jakeret / unet Goto Github PK

Hi!

While running the notebook 'oxford_pets.ipynb', I ran into the following issue:

`AttributeError Traceback (most recent call last)
in
1 LEARNING_RATE = 1e-3
----> 2 unet_model = unet.build_model(*oxford_iiit_pet.IMAGE_SIZE,
3 channels=oxford_iiit_pet.channels,
4 num_classes=oxford_iiit_pet.classes,
5 layer_depth=5,

AttributeError: module 'unet' has no attribute 'build_model'`

What could it be?

Thanks!

In your code you write that there are 3 classes in the dataset. Where does it come from? On TF datasets page it says that there are 37 classes.

Also, what is the meaning of masks in the dataset? I've seen that there is only one channel for each mask so it isn't clear what should it mean for 3 classes

As per the code, if the number of input channels is>3 it just uses the first 3 channels.
The input I want to give consists of 6 input channels, first three being rgb and the other 3 being some specific inputs I want to give to the model for my desired use case.
How can we make the model input all 6 channels?

I ran the scripts/oxford_iiit_pet.py and got a saved model in model_path.

now I would like to load this model with:
model = tf.keras.models.load_model(model_path)

but I get:

ValueError: Unable to restore custom object of type _tf_keras_metric currently. Please make sure that the layer implements get_configand from_config when saving. In addition, please use the custom_objects arg when calling load_model().

I searched this question on Stack Overflow and found that it worked when I changed the code to this:
model = tf.keras.models.load_model(model_path, custom_objects = {"mean_iou": mean_iou, "dice_coefficient": dice_coefficient})

so, I think in order to deserialize the model more conveniently, these two metrics should subclass tf.keras.metrics.Metric

I am getting the error below right after executing this command:

unet_model = tf.keras.models.load_model("M:\\ant\\2021-06-04T22-22_27", custom_objects=custom_objects)

File "segmenter2-generator.py", line 152, in evaluate unet_model = tf.keras.models.load_model("M:\\anthurium\\ant\\2021-06-04T22-22_27", custom_objects=None, compile=False) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\saving\save.py", line 206, in load_model return saved_model_load.load(filepath, compile, options) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 155, in load keras_loader.finalize_objects() File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 626, in finalize_objects self._reconstruct_all_models() File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 645, in _reconstruct_all_models self._reconstruct_model(model_id, model, layers) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 692, in _reconstruct_model config, created_layers={layer.name: layer for layer in layers}) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\engine\functional.py", line 1289, in reconstruct_from_config process_node(layer, node_data) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\engine\functional.py", line 1237, in process_node output_tensors = layer(input_tensors, **kwargs) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 970, in __call__ input_list) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1108, in _functional_construction_call inputs, input_masks, args, kwargs) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 840, in _keras_tensor_symbolic_call return self._infer_output_signature(inputs, args, kwargs, input_masks) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 880, in _infer_output_signature outputs = call_fn(inputs, *args, **kwargs) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\saving\saved_model\utils.py", line 69, in return_outputs_and_add_losses outputs, losses = fn(*args, **kwargs) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\saving\saved_model\utils.py", line 167, in wrap_with_training_arg lambda: replace_training_and_call(False)) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\utils\control_flow_util.py", line 110, in smart_cond pred, true_fn=true_fn, false_fn=false_fn, name=name) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\smart_cond.py", line 56, in smart_cond return false_fn() File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\saving\saved_model\utils.py", line 167, in <lambda> lambda: replace_training_and_call(False)) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\saving\saved_model\utils.py", line 163, in replace_training_and_call return wrapped_call(*args, **kwargs) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\eager\def_function.py", line 889, in __call__ result = self._call(*args, **kwds) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\eager\def_function.py", line 933, in _call self._initialize(args, kwds, add_initializers_to=initializers) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\eager\def_function.py", line 764, in _initialize *args, **kwds)) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\eager\function.py", line 3050, in _get_concrete_function_internal_garbage_collected graph_function, _ = self._maybe_define_function(args, kwargs) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\eager\function.py", line 3397, in _maybe_define_function self._function_spec.canonicalize_function_inputs(*args, **kwargs) File "C:\Users\JP\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\eager\function.py", line 2752, in canonicalize_function_inputs self.signature_summary(), missing_args[0])) TypeError: f(inputs, training, training, training, training, *, training, training) missing 1 required argument: training

Any tips what is causing this and how to solve this?

Thanks

after saving my model like this I can't reopen it
unet_model.save(r'C:\Users\ggrimard\Documents\Models/UnetTake2')

modelPath = r'C:\Users\ggrimard\Documents\Models/UnetTake2'
from unet import custom_objects
reconstructed_model = tf.keras.models.load_model(modelPath, custom_objects=custom_objects)

Looks like the predictions are all completely masked to black images. I could replicate it on my end and got accurate predictions. I would love to fix it.

I want to install this in miniconda by inputing'python setup.py install' in Anaconda Prompt .But Error: AttributeError: 'Configuration' object has no attribute 'parentdir_prefix_version' Would you tell me why?Thanks!!!

Trying to train on images with more than three channels raises a ValueError. I fixed the bug with a really simple patch to unet.utils.to_rgb() and can submit a pull request, if you like.

Here's the full error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-8-220be538e83e> in <module>
----> 1 trainer.fit(model4, data)

~/.local/share/virtualenvs/GitHub-mrs5Yhkq/lib/python3.7/site-packages/unet/trainer.py in fit(self, model, train_dataset, validation_dataset, test_dataset, epochs, batch_size, **fit_kwargs)
     94                             epochs=epochs,
     95                             callbacks=callbacks,
---> 96                             **fit_kwargs)
     97 
     98         self.evaluate(model, test_dataset, prediction_shape)

~/.local/share/virtualenvs/GitHub-mrs5Yhkq/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
     64   def _method_wrapper(self, *args, **kwargs):
     65     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
---> 66       return method(self, *args, **kwargs)
     67 
     68     # Running inside `run_distribute_coordinator` already.

~/.local/share/virtualenvs/GitHub-mrs5Yhkq/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
    874           epoch_logs.update(val_logs)
    875 
--> 876         callbacks.on_epoch_end(epoch, epoch_logs)
    877         if self.stop_training:
    878           break

~/.local/share/virtualenvs/GitHub-mrs5Yhkq/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py in on_epoch_end(self, epoch, logs)
    363     logs = self._process_logs(logs)
    364     for callback in self.callbacks:
--> 365       callback.on_epoch_end(epoch, logs)
    366 
    367   def on_train_batch_begin(self, batch, logs=None):

~/.local/share/virtualenvs/GitHub-mrs5Yhkq/lib/python3.7/site-packages/unet/callbacks.py in on_epoch_end(self, epoch, logs)
     32         self._log_histogramms(epoch, predictions)
     33 
---> 34         self._log_image_summaries(epoch, predictions)
     35 
     36         self.file_writer.flush()

~/.local/share/virtualenvs/GitHub-mrs5Yhkq/lib/python3.7/site-packages/unet/callbacks.py in _log_image_summaries(self, epoch, predictions)
     50                                  utils.to_rgb(cropped_labels[..., :1].numpy()),
     51                                  utils.to_rgb(mask)),
---> 52                                 axis=2)
     53 
     54         with self.file_writer.as_default():

<__array_function__ internals> in concatenate(*args, **kwargs)

ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 3, the array at index 0 has size 4 and the array at index 1 has size 3

And a quick script to reproduce the error:

import unet
import tensorflow as tf

X = tf.random.normal((100,256,256,4))
Y_flat = tf.random.categorical(tf.math.log([[0.5, 0.5]]),100*256*256)
Y = tf.reshape(Y_flat,(100,256,256))
Y_onehot = tf.one_hot(Y,2)

data = tf.data.Dataset.from_tensor_slices((X,Y_onehot))
train_data = data.take(75)
test_data = data.skip(75)

model4 = unet.build_model(256,256,channels=4,padding="same")
unet.finalize_model(model4,loss=tf.keras.losses.categorical_crossentropy)
trainer = unet.Trainer()

trainer.fit(model4, data)

Thanks much for your project and making your source available to others! : ) Wondering if someone may be able to help with an issue I'm having.

I took the code from the circle.ipynb demo and made a .py demo from it (copy / paste). Everything is working great there. However, when I change layer_depth=3 to a new value, I get ConcatOp errors.

I change:
unet_model = unet.build_model(channels=circles.channels, num_classes=circles.classes, layer_depth=3, filters_root=16)

To be:
unet_model = unet.build_model(channels=circles.channels, num_classes=circles.classes, layer_depth=4, filters_root=16)

Then I see errors when I try to run:
tensorflow.python.framework.errors_impl.InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [1,64,35,35] vs. shape[1] = [1,64,34,34] [[node unet/crop_concat_block/concat (defined at /home/avose/workspace/butterfly/synth/bf_unet/unet/unet.py:130) ]] [Op:__inference_distributed_function_2008]

Any tips would be greatly appreciated. I have also been playing with the TF1.x version of unet, and that version does seem to allow me to change the layer depth option without giving any errors.

Hi, Does this work with Python 3.10 and TensorFlow 2.8?

I'm trying to load a saved Unet model in OpenCV using dnn.readNetFromTensorflow and dnn.readNetFromONNX, but both methods are failing. The first one hangs and the second results in an obscure error.

> Node [[email protected]]:(onnx_node!StatefulPartitionedCall/unet/crop_concat_block/strided_slice_6) parse error: OpenCV(4.6.0) /Users/xperience/actions-runner/_work/opencv-python/opencv-python/opencv/modules/dnn/src/onnx/onnx_importer.cpp:1345: error: (-215:Assertion failed) axes[i - 1] == axes[i] - 1 in function 'parseSlice'

Has anyone done this before?

Heya,
I'm trying to use the model to perform some image separation using the mnist database.
I literally copy your code and modify some parameters to match with the mnist database, but I get this weird mesage:

If you're wondering, here's how I create the train and test dataset:

(mnist_x_train, mnist_y_train), (mnist_x_test, mnist_y_test) = mnist.load_data()

Not sure what this is related to, any ideas?

Thanks,
Mat