transfer learning broken using weights from google drive

train.py with --weights=./yolov4.weights cause an assert error under utils.load_weights If assert len(wf.read()) == 0, 'failed to read all data' is commented the training start with nan

=> STEP   63   lr: 0.000027   giou_loss:  nan   conf_loss:  nan   prob_loss:  nan   total_loss:  nan
=> STEP   64   lr: 0.000028   giou_loss:  nan   conf_loss:  nan   prob_loss:  nan   total_loss:  nan

[bgamini]

I solved the problem by using pickle, but definitely this is not the correct way 😃

weights_to_pickle.py:

from core.yolov4 import YOLOv4, decode
import tensorflow as tf
import core.utils as utils
import pickle

input_layer = tf.keras.layers.Input([608, 608, 3])
feature_maps = YOLOv4(input_layer, NUM_CLASS=80)
model = tf.keras.Model(input_layer, feature_maps)
utils.load_weights(model, "./data/yolov4.weights")
weights = []
for layer in model.layers:
    weights.append(layer.get_weights())
pickle.dump(weights, open("./data/yolov4.pkl", 'wb'))

and adding this to train.py file after line 78:

elif FLAGS.weights.split(".")[len(FLAGS.weights.split(".")) - 1] == "pkl":
    weights = pickle.load(open(FLAGS.weights, 'rb'))
    num_loaded_layers = 0
    for i, w in enumerate(weights):
        try:
            model.layers[i].set_weights(w)
        except:
            num_loaded_layers = i
            break
    print("=========", num_loaded_layers ,"layers loaded successfully ==========")

Note: you can use model.layers[i].trainable = False

[hunglc007]

I updated mish activation to fix nan loss issue.