ValueError: Invalid value for argument filters. Expected a strictly positive value. Received filters=0.
Opened this issue · 2 comments
stnridy commented
Check if applicable
- I used Google/Bing/other search engines to thoroughly research my question and DID NOT find any suitable answers
- Additionally I went through the issues in this repository/MaixPy/Tensorflow repositories and DID NOT find any suitable answers
Describe the bug
- I try to use theegacy-yolov2 branch as mentioned in TinkerGen/Mark-micropython#1
%load_ext tensorboard
#we need imgaug 0.4 for image augmentations to work properly, see https://stackoverflow.com/questions/62580797/in-colab-doing-image-data-augmentation-with-imgaug-is-not-working-as-intended
!pip uninstall -y imgaug && pip uninstall -y albumentations && pip install imgaug==0.4
!pip install --upgrade --no-cache-dir gdown
!git clone -b legacy-yolov2 https://github.com/AIWintermuteAI/aXeleRate.git
import sys
sys.path.append('/content/aXeleRate')
from axelerate import setup_training, setup_inference
- below is my config
config = {
"model":{
"type": "Detector",
"architecture": "MobileNet7_5",
"input_size": 224,
"anchors": [[[0.76120044, 0.57155991], [0.6923348, 0.88535553], [0.47163042, 0.34163313]]],
"labels": ["licence"],
"obj_thresh" : 0.7,
"iou_thresh" : 0.5,
"coord_scale" : 1.0,
"class_scale" : 1.0,
"object_scale" : 5.0,
"no_object_scale" : 1.0
},
"weights" : {
"full": "",
"backend": "imagenet"
},
"train" : {
"actual_epoch": 100,
"train_image_folder": "/content/MyDrive/MyDrive/ALPR/im",
"train_annot_folder": "/content/MyDrive/MyDrive/ALPR/images",
"train_times": 1,
"valid_image_folder": "/content/MyDrive/MyDrive/ALPR/validation",
"valid_annot_folder": "/content/MyDrive/MyDrive/ALPR/validation_annot",
"valid_times": 1,
"valid_metric": "recall",
"batch_size": 4,
"learning_rate": 1e-4,
"saved_folder": F"/content/MyDrive/MyDrive/ALPR/ww44p3_result",
"first_trainable_layer": "",
"augmentation": True,
"is_only_detect" : False
},
"converter" : {
"type": ["k210"]
}
}
Expected behavior
Expected to be able to train the model
Platform
- Version/commit number of aXeleRate --> legacy-yolov2
Relevant log output
ValueError Traceback (most recent call last)
<ipython-input-14-bbd5d1611139> in <cell line: 3>()
1 from keras import backend as K
2 K.clear_session()
----> 3 model_path = setup_training(config_dict=config)
7 frames
/content/aXeleRate/axelerate/train.py in setup_training(config_file, config_dict)
163 os.makedirs(dirname)
164
--> 165 return(train_from_config(config, dirname))
166
167
/content/aXeleRate/axelerate/train.py in train_from_config(config, project_folder)
114
115 # 1. Construct the model
--> 116 yolo = create_yolo(config['model']['architecture'],
117 labels,
118 input_size,
/content/aXeleRate/axelerate/networks/yolo/frontend.py in create_yolo(architecture, labels, input_size, anchors, coord_scale, class_scale, object_scale, no_object_scale, weights)
34 n_classes = len(labels)
35 n_boxes = int(len(anchors)/2)
---> 36 yolo_network = create_yolo_network(architecture, input_size, n_classes, n_boxes, weights)
37 yolo_loss = YoloLoss(yolo_network.get_grid_size(),
38 n_classes,
/content/aXeleRate/axelerate/networks/yolo/backend/network.py in create_yolo_network(architecture, input_size, nb_classes, nb_box, weights)
14 weights):
15 feature_extractor = create_feature_extractor(architecture, input_size, weights)
---> 16 yolo_net = YoloNetwork(feature_extractor,
17 input_size,
18 nb_classes,
/content/aXeleRate/axelerate/networks/yolo/backend/network.py in __init__(self, feature_extractor, input_size, nb_classes, nb_box)
33
34 # make the object detection layer
---> 35 output_tensor = Conv2D(nb_box * (4 + 1 + nb_classes), (1,1), strides=(1,1),
36 padding='same',
37 name='detection_layer_{}'.format(nb_box * (4 + 1 + nb_classes)),
/usr/local/lib/python3.10/dist-packages/keras/src/dtensor/utils.py in _wrap_function(layer_instance, *args, **kwargs)
94 layout_args[variable_name + "_layout"] = layout
95
---> 96 init_method(layer_instance, *args, **kwargs)
97
98 # Inject the layout parameter after the invocation of __init__()
/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/conv2d.py in __init__(self, filters, kernel_size, strides, padding, data_format, dilation_rate, groups, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs)
177 **kwargs
178 ):
--> 179 super().__init__(
180 rank=2,
181 filters=filters,
/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py in __init__(self, rank, filters, kernel_size, strides, padding, data_format, dilation_rate, groups, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, trainable, name, conv_op, **kwargs)
127 filters = int(filters)
128 if filters is not None and filters <= 0:
--> 129 raise ValueError(
130 "Invalid value for argument `filters`. "
131 "Expected a strictly positive value. "
ValueError: Invalid value for argument `filters`. Expected a strictly positive value. Received filters=0.
https://colab.research.google.com/drive/1ZVO7KDgd_X1aEVd7Jh6gAFDNlQLxhCiJ#scrollTo=AjQngERURAWImuaz919 commented
Hi, @AIWintermuteAI, I see your issues closed about implementing YOLOv3 on the Maix board. -> #63
Since there is an option to use yolov3 in your Readme, can you provide the pre-compiled firmware that has been deleted before?
AIWintermuteAI commented
Hi @muaz919 !
Please create a separate issue as this is unrelated.