quantization

问题 Here is the example I am testing on MNIST dataset for quantization. I am testing my model using below code: import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data from tensorflow.python.framework import graph_util from tensorflow.core.framework import graph_pb2 import numpy as np def test_model(model_file,x_in): with tf.Session() as sess: with open(model_file, "rb") as f: output_graph_def = graph_pb2.GraphDef() output_graph_def.ParseFromString(f.read()) _ = tf

问题 Using tflite and getting properties of interpreter like : print(interpreter.get_input_details()) [{'name': 'input_1_1', 'index': 47, 'shape': array([ 1, 128, 128, 3], dtype=int32), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.003921568859368563, 0)}] What does 'quantization': (0.003921568859368563, 0) mean? 回答1: It means quantization parameters values: scale and zero_point of input tensor. This is necessary to convert a quantized uint8 number q to floating point number f using formula:

问题 TensorFlow website claims that Quantization provides up to 3x lower latency on mobile devices: https://www.tensorflow.org/lite/performance/post_training_quantization I tried to verify this claim, and found that Quantized models are 45%-75% SLOWER than Float models in spite of being almost 4 times smaller in size. Needless to say, this is very disappointing and conflicts with Google's claims. My test uses Google's official MnasNet model: https://storage.googleapis.com/mnasnet/checkpoints

问题 TensorFlow website claims that Quantization provides up to 3x lower latency on mobile devices: https://www.tensorflow.org/lite/performance/post_training_quantization I tried to verify this claim, and found that Quantized models are 45%-75% SLOWER than Float models in spite of being almost 4 times smaller in size. Needless to say, this is very disappointing and conflicts with Google's claims. My test uses Google's official MnasNet model: https://storage.googleapis.com/mnasnet/checkpoints

问题 So I'm working on a way to extract dominant colors as perceived by humans from an image. As an example, here's a photo: https://500px.com/photo/63897015/looking-out-for-her-kittens-by-daniel-paulsson Most humans would think the 'dominant' color is that piercing azure of the eyes. Using standard quantization, however, that blue disappears completely when you drop below 16 colors or so. The eyes only take up 0.2% of the canvas, so going for the average doesn't work at all. Project Details : I'm

问题 I am using tensorflow 1.1 version. I want to quantize inception_resnet_v2 model. The quantization method using bazel build tensorflow/tools/quantization/tools:quantize_graph bazel-bin/tensorflow/tools/quantization/tools/quantize_graph \ --input=/tmp/classify_image_graph_def.pb \ --output_node_names="softmax" --output=/tmp/quantized_graph.pb \ --mode=eightbit this doesn't give accurate results. For inception_v3 the results are okay but for inception_resnet_v2 it doesn't work (0% accuracy for

问题 I am using tensorflow 1.1 version. I want to quantize inception_resnet_v2 model. The quantization method using bazel build tensorflow/tools/quantization/tools:quantize_graph bazel-bin/tensorflow/tools/quantization/tools/quantize_graph \ --input=/tmp/classify_image_graph_def.pb \ --output_node_names="softmax" --output=/tmp/quantized_graph.pb \ --mode=eightbit this doesn't give accurate results. For inception_v3 the results are okay but for inception_resnet_v2 it doesn't work (0% accuracy for

问题 Following the idea of @PhiLho's answer to How to convert a BufferedImage to 8 bit?, I want to use ColorQuantizerDescriptor to convert a BufferedImage , imageType TYPE_INT_RGB, but RenderedOp#getColorModel() is throwing the following exception: java.lang.IllegalArgumentException: The specified ColorModel is incompatible with the image SampleModel. at javax.media.jai.PlanarImage.setImageLayout(PlanarImage.java:541) at javax.media.jai.RenderedOp.createRendering(RenderedOp.java:878) at javax

问题 I have float 32 numbers (let's say positive numbers) in numpy format. I want to convert them to fixed point numbers with predefined number of bits to reduce precision. For example, number 3.1415926 becomes 3.25 in matlab by using function num2fixpt. The command is num2fixpt(3.1415926,sfix(5),2^(1 + 2-5), 'Nearest','on') which says 3 bits for integer part, 2 bits for fractional part. Can I do the same thing using Python 回答1: You can do it if you understand how IEEE floating point notation

问题 I'm developing a custom thin-client server that serves rendered webpages to its clients. Server is running on multicore Linux box, with Webkit providing the html rendering engine. The only problem is the fact that clients display is limited with a 4bit (16 colors) grayscale palette. I'm currently using LibGraphicsMagick to dither images (RGB->4bit grayscale), which is an apparent bottleneck in the server performance. Profiling shows that more than 70% of time is spent running GraphicsMagick