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Resnet-10 and Resnet-50 both hang device - Help would be appreciated

I'm working on pervasive device that had support for the caffe framework and I'm attempting to use resnet-10 from opencv to do face detection (https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector). The graph file compiles fine, and the first pass even executes fine and then the device hangs. The following is the trace I'm getting from the device

 

[INFO] Get Tensor Descriptor Structures

 

[INFO] create input and output Fifos

 

I: [ 0] ncFifoCreate:2126 Init fifo

 

I: [ 0] ncFifoCreate:2126 Init fifo

 

I: [ 0] ncFifoAllocate:2321 Creating fifo

 

I: [ 0] ncFifoAllocate:2321 Creating fifo

 

[INFO] return input and output Fifos

 

[INFO] Sending Image to NCS device]

 

D: [ 0] ncFifoWriteElem:2592 No layout conversion is needed 0

 

D: [ 0] convertDataTypeAndLayout:170 src data type 1 dst data type 0

 

D: [ 0] convertDataTypeAndLayout:172 SRC: w 300 h 300 c 3 w_s 12 h_s 3600 c_s 4

 

D: [ 0] convertDataTypeAndLayout:174 DST: w 300 h 300 c 3 w_s 6 h_s 1800 c_s 2

 

D: [ 0] ncFifoWriteElem:2617 write count 0 num_elements 2 userparam 0x75a86418

 

[INFO Sendiing to NCS Queue]

 

I: [ 0] ncGraphQueueInference:3048 trigger start

 

I: [ 0] ncGraphQueueInference:3145 trigger end

 

D: [ 0] ncFifoReadElem:2686 No layout conversion is needed 0

 

D: [ 0] convertDataTypeAndLayout:170 src data type 0 dst data type 1

 

D: [ 0] convertDataTypeAndLayout:172 SRC: w 7 h 201 c 1 w_s 2 h_s 14 c_s 2

 

D: [ 0] convertDataTypeAndLayout:174 DST: w 7 h 201 c 1 w_s 4 h_s 28 c_s 4

 

D: [ 0] ncFifoReadElem:2714 num_elements 2 userparam 0x75a86558 output length 5628

 

[INFO] Image prdictions returned from NCS

 

1

 

1

 

[INFO] Sending Image to NCS device]

 

D: [ 0] ncFifoWriteElem:2592 No layout conversion is needed 0

 

D: [ 0] convertDataTypeAndLayout:170 src data type 1 dst data type 0

 

D: [ 0] convertDataTypeAndLayout:172 SRC: w 300 h 300 c 3 w_s 12 h_s 3600 c_s 4

 

D: [ 0] convertDataTypeAndLayout:174 DST: w 300 h 300 c 3 w_s 6 h_s 1800 c_s 2

 

D: [ 0] ncFifoWriteElem:2617 write count 1 num_elements 2 userparam 0x75a86418

 

[INFO Sendiing to NCS Queue]

 

I: [ 0] ncGraphQueueInference:3048 trigger start

 

E: [ 0] dispatcherEventReceive:200 dispatcherEventReceive() Read failed -1

 

I notice in the SDK readme that this model had not been tested but Resnet-50 had, so I tried that Model with the same results. The trace from that run can be found below

 

[INFO] Get Tensor Descriptor Structures

 

[INFO] create input and output Fifos

 

I: [ 0] ncFifoCreate:2126 Init fifo

 

I: [ 0] ncFifoCreate:2126 Init fifo

 

I: [ 0] ncFifoAllocate:2321 Creating fifo

 

I: [ 0] ncFifoAllocate:2321 Creating fifo

 

[INFO] return input and output Fifos

 

[INFO] Sending Image to NCS device]

 

D: [ 0] ncFifoWriteElem:2592 No layout conversion is needed 0

 

D: [ 0] convertDataTypeAndLayout:170 src data type 1 dst data type 0

 

D: [ 0] convertDataTypeAndLayout:172 SRC: w 224 h 224 c 3 w_s 12 h_s 2688 c_s 4

 

D: [ 0] convertDataTypeAndLayout:174 DST: w 224 h 224 c 3 w_s 6 h_s 1344 c_s 2

 

D: [ 0] ncFifoWriteElem:2617 write count 0 num_elements 2 userparam 0x75ac4468

 

[INFO Sendiing to NCS Queue]

 

I: [ 0] ncGraphQueueInference:3048 trigger start

 

I: [ 0] ncGraphQueueInference:3145 trigger end

 

D: [ 0] ncFifoReadElem:2686 No layout conversion is needed 1

 

D: [ 0] convertDataTypeAndLayout:170 src data type 0 dst data type 1

 

D: [ 0] convertDataTypeAndLayout:172 SRC: w 1 h 1 c 1000 w_s 2000 h_s 2000 c_s 2

 

D: [ 0] convertDataTypeAndLayout:174 DST: w 1 h 1 c 1000 w_s 4000 h_s 4000 c_s 4

 

D: [ 0] ncFifoReadElem:2714 num_elements 2 userparam 0x75ac45a8 output length 4000

 

[INFO] Image prdictions returned from NCS

 

0

 

0

 

[INFO] Sending Image to NCS device]

 

D: [ 0] ncFifoWriteElem:2592 No layout conversion is needed 0

 

D: [ 0] convertDataTypeAndLayout:170 src data type 1 dst data type 0

 

D: [ 0] convertDataTypeAndLayout:172 SRC: w 224 h 224 c 3 w_s 12 h_s 2688 c_s 4

 

D: [ 0] convertDataTypeAndLayout:174 DST: w 224 h 224 c 3 w_s 6 h_s 1344 c_s 2

 

D: [ 0] ncFifoWriteElem:2617 write count 1 num_elements 2 userparam 0x75ac4468

 

[INFO Sendiing to NCS Queue]

 

I: [ 0] ncGraphQueueInference:3048 trigger start

 

E: [ 0] dispatcherEventReceive:200 dispatcherEventReceive() Read failed -1

 

The Device works fine for other SSD models I have tried so I don't think I have a hardware problem. The frustrating thing on this problem is it works the first pass fine. Any help would be greatly appreciated.

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@sggriset Thanks for reporting this issue. I've also seen these hangs with the same networks and they usually happen during fifo.read_elem(). Can you provide your python code for testing and issue reproduction? Thanks.

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Thank you for your quick response. The following code is set up for resnet-10 SSD from opencv. If you adjust the dimensions you can test resnet-50 also. Assuming you have the graph files so here you go

 

# import necessary libs import usb.core # from pyusb import usb.util # from pyusb import mvnc.mvncapi as mvnc from threading import Thread import numpy as np import time import cv2 import os VID = 0x03e7 PID = 0x2150 dim_prep = (300, 300) graph_path = os.path.relpath("graphs/facecvgraph") out_fifo = 'faceout' in_fifo = 'facein' graph_name = 'facegraph' class WebcamVideoStream: def __init__(self, src = 0): # initialize the video camera stream and read the frist frame # from the stream self.stream = cv2.VideoCapture(src) # camera returns a tuple grabbed = boolean value whether the frame was read # frame = the video frame itself (self.grabbed, self.frame) = self.stream.read() # initialize the variable used to indicate if the thread should # be stopped self.stopped = False def start(self): # start the thread to read frames from the video stream Thread(target = self.update, args=()).start() return self def update(self): # keep looping infinitely until the tread is stopped while True: # if the thread indicator variable is set, stop the thread if self.stopped: return # otherwise read the next frame from the stream (self.grabbed, self.frame) = self.stream.read() def read(self): # return the frame most recently read return self.frame def stop(self): # indicate that the thread should be stopped self.stopped = True def preprocess_image(input_image, dim): # preprocess the image preprocessed = cv2.resize(input_image, dim) preprocessed = preprocessed - 127.5 preprocessed = preprocessed * 0.007843 preprocessed = preprocessed.astype(np.float32) # return the image to the callling function return preprocessed # Load graph files and return graph object def load_graph(device, Graphpath, graph_name): # Initalize a graph print(Graphpath) print("[INFO] loading the graph file into NCS memory") with open(Graphpath, mode='rb') as f: graph_buffer = f.read() print("[INFO] initialize graph object ") # Initalize a graph object graph = mvnc.Graph(graph_name) # Allocate the graph to the device print("[INFO] allocating the graph to the NCS") graph.allocate(device, graph_buffer) # return graph object return graph def get_tensors(graph, device, input_name, output_name): print("[INFO] Get Tensor Descriptor Structures") input_descriptors = graph.get_option(mvnc.GraphOption.RO_INPUT_TENSOR_DESCRIPTORS) output_descriptors = graph.get_option(mvnc.GraphOption.RO_OUTPUT_TENSOR_DESCRIPTORS) print("[INFO] create input and output Fifos") input_fifo = mvnc.Fifo(input_name, mvnc.FifoType.HOST_WO) output_fifo = mvnc.Fifo(output_name, mvnc.FifoType.HOST_RO) input_fifo.allocate(device, input_descriptors[0], 2) output_fifo.allocate(device, output_descriptors[0], 2) print("[INFO] return input and output Fifos") return input_fifo, output_fifo def predict(image, graph, input_fifo, output_fifo): # preprocess the image # send the image to the NCS and run a forward-pass to grab the # netowrk predictions print("[INFO] Sending Image to NCS device]") input_fifo.write_elem(image, None) print("[INFO Sendiing to NCS Queue]") graph.queue_inference(input_fifo, output_fifo) (output, _ ) = output_fifo.read_elem() print("[INFO] Image predictions returned from NCS") # grab the number of valid object predictions from the output num_valid_boxes = int(output[0]) predictions = [] for box_index in range(num_valid_boxes): # calculate the base index into our array so we can extract # bound box information base_index = 7 + box_index * 7 # boxes with non-finite(inf, nan, etc) numbers must be # ignored if(not np.isfinite(output[base_index]) or not np.isfinite(output[base_index + 1]) or not np.isfinite(output[base_index + 2]) or not np.isfinite(output[base_index + 3]) or not np.isfinite(output[base_index + 4]) or not np.isfinite(output[base_index + 5]) or not np.isfinite(output[base_index + 6])): continue # extract the image width and height and clip the boxes to the # image size in case network returns boxes outside of the image # boundaries (h, w) = image.shape[:2] x1 = max(0, int(output[base_index + 3] * w)) y1 = max(0, int(output[base_index + 4] * h)) x2 = min(w, int(output[base_index + 5] * w)) y2 = min(h, int(output[base_index + 6] * h)) # grab the prediction class label, confidence, # and bounding box (x, y) - coordinates pred_class = int(output[base_index + 1]) pred_conf = output[base_index + 2] pred_boxpts = ((x1, y1), (x2, y2)) # create prediction tuple and append the prediction to the # predictions list prediction = (pred_class, pred_conf, pred_boxpts) predictions.append(prediction) print(len(predictions)) # return the list of predictions to the calling function return predictions def main(): mob_device = usb.core.find(idVendor=VID, idProduct=PID) if not mob_device: print ("[INFO] Movidius Device Not Found") exit(1) print ("[INFO] Found Movidius Device") # set the logging level for the Movidius device mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 0) devices = mvnc.enumerate_devices() if(len(devices) < 1): print("[INFO] Error - no Movidius devices detected") exit(1) # get the first NCS device dev = mvnc.Device(devices[0]) try: dev.open() except: print("[INFO] Error - Could not open NCS device.") exit(1) # load graph file my_graph = load_graph(dev, graph_path, graph_name) # set up Fifos inputfifo, outputfifo = get_tensors(my_graph, dev, in_fifo, out_fifo) vs = WebcamVideoStream(src = 0).start() # let camera warm up time.sleep(2.0) while True: frame = vs.read() frame = preprocess_image(frame, dim_prep) key = key = cv2.waitKey(1) & 0xFF if key == 'q': break predictions = predict(frame, my_graph, inputfifo, outputfifo) print(len(predictions)) cv2.destroyAllWindows() vs.stop() input_fifo.destroy() output_fifo.destroy() dev.destroy() exit(0) if __name__ == '__main__': main()
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Tome_at_Intel

 

Any progress on reproduction of problem? Do you have what you need to reproduce the problem? Or do you need to supply you the graph files and model?

 

Let me know again thank you for your help

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@sggriset If you could also provide the model file with the prototxt file, it would be helpful to me. Thanks again.

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Tome_at_Intel

 

You can find the caffe model file at the following url

 

https://github.com/opencv/opencv_3rdparty/tree/dnn_samples_face_detector_20170830

 

You can find the prototxt file (deploy.prototxt) file at the following url https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector

 

Let me know what else I can do for you. Thank you for your help and enjoy your weekend.

 

Steve

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Hello, I'm interested too, this model runs fine on OpenCV.

 

Thanks!

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Any update on this issue would be greatly appreciated

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I'm interested too. Any update on this issue would be greatly appreciated

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Still working on this issue. Hang tight guys.

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Tome_at_Intel

 

Any update?

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No updates to report as of right now. Still looking into the root cause.

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Hello,@Tome_at_Intel, any update on this issue?

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@xzongbao We apologize that this issue is affecting our users, however at the moment, we don't have any updates that we can share yet. We greatly appreciate your patience while we work on resolving this issue.

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@xzongbao @sggriset Please try running your models with the latest NCSDK v 2.08 and let me know if your issue is resolved. Thanks.

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Tome_at_Intel

 

Sorry for the slow response I had to rebuild my systems to test NCSDK v 2.08 release. It worked fine but it does require you to rebuild the graph file under NCSDK v 2.08. But thank you to Intel team for your fix

 

Steve

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@Tome_at_Intel

 

I have upgraded to NCSDK v2.08. It‘s ok to run the demo ResNet-18 of the ncappzoo. But for my ResNet-34 model, a compilation error has occurred. The compilation is OK before upgrading.

 

Network Input tensors ['data#162']

 

Network Output tensors ['prob#323']

 

Traceback (most recent call last):

 

File "/usr/local/bin/mvNCCompile", line 206, in

 

create_graph(args.network, args.image, args.inputnode, args.outputnode, args.outfile, args.nshaves, args.inputsize, args.weights, args.explicit_concat, args.ma2480, args.scheduler, args.new_parser, args.cpp, args)

 

File "/usr/local/bin/mvNCCompile", line 185, in create_graph

 

load_ret = load_network(args, parser, myriad_config)

 

File "/usr/local/bin/ncsdk/Controllers/Scheduler.py", line 130, in load_network

 

net, graphFile, finalLayers = serializeNewFmt(parsedLayers, arguments, myriad_conf, input_data)

 

File "/usr/local/bin/ncsdk/Controllers/Parsers/Phases.py", line 508, in serializeNewFmt

 

adp.transform(parsedLayers) # TODO: DFS Transform?

 

File "/usr/local/bin/ncsdk/Controllers/Adaptor.py", line 200, in transform

 

self.net.attach(NetworkStageEmulator(l))

 

File "/usr/local/bin/ncsdk/Controllers/Adaptor.py", line 325, in init

 

self.specific_fields()

 

File "/usr/local/bin/ncsdk/Controllers/Adaptor.py", line 333, in specific_fields

 

self.definition.adapt_fields(self, _or)

 

File "/usr/local/bin/ncsdk/Models/StageDefinitions/Relu_Op.py", line 50, in adapt_fields

 

emulator.dataBUF = BufferEmulator(i.resolve())

 

File "/usr/local/bin/ncsdk/Controllers/Tensor.py", line 200, in resolve

 

rt = ResolvedTensor(self)

 

File "/usr/local/bin/ncsdk/Controllers/Tensor.py", line 310, in init

 

self.__data = np.zeros(tensor.shape)

 

MemoryError

 

Makefile:52: recipe for target 'compile' failed

 

make: *** [compile] Error 1
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