Convert yolov4-tiny to transflow lite: ValueError: cannot reshape array of size 374698 into shape (256,256,3,3)

As I try to covert my yolov4-tiny custom weight to tftile, it always happen.

This is what I input:

python save_model.py --weights ./data/yolov4-tiny-obj-food_final.weights --output ./checkpoints/yolov4-tiny-416-tflite --input_size 416 --model yolov4 --framework tflite

And the wrong message appear.

conv_weights = conv_weights.reshape(conv_shape).transpose([2, 3, 1, 0])
ValueError: cannot reshape array of size 374698 into shape (256,256,3,3)

I have checked my labels.txt and there is no space or more lines.Also, I have changed the name in config.py.

Is there any way to solve this problem?

Thanks for help!

Attach part of my code, hope it helps.

Here is github:https://github.com/piggychu0w0/food-image-detection

.cfg:

[convolutional]
size=1
stride=1
pad=1
filters=21
activation=linear

[yolo]
mask = 3,4,5
anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
classes=2
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
ignore_thresh = .7
truth_thresh = 1
random=0
resize=1.5
nms_kind=greedynms
beta_nms=0.6

.names:

rice
toast

Answer

Short answer

You have to add --tiny to the command. Which, from the command you gave in the question, will be.

python save_model.py --weights ./data/yolov4-tiny-obj-food_final.weights --output ./checkpoints/yolov4-tiny-416-tflite --input_size 416 --model yolov4 --framework tflite --tiny

Long answer

You see, there’s this line that if you set it to True (by adding --tiny) it will make the load_weights() uses layer_size = 21 here instead of layer_size = 110 here.

The problem here is that the weights you have and the np.fromfile command actually give you a big chunk of 1 dimensional array, in this particular file it’s size is (5882629,), and then you have to allocate those variable one by one to the layers.

So, when you create the big model instead of tiny. The tiny weighs file runs out of the variable at 49th layers, and hilariously with such a big prime number.