I have a ResNet type of model, that I have simplified and that I would like to use with OpenCV.
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 50, 50, 1)] 0
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 50, 50, 1) 4 input_1[0][0]
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 50, 50, 20) 200 batch_normalization[0][0]
__________________________________________________________________________________________________
re_lu (ReLU) (None, 50, 50, 20) 0 conv2d[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 50, 50, 20) 80 re_lu[0][0]
__________________________________________________________________________________________________
...
conv2d_13 (Conv2D) (None, 13, 13, 80) 57680 batch_normalization_12[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, 13, 13, 80) 0 conv2d_14[0][0]
conv2d_13[0][0]
__________________________________________________________________________________________________
re_lu_12 (ReLU) (None, 13, 13, 80) 0 add_5[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 13, 13, 80) 320 re_lu_12[0][0]
__________________________________________________________________________________________________
average_pooling2d (AveragePooli (None, 3, 3, 80) 0 batch_normalization_13[0][0]
__________________________________________________________________________________________________
flatten (Flatten) (None, 720) 0 average_pooling2d[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 2) 1442 flatten[0][0]
==================================================================================================
I followed the tutorial to freeze the model and to optimize it using Tensorflow 1.5 tensorflow.python.tools.optimize_for_inference tool.
- https://medium.com/@sebastingarcaacosta/how-to-export-a-tensorflow-2-x-keras-model-to-a-frozen-and-optimized-graph-39740846d9eb
All steps worked fine and now I can load my model with OpenCV both with
net = cv2.dnn.readNetFromTensorflow("optmized_graph.pb", "optmized_graph.pbtxt")
# or
net = cv2.dnn.readNet("optmized_graph.pb", "optmized_graph.pbtxt")
But I'm getting some weird error, when I try to get the predictions:
image = np.random.rand(1, 50, 50).astype("float32")
net.setInput(image, 'x')
# or
image = np.random.rand(1, 50, 50, 1).astype("float32")
net.setInput(image, 'x')
The error is
[ERROR:0] global /tmp/pip-req-build-6amqbhlx/opencv/modules/dnn/src/dnn.cpp (3441) getLayerShapesRecursively OPENCV/DNN: [Convolution]:(mask-try4/conv2d/Conv2D): ge
tMemoryShapes() throws exception. inputs=1 outputs=0/1 blobs=2
[ERROR:0] global /tmp/pip-req-build-6amqbhlx/opencv/modules/dnn/src/dnn.cpp (3447) getLayerShapesRecursively input[0] = [ 1 50 ]
[ERROR:0] global /tmp/pip-req-build-6amqbhlx/opencv/modules/dnn/src/dnn.cpp (3455) getLayerShapesRecursively blobs[0] = CV_32FC1 [ 20 1 3 3 ]
[ERROR:0] global /tmp/pip-req-build-6amqbhlx/opencv/modules/dnn/src/dnn.cpp (3455) getLayerShapesRecursively blobs[1] = CV_32FC1 [ 20 1 ]
[ERROR:0] global /tmp/pip-req-build-6amqbhlx/opencv/modules/dnn/src/dnn.cpp (3457) getLayerShapesRecursively Exception message: OpenCV(4.4.0) /tmp/pip-req-build-6am
qbhlx/opencv/modules/dnn/src/layers/convolution_layer.cpp:348: error: (-215:Assertion failed) ngroups > 0 && inpCn % ngroups == 0 && outCn % ngroups == 0 in functio
n 'getMemoryShapes'
Traceback (most recent call last):
File "test_with_opencv.py", line 17, in <module>
detections = net.forward()
cv2.error: OpenCV(4.4.0) /tmp/pip-req-build-6amqbhlx/opencv/modules/dnn/src/layers/convolution_layer.cpp:348: error: (-215:Assertion failed) ngroups > 0 && inpCn %
ngroups == 0 && outCn % ngroups == 0 in function 'getMemoryShapes'
My questions are:
- What I'm doing wrong?
- How can I overcome the error and get the model to work?
- My the shape in the error message is
input[0] = [ 1 50 ]? (I'm passing(1,50,50))
Versions:
- Tensorflow: 2.3.0
- OpenCV: 4.4.0.44
- Python: 3.6.9
Post on StackOverflow: https://stackoverflow.com/questions/64425280/freezing-and-optimizing-tensorflow-2-3-0-resnet-like-model-for-opencv
