Assertion error while running train_unet.py

[prativadas]

assert len(img.shape) == 3 and img.shape[0] > sz and img.shape[1] > sz and img.shape[0:2] == mask.shape[0:2]
this is getting Assertion error while running train_unet.py

how to resolve this?

[prativadas]

when i change this line in train_unet train_xsz = int(3/4 * img_m.shape[0]) to this
train_xsz = int(img_m.shape[0]*(0.75)) the error goes away and model starts training

[onimonipea]

I had the same error. My problem was caused by a difference in data structures. The imagery I wanted to train on was already correctly formatted, so I did not need to use numpy.transpose() to get img.shape and mask.shape in the proper order (X, Y, channels). Changing train_unet.py to transpose the data into the correct format should help you clear the assertion check.

[v-bryzgalin]

when i change this line in train_unet train_xsz = int(3/4 * img_m.shape[0]) to this
train_xsz = int(img_m.shape[0]*(0.75)) the error goes away and model starts training

Thank you, it was useful!

[nacerader]

Hello , can somone help me please in this code , when i run the Train_unet i get this error :

start train net
Generated 4000 patches
Generated 1000 patches

---

InvalidArgumentError Traceback (most recent call last)
~\anaconda3\envs\tf3\lib\site-packages\tensorflow_core\python\framework\ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1618 try:
-> 1619 c_op = c_api.TF_FinishOperation(op_desc)
1620 except errors.InvalidArgumentError as e:

InvalidArgumentError: Negative dimension size caused by subtracting 2 from 1 for 'max_pooling2d_20/MaxPool' (op: 'MaxPool') with input shapes: [?,1,1,512].

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last)
in
18 return model
19
---> 20 train_net()

in train_net()
3 x_train, y_train = get_patches(X_DICT_TRAIN, Y_DICT_TRAIN, n_patches=TRAIN_SZ, sz=PATCH_SZ)
4 x_val, y_val = get_patches(X_DICT_VALIDATION, Y_DICT_VALIDATION, n_patches=VAL_SZ, sz=PATCH_SZ)
----> 5 model = get_model()
6 if os.path.isfile(weights_path):
7 model.load_weights(weights_path)

in get_model()
1 def get_model():
----> 2 return unet_model(N_CLASSES, PATCH_SZ, n_channels=N_BANDS, upconv=UPCONV, class_weights=CLASS_WEIGHTS)
3
4
5 weights_path = 'weights'

~\Desktop\Unet\deep-unet-for-satellite-image-segmentation-master\unet_model.py in unet_model(n_classes, im_sz, n_channels, n_filters_start, growth_factor, upconv, class_weights)
44 conv4_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool4_1)
45 conv4_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv4_1)
---> 46 pool4_2 = MaxPooling2D(pool_size=(2, 2))(conv4_1)
47 pool4_2 = Dropout(droprate)(pool4_2)
48

~\anaconda3\envs\tf3\lib\site-packages\keras\backend\tensorflow_backend.py in symbolic_fn_wrapper(*args, **kwargs)
73 if _SYMBOLIC_SCOPE.value:
74 with get_graph().as_default():
---> 75 return func(*args, **kwargs)
76 else:
77 return func(*args, **kwargs)

~\anaconda3\envs\tf3\lib\site-packages\keras\engine\base_layer.py in call(self, inputs, **kwargs)
487 # Actually call the layer,
488 # collecting output(s), mask(s), and shape(s).
--> 489 output = self.call(inputs, **kwargs)
490 output_mask = self.compute_mask(inputs, previous_mask)
491

~\anaconda3\envs\tf3\lib\site-packages\keras\layers\pooling.py in call(self, inputs)
203 strides=self.strides,
204 padding=self.padding,
--> 205 data_format=self.data_format)
206 return output
207

~\anaconda3\envs\tf3\lib\site-packages\keras\layers\pooling.py in _pooling_function(self, inputs, pool_size, strides, padding, data_format)
266 output = K.pool2d(inputs, pool_size, strides,
267 padding, data_format,
--> 268 pool_mode='max')
269 return output
270

~\anaconda3\envs\tf3\lib\site-packages\keras\backend\tensorflow_backend.py in pool2d(x, pool_size, strides, padding, data_format, pool_mode)
4070 x = tf.nn.max_pool(x, pool_size, strides,
4071 padding=padding,
-> 4072 data_format=tf_data_format)
4073 elif pool_mode == 'avg':
4074 x = tf.nn.avg_pool(x, pool_size, strides,

~\anaconda3\envs\tf3\lib\site-packages\tensorflow_core\python\ops\nn_ops.py in max_pool_v2(input, ksize, strides, padding, data_format, name)
3824 padding=padding,
3825 data_format=data_format,
-> 3826 name=name)
3827 # pylint: enable=redefined-builtin
3828

~\anaconda3\envs\tf3\lib\site-packages\tensorflow_core\python\ops\gen_nn_ops.py in max_pool(input, ksize, strides, padding, data_format, name)
5198 _, _, _op, _outputs = _op_def_library._apply_op_helper(
5199 "MaxPool", input=input, ksize=ksize, strides=strides, padding=padding,
-> 5200 data_format=data_format, name=name)
5201 _result = _outputs[:]
5202 if _execute.must_record_gradient():

~\anaconda3\envs\tf3\lib\site-packages\tensorflow_core\python\framework\op_def_library.py in _apply_op_helper(op_type_name, name, **keywords)
740 op = g._create_op_internal(op_type_name, inputs, dtypes=None,
741 name=scope, input_types=input_types,
--> 742 attrs=attr_protos, op_def=op_def)
743
744 # outputs is returned as a separate return value so that the output

~\anaconda3\envs\tf3\lib\site-packages\tensorflow_core\python\framework\func_graph.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
593 return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access
594 op_type, inputs, dtypes, input_types, name, attrs, op_def,
--> 595 compute_device)
596
597 def capture(self, tensor, name=None, shape=None):

~\anaconda3\envs\tf3\lib\site-packages\tensorflow_core\python\framework\ops.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
3320 input_types=input_types,
3321 original_op=self._default_original_op,
-> 3322 op_def=op_def)
3323 self._create_op_helper(ret, compute_device=compute_device)
3324 return ret

~\anaconda3\envs\tf3\lib\site-packages\tensorflow_core\python\framework\ops.py in init(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
1784 op_def, inputs, node_def.attr)
1785 self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
-> 1786 control_input_ops)
1787 name = compat.as_str(node_def.name)
1788 # pylint: enable=protected-access

~\anaconda3\envs\tf3\lib\site-packages\tensorflow_core\python\framework\ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1620 except errors.InvalidArgumentError as e:
1621 # Convert to ValueError for backwards compatibility.
-> 1622 raise ValueError(str(e))
1623
1624 return c_op

ValueError: Negative dimension size caused by subtracting 2 from 1 for 'max_pooling2d_20/MaxPool' (op: 'MaxPool') with input shapes: [?,1,1,512].