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Intro to deep learning for medical imaging lesson, by MD.ai

License: Apache License 2.0

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ml-lessons's Issues

Intro to deep learning for medical imaging: tutorial lessons

Lesson 1 - Hello World, Image Classification
Chest vs. Abdomen, three implementations, (1) Keras, (2) Fast.ai and (3) Keras with TF Records
Lesson 2 - Semantic Segmentation (U-nets)
Lesson 3 - Object Detection with Instance Segmentation (Mask R-CNN)

Permission denied for git clone from GCP

Clone ml-lessons repo from github on instance
After changing directory to /opt/deeplearning/workspace and running
git clone https://github.com/mdai/ml-lessons.git
I get this error:

fatal: could not create work tree dir 'ml-lessons': Permission denied

Any idea why this is happening?

InvalidArgumentError

Hi
I am trying to run the notebook : lesson3-rsna-pneumonia-detection-kaggle.ipynb verbatim on my Ubuntu machine.

I get an InvalidArgumentError. This error does not happen if i run it in Google Colab but it does on two different Ubuntu machines that I tried.

This happens in this cell of code.

NUM_EPOCHS = 1
# Train Mask-RCNN Model
import warnings
warnings.filterwarnings("ignore")
model.train(dataset_train, dataset_val,
            learning_rate=config.LEARNING_RATE,
            epochs=NUM_EPOCHS,
            layers='all',
            augmentation=augmentation)

All Python modules for mrcnn have been installed from their requiremnt.txt file so I dont see any reason for a version mismatch.

Any suggestions would be appreciated.

Here's the complete stack trace.

Epoch 1/1
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1277     try:
-> 1278       return fn(*args)
   1279     except errors.OpError as e:

/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
   1262       return self._call_tf_sessionrun(
-> 1263           options, feed_dict, fetch_list, target_list, run_metadata)
   1264 

/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
   1349         self._session, options, feed_dict, fetch_list, target_list,
-> 1350         run_metadata)
   1351 

InvalidArgumentError: indices[209] = 980 is not in [0, 960)
	 [[Node: ROI/GatherV2_23 = GatherV2[Taxis=DT_INT32, Tindices=DT_INT32, Tparams=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](ROI/strided_slice_48, ROI/strided_slice_49, training/SGD/gradients/roi_align_classifier/concat_grad/mod)]]

During handling of the above exception, another exception occurred:

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-32-3c633e2e5bb2> in <module>()
      8             epochs=NUM_EPOCHS,
      9             layers='all',
---> 10             augmentation=augmentation)

/usr/local/lib/python3.5/dist-packages/mask_rcnn-2.1-py3.5.egg/mrcnn/model.py in train(self, train_dataset, val_dataset, learning_rate, epochs, layers, augmentation, custom_callbacks, no_augmentation_sources)
   2379             max_queue_size=100,
   2380             workers=workers,
-> 2381             use_multiprocessing=True,
   2382         )
   2383         self.epoch = max(self.epoch, epochs)

/usr/local/lib/python3.5/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     85                 warnings.warn('Update your `' + object_name +
     86                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 87             return func(*args, **kwargs)
     88         wrapper._original_function = func
     89         return wrapper

/usr/local/lib/python3.5/dist-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   2040                     outs = self.train_on_batch(x, y,
   2041                                                sample_weight=sample_weight,
-> 2042                                                class_weight=class_weight)
   2043 
   2044                     if not isinstance(outs, list):

/usr/local/lib/python3.5/dist-packages/keras/engine/training.py in train_on_batch(self, x, y, sample_weight, class_weight)
   1760             ins = x + y + sample_weights
   1761         self._make_train_function()
-> 1762         outputs = self.train_function(ins)
   1763         if len(outputs) == 1:
   1764             return outputs[0]

/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
   2271         updated = session.run(self.outputs + [self.updates_op],
   2272                               feed_dict=feed_dict,
-> 2273                               **self.session_kwargs)
   2274         return updated[:len(self.outputs)]
   2275 

/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    875     try:
    876       result = self._run(None, fetches, feed_dict, options_ptr,
--> 877                          run_metadata_ptr)
    878       if run_metadata:
    879         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1098     if final_fetches or final_targets or (handle and feed_dict_tensor):
   1099       results = self._do_run(handle, final_targets, final_fetches,
-> 1100                              feed_dict_tensor, options, run_metadata)
   1101     else:
   1102       results = []

/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1270     if handle is None:
   1271       return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1272                            run_metadata)
   1273     else:
   1274       return self._do_call(_prun_fn, handle, feeds, fetches)

/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1289         except KeyError:
   1290           pass
-> 1291       raise type(e)(node_def, op, message)
   1292 
   1293   def _extend_graph(self):

InvalidArgumentError: indices[209] = 980 is not in [0, 960)
	 [[Node: ROI/GatherV2_23 = GatherV2[Taxis=DT_INT32, Tindices=DT_INT32, Tparams=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](ROI/strided_slice_48, ROI/strided_slice_49, training/SGD/gradients/roi_align_classifier/concat_grad/mod)]]

Caused by op 'ROI/GatherV2_23', defined at:
  File "/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/lib/python3.5/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py", line 16, in <module>
    app.launch_new_instance()
  File "/usr/local/lib/python3.5/dist-packages/traitlets/config/application.py", line 658, in launch_instance
    app.start()
  File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelapp.py", line 477, in start
    ioloop.IOLoop.instance().start()
  File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/ioloop.py", line 177, in start
    super(ZMQIOLoop, self).start()
  File "/usr/local/lib/python3.5/dist-packages/tornado/ioloop.py", line 888, in start
    handler_func(fd_obj, events)
  File "/usr/local/lib/python3.5/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
    self._handle_recv()
  File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
    self._run_callback(callback, msg)
  File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
    callback(*args, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell
    handler(stream, idents, msg)
  File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
    user_expressions, allow_stdin)
  File "/usr/local/lib/python3.5/dist-packages/ipykernel/ipkernel.py", line 196, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/usr/local/lib/python3.5/dist-packages/ipykernel/zmqshell.py", line 533, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2698, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2802, in run_ast_nodes
    if self.run_code(code, result):
  File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2862, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-30-6cd33d3ef12a>", line 1, in <module>
    model = modellib.MaskRCNN(mode='training', config=config, model_dir=MODEL_DIR)
  File "/usr/local/lib/python3.5/dist-packages/mask_rcnn-2.1-py3.5.egg/mrcnn/model.py", line 1845, in __init__
    self.keras_model = self.build(mode=mode, config=config)
  File "/usr/local/lib/python3.5/dist-packages/mask_rcnn-2.1-py3.5.egg/mrcnn/model.py", line 1973, in build
    config=config)([rpn_class, rpn_bbox, anchors])
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 602, in __call__
    output = self.call(inputs, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/mask_rcnn-2.1-py3.5.egg/mrcnn/model.py", line 296, in call
    names=["pre_nms_anchors"])
  File "/usr/local/lib/python3.5/dist-packages/mask_rcnn-2.1-py3.5.egg/mrcnn/utils.py", line 829, in batch_slice
    output_slice = graph_fn(*inputs_slice)
  File "/usr/local/lib/python3.5/dist-packages/mask_rcnn-2.1-py3.5.egg/mrcnn/model.py", line 294, in <lambda>
    pre_nms_anchors = utils.batch_slice([anchors, ix], lambda a, x: tf.gather(a, x),
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/array_ops.py", line 2659, in gather
    return gen_array_ops.gather_v2(params, indices, axis, name=name)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 3142, in gather_v2
    "GatherV2", params=params, indices=indices, 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 454, in new_func
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3155, in create_op
    op_def=op_def)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1717, in __init__
    self._traceback = tf_stack.extract_stack()

InvalidArgumentError (see above for traceback): indices[209] = 980 is not in [0, 960)
	 [[Node: ROI/GatherV2_23 = GatherV2[Taxis=DT_INT32, Tindices=DT_INT32, Tparams=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](ROI/strided_slice_48, ROI/strided_slice_49, training/SGD/gradients/roi_align_classifier/concat_grad/mod)]]

Lesson2: Why can't I get a good segmentation result as yours?

I have run lesson2 ipynb in Colab several times, but I couldn't get a good segmentation result though the training stage is normal(val loss 0.010, val acc 0.95). In some cases, the masks don't even exist. I don't know whether this is because the model is not good or because the dataset is very small, maybe both. I have also changed another dataset which has 566 images, however I still can't get a good mask.
Now maybe I should try other implementation of U-Net, but I still hope there are someone who can explain to me.

lesson1-xray-images-classification.ipynb : Exception Call project.prepare() first.

I understand that the notebook is a bit old. Anyway I tried it with the latest release of mdai-python-client and inspite of calling project.prepare() , it raised the error.

I didn't dive too deep into the code, but I called some private methods and found an exception which was not raised when prepare was called:-
ValueError: Unable to create UID from L_lVzGql

Error in "lesson3..." notebook when setting GPU count > 1

If I set GPU_COUNT = 2 in DetectorConfig and then run model = modellib.MaskRCNN(mode='training', config=config, model_dir=MODEL_DIR) I get the error pasted below.

This is not a big deal--running this notebook with 1 GPU is fine. I just thought to post this in case anyone else had this problem.

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
~/miniconda3/envs/kgconda/lib/python3.6/site-packages/keras/engine/network.py in __setattr__(self, name, value)
    312             try:
--> 313                 is_graph_network = self._is_graph_network
    314             except AttributeError:

~/miniconda3/envs/kgconda/lib/python3.6/site-packages/mrcnn/parallel_model.py in __getattribute__(self, attrname)
     45             return getattr(self.inner_model, attrname)
---> 46         return super(ParallelModel, self).__getattribute__(attrname)
     47 

AttributeError: 'ParallelModel' object has no attribute '_is_graph_network'

During handling of the above exception, another exception occurred:

RuntimeError                              Traceback (most recent call last)
<ipython-input-87-6f119e8b220d> in <module>()
----> 1 model = modellib.MaskRCNN(mode='training', config=config, model_dir=MODEL_DIR)

~/miniconda3/envs/kgconda/lib/python3.6/site-packages/mrcnn/model.py in __init__(self, mode, config, model_dir)
   1843         self.model_dir = model_dir
   1844         self.set_log_dir()
-> 1845         self.keras_model = self.build(mode=mode, config=config)
   1846 
   1847     def build(self, mode, config):

~/miniconda3/envs/kgconda/lib/python3.6/site-packages/mrcnn/model.py in build(self, mode, config)
   2068         if config.GPU_COUNT > 1:
   2069             from mrcnn.parallel_model import ParallelModel
-> 2070             model = ParallelModel(model, config.GPU_COUNT)
   2071 
   2072         return model

~/miniconda3/envs/kgconda/lib/python3.6/site-packages/mrcnn/parallel_model.py in __init__(self, keras_model, gpu_count)
     33         gpu_count: Number of GPUs. Must be > 1
     34         """
---> 35         self.inner_model = keras_model
     36         self.gpu_count = gpu_count
     37         merged_outputs = self.make_parallel()

~/miniconda3/envs/kgconda/lib/python3.6/site-packages/keras/engine/network.py in __setattr__(self, name, value)
    314             except AttributeError:
    315                 raise RuntimeError(
--> 316                     'It looks like you are subclassing `Model` and you '
    317                     'forgot to call `super(YourClass, self).__init__()`.'
    318                     ' Always start with this line.')

RuntimeError: It looks like you are subclassing `Model` and you forgot to call `super(YourClass, self).__init__()`. Always start with this line.

Lesson 4?

Will there be a lesson 4?

Exception: Call project.prepare() first: in lesson3-rsna-pneumonia-detection-mdai-client-lib

Hi there,
I was testing lesson3-rsna-pneumonia-detection-mdai-client-lib, and encountered an issue on this sectionof the code:

train_dataset, valid_dataset = mdai.common_utils.train_test_split(dataset)

error encountered is as follows:

`Exception Traceback (most recent call last)
in ()
----> 1 train_dataset, valid_dataset = mdai.common_utils.train_test_split(dataset)

1 frames
/usr/local/lib/python3.6/dist-packages/mdai/preprocess.py in get_image_ids(self, verbose)
296 """
297 if not self.image_ids:
--> 298 raise Exception("Call project.prepare() first.")
299
300 if verbose:

Exception: Call project.prepare() first.`

Meanwhile:
dataset = p.get_dataset_by_id('D_VolK4E') dataset.prepare()

had already been executed.

Any clues?

InvalidArgumentError

Hi
I am trying to run the following cell

import warnings
warnings.filterwarnings("ignore")
model.train(dataset_train, dataset_val,learning_rate=config.LEARNING_RATE, epochs=NUM_EPOCHS, layers='all',augmentation=augmentation)

And I am getting this error

File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1335, in _do_call
raise type(e)(node_def, op, message)

InvalidArgumentError: indices[553] = 960 is not in [0, 960)
[[Node: ROI_4/GatherV2_20 = GatherV2[Taxis=DT_INT32, Tindices=DT_INT32, Tparams=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](ROI_4/strided_slice_42, ROI_4/strided_slice_43, training_5/SGD/gradients/roi_align_classifier_4/concat_grad/mod)]]

Caused by op 'ROI_4/GatherV2_20', defined at:
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 269, in
main()
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 265, in main
kernel.start()
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 486, in start
self.io_loop.start()
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 127, in start
self.asyncio_loop.run_forever()
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\asyncio\base_events.py", line 422, in run_forever
self._run_once()
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\asyncio\base_events.py", line 1432, in _run_once
handle._run()
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\asyncio\events.py", line 145, in _run
self._callback(*self._args)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\tornado\ioloop.py", line 759, in _run_callback
ret = callback()
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\tornado\stack_context.py", line 276, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 263, in enter_eventloop
self.eventloop(self)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\ipykernel\eventloops.py", line 134, in loop_qt5
return loop_qt4(kernel)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\ipykernel\eventloops.py", line 122, in loop_qt4
_loop_qt(kernel.app)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\ipykernel\eventloops.py", line 106, in loop_qt
app.exec()
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\ipykernel\eventloops.py", line 39, in process_stream_events
kernel.do_one_iteration()
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 298, in do_one_iteration
stream.flush(zmq.POLLIN, 1)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 357, in flush
self._handle_recv()
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 480, in _handle_recv
self._run_callback(callback, msg)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 432, in _run_callback
callback(*args, **kwargs)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\tornado\stack_context.py", line 276, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2662, in run_cell
raw_cell, store_history, silent, shell_futures)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2785, in _run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2909, in run_ast_nodes
if self.run_code(code, result):
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2963, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "", line 1, in
runfile('E:/Spyder/pneumonia22.py', wdir='E:/Spyder')
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 705, in runfile
execfile(filename, namespace)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "E:/Spyder/pneumonia22.py", line 209, in
model = modellib.MaskRCNN(mode='training', config=config, model_dir=MODEL_DIR)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\mask_rcnn-2.1-py3.6.egg\mrcnn\model.py", line 1845, in init
self.keras_model = self.build(mode=mode, config=config)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\mask_rcnn-2.1-py3.6.egg\mrcnn\model.py", line 1973, in build
config=config)([rpn_class, rpn_bbox, anchors])
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\keras\engine\topology.py", line 617, in call
output = self.call(inputs, **kwargs)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\mask_rcnn-2.1-py3.6.egg\mrcnn\model.py", line 296, in call
names=["pre_nms_anchors"])
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\mask_rcnn-2.1-py3.6.egg\mrcnn\utils.py", line 829, in batch_slice
output_slice = graph_fn(*inputs_slice)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\mask_rcnn-2.1-py3.6.egg\mrcnn\model.py", line 294, in
pre_nms_anchors = utils.batch_slice([anchors, ix], lambda a, x: tf.gather(a, x),
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py", line 2666, in gather
return gen_array_ops.gather_v2(params, indices, axis, name=name)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 3760, in gather_v2
"GatherV2", params=params, indices=indices, axis=axis, name=name)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3414, in create_op
op_def=op_def)
File "C:\Users\SIDDHESHWAR\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1740, in init
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): indices[553] = 960 is not in [0, 960)
[[Node: ROI_4/GatherV2_20 = GatherV2[Taxis=DT_INT32, Tindices=DT_INT32, Tparams=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](ROI_4/strided_slice_42, ROI_4/strided_slice_43, training_5/SGD/gradients/roi_align_classifier_4/concat_grad/mod)]]

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Intro to deep learning for medical imaging: tutorial lessons

Permission denied for git clone from GCP

InvalidArgumentError

Lesson2: Why can't I get a good segmentation result as yours?

lesson1-xray-images-classification.ipynb : Exception Call project.prepare() first.

Error in "lesson3..." notebook when setting GPU count > 1

Lesson 4?

Exception: Call project.prepare() first: in lesson3-rsna-pneumonia-detection-mdai-client-lib

InvalidArgumentError

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