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Author: MarcSteven

quantization.py

@@ -0,0 +1,38 @@
+import sys
+import coremltools
+from coremltools.models.neural_network.quantization_utils import *
+
+
+def quantize(file,bits,functions):
+
+
+    
+    """
+    processes a file to quantize it for each bit_per-weight
+    and function listed.
+    def quantize(file, bits, functions):
+    
+    Processes a file to quantize it for each bit-per-weight 
+    and function listed.
+    file : Core ML file to process (example : mymodel.mlmodel)
+    bits : Array of bit per weight (example : [16,8,6,4,2,1])
+    functions : Array of distribution functions (example : ["linear", "linear_lut", "kmeans"])
+    """
+    if not file.endswith(".mlmodel"): return # We only consider .mlmodel files
+    model_name = file.split(".")[0]
+    model = coremltools.models.MLModel(file)
+    for function in functions :
+        for bit in bits:
+            print("--------------------------------------------------------------------")
+            print("Processing "+model_name+" for "+str(bit)+"-bits with "+function+" function")
+            sys.stdout.flush()
+            quantized_model = quantize_weights(model, bit, function)
+            sys.stdout.flush()
+            quantized_model.author = "Alexis Creuzot" 
+            quantized_model.short_description = str(bit)+"-bit per quantized weight, using "+function+"."
+            quantized_model.save(model_name+"_"+function+"_"+str(bit)+".mlmodel")
+
+# Launch quantization
+quantize("CNNEmotions.mlmodel", 
+        [6,4,3,2], 
+        ["linear"])