>python pspnet.py
Namespace(flip=False, id='0', input_path='example_images/ade20k.jpg', model='pspnet50_ade20k', multi_scale=False, output_path='example_results/ade20k.jpg', sliding=False)
No Keras model & weights found, import from npy weights.
Building a PSPNet based on ResNet 50 expecting inputs of shape (473, 473) predicting 150 classes
PSP module will interpolate to a final feature map size of (60, 60)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/home/hab/libs/PSPNet-Keras-tensorflow/pspnet.py in <module>()
    263         if "pspnet50" in args.model:
    264             pspnet = PSPNet50(nb_classes=150, input_shape=(473, 473),
--> 265                               weights=args.model)
    266         elif "pspnet101" in args.model:
    267             if "cityscapes" in args.model:

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet.py in __init__(self, nb_classes, weights, input_shape)
    140         """Instanciate a PSPNet50."""
    141         PSPNet.__init__(self, nb_classes=nb_classes, resnet_layers=50,
--> 142                         input_shape=input_shape, weights=weights)
    143 
    144 

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet.py in __init__(self, nb_classes, resnet_layers, input_shape, weights)
     45             self.model = layers.build_pspnet(nb_classes=nb_classes,
     46                                              resnet_layers=resnet_layers,
---> 47                                              input_shape=self.input_shape)
     48             self.set_npy_weights(weights)
     49 

/home/hab/libs/PSPNet-Keras-tensorflow/layers_builder.py in build_pspnet(nb_classes, resnet_layers, input_shape, activation)
    210     inp = Input((input_shape[0], input_shape[1], 3))
    211     res = ResNet(inp, layers=resnet_layers)
--> 212     psp = build_pyramid_pooling_module(res, input_shape)
    213 
    214     x = Conv2D(512, (3, 3), strides=(1, 1), padding="same", name="conv5_4",

/home/hab/libs/PSPNet-Keras-tensorflow/layers_builder.py in build_pyramid_pooling_module(res, input_shape)
    190     print("PSP module will interpolate to a final feature map size of %s" % (feature_map_size, ))
    191 
--> 192     interp_block1 = interp_block(res, 6, feature_map_size, str_lvl=1)
    193     interp_block2 = interp_block(res, 3, feature_map_size, str_lvl=2)
    194     interp_block3 = interp_block(res, 2, feature_map_size, str_lvl=3)

/home/hab/libs/PSPNet-Keras-tensorflow/layers_builder.py in interp_block(prev_layer, level, feature_map_shape, str_lvl)
    175     kernel = (10*level, 10*level)
    176     strides = (10*level, 10*level)
--> 177     prev_layer = AveragePooling2D(kernel, strides=strides)(prev_layer)
    178     prev_layer = Conv2D(512, (1, 1), strides=(1, 1), name=names[0],
    179                         use_bias=False)(prev_layer)

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet_ve/lib/python3.5/site-packages/keras/engine/topology.py in __call__(self, inputs, **kwargs)
    600 
    601             # Actually call the layer, collecting output(s), mask(s), and shape(s).
--> 602             output = self.call(inputs, **kwargs)
    603             output_mask = self.compute_mask(inputs, previous_mask)
    604 

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet_ve/lib/python3.5/site-packages/keras/layers/pooling.py in call(self, inputs)
    152                                         strides=self.strides,
    153                                         padding=self.padding,
--> 154                                         data_format=self.data_format)
    155         return output
    156 

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet_ve/lib/python3.5/site-packages/keras/layers/pooling.py in _pooling_function(self, inputs, pool_size, strides, padding, data_format)
    269                           padding, data_format):
    270         output = K.pool2d(inputs, pool_size, strides,
--> 271                           padding, data_format, pool_mode='avg')
    272         return output
    273 

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet_ve/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py in pool2d(x, pool_size, strides, padding, data_format, pool_mode)
   3386         x = tf.nn.max_pool(x, pool_size, strides, padding=padding)
   3387     elif pool_mode == 'avg':
-> 3388         x = tf.nn.avg_pool(x, pool_size, strides, padding=padding)
   3389     else:
   3390         raise ValueError('Invalid pooling mode:', pool_mode)

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet_ve/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py in avg_pool(value, ksize, strides, padding, data_format, name)
   1742                                 padding=padding,
   1743                                 data_format=data_format,
-> 1744                                 name=name)
   1745 
   1746 

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet_ve/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py in _avg_pool(value, ksize, strides, padding, data_format, name)
     44   result = _op_def_lib.apply_op("AvgPool", value=value, ksize=ksize,
     45                                 strides=strides, padding=padding,
---> 46                                 data_format=data_format, name=name)
     47   return result
     48 

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet_ve/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py in apply_op(self, op_type_name, name, **keywords)
    765         op = g.create_op(op_type_name, inputs, output_types, name=scope,
    766                          input_types=input_types, attrs=attr_protos,
--> 767                          op_def=op_def)
    768         if output_structure:
    769           outputs = op.outputs

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet_ve/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in create_op(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)
   2630                     original_op=self._default_original_op, op_def=op_def)
   2631     if compute_shapes:
-> 2632       set_shapes_for_outputs(ret)
   2633     self._add_op(ret)
   2634     self._record_op_seen_by_control_dependencies(ret)

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet_ve/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in set_shapes_for_outputs(op)
   1909       shape_func = _call_cpp_shape_fn_and_require_op
   1910 
-> 1911   shapes = shape_func(op)
   1912   if shapes is None:
   1913     raise RuntimeError(

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet_ve/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in call_with_requiring(op)
   1859 
   1860   def call_with_requiring(op):
-> 1861     return call_cpp_shape_fn(op, require_shape_fn=True)
   1862 
   1863   _call_cpp_shape_fn_and_require_op = call_with_requiring

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet_ve/lib/python3.5/site-packages/tensorflow/python/framework/common_shapes.py in call_cpp_shape_fn(op, require_shape_fn)
    593     res = _call_cpp_shape_fn_impl(op, input_tensors_needed,
    594                                   input_tensors_as_shapes_needed,
--> 595                                   require_shape_fn)
    596     if not isinstance(res, dict):
    597       # Handles the case where _call_cpp_shape_fn_impl calls unknown_shape(op).

/home/hab/libs/PSPNet-Keras-tensorflow/pspnet_ve/lib/python3.5/site-packages/tensorflow/python/framework/common_shapes.py in _call_cpp_shape_fn_impl(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)
    657       missing_shape_fn = True
    658     else:
--> 659       raise ValueError(err.message)
    660 
    661   if missing_shape_fn:

ValueError: Negative dimension size caused by subtracting 60 from 1 for 'average_pooling2d_2/AvgPool' (op: 'AvgPool') with input shapes: [?,60,1,2048].