def get_ops(self):
mul_dtypes = (self.data.dtype, FixedPoint(16, 15))
rshift_dtype = FixedPoint(self.data.dtype.bits + 16,
self.data.dtype.frac_bits + 15)
cmp_dtypes = (self.data.dtype)
return {
Ops.MUL(mul_dtypes): self.data.size,
Ops.RSHIFT(rshift_dtype): self.data.size,
Ops.CMP(cmp_dtypes): self.data.size
}
def dnnweaver_init_bias(g, scope, *args, **kwargs):
fname = "cmstack.codegen.dnnweavergen.dnnweaver2.get_tensor"
with g.as_default():
with g.name_scope(scope):
return get_func(fname)(*args, dtype=FixedPoint(32, 22), **kwargs)
def dnnweaver_init_mean(g, scope, input_op, *args, **kwargs):
fname = "cmstack.codegen.dnnweavergen.dnnweaver2.get_tensor"
with g.as_default():
with g.name_scope(scope):
with g.name_scope(input_op):
return get_func(fname)(*args,
dtype=FixedPoint(16, 9),
**kwargs)
def yolo_convolution(tensor_in,
filters=32,
kernel_size=3,
batch_normalize=True,
act='leakyReLU',
c_dtype=None,
w_dtype=None,
s_dtype=None,
bn_dtype=None):
input_channels = tensor_in.shape[-1]
weights = get_tensor(shape=(filters, kernel_size, kernel_size,
input_channels),
name='weights',
dtype=w_dtype)
biases = get_tensor(shape=(filters),
name='biases',
dtype=FixedPoint(
32, w_dtype.frac_bits + tensor_in.dtype.frac_bits))
conv = conv2D(tensor_in, weights, biases, pad='SAME', dtype=c_dtype)
if batch_normalize:
with get_default_graph().name_scope('batch_norm'):
mean = get_tensor(shape=(filters),
name='mean',
dtype=FixedPoint(16, c_dtype.frac_bits))
scale = get_tensor(shape=(filters), name='scale', dtype=s_dtype)
bn = batch_norm(conv, mean=mean, scale=scale, dtype=bn_dtype)
else:
bn = conv
if act == 'leakyReLU':
with get_default_graph().name_scope(act):
act = leakyReLU(bn, dtype=bn.dtype)
elif act == 'linear':
with get_default_graph().name_scope(act):
act = bn
else:
raise ValueError('Unknown activation type {}'.format(act))
return act
def dnnweaver_conv2d(g, scope, *args, **kwargs):
fname = "cmstack.codegen.dnnweavergen.dnnweaver2.tensorOps.cnn.conv2D"
with g.as_default():
with g.name_scope(scope):
dtype_counters['cout'] += 1
return get_func(fname)(
*args,
dtype=FixedPoint(16, dtype_map['cout'][dtype_counters['cout'] -
1]),
**kwargs)
def get_graph_init(train=False):
g = Graph('YOLOv2-Test: 16-bit',
dataset='imagenet',
log_level=logging.INFO)
batch_size = 1
with g.as_default():
with g.name_scope('inputs'):
i = get_tensor(shape=(batch_size, 416, 416, 3),
name='data',
dtype=FQDtype.FXP16,
trainable=False)
with g.name_scope('conv0'):
conv0 = yolo_convolution(i,
filters=16,
kernel_size=3,
batch_normalize=True,
act='leakyReLU',
w_dtype=FixedPoint(16, 14),
c_dtype=FixedPoint(16, 12),
s_dtype=FixedPoint(16, 9),
bn_dtype=FixedPoint(16, 8))
with g.name_scope('pool0'):
pool0 = maxPool(conv0,
pooling_kernel=(1, 2, 2, 1),
stride=(1, 2, 2, 1),
pad='VALID')
with g.name_scope('conv1'):
conv1 = yolo_convolution(pool0,
filters=32,
kernel_size=3,
batch_normalize=True,
act='leakyReLU',
w_dtype=FixedPoint(16, 14),
c_dtype=FixedPoint(16, 8),
s_dtype=FixedPoint(16, 14),
bn_dtype=FixedPoint(16, 8))
with g.name_scope('pool1'):
pool1 = maxPool(conv1,
pooling_kernel=(1, 2, 2, 1),
stride=(1, 2, 2, 1),
pad='VALID')
with g.name_scope('conv2'):
conv2 = yolo_convolution(
pool1,
filters=64,
kernel_size=3,
batch_normalize=True,
act='leakyReLU',
# batch_normalize=False, act='linear',
w_dtype=FixedPoint(16, 14),
c_dtype=FixedPoint(16, 10),
s_dtype=FixedPoint(16, 13),
bn_dtype=FixedPoint(16, 9))
with g.name_scope('pool2'):
pool2 = maxPool(conv2,
pooling_kernel=(1, 2, 2, 1),
stride=(1, 2, 2, 1),
pad='VALID')
with g.name_scope('conv3'):
conv3 = yolo_convolution(pool2,
filters=128,
kernel_size=3,
batch_normalize=True,
act='leakyReLU',
w_dtype=FixedPoint(16, 14),
c_dtype=FixedPoint(16, 10),
s_dtype=FixedPoint(16, 13),
bn_dtype=FixedPoint(16, 10))
with g.name_scope('pool3'):
pool3 = maxPool(conv3,
pooling_kernel=(1, 2, 2, 1),
stride=(1, 2, 2, 1),
pad='VALID')
with g.name_scope('conv4'):
conv4 = yolo_convolution(pool3,
filters=256,
kernel_size=3,
batch_normalize=True,
act='leakyReLU',
w_dtype=FixedPoint(16, 14),
c_dtype=FixedPoint(16, 11),
s_dtype=FixedPoint(16, 13),
bn_dtype=FixedPoint(16, 10))
with g.name_scope('pool4'):
pool4 = maxPool(conv4,
pooling_kernel=(1, 2, 2, 1),
stride=(1, 2, 2, 1),
pad='VALID')
with g.name_scope('conv5'):
conv5 = yolo_convolution(pool4,
filters=512,
kernel_size=3,
batch_normalize=True,
act='leakyReLU',
w_dtype=FixedPoint(16, 14),
c_dtype=FixedPoint(16, 12),
s_dtype=FixedPoint(16, 13),
bn_dtype=FixedPoint(16, 11))
with g.name_scope('pool5'):
pool5 = maxPool(conv5,
pooling_kernel=(1, 2, 2, 1),
stride=(1, 1, 1, 1),
pad=((0, 0), (0, 1), (0, 1), (0, 0)))
with g.name_scope('conv6'):
conv6 = yolo_convolution(pool5,
filters=1024,
kernel_size=3,
batch_normalize=True,
act='leakyReLU',
w_dtype=FixedPoint(16, 14),
c_dtype=FixedPoint(16, 12),
s_dtype=FixedPoint(16, 11),
bn_dtype=FixedPoint(16, 9))
with g.name_scope('conv7'):
conv7 = yolo_convolution(conv6,
filters=1024,
kernel_size=3,
batch_normalize=True,
act='leakyReLU',
w_dtype=FixedPoint(16, 14),
c_dtype=FixedPoint(16, 11),
s_dtype=FixedPoint(16, 14),
bn_dtype=FixedPoint(16, 12))
with g.name_scope('conv8'):
conv8 = yolo_convolution(conv7,
filters=125,
kernel_size=1,
batch_normalize=False,
act='linear',
w_dtype=FixedPoint(16, 14),
c_dtype=FixedPoint(16, 11))
return g
def _get_output_dtype(self):
total_bits = 64
total_frac_bits = self.data.dtype.frac_bits + self.weights.dtype.frac_bits
return FixedPoint(total_bits, total_frac_bits)
def _get_output_dtype(self):
return FixedPoint(
32, self.data.dtype.frac_bits + self.scale.dtype.frac_bits)