first commit

Author: anilsathyan7

README.md

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+# Portrait-Segmentation

checkpoints/deconv_model-260-0.06.hdf5

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+import numpy as np
+import keras
+import sys
+from keras.models import load_model
+
+
+
+# Load a trained model checkpoint
+model=load_model(sys.argv[1])
+
+# Load a test dataset (UINT8)
+new_xtest=np.load('data/test_xtrain.npy')
+new_ytest=np.load('data/test_ytrain.npy')
+
+# Evaluate model 
+score = model.evaluate(np.float32(new_xtest/255.0), np.float32(new_ytest/255.0), verbose=0)
+
+# Print loss and accuracy
+print('Test loss:', score[0])
+print('Test accuracy:', score[1])
+
+# Sample run: python eval.py checkpoints/up_super_model-102-0.06.hdf5

checkpoints/up_super_model-102-0.06.hdf5

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+import numpy as np
+from PIL import Image
+import tensorflow as tf
+import keras
+from keras.models import Model
+import sys
+from keras.models import load_model
+from keras.layers import Input, Flatten
+import matplotlib.pyplot as plt
+from kito import reduce_keras_model # Ensure kito is installed
+
+# Load the model (output of training - checkpoint)
+model=load_model(sys.argv[1])
+
+# Fold batch norms
+model_reduced = reduce_keras_model(model)
+model_reduced.save('bilinear_bnoptimized_munet.h5') # Use this model in PC
+
+# Flatten output and save model (Optimize for phone)
+output = model_reduced.output
+newout=Flatten()(output)
+new_model=Model(model_reduced.input,newout)
+
+new_model.save('bilinear_fin_munet.h5')
+
+
+# For Float32 Model
+converter = tf.lite.TFLiteConverter.from_keras_model_file('bilinear_fin_munet.h5')
+tflite_model = converter.convert()
+open("bilinear_fin_munet.tflite", "wb").write(tflite_model)
+
+
+#For UINT8 Quantization
+converter = tf.lite.TFLiteConverter.from_keras_model_file('bilinear_fin_munet.h5')
+#converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE] 
+converter.post_training_quantize=True
+tflite_model = converter.convert()
+open("bilinear_fin_munet_uint8.tflite", "wb").write(tflite_model)
+
+
+#For Float16 Quantization (Requires TF 1.15 or above)
+converter = tf.lite.TFLiteConverter.from_keras_model_file('bilinear_fin_munet.h5')
+converter.optimizations = [tf.lite.Optimize.DEFAULT]
+converter.target_spec.supported_types = [tf.lite.constants.FLOAT16]
+tflite_model = converter.convert()
+open("bilinear_fin_munet_fp16.tflite", "wb").write(tflite_model)
+
+
+# Sample run: python export.py checkpoints/up_super_model-102-0.06.hdf5
+