training on ADE 20k dataset exception

i have downloaded the ADE 20k dataset and convert each png to width of 512px.

the pandas dataframe generated as below(l is a list filled with resized png path)

` d = pd.DataFrame(l, columns=['path'])

d.to_hdf("noise.h5", key='df') `, and directories.train has been set to noise.h5 file path, during training, exception throwed as below

` Training on dataset cityscapes

Building computational graph ...

Training on cityscapes

<------------ Building global image generator architecture ------------>

Sampling noise...

Real image shape: [None, None, None, 3]

Reconstruction shape: [None, 512, 1024, 3]

<------------ Building multiscale discriminator architecture ------------>

Building discriminator D(x)

Shape of x: [None, None, None, 3]

Shape of x downsampled by factor 2: [None, None, None, 3]

Shape of x downsampled by factor 4: [None, None, None, 3]

<------------ Building multiscale discriminator architecture ------------>

Building discriminator D(G(z))

Shape of x: [None, 512, 1024, 3]

Shape of x downsampled by factor 2: [None, 256, 512, 3]

Shape of x downsampled by factor 4: [None, 128, 256, 3]2018-11-13 16:59:16.311330: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FM A 2018-11-13 16:59:27.145299: W tensorflow/core/kernels/data/cache_dataset_ops.cc:770] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the datasetwill be discarded. This can happen if you have an input pipeline similar to dataset.cache().take(k).repeat(). You should use dataset.take(k).cache().repeat() instead. Traceback (most recent call last): File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1334, in _do_call return fn(*args) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1319, in _run_fn options, feed_dict, fetch_list, target_list, run_metadata) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1407, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [1,24,32,8] vs. shape[1] = [1,32,64,8] [[{{node generator/concat}} = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](generator/quantizer_image/Round, generator/noise_generator/conv_out/conv2d/BiasAdd, generator/quantizer_image/ArgMin/dimension)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "train.py", line 119, in main() File "train.py", line 116, in main train(config_train, args) File "train.py", line 70, in train start_time, epoch, args.name, G_loss_best, D_loss_best) File "/home/xiakai/software/generative-compression-master/utils.py", line 78, in run_diagnostics G_loss, D_loss, summary = sess.run([model.G_loss, model.D_loss, model.merge_op], feed_dict=feed_dict_test) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 929, in run run_metadata_ptr) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1152, in _run feed_dict_tensor, options, run_metadata) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1328, in _do_run run_metadata) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1348, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [1,24,32,8] vs. shape[1] = [1,32,64,8] [[node generator/concat (defined at /home/xiakai/software/generative-compression-master/model.py:77) = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](generator/quantizer_image/Round, generator/noise_generator/conv_out/conv2d/BiasAdd, generator/quantizer_image/ArgMin/dimension)]]

Caused by op 'generator/concat', defined at: File "train.py", line 119, in main() File "train.py", line 116, in main train(config_train, args) File "train.py", line 34, in train gan = Model(config, paths, name=args.name, dataset=args.dataset) File "/home/xiakai/software/generative-compression-master/model.py", line 77, in init self.z = tf.concat([self.w_hat, Gv], axis=-1) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/array_ops.py", line 1124, in concat return gen_array_ops.concat_v2(values=values, axis=axis, name=name) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 1033, in concat_v2 "ConcatV2", values=values, axis=axis, name=name) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func return func(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3274, in create_op op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1770, in init self._traceback = tf_stack.extract_stack()

InvalidArgumentError (see above for traceback): ConcatOp : Dimensions of inputs should match: shape[0] = [1,24,32,8] vs. shape[1] = [1,32,64,8] [[node generator/concat (defined at /home/xiakai/software/generative-compression-master/model.py:77) = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](generator/quantizer_image/Round, generator/noise_generator/conv_out/conv2d/BiasAdd, generator/quantizer_image/ArgMin/dimension)]] `

how could i fix it. thantks

Justin-Tan / generative-compression

training on ADE 20k dataset exception #25