coremltools | Core ML tools contain supporting tools for Core ML model | Machine Learning library
kandi X-RAY | coremltools Summary
kandi X-RAY | coremltools Summary
Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
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Top functions reviewed by kandi - BETA
- Perform quantization on the nn_spec .
- Converts LSTM to LSTM
- Converts a model into Keras model .
- Convert a source to a model .
- Add a convolution .
- Converts a model to a spec model .
- Convert a sklearn model to a skk model .
- Convert LSTM to LSTM
- Re - allocates the tensor array .
- Checks whether the feature list is valid or not
coremltools Key Features
coremltools Examples and Code Snippets
Lines of Code : 4
License : No License
Copy# pip install tensorflow==1.1.0
# pip install keras==2.0.4
# pip install coremltools==0.4.0
pip install -r requirements.txt
def dense_from_coo(i, v):
rows = i[0].max()+1
cols = i[1].max()+1
out = torch.zeros(rows, cols)
out[i[0], i[1]] = v
return out
print(dense_from_coo(i,v))
>>> tensor([[0., 0., 3.],
[4., 0., 5.]])
pclass_labels = list(range(1, 43))
class_labels = [str(x) for x in class_labels] # add this line
source path_to_virtualenv/bin/activate
pip install
model.predict( {"img": img } )
input {
name: "image"
shortDescription: "Input image to be classified"
....
}
model.predict( {"image": img} )
pip install -U git+https://github.com/apple/coremltools.git
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224
del spec.description.input[0].shape[0]
import coremltools
category_output_labels = ['dress', 'shirt', 'trousers']
color_output_labels = ['black', 'blue', 'green', 'pink', 'red',
'white']
coreml_model = coremltools.converters.keras.convert('./fashion_multi_output_v02.h5',
pip install --upgrade pip
pip install –U virtualenv
cd ~
virtualenv coreml
source coreml/bin/activate
pip install -U coremltools
Community Discussions
Trending Discussions on coremltools
QUESTION
I would like to replace the torch.sparse function using the other Pytorch function.
...ANSWER
Answered 2021-May-20 at 12:48If I understand sparse_coo format correctly, the two rows of i are just the coordinates at which to copy the values of v. Which means that you can instead create you matrix like :
QUESTION
I would like to replace the torch.norm function using the other Pytorch function.
I was able to replace torch.norm in the case where x is not a matrix, as shown in the following code.
ANSWER
Answered 2021-May-09 at 15:57You could try the following:
QUESTION
I am converting my model to coreml using coremltools 4.1 and for some reason I am getting this error: TypeError: 'InputLayer' object is not iterable and on line coreml_model = coremltools.converters.keras.convert(model). can anyone what I have done wrong in my code. thanks for the help. here is my snippet.
ANSWER
Answered 2021-Apr-23 at 10:27Don't use coremltools.converters.keras.convert but coremltools.convert(...). See the docs for details: https://coremltools.readme.io/docs/introductory-quickstart
QUESTION
Please bear with me. I'm new to CoreML and machine learning. I have a CoreML model that I was able to convert from a research paper implementation that used Caffe. It's a CSRNet, the objective being crowd-counting. After much wrangling, I'm able to load the MLmodel into Python using Coremltools, pre-process an image using Pillow and predict an output. The result is a MultiArray (from a density map), which I've then processed further to derive the actual numerical prediction.
How do I add a custom layer as an output to the model that takes the current output and performs the following functionality? I've read numerous articles, and am still at a loss. (Essentially, it sums the values all the values in the MultiArray) I'd like to be able to save the model/ layer and import it into Xcode so that the MLModel result is a single numerical value, and not a MultiArray.
This is the code I'm currently using to convert the output from the model into a number (in Python):
...ANSWER
Answered 2021-Apr-01 at 10:21You can add a ReduceSumLayerParams to the end of the model. You'll need to do this in Python by hand. If you set its reduceAll parameter to true, it will compute the sum over the entire tensor.
However, in my opinion, it's just as easy to use the model as-is, and in your Swift code grab a pointer to the MLMultiArray's data and use vDSP.sum(a) to compute the sum.
QUESTION
I try to convert my model from Tensorflow to Coreml however I get below error. Isn't it possible to convert instance normalization layer to CoreML? Any workaround to overcome?
ValueError Traceback (most recent call last) in () 6 7 model = ct.convert( ----> 8 tf_keras_model )
6 frames /usr/local/lib/python3.6/dist-packages/coremltools/converters/mil/mil/block.py in remove_ops(self, existing_ops) 700 + "used by ops {}" 701 ) --> 702 raise ValueError(msg.format(op.name, i, v.name, child_op_names)) 703 # Check that the output Var isn't block's output 704 if v in self._outputs:
ValueError: Cannot delete op 'Generator/StatefulPartitionedCall/StatefulPartitionedCall/encoder_down_resblock_0/instance_norm_0/Shape' with active output at id 0: 'Generator/StatefulPartitionedCall/StatefulPartitionedCall/encoder_down_resblock_0/instance_norm_0/Shape' used by ops ['Generator/StatefulPartitionedCall/StatefulPartitionedCall/encoder_down_resblock_0/instance_norm_0/strided_slice'] SEARCH STACK OVERFLOW
...ANSWER
Answered 2021-Jan-28 at 18:12I use keras-contrib instead and it works fine. Please see issue and its solution below. It is still open for tensorflow_addons.
QUESTION
I am following this tutorial to build a simple deep learning app for traffic sign recognition. link
I made an own model, and I also tried it with the model in this repository: link
When I run the app from xcode on my iPhone, I can see the picture of the camera, but the text always says "Label", no matter what is on the screen. The only thing I modified from the tutorial is that I hardcoded the classes before converting to mlmodel:
...ANSWER
Answered 2020-Nov-17 at 10:16I don't know if this fixes it, but in your conversion script try the following:
QUESTION
I am trying to use coremltools to convert a keras model to mlmodel. I am getting the following code:
...ANSWER
Answered 2020-Nov-10 at 11:37it seems to be an issue related to a recent release of version 3.0.0 of h5yp, so either you can use a previous version of h5yp (pip install tensorflow h5py<3.0.0) or you can try to remove .decode('utf-8') from tensorflow/python/keras/saving/hdf5_format.py. I know they are not the answer you are looking for but seems to be the way out. Otherwise you can just wait for an update.
QUESTION
I have a CoreML model and I've added information to the model using coremltools:
...ANSWER
Answered 2020-Oct-28 at 10:36MyModel is not an MLModel object but a class that is specifically generated for your model. However, it does have an MLModel object inside, in the model property.
You can access the description like so:
QUESTION
I have converted a Keras model to a MLModel using coremltools 4.0 with limited success.
It works but only if I use an MLMultiArray for the output and covert to an image. Converting to an image takes magnitudes longer than inferencing; making it unusable.
If I try to change the MLModel spec to use images for output I get this error running prediction:
Failed to convert output Identity to image:
NSUnderlyingError=0x2809bad00 {Error Domain=com.apple.CoreML Code=0 "Invalid array shape ( 2048, 2048, 3 ) for converting to gray image"
Even though I have specified RGB for the output color:
output { name: "Identity" type { imageType { width: 2048 height: 2048 colorSpace: RGB } } }
If I use a MultiArray (that works) Xcode reports:
output: Float32 1 x 2048 x 2048 x 3 array
I suspect the issue is the first dimension, which is the batch number but no dimensions are shown, so I can't delete the batch dimension:
output { name: "Identity" type { multiArrayType { dataType: FLOAT32 } } }
I don't think I can just add an output shape to the Keras Conv2D output layer because it has multiple inbound nodes with different shapes. Here are the output shapes:
...ANSWER
Answered 2020-Jul-21 at 09:17In Core ML the order of the dimensions is (channels, height, width) so it expects to see a 3 x 2048 x 2048 output instead of 2048 x 2048 x 3.
Note that you also need to make sure the output pixels are in the range [0, 255] instead of [0, 1] which is probably what your Keras model gives you.
QUESTION
I am new user of coremltool, currently I am able to convert my TF model, and run it in python. However the output layer of my model is automatically converted to float instead of int32. I have to perform myself in python a rollback conversion.
...ANSWER
Answered 2020-Sep-19 at 11:47You can change the datatype of the model's outputs to INT32 using the coremltools Python library. That will automatically cast those numbers from floats to integers.
Something like this:
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Install coremltools
You can use coremltools like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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