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I mean, what's the model the model_c use to train? the L_Resnet_E_IR.py or some other *.py?
because, when i use the latest code to restore as follow:
if args.ckpt_file:
model_path = tf.train.latest_checkpoint(args.ckpt_file)
print('restore model from model_path:{} {}'.format(model_path, args.ckpt_file))
saver.restore(sess, model_path)
else:
print('re train model')
sess.run(tf.global_variables_initializer())
where,model_path is InsightFace_iter_best_1950000.ckpt, but it fails, the tf report is:
Key resnet_v1_50/block1/unit_1/bottleneck_v1/conv1/kernel not found in checkpoint
so, can you tell me how can i to do restore from the model_c in the right way? and is there something
different in model layer between mgpu and single gpu??? thank you very much!
The text was updated successfully, but these errors were encountered:
I mean, what's the model the model_c use to train? the L_Resnet_E_IR.py or some other *.py?
because, when i use the latest code to restore as follow:
if args.ckpt_file:
model_path = tf.train.latest_checkpoint(args.ckpt_file)
print('restore model from model_path:{} {}'.format(model_path, args.ckpt_file))
saver.restore(sess, model_path)
else:
print('re train model')
sess.run(tf.global_variables_initializer())
where,model_path is InsightFace_iter_best_1950000.ckpt, but it fails, the tf report is:
Key resnet_v1_50/block1/unit_1/bottleneck_v1/conv1/kernel not found in checkpoint
so, can you tell me how can i to do restore from the model_c in the right way? and is there something
different in model layer between mgpu and single gpu??? thank you very much!
The text was updated successfully, but these errors were encountered: