I have made a Object Detector using Keras.
input_1 (InputLayer) (None, None, None, 3)
fcn_conv0 (Conv2D) (None, None, None, 32)
fcn_conv1 (Conv2D) (None, None, None, 64)
fcn_conv2 (Conv2D) (None, None, None, 128)
max_pooling2d_1 (MaxPooling2 (None, None, None, 128)
fcn_conv3 (Conv2D) (None, None, None, 64)
max_pooling2d_2 (MaxPooling2 (None, None, None, 64)
fcn_conv4 (Conv2D) (None, None, None, 32)
max_pooling2d_3 (MaxPooling2 (None, None, None, 32)
fcn_conv5 (Conv2D) (None, None, None, 32)
fcn_conv6 (Conv2D) (None, None, None, 512)
fcn_conv7 (Conv2D) (None, None, None, 6)
global_max_pooling2d_1 (Glob (None, 6)
Since I am dealing with variable sized object, I have kept the input as None and made a custom loss.
But after converting my Keras model to Tensorflow model and compiling using mvNCCompile command line tool it throws an error that
input_data = np.random.uniform(0, 1, shape)
File "mtrand.pyx", line 1307, in mtrand.RandomState.uniform
File "mtrand.pyx", line 242, in mtrand.cont2_array_sc
TypeError: 'NoneType' object cannot be interpreted as an integer
Command I am using is:
mvNCCompile tf_model.meta -in=input_1 -on=fcn_conv11/BiasAdd
I've the TF_Model folder which contains checkpoint file, .meta file, .data file, .index file.
Kindly help. I am stuck with these for the past one week
@bumzo The NCSDK doesn't support models that use variable input sizes. For reference, the NCSDK takes a model and compiles a version of that model in the form of a static Movidius graph file. The input size is set for the graph file and cannot be changed. You can always resize input images to a set input size resolution, but I understand that based on what you are doing, it may not be suitable. Currently there isn't a workaround for implementing variable input sizes.
@bumzo The input size for Tiny Yolo is set at 448x448. This doesn't change. When using input images that are of a different resolution, the images are resized and passed through the model.
Hi, I am getting the same exact problem but I took a Resnet Inception V2 model from TensorFlow Zoo which should be supported and I retrained it on my custom dataset. After retraining, I took the weights from inference folder and tried to use the mvncCompile on them and got the same exact error. How can I change the input size to 1 in this case? @bumzo @Tome_at_Intel