diff --git a/README.md b/README.md index 982f12bf5..60d13f183 100644 --- a/README.md +++ b/README.md @@ -47,6 +47,7 @@ Some of the development is currently happening in the [llama.cpp](https://github - [X] Example of ChatGLM inference [li-plus/chatglm.cpp](https://github.com/li-plus/chatglm.cpp) - [X] Example of Stable Diffusion inference [leejet/stable-diffusion.cpp](https://github.com/leejet/stable-diffusion.cpp) - [X] Example of Qwen inference [QwenLM/qwen.cpp](https://github.com/QwenLM/qwen.cpp) +- [X] Example of YOLO inference [examples/yolo](https://github.com/ggerganov/ggml/tree/master/examples/yolo) ## Whisper inference (example) diff --git a/ci/run.sh b/ci/run.sh index 15fc39e58..6b12b4aec 100644 --- a/ci/run.sh +++ b/ci/run.sh @@ -264,6 +264,43 @@ function gg_sum_sam { gg_printf '```\n' } +# yolo + +function gg_run_yolo { + cd ${SRC} + + gg_wget models-mnt/yolo/ https://pjreddie.com/media/files/yolov3-tiny.weights + gg_wget models-mnt/yolo/ https://raw.githubusercontent.com/pjreddie/darknet/master/data/dog.jpg + + cd build-ci-release + cp -r ../examples/yolo/data . + + set -e + + path_models="../models-mnt/yolo/" + + python3 ../examples/yolo/convert-yolov3-tiny.py ${path_models}/yolov3-tiny.weights + + (time ./bin/yolov3-tiny -m yolov3-tiny.gguf -i ${path_models}/dog.jpg ) 2>&1 | tee -a $OUT/${ci}-main.log + + grep -q "dog: 57%" $OUT/${ci}-main.log + grep -q "car: 52%" $OUT/${ci}-main.log + grep -q "truck: 56%" $OUT/${ci}-main.log + grep -q "bicycle: 59%" $OUT/${ci}-main.log + + set +e +} + +function gg_sum_yolo { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Run YOLO\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-main.log)" + gg_printf '```\n' +} + # mpt function gg_run_mpt { @@ -324,6 +361,7 @@ test $ret -eq 0 && gg_run gpt_2 test $ret -eq 0 && gg_run mnist test $ret -eq 0 && gg_run whisper test $ret -eq 0 && gg_run sam +test $ret -eq 0 && gg_run yolo if [ -z $GG_BUILD_LOW_PERF ]; then if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 16 ]; then diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index e3404fb8b..340f6470d 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -28,3 +28,4 @@ add_subdirectory(replit) add_subdirectory(mpt) add_subdirectory(starcoder) add_subdirectory(sam) +add_subdirectory(yolo) diff --git a/examples/yolo/CMakeLists.txt b/examples/yolo/CMakeLists.txt new file mode 100644 index 000000000..0c068cea2 --- /dev/null +++ b/examples/yolo/CMakeLists.txt @@ -0,0 +1,6 @@ +# +# yolov3-tiny + +set(TEST_TARGET yolov3-tiny) +add_executable(${TEST_TARGET} yolov3-tiny.cpp yolo-image.cpp) +target_link_libraries(${TEST_TARGET} PRIVATE ggml common) diff --git a/examples/yolo/README.md b/examples/yolo/README.md new file mode 100644 index 000000000..0e69dc996 --- /dev/null +++ b/examples/yolo/README.md @@ -0,0 +1,52 @@ +This example shows how to implement YOLO object detection with ggml using pretrained model. + +# YOLOv3-tiny + +Download the model weights: + +```bash +$ wget https://pjreddie.com/media/files/yolov3-tiny.weights +$ sha1sum yolov3-tiny.weights +40f3c11883bef62fd850213bc14266632ed4414f yolov3-tiny.weights +``` + +Convert the weights to GGUF format: + +```bash +$ ./convert-yolov3-tiny.py yolov3-tiny.weights +yolov3-tiny.weights converted to yolov3-tiny.gguf +``` + +Object detection: + +```bash +$ wget https://raw.githubusercontent.com/pjreddie/darknet/master/data/dog.jpg +$ ./yolov3-tiny -m yolov3-tiny.gguf -i dog.jpg +Layer 0 output shape: 416 x 416 x 16 x 1 +Layer 1 output shape: 208 x 208 x 16 x 1 +Layer 2 output shape: 208 x 208 x 32 x 1 +Layer 3 output shape: 104 x 104 x 32 x 1 +Layer 4 output shape: 104 x 104 x 64 x 1 +Layer 5 output shape: 52 x 52 x 64 x 1 +Layer 6 output shape: 52 x 52 x 128 x 1 +Layer 7 output shape: 26 x 26 x 128 x 1 +Layer 8 output shape: 26 x 26 x 256 x 1 +Layer 9 output shape: 13 x 13 x 256 x 1 +Layer 10 output shape: 13 x 13 x 512 x 1 +Layer 11 output shape: 13 x 13 x 512 x 1 +Layer 12 output shape: 13 x 13 x 1024 x 1 +Layer 13 output shape: 13 x 13 x 256 x 1 +Layer 14 output shape: 13 x 13 x 512 x 1 +Layer 15 output shape: 13 x 13 x 255 x 1 +Layer 18 output shape: 13 x 13 x 128 x 1 +Layer 19 output shape: 26 x 26 x 128 x 1 +Layer 20 output shape: 26 x 26 x 384 x 1 +Layer 21 output shape: 26 x 26 x 256 x 1 +Layer 22 output shape: 26 x 26 x 255 x 1 +dog: 57% +car: 52% +truck: 56% +car: 62% +bicycle: 59% +Detected objects saved in 'predictions.jpg' (time: 0.357000 sec.) +``` \ No newline at end of file diff --git a/examples/yolo/convert-yolov3-tiny.py b/examples/yolo/convert-yolov3-tiny.py new file mode 100755 index 000000000..4c479919b --- /dev/null +++ b/examples/yolo/convert-yolov3-tiny.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python3 +import sys +import gguf +import numpy as np + +def save_conv2d_layer(f, gguf_writer, prefix, inp_c, filters, size, batch_normalize=True): + biases = np.fromfile(f, dtype=np.float32, count=filters) + gguf_writer.add_tensor(prefix + "_biases", biases, raw_shape=(1, filters, 1, 1)) + + if batch_normalize: + scales = np.fromfile(f, dtype=np.float32, count=filters) + gguf_writer.add_tensor(prefix + "_scales", scales, raw_shape=(1, filters, 1, 1)) + rolling_mean = np.fromfile(f, dtype=np.float32, count=filters) + gguf_writer.add_tensor(prefix + "_rolling_mean", rolling_mean, raw_shape=(1, filters, 1, 1)) + rolling_variance = np.fromfile(f, dtype=np.float32, count=filters) + gguf_writer.add_tensor(prefix + "_rolling_variance", rolling_variance, raw_shape=(1, filters, 1, 1)) + + weights_count = filters * inp_c * size * size + l0_weights = np.fromfile(f, dtype=np.float32, count=weights_count) + ## ggml doesn't support f32 convolution yet, use f16 instead + l0_weights = l0_weights.astype(np.float16) + gguf_writer.add_tensor(prefix + "_weights", l0_weights, raw_shape=(filters, inp_c, size, size)) + + +if __name__ == '__main__': + if len(sys.argv) != 2: + print("Usage: %s " % sys.argv[0]) + sys.exit(1) + outfile = 'yolov3-tiny.gguf' + gguf_writer = gguf.GGUFWriter(outfile, 'yolov3-tiny') + + f = open(sys.argv[1], 'rb') + f.read(20) # skip header + save_conv2d_layer(f, gguf_writer, "l0", 3, 16, 3) + save_conv2d_layer(f, gguf_writer, "l1", 16, 32, 3) + save_conv2d_layer(f, gguf_writer, "l2", 32, 64, 3) + save_conv2d_layer(f, gguf_writer, "l3", 64, 128, 3) + save_conv2d_layer(f, gguf_writer, "l4", 128, 256, 3) + save_conv2d_layer(f, gguf_writer, "l5", 256, 512, 3) + save_conv2d_layer(f, gguf_writer, "l6", 512, 1024, 3) + save_conv2d_layer(f, gguf_writer, "l7", 1024, 256, 1) + save_conv2d_layer(f, gguf_writer, "l8", 256, 512, 3) + save_conv2d_layer(f, gguf_writer, "l9", 512, 255, 1, batch_normalize=False) + save_conv2d_layer(f, gguf_writer, "l10", 256, 128, 1) + save_conv2d_layer(f, gguf_writer, "l11", 384, 256, 3) + save_conv2d_layer(f, gguf_writer, "l12", 256, 255, 1, batch_normalize=False) + f.close() + + gguf_writer.write_header_to_file() + gguf_writer.write_kv_data_to_file() + gguf_writer.write_tensors_to_file() + gguf_writer.close() + print("{} converted to {}".format(sys.argv[1], outfile)) diff --git a/examples/yolo/data/coco.names b/examples/yolo/data/coco.names new file mode 100644 index 000000000..ca76c80b5 --- /dev/null +++ b/examples/yolo/data/coco.names @@ -0,0 +1,80 @@ +person +bicycle +car +motorbike +aeroplane +bus +train +truck +boat +traffic light +fire hydrant +stop sign +parking meter +bench +bird +cat +dog +horse +sheep +cow +elephant +bear +zebra +giraffe +backpack +umbrella +handbag +tie +suitcase +frisbee +skis +snowboard +sports ball +kite +baseball bat +baseball glove +skateboard +surfboard +tennis racket +bottle +wine glass +cup +fork +knife +spoon +bowl +banana +apple +sandwich +orange +broccoli +carrot +hot dog +pizza +donut +cake +chair +sofa +pottedplant +bed +diningtable +toilet +tvmonitor +laptop +mouse +remote +keyboard +cell phone +microwave +oven 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file mode 100644 index 000000000..f7b9e098e Binary files /dev/null and b/examples/yolo/data/labels/99_7.png differ diff --git a/examples/yolo/yolo-image.cpp b/examples/yolo/yolo-image.cpp new file mode 100644 index 000000000..ceddc4182 --- /dev/null +++ b/examples/yolo/yolo-image.cpp @@ -0,0 +1,210 @@ +#define STB_IMAGE_IMPLEMENTATION +#include "stb_image.h" +#define STB_IMAGE_WRITE_IMPLEMENTATION +#include "stb_image_write.h" + +#include "yolo-image.h" + +static void draw_box(yolo_image & a, int x1, int y1, int x2, int y2, float r, float g, float b) +{ + if (x1 < 0) x1 = 0; + if (x1 >= a.w) x1 = a.w-1; + if (x2 < 0) x2 = 0; + if (x2 >= a.w) x2 = a.w-1; + + if (y1 < 0) y1 = 0; + if (y1 >= a.h) y1 = a.h-1; + if (y2 < 0) y2 = 0; + if (y2 >= a.h) y2 = a.h-1; + + for (int i = x1; i <= x2; ++i){ + a.data[i + y1*a.w + 0*a.w*a.h] = r; + a.data[i + y2*a.w + 0*a.w*a.h] = r; + + a.data[i + y1*a.w + 1*a.w*a.h] = g; + a.data[i + y2*a.w + 1*a.w*a.h] = g; + + a.data[i + y1*a.w + 2*a.w*a.h] = b; + a.data[i + y2*a.w + 2*a.w*a.h] = b; + } + for (int i = y1; i <= y2; ++i){ + a.data[x1 + i*a.w + 0*a.w*a.h] = r; + a.data[x2 + i*a.w + 0*a.w*a.h] = r; + + a.data[x1 + i*a.w + 1*a.w*a.h] = g; + a.data[x2 + i*a.w + 1*a.w*a.h] = g; + + a.data[x1 + i*a.w + 2*a.w*a.h] = b; + a.data[x2 + i*a.w + 2*a.w*a.h] = b; + } +} + +void draw_box_width(yolo_image & a, int x1, int y1, int x2, int y2, int w, float r, float g, float b) +{ + for (int i = 0; i < w; ++i) { + draw_box(a, x1+i, y1+i, x2-i, y2-i, r, g, b); + } +} + +bool save_image(const yolo_image & im, const char *name, int quality) +{ + uint8_t *data = (uint8_t*)calloc(im.w*im.h*im.c, sizeof(uint8_t)); + for (int k = 0; k < im.c; ++k) { + for (int i = 0; i < im.w*im.h; ++i) { + data[i*im.c+k] = (uint8_t) (255*im.data[i + k*im.w*im.h]); + } + } + int success = stbi_write_jpg(name, im.w, im.h, im.c, data, quality); + free(data); + if (!success) { + fprintf(stderr, "Failed to write image %s\n", name); + return false; + } + return true; +} + +bool load_image(const char *fname, yolo_image & img) +{ + int w, h, c; + uint8_t * data = stbi_load(fname, &w, &h, &c, 3); + if (!data) { + return false; + } + c = 3; + img.w = w; + img.h = h; + img.c = c; + img.data.resize(w*h*c); + for (int k = 0; k < c; ++k){ + for (int j = 0; j < h; ++j){ + for (int i = 0; i < w; ++i){ + int dst_index = i + w*j + w*h*k; + int src_index = k + c*i + c*w*j; + img.data[dst_index] = (float)data[src_index]/255.; + } + } + } + stbi_image_free(data); + return true; +} + +static yolo_image resize_image(const yolo_image & im, int w, int h) +{ + yolo_image resized(w, h, im.c); + yolo_image part(w, im.h, im.c); + float w_scale = (float)(im.w - 1) / (w - 1); + float h_scale = (float)(im.h - 1) / (h - 1); + for (int k = 0; k < im.c; ++k){ + for (int r = 0; r < im.h; ++r) { + for (int c = 0; c < w; ++c) { + float val = 0; + if (c == w-1 || im.w == 1){ + val = im.get_pixel(im.w-1, r, k); + } else { + float sx = c*w_scale; + int ix = (int) sx; + float dx = sx - ix; + val = (1 - dx) * im.get_pixel(ix, r, k) + dx * im.get_pixel(ix+1, r, k); + } + part.set_pixel(c, r, k, val); + } + } + } + for (int k = 0; k < im.c; ++k){ + for (int r = 0; r < h; ++r){ + float sy = r*h_scale; + int iy = (int) sy; + float dy = sy - iy; + for (int c = 0; c < w; ++c){ + float val = (1-dy) * part.get_pixel(c, iy, k); + resized.set_pixel(c, r, k, val); + } + if (r == h-1 || im.h == 1) continue; + for (int c = 0; c < w; ++c){ + float val = dy * part.get_pixel(c, iy+1, k); + resized.add_pixel(c, r, k, val); + } + } + } + return resized; +} + +static void embed_image(const yolo_image & source, yolo_image & dest, int dx, int dy) +{ + for (int k = 0; k < source.c; ++k) { + for (int y = 0; y < source.h; ++y) { + for (int x = 0; x < source.w; ++x) { + float val = source.get_pixel(x, y, k); + dest.set_pixel(dx+x, dy+y, k, val); + } + } + } +} + +yolo_image letterbox_image(const yolo_image & im, int w, int h) +{ + int new_w = im.w; + int new_h = im.h; + if (((float)w/im.w) < ((float)h/im.h)) { + new_w = w; + new_h = (im.h * w)/im.w; + } else { + new_h = h; + new_w = (im.w * h)/im.h; + } + yolo_image resized = resize_image(im, new_w, new_h); + yolo_image boxed(w, h, im.c); + boxed.fill(0.5); + embed_image(resized, boxed, (w-new_w)/2, (h-new_h)/2); + return boxed; +} + +static yolo_image tile_images(const yolo_image & a, const yolo_image & b, int dx) +{ + if (a.w == 0) { + return b; + } + yolo_image c(a.w + b.w + dx, (a.h > b.h) ? a.h : b.h, a.c); + c.fill(1.0f); + embed_image(a, c, 0, 0); + embed_image(b, c, a.w + dx, 0); + return c; +} + +static yolo_image border_image(const yolo_image & a, int border) +{ + yolo_image b(a.w + 2*border, a.h + 2*border, a.c); + b.fill(1.0f); + embed_image(a, b, border, border); + return b; +} + +yolo_image get_label(const std::vector & alphabet, const std::string & label, int size) +{ + size = size/10; + size = std::min(size, 7); + yolo_image result(0,0,0); + for (int i = 0; i < (int)label.size(); ++i) { + int ch = label[i]; + yolo_image img = alphabet[size*128 + ch]; + result = tile_images(result, img, -size - 1 + (size+1)/2); + } + return border_image(result, (int)(result.h*.25)); +} + +void draw_label(yolo_image & im, int row, int col, const yolo_image & label, const float * rgb) +{ + int w = label.w; + int h = label.h; + if (row - h >= 0) { + row = row - h; + } + for (int j = 0; j < h && j + row < im.h; j++) { + for (int i = 0; i < w && i + col < im.w; i++) { + for (int k = 0; k < label.c; k++) { + float val = label.get_pixel(i, j, k); + im.set_pixel(i + col, j + row, k, rgb[k] * val); + } + } + } +} \ No newline at end of file diff --git a/examples/yolo/yolo-image.h b/examples/yolo/yolo-image.h new file mode 100644 index 000000000..124e1cd29 --- /dev/null +++ b/examples/yolo/yolo-image.h @@ -0,0 +1,39 @@ +#pragma once + +#include +#include +#include + +struct yolo_image { + int w, h, c; + std::vector data; + + yolo_image() : w(0), h(0), c(0) {} + yolo_image(int w, int h, int c) : w(w), h(h), c(c), data(w*h*c) {} + + float get_pixel(int x, int y, int c) const { + assert(x >= 0 && x < w && y >= 0 && y < h && c >= 0 && c < this->c); + return data[c*w*h + y*w + x]; + } + + void set_pixel(int x, int y, int c, float val) { + assert(x >= 0 && x < w && y >= 0 && y < h && c >= 0 && c < this->c); + data[c*w*h + y*w + x] = val; + } + + void add_pixel(int x, int y, int c, float val) { + assert(x >= 0 && x < w && y >= 0 && y < h && c >= 0 && c < this->c); + data[c*w*h + y*w + x] += val; + } + + void fill(float val) { + std::fill(data.begin(), data.end(), val); + } +}; + +bool load_image(const char *fname, yolo_image & img); +void draw_box_width(yolo_image & a, int x1, int y1, int x2, int y2, int w, float r, float g, float b); +yolo_image letterbox_image(const yolo_image & im, int w, int h); +bool save_image(const yolo_image & im, const char *name, int quality); +yolo_image get_label(const std::vector & alphabet, const std::string & label, int size); +void draw_label(yolo_image & im, int row, int col, const yolo_image & label, const float * rgb); diff --git a/examples/yolo/yolov3-tiny.cpp b/examples/yolo/yolov3-tiny.cpp new file mode 100644 index 000000000..7c69fce44 --- /dev/null +++ b/examples/yolo/yolov3-tiny.cpp @@ -0,0 +1,525 @@ +#include "ggml/ggml.h" +#include "yolo-image.h" + +#include +#include +#include +#include +#include +#include +#include +#include + +#if defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + +struct conv2d_layer { + struct ggml_tensor * weights; + struct ggml_tensor * biases; + struct ggml_tensor * scales; + struct ggml_tensor * rolling_mean; + struct ggml_tensor * rolling_variance; + int padding = 1; + bool batch_normalize = true; + bool activate = true; // true for leaky relu, false for linear +}; + +struct yolo_model { + int width = 416; + int height = 416; + std::vector conv2d_layers; + struct ggml_context * ctx; +}; + +struct yolo_layer { + int classes = 80; + std::vector mask; + std::vector anchors; + struct ggml_tensor * predictions; + + yolo_layer(int classes, const std::vector & mask, const std::vector & anchors, struct ggml_tensor * predictions) + : classes(classes), mask(mask), anchors(anchors), predictions(predictions) + { } + + int entry_index(int location, int entry) const { + int w = predictions->ne[0]; + int h = predictions->ne[1]; + int n = location / (w*h); + int loc = location % (w*h); + return n*w*h*(4+classes+1) + entry*w*h + loc; + } +}; + +struct box { + float x, y, w, h; +}; + +struct detection { + box bbox; + std::vector prob; + float objectness; +}; + +static bool load_model(const std::string & fname, yolo_model & model) { + struct gguf_init_params params = { + /*.no_alloc =*/ false, + /*.ctx =*/ &model.ctx, + }; + gguf_context * ctx = gguf_init_from_file(fname.c_str(), params); + if (!ctx) { + fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__); + return false; + } + model.width = 416; + model.height = 416; + model.conv2d_layers.resize(13); + model.conv2d_layers[7].padding = 0; + model.conv2d_layers[9].padding = 0; + model.conv2d_layers[9].batch_normalize = false; + model.conv2d_layers[9].activate = false; + model.conv2d_layers[10].padding = 0; + model.conv2d_layers[12].padding = 0; + model.conv2d_layers[12].batch_normalize = false; + model.conv2d_layers[12].activate = false; + for (int i = 0; i < (int)model.conv2d_layers.size(); i++) { + char name[256]; + snprintf(name, sizeof(name), "l%d_weights", i); + model.conv2d_layers[i].weights = ggml_get_tensor(model.ctx, name); + snprintf(name, sizeof(name), "l%d_biases", i); + model.conv2d_layers[i].biases = ggml_get_tensor(model.ctx, name); + if (model.conv2d_layers[i].batch_normalize) { + snprintf(name, sizeof(name), "l%d_scales", i); + model.conv2d_layers[i].scales = ggml_get_tensor(model.ctx, name); + snprintf(name, sizeof(name), "l%d_rolling_mean", i); + model.conv2d_layers[i].rolling_mean = ggml_get_tensor(model.ctx, name); + snprintf(name, sizeof(name), "l%d_rolling_variance", i); + model.conv2d_layers[i].rolling_variance = ggml_get_tensor(model.ctx, name); + } + } + return true; +} + +static bool load_labels(const char * filename, std::vector & labels) +{ + std::ifstream file_in(filename); + if (!file_in) { + return false; + } + std::string line; + while (std::getline(file_in, line)) { + labels.push_back(line); + } + GGML_ASSERT(labels.size() == 80); + return true; +} + +static bool load_alphabet(std::vector & alphabet) +{ + alphabet.resize(8 * 128); + for (int j = 0; j < 8; j++) { + for (int i = 32; i < 127; i++) { + char fname[256]; + sprintf(fname, "data/labels/%d_%d.png", i, j); + if (!load_image(fname, alphabet[j*128 + i])) { + fprintf(stderr, "Cannot load '%s'\n", fname); + return false; + } + } + } + return true; +} + +static ggml_tensor * apply_conv2d(ggml_context * ctx, ggml_tensor * input, const conv2d_layer & layer) +{ + struct ggml_tensor * result = ggml_conv_2d(ctx, layer.weights, input, 1, 1, layer.padding, layer.padding, 1, 1); + if (layer.batch_normalize) { + result = ggml_sub(ctx, result, ggml_repeat(ctx, layer.rolling_mean, result)); + result = ggml_div(ctx, result, ggml_sqrt(ctx, ggml_repeat(ctx, layer.rolling_variance, result))); + result = ggml_mul(ctx, result, ggml_repeat(ctx, layer.scales, result)); + } + result = ggml_add(ctx, result, ggml_repeat(ctx, layer.biases, result)); + if (layer.activate) { + result = ggml_leaky(ctx, result); + } + return result; +} + +static void activate_array(float * x, const int n) +{ + // logistic activation + for (int i = 0; i < n; i++) { + x[i] = 1./(1. + exp(-x[i])); + } +} + +static void apply_yolo(yolo_layer & layer) +{ + int w = layer.predictions->ne[0]; + int h = layer.predictions->ne[1]; + int N = layer.mask.size(); + float * data = ggml_get_data_f32(layer.predictions); + for (int n = 0; n < N; n++) { + int index = layer.entry_index(n*w*h, 0); + activate_array(data + index, 2*w*h); + index = layer.entry_index(n*w*h, 4); + activate_array(data + index, (1+layer.classes)*w*h); + } +} + +static box get_yolo_box(const yolo_layer & layer, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride) +{ + float * predictions = ggml_get_data_f32(layer.predictions); + box b; + b.x = (i + predictions[index + 0*stride]) / lw; + b.y = (j + predictions[index + 1*stride]) / lh; + b.w = exp(predictions[index + 2*stride]) * layer.anchors[2*n] / w; + b.h = exp(predictions[index + 3*stride]) * layer.anchors[2*n+1] / h; + return b; +} + +static void correct_yolo_box(box & b, int im_w, int im_h, int net_w, int net_h) +{ + int new_w = 0; + int new_h = 0; + if (((float)net_w/im_w) < ((float)net_h/im_h)) { + new_w = net_w; + new_h = (im_h * net_w)/im_w; + } else { + new_h = net_h; + new_w = (im_w * net_h)/im_h; + } + b.x = (b.x - (net_w - new_w)/2./net_w) / ((float)new_w/net_w); + b.y = (b.y - (net_h - new_h)/2./net_h) / ((float)new_h/net_h); + b.w *= (float)net_w/new_w; + b.h *= (float)net_h/new_h; +} + +static void get_yolo_detections(const yolo_layer & layer, std::vector & detections, int im_w, int im_h, int netw, int neth, float thresh) +{ + int w = layer.predictions->ne[0]; + int h = layer.predictions->ne[1]; + int N = layer.mask.size(); + float * predictions = ggml_get_data_f32(layer.predictions); + std::vector result; + for (int i = 0; i < w*h; i++) { + for (int n = 0; n < N; n++) { + int obj_index = layer.entry_index(n*w*h + i, 4); + float objectness = predictions[obj_index]; + if (objectness <= thresh) { + continue; + } + detection det; + int box_index = layer.entry_index(n*w*h + i, 0); + int row = i / w; + int col = i % w; + det.bbox = get_yolo_box(layer, layer.mask[n], box_index, col, row, w, h, netw, neth, w*h); + correct_yolo_box(det.bbox, im_w, im_h, netw, neth); + det.objectness = objectness; + det.prob.resize(layer.classes); + for (int j = 0; j < layer.classes; j++) { + int class_index = layer.entry_index(n*w*h + i, 4 + 1 + j); + float prob = objectness*predictions[class_index]; + det.prob[j] = (prob > thresh) ? prob : 0; + } + detections.push_back(det); + } + } +} + +static float overlap(float x1, float w1, float x2, float w2) +{ + float l1 = x1 - w1/2; + float l2 = x2 - w2/2; + float left = l1 > l2 ? l1 : l2; + float r1 = x1 + w1/2; + float r2 = x2 + w2/2; + float right = r1 < r2 ? r1 : r2; + return right - left; +} + +static float box_intersection(const box & a, const box & b) +{ + float w = overlap(a.x, a.w, b.x, b.w); + float h = overlap(a.y, a.h, b.y, b.h); + if (w < 0 || h < 0) return 0; + float area = w*h; + return area; +} + +static float box_union(const box & a, const box & b) +{ + float i = box_intersection(a, b); + float u = a.w*a.h + b.w*b.h - i; + return u; +} + +static float box_iou(const box & a, const box & b) +{ + return box_intersection(a, b)/box_union(a, b); +} + +static void do_nms_sort(std::vector & dets, int classes, float thresh) +{ + int k = (int)dets.size()-1; + for (int i = 0; i <= k; ++i) { + if (dets[i].objectness == 0) { + std::swap(dets[i], dets[k]); + --k; + --i; + } + } + int total = k+1; + for (int k = 0; k < classes; ++k) { + std::sort(dets.begin(), dets.begin()+total, [=](const detection & a, const detection & b) { + return a.prob[k] > b.prob[k]; + }); + for (int i = 0; i < total; ++i) { + if (dets[i].prob[k] == 0) { + continue; + } + box a = dets[i].bbox; + for (int j = i+1; j < total; ++j){ + box b = dets[j].bbox; + if (box_iou(a, b) > thresh) { + dets[j].prob[k] = 0; + } + } + } + } +} + +static float get_color(int c, int x, int max) +{ + float colors[6][3] = { {1,0,1}, {0,0,1}, {0,1,1}, {0,1,0}, {1,1,0}, {1,0,0} }; + float ratio = ((float)x/max)*5; + int i = floor(ratio); + int j = ceil(ratio); + ratio -= i; + float r = (1-ratio) * colors[i][c] + ratio*colors[j][c]; + return r; +} + +static void draw_detections(yolo_image & im, const std::vector & dets, float thresh, const std::vector & labels, const std::vector & alphabet) +{ + int classes = (int)labels.size(); + for (int i = 0; i < (int)dets.size(); i++) { + std::string labelstr; + int cl = -1; + for (int j = 0; j < (int)dets[i].prob.size(); j++) { + if (dets[i].prob[j] > thresh) { + if (cl < 0) { + labelstr = labels[j]; + cl = j; + } else { + labelstr += ", "; + labelstr += labels[j]; + } + printf("%s: %.0f%%\n", labels[j].c_str(), dets[i].prob[j]*100); + } + } + if (cl >= 0) { + int width = im.h * .006; + int offset = cl*123457 % classes; + float red = get_color(2,offset,classes); + float green = get_color(1,offset,classes); + float blue = get_color(0,offset,classes); + float rgb[3]; + + rgb[0] = red; + rgb[1] = green; + rgb[2] = blue; + box b = dets[i].bbox; + + int left = (b.x-b.w/2.)*im.w; + int right = (b.x+b.w/2.)*im.w; + int top = (b.y-b.h/2.)*im.h; + int bot = (b.y+b.h/2.)*im.h; + + if (left < 0) left = 0; + if (right > im.w-1) right = im.w-1; + if (top < 0) top = 0; + if (bot > im.h-1) bot = im.h-1; + + draw_box_width(im, left, top, right, bot, width, red, green, blue); + yolo_image label = get_label(alphabet, labelstr, (im.h*.03)); + draw_label(im, top + width, left, label, rgb); + } + } +} + +static void print_shape(int layer, const ggml_tensor * t) +{ + printf("Layer %2d output shape: %3d x %3d x %4d x %3d\n", layer, (int)t->ne[0], (int)t->ne[1], (int)t->ne[2], (int)t->ne[3]); +} + +void detect(yolo_image & img, const yolo_model & model, float thresh, const std::vector & labels, const std::vector & alphabet) +{ + static size_t buf_size = 20000000 * sizeof(float) * 4; + 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 = {}; + std::vector detections; + + yolo_image sized = letterbox_image(img, model.width, model.height); + struct ggml_tensor * input = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, model.width, model.height, 3, 1); + std::memcpy(input->data, sized.data.data(), ggml_nbytes(input)); + ggml_set_name(input, "input"); + + struct ggml_tensor * result = apply_conv2d(ctx0, input, model.conv2d_layers[0]); + print_shape(0, result); + result = ggml_pool_2d(ctx0, result, GGML_OP_POOL_MAX, 2, 2, 2, 2, 0, 0); + print_shape(1, result); + result = apply_conv2d(ctx0, result, model.conv2d_layers[1]); + print_shape(2, result); + result = ggml_pool_2d(ctx0, result, GGML_OP_POOL_MAX, 2, 2, 2, 2, 0, 0); + print_shape(3, result); + result = apply_conv2d(ctx0, result, model.conv2d_layers[2]); + print_shape(4, result); + result = ggml_pool_2d(ctx0, result, GGML_OP_POOL_MAX, 2, 2, 2, 2, 0, 0); + print_shape(5, result); + result = apply_conv2d(ctx0, result, model.conv2d_layers[3]); + print_shape(6, result); + result = ggml_pool_2d(ctx0, result, GGML_OP_POOL_MAX, 2, 2, 2, 2, 0, 0); + print_shape(7, result); + result = apply_conv2d(ctx0, result, model.conv2d_layers[4]); + struct ggml_tensor * layer_8 = result; + print_shape(8, result); + result = ggml_pool_2d(ctx0, result, GGML_OP_POOL_MAX, 2, 2, 2, 2, 0, 0); + print_shape(9, result); + result = apply_conv2d(ctx0, result, model.conv2d_layers[5]); + print_shape(10, result); + result = ggml_pool_2d(ctx0, result, GGML_OP_POOL_MAX, 2, 2, 1, 1, 0.5, 0.5); + print_shape(11, result); + result = apply_conv2d(ctx0, result, model.conv2d_layers[6]); + print_shape(12, result); + result = apply_conv2d(ctx0, result, model.conv2d_layers[7]); + struct ggml_tensor * layer_13 = result; + print_shape(13, result); + result = apply_conv2d(ctx0, result, model.conv2d_layers[8]); + print_shape(14, result); + result = apply_conv2d(ctx0, result, model.conv2d_layers[9]); + struct ggml_tensor * layer_15 = result; + print_shape(15, result); + result = apply_conv2d(ctx0, layer_13, model.conv2d_layers[10]); + print_shape(18, result); + result = ggml_upscale(ctx0, result, 2); + print_shape(19, result); + result = ggml_concat(ctx0, result, layer_8); + print_shape(20, result); + result = apply_conv2d(ctx0, result, model.conv2d_layers[11]); + print_shape(21, result); + result = apply_conv2d(ctx0, result, model.conv2d_layers[12]); + struct ggml_tensor * layer_22 = result; + print_shape(22, result); + + ggml_build_forward_expand(&gf, layer_15); + ggml_build_forward_expand(&gf, layer_22); + ggml_graph_compute_with_ctx(ctx0, &gf, 1); + + yolo_layer yolo16{ 80, {3, 4, 5}, {10, 14, 23, 27, 37,58, 81, 82, 135, 169, 344, 319}, layer_15}; + apply_yolo(yolo16); + get_yolo_detections(yolo16, detections, img.w, img.h, model.width, model.height, thresh); + + yolo_layer yolo23{ 80, {0, 1, 2}, {10, 14, 23, 27, 37,58, 81, 82, 135, 169, 344, 319}, layer_22}; + apply_yolo(yolo23); + get_yolo_detections(yolo23, detections, img.w, img.h, model.width, model.height, thresh); + + do_nms_sort(detections, yolo23.classes, .45); + draw_detections(img, detections, thresh, labels, alphabet); + ggml_free(ctx0); +} + +struct yolo_params { + float thresh = 0.5; + std::string model = "yolov3-tiny.gguf"; + std::string fname_inp = "input.jpg"; + std::string fname_out = "predictions.jpg"; +}; + +void yolo_print_usage(int argc, char ** argv, const yolo_params & params) { + fprintf(stderr, "usage: %s [options]\n", argv[0]); + fprintf(stderr, "\n"); + fprintf(stderr, "options:\n"); + fprintf(stderr, " -h, --help show this help message and exit\n"); + fprintf(stderr, " -th T, --thresh T detection threshold (default: %.2f)\n", params.thresh); + fprintf(stderr, " -m FNAME, --model FNAME\n"); + fprintf(stderr, " model path (default: %s)\n", params.model.c_str()); + fprintf(stderr, " -i FNAME, --inp FNAME\n"); + fprintf(stderr, " input file (default: %s)\n", params.fname_inp.c_str()); + fprintf(stderr, " -o FNAME, --out FNAME\n"); + fprintf(stderr, " output file (default: %s)\n", params.fname_out.c_str()); + fprintf(stderr, "\n"); +} + +bool yolo_params_parse(int argc, char ** argv, yolo_params & params) { + for (int i = 1; i < argc; i++) { + std::string arg = argv[i]; + + if (arg == "-th" || arg == "--thresh") { + params.thresh = std::stof(argv[++i]); + } else if (arg == "-m" || arg == "--model") { + params.model = argv[++i]; + } else if (arg == "-i" || arg == "--inp") { + params.fname_inp = argv[++i]; + } else if (arg == "-o" || arg == "--out") { + params.fname_out = argv[++i]; + } else if (arg == "-h" || arg == "--help") { + yolo_print_usage(argc, argv, params); + exit(0); + } else { + fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); + yolo_print_usage(argc, argv, params); + exit(0); + } + } + + return true; +} + +int main(int argc, char *argv[]) +{ + ggml_time_init(); + yolo_model model; + + yolo_params params; + if (!yolo_params_parse(argc, argv, params)) { + return 1; + } + if (!load_model(params.model, model)) { + fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str()); + return 1; + } + yolo_image img(0,0,0); + if (!load_image(params.fname_inp.c_str(), img)) { + fprintf(stderr, "%s: failed to load image from '%s'\n", __func__, params.fname_inp.c_str()); + return 1; + } + std::vector labels; + if (!load_labels("data/coco.names", labels)) { + fprintf(stderr, "%s: failed to load labels from 'data/coco.names'\n", __func__); + return 1; + } + std::vector alphabet; + if (!load_alphabet(alphabet)) { + fprintf(stderr, "%s: failed to load alphabet\n", __func__); + return 1; + } + const int64_t t_start_ms = ggml_time_ms(); + detect(img, model, params.thresh, labels, alphabet); + const int64_t t_detect_ms = ggml_time_ms() - t_start_ms; + if (!save_image(img, params.fname_out.c_str(), 80)) { + fprintf(stderr, "%s: failed to save image to '%s'\n", __func__, params.fname_out.c_str()); + return 1; + } + printf("Detected objects saved in '%s' (time: %f sec.)\n", params.fname_out.c_str(), t_detect_ms / 1000.0f); + ggml_free(model.ctx); + return 0; +} diff --git a/include/ggml/ggml.h b/include/ggml/ggml.h index 08bff5511..a8c2a4ad9 100644 --- a/include/ggml/ggml.h +++ b/include/ggml/ggml.h @@ -451,6 +451,7 @@ extern "C" { GGML_UNARY_OP_GELU, GGML_UNARY_OP_GELU_QUICK, GGML_UNARY_OP_SILU, + GGML_UNARY_OP_LEAKY }; enum ggml_object_type { @@ -943,6 +944,10 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_leaky( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_relu_inplace( struct ggml_context * ctx, struct ggml_tensor * a); @@ -1463,6 +1468,8 @@ extern "C" { int s0, // stride int p0); // padding + // the result will have 2*p0 padding for the first dimension + // and 2*p1 padding for the second dimension GGML_API struct ggml_tensor * ggml_pool_2d( struct ggml_context * ctx, struct ggml_tensor * a, @@ -1471,8 +1478,8 @@ extern "C" { int k1, int s0, int s1, - int p0, - int p1); + float p0, + float p1); // nearest interpolate // used in stable-diffusion diff --git a/requirements.txt b/requirements.txt index 9be8160aa..a9fd9604e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,4 +4,5 @@ sentencepiece==0.1.98 torch==2.0.1 torchaudio==2.0.2 torchvision==0.15.2 -transformers==4.29.2 \ No newline at end of file +transformers==4.29.2 +gguf==0.4.5 diff --git a/src/ggml.c b/src/ggml.c index 6f66bab05..5b1e8c628 100644 --- a/src/ggml.c +++ b/src/ggml.c @@ -3834,6 +3834,7 @@ inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } +inline static void ggml_vec_leaky_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.1f*x[i]; } static const float GELU_COEF_A = 0.044715f; static const float GELU_QUICK_COEF = -1.702f; @@ -6251,6 +6252,14 @@ struct ggml_tensor * ggml_relu_inplace( return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_RELU); } +// ggml_leaky + +struct ggml_tensor * ggml_leaky( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_LEAKY); +} + // ggml_gelu struct ggml_tensor * ggml_gelu( @@ -7855,7 +7864,7 @@ struct ggml_tensor * ggml_conv_transpose_2d_p0( // ggml_pool_* -static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, int p) { +static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, float p) { return (ins + 2 * p - ks) / s + 1; } @@ -7902,8 +7911,8 @@ struct ggml_tensor * ggml_pool_2d( int k1, int s0, int s1, - int p0, - int p1) { + float p0, + float p1) { bool is_node = false; @@ -7911,7 +7920,6 @@ struct ggml_tensor * ggml_pool_2d( GGML_ASSERT(false); // TODO: implement backward is_node = true; } - const int64_t ne[3] = { ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), ggml_calc_pool_output_size(a->ne[1], k1, s1, p1), @@ -11342,6 +11350,48 @@ static void ggml_compute_forward_silu( } } +// ggml_compute_forward_leaky + +static void ggml_compute_forward_leaky_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + assert(params->ith == 0); + assert(ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert(dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_leaky_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_leaky( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_leaky_f32(params, src0, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_silu_back static void ggml_compute_forward_silu_back_f32( @@ -14995,14 +15045,11 @@ static void ggml_compute_forward_pool_1d( ggml_compute_forward_pool_1d_sk_p0(params, op, src0, k0, dst); } -// ggml_compute_forward_pool_2d_sk_p0 +// ggml_compute_forward_pool_2d -static void ggml_compute_forward_pool_2d_sk_p0( +static void ggml_compute_forward_pool_2d( const struct ggml_compute_params * params, - const enum ggml_op_pool op, const struct ggml_tensor * src, - const int k0, - const int k1, struct ggml_tensor * dst) { assert(src->type == GGML_TYPE_F32); assert(params->ith == 0); @@ -15011,6 +15058,14 @@ static void ggml_compute_forward_pool_2d_sk_p0( return; } + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = opts[0]; + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; const char * cdata = (const char*)src->data; const char * const data_end = cdata + ggml_nbytes(src); @@ -15021,6 +15076,8 @@ static void ggml_compute_forward_pool_2d_sk_p0( float * dplane = (float *)dst->data; const int ka = k0 * k1; + const int offset0 = -p0; + const int offset1 = -p1; while (cdata < data_end) { for (int oy = 0; oy < py; ++oy) { @@ -15033,13 +15090,15 @@ static void ggml_compute_forward_pool_2d_sk_p0( case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } - const int ix = ox * k0; - const int iy = oy * k1; + const int ix = offset0 + ox * s0; + const int iy = offset1 + oy * s1; for (int ky = 0; ky < k1; ++ky) { + if (iy + ky < 0 || iy + ky >= src->ne[1]) continue; const float * const srow = (const float *)(cdata + src->nb[1] * (iy + ky)); for (int kx = 0; kx < k0; ++kx) { int j = ix + kx; + if (j < 0 || j >= src->ne[0]) continue; switch (op) { case GGML_OP_POOL_AVG: *out += srow[j]; break; case GGML_OP_POOL_MAX: if (srow[j] > *out) *out = srow[j]; break; @@ -15060,29 +15119,6 @@ static void ggml_compute_forward_pool_2d_sk_p0( } } -// ggml_compute_forward_pool_2d - -static void ggml_compute_forward_pool_2d( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - struct ggml_tensor * dst) { - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; - GGML_ASSERT(p0 == 0); - GGML_ASSERT(p1 == 0); // padding not supported - GGML_ASSERT(k0 == s0); - GGML_ASSERT(k1 == s1); // only s = k supported - - ggml_compute_forward_pool_2d_sk_p0(params, op, src0, k0, k1, dst); -} - // ggml_compute_forward_upscale static void ggml_compute_forward_upscale_f32( @@ -16284,6 +16320,10 @@ static void ggml_compute_forward_unary( { ggml_compute_forward_silu(params, src0, dst); } break; + case GGML_UNARY_OP_LEAKY: + { + ggml_compute_forward_leaky(params, src0, dst); + } break; default: { GGML_ASSERT(false); @@ -18731,6 +18771,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { case GGML_UNARY_OP_TANH: case GGML_UNARY_OP_ELU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_LEAKY: { n_tasks = 1; } break;