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Add the dense image positional encoding in SAM state #401

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Jul 20, 2023
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Add the dense image positional encoding in SAM state #401

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93 changes: 92 additions & 1 deletion examples/sam/main.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -114,6 +114,7 @@ struct sam_state {
struct ggml_tensor * embd_img;
struct ggml_tensor * embd_prompt_sparse;
struct ggml_tensor * embd_prompt_dense;
struct ggml_tensor * pe_img_dense;

struct ggml_context * ctx;

Expand Down Expand Up @@ -532,7 +533,7 @@ bool sam_model_load(const std::string & fname, sam_model & model) {

// key + value memory
{
const auto & hparams = model.hparams;
// const auto & hparams = model.hparams;

// TODO
}
Expand Down Expand Up @@ -630,6 +631,88 @@ bool sam_model_load(const std::string & fname, sam_model & model) {
return true;
}

bool sam_fill_dense_pe(
const sam_model & model,
sam_state & state,
int n_threads) {
const auto & hparams = model.hparams;
const auto & enc = model.enc_prompt;

const int32_t n_img_embd = hparams.n_img_embd();
const float n_img_embd_inv = 1.0f / n_img_embd;

static size_t buf_size = 256u*1024*1024;
static void * buf = malloc(buf_size);

struct ggml_init_params params = {
/*.mem_size =*/ buf_size,
/*.mem_buffer =*/ buf,
/*.no_alloc =*/ false,
};

struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph gf = {};

struct ggml_tensor * xy_embed_stacked = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 2, n_img_embd, n_img_embd);

{
float * data = (float *) ggml_get_data(xy_embed_stacked);
for (int i = 0; i < n_img_embd; ++i) {
const int row = 2*i*n_img_embd;
const float y_val = 2 * (i + 0.5f) * n_img_embd_inv - 1;
for (int j = 0; j < n_img_embd; ++j) {
const float x_val = 2 * (j + 0.5f) * n_img_embd_inv - 1;
data[row + 2*j + 0] = x_val;
data[row + 2*j + 1] = y_val;
}
}
}

struct ggml_tensor * cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, enc.pe)), xy_embed_stacked);

cur = ggml_scale(ctx0, cur, ggml_new_f32(ctx0, 2.0f*M_PI));

// concat
// ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/prompt_encoder.py#L192
{
struct ggml_tensor * t_sin = ggml_map_unary_f32(ctx0, cur, ggml_sam_sin);
struct ggml_tensor * t_cos = ggml_map_unary_f32(ctx0, cur, ggml_sam_cos);

cur = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, t_sin->ne[0] + t_cos->ne[0], cur->ne[1], cur->ne[2]);

ggml_build_forward_expand(&gf, ggml_cpy(ctx0, t_sin, ggml_view_3d(ctx0, cur, t_sin->ne[0], t_sin->ne[1], t_sin->ne[2], cur->nb[1], cur->nb[2], 0)));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, t_cos, ggml_view_3d(ctx0, cur, t_sin->ne[0], t_sin->ne[1], t_sin->ne[2], cur->nb[1], cur->nb[2], t_sin->nb[1])));
}

cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 2, 0, 1, 3));

// TODO: avoid copy
cur = ggml_cpy(ctx0, cur, state.pe_img_dense);

// run the computation
ggml_set_name(cur, "check");
ggml_build_forward_expand(&gf, cur);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);

// auto * t = ggml_get_tensor(ctx0, "check");
// auto print_t_f32 = [&](struct ggml_tensor * t) {
// float * data = (float *)t->data;
// printf("dims: %jd %jd %jd %jd f32\n", t->ne[0], t->ne[1], t->ne[2], t->ne[3]);
// for (int i = 0; i < 256; i++) {
// printf("%f ", data[256*64*63 + 63*256 + i]);
// }
// printf("\n");
// double sum = 0.0;
// for (int i = 0; i < ggml_nelements(t); i++) {
// sum += data[i];
// }
// printf("sum: %f\n", sum);
// };
// print_t_f32(t);

return true;
}

bool sam_encode_image(
const sam_model & model,
sam_state & state,
Expand Down Expand Up @@ -1254,6 +1337,14 @@ int main(int argc, char ** argv) {

state.embd_prompt_dense = ggml_new_tensor_3d(state.ctx, GGML_TYPE_F32,
model.hparams.n_img_embd(), model.hparams.n_img_embd(), model.hparams.n_enc_out_chans);

state.pe_img_dense = ggml_new_tensor_3d(state.ctx, GGML_TYPE_F32,
model.hparams.n_img_embd(), model.hparams.n_img_embd(), model.hparams.n_enc_out_chans);
}

if (!sam_fill_dense_pe(model, state, params.n_threads)) {
fprintf(stderr, "%s: failed to get dense positional encoding\n", __func__);
return 1;
}

if (!sam_encode_image(model, state, img1, params.n_threads)) {
Expand Down