I've tried this equation but still getting error and not updated automatically.
Let's say given throttle value 1500
NewValue = (((OldValue - OldMin) * (NewMax - NewMin)) / (OldMax - OldMin)) + NewMin
self.prev_values = [0,0,0]
self.min_values = [0, 0, 0, 0]
self.max_values = [1024, 1024, 1024, 1024]
self.setpoint_euler[3] = (((self.setpoint_cmd[3] - self.min_values[3]) * (1024 - 0)) /(2000 -1000))+0
1.024 * (value - 1000)
if you value 1500
1.024 * (1500 - 1000) = 512
if you value 2000
1.024 * (2000 - 1000) = 1024
if you value 1000
1.024 * (1000 - 1000) = 0
apply round or something btw
I am trying to do a batch detection using the Darknet\YoloV4. It works for one batch, then the second batch fails with CUDA error. Am I missing something else on below snippet? And what are the right parameters for Batch for RTX GPU card, How to determine the right Batch size?
My system configuration is like below -
System: Host: ubox Kernel: 5.4.0-42-generic x86_64 bits: 64
Desktop: Gnome 3.28.4 Distro: Ubuntu 18.04.4 LTS
Machine: Device: desktop System: Alienware product: Alienware Aurora R9 v: 1.0.7 serial: N/A
Mobo: Alienware model: 0T76PD v: A01 serial: N/A
UEFI: Alienware v: 1.0.7 date: 12/23/2019
CPU: 8 core Intel Core i7-9700K (-MCP-) cache: 12288 KB
clock speeds: max: 4900 MHz 1: 800 MHz 2: 800 MHz 3: 800 MHz
4: 800 MHz 5: 801 MHz 6: 803 MHz 7: 808 MHz 8: 810 MHz
Graphics: Card-1: Intel Device 3e98
Card-2: NVIDIA Device 1e84
Display Server: x11 (X.Org 1.20.8 )
drivers: modesetting,nvidia (unloaded: fbdev,vesa,nouveau)
Resolution: 2560x1440#59.95hz, 1920x1080#60.00hz
OpenGL: renderer: GeForce RTX 2070 SUPER/PCIe/SSE2
version: 4.6.0 NVIDIA 450.57
I am getting CUDA Error: out of memory when I perform performBatchDetectV2() of batch size 3.
How to do Batching on Yolov4 architecture properly? In my use case, I am getting frames from the camera, I want to batch 10 frames into one and call below. The below function works perfectly if I call just once, meaning it throws Cuda error on the second batch of frames.
def performBatchDetectV2(image_list, thresh= 0.25, configPath = "./cfg/yolov4.cfg", weightPath = "yolov4.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3):
net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size)
meta = load_meta(metaPath.encode('utf-8'))
pred_height, pred_width, c = image_list[0].shape
net_width, net_height = (network_width(net), network_height(net))
img_list = []
for custom_image_bgr in image_list:
custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB)
custom_image = cv2.resize(
custom_image, (net_width, net_height), interpolation=cv2.INTER_NEAREST)
custom_image = custom_image.transpose(2, 0, 1)
img_list.append(custom_image)
arr = np.concatenate(img_list, axis=0)
arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0
data = arr.ctypes.data_as(POINTER(c_float))
im = IMAGE(net_width, net_height, c, data)
batch_dets = network_predict_batch(net, im, batch_size, pred_width,
pred_height, thresh, hier_thresh, None, 0, 0)
batch_boxes = []
batch_scores = []
batch_classes = []
for b in range(batch_size):
num = batch_dets[b].num
dets = batch_dets[b].dets
if nms:
do_nms_obj(dets, num, meta.classes, nms)
boxes = []
scores = []
classes = []
for i in range(num):
det = dets[i]
score = -1
label = None
for c in range(det.classes):
p = det.prob[c]
if p > score:
score = p
label = c
if score > thresh:
box = det.bbox
left, top, right, bottom = map(int,(box.x - box.w / 2, box.y - box.h / 2,
box.x + box.w / 2, box.y + box.h / 2))
boxes.append((top, left, bottom, right))
scores.append(score)
classes.append(label)
# boxColor = (int(255 * (1 - (score ** 2))), int(255 * (score ** 2)), 0)
# cv2.rectangle(image_list[b], (left, top),
# (right, bottom), boxColor, 2)
# cv2.imwrite(os.path.basename(img_samples[b]),image_list[b])
batch_boxes.append(boxes)
batch_scores.append(scores)
batch_classes.append(classes)
free_batch_detections(batch_dets, batch_size)
return batch_boxes, batch_scores, batch_classes
The problem is, every time you did loaded the network when you call the performBatchDetectV2 method. So you have to load the network one time with constant batch size and use your loaded network for the prediction.
set your netwrok variable as global. Because you are
This is your function
def performBatchDetectV2(image_list, thresh= 0.25, configPath = "./cfg/yolov4.cfg", weightPath = "yolov4.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3):
net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size)
meta = load_meta(metaPath.encode('utf-8'))
<your code>
Change to this
configPath = "./cfg/yolov4.cfg"
weightPath = "yolov4.weights"
metaPath= "./cfg/coco.data",
net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size)
meta = load_meta(metaPath.encode('utf-8'))
def performBatchDetectV2(image_list, thresh= 0.25, hier_thresh=.5, nms=.45, batch_size=3):
global net, meta
<your code>
I have two different rates that the user can make for his teacher , i want to convert the total of each rate in pixels so i can have the progress bar effect, for example:
maximum_pixels = 100 #maximum width
services = 4.5 #width: 95px
professionalism = 5.0 #width: 100px
total_percentage = maximum_pixels * services / maximum_pixels
How can i implement that in my code ?
maxAllowed = 100
minAllowed = 0
unscaledNum = 3
_min = 0
_max = 5
((maxAllowed - minAllowed) * (unscaledNum - _min) / (_max - _min) + minAllowed)
Result:
60.0
newarray = [x + 0.5 for x in range(1, 10)]
this code will give me following result:
newarray
[1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5]
Instead of adding 0.5 with x I want to increase my x by 0.5 for each 1 increment of x. The output suppose to be
newarray=[0.5,1,1.5,2,2.5......5.5].
Keep in mind that my range must be fix in 1 to 10. What can be better approach to make that?
[0.5 * x for x in range(1, 12)]
Will do the thing, I'm afraid generating that array with range(1, 10) is impossible
My code acts like 2d matrix muliplication ( http://gpgpu-computing4.blogspot.de/2009/09/matrix-multiplication-2-opencl.html).
The dimenstions of the matrixes are (1000*1000 and 10000*10000 and 100000*100000).
My Hardware is: NVIDIA Corporation GM204 [GeForce GTX 980] (MAX_WORK_GROUP_SIZES: 1024 1024 64).
The question is:
I have got Some Confusing Results depends on local_item_size and I need to understand what is happened?
1000 X 1000 matrixes & local_item_size = 16 : INVALID_WORKGROUP_SIZE.
1000 X 1000 matrixes & local_item_size = 8 : WORKS :).
1000 X 1000 matrixes & local_item_size = 10 : WORKS :) (The Execution time when 8 was better).
10000 X 10000 matrixes & local_item_size = 8 or 16: CL_OUT_OF_RESOURCES.
Thanks in advance,
To your second question, this is the reasoning behind:
1000 / 8 = 125, ok
1000 / 16 = 62.5, wrong! INVALID_WORKGROUP_SIZE
1000 / 10 = 100 ok, but 10 and multiples of 10, will never fully use the GPU cores.
IE: If you have 16 warps, 6 are wasted, if you have 32, 2 are wasted, and so on.
10000x10000 = 400MB(at least, if using floats) for just the input, so something is getting too big for the memory, therefore CL_OUT_OF_RESOURCES