Tensorflow implementation of "Learning Deconvolution Network for Semantic Segmentation"

Hi Fabian, sorry this might not entirely constitute an "issue", but I was wondering if it is normal to have training times of 1-5 seconds per step? Meaning that 1000 steps of GD might take ~1 hour? This seems quite slow based on other networks I've trained (on my GTX 1080 GPU), particularly with a single image per batch, but perhaps this is entirely down to the significant number of parameters for this very deep model. Just wanted to make sure it seemed like everything was installed and running properly on my machine anyway as a sanity check! Thanks!

Hi, I have saw that in this code you use the placeholder with fixed shape? I really want to know how to deal with images with different size in semantic segmentation.

The Error appears in line 147 respectively in line 234. Maybe there is some flooring or ceiling during the input size and or conv layers.

Steps to reproduce:

cd /your/path/to/Tensorflow-DeconvNet-Segmentation/
python3 main.py

Error: Caused by op 'concat_1', defined at: File "main.py", line 4, in deconvNet = DeconvNet(use_cpu=True) File "/home/fabian/workspace/Tensorflow-DeconvNet-Segmentation/DeconvNet.py", line 15, in init self.build(use_cpu=use_cpu) File "/home/fabian/workspace/Tensorflow-DeconvNet-Segmentation/DeconvNet.py", line 147, in build unpool_4 = self.unpool_layer2x2(deconv_5_1, self.unravel_argmax(pool_4_argmax, tf.to_int64(tf.shape(conv_4_3)))) File "/home/fabian/workspace/Tensorflow-DeconvNet-Segmentation/DeconvNet.py", line 234, in unpool_layer2x2 t = tf.concat(3, [t2, t1]) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/array_ops.py", line 872, in concat name=name) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 436, in _concat values=values, name=name) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 749, in apply_op op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2380, in create_op original_op=self._default_original_op, op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1298, in init self._traceback = _extract_stack()

Thanks for sharing good stuff! Reviewing this code, and found that in file DeconvNet.py ReLU used only once in convolution layer, not in deconvolution layer. In ref paper they are using. Is this correct? (For me, looks like problem arrives from calc of output score values that don't need activation function, linear op, but deconv need relu..)

There are some errors I've found recently when trying to reproduce the result.

1. restore_session -> doesn't have 'self' as param
2. in predict: feed_dict={image:[image]} -> {self.x:[image]}

Can I compile and execute this code on windows? I tried like

"pip install -r requirement.txt Then get error: Could not open requirements file: [Errno 2] No such file or directory: 'requirement.txt'"

I think I may not using right command. I am very beginner at this.......:(

Hi Fabian,

When experimenting with your code of UnpoolLayerTest.ipynb, I got the following error message. Would you like to take a look at it? I did not change your code. Thanks.

Traceback (most recent call last): File "UnpoolLayerTest.py", line 56, in <module> unpool_1 = unpool_layer2x2(pool_1, unravel_argmax(argmax, tf.to_int64(tf.shape(x)))) File "UnpoolLayerTest.py", line 17, in unpool_layer2x2 output = tf.zeros([x_shape[1] * 2, x_shape[2] * 2, x_shape[3]]) File "/develop/tfw/lib/python3.4/site-packages/tensorflow/python/ops/array_ops.py", line 620, in zeros output = constant(0, shape=shape, dtype=dtype, name=name) File "/develop/tfw/lib/python3.4/site-packages/tensorflow/python/ops/constant_op.py", line 162, in constant tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape)) File "/develop/tfw/lib/python3.4/site-packages/tensorflow/python/framework/tensor_util.py", line 363, in make_tensor_proto shape = [int(dim) for dim in shape] File "/develop/tfw/lib/python3.4/site-packages/tensorflow/python/framework/tensor_util.py", line 363, in <listcomp> shape = [int(dim) for dim in shape] TypeError: int() argument must be a string, a bytes-like object or a number, not 'Tensor'

Hello, I read the original paper and there was no deconv layer with 32 filters, but in the code I saw this:

deconv_1_1 = self.deconv_layer(deconv_1_2, [3, 3, 32, 64], 32, 'deconv_1_1')

I really appreciate if someone helps me with that.

Hi? I am currently working to make PR of DeconvNetPipeline.py with batch normalization. I wonder, however, why the logits' last dimension is 21 at the end of the network, while ? Does self.y has same 21 at final dimension?

Thank you

Hi! I wonder why batch normalization and activation function is not used in the code. Also, another thing that I am curious about is that filter size of 'fc_6' is 77 in this code. however , the filter width&height should be variable to make fc_7 11 vector.

Thank you.

Traceback (most recent call last): File "img_to_tfrecords.py", line 48, in iQ,imgLen,mQ,segLen=read_filelist(args.imgpath,args.segpath)
File "img_to_tfrecords.py", line 20, in read_filelist imgFiles=tf.train.string_input_producer(imgList,shuffle=False) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py", line 217, in string_input_producer raise ValueError(not_null_err) ValueError: string_input_producer requires a non-null input tensor

I am wondering whether the shape of this input must be (224, 224, 3). If it is not this shape, I just need to change the kernel_size in fc6 in the code to the width and height of pool5, right?

fc6 = Conv2D(512, 7, use_bias=False, padding='valid', name='fc6')(pool5) #4096
fc6 = BatchNormalization(name='batchnorm_fc6')(fc6)
fc6 = Activation('relu', name='relu_fc6')(fc6)

I have been reading thorugh the code for the function "MaxUnpoolWithArgmax" since I'm interested in the logic to extract the indexes [b,y,x,c] from the argmax tensor. I cannot understand the maths behind to extract the index "y" and "x". Could you elaborate a bit on how did you get these expressions? :

• y = argmax // (output_shape[2] * output_shape[3])

• x = argmax % (output_shape[2] * output_shape[3]) // output_shape[3]

Thank you so much Pedro

Hello everyone ! Do you have the Well trained models . I want to see the the effect of the network . As you all know, the network is training very slowly . . .Thanks ! !

I know this question should not be an issue, but I would like to read some documentation on the unpool_layer2x2 method in DeconvNet.py code so as to understand what is going on in this code.

Any help is much appreciated!!

hi i run the code DeconvNetPipeline.py and while the traning i got something like this: 2017-01-26 15:01:00.156146: step 85, loss = 39709622646341632.00 (3.6 examples/sec; 2.809 sec/batch) 2017-01-26 15:01:03.019399: step 86, loss = 34950307108618240.00 (3.5 examples/sec; 2.863 sec/batch) 2017-01-26 15:01:05.870122: step 87, loss = 37934860555255808.00 (3.5 examples/sec; 2.851 sec/batch) its error? Also he break in the middle and because the line: except tf.errors.OutOfRangeError: what its mean? thenk you