I tried to apply INT8bit quantization before FloatingPoint32bit Matrix Multiplication, then requantize accumulated INT32bit output to INT8bit. After all, I guess there’s a couple of mix-ups somewhere in the process. I feel stuck in spotting those trouble spots. data flow [Affine Quantization]: input(fp32) -> quant(int8) ____ matmul(int32) -> requant(int8) ->deq(fp32) input(fp32) -> quant(int8) —-/ My ..
I am training a Model in Tensorflow with a variable batchsize (Input: [None, 320, 240, 3]). The problem is during post-training quantization I can not have any dynamic input, thus no "None" and with edgetpu compiler I can not have batchsizes greater than 1. My current approach is to train one more epoch with a ..
I’m working on something called Federated Learning. In this project, resource constraints are simulated, and the local model updates thus have to be compressed. This does not have to be efficient in any way, we just want to see the effects that this form of compression would have on convergence and convergence time. I have ..
I was able to successfully quantise a pytorch model for huggingface text classification with intel lpot(neural compressor) I now have the fp32 model and quantised int8 models in my machine. For inference I loaded the quantised lpot model with the below code model = AutoModelForSequenceClassification.from_pretrained(‘fp32/model/path’) from lpot.utils.pytorch import load modellpot = load("path/to/lpotmodel/", model) I am ..
I want to use a generator to quantize a LSTM model. Questions I start with the question as this is quite a long post. I actually want to know if you have manged to quantize (int8) a LSTM model with post training quantization. I tried it different TF versions but always bumped into an error. ..
I’ve tried QAT implementation in my model training script. I’m using functional API for the model creation. Steps that I followed to implement QAT API, Build the model acrhitecture Inserted the appropriate quantize_model function Train the model Let me provide you the code snippet for more clearance words_input = Input(shape=(None,),dtype=’int32′,name=’words_input’) words = Embedding(input_dim=wordEmbeddings.shape, output_dim=wordEmbeddings.shape, weights=[wordEmbeddings], ..
I’ve created a Bayesian neural network and I was trying to use Pytorch QAT, but when I check the model dimension after training and model conversion it’s still the same as before the conversion. I’ve tried to use the same code but using a standard MLP and the is an x4 reduction in the model ..
I am planning to write a color quantization algorithm technique based on ant tree algorithm/Particle Swarm Optimization. I have not done much work in image processing area. Would you recommend me some lesson or maybe GitHub for reference? Source: Python..
So I have the following code snipped which works till the last line where i enter the interpreter.invoke() input_data10 = np.expand_dims(input_text[1:1001], axis=1) interpreter.resize_tensor_input(input_details[‘index’], [1000, 1, 100]) interpreter.allocate_tensors() interpreter.set_tensor(input_details[‘index’], input_data10) interpreter.allocate_tensors() interpreter.invoke() The error I am getting is this one: ————————————————————————— RuntimeError Traceback (most recent call last) <ipython-input-51-7d35ed1dfe14> in <module> —-> 1 interpreter.invoke() /usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/interpreter.py in invoke(self) ..
Following this example of K means clustering I want to recreate the same – only I’m very keen for the final image to contain just the quantized colours (+ white background). As it is, the colour bars get smooshed together to create a pixel line of blended colours. I’ve tried : # OpenCV and Python ..