Hi, I tried to run the model but I get the following error

Epoch 1/100 Traceback (most recent call last): File "/home/user/.local/lib/python3.5/site-packages/keras/utils/data_utils.py", line 578, in get inputs = self.queue.get(block=True).get() File "/usr/lib/python3.5/multiprocessing/pool.py", line 608, in get raise self._value File "/usr/lib/python3.5/multiprocessing/pool.py", line 119, in worker result = (True, func(*args, **kwds)) File "/home/user/.local/lib/python3.5/site-packages/keras/utils/data_utils.py", line 401, in get_index return _SHARED_SEQUENCES[uid][i] File "/home/user/Desktop/Keras-ICNet/utils.py", line 95, in getitem self.Y1[n] = to_categorical(cv2.resize(label, (label.shape[1]//4, label.shape[0]//4)), self.n_classes).reshape((label.shape[0]//4, label.shape[1]//4, -1))
File "/home/user/.local/lib/python3.5/site-packages/keras/utils/np_utils.py", line 31, in to_categorical categorical[np.arange(n), y] = 1 IndexError: index 117 is out of bounds for axis 1 with size 66

The above exception was the direct cause of the following exception:

Traceback`(most recent call last): File "train", line 63, in use_multiprocessing=False, shuffle=True, max_queue_size=10, initial_epoch=opt.epoch)
File "/home/user/.local/lib/python3.5/site-packages/keras/legacy/interfaces.py", line 91, in wrapper return func(*args, **kwargs) File "/home/user/.local/lib/python3.5/site-packages/keras/engine/training.py", line 2192, in fit_generator generator_output = next(output_generator) File "/home/user/.local/lib/python3.5/site-packages/keras/utils/data_utils.py", line 584, in get six.raise_from(StopIteration(e), e) File "", line 3, in raise_from StopIteration: index 117 is out of bounds for axis 1 with size 66

Any ideas ?

Many thanks

Ok that's my bad, the keras implementation is very fast too ! I ran it on 20 images on a single batch and as you can see it goes from 1 FPS to 90 FPS after only one prediction !

[GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.7335]

Generated segmentations in 0.879213809967041 seconds -- 1.1373797689068224 FPS Generated segmentations in 0.011792182922363281 seconds -- 84.80194096239386 FPS Generated segmentations in 0.011512994766235352 seconds -- 86.85837354262875 FPS Generated segmentations in 0.01149892807006836 seconds -- 86.96462782500518 FPS Generated segmentations in 0.011469125747680664 seconds -- 87.1906038873298 FPS Generated segmentations in 0.011708974838256836 seconds -- 85.40457331351429 FPS Generated segmentations in 0.011548042297363281 seconds -- 86.59476422495665 FPS Generated segmentations in 0.011418342590332031 seconds -- 87.57838469890586 FPS Generated segmentations in 0.01206517219543457 seconds -- 82.88319336033989 FPS Generated segmentations in 0.011431455612182617 seconds -- 87.47792354057606 FPS Generated segmentations in 0.0111236572265625 seconds -- 89.89849108367626 FPS Generated segmentations in 0.010841846466064453 seconds -- 92.23521132955095 FPS Generated segmentations in 0.010785341262817383 seconds -- 92.71843844640449 FPS Generated segmentations in 0.010878801345825195 seconds -- 91.92189177935086 FPS Generated segmentations in 0.010807514190673828 seconds -- 92.52821530994926 FPS Generated segmentations in 0.010857343673706055 seconds -- 92.10355958628868 FPS Generated segmentations in 0.010789155960083008 seconds -- 92.68565619958898 FPS Generated segmentations in 0.010899066925048828 seconds -- 91.75097344358402 FPS Generated segmentations in 0.010824441909790039 seconds -- 92.38351578159072 FPS Generated segmentations in 0.010749101638793945 seconds -- 93.03103027614506 FPS

note that it doesn't count the image loading and parsing

So you might want to use this repo, it's well coded and easy to understand

Originally posted by @MathiasGilson in https://github.com/aitorzip/Keras-ICNet/issues/2#issuecomment-377172106

Hi, thank you for your work. One thing I want to point out here is that you have some tiny difference compared with original implementaion, which may lead to more latency. Your implementation here is conv + activation + bn as shown below:

y = Conv2D(32, 3, strides=2, padding='same', activation='relu', name='conv1_1_3x3_s2')(y)
y = BatchNormalization(name='conv1_1_3x3_s2_bn')(y)

But the original caffe implementaion is conv + bn + activation, which normally won't have much differnce, but this kind of order make it possible to merge bn into conv, thus leads to smaller latency.

After I trained on my own dataset with width 800 height 480 without checkpoint successfully, I fill in the checkpoint parameter and tried the test image and this error showed up. The exact error is like this:

Traceback (most recent call last): File "train", line 55, in net = load_model(opt.checkpoint) File "C:\DDDDZZZZYYYY\envs\kerass\lib\site-packages\keras\engine\saving.py", line 419, in load_model model = _deserialize_model(f, custom_objects, compile) File "C:\DDDDZZZZYYYY\envs\kerass\lib\site-packages\keras\engine\saving.py", line 225, in deserialize_model model = model_from_config(model_config, custom_objects=custom_objects) File "C:\DDDDZZZZYYYY\envs\kerass\lib\site-packages\keras\engine\saving.py", line 458, in model_from_config return deserialize(config, custom_objects=custom_objects) File "C:\DDDDZZZZYYYY\envs\kerass\lib\site-packages\keras\layers_init.py", line 55, in deserialize printable_module_name='layer') File "C:\DDDDZZZZYYYY\envs\kerass\lib\site-packages\keras\utils\generic_utils.py", line 145, in deserialize_keras_object list(custom_objects.items()))) File "C:\DDDDZZZZYYYY\envs\kerass\lib\site-packages\keras\engine\network.py", line 1032, in from_config process_node(layer, node_data) File "C:\DDDDZZZZYYYY\envs\kerass\lib\site-packages\keras\engine\network.py", line 991, in process_node layer(unpack_singleton(input_tensors), **kwargs) File "C:\DDDDZZZZYYYY\envs\kerass\lib\site-packages\keras\engine\base_layer.py", line 457, in call output = self.call(inputs, **kwargs) File "C:\DDDDZZZZYYYY\envs\kerass\lib\site-packages\keras\layers\core.py", line 687, in call return self.function(inputs, **arguments) File "C:\Users\In-Car DVR\Desktop\DZY-work\Keras-ICNet-master\model.py", line 195, in y = Lambda(lambda x: tf.image.resize_bilinear(x, size=(int(x.shape[1])//2, int(x.shape[2])//2)), name='data_sub2')(x) NameError: name 'tf' is not defined

The training process showed the same error when I put in the checkpoint parameters. Is the file in the output folder the checkpoint file? Or the checkpoint file is in some other locations?

Thanks in advance!

Hi,

I have trained the model and I am trying to visualize the predictions, however I am having trouble. The error occurs on the second last line; output = apply_color_map(np.argmax(y[0], axis=-1), labels) I also noticed that there is y[1] and y[3], as well.

Can anyone help me please?

Hi, I wonder to know your accuracy of the predicted results. How did you calculate the testing results? IoU or other methods? Because I apply this program to my own dataset, I get the very low accuracy.

@aitorzip Hi, thanks your great repo, when I train in my dataset, I find such following , every new epoch loss fall from the beginning ，not from the last epoch , why does it happen ?

In the original PSPnet ,there appear to be a projection conv after the feature map is pooled, but it seems to be lefted out in this repo. So can a convolution between pooling and upsample help improve performance, did anyone test that?