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llama.cpp
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fix: test-1.jpg ORC issue with small (640) resolution 

setting min-resolution base (1024) max large (1280) for dynamic-resolution
This commit is contained in:
Saba Fallah 2025-12-10 20:20:55 +01:00
parent 016140699f
commit ed944cd25b
1 changed files with 85 additions and 160 deletions
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245
tools/mtmd/clip.cpp
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@ -2330,11 +2330,11 @@ private:
int qk = q_size * k_size; int qk = q_size * k_size;
cur = ggml_reshape_3d( cur = ggml_reshape_3d(
ctx, ctx,
ggml_get_rows(ctx, cur, ggml_reshape_1d(ctx, indices, qk)), ggml_get_rows(ctx, cur, ggml_reshape_1d(ctx, indices, qk)),
C, k_size, q_size C, k_size, q_size
); );
return cur; // [C, k_size, q_size] return cur; // [C, k_size, q_size]
} }
@ -2696,7 +2696,7 @@ private:
ggml_tensor * rel_pos_indices_local; ggml_tensor * rel_pos_indices_local;
ggml_tensor * rel_pos_indices_global; ggml_tensor * rel_pos_indices_global;
rel_pos_indices_local = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, window, window); rel_pos_indices_local = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, window, window);
rel_pos_indices_global = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, inpL->ne[1], inpL->ne[2]); rel_pos_indices_global = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, inpL->ne[1], inpL->ne[2]);
ggml_set_name(rel_pos_indices_local, "rel_pos_indices_local"); ggml_set_name(rel_pos_indices_local, "rel_pos_indices_local");
@ -2738,7 +2738,7 @@ private:
const int64_t h0 = cur->ne[2]; const int64_t h0 = cur->ne[2];
ggml_tensor * indices; ggml_tensor * indices;
if (hparams.is_global_attn(il)) { if (hparams.is_global_attn(il)) {
indices = rel_pos_indices_global; indices = rel_pos_indices_global;
} else { } else {
@ -3292,7 +3292,7 @@ struct clip_model_loader {
hparams.patch_size = 16; hparams.patch_size = 16;
hparams.image_size = 1024; hparams.image_size = 1024;
hparams.warmup_image_size = 1024; hparams.warmup_image_size = 1024;
get_u32(KEY_SAM_N_BLOCK, hparams.sam_n_layer, true); get_u32(KEY_SAM_N_BLOCK, hparams.sam_n_layer, true);
get_u32(KEY_SAM_N_HEAD, hparams.sam_n_head, true); get_u32(KEY_SAM_N_HEAD, hparams.sam_n_head, true);
get_u32(KEY_SAM_N_EMBD, hparams.sam_n_embd, true); get_u32(KEY_SAM_N_EMBD, hparams.sam_n_embd, true);
@ -4474,7 +4474,7 @@ private:
if (xmax > inSize) { if (xmax > inSize) {
xmax = inSize; xmax = inSize;
} }
xcnt = xmax - xmin; xcnt = xmax - xmin;
// Compute filter weights for each contributing input pixel // Compute filter weights for each contributing input pixel
@ -5210,165 +5210,90 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
} }
} }
} break; } break;
case PROJECTOR_TYPE_DEEPSEEKOCR: case PROJECTOR_TYPE_DEEPSEEKOCR:
{ {
const int native_resolutions[] = { const int native_resolutions[] = {
512 /* tiny */, 640 /* small */, 1024 /* base */, 1280 /* large */ /* 512 tiny ,640 small ,*/ 1024 /* base */, 1280 /* large */
}; };
// original image size // original image size
const int orig_w = original_size.width; const int orig_w = original_size.width;
const int orig_h = original_size.height; const int orig_h = original_size.height;
const int orig_area = orig_h * orig_w; const int orig_area = orig_h * orig_w;
std::array<uint8_t, 3u> color; std::array<uint8_t, 3u> color;
for (int i = 0; i < 3; i++) { for (int i = 0; i < 3; i++) {
color[i] = (int)(255 * params.image_mean[i]); color[i] = (int)(255 * params.image_mean[i]);
} }
int mode_i = 0; int mode_i = 0;
int min_diff = orig_area; int min_diff = orig_area;
for (int i = 0; i < 4; i++) { for (int i = 0; i < 2; i++) {
int r = native_resolutions[i]; int r = native_resolutions[i];
if (std::abs(orig_area - r*r) < min_diff) { if (std::abs(orig_area - r * r) < min_diff) {
mode_i = i; mode_i = i;
min_diff = std::abs(orig_area - r*r); min_diff = std::abs(orig_area - r * r);
}
}
if (mode_i < 2) {
/* Native Resolution (Tiny/Small) */
const int image_size = native_resolutions[mode_i];
// Just resize the image to image_size × image_size
clip_image_u8_ptr resized_img(clip_image_u8_init());
img_tool::resize(*img, *resized_img,
clip_image_size{image_size, image_size},
img_tool::RESIZE_ALGO_BICUBIC_PILLOW, false, color); // Match PIL default
clip_image_f32_ptr res(clip_image_f32_init());
normalize_image_u8_to_f32(*resized_img, *res, params.image_mean, params.image_std);
res_imgs->entries.push_back(std::move(res));
res_imgs->grid_x = 1;
res_imgs->grid_y = 1;
}
else if (mode_i < 4) {
/* Native Resolution (Base/Large) */
const int image_size = native_resolutions[mode_i];
// Resize maintaining aspect ratio, then pad to square
float scale = std::min(
static_cast<float>(image_size) / orig_w,
static_cast<float>(image_size) / orig_h
);
int new_w = static_cast<int>(orig_w * scale);
int new_h = static_cast<int>(orig_h * scale);
clip_image_u8_ptr scaled_img(clip_image_u8_init());
img_tool::resize(*img, *scaled_img, clip_image_size{new_w, new_h},
img_tool::RESIZE_ALGO_BICUBIC_PILLOW, true, color);
// Use mean color for padding
unsigned char pad_r = static_cast<unsigned char>(params.image_mean[0] * 255.0f);
unsigned char pad_g = static_cast<unsigned char>(params.image_mean[1] * 255.0f);
unsigned char pad_b = static_cast<unsigned char>(params.image_mean[2] * 255.0f);
// Pad to image_size × image_size (center padding)
clip_image_u8_ptr padded_img(clip_image_u8_init());
padded_img->nx = image_size;
padded_img->ny = image_size;
padded_img->buf.resize(image_size * image_size * 3); // black padding
// Fill with mean color
for (int i = 0; i < image_size * image_size; ++i) {
padded_img->buf[i * 3 + 0] = pad_r;
padded_img->buf[i * 3 + 1] = pad_g;
padded_img->buf[i * 3 + 2] = pad_b;
}
// Calculate padding offsets (center the image)
int pad_x = (image_size - new_w) / 2;
int pad_y = (image_size - new_h) / 2;
// Copy scaled image into padded canvas
for (int y = 0; y < new_h; ++y) {
for (int x = 0; x < new_w; ++x) {
int src_idx = (y * new_w + x) * 3;
int dst_idx = ((y + pad_y) * image_size + (x + pad_x)) * 3;
padded_img->buf[dst_idx + 0] = scaled_img->buf[src_idx + 0];
padded_img->buf[dst_idx + 1] = scaled_img->buf[src_idx + 1];
padded_img->buf[dst_idx + 2] = scaled_img->buf[src_idx + 2];
}
}
// Normalize and output
clip_image_f32_ptr res(clip_image_f32_init());
normalize_image_u8_to_f32(*padded_img, *res, params.image_mean, params.image_std);
res_imgs->entries.push_back(std::move(res));
res_imgs->grid_x = 1;
res_imgs->grid_y = 1;
}
else {
GGML_ABORT("DeepSeek-OCR hasn't supported Gundam/Gundam-Master yet");
/* Dynamic Resolution (Gundam/Gundam-Master) */
// configurable, or read from params
const int min_num = 2;
const int max_num = 9;
const int image_size = (mode_i == 4) ? 640 : 1024;
// original image size
const int orig_w = original_size.width;
const int orig_h = original_size.height;
// create overview image (thumbnail)
clip_image_u8_ptr overview_img(clip_image_u8_init());
img_tool::resize(*img, *overview_img, { image_size, image_size },
img_tool::RESIZE_ALGO_BICUBIC_PILLOW, true, color);
clip_image_f32_ptr overview_f32(clip_image_f32_init());
normalize_image_u8_to_f32(*overview_img, *overview_f32, params.image_mean, params.image_std);
res_imgs->entries.push_back(std::move(overview_f32));
// build candidate grids (cols, rows)
auto target_ratios = ds_build_target_ratios(min_num, max_num);
// pick the grid that best matches the original aspect ratio
const float aspect_ratio = static_cast<float>(orig_w) / static_cast<float>(orig_h);
auto best = ds_find_closest_ratio(aspect_ratio, target_ratios, orig_w, orig_h, image_size);
const int grid_cols = best.first; // how many tiles horizontally
const int grid_rows = best.second; // how many tiles vertically
// resize to refined size (no padding, direct resize)
clip_image_u8_ptr refined_img(clip_image_u8_init());
img_tool::resize(*img, *refined_img, { image_size * grid_cols, image_size * grid_rows },
img_tool::RESIZE_ALGO_BICUBIC_PILLOW, false);
// crop slices from the refined image
for (int r = 0; r < grid_rows; ++r) {
for (int c = 0; c < grid_cols; ++c) {
const int x = c * image_size;
const int y = r * image_size;
// crop the slice
clip_image_u8_ptr slice_img(clip_image_u8_init());
img_tool::crop(*refined_img, *slice_img, x, y, image_size, image_size);
// normalize and add to results
clip_image_f32_ptr slice_f32(clip_image_f32_init());
normalize_image_u8_to_f32(*slice_img, *slice_f32, params.image_mean, params.image_std);
res_imgs->entries.push_back(std::move(slice_f32));
}
}
// keep the grid info — the model may need to know how to reassemble / attend
res_imgs->grid_x = grid_cols;
res_imgs->grid_y = grid_rows;
} }
} }
break;
/* Native Resolution (Base/Large) */
const int image_size = native_resolutions[mode_i];
// Resize maintaining aspect ratio, then pad to square
float scale = std::min(
static_cast<float>(image_size) / orig_w,
static_cast<float>(image_size) / orig_h
);
int new_w = static_cast<int>(orig_w * scale);
int new_h = static_cast<int>(orig_h * scale);
clip_image_u8_ptr scaled_img(clip_image_u8_init());
img_tool::resize(*img, *scaled_img, clip_image_size{new_w, new_h},
img_tool::RESIZE_ALGO_BICUBIC_PILLOW, true, color);
// Use mean color for padding
unsigned char pad_r = static_cast<unsigned char>(params.image_mean[0] * 255.0f);
unsigned char pad_g = static_cast<unsigned char>(params.image_mean[1] * 255.0f);
unsigned char pad_b = static_cast<unsigned char>(params.image_mean[2] * 255.0f);
// Pad to image_size × image_size (center padding)
clip_image_u8_ptr padded_img(clip_image_u8_init());
padded_img->nx = image_size;
padded_img->ny = image_size;
padded_img->buf.resize(image_size * image_size * 3); // black padding
// Fill with mean color
for (int i = 0; i < image_size * image_size; ++i)
{
padded_img->buf[i * 3 + 0] = pad_r;
padded_img->buf[i * 3 + 1] = pad_g;
padded_img->buf[i * 3 + 2] = pad_b;
}
// Calculate padding offsets (center the image)
int pad_x = (image_size - new_w) / 2;
int pad_y = (image_size - new_h) / 2;
// Copy scaled image into padded canvas
for (int y = 0; y < new_h; ++y){
for (int x = 0; x < new_w; ++x){
int src_idx = (y * new_w + x) * 3;
int dst_idx = ((y + pad_y) * image_size + (x + pad_x)) * 3;
padded_img->buf[dst_idx + 0] = scaled_img->buf[src_idx + 0];
padded_img->buf[dst_idx + 1] = scaled_img->buf[src_idx + 1];
padded_img->buf[dst_idx + 2] = scaled_img->buf[src_idx + 2];
}
}
// Normalize and output
clip_image_f32_ptr res(clip_image_f32_init());
normalize_image_u8_to_f32(*padded_img, *res, params.image_mean, params.image_std);
res_imgs->entries.push_back(std::move(res));
res_imgs->grid_x = 1;
res_imgs->grid_y = 1;
}
break;
default: default:
LOG_ERR("%s: unsupported projector type %d\n", __func__, ctx->proj_type()); LOG_ERR("%s: unsupported projector type %d\n", __func__, ctx->proj_type());