mirror of https://github.com/AlexeyAB/darknet.git
AlexeyAB
6 years ago
parent
c56931dd75
commit
3ff5084590
30 changed files with 1830 additions and 315 deletions
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rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\python.exe darknet_video.py |
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C:\Python27\python.exe darknet_video.py |
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pause |
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@ -0,0 +1,320 @@ |
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from ctypes import * |
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import math |
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import random |
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import os |
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import cv2 |
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import numpy as np |
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import time |
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def sample(probs): |
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s = sum(probs) |
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probs = [a/s for a in probs] |
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r = random.uniform(0, 1) |
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for i in range(len(probs)): |
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r = r - probs[i] |
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if r <= 0: |
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return i |
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return len(probs)-1 |
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|
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|
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def c_array(ctype, values): |
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arr = (ctype*len(values))() |
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arr[:] = values |
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return arr |
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|
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class BOX(Structure): |
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_fields_ = [("x", c_float), |
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("y", c_float), |
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("w", c_float), |
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("h", c_float)] |
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class DETECTION(Structure): |
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_fields_ = [("bbox", BOX), |
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("classes", c_int), |
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("prob", POINTER(c_float)), |
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("mask", POINTER(c_float)), |
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("objectness", c_float), |
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("sort_class", c_int)] |
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class IMAGE(Structure): |
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_fields_ = [("w", c_int), |
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("h", c_int), |
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("c", c_int), |
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("data", POINTER(c_float))] |
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class METADATA(Structure): |
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_fields_ = [("classes", c_int), |
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("names", POINTER(c_char_p))] |
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hasGPU = True |
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lib = CDLL("yolo_cpp_dll.dll", RTLD_GLOBAL) |
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lib.network_width.argtypes = [c_void_p] |
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lib.network_width.restype = c_int |
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lib.network_height.argtypes = [c_void_p] |
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lib.network_height.restype = c_int |
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predict = lib.network_predict |
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predict.argtypes = [c_void_p, POINTER(c_float)] |
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predict.restype = POINTER(c_float) |
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if hasGPU: |
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set_gpu = lib.cuda_set_device |
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set_gpu.argtypes = [c_int] |
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make_image = lib.make_image |
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make_image.argtypes = [c_int, c_int, c_int] |
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make_image.restype = IMAGE |
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get_network_boxes = lib.get_network_boxes |
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get_network_boxes.argtypes = \ |
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[c_void_p, c_int, c_int, c_float, c_float, POINTER( |
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c_int), c_int, POINTER(c_int), c_int] |
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get_network_boxes.restype = POINTER(DETECTION) |
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make_network_boxes = lib.make_network_boxes |
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make_network_boxes.argtypes = [c_void_p] |
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make_network_boxes.restype = POINTER(DETECTION) |
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free_detections = lib.free_detections |
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free_detections.argtypes = [POINTER(DETECTION), c_int] |
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free_ptrs = lib.free_ptrs |
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free_ptrs.argtypes = [POINTER(c_void_p), c_int] |
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network_predict = lib.network_predict |
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network_predict.argtypes = [c_void_p, POINTER(c_float)] |
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reset_rnn = lib.reset_rnn |
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reset_rnn.argtypes = [c_void_p] |
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load_net = lib.load_network |
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load_net.argtypes = [c_char_p, c_char_p, c_int] |
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load_net.restype = c_void_p |
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load_net_custom = lib.load_network_custom |
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load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int] |
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load_net_custom.restype = c_void_p |
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do_nms_obj = lib.do_nms_obj |
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do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] |
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do_nms_sort = lib.do_nms_sort |
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do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] |
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free_image = lib.free_image |
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free_image.argtypes = [IMAGE] |
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letterbox_image = lib.letterbox_image |
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letterbox_image.argtypes = [IMAGE, c_int, c_int] |
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letterbox_image.restype = IMAGE |
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load_meta = lib.get_metadata |
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lib.get_metadata.argtypes = [c_char_p] |
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lib.get_metadata.restype = METADATA |
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load_image = lib.load_image_color |
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load_image.argtypes = [c_char_p, c_int, c_int] |
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load_image.restype = IMAGE |
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rgbgr_image = lib.rgbgr_image |
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rgbgr_image.argtypes = [IMAGE] |
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predict_image = lib.network_predict_image |
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predict_image.argtypes = [c_void_p, IMAGE] |
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predict_image.restype = POINTER(c_float) |
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def array_to_image(arr): |
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import numpy as np |
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arr = arr.transpose(2, 0, 1) |
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c = arr.shape[0] |
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h = arr.shape[1] |
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w = arr.shape[2] |
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arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 |
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data = arr.ctypes.data_as(POINTER(c_float)) |
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im = IMAGE(w, h, c, data) |
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return im, arr |
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def classify(net, meta, im): |
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out = predict_image(net, im) |
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res = [] |
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for i in range(meta.classes): |
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if altNames is None: |
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nameTag = meta.names[i] |
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else: |
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nameTag = altNames[i] |
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res.append((nameTag, out[i])) |
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res = sorted(res, key=lambda x: -x[1]) |
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return res |
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def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug=False): |
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im, arr = array_to_image(image) |
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if debug: |
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print("Loaded image") |
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num = c_int(0) |
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if debug: |
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print("Assigned num") |
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pnum = pointer(num) |
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if debug: |
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print("Assigned pnum") |
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predict_image(net, im) |
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if debug: |
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print("did prediction") |
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# dets = get_network_boxes( |
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# net, image.shape[1], image.shape[0], |
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# thresh, hier_thresh, |
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# None, 0, pnum, 0) # OpenCV |
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dets = get_network_boxes(net, im.w, im.h, |
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thresh, hier_thresh, None, 0, pnum, 0) |
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if debug: |
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print("Got dets") |
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num = pnum[0] |
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if debug: |
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print("got zeroth index of pnum") |
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if nms: |
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do_nms_sort(dets, num, meta.classes, nms) |
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if debug: |
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print("did sort") |
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res = [] |
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if debug: |
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print("about to range") |
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for j in range(num): |
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if debug: |
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print("Ranging on "+str(j)+" of "+str(num)) |
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if debug: |
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print("Classes: "+str(meta), meta.classes, meta.names) |
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for i in range(meta.classes): |
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if debug: |
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print("Class-ranging on "+str(i)+" of " + |
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str(meta.classes)+"= "+str(dets[j].prob[i])) |
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if dets[j].prob[i] > 0: |
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b = dets[j].bbox |
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if altNames is None: |
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nameTag = meta.names[i] |
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else: |
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nameTag = altNames[i] |
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if debug: |
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print("Got bbox", b) |
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print(nameTag) |
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print(dets[j].prob[i]) |
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print((b.x, b.y, b.w, b.h)) |
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res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h))) |
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if debug: |
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print("did range") |
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res = sorted(res, key=lambda x: -x[1]) |
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if debug: |
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print("did sort") |
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# free_image(im) |
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if debug: |
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print("freed image") |
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free_detections(dets, num) |
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if debug: |
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print("freed detections") |
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return res |
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def convertBack(x, y, w, h): |
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xmin = int(round(x - (w / 2))) |
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xmax = int(round(x + (w / 2))) |
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ymin = int(round(y - (h / 2))) |
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ymax = int(round(y + (h / 2))) |
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return xmin, ymin, xmax, ymax |
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def cvDrawBoxes(detections, img): |
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for detection in detections: |
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x, y, w, h = detection[2][0],\ |
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detection[2][1],\ |
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detection[2][2],\ |
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detection[2][3] |
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xmin, ymin, xmax, ymax = convertBack( |
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float(x), float(y), float(w), float(h)) |
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pt1 = (xmin, ymin) |
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pt2 = (xmax, ymax) |
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cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2) |
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cv2.putText(img, |
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detection[0].decode() + |
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" [" + str(round(detection[1] * 100, 2)) + "]", |
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(pt1[0], pt1[1] + 20), cv2.FONT_HERSHEY_SIMPLEX, 1, |
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[0, 255, 0], 4) |
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return img |
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netMain = None |
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metaMain = None |
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altNames = None |
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def YOLO(): |
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global metaMain, netMain, altNames |
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configPath = "./cfg/yolov3.cfg" |
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weightPath = "./yolov3.weights" |
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metaPath = "./cfg/coco.data" |
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if not os.path.exists(configPath): |
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raise ValueError("Invalid config path `" + |
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os.path.abspath(configPath)+"`") |
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if not os.path.exists(weightPath): |
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raise ValueError("Invalid weight path `" + |
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os.path.abspath(weightPath)+"`") |
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if not os.path.exists(metaPath): |
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raise ValueError("Invalid data file path `" + |
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os.path.abspath(metaPath)+"`") |
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if netMain is None: |
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netMain = load_net_custom(configPath.encode( |
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"ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 |
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if metaMain is None: |
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metaMain = load_meta(metaPath.encode("ascii")) |
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if altNames is None: |
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try: |
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with open(metaPath) as metaFH: |
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metaContents = metaFH.read() |
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import re |
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match = re.search("names *= *(.*)$", metaContents, |
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re.IGNORECASE | re.MULTILINE) |
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if match: |
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result = match.group(1) |
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else: |
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result = None |
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try: |
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if os.path.exists(result): |
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with open(result) as namesFH: |
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namesList = namesFH.read().strip().split("\n") |
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altNames = [x.strip() for x in namesList] |
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except TypeError: |
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pass |
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except Exception: |
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pass |
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#cap = cv2.VideoCapture(0) |
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cap = cv2.VideoCapture("test.mp4") |
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cap.set(3, 1280) |
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cap.set(4, 720) |
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out = cv2.VideoWriter( |
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"output.avi", cv2.VideoWriter_fourcc(*"MJPG"), 10.0, |
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(lib.network_width(netMain), lib.network_height(netMain))) |
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print("Starting the YOLO loop...") |
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while True: |
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prev_time = time.time() |
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ret, frame_read = cap.read() |
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frame_rgb = cv2.cvtColor(frame_read, cv2.COLOR_BGR2RGB) |
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frame_resized = cv2.resize(frame_rgb, |
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(lib.network_width(netMain), |
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lib.network_height(netMain)), |
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interpolation=cv2.INTER_LINEAR) |
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detections = detect(netMain, metaMain, frame_resized, thresh=0.25) |
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image = cvDrawBoxes(detections, frame_resized) |
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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print(1/(time.time()-prev_time)) |
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cap.release() |
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out.release() |
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if __name__ == "__main__": |
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YOLO() |
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@ -0,0 +1,320 @@ |
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from ctypes import * |
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import math |
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import random |
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import os |
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import cv2 |
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import numpy as np |
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import time |
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def sample(probs): |
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s = sum(probs) |
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probs = [a/s for a in probs] |
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r = random.uniform(0, 1) |
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for i in range(len(probs)): |
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r = r - probs[i] |
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if r <= 0: |
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return i |
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return len(probs)-1 |
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def c_array(ctype, values): |
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arr = (ctype*len(values))() |
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arr[:] = values |
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return arr |
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class BOX(Structure): |
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_fields_ = [("x", c_float), |
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("y", c_float), |
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("w", c_float), |
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("h", c_float)] |
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class DETECTION(Structure): |
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_fields_ = [("bbox", BOX), |
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("classes", c_int), |
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("prob", POINTER(c_float)), |
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("mask", POINTER(c_float)), |
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("objectness", c_float), |
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("sort_class", c_int)] |
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class IMAGE(Structure): |
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_fields_ = [("w", c_int), |
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("h", c_int), |
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("c", c_int), |
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("data", POINTER(c_float))] |
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class METADATA(Structure): |
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_fields_ = [("classes", c_int), |
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("names", POINTER(c_char_p))] |
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hasGPU = True |
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lib = CDLL("./libdarknet.so", RTLD_GLOBAL) |
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lib.network_width.argtypes = [c_void_p] |
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lib.network_width.restype = c_int |
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lib.network_height.argtypes = [c_void_p] |
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lib.network_height.restype = c_int |
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predict = lib.network_predict |
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predict.argtypes = [c_void_p, POINTER(c_float)] |
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predict.restype = POINTER(c_float) |
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if hasGPU: |
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set_gpu = lib.cuda_set_device |
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set_gpu.argtypes = [c_int] |
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make_image = lib.make_image |
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make_image.argtypes = [c_int, c_int, c_int] |
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make_image.restype = IMAGE |
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get_network_boxes = lib.get_network_boxes |
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get_network_boxes.argtypes = \ |
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[c_void_p, c_int, c_int, c_float, c_float, POINTER( |
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c_int), c_int, POINTER(c_int), c_int] |
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get_network_boxes.restype = POINTER(DETECTION) |
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make_network_boxes = lib.make_network_boxes |
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make_network_boxes.argtypes = [c_void_p] |
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make_network_boxes.restype = POINTER(DETECTION) |
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free_detections = lib.free_detections |
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free_detections.argtypes = [POINTER(DETECTION), c_int] |
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free_ptrs = lib.free_ptrs |
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free_ptrs.argtypes = [POINTER(c_void_p), c_int] |
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network_predict = lib.network_predict |
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network_predict.argtypes = [c_void_p, POINTER(c_float)] |
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reset_rnn = lib.reset_rnn |
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reset_rnn.argtypes = [c_void_p] |
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load_net = lib.load_network |
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load_net.argtypes = [c_char_p, c_char_p, c_int] |
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load_net.restype = c_void_p |
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load_net_custom = lib.load_network_custom |
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load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int] |
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load_net_custom.restype = c_void_p |
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do_nms_obj = lib.do_nms_obj |
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do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] |
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do_nms_sort = lib.do_nms_sort |
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do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] |
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free_image = lib.free_image |
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free_image.argtypes = [IMAGE] |
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letterbox_image = lib.letterbox_image |
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letterbox_image.argtypes = [IMAGE, c_int, c_int] |
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letterbox_image.restype = IMAGE |
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load_meta = lib.get_metadata |
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lib.get_metadata.argtypes = [c_char_p] |
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lib.get_metadata.restype = METADATA |
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load_image = lib.load_image_color |
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load_image.argtypes = [c_char_p, c_int, c_int] |
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load_image.restype = IMAGE |
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rgbgr_image = lib.rgbgr_image |
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rgbgr_image.argtypes = [IMAGE] |
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predict_image = lib.network_predict_image |
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predict_image.argtypes = [c_void_p, IMAGE] |
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predict_image.restype = POINTER(c_float) |
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def array_to_image(arr): |
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import numpy as np |
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arr = arr.transpose(2, 0, 1) |
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c = arr.shape[0] |
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h = arr.shape[1] |
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w = arr.shape[2] |
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arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 |
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data = arr.ctypes.data_as(POINTER(c_float)) |
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im = IMAGE(w, h, c, data) |
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return im, arr |
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|
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def classify(net, meta, im): |
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out = predict_image(net, im) |
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res = [] |
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for i in range(meta.classes): |
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if altNames is None: |
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nameTag = meta.names[i] |
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else: |
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nameTag = altNames[i] |
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res.append((nameTag, out[i])) |
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res = sorted(res, key=lambda x: -x[1]) |
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return res |
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def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug=False): |
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im, arr = array_to_image(image) |
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if debug: |
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print("Loaded image") |
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num = c_int(0) |
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if debug: |
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print("Assigned num") |
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pnum = pointer(num) |
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if debug: |
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print("Assigned pnum") |
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predict_image(net, im) |
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if debug: |
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print("did prediction") |
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# dets = get_network_boxes( |
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# net, image.shape[1], image.shape[0], |
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# thresh, hier_thresh, |
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# None, 0, pnum, 0) # OpenCV |
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dets = get_network_boxes(net, im.w, im.h, |
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thresh, hier_thresh, None, 0, pnum, 0) |
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if debug: |
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print("Got dets") |
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num = pnum[0] |
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if debug: |
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print("got zeroth index of pnum") |
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if nms: |
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do_nms_sort(dets, num, meta.classes, nms) |
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if debug: |
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print("did sort") |
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res = [] |
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if debug: |
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print("about to range") |
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for j in range(num): |
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if debug: |
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print("Ranging on "+str(j)+" of "+str(num)) |
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if debug: |
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print("Classes: "+str(meta), meta.classes, meta.names) |
||||
for i in range(meta.classes): |
||||
if debug: |
||||
print("Class-ranging on "+str(i)+" of " + |
||||
str(meta.classes)+"= "+str(dets[j].prob[i])) |
||||
if dets[j].prob[i] > 0: |
||||
b = dets[j].bbox |
||||
if altNames is None: |
||||
nameTag = meta.names[i] |
||||
else: |
||||
nameTag = altNames[i] |
||||
if debug: |
||||
print("Got bbox", b) |
||||
print(nameTag) |
||||
print(dets[j].prob[i]) |
||||
print((b.x, b.y, b.w, b.h)) |
||||
res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h))) |
||||
if debug: |
||||
print("did range") |
||||
res = sorted(res, key=lambda x: -x[1]) |
||||
if debug: |
||||
print("did sort") |
||||
# free_image(im) |
||||
if debug: |
||||
print("freed image") |
||||
free_detections(dets, num) |
||||
if debug: |
||||
print("freed detections") |
||||
return res |
||||
|
||||
|
||||
def convertBack(x, y, w, h): |
||||
xmin = int(round(x - (w / 2))) |
||||
xmax = int(round(x + (w / 2))) |
||||
ymin = int(round(y - (h / 2))) |
||||
ymax = int(round(y + (h / 2))) |
||||
return xmin, ymin, xmax, ymax |
||||
|
||||
|
||||
def cvDrawBoxes(detections, img): |
||||
for detection in detections: |
||||
x, y, w, h = detection[2][0],\ |
||||
detection[2][1],\ |
||||
detection[2][2],\ |
||||
detection[2][3] |
||||
xmin, ymin, xmax, ymax = convertBack( |
||||
float(x), float(y), float(w), float(h)) |
||||
pt1 = (xmin, ymin) |
||||
pt2 = (xmax, ymax) |
||||
cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2) |
||||
cv2.putText(img, |
||||
detection[0].decode() + |
||||
" [" + str(round(detection[1] * 100, 2)) + "]", |
||||
(pt1[0], pt1[1] + 20), cv2.FONT_HERSHEY_SIMPLEX, 1, |
||||
[0, 255, 0], 4) |
||||
return img |
||||
|
||||
|
||||
netMain = None |
||||
metaMain = None |
||||
altNames = None |
||||
|
||||
|
||||
def YOLO(): |
||||
global metaMain, netMain, altNames |
||||
configPath = "./cfg/yolov3.cfg" |
||||
weightPath = "./yolov3.weights" |
||||
metaPath = "./cfg/coco.data" |
||||
if not os.path.exists(configPath): |
||||
raise ValueError("Invalid config path `" + |
||||
os.path.abspath(configPath)+"`") |
||||
if not os.path.exists(weightPath): |
||||
raise ValueError("Invalid weight path `" + |
||||
os.path.abspath(weightPath)+"`") |
||||
if not os.path.exists(metaPath): |
||||
raise ValueError("Invalid data file path `" + |
||||
os.path.abspath(metaPath)+"`") |
||||
if netMain is None: |
||||
netMain = load_net_custom(configPath.encode( |
||||
"ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 |
||||
if metaMain is None: |
||||
metaMain = load_meta(metaPath.encode("ascii")) |
||||
if altNames is None: |
||||
try: |
||||
with open(metaPath) as metaFH: |
||||
metaContents = metaFH.read() |
||||
import re |
||||
match = re.search("names *= *(.*)$", metaContents, |
||||
re.IGNORECASE | re.MULTILINE) |
||||
if match: |
||||
result = match.group(1) |
||||
else: |
||||
result = None |
||||
try: |
||||
if os.path.exists(result): |
||||
with open(result) as namesFH: |
||||
namesList = namesFH.read().strip().split("\n") |
||||
altNames = [x.strip() for x in namesList] |
||||
except TypeError: |
||||
pass |
||||
except Exception: |
||||
pass |
||||
#cap = cv2.VideoCapture(0) |
||||
cap = cv2.VideoCapture("test.mp4") |
||||
cap.set(3, 1280) |
||||
cap.set(4, 720) |
||||
out = cv2.VideoWriter( |
||||
"output.avi", cv2.VideoWriter_fourcc(*"MJPG"), 10.0, |
||||
(lib.network_width(netMain), lib.network_height(netMain))) |
||||
print("Starting the YOLO loop...") |
||||
while True: |
||||
prev_time = time.time() |
||||
ret, frame_read = cap.read() |
||||
frame_rgb = cv2.cvtColor(frame_read, cv2.COLOR_BGR2RGB) |
||||
frame_resized = cv2.resize(frame_rgb, |
||||
(lib.network_width(netMain), |
||||
lib.network_height(netMain)), |
||||
interpolation=cv2.INTER_LINEAR) |
||||
detections = detect(netMain, metaMain, frame_resized, thresh=0.25) |
||||
image = cvDrawBoxes(detections, frame_resized) |
||||
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
||||
print(1/(time.time()-prev_time)) |
||||
cap.release() |
||||
out.release() |
||||
|
||||
if __name__ == "__main__": |
||||
YOLO() |
||||
@ -0,0 +1,793 @@ |
||||
#ifndef DARKNET_API |
||||
#define DARKNET_API |
||||
|
||||
#if defined(_MSC_VER) && _MSC_VER < 1900 |
||||
#define inline __inline |
||||
#endif |
||||
|
||||
#include <stdlib.h> |
||||
#include <stdio.h> |
||||
#include <string.h> |
||||
#include <pthread.h> |
||||
#include <stdint.h> |
||||
|
||||
#ifdef LIB_EXPORTS |
||||
#if defined(_MSC_VER) |
||||
#define LIB_API __declspec(dllexport) |
||||
#else |
||||
#define LIB_API __attribute__((visibility("default"))) |
||||
#endif |
||||
#else |
||||
#if defined(_MSC_VER) |
||||
#define LIB_API |
||||
#else |
||||
#define LIB_API |
||||
#endif |
||||
#endif |
||||
|
||||
#ifdef GPU |
||||
#define BLOCK 512 |
||||
|
||||
#include "cuda_runtime.h" |
||||
#include "curand.h" |
||||
#include "cublas_v2.h" |
||||
|
||||
#ifdef CUDNN |
||||
#include "cudnn.h" |
||||
#endif |
||||
#endif |
||||
|
||||
#ifdef __cplusplus |
||||
extern "C" { |
||||
#endif |
||||
|
||||
struct network; |
||||
typedef struct network network; |
||||
|
||||
struct network_state; |
||||
typedef struct network_state; |
||||
|
||||
struct layer; |
||||
typedef struct layer layer; |
||||
|
||||
struct image; |
||||
typedef struct image image; |
||||
|
||||
struct detection; |
||||
typedef struct detection detection; |
||||
|
||||
struct load_args; |
||||
typedef struct load_args load_args; |
||||
|
||||
struct data; |
||||
typedef struct data data; |
||||
|
||||
struct metadata; |
||||
typedef struct metadata metadata; |
||||
|
||||
struct tree; |
||||
typedef struct tree tree; |
||||
|
||||
|
||||
#define SECRET_NUM -1234 |
||||
extern int gpu_index; |
||||
|
||||
// option_list.h
|
||||
typedef struct metadata { |
||||
int classes; |
||||
char **names; |
||||
} metadata; |
||||
|
||||
|
||||
// tree.h
|
||||
typedef struct tree { |
||||
int *leaf; |
||||
int n; |
||||
int *parent; |
||||
int *child; |
||||
int *group; |
||||
char **name; |
||||
|
||||
int groups; |
||||
int *group_size; |
||||
int *group_offset; |
||||
} tree; |
||||
|
||||
|
||||
// activations.h
|
||||
typedef enum { |
||||
LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU |
||||
}ACTIVATION; |
||||
|
||||
// image.h
|
||||
typedef enum{ |
||||
PNG, BMP, TGA, JPG |
||||
} IMTYPE; |
||||
|
||||
// activations.h
|
||||
typedef enum{ |
||||
MULT, ADD, SUB, DIV |
||||
} BINARY_ACTIVATION; |
||||
|
||||
// layer.h
|
||||
typedef enum { |
||||
CONVOLUTIONAL, |
||||
DECONVOLUTIONAL, |
||||
CONNECTED, |
||||
MAXPOOL, |
||||
SOFTMAX, |
||||
DETECTION, |
||||
DROPOUT, |
||||
CROP, |
||||
ROUTE, |
||||
COST, |
||||
NORMALIZATION, |
||||
AVGPOOL, |
||||
LOCAL, |
||||
SHORTCUT, |
||||
ACTIVE, |
||||
RNN, |
||||
GRU, |
||||
LSTM, |
||||
CRNN, |
||||
BATCHNORM, |
||||
NETWORK, |
||||
XNOR, |
||||
REGION, |
||||
YOLO, |
||||
ISEG, |
||||
REORG, |
||||
REORG_OLD, |
||||
UPSAMPLE, |
||||
LOGXENT, |
||||
L2NORM, |
||||
BLANK |
||||
} LAYER_TYPE; |
||||
|
||||
// layer.h
|
||||
typedef enum{ |
||||
SSE, MASKED, L1, SEG, SMOOTH,WGAN |
||||
} COST_TYPE; |
||||
|
||||
// layer.h
|
||||
typedef struct update_args { |
||||
int batch; |
||||
float learning_rate; |
||||
float momentum; |
||||
float decay; |
||||
int adam; |
||||
float B1; |
||||
float B2; |
||||
float eps; |
||||
int t; |
||||
} update_args; |
||||
|
||||
// layer.h
|
||||
struct layer { |
||||
LAYER_TYPE type; |
||||
ACTIVATION activation; |
||||
COST_TYPE cost_type; |
||||
void(*forward) (struct layer, struct network_state); |
||||
void(*backward) (struct layer, struct network_state); |
||||
void(*update) (struct layer, int, float, float, float); |
||||
void(*forward_gpu) (struct layer, struct network_state); |
||||
void(*backward_gpu) (struct layer, struct network_state); |
||||
void(*update_gpu) (struct layer, int, float, float, float); |
||||
int batch_normalize; |
||||
int shortcut; |
||||
int batch; |
||||
int forced; |
||||
int flipped; |
||||
int inputs; |
||||
int outputs; |
||||
int nweights; |
||||
int nbiases; |
||||
int extra; |
||||
int truths; |
||||
int h, w, c; |
||||
int out_h, out_w, out_c; |
||||
int n; |
||||
int max_boxes; |
||||
int groups; |
||||
int size; |
||||
int side; |
||||
int stride; |
||||
int reverse; |
||||
int flatten; |
||||
int spatial; |
||||
int pad; |
||||
int sqrt; |
||||
int flip; |
||||
int index; |
||||
int binary; |
||||
int xnor; |
||||
int use_bin_output; |
||||
int steps; |
||||
int hidden; |
||||
int truth; |
||||
float smooth; |
||||
float dot; |
||||
float angle; |
||||
float jitter; |
||||
float saturation; |
||||
float exposure; |
||||
float shift; |
||||
float ratio; |
||||
float learning_rate_scale; |
||||
float clip; |
||||
int focal_loss; |
||||
int noloss; |
||||
int softmax; |
||||
int classes; |
||||
int coords; |
||||
int background; |
||||
int rescore; |
||||
int objectness; |
||||
int does_cost; |
||||
int joint; |
||||
int noadjust; |
||||
int reorg; |
||||
int log; |
||||
int tanh; |
||||
int *mask; |
||||
int total; |
||||
float bflops; |
||||
|
||||
int adam; |
||||
float B1; |
||||
float B2; |
||||
float eps; |
||||
|
||||
int t; |
||||
|
||||
float alpha; |
||||
float beta; |
||||
float kappa; |
||||
|
||||
float coord_scale; |
||||
float object_scale; |
||||
float noobject_scale; |
||||
float mask_scale; |
||||
float class_scale; |
||||
int bias_match; |
||||
int random; |
||||
float ignore_thresh; |
||||
float truth_thresh; |
||||
float thresh; |
||||
float focus; |
||||
int classfix; |
||||
int absolute; |
||||
|
||||
int onlyforward; |
||||
int stopbackward; |
||||
int dontload; |
||||
int dontsave; |
||||
int dontloadscales; |
||||
int numload; |
||||
|
||||
float temperature; |
||||
float probability; |
||||
float scale; |
||||
|
||||
char * cweights; |
||||
int * indexes; |
||||
int * input_layers; |
||||
int * input_sizes; |
||||
int * map; |
||||
int * counts; |
||||
float ** sums; |
||||
float * rand; |
||||
float * cost; |
||||
float * state; |
||||
float * prev_state; |
||||
float * forgot_state; |
||||
float * forgot_delta; |
||||
float * state_delta; |
||||
float * combine_cpu; |
||||
float * combine_delta_cpu; |
||||
|
||||
float *concat; |
||||
float *concat_delta; |
||||
|
||||
float *binary_weights; |
||||
|
||||
float *biases; |
||||
float *bias_updates; |
||||
|
||||
float *scales; |
||||
float *scale_updates; |
||||
|
||||
float *weights; |
||||
float *weight_updates; |
||||
|
||||
char *align_bit_weights_gpu; |
||||
float *mean_arr_gpu; |
||||
float *align_workspace_gpu; |
||||
float *transposed_align_workspace_gpu; |
||||
int align_workspace_size; |
||||
|
||||
char *align_bit_weights; |
||||
float *mean_arr; |
||||
int align_bit_weights_size; |
||||
int lda_align; |
||||
int new_lda; |
||||
int bit_align; |
||||
|
||||
float *col_image; |
||||
float * delta; |
||||
float * output; |
||||
float * loss; |
||||
float * squared; |
||||
float * norms; |
||||
|
||||
float * spatial_mean; |
||||
float * mean; |
||||
float * variance; |
||||
|
||||
float * mean_delta; |
||||
float * variance_delta; |
||||
|
||||
float * rolling_mean; |
||||
float * rolling_variance; |
||||
|
||||
float * x; |
||||
float * x_norm; |
||||
|
||||
float * m; |
||||
float * v; |
||||
|
||||
float * bias_m; |
||||
float * bias_v; |
||||
float * scale_m; |
||||
float * scale_v; |
||||
|
||||
|
||||
float *z_cpu; |
||||
float *r_cpu; |
||||
float *h_cpu; |
||||
float * prev_state_cpu; |
||||
|
||||
float *temp_cpu; |
||||
float *temp2_cpu; |
||||
float *temp3_cpu; |
||||
|
||||
float *dh_cpu; |
||||
float *hh_cpu; |
||||
float *prev_cell_cpu; |
||||
float *cell_cpu; |
||||
float *f_cpu; |
||||
float *i_cpu; |
||||
float *g_cpu; |
||||
float *o_cpu; |
||||
float *c_cpu; |
||||
float *dc_cpu; |
||||
|
||||
float * binary_input; |
||||
|
||||
struct layer *input_layer; |
||||
struct layer *self_layer; |
||||
struct layer *output_layer; |
||||
|
||||
struct layer *reset_layer; |
||||
struct layer *update_layer; |
||||
struct layer *state_layer; |
||||
|
||||
struct layer *input_gate_layer; |
||||
struct layer *state_gate_layer; |
||||
struct layer *input_save_layer; |
||||
struct layer *state_save_layer; |
||||
struct layer *input_state_layer; |
||||
struct layer *state_state_layer; |
||||
|
||||
struct layer *input_z_layer; |
||||
struct layer *state_z_layer; |
||||
|
||||
struct layer *input_r_layer; |
||||
struct layer *state_r_layer; |
||||
|
||||
struct layer *input_h_layer; |
||||
struct layer *state_h_layer; |
||||
|
||||
struct layer *wz; |
||||
struct layer *uz; |
||||
struct layer *wr; |
||||
struct layer *ur; |
||||
struct layer *wh; |
||||
struct layer *uh; |
||||
struct layer *uo; |
||||
struct layer *wo; |
||||
struct layer *uf; |
||||
struct layer *wf; |
||||
struct layer *ui; |
||||
struct layer *wi; |
||||
struct layer *ug; |
||||
struct layer *wg; |
||||
|
||||
tree *softmax_tree; |
||||
|
||||
size_t workspace_size; |
||||
|
||||
#ifdef GPU |
||||
int *indexes_gpu; |
||||
|
||||
float *z_gpu; |
||||
float *r_gpu; |
||||
float *h_gpu; |
||||
|
||||
float *temp_gpu; |
||||
float *temp2_gpu; |
||||
float *temp3_gpu; |
||||
|
||||
float *dh_gpu; |
||||
float *hh_gpu; |
||||
float *prev_cell_gpu; |
||||
float *cell_gpu; |
||||
float *f_gpu; |
||||
float *i_gpu; |
||||
float *g_gpu; |
||||
float *o_gpu; |
||||
float *c_gpu; |
||||
float *dc_gpu; |
||||
|
||||
// adam
|
||||
float *m_gpu; |
||||
float *v_gpu; |
||||
float *bias_m_gpu; |
||||
float *scale_m_gpu; |
||||
float *bias_v_gpu; |
||||
float *scale_v_gpu; |
||||
|
||||
float * combine_gpu; |
||||
float * combine_delta_gpu; |
||||
|
||||
float * prev_state_gpu; |
||||
float * forgot_state_gpu; |
||||
float * forgot_delta_gpu; |
||||
float * state_gpu; |
||||
float * state_delta_gpu; |
||||
float * gate_gpu; |
||||
float * gate_delta_gpu; |
||||
float * save_gpu; |
||||
float * save_delta_gpu; |
||||
float * concat_gpu; |
||||
float * concat_delta_gpu; |
||||
|
||||
float *binary_input_gpu; |
||||
float *binary_weights_gpu; |
||||
|
||||
float * mean_gpu; |
||||
float * variance_gpu; |
||||
|
||||
float * rolling_mean_gpu; |
||||
float * rolling_variance_gpu; |
||||
|
||||
float * variance_delta_gpu; |
||||
float * mean_delta_gpu; |
||||
|
||||
float * col_image_gpu; |
||||
|
||||
float * x_gpu; |
||||
float * x_norm_gpu; |
||||
float * weights_gpu; |
||||
float * weight_updates_gpu; |
||||
float * weight_change_gpu; |
||||
|
||||
float * weights_gpu16; |
||||
float * weight_updates_gpu16; |
||||
|
||||
float * biases_gpu; |
||||
float * bias_updates_gpu; |
||||
float * bias_change_gpu; |
||||
|
||||
float * scales_gpu; |
||||
float * scale_updates_gpu; |
||||
float * scale_change_gpu; |
||||
|
||||
float * output_gpu; |
||||
float * loss_gpu; |
||||
float * delta_gpu; |
||||
float * rand_gpu; |
||||
float * squared_gpu; |
||||
float * norms_gpu; |
||||
#ifdef CUDNN |
||||
cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc; |
||||
cudnnTensorDescriptor_t srcTensorDesc16, dstTensorDesc16; |
||||
cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc; |
||||
cudnnTensorDescriptor_t dsrcTensorDesc16, ddstTensorDesc16; |
||||
cudnnTensorDescriptor_t normTensorDesc, normDstTensorDesc, normDstTensorDescF16; |
||||
cudnnFilterDescriptor_t weightDesc, weightDesc16; |
||||
cudnnFilterDescriptor_t dweightDesc, dweightDesc16; |
||||
cudnnConvolutionDescriptor_t convDesc; |
||||
cudnnConvolutionFwdAlgo_t fw_algo, fw_algo16; |
||||
cudnnConvolutionBwdDataAlgo_t bd_algo, bd_algo16; |
||||
cudnnConvolutionBwdFilterAlgo_t bf_algo, bf_algo16; |
||||
cudnnPoolingDescriptor_t poolingDesc; |
||||
#endif // CUDNN
|
||||
#endif // GPU
|
||||
}; |
||||
|
||||
|
||||
// network.h
|
||||
typedef enum { |
||||
CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM |
||||
} learning_rate_policy; |
||||
|
||||
// network.h
|
||||
typedef struct network { |
||||
int n; |
||||
int batch; |
||||
uint64_t *seen; |
||||
int *t; |
||||
float epoch; |
||||
int subdivisions; |
||||
layer *layers; |
||||
float *output; |
||||
learning_rate_policy policy; |
||||
|
||||
float learning_rate; |
||||
float momentum; |
||||
float decay; |
||||
float gamma; |
||||
float scale; |
||||
float power; |
||||
int time_steps; |
||||
int step; |
||||
int max_batches; |
||||
float *scales; |
||||
int *steps; |
||||
int num_steps; |
||||
int burn_in; |
||||
int cudnn_half; |
||||
|
||||
int adam; |
||||
float B1; |
||||
float B2; |
||||
float eps; |
||||
|
||||
int inputs; |
||||
int outputs; |
||||
int truths; |
||||
int notruth; |
||||
int h, w, c; |
||||
int max_crop; |
||||
int min_crop; |
||||
float max_ratio; |
||||
float min_ratio; |
||||
int center; |
||||
int flip; // horizontal flip 50% probability augmentaiont for classifier training (default = 1)
|
||||
float angle; |
||||
float aspect; |
||||
float exposure; |
||||
float saturation; |
||||
float hue; |
||||
int random; |
||||
int small_object; |
||||
|
||||
int gpu_index; |
||||
tree *hierarchy; |
||||
|
||||
float *input; |
||||
float *truth; |
||||
float *delta; |
||||
float *workspace; |
||||
int train; |
||||
int index; |
||||
float *cost; |
||||
float clip; |
||||
|
||||
#ifdef GPU |
||||
//float *input_gpu;
|
||||
//float *truth_gpu;
|
||||
float *delta_gpu; |
||||
float *output_gpu; |
||||
|
||||
float *input_state_gpu; |
||||
|
||||
float **input_gpu; |
||||
float **truth_gpu; |
||||
float **input16_gpu; |
||||
float **output16_gpu; |
||||
size_t *max_input16_size; |
||||
size_t *max_output16_size; |
||||
int wait_stream; |
||||
#endif |
||||
} network; |
||||
|
||||
// network.h
|
||||
typedef struct network_state { |
||||
float *truth; |
||||
float *input; |
||||
float *delta; |
||||
float *workspace; |
||||
int train; |
||||
int index; |
||||
network net; |
||||
} network_state; |
||||
|
||||
//typedef struct {
|
||||
// int w;
|
||||
// int h;
|
||||
// float scale;
|
||||
// float rad;
|
||||
// float dx;
|
||||
// float dy;
|
||||
// float aspect;
|
||||
//} augment_args;
|
||||
|
||||
// image.h
|
||||
typedef struct image { |
||||
int w; |
||||
int h; |
||||
int c; |
||||
float *data; |
||||
} image; |
||||
|
||||
//typedef struct {
|
||||
// int w;
|
||||
// int h;
|
||||
// int c;
|
||||
// float *data;
|
||||
//} image;
|
||||
|
||||
// box.h
|
||||
typedef struct box { |
||||
float x, y, w, h; |
||||
} box; |
||||
|
||||
// box.h
|
||||
typedef struct detection{ |
||||
box bbox; |
||||
int classes; |
||||
float *prob; |
||||
float *mask; |
||||
float objectness; |
||||
int sort_class; |
||||
} detection; |
||||
|
||||
// matrix.h
|
||||
typedef struct matrix { |
||||
int rows, cols; |
||||
float **vals; |
||||
} matrix; |
||||
|
||||
// data.h
|
||||
typedef struct data { |
||||
int w, h; |
||||
matrix X; |
||||
matrix y; |
||||
int shallow; |
||||
int *num_boxes; |
||||
box **boxes; |
||||
} data; |
||||
|
||||
// data.h
|
||||
typedef enum { |
||||
CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA, LETTERBOX_DATA, REGRESSION_DATA, SEGMENTATION_DATA, INSTANCE_DATA, ISEG_DATA |
||||
} data_type; |
||||
|
||||
// data.h
|
||||
typedef struct load_args { |
||||
int threads; |
||||
char **paths; |
||||
char *path; |
||||
int n; |
||||
int m; |
||||
char **labels; |
||||
int h; |
||||
int w; |
||||
int c; // color depth
|
||||
int out_w; |
||||
int out_h; |
||||
int nh; |
||||
int nw; |
||||
int num_boxes; |
||||
int min, max, size; |
||||
int classes; |
||||
int background; |
||||
int scale; |
||||
int center; |
||||
int coords; |
||||
int small_object; |
||||
float jitter; |
||||
int flip; |
||||
float angle; |
||||
float aspect; |
||||
float saturation; |
||||
float exposure; |
||||
float hue; |
||||
data *d; |
||||
image *im; |
||||
image *resized; |
||||
data_type type; |
||||
tree *hierarchy; |
||||
} load_args; |
||||
|
||||
// data.h
|
||||
typedef struct box_label { |
||||
int id; |
||||
float x, y, w, h; |
||||
float left, right, top, bottom; |
||||
} box_label; |
||||
|
||||
// list.h
|
||||
//typedef struct node {
|
||||
// void *val;
|
||||
// struct node *next;
|
||||
// struct node *prev;
|
||||
//} node;
|
||||
|
||||
// list.h
|
||||
//typedef struct list {
|
||||
// int size;
|
||||
// node *front;
|
||||
// node *back;
|
||||
//} list;
|
||||
|
||||
// -----------------------------------------------------
|
||||
|
||||
|
||||
// parser.c
|
||||
LIB_API network *load_network(char *cfg, char *weights, int clear); |
||||
LIB_API network *load_network_custom(char *cfg, char *weights, int clear, int batch); |
||||
LIB_API network *load_network(char *cfg, char *weights, int clear); |
||||
|
||||
// network.c
|
||||
LIB_API load_args get_base_args(network *net); |
||||
|
||||
// box.h
|
||||
LIB_API void do_nms_sort(detection *dets, int total, int classes, float thresh); |
||||
LIB_API void do_nms_obj(detection *dets, int total, int classes, float thresh); |
||||
|
||||
// network.h
|
||||
LIB_API float *network_predict(network net, float *input); |
||||
LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); |
||||
LIB_API void free_detections(detection *dets, int n); |
||||
LIB_API void fuse_conv_batchnorm(network net); |
||||
LIB_API void calculate_binary_weights(network net); |
||||
|
||||
LIB_API layer* get_network_layer(network* net, int i); |
||||
LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); |
||||
LIB_API detection *make_network_boxes(network *net, float thresh, int *num); |
||||
LIB_API void reset_rnn(network *net); |
||||
LIB_API float *network_predict_image(network *net, image im); |
||||
LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net); |
||||
LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map); |
||||
LIB_API int network_width(network *net); |
||||
LIB_API int network_height(network *net); |
||||
LIB_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm); |
||||
|
||||
// image.h
|
||||
LIB_API image resize_image(image im, int w, int h); |
||||
LIB_API image letterbox_image(image im, int w, int h); |
||||
LIB_API void rgbgr_image(image im); |
||||
LIB_API image make_image(int w, int h, int c); |
||||
LIB_API image load_image_color(char *filename, int w, int h); |
||||
LIB_API void free_image(image m); |
||||
|
||||
// layer.h
|
||||
LIB_API void free_layer(layer); |
||||
|
||||
// data.c
|
||||
LIB_API void free_data(data d); |
||||
LIB_API pthread_t load_data(load_args args); |
||||
LIB_API pthread_t load_data_in_thread(load_args args); |
||||
|
||||
// cuda.h
|
||||
LIB_API void cuda_pull_array(float *x_gpu, float *x, size_t n); |
||||
LIB_API void cuda_set_device(int n); |
||||
|
||||
// utils.h
|
||||
LIB_API void free_ptrs(void **ptrs, int n); |
||||
LIB_API void top_k(float *a, int n, int k, int *index); |
||||
|
||||
// tree.h
|
||||
LIB_API tree *read_tree(char *filename); |
||||
|
||||
// option_list.h
|
||||
LIB_API metadata get_metadata(char *file); |
||||
|
||||
|
||||
#ifdef __cplusplus |
||||
} |
||||
#endif // __cplusplus
|
||||
#endif // DARKNET_API
|
||||
Loading…
Reference in new issue