ref: f74bff74f6c3961eb4e3b73cd38a4aff71aff5f4
dir: /dnn/training_tf2/keraslayerdump.py/
""" helper functions for dumping some Keras layers to C files """
import numpy as np
def printVector(f, vector, name, dtype='float', dotp=False, static=True):
""" prints vector as one-dimensional C array """
if dotp:
vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8))
vector = vector.transpose((2, 0, 3, 1))
v = np.reshape(vector, (-1))
if static:
f.write('static const {} {}[{}] = {{\n '.format(dtype, name, len(v)))
else:
f.write('const {} {}[{}] = {{\n '.format(dtype, name, len(v)))
for i in range(0, len(v)):
f.write('{}'.format(v[i]))
if (i!=len(v)-1):
f.write(',')
else:
break;
if (i%8==7):
f.write("\n ")
else:
f.write(" ")
f.write('\n};\n\n')
return vector
def printSparseVector(f, A, name, have_diag=True):
N = A.shape[0]
M = A.shape[1]
W = np.zeros((0,), dtype='int')
W0 = np.zeros((0,))
if have_diag:
diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])])
A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N]))
A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N]))
A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))
printVector(f, diag, name + '_diag')
AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int')
idx = np.zeros((0,), dtype='int')
for i in range(M//8):
pos = idx.shape[0]
idx = np.append(idx, -1)
nb_nonzero = 0
for j in range(N//4):
block = A[j*4:(j+1)*4, i*8:(i+1)*8]
qblock = AQ[j*4:(j+1)*4, i*8:(i+1)*8]
if np.sum(np.abs(block)) > 1e-10:
nb_nonzero = nb_nonzero + 1
idx = np.append(idx, j*4)
vblock = qblock.transpose((1,0)).reshape((-1,))
W0 = np.concatenate([W0, block.reshape((-1,))])
W = np.concatenate([W, vblock])
idx[pos] = nb_nonzero
f.write('#ifdef DOT_PROD\n')
printVector(f, W, name, dtype='qweight')
f.write('#else /*DOT_PROD*/\n')
printVector(f, W0, name, dtype='qweight')
f.write('#endif /*DOT_PROD*/\n')
printVector(f, idx, name + '_idx', dtype='int')
return AQ
def dump_sparse_gru(self, f, hf):
name = 'sparse_' + self.name
print("printing layer " + name + " of type sparse " + self.__class__.__name__)
weights = self.get_weights()
qweights = printSparseVector(f, weights[1], name + '_recurrent_weights')
printVector(f, weights[-1], name + '_bias')
subias = weights[-1].copy()
subias[1,:] = subias[1,:] - np.sum(qweights*(1./128),axis=0)
printVector(f, subias, name + '_subias')
if hasattr(self, 'activation'):
activation = self.activation.__name__.upper()
else:
activation = 'TANH'
if hasattr(self, 'reset_after') and not self.reset_after:
reset_after = 0
else:
reset_after = 1
neurons = weights[0].shape[1]//3
max_rnn_neurons = neurons
f.write('const SparseGRULayer {} = {{\n {}_bias,\n {}_subias,\n {}_recurrent_weights_diag,\n {}_recurrent_weights,\n {}_recurrent_weights_idx,\n {}, ACTIVATION_{}, {}\n}};\n\n'
.format(name, name, name, name, name, name, weights[0].shape[1]//3, activation, reset_after))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('extern const SparseGRULayer {};\n\n'.format(name));
return max_rnn_neurons
def dump_gru_layer(self, f, hf, dotp=False, sparse=False):
name = self.name
print("printing layer " + name + " of type " + self.__class__.__name__)
weights = self.get_weights()
if sparse:
qweight = printSparseVector(f, weights[0], name + '_weights', have_diag=False)
else:
qweight = printVector(f, weights[0], name + '_weights')
if dotp:
f.write('#ifdef DOT_PROD\n')
qweight2 = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127)
printVector(f, qweight2, name + '_recurrent_weights', dotp=True, dtype='qweight')
f.write('#else /*DOT_PROD*/\n')
else:
qweight2 = weights[1]
printVector(f, weights[1], name + '_recurrent_weights')
if dotp:
f.write('#endif /*DOT_PROD*/\n')
printVector(f, weights[-1], name + '_bias')
subias = weights[-1].copy()
subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0)
subias[1,:] = subias[1,:] - np.sum(qweight2*(1./128.),axis=0)
printVector(f, subias, name + '_subias')
if hasattr(self, 'activation'):
activation = self.activation.__name__.upper()
else:
activation = 'TANH'
if hasattr(self, 'reset_after') and not self.reset_after:
reset_after = 0
else:
reset_after = 1
neurons = weights[0].shape[1]//3
max_rnn_neurons = neurons
f.write('const GRULayer {} = {{\n {}_bias,\n {}_subias,\n {}_weights,\n NULL,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}, {}\n}};\n\n'
.format(name, name, name, name, name, weights[0].shape[0], weights[0].shape[1]//3, activation, reset_after))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('extern const GRULayer {};\n\n'.format(name));
return max_rnn_neurons
def dump_dense_layer_impl(name, weights, bias, activation, f, hf):
printVector(f, weights, name + '_weights')
printVector(f, bias, name + '_bias')
f.write('const DenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, weights.shape[0], weights.shape[1], activation))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
hf.write('extern const DenseLayer {};\n\n'.format(name));
def dump_dense_layer(self, f, hf):
name = self.name
print("printing layer " + name + " of type " + self.__class__.__name__)
weights = self.get_weights()
activation = self.activation.__name__.upper()
dump_dense_layer_impl(name, weights[0], weights[1], activation, f, hf)
return False
def dump_conv1d_layer(self, f, hf):
name = self.name
print("printing layer " + name + " of type " + self.__class__.__name__)
weights = self.get_weights()
printVector(f, weights[0], name + '_weights')
printVector(f, weights[-1], name + '_bias')
activation = self.activation.__name__.upper()
max_conv_inputs = weights[0].shape[1]*weights[0].shape[0]
f.write('const Conv1DLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[2]))
hf.write('#define {}_STATE_SIZE ({}*{})\n'.format(name.upper(), weights[0].shape[1], (weights[0].shape[0]-1)))
hf.write('#define {}_DELAY {}\n'.format(name.upper(), (weights[0].shape[0]-1)//2))
hf.write('extern const Conv1DLayer {};\n\n'.format(name));
return max_conv_inputs