[box_index + 1]) + row) / l.h * h;
+ }
+ boxes[index].w = pow(logistic_activate(predictions[box_index + 2]), (l.sqrt?2:1)) * w;
+ boxes[index].h = pow(logistic_activate(predictions[box_index + 3]), (l.sqrt?2:1)) * h;
+ if(1){
+ boxes[index].x = ((col + .5)/l.w + predictions[box_index + 0] * .5) * w;
+ boxes[index].y = ((row + .5)/l.h + predictions[box_index + 1] * .5) * h;
+ boxes[index].w = (exp(predictions[box_index + 2]) * .5) * w;
+ boxes[index].h = (exp(predictions[box_index + 3]) * .5) * h;
+ }
+ for(j = 0; j < l.classes; ++j){
+ int class_index = index * (l.classes + 5) + 5;
+ float prob = scale*predictions[class_index+j];
+ probs[index][j] = (prob > thresh) ? prob : 0;
+ }
+ if(only_objectness){
+ probs[index][0] = scale;
+ }
+ }
+ }
+}
+
+#ifdef GPU
+
+void forward_region_layer_gpu(const region_layer l, network_state state)
+{
+ /*
+ if(!state.train){
+ copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
+ return;
+ }
+ */
+
+ float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
+ float *truth_cpu = 0;
+ if(state.truth){
+ int num_truth = l.batch*l.truths;
+ truth_cpu = calloc(num_truth, sizeof(float));
+ cuda_pull_array(state.truth, truth_cpu, num_truth);
+ }
+ cuda_pull_array(state.input, in_cpu, l.batch*l.inputs);
+ network_state cpu_state = state;
+ cpu_state.train = state.train;
+ cpu_state.truth = truth_cpu;
+ cpu_state.input = in_cpu;
+ forward_region_layer(l, cpu_state);
+ cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
+ cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
+ free(cpu_state.input);
+ if(cpu_state.truth) free(cpu_state.truth);
+}
+
+void backward_region_layer_gpu(region_layer l, network_state state)
+{
+ axpy_ongpu(l.batch*l.outputs, 1, l.delta_gpu, 1, state.delta, 1);
+ //copy_ongpu(l.batch*l.inputs, l.delta_gpu, 1, state.delta, 1);
+}
+#endif
+
--
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