def main():
# config
binary_mode = True
epoch = 4
mini_batch = 32
training_modulation_size = 3
inference_modulation_size = 3
# load MNIST data
td = bb.LoadMnist.load()
batch_size = len(td.x_train)
print('batch_size =', batch_size)
############################
# Learning
############################
# create network
main_net = bb.Sequential.create()
main_net.add(bb.DenseAffine.create(output_shape=[1024]))
main_net.add(bb.BatchNormalization.create())
main_net.add(bb.ReLU.create())
main_net.add(bb.DenseAffine.create([512]))
main_net.add(bb.BatchNormalization.create())
main_net.add(bb.ReLU.create())
main_net.add(bb.DenseAffine.create(td.t_shape))
if binary_mode:
main_net.add(bb.BatchNormalization.create())
main_net.add(bb.ReLU.create())
# wrapping with binary modulator
net = bb.Sequential.create()
net.add(
bb.BinaryModulation.create(
main_net, training_modulation_size=training_modulation_size))
net.add(bb.Reduce.create(td.t_shape))
net.set_input_shape(td.x_shape)
# print model information
print(net.get_info())
# set binary mode
if binary_mode:
net.send_command("binary true")
else:
net.send_command("binary false")
# learning
print('\n[learning]')
loss = bb.LossSoftmaxCrossEntropy.create()
metrics = bb.MetricsCategoricalAccuracy.create()
optimizer = bb.OptimizerAdam.create()
optimizer.set_variables(net.get_parameters(), net.get_gradients())
runner = bb.Runner(net, "mnist-sparse-lut6-simple", loss, metrics,
optimizer)
runner.fitting(td, epoch_size=epoch, mini_batch_size=mini_batch)
net.add(bb.LoweringConvolution.create(cnv2_sub, 3, 3))
net.add(bb.LoweringConvolution.create(cnv3_sub, 3, 3))
net.add(bb.MaxPooling.create(2, 2))
net.add(bb.DenseAffine.create([512]))
net.add(bb.BatchNormalization.create())
net.add(bb.ReLU.create())
net.add(bb.DenseAffine.create([10]))
net.set_input_shape(td['x_shape'])
# set no binary mode
net.send_command("binary false")
# print model information
print(net.get_info())
# learning
print('\n[learning]')
loss = bb.LossSoftmaxCrossEntropy.create()
metrics = bb.MetricsCategoricalAccuracy.create()
optimizer = bb.OptimizerAdam.create()
optimizer.set_variables(net.get_parameters(), net.get_gradients())
runner = bb.Runner(net, "cifar10-fp32-dense-cnn", loss, metrics, optimizer)
runner.fitting(td,
epoch_size=epoch,
mini_batch_size=mini_batch,
file_read=file_read,
file_write=file_write)
net.add(bb.BinaryModulation.create(main_net, training_modulation_size=15))
net.add(bb.Reduce.create(td.t_shape))
net.set_input_shape(td.x_shape)
print(net.get_info())
loss = bb.LossSoftmaxCrossEntropy.create()
metrics = bb.MetricsCategoricalAccuracy.create()
optimizer = bb.OptimizerAdam.create()
optimizer.set_variables(net.get_parameters(), net.get_gradients())
batch_size = len(td.x_train)
print('batch_size =', batch_size)
runner = bb.Runner(net, "mnist-mlp-sparse-lut6", loss, metrics, optimizer)
runner.fitting(td, epoch_size=3, mini_batch_size=16)
sys.exit(0)
if False:
runner = bb.CRunner.create("mnist-mlp-sparse-lut6", net, loss, metrics,
optimizer)
runner.fitting(td, epoch_size=1, batch_size=16)
loss.clear()
metrics.clear()
x_train = td.x_train
t_train = td.t_train
def main():
# config
epoch = 4
mini_batch = 32
training_modulation_size = 3
inference_modulation_size = 3
# load data
td = bb.load_cifar10()
batch_size = len(td['x_train'])
print('batch_size =', batch_size)
############################
# Learning
############################
# create layer
layer_sl0 = bb.SparseLut6.create([1024])
layer_sl1 = bb.SparseLut6.create([480])
layer_sl2 = bb.SparseLut6.create([70])
# create network
main_net = bb.Sequential.create()
main_net.add(layer_sl0)
main_net.add(layer_sl1)
main_net.add(layer_sl2)
# wrapping with binary modulator
net = bb.Sequential.create()
net.add(bb.BinaryModulation.create(main_net, training_modulation_size=training_modulation_size))
net.add(bb.Reduce.create(td['t_shape']))
net.set_input_shape(td['x_shape'])
# set binary mode
net.send_command('binary true')
# print model information
print(net.get_info())
# learning
print('\n[learning]')
loss = bb.LossSoftmaxCrossEntropy.create()
metrics = bb.MetricsCategoricalAccuracy.create()
optimizer = bb.OptimizerAdam.create()
optimizer.set_variables(net.get_parameters(), net.get_gradients())
runner = bb.Runner(net, "cifar10-sparse-lut6-simple", loss, metrics, optimizer)
runner.fitting(td, epoch_size=epoch, mini_batch_size=mini_batch, file_read=True, file_write=True)
################################
# convert to FPGA
################################
print('\n[convert to Binary LUT]')
# LUT-network
layer_bl0 = bb.BinaryLut6.create(layer_sl0.get_output_shape())
layer_bl1 = bb.BinaryLut6.create(layer_sl1.get_output_shape())
layer_bl2 = bb.BinaryLut6.create(layer_sl2.get_output_shape())
lut_net = bb.Sequential.create()
lut_net.add(layer_bl0)
lut_net.add(layer_bl1)
lut_net.add(layer_bl2)
# evaluate network
eval_net = bb.Sequential.create()
eval_net.add(bb.BinaryModulation.create(lut_net, inference_modulation_size=inference_modulation_size))
eval_net.add(bb.Reduce.create(td['t_shape']))
# set input shape
eval_net.set_input_shape(td['x_shape'])
# parameter copy
print('parameter copy to binary LUT-Network')
layer_bl0.import_parameter(layer_sl0)
layer_bl1.import_parameter(layer_sl1)
layer_bl2.import_parameter(layer_sl2)
# evaluate network
print('evaluate LUT-Network')
lut_runner = bb.Runner(eval_net, "cifar10-binary-lut6-simple",
bb.LossSoftmaxCrossEntropy.create(),
bb.MetricsCategoricalAccuracy.create())
lut_runner.evaluation(td, mini_batch_size=mini_batch)
# write Verilog
print('write verilog file')
with open('Cifar10LutSimple.v', 'w') as f:
f.write('`timescale 1ns / 1ps\n\n')
f.write(bb.make_verilog_from_lut('Cifar10LutSimple', [layer_bl0, layer_bl1, layer_bl2]))
def main():
epoch = 1
mini_batch = 32
training_modulation_size = 1
inference_modulation_size = 1
# load MNIST data
td = bb.LoadMnist.load()
batch_size = len(td.x_train)
print('batch_size =', batch_size)
############################
# Learning
############################
# create layer
layer_cnv0_sl0 = bb.SparseLut6.create([192])
layer_cnv0_sl1 = bb.SparseLut6.create([32])
layer_cnv1_sl0 = bb.SparseLut6.create([192])
layer_cnv1_sl1 = bb.SparseLut6.create([32])
layer_cnv2_sl0 = bb.SparseLut6.create([384])
layer_cnv2_sl1 = bb.SparseLut6.create([64])
layer_cnv3_sl0 = bb.SparseLut6.create([384])
layer_cnv3_sl1 = bb.SparseLut6.create([64])
layer_sl4 = bb.SparseLut6.create([420])
layer_sl5 = bb.SparseLut6.create([70])
# main network
cnv0_sub = bb.Sequential.create()
cnv0_sub.add(layer_cnv0_sl0)
cnv0_sub.add(layer_cnv0_sl1)
cnv1_sub = bb.Sequential.create()
cnv1_sub.add(layer_cnv1_sl0)
cnv1_sub.add(layer_cnv1_sl1)
cnv2_sub = bb.Sequential.create()
cnv2_sub.add(layer_cnv2_sl0)
cnv2_sub.add(layer_cnv2_sl1)
cnv3_sub = bb.Sequential.create()
cnv3_sub.add(layer_cnv3_sl0)
cnv3_sub.add(layer_cnv3_sl1)
main_net = bb.Sequential.create()
main_net.add(bb.LoweringConvolution.create(cnv0_sub, 3, 3))
main_net.add(bb.LoweringConvolution.create(cnv1_sub, 3, 3))
main_net.add(bb.MaxPooling.create(2, 2))
main_net.add(bb.LoweringConvolution.create(cnv2_sub, 3, 3))
main_net.add(bb.LoweringConvolution.create(cnv3_sub, 3, 3))
main_net.add(bb.MaxPooling.create(2, 2))
main_net.add(layer_sl4)
main_net.add(layer_sl5)
# wrapping with binary modulator
net = bb.Sequential.create()
net.add(
bb.BinaryModulation.create(
main_net, training_modulation_size=training_modulation_size))
net.add(bb.Reduce.create(td.t_shape))
net.set_input_shape(td.x_shape)
# print model information
print(net.get_info())
# learning
print('\n[learning]')
loss = bb.LossSoftmaxCrossEntropy.create()
metrics = bb.MetricsCategoricalAccuracy.create()
optimizer = bb.OptimizerAdam.create()
optimizer.set_variables(net.get_parameters(), net.get_gradients())
runner = bb.Runner(net, "mnist-sparse-lut6-cnn", loss, metrics, optimizer)
runner.fitting(td, epoch_size=epoch, mini_batch_size=mini_batch)
################################
# convert to FPGA
################################
print('\n[convert to Binary LUT]')
# LUT-network
layer_cnv0_bl0 = bb.BinaryLut6.create(layer_cnv0_sl0.get_output_shape())
layer_cnv0_bl1 = bb.BinaryLut6.create(layer_cnv0_sl1.get_output_shape())
layer_cnv1_bl0 = bb.BinaryLut6.create(layer_cnv1_sl0.get_output_shape())
layer_cnv1_bl1 = bb.BinaryLut6.create(layer_cnv1_sl1.get_output_shape())
layer_cnv2_bl0 = bb.BinaryLut6.create(layer_cnv2_sl0.get_output_shape())
layer_cnv2_bl1 = bb.BinaryLut6.create(layer_cnv2_sl1.get_output_shape())
layer_cnv3_bl0 = bb.BinaryLut6.create(layer_cnv3_sl0.get_output_shape())
layer_cnv3_bl1 = bb.BinaryLut6.create(layer_cnv3_sl1.get_output_shape())
layer_bl4 = bb.BinaryLut6.create(layer_sl4.get_output_shape())
layer_bl5 = bb.BinaryLut6.create(layer_sl5.get_output_shape())
cnv0_sub = bb.Sequential.create()
cnv0_sub.add(layer_cnv0_bl0)
cnv0_sub.add(layer_cnv0_bl1)
cnv1_sub = bb.Sequential.create()
cnv1_sub.add(layer_cnv1_bl0)
cnv1_sub.add(layer_cnv1_bl1)
cnv2_sub = bb.Sequential.create()
cnv2_sub.add(layer_cnv2_bl0)
cnv2_sub.add(layer_cnv2_bl1)
cnv3_sub = bb.Sequential.create()
cnv3_sub.add(layer_cnv3_bl0)
cnv3_sub.add(layer_cnv3_bl1)
cnv4_sub = bb.Sequential.create()
cnv4_sub.add(layer_bl4)
cnv4_sub.add(layer_bl5)
cnv0 = bb.LoweringConvolution.create(cnv0_sub, 3, 3)
cnv1 = bb.LoweringConvolution.create(cnv1_sub, 3, 3)
pol0 = bb.MaxPooling.create(2, 2)
cnv2 = bb.LoweringConvolution.create(cnv2_sub, 3, 3)
cnv3 = bb.LoweringConvolution.create(cnv3_sub, 3, 3)
pol1 = bb.MaxPooling.create(2, 2)
cnv4 = bb.LoweringConvolution.create(cnv4_sub, 4, 4)
lut_net = bb.Sequential.create()
lut_net.add(cnv0)
lut_net.add(cnv1)
lut_net.add(pol0)
lut_net.add(cnv2)
lut_net.add(cnv3)
lut_net.add(pol1)
lut_net.add(cnv4)
# evaluate network
eval_net = bb.Sequential.create()
eval_net.add(
bb.BinaryModulation.create(
lut_net, inference_modulation_size=inference_modulation_size))
eval_net.add(bb.Reduce.create(td.t_shape))
# set input shape
eval_net.set_input_shape(td.x_shape)
# parameter copy
print('parameter copy to binary LUT-Network')
layer_cnv0_bl0.import_parameter(layer_cnv0_sl0)
layer_cnv0_bl1.import_parameter(layer_cnv0_sl1)
layer_cnv1_bl0.import_parameter(layer_cnv1_sl0)
layer_cnv1_bl1.import_parameter(layer_cnv1_sl1)
layer_cnv2_bl0.import_parameter(layer_cnv2_sl0)
layer_cnv2_bl1.import_parameter(layer_cnv2_sl1)
layer_cnv3_bl0.import_parameter(layer_cnv3_sl0)
layer_cnv3_bl1.import_parameter(layer_cnv3_sl1)
layer_bl4.import_parameter(layer_sl4)
layer_bl5.import_parameter(layer_sl5)
# evaluate network
print('evaluate LUT-Network')
lut_runner = bb.Runner(eval_net, "mnist-binary-lut6-cnn",
bb.LossSoftmaxCrossEntropy.create(),
bb.MetricsCategoricalAccuracy.create())
lut_runner.evaluation(td, mini_batch_size=mini_batch)
# write Verilog
print('write verilog file')
with open('MnistLutCnn.v', 'w') as f:
f.write('`timescale 1ns / 1ps\n\n')
f.write(
bb.get_verilog_axi4s_from_lut_cnn('MnistLutCnnCnv0',
[cnv0, cnv1, pol0]))
f.write(
bb.get_verilog_axi4s_from_lut_cnn('MnistLutCnnCnv1',
[cnv2, cnv3, pol1]))
f.write(bb.get_verilog_axi4s_from_lut_cnn('MnistLutCnnCnv2', [cnv4]))
def main():
epoch = 4
mini_batch = 32
training_modulation_size = 7
test_modulation_size = 7
# load MNIST data
td = bb.load_mnist()
# create layer
layer_sl0 = bb.SparseLut6.create([1024])
layer_sl1 = bb.SparseLut6.create([480])
layer_sl2 = bb.SparseLut6.create([70])
# create network
main_net = bb.Sequential.create()
main_net.add(layer_sl0)
main_net.add(layer_sl1)
main_net.add(layer_sl2)
# wrapping with binary modulator
net = bb.Sequential.create()
net.add(
bb.BinaryModulation.create(
main_net, training_modulation_size=training_modulation_size))
net.add(bb.Reduce.create(td['t_shape']))
net.set_input_shape(td['x_shape'])
print(net.get_info())
loss = bb.LossSoftmaxCrossEntropy.create()
metrics = bb.MetricsCategoricalAccuracy.create()
optimizer = bb.OptimizerAdam.create()
optimizer.set_variables(net.get_parameters(), net.get_gradients())
batch_size = len(td['x_train'])
print('batch_size =', batch_size)
runner = bb.Runner(net, "mnist-sparse-lut6-simple", loss, metrics,
optimizer)
runner.fitting(td, epoch_size=epoch, mini_batch_size=mini_batch)
# LUT-network
layer_bl0 = bb.BinaryLut6Bit.create(layer_sl0.get_output_shape())
layer_bl1 = bb.BinaryLut6Bit.create(layer_sl1.get_output_shape())
layer_bl2 = bb.BinaryLut6Bit.create(layer_sl2.get_output_shape())
lut_net = bb.Sequential.create()
lut_net.add(layer_bl0)
lut_net.add(layer_bl1)
lut_net.add(layer_bl2)
# evaluation network
eval_net = bb.Sequential.create()
eval_net.add(
bb.BinaryModulationBit.create(
lut_net, inference_modulation_size=test_modulation_size))
eval_net.add(bb.Reduce.create(td['t_shape']))
# set input shape
eval_net.set_input_shape(td['x_shape'])
# import table
print('parameter copy to binary LUT-Network')
layer_bl0.import_parameter(layer_sl0)
layer_bl1.import_parameter(layer_sl1)
layer_bl2.import_parameter(layer_sl2)
# evaluation
lut_runner = bb.Runner(eval_net, "mnist-binary-lut6-simple",
bb.LossSoftmaxCrossEntropy.create(),
bb.MetricsCategoricalAccuracy.create())
lut_runner.evaluation(td, mini_batch_size=mini_batch)
# Verilog 出力
with open('MnistLutSimple.v', 'w') as f:
f.write('`timescale 1ns / 1ps\n\n')
f.write(
bb.make_verilog_from_lut_bit('MnistLutSimple',
[layer_bl0, layer_bl1, layer_bl2]))
def main():
binary_mode = False
epoch = 8
mini_batch = 32
training_modulation_size = 3
inference_modulation_size = 3
# load data
td = bb.load_cifar10()
batch_size = len(td['x_train'])
print('batch_size =', batch_size)
############################
# Learning
############################
# create layer
layer_cnv0_affine = bb.DenseAffine.create([32])
layer_cnv0_batchnorm = bb.BatchNormalization.create()
layer_cnv0_actibation = bb.ReLU.create()
layer_cnv1_affine = bb.DenseAffine.create([32])
layer_cnv1_batchnorm = bb.BatchNormalization.create()
layer_cnv1_actibation = bb.ReLU.create()
layer_cnv2_affine = bb.DenseAffine.create([64])
layer_cnv2_batchnorm = bb.BatchNormalization.create()
layer_cnv2_actibation = bb.ReLU.create()
layer_cnv3_affine = bb.DenseAffine.create([64])
layer_cnv3_batchnorm = bb.BatchNormalization.create()
layer_cnv3_actibation = bb.ReLU.create()
layer_cnv3_affine = bb.DenseAffine.create([512])
layer_cnv3_batchnorm = bb.BatchNormalization.create()
layer_cnv3_actibation = bb.ReLU.create()
# main network
cnv0_sub = bb.Sequential.create()
cnv0_sub.add(layer_cnv0_affine)
cnv0_sub.add(layer_cnv0_batchnorm)
cnv0_sub.add(layer_cnv0_actibation)
cnv1_sub = bb.Sequential.create()
cnv1_sub.add(layer_cnv1_affine)
cnv1_sub.add(layer_cnv1_batchnorm)
cnv1_sub.add(layer_cnv1_actibation)
cnv2_sub = bb.Sequential.create()
cnv2_sub.add(layer_cnv2_affine)
cnv2_sub.add(layer_cnv2_batchnorm)
cnv2_sub.add(layer_cnv2_actibation)
cnv3_sub = bb.Sequential.create()
cnv3_sub.add(layer_cnv3_affine)
cnv3_sub.add(layer_cnv3_batchnorm)
cnv3_sub.add(layer_cnv3_actibation)
main_net = bb.Sequential.create()
main_net.add(bb.LoweringConvolution.create(cnv0_sub, 3, 3))
main_net.add(bb.LoweringConvolution.create(cnv1_sub, 3, 3))
main_net.add(bb.MaxPooling.create(2, 2))
main_net.add(bb.LoweringConvolution.create(cnv2_sub, 3, 3))
main_net.add(bb.LoweringConvolution.create(cnv3_sub, 3, 3))
main_net.add(bb.MaxPooling.create(2, 2))
main_net.add(bb.DenseAffine.create([512]))
main_net.add(bb.BatchNormalization.create())
main_net.add(bb.ReLU.create())
main_net.add(bb.DenseAffine.create([10]))
if binary_mode:
main_net.add(bb.ReLU.create())
# wrapping with binary modulator
net = bb.Sequential.create()
net.add(
bb.BinaryModulation.create(
main_net, training_modulation_size=training_modulation_size))
net.add(bb.Reduce.create(td['t_shape']))
net.set_input_shape(td['x_shape'])
# set no binary mode
if binary_mode:
net.send_command("binary true")
else:
net.send_command("binary false")
# print model information
print(net.get_info())
# learning
print('\n[learning]')
loss = bb.LossSoftmaxCrossEntropy.create()
metrics = bb.MetricsCategoricalAccuracy.create()
optimizer = bb.OptimizerAdam.create()
optimizer.set_variables(net.get_parameters(), net.get_gradients())
runner = bb.Runner(net, "cifar10-dense-cnn", loss, metrics, optimizer)
runner.fitting(td,
epoch_size=epoch,
mini_batch_size=mini_batch,
file_read=True,
file_write=True)
def main():
epoch = 4
mini_batch = 32
training_modulation_size = 3
inference_modulation_size = 3
# load data
td = bb.load_cifar10()
batch_size = len(td['x_train'])
print('batch_size =', batch_size)
############################
# Learning
############################
# create layer
layer_cnv0_sl0 = bb.SparseLut6Bit.create([192])
layer_cnv0_sl1 = bb.SparseLut6Bit.create([32])
layer_cnv1_sl0 = bb.SparseLut6Bit.create([1152])
layer_cnv1_sl1 = bb.SparseLut6Bit.create([192])
layer_cnv1_sl2 = bb.SparseLut6Bit.create([32])
layer_cnv2_sl0 = bb.SparseLut6Bit.create([2304])
layer_cnv2_sl1 = bb.SparseLut6Bit.create([384])
layer_cnv2_sl2 = bb.SparseLut6Bit.create([64])
layer_cnv3_sl0 = bb.SparseLut6Bit.create([2384])
layer_cnv3_sl1 = bb.SparseLut6Bit.create([384])
layer_cnv3_sl2 = bb.SparseLut6Bit.create([64])
layer_sl4 = bb.SparseLut6Bit.create([18432])
layer_sl5 = bb.SparseLut6Bit.create([3072])
layer_sl6 = bb.SparseLut6Bit.create([512])
layer_sl7 = bb.SparseLut6Bit.create([2160])
layer_sl8 = bb.SparseLut6Bit.create([360])
layer_sl9 = bb.SparseLut6Bit.create([60])
layer_sl10 = bb.SparseLut6Bit.create([10])
# main network
cnv0_sub = bb.Sequential.create()
cnv0_sub.add(layer_cnv0_sl0)
cnv0_sub.add(layer_cnv0_sl1)
cnv1_sub = bb.Sequential.create()
cnv1_sub.add(layer_cnv1_sl0)
cnv1_sub.add(layer_cnv1_sl1)
cnv1_sub.add(layer_cnv1_sl2)
cnv2_sub = bb.Sequential.create()
cnv2_sub.add(layer_cnv2_sl0)
cnv2_sub.add(layer_cnv2_sl1)
cnv2_sub.add(layer_cnv2_sl2)
cnv3_sub = bb.Sequential.create()
cnv3_sub.add(layer_cnv3_sl0)
cnv3_sub.add(layer_cnv3_sl1)
cnv3_sub.add(layer_cnv3_sl2)
main_net = bb.Sequential.create()
main_net.add(bb.LoweringConvolutionBit.create(cnv0_sub, 3, 3))
main_net.add(bb.LoweringConvolutionBit.create(cnv1_sub, 3, 3))
main_net.add(bb.MaxPoolingBit.create(2, 2))
main_net.add(bb.LoweringConvolutionBit.create(cnv2_sub, 3, 3))
main_net.add(bb.LoweringConvolutionBit.create(cnv3_sub, 3, 3))
main_net.add(bb.MaxPoolingBit.create(2, 2))
main_net.add(layer_sl4)
main_net.add(layer_sl5)
main_net.add(layer_sl6)
main_net.add(layer_sl7)
main_net.add(layer_sl8)
main_net.add(layer_sl9)
main_net.add(layer_sl10)
# wrapping with binary modulator
net = bb.Sequential.create()
net.add(
bb.BinaryModulationBit.create(
main_net, training_modulation_size=training_modulation_size))
net.add(bb.Reduce.create(td['t_shape']))
net.set_input_shape(td['x_shape'])
# set binary mode
net.send_command("binary true")
# print model information
print(net.get_info())
# learning
print('\n[learning]')
loss = bb.LossSoftmaxCrossEntropy.create()
metrics = bb.MetricsCategoricalAccuracy.create()
optimizer = bb.OptimizerAdam.create()
optimizer.set_variables(net.get_parameters(), net.get_gradients())
runner = bb.Runner(net, "cifar10-sparse-lut6-cnn", loss, metrics,
optimizer)
runner.fitting(td,
epoch_size=epoch,
mini_batch_size=mini_batch,
file_read=True,
file_write=True)
net.add(bb.StochasticLut6.create([10 * 6 * 6]))
net.add(bb.StochasticBatchNormalization.create())
net.add(bb.StochasticLut6.create([10 * 6]))
net.add(bb.StochasticBatchNormalization.create())
net.add(bb.StochasticLut6.create([10]))
net.set_input_shape(td['x_shape'])
# set no binary mode
net.send_command("binary false")
net.send_command("lut_binarize false")
# print model information
print(net.get_info())
# learning
print('\n[learning]')
loss = bb.LossSoftmaxCrossEntropy.create()
metrics = bb.MetricsCategoricalAccuracy.create()
optimizer = bb.OptimizerAdam.create()
optimizer.set_variables(net.get_parameters(), net.get_gradients())
runner = bb.Runner(net, "cifar10-fp32-sparse-lut-cnn", loss, metrics,
optimizer)
runner.fitting(td,
epoch_size=epoch,
mini_batch_size=mini_batch,
file_read=file_read,
file_write=file_write)
def main():
epoch = 8
mini_batch = 32
training_modulation_size = 7
test_modulation_size = 7
# load MNIST data
td = bb.load_mnist()
# set teaching signnal
td['t_shape'] = td['x_shape']
td['t_train'] = td['x_train']
td['t_test'] = td['x_test']
# create layer
layer_enc_sl0 = bb.SparseLut6.create([32 * 6 * 6 * 6])
layer_enc_sl1 = bb.SparseLut6.create([32 * 6 * 6])
layer_enc_sl2 = bb.SparseLut6.create([32 * 6])
layer_enc_sl3 = bb.SparseLut6.create([32])
layer_dec_sl2 = bb.SparseLut6.create([28 * 28 * 6 * 6])
layer_dec_sl1 = bb.SparseLut6.create([28 * 28 * 6])
layer_dec_sl0 = bb.SparseLut6.create([28 * 28], False) # diable BatchNorm
# create network
main_net = bb.Sequential.create()
main_net.add(layer_enc_sl0)
main_net.add(layer_enc_sl1)
main_net.add(layer_enc_sl2)
main_net.add(layer_enc_sl3)
main_net.add(layer_dec_sl2)
main_net.add(layer_dec_sl1)
main_net.add(layer_dec_sl0)
# wrapping with binary modulator
net = bb.Sequential.create()
net.add(
bb.BinaryModulation.create(
main_net, training_modulation_size=training_modulation_size))
net.set_input_shape(td['x_shape'])
print(net.get_info())
loss = bb.LossMeanSquaredError.create()
metrics = bb.MetricsMeanSquaredError.create()
optimizer = bb.OptimizerAdam.create()
optimizer.set_variables(net.get_parameters(), net.get_gradients())
batch_size = len(td['x_train'])
print('batch_size =', batch_size)
# runner = bb.Runner(net, "mnist-autoencoder-sparse-lut6-simple", loss, metrics, optimizer)
# runner.fitting(td, epoch_size=epoch, mini_batch_size=mini_batch)
result_img = None
x_train = td['x_train']
t_train = td['t_train']
x_test = td['x_test']
t_test = td['t_test']
x_buf = bb.FrameBuffer()
t_buf = bb.FrameBuffer()
for epoch_num in range(epoch):
# train
for index in tqdm(range(0, batch_size, mini_batch)):
mini_batch_size = min(mini_batch, batch_size - index)
x_buf.resize(mini_batch_size, td['x_shape'], bb.TYPE_FP32)
x_buf.set_data(x_train[index:index + mini_batch_size])
y_buf = net.forward(x_buf)
t_buf.resize(mini_batch_size, td['t_shape'], bb.TYPE_FP32)
t_buf.set_data(t_train[index:index + mini_batch_size])
dy_buf = loss.calculate_loss(y_buf, t_buf, mini_batch_size)
metrics.calculate_metrics(y_buf, t_buf)
dx_buf = net.backward(dy_buf)
optimizer.update()
cv2.waitKey(1)
print('loss =', loss.get_loss())
print('metrics =', metrics.get_metrics())
# test
x_buf.resize(16, td['x_shape'], bb.TYPE_FP32)
x_buf.set_data(x_test[0:16])
y_buf = net.forward(x_buf)
if result_img is None:
x_img = make_image_block(x_buf.get_data())
cv2.imwrite('mnist-autoencoder-sparse-lut6-simple_x.png',
x_img * 255)
result_img = x_img
y_img = make_image_block(y_buf.get_data())
cv2.imwrite('mnist-autoencoder-sparse-lut6-simple_%d.png' % epoch_num,
y_img * 255)
result_img = np.vstack((result_img, y_img))
cv2.imshow('result_img', result_img)
cv2.waitKey(1)
cv2.destroyAllWindows()
cv2.imwrite("mnist-autoencoder-sparse-lut6-simple.png", result_img * 255)
# LUT-network
layer_enc_bl0 = bb.BinaryLut6Bit.create(layer_enc_sl0.get_output_shape())
layer_enc_bl1 = bb.BinaryLut6Bit.create(layer_enc_sl1.get_output_shape())
layer_enc_bl2 = bb.BinaryLut6Bit.create(layer_enc_sl2.get_output_shape())
layer_enc_bl3 = bb.BinaryLut6Bit.create(layer_enc_sl3.get_output_shape())
layer_dec_bl2 = bb.BinaryLut6Bit.create(layer_dec_sl2.get_output_shape())
layer_dec_bl1 = bb.BinaryLut6Bit.create(layer_dec_sl1.get_output_shape())
layer_dec_bl0 = bb.BinaryLut6Bit.create(layer_dec_sl0.get_output_shape())
lut_net = bb.Sequential.create()
lut_net.add(layer_enc_bl0)
lut_net.add(layer_enc_bl1)
lut_net.add(layer_enc_bl2)
lut_net.add(layer_enc_bl3)
lut_net.add(layer_dec_bl2)
lut_net.add(layer_dec_bl1)
lut_net.add(layer_dec_bl0)
# evaluation network
eval_net = bb.Sequential.create()
eval_net.add(
bb.BinaryModulationBit.create(
lut_net, inference_modulation_size=test_modulation_size))
eval_net.add(bb.Reduce.create(td['t_shape']))
# set input shape
eval_net.set_input_shape(td['x_shape'])
# import table
print('parameter copy to binary LUT-Network')
layer_enc_bl0.import_parameter(layer_enc_sl0)
layer_enc_bl1.import_parameter(layer_enc_sl1)
layer_enc_bl2.import_parameter(layer_enc_sl2)
layer_enc_bl3.import_parameter(layer_enc_sl3)
layer_dec_bl2.import_parameter(layer_dec_sl2)
layer_dec_bl1.import_parameter(layer_dec_sl1)
layer_dec_bl0.import_parameter(layer_dec_sl0)
# evaluation
lut_runner = bb.Runner(eval_net, "mnist-autoencpder-binary-lut6-simple",
bb.LossMeanSquaredError.create(),
bb.MetricsMeanSquaredError.create())
lut_runner.evaluation(td, mini_batch_size=mini_batch)
# Verilog 出力
with open('MnistAeLutSimple.v', 'w') as f:
f.write('`timescale 1ns / 1ps\n\n')
f.write(
bb.make_verilog_from_lut_bit('MnistAeLutSimple', [
layer_enc_bl0, layer_enc_bl1, layer_enc_bl2, layer_dec_bl2,
layer_dec_bl1, layer_dec_bl0
]))