Mask-RCNN-Shiny
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Published
20 hours ago •
huuuuusy
huuuuusy
AttributeError: module 'tensorflow' has no attribute 'log'
Hi, I was running the Demo-Image.ipynb and stumbled on this error when running [7] COCO dataset object names:
AttributeError Traceback (most recent call last)
<ipython-input-7-cbc8b676b8a3> in <module>
1 # COCO dataset object names
----> 2 model = modellib.MaskRCNN(
3 mode="inference", model_dir=MODEL_DIR, config=config
4 )
5 model.load_weights(COCO_MODEL_PATH, by_name=True)
~\Desktop\AR_test\models\pretrained-models\R-CNN-mask\Mask-RCNN-Shiny-master\mrcnn\model.py in __init__(self, mode, config, model_dir)
1821 self.model_dir = model_dir
1822 self.set_log_dir()
-> 1823 self.keras_model = self.build(mode=mode, config=config)
1824
1825 def build(self, mode, config):
~\Desktop\AR_test\models\pretrained-models\R-CNN-mask\Mask-RCNN-Shiny-master\mrcnn\model.py in build(self, mode, config)
2014 # Proposal classifier and BBox regressor heads
2015 mrcnn_class_logits, mrcnn_class, mrcnn_bbox =\
-> 2016 fpn_classifier_graph(rpn_rois, mrcnn_feature_maps, input_image_meta,
2017 config.POOL_SIZE, config.NUM_CLASSES,
2018 train_bn=config.TRAIN_BN)
~\Desktop\AR_test\models\pretrained-models\R-CNN-mask\Mask-RCNN-Shiny-master\mrcnn\model.py in fpn_classifier_graph(rois, feature_maps, image_meta, pool_size, num_classes, train_bn)
916 # ROI Pooling
917 # Shape: [batch, num_boxes, pool_height, pool_width, channels]
--> 918 x = PyramidROIAlign([pool_size, pool_size],
919 name="roi_align_classifier")([rois, image_meta] + feature_maps)
920 # Two 1024 FC layers (implemented with Conv2D for consistency)
c:\users\tukuru0005\pythonstuff\envs\imageseg\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__(self, *args, **kwargs)
920 not base_layer_utils.is_in_eager_or_tf_function()):
921 with auto_control_deps.AutomaticControlDependencies() as acd:
--> 922 outputs = call_fn(cast_inputs, *args, **kwargs)
923 # Wrap Tensors in `outputs` in `tf.identity` to avoid
924 # circular dependencies.
c:\users\tukuru0005\pythonstuff\envs\imageseg\lib\site-packages\tensorflow\python\autograph\impl\api.py in wrapper(*args, **kwargs)
263 except Exception as e: # pylint:disable=broad-except
264 if hasattr(e, 'ag_error_metadata'):
--> 265 raise e.ag_error_metadata.to_exception(e)
266 else:
267 raise
AttributeError: in user code:
C:\Users\tukuru0005\Desktop\AR_test\models\pretrained-models\R-CNN-mask\Mask-RCNN-Shiny-master\mrcnn\model.py:387 call *
roi_level = log2_graph(tf.sqrt(h * w) / (224.0 / tf.sqrt(image_area)))
C:\Users\tukuru0005\Desktop\AR_test\models\pretrained-models\R-CNN-mask\Mask-RCNN-Shiny-master\mrcnn\model.py:338 log2_graph *
return tf.log(x) / tf.log(2.0)
AttributeError: module 'tensorflow' has no attribute 'log'
I tried both my PC (using different tensorflow versions) and google colab but it throws a similar error on colab:
AttributeError Traceback (most recent call last)
<ipython-input-21-cbc8b676b8a3> in <module>()
1 # COCO dataset object names
2 model = modellib.MaskRCNN(
----> 3 mode="inference", model_dir=MODEL_DIR, config=config
4 )
5 model.load_weights(COCO_MODEL_PATH, by_name=True)
6 frames
/content/Mask-RCNN-Shiny/mrcnn/model.py in __init__(self, mode, config, model_dir)
1821 self.model_dir = model_dir
1822 self.set_log_dir()
-> 1823 self.keras_model = self.build(mode=mode, config=config)
1824
1825 def build(self, mode, config):
/content/Mask-RCNN-Shiny/mrcnn/model.py in build(self, mode, config)
2016 fpn_classifier_graph(rpn_rois, mrcnn_feature_maps, input_image_meta,
2017 config.POOL_SIZE, config.NUM_CLASSES,
-> 2018 train_bn=config.TRAIN_BN)
2019
2020 # Detections
/content/Mask-RCNN-Shiny/mrcnn/model.py in fpn_classifier_graph(rois, feature_maps, image_meta, pool_size, num_classes, train_bn)
917 # Shape: [batch, num_boxes, pool_height, pool_width, channels]
918 x = PyramidROIAlign([pool_size, pool_size],
--> 919 name="roi_align_classifier")([rois, image_meta] + feature_maps)
920 # Two 1024 FC layers (implemented with Conv2D for consistency)
921 x = KL.TimeDistributed(KL.Conv2D(1024, (pool_size, pool_size), padding="valid"),
/usr/local/lib/python3.6/dist-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)
/usr/local/lib/python3.6/dist-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
/content/Mask-RCNN-Shiny/mrcnn/model.py in call(self, inputs)
385 # e.g. a 224x224 ROI (in pixels) maps to P4
386 image_area = tf.cast(image_shape[0] * image_shape[1], tf.float32)
--> 387 roi_level = log2_graph(tf.sqrt(h * w) / (224.0 / tf.sqrt(image_area)))
388 roi_level = tf.minimum(5, tf.maximum(
389 2, 4 + tf.cast(tf.round(roi_level), tf.int32)))
/content/Mask-RCNN-Shiny/mrcnn/model.py in log2_graph(x)
336 def log2_graph(x):
337 """Implementatin of Log2. TF doesn't have a native implemenation."""
--> 338 return tf.log(x) / tf.log(2.0)
339
340
AttributeError: module 'tensorflow' has no attribute 'log'
according to this reply we should replace tf.log() by tf.math.log(), I haven't tried it yet but it may be good to update the repository as well
after following the reply I replaced in model.py by:
#line 338
tf.log() -> tf.math.log()
#lines 716 and 754
tf.sets.set_intersection() -> tf.sets.intersection()
#lines 718 and 756
tf.sparse_tensor_to_dense() -> tf.sparse.to_dense()
#line 768
tf.to_float() -> tf.cast([tf.gather(class_ids, keep)], tf.float32)[..., tf.newaxis]
and a new error appeared on the same section:
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs, op_def)
1653 try:
-> 1654 c_op = pywrap_tf_session.TF_FinishOperation(op_desc)
1655 except errors.InvalidArgumentError as e:
InvalidArgumentError: Shape must be rank 2 but is rank 3 for '{{node mrcnn_detection/concat_1}} = ConcatV2[N=3, T=DT_FLOAT, Tidx=DT_INT32](mrcnn_detection/GatherV2_6, mrcnn_detection/strided_slice_28, mrcnn_detection/strided_slice_29, mrcnn_detection/concat_1/axis)' with input shapes: [?,4], [1,?,1], [?,1], [].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
16 frames
<ipython-input-10-cbc8b676b8a3> in <module>()
1 # COCO dataset object names
2 model = modellib.MaskRCNN(
----> 3 mode="inference", model_dir=MODEL_DIR, config=config
4 )
5 model.load_weights(COCO_MODEL_PATH, by_name=True)
/content/drive/My Drive/Mask-RCNN-Shiny-master/mrcnn/model.py in __init__(self, mode, config, model_dir)
1821 self.model_dir = model_dir
1822 self.set_log_dir()
-> 1823 self.keras_model = self.build(mode=mode, config=config)
1824
1825 def build(self, mode, config):
/content/drive/My Drive/Mask-RCNN-Shiny-master/mrcnn/model.py in build(self, mode, config)
2022 # normalized coordinates
2023 detections = DetectionLayer(config, name="mrcnn_detection")(
-> 2024 [rpn_rois, mrcnn_class, mrcnn_bbox, input_image_meta])
2025
2026 # Create masks for detections
/usr/local/lib/python3.6/dist-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)
/usr/local/lib/python3.6/dist-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
/content/drive/My Drive/Mask-RCNN-Shiny-master/mrcnn/model.py in call(self, inputs)
807 [rois, mrcnn_class, mrcnn_bbox, window],
808 lambda x, y, w, z: refine_detections_graph(x, y, w, z, self.config),
--> 809 self.config.IMAGES_PER_GPU)
810
811 # Reshape output
/content/drive/My Drive/Mask-RCNN-Shiny-master/mrcnn/utils.py in batch_slice(inputs, graph_fn, batch_size, names)
823 for i in range(batch_size):
824 inputs_slice = [x[i] for x in inputs]
--> 825 output_slice = graph_fn(*inputs_slice)
826 if not isinstance(output_slice, (tuple, list)):
827 output_slice = [output_slice]
/content/drive/My Drive/Mask-RCNN-Shiny-master/mrcnn/model.py in <lambda>(x, y, w, z)
806 detections_batch = utils.batch_slice(
807 [rois, mrcnn_class, mrcnn_bbox, window],
--> 808 lambda x, y, w, z: refine_detections_graph(x, y, w, z, self.config),
809 self.config.IMAGES_PER_GPU)
810
/content/drive/My Drive/Mask-RCNN-Shiny-master/mrcnn/model.py in refine_detections_graph(rois, probs, deltas, window, config)
768 tf.cast([tf.gather(class_ids, keep)], tf.float32)[..., tf.newaxis],
769 tf.gather(class_scores, keep)[..., tf.newaxis]
--> 770 ], axis=1)
771
772 # Pad with zeros if detections < DETECTION_MAX_INSTANCES
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
178 """Call target, and fall back on dispatchers if there is a TypeError."""
179 try:
--> 180 return target(*args, **kwargs)
181 except (TypeError, ValueError):
182 # Note: convert_to_eager_tensor currently raises a ValueError, not a
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py in concat(values, axis, name)
1604 dtype=dtypes.int32).get_shape().assert_has_rank(0)
1605 return identity(values[0], name=name)
-> 1606 return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
1607
1608
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py in concat_v2(values, axis, name)
1187 _attr_N = len(values)
1188 _, _, _op, _outputs = _op_def_library._apply_op_helper(
-> 1189 "ConcatV2", values=values, axis=axis, name=name)
1190 _result = _outputs[:]
1191 if _execute.must_record_gradient():
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(op_type_name, name, **keywords)
742 op = g._create_op_internal(op_type_name, inputs, dtypes=None,
743 name=scope, input_types=input_types,
--> 744 attrs=attr_protos, op_def=op_def)
745
746 # `outputs` is returned as a separate return value so that the output
/usr/local/lib/python3.6/dist-packages/tensorflow/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):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
3325 input_types=input_types,
3326 original_op=self._default_original_op,
-> 3327 op_def=op_def)
3328 self._create_op_helper(ret, compute_device=compute_device)
3329 return ret
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
1815 op_def = self._graph._get_op_def(node_def.op)
1816 self._c_op = _create_c_op(self._graph, node_def, inputs,
-> 1817 control_input_ops, op_def)
1818 name = compat.as_str(node_def.name)
1819 # pylint: enable=protected-access
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs, op_def)
1655 except errors.InvalidArgumentError as e:
1656 # Convert to ValueError for backwards compatibility.
-> 1657 raise ValueError(str(e))
1658
1659 return c_op
ValueError: Shape must be rank 2 but is rank 3 for '{{node mrcnn_detection/concat_1}} = ConcatV2[N=3, T=DT_FLOAT, Tidx=DT_INT32](mrcnn_detection/GatherV2_6, mrcnn_detection/strided_slice_28, mrcnn_detection/strided_slice_29, mrcnn_detection/concat_1/axis)' with input shapes: [?,4], [1,?,1], [?,1], [].
I understand it has something to do with the dimensions but both test images had the same size, different channels. any ideas? should I close this issue and open a new one with this information?