def _add_assign_add():
scale = tf.placeholder("double", name="pok/update/scale")
typedScales = {
scale.dtype: scale,
}
group_assign = []
group_assign_add = []
group_assign_add_quantized = []
for v in tf.trainable_variables():
# Input variables
name = "pok/update/var/" + v.name.replace(':', '/')
vin = tf.placeholder(v.dtype, v.shape, name=name)
typedScale = typedScales.get(v.dtype)
if typedScale is None:
typedScale = tf.cast(scale, v.dtype)
typedScales[v.dtype] = typedScale
group_assign.append(v.assign(vin))
assign_add = v.assign_add(vin * typedScale)
group_assign_add.append(assign_add)
# Add quantize assign_add + variable export
if v.dtype.base_dtype != tf.float32:
group_assign_add_quantized.append(assign_add)
continue
# Quantized output weights
flat_name = v.name.replace(':', '/')
name = "pok/quant/out/" + flat_name
minv = tf.reduce_min(v, name=name + '/min')
maxv = tf.reduce_max(v, name=name + '/max')
quantized_dtype = tf.quint8
quantized = tf.quantize_v2(v, minv, maxv, quantized_dtype, name=name)
# Quantized input weights
name = "pok/quant/in/" + flat_name
inp = tf.placeholder(quantized_dtype, v.shape, name=name)
minp = tf.placeholder(tf.float32, name=name + '/min')
maxp = tf.placeholder(tf.float32, name=name + '/max')
dequantized = tf.dequantize(inp, minp, maxp)
assign_add = v.assign_add(dequantized * typedScale)
group_assign_add_quantized.append(assign_add)
tf.group(*group_assign, name="pok/update/assign")
tf.group(*group_assign_add, name="pok/update/assign_add")
tf.group(*group_assign_add_quantized, name="pok/update/assign_add_quant")
def model_tf(x): # return x return tf.quantize_v2(x, 0.0, 75.4, tf.quint8, name='ys')
def cvt_x(x):
return tf.quantize_v2(x, -1.0, 3.0, tf.quint8)
saver = tf.train.import_meta_graph('./LenetParam.meta')
saver.restore(sess, './LenetParam')
suffix = ["_w:0", "_b:0"]
paramDict = {}
for pr in suffix:
lay_name = [
v.name for v in tf.trainable_variables() if v.name.endswith(pr)
]
for v in lay_name:
print(v)
curLay = [a for a in tf.trainable_variables() if (a.name == v)]
curWt = curLay[0].eval()
print(curWt.dtype)
quantWt = tf.quantize_v2(curWt,
tf.reduce_min(curWt),
tf.reduce_max(curWt),
tf.qint16,
mode="MIN_FIRST",
name="quant32to16")
chk = sess.run(quantWt)
paramDict.update({v: chk})
print(list(paramDict.keys()))
#chkVal = chk[0].astype('int16')
#chkRange = [chk[1],chk[2]]
#fName = "./quant16/"+v+"32to16.npy"
#print(fName)
#with open(fName,'wb') as f:
# np.save(f,chkVal)
#fName = "./quant16/"+v+"32to16MinMax.npy"
#print(fName)
#with open(fName,'wb') as f:
# np.save(f,chkRange)
def model_tf(x):
return tf.quantize_v2(x, -1.0, 3.0, tf.quint8, name='ys')
def model_tf(x):
return tf.quantize_v2(x, 0.0, 108.4, tf.quint8, name='ys')
def model(x):
variables = {}
y = tf.quantize_v2(x, -2.0, 2.0, tf.quint8, name='ys')
return y, variables
def model_tf(x): # return x return tf.quantize_v2(x, -3.0, 9.0, tf.quint8, name='ys')
def cvt_x(x):
return tf.quantize_v2(x, -1, 3, tf.quint8)
