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I'm working on translating ONet(the final part of MTCNN) to NCS graph.
The model was translated into NCS graph without any errors. But when I do the test on it, the results are just so far away from the origin caffe model. There must be some calculating things misunderstood by the translating, but I don't know how to find it..
The origin caffe model is here 48net.caffemodel, and 48net.prototxt.
I'm not sure if the warning message shows the problem:
mvNCCompile v02.00, Copyright @ Intel Corporation 2017
Fusing Pad and Convolution2D
Fusing BatchNorm and Scale after Convolution
Replacing BN with Bias&Scale
Fusing Permute and Flatten
Fusing Eltwise and Relu
Eliminate layers that have been parsed as NoOp
Evaluating input and weigths for each hw layer
--------------------------------------
----------------------
----------------------
# Network Input tensors ['data#21']
# Network Output tensors ['conv6-3#30']
Blob generated
It seems that some ops are fused. Would it change the calculation in caffe model?
Here is my test code.
import cv2
import numpy as np
import caffe
caffe.set_device(0)
caffe.set_mode_gpu()
ONet = caffe.Net("tmp/48net.prototxt", "tmp/48net.caffemodel", caffe.TEST)
from mvnc import mvncapi
devices = mvncapi.enumerate_devices()
if len(devices) > 0:
device = mvncapi.Device(devices[0])
device.open()
with open("tmp/graph", mode="rb") as f:
data = f.read()
graph = mvncapi.Graph('graph')
input_fifo, output_fifo = graph.allocate_with_fifos(device,data)
print("inside")
else:
print("no device")
def test():
with open("../align_label.txt") as f:
lines = f.readlines()
for line in lines:
words = line.strip().split()
img_path = "../Images/"+ words[0]
raw_img = cv2.imread(img_path)
showvalidate(raw_img, words)
process(raw_img)
process_ncs(raw_img)
def testwithoutvalidate():
img_path = "test.jpg"
img = cv2.imread(img_path)
process(img)
process_ncs(img)
def process(raw_img):
(h,w,c) = raw_img.shape
test_img = raw_img.copy()
inp_img = cv2.resize(test_img.copy(),(48,48))
inp_img = np.swapaxes(inp_img, 0, 2)
inp_img = (inp_img - 127.5) / 127.5
ONet.blobs['data'].data[...] = inp_img
out = ONet.forward()
#test_pts = out['conv6-3'][0]
test_pts = ONet.blobs['conv6-3'].data[0]
print("test_shape:", test_pts.shape)
print("test:",test_pts)
for i in range(5):
cv2.circle(test_img,(int(test_pts[i+0] * w),int(test_pts[i+5] * h)),2,(0,255,0))
cv2.imshow("test",test_img)
cv2.waitKey(0)
def process_ncs(raw_img):
(h,w,c) = raw_img.shape
test_img = raw_img.copy()
inp_img = cv2.resize(test_img.copy(),(48,48))
inp_img = (inp_img - 127.5) / 127.5
inp_img = inp_img.astype(np.float32)
graph.queue_inference_with_fifo_elem(input_fifo, output_fifo, inp_img, None)
test_pts, _ = output_fifo.read_elem()
print("test2:", test_pts)
for i in range(5):
cv2.circle(test_img,(int(test_pts[i+0] * w),int(test_pts[i+5] * h)),2,(0,255,0))
cv2.imshow("test2",test_img)
cv2.waitKey(0)
def showvalidate(raw_img, words):
(h,w,c) = raw_img.shape
validate_pts = words[1:]
print("validate:",validate_pts)
validate_img = raw_img.copy()
for i in range(5):
cv2.circle(validate_img,(int(float(validate_pts[i*2+0]) * w),int(float(validate_pts[i*2+1]) * h)),2,(0,255,0))
cv2.imshow("validate",validate_img)
cv2.waitKey(0)
try:
#test()
testwithoutvalidate()
except Exception as e:
print(e)
finally:
input_fifo.destroy()
output_fifo.destroy()
graph.destroy()
- Tags:
- Caffe
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ok, I got the information in the ncsdk2 release
so the prelu is supported now, but MTCNN still gives unexpected results.
I still want to ask. @Tome_at_Intel hi, have you found what's wrong with MTCNN? It seems the ncs will fuse some ops, is that the problem? Because all the layers in ONet are supported.
![](/skins/images/7FC17B7B85029576C25F1E43CE255B51/responsive_peak/images/icon_anonymous_message.png)
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