I'm running Keras with CNTK backend.
I'm using Python 3.5 GPU-1bit-SGD on Windows 10.
Followed the instruction as given here to install and test the set-up. And everything works!
However, when I run my own code, I get this error:
numBins = y_pred.get_shape().as_list()[1]
File "C:\Users\abisw\AppData\Local\Continuum\Anaconda3\envs\cntkkeraspy35\lib\site-packages\cntk\ops\functions.py", line 480, in __getattr__
return getattr(outputs[0], name)
File "C:\Users\abisw\AppData\Local\Continuum\Anaconda3\envs\cntkkeraspy35\lib\site-packages\cntk\cntk_py.py", line 1125, in <lambda>
__getattr__ = lambda self, name: _swig_getattr(self, Variable, name)
File "C:\Users\abisw\AppData\Local\Continuum\Anaconda3\envs\cntkkeraspy35\lib\site-packages\cntk\cntk_py.py", line 83, in _swig_getattr
raise AttributeError("'%s' object has no attribute '%s'" % (class_type.__name__, name))
AttributeError: 'Variable' object has no attribute 'get_shape'
Any idea what's wrong?
you are mixing keras code with tensorflow code, get_shape is a tensorflow grammar, not keras grammar. If you want the variable shape, you should write:
from keras import K
K.int_shape(y_pred)
Related
Goal: instantiate unet_learner() using weights.
weights is a str that I bring in from a user-defined .yaml file; hence eval().
file_path and training are classes that hold parameters.
Code:
import numpy as np
from fastai.vision.all import *
def train(dls, file_path, training):
labels = np.loadtxt(file_path.labels, dtype=str)
weights = torch.tensor(eval(training.weights))
print('#################')
print(weights)
print(type(weights))
print('#################')
learner = unet_learner(dls, training.architecture,loss_func=CrossEntropyLossFlat(
axis=1,
weight=weights)
)
return learner.load(file_path.weights)
Placing torch.tensor() around weights again in the parameter line doesn't help. Same error.
Traceback:
(venv) me#ubuntu-pcs:~/PycharmProjects/project$ python pdl1_lung_train/main.py
/home/me/miniconda3/envs/venv/lib/python3.7/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /opt/conda/conda-bld/pytorch_1607370156314/work/c10/cuda/CUDAFunctions.cpp:100.)
return torch._C._cuda_getDeviceCount() > 0
#################
tensor([0.4000, 0.9000])
<class 'torch.Tensor'>
#################
Traceback (most recent call last):
File "pdl1_lung_train/main.py", line 27, in <module>
main(ROOT)
File "pdl1_lung_train/main.py", line 19, in main
learner = train(dls, file_path, training)
File "/home/me/PycharmProjects/project/pdl1_lung_train/train.py", line 16, in train
weight=weights))
File "/home/me/miniconda3/envs/venv/lib/python3.7/site-packages/fastai/vision/learner.py", line 267, in unet_learner
model = create_unet_model(arch, n_out, img_size, pretrained=pretrained, **kwargs)
File "/home/me/miniconda3/envs/venv/lib/python3.7/site-packages/fastai/vision/learner.py", line 243, in create_unet_model
model = arch(pretrained)
TypeError: 'str' object is not callable
Please let me know if I need to add other info. to post.
I might be wrong but I think your training.architecture is a string. But according to unet_learner documentation it has to be callable.
I am saving the MLFlow model using databricks. Below are the details:
artifact_path: model
databricks_runtime: 8.4.x-gpu-ml-scala2.12
flavors:
python_function:
data: data
env: conda.yaml
loader_module: mlflow.pytorch
pickle_module_name: mlflow.pytorch.pickle_module
python_version: 3.8.8
pytorch:
model_data: data
pytorch_version: 1.9.0+cu102
I am not able to locally load the model using Python 3.7, whereas it works well with Python 3.9.
Any idea what could be the possible resolution to this?
Error Trace:
File "/Users/danishm/opt/miniconda3/envs/py37/lib/python3.7/site-packages/mlflow/pytorch/__init__.py", line 714, in load_model
return _load_model(path=torch_model_artifacts_path, **kwargs)
File "/Users/danishm/opt/miniconda3/envs/py37/lib/python3.7/site-packages/mlflow/pytorch/__init__.py", line 626, in _load_model
return torch.load(model_path, **kwargs)
File "/Users/danishm/opt/miniconda3/envs/py37/lib/python3.7/site-packages/torch/serialization.py", line 607, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "/Users/danishm/opt/miniconda3/envs/py37/lib/python3.7/site-packages/torch/serialization.py", line 882, in _load
result = unpickler.load()
TypeError: code() takes at most 15 arguments (16 given)
I have tried specifying the pickle module name explicitly as mlflow.pytorch.load_model(ML_MODEL_PATH,pickle_module=mlflow.pytorch.pickle_module)
But still getting the same error.
I recently read a paper about UNet++，and I want to implement this structure with tensorflow-2.0 and keras customized model. As the structure is so complicated, I decided to manage the keras layers by a dictionary. Everything went well in training, but an error occurred while saving the model. Here is a minimum code to show the error:
class DicModel(tf.keras.Model):
def __init__(self):
super(DicModel, self).__init__(name='SequenceEECNN')
self.c = {}
self.c[0] = tf.keras.Sequential([
tf.keras.layers.Conv2D(32, 3,activation='relu',padding='same'),
tf.keras.layers.BatchNormalization()]
)
self.c[1] = tf.keras.layers.Conv2D(3,3,activation='softmax',padding='same')
def call(self,images):
x = self.c[0](images)
x = self.c[1](x)
return x
X_train,y_train = load_data()
X_test,y_test = load_data()
class_weight.compute_class_weight('balanced',np.ravel(np.unique(y_train)),np.ravel(y_train))
model = DicModel()
model_name = 'test'
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir='logs/'+model_name+'/')
early_stop_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss',patience=100,mode='min')
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
loss=tf.keras.losses.sparse_categorical_crossentropy,
metrics=['accuracy'])
results = model.fit(X_train,y_train,batch_size=4,epochs=5,validation_data=(X_test,y_test),
callbacks=[tensorboard_callback,early_stop_callback],
class_weight=[0.2,2.0,100.0])
model.save_weights('model/'+model_name,save_format='tf')
The error information is:
Traceback (most recent call last):
File "/media/xrzhang/Data/ZHS/Research/CNN-TF2/learn_tf2/test_model.py", line 61, in \<module>
model.save_weights('model/'+model_name,save_format='tf')
File "/media/xrzhang/Data/ZHS/Research/CNN-TF2/venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py", line 1328, in save_weights
self.\_trackable_saver.save(filepath, session=session)
File "/media/xrzhang/Data/ZHS/Research/CNN-TF2/venv/lib/python3.6/site-packages/tensorflow/python/training/tracking/util.py", line 1106, in save
file_prefix=file_prefix_tensor, object_graph_tensor=object_graph_tensor)
File "/media/xrzhang/Data/ZHS/Research/CNN-TF2/venv/lib/python3.6/site-packages/tensorflow/python/training/tracking/util.py", line 1046, in \_save_cached_when_graph_building
object_graph_tensor=object_graph_tensor)
File "/media/xrzhang/Data/ZHS/Research/CNN-TF2/venv/lib/python3.6/site-packages/tensorflow/python/training/tracking/util.py", line 1014, in \_gather_saveables
feed_additions) = self.\_graph_view.serialize_object_graph()
File "/media/xrzhang/Data/ZHS/Research/CNN-TF2/venv/lib/python3.6/site-packages/tensorflow/python/training/tracking/graph_view.py", line 379, in serialize_object_graph
trackable_objects, path_to_root = self.\_breadth_first_traversal()
File "/media/xrzhang/Data/ZHS/Research/CNN-TF2/venv/lib/python3.6/site-packages/tensorflow/python/training/tracking/graph_view.py", line 199, in \_breadth_first_traversal
for name, dependency in self.list_dependencies(current_trackable):
File "/media/xrzhang/Data/ZHS/Research/CNN-TF2/venv/lib/python3.6/site-packages/tensorflow/python/training/tracking/graph_view.py", line 159, in list_dependencies
return obj.\_checkpoint_dependencies
File "/media/xrzhang/Data/ZHS/Research/CNN-TF2/venv/lib/python3.6/site-packages/tensorflow/python/training/tracking/data_structures.py", line 690, in \_\_getattribute\_\_
return object.\_\_getattribute\_\_(self, name)
File "/media/xrzhang/Data/ZHS/Research/CNN-TF2/venv/lib/python3.6/site-packages/tensorflow/python/training/tracking/data_structures.py", line 732, in \_checkpoint_dependencies
"ignored." % (self,))
ValueError: Unable to save the object {0: \<tensorflow.python.keras.engine.sequential.Sequential object at 0x7fb5c6c36588>, 1: \<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fb5c6c36630>} (a dictionary wrapper constructed automatically on attribute assignment). The wrapped dictionary contains a non-string key which maps to a trackable object or mutable data structure.
If you don't need this dictionary checkpointed, wrap it in a tf.contrib.checkpoint.NoDependency object; it will be automatically un-wrapped and subsequently ignored.
The tf.contrib.checkpoint.NoDependency seems has been removed from Tensorflow-2.0 (https://medium.com/tensorflow/whats-coming-in-tensorflow-2-0-d3663832e9b8). How can I fix this issue? Or should I just give up using dictionary in customized Keras Model. Thank you for your time and helps!
Use string keys. For some reason tensorflow doesn't like int keys.
The exception message was incorrect in Tensorflow 2.0 and has been fixed in 2.2
You can avoid the problem by wrapping the c attribute like this
from tensorflow.python.training.tracking.data_structures import NoDependency
self.c = NoDependency({})
For more details check this issue.
I'm trying to convert this t7 model to pytorch or to caffe or to caffe2 or to any other model..
this is what I get when converting to pytorch with the code from:
https://github.com/clcarwin/convert_torch_to_pytorch
has anyone had this error or know what to do with it?
roy#roy-Lenovo:~/convert_torch_to_pytorch$ python convert_torch.py -m model_a.t7
Traceback (most recent call last):
File "convert_torch.py", line 314, in <module>
torch_to_pytorch(args.model,args.output)
File "convert_torch.py", line 262, in torch_to_pytorch
slist = lua_recursive_source(lnn.Sequential().add(model))
File "/home/roy/.local/lib/python2.7/site-packages/torch/legacy/nn/Sequential.py", line 15, in add
self.output = module.output
File "/home/roy/.local/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 99, in __getattr__
return self._obj.get(k)
AttributeError: 'list' object has no attribute 'get'
I have seen problems similar to mine here on Stack Overflow, but not exactly the same. I can reshape when using fully-connected NN layers, but not with Conv1D layers. Here's a minimal example. I'm using TF 1.4.0 on Python 3.6.3.
import tensorflow as tf
# fully connected
fc = tf.placeholder(tf.float32, [None,12])
fc = tf.contrib.layers.fully_connected(fc, 12)
fc = tf.contrib.layers.fully_connected(fc, 6)
fc = tf.reshape(fc, [-1,3,2])
# convolutional
con = tf.placeholder(tf.float32, [None,50,4])
con = tf.layers.Conv1D(con, 12, 3, activation=tf.nn.relu)
con = tf.layers.Conv1D(con, 6, 3, activation=tf.nn.relu)
con = tf.reshape(con, [-1,50,3,2])
Here is the output (yes, I'm aware of the RuntimeWarning. The messages I have found which discuss it suggest that it's harmless, but if you know otherwise, please share!):
/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
return f(*args, **kwds)
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_util.py", line 468, in make_tensor_proto
str_values = [compat.as_bytes(x) for x in proto_values]
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_util.py", line 468, in <listcomp>
str_values = [compat.as_bytes(x) for x in proto_values]
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/compat.py", line 65, in as_bytes
(bytes_or_text,))
TypeError: Expected binary or unicode string, got <tensorflow.python.layers.convolutional.Conv1D object at 0x7fa67e0d1a20>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "minimal reshape example.py", line 16, in <module>
con = tf.reshape(con, [-1,width,3,2])
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 3938, in reshape
"Reshape", tensor=tensor, shape=shape, name=name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py", line 513, in _apply_op_helper
raise err
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py", line 510, in _apply_op_helper
preferred_dtype=default_dtype)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 926, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py", line 229, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py", line 208, in constant
value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_util.py", line 472, in make_tensor_proto
"supported type." % (type(values), values))
TypeError: Failed to convert object of type <class 'tensorflow.python.layers.convolutional.Conv1D'> to Tensor. Contents: <tensorflow.python.layers.convolutional.Conv1D object at 0x7fa67e0d1a20>. Consider casting elements to a supported type.
My code fails at con = tf.reshape(con, [-1,50,3,2]). Yet the pattern is nearly identical to the pattern that I use for the fully-connected graph, fc.
I made nets very similar to these work in the higher-level API for TensorFlow called TFLearn. However, TFLearn's DNN Estimator object is having trouble managing a tf.Session correctly. After over a month, I have yet to resolve the issue with TFLearn's developers on GitHub.
I don't mind using TensorFlow's native Estimator, but I have to solve this reshape problem to achieve it.
Well, I found the error: tf.layers.Conv1D != tf.layers.conv1d. Changing the former to the latter eliminated the error. Let the TensorFlow / Python user beware!
Even though TensorFlow seems to avoid Python's object model (which is probably necessary, given the possibility of distributed, low-level computation), there are in fact a few genuine classes in the Python API. The class constructors can accept many (all?) of the same arguments as the similarly-named convenience functions.