def quant_model(float_model,quant_model,batchsize,tfrec_dir,evaluate):
'''
Quantize the floating-point model
Save to HDF5 file
'''
# make folder for saving quantized model
head_tail = os.path.split(quant_model)
os.makedirs(head_tail[0], exist_ok = True)
# load the floating point trained model
float_model = load_model(float_model)
# get input dimensions of the floating-point model
height = float_model.input_shape[1]
width = float_model.input_shape[2]
# make TFRecord dataset and image processing pipeline
quant_dataset = input_fn_quant(tfrec_dir, batchsize, height, width)
# run quantization
quantizer = vitis_quantize.VitisQuantizer(float_model)
quantized_model = quantizer.quantize_model(calib_dataset=quant_dataset)
# saved quantized model
quantized_model.save(quant_model)
print('Saved quantized model to',quant_model)
if (evaluate):
'''
Evaluate quantized model
'''
print('\n'+DIVIDER)
print ('Evaluating quantized model..')
print(DIVIDER+'\n')
test_dataset = input_fn_test(tfrec_dir, batchsize, height, width)
quantized_model.compile(optimizer=Adam(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
scores = quantized_model.evaluate(test_dataset,
verbose=0)
print('Quantized model accuracy: {0:.4f}'.format(scores[1]*100),'%')
print('\n'+DIVIDER)
return
yield num
step_num = step_number()
# model to be evaluated
eval_model = mobilenetv2(input_shape=(input_height, input_width, input_chan),
classes=2,
alpha=1.0,
incl_softmax=False)
eval_model.compile(loss=SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# test dataset
test_dataset = input_fn_test(tfrec_dir, batchsize)
# eval function
def evaluate(checkpoint_path=''):
eval_model.load_weights(checkpoint_path)
scores = eval_model.evaluate(test_dataset)
eval_metric_ops = {'accuracy': scores[1]}
print('*** Accuracy after', next(step_num), 'steps:', scores[-1], '***')
return eval_metric_ops
if __name__ == '__main__':
path = './tf_ckpt/tf_float.ckpt'
evaluate(path)
def train(input_height,input_width,input_chan,tfrec_dir,batchsize,learnrate,epochs,chkpt_dir,tboard):
def step_decay(epoch):
"""
Learning rate scheduler used by callback
Reduces learning rate depending on number of epochs
"""
lr = learnrate
if epoch > 200:
lr /= 100
elif epoch > 10:
lr /= 10
return lr
'''
Define the model
'''
model = customcnn(input_shape=(input_height, input_width, input_chan),classes=2,filters=[8,16,32,64,128])
print('\n'+DIVIDER)
print(' Model Summary')
print(DIVIDER)
print(model.summary())
print("Model Inputs: {ips}".format(ips=(model.inputs)))
print("Model Outputs: {ops}".format(ops=(model.outputs)))
'''
tf.data pipelines
'''
# train and test folders
train_dataset = input_fn_trn(tfrec_dir,batchsize,input_height,input_width)
test_dataset = input_fn_test(tfrec_dir,batchsize,input_height,input_width)
'''
Call backs
'''
tb_call = TensorBoard(log_dir=tboard)
chkpt_call = ModelCheckpoint(filepath=os.path.join(chkpt_dir,'f_model.h5'),
monitor='val_accuracy',
verbose=1,
save_best_only=True)
lr_scheduler_call = LearningRateScheduler(schedule=step_decay,
verbose=1)
callbacks_list = [tb_call, chkpt_call, lr_scheduler_call]
'''
Compile model
Adam optimizer to change weights & biases
Loss function is categorical crossentropy
'''
model.compile(optimizer=Adam(learning_rate=learnrate),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
'''
Training
'''
print('\n'+DIVIDER)
print(' Training model with training set..')
print(DIVIDER)
# make folder for saving trained model checkpoint
os.makedirs(chkpt_dir, exist_ok = True)
# run training
train_history=model.fit(train_dataset,
epochs=epochs,
steps_per_epoch=17500//batchsize,
validation_data=test_dataset,
validation_steps=None,
callbacks=callbacks_list,
verbose=1)
print("\nTensorBoard can be opened with the command: tensorboard --logdir={dir} --host localhost --port 6006".format(dir=tboard))
return
def train(input_ckpt,output_ckpt,tfrec_dir,tboard_dir,input_height,input_width, \
input_chan,batchsize,epochs,learnrate,target_acc):
'''
tf.data pipelines
'''
# train and test folders
train_dataset = input_fn_trn(tfrec_dir, batchsize)
test_dataset = input_fn_test(tfrec_dir, batchsize)
'''
Call backs
'''
tb_call = TensorBoard(log_dir=tboard_dir)
chkpt_call = ModelCheckpoint(filepath=output_ckpt,
monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=True)
early_stop_call = EarlyStoponAcc(target_acc)
callbacks_list = [tb_call, chkpt_call, early_stop_call]
# if required, tf.set_pruning_mode must be set before defining the model
if (input_ckpt != ''):
tf.set_pruning_mode()
'''
Define the model
'''
model = mobilenetv2(input_shape=(input_height, input_width, input_chan),
classes=2,
alpha=1.0,
incl_softmax=False)
'''
Compile model
Adam optimizer to change weights & biases
Loss function is categorical crossentropy
'''
model.compile(optimizer=tf.train.AdamOptimizer(learning_rate=learnrate),
loss=SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
'''
If an input checkpoint is specified then assume we are fine-tuning a pruned model,
so load the weights into the model, otherwise we are training from scratch
'''
if (input_ckpt != ''):
print('Loading checkpoint - fine-tuning from', input_ckpt)
model.load_weights(input_ckpt)
else:
print('Training from scratch..')
print('\n' + DIVIDER)
print(' Model Summary')
print(DIVIDER)
print(model.summary())
print("Model Inputs: {ips}".format(ips=(model.inputs)))
print("Model Outputs: {ops}".format(ops=(model.outputs)))
'''
Training
'''
print('\n' + DIVIDER)
print(' Training model with training set..')
print(DIVIDER)
# make folder for saving trained model checkpoint
os.makedirs(os.path.dirname(output_ckpt), exist_ok=True)
# run training
train_history = model.fit(train_dataset,
epochs=epochs,
steps_per_epoch=20000 // batchsize,
validation_data=test_dataset,
validation_steps=5000 // batchsize,
callbacks=callbacks_list,
verbose=1)
'''
save just the model architecture (no weights) to a JSON file
'''
with open(os.path.join(os.path.dirname(output_ckpt), 'baseline_arch.json'),
'w') as f:
f.write(model.to_json())
print(
"\nTensorBoard can be opened with the command: tensorboard --logdir={dir} --host localhost --port 6006"
.format(dir=tboard_dir))
return