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juglab / n2v

This is the implementation of Noise2Void training.
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n2v example notebooks throw errors #98

Open Histo23 opened 4 years ago

Histo23 commented 4 years ago

Hi, after having used the n2v notebook for 3D denoising with my own images for quite some time it has stopped working recently. I went back and tried to run the example notebooks for training for 3D and 2D-RGB with the provided example data and get the same error when running the actual training. I run csbdeep 0.5.2 and n2v 0.2.1 (if I try to use csbdeep 0.6.0 I get "n2v 0.2.1 has requirement csbdeep<0.6.0,>=0.4.0, but you'll have csbdeep 0.6.0 which is incompatible.")

Here is the error that shows up after executing "history = model.train(X, X_val)"

8 blind-spots will be generated per training patch of size (64, 64). Preparing validation data: 100%|██████████| 848/848 [00:00<00:00, 1293.30it/s] WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

WARNING:tensorflow: The TensorFlow contrib module will not be included in TensorFlow 2.0. For more information, please see:

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/csbdeep/utils/tf.py:245: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/csbdeep/utils/tf.py:273: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/csbdeep/utils/tf.py:280: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

Epoch 1/25 39/39 [==============================] - 18s 464ms/step - loss: 0.7861 - n2v_mse: 0.7861 - n2v_abs: 0.7184 - val_loss: 0.7623 - val_n2v_mse: 0.7605 - val_n2v_abs: 0.7256 WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/n2v/models/n2v_standard.py:316: The name tf.Summary is deprecated. Please use tf.compat.v1.Summary instead.

AttributeError Traceback (most recent call last)

in () ----> 1 history = model.train(X, X_val) 5 frames /usr/local/lib/python3.6/dist-packages/n2v/models/n2v_standard.py in on_epoch_end(self, epoch, logs) 316 summary = tf.Summary() 317 summary_value = summary.value.add() --> 318 summary_value.simple_value = value.item() 319 summary_value.tag = name 320 self.writer.add_summary(summary, epoch) AttributeError: 'float' object has no attribute 'item' ------------------------------------------------------ **Also running the 3d n2v prediction notebook on a previously trained model gives an error when executing:** **execution of:** model_name = '300_32_test_dapi_works' basedir = '/content/gdrive/My Drive/CARE/n2v_3D/models' model = N2V(config=None, name=model_name, basedir=basedir) **leads to:** > --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) in () 2 model_name = '300_32_test_dapi_works' 3 basedir = '/content/gdrive/My Drive/CARE/n2v_3D/models' ----> 4 model = N2V(config=None, name=model_name, basedir=basedir) 2 frames /usr/local/lib/python3.6/dist-packages/n2v/models/n2v_config.py in is_valid(self, return_invalid) 235 'normal_fitted', 'identity'] 236 ok['n2v_neighborhood_radius']= _is_int(self.n2v_neighborhood_radius, 0) --> 237 ok['single_net_per_channel'] = isinstance( self.single_net_per_channel, bool ) 238 239 if self.structN2Vmask is None: AttributeError: 'N2VConfig' object has no attribute 'single_net_per_channel' **I just wonder what the reason might be.** **Best,** **Christian**
tibuch commented 3 years ago

Hi @Histo23,

Thank you for reporting.

I will try to reproduce the first error. Which keras version are you using?

The second error seems to come from the new config-parameter single_net_per_channel which was introduced in v0.2.1. This new option is turned on by default and trains an independent U-Net for each channel of a multi-channel image to avoid channel-bleed through artifacts. You could hack the config of old N2V training runs, by adding "single_net_per_channel": False.