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quant evaluation export bugfix

This commit is contained in:
yuchaojie 2020-09-18 11:41:16 +08:00
parent b7183ded66
commit 07fc1eb455
6 changed files with 42 additions and 32 deletions
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7
mindspore/nn/layer/quant.py
View File

@ -1364,10 +1364,6 @@ class QuantBlock(Cell):
self.has_bias = bias is not None
self.activation = activation
self.has_act = activation is not None
if isinstance(activation, ReLU):
self.activation = None
self.has_act = False
self.dequant.add_prim_attr("relu_flag", True)
self.bias_add = P.BiasAdd()
def construct(self, x):
@ -1376,9 +1372,10 @@ class QuantBlock(Cell):
x = self.core_op(x, self.weight, self.bias)
else:
x = self.core_op(x, self.weight)
x = self.dequant(x, self.dequant_scale)
x = F.cast(x, mstype.float32)
if self.has_act:
x = self.activation(x)
x = self.dequant(x, self.dequant_scale)
return x
def extend_repr(self):

16
mindspore/train/quant/quant.py
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@ -368,12 +368,12 @@ class ExportToQuantInferNetwork:
scale_w, zp_w, _, _ = \
quant_utils.scale_zp_max_min_from_fake_quant_cell(cell_core.fake_quant_weight, np_type)
scale_a_out, _, param_dict["output_maxq"], param_dict["output_minq"] = \
_, _, param_dict["output_maxq"], param_dict["output_minq"] = \
quant_utils.scale_zp_max_min_from_fake_quant_cell(fake_quant_a_out, np_type)
info = self.quant_info_table.get(w_minq_name, None)
if info:
fack_quant_a_in_op, minq_name = info
fake_quant_a_in_op, minq_name = info
if minq_name == 'input':
scale_a_in, zp_a_in = self.input_scale, self.input_zero_point
else:
@ -381,17 +381,17 @@ class ExportToQuantInferNetwork:
minq = self.all_parameters[minq_name]
if self.is_mindir:
scale_a_in, zp_a_in, param_dict["input_maxq"], param_dict["input_minq"] = \
quant_utils.scale_zp_max_min_from_data(fack_quant_a_in_op, minq, maxq, np_type)
quant_utils.scale_zp_max_min_from_data(fake_quant_a_in_op, minq, maxq, np_type)
else:
scale_a_in, zp_a_in = quant_utils.scale_zp_from_data(fack_quant_a_in_op, minq, maxq, np_type)
scale_a_in, zp_a_in = quant_utils.scale_zp_from_data(fake_quant_a_in_op, minq, maxq, np_type)
else:
logger.warning(f"Do not find `fake_quant` from input with `fake_quant.minq` {w_minq_name}")
logger.warning(f"Can not find `fake_quant` from input with `fake_quant.minq` {w_minq_name}")
return None
# Build the `Quant` `Dequant` op.
# Quant only support perlayer version. Need check here.
quant_op = inner.Quant(1 / float(scale_a_in), float(zp_a_in))
scale_deq = scale_a_out * scale_w
scale_deq = scale_a_in * scale_w
dequant_op = inner.Dequant()
if isinstance(activation, _AddFakeQuantAfterSubCell):
@ -414,7 +414,9 @@ class ExportToQuantInferNetwork:
weight_b = weight
bias_b = bias
# apply the quant
weight = quant_utils.weight2int(weight, scale_w, zp_w)
fake_quant_weight_op = cell_core.fake_quant_weight.fake_quant_infer
weight = quant_utils.weight2int(weight, scale_w, zp_w, np_type, fake_quant_weight_op.num_bits,
fake_quant_weight_op.narrow_range)
if bias is not None:
bias = Tensor(bias / scale_a_in / scale_w, mstype.int32)

44
mindspore/train/quant/quant_utils.py
View File

@ -29,7 +29,7 @@ def cal_quantization_params(input_min,
Args:
input_min (numpy.ndarray): The dimension of channel or 1.
input_max (numpy.ndarray): The dimension of channel or 1.
data_type (numpy type) : Can ben numpy int8, numpy uint8.
data_type (numpy type) : Can be numpy int8, numpy uint8.
num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
symmetric (bool): Whether the quantization algorithm is symmetric or not. Default: False.
narrow_range (bool): Whether the quantization algorithm uses narrow range or not. Default: False.
@ -52,10 +52,12 @@ def cal_quantization_params(input_min,
if data_type == np.int8:
quant_min = 0 - 2 ** (num_bits - 1)
quant_max = 2 ** (num_bits - 1)
else:
quant_max = 2 ** (num_bits - 1) - 1
elif data_type == np.uint8:
quant_min = 0
quant_max = 2 ** num_bits - 1
else:
raise ValueError("Unsupported datatype({})".format(data_type))
if narrow_range:
quant_min = quant_min + 1
@ -69,22 +71,13 @@ def cal_quantization_params(input_min,
if symmetric:
zp = np.zeros(input_min.shape)
else:
zp_from_min = quant_min - input_min / scale
zp_from_max = quant_max - input_max / scale
zp_from_min_error = np.abs(quant_min) + np.abs(input_min / scale)
zp_from_max_error = np.abs(quant_max) + np.abs(input_max / scale)
zp_double = zp_from_min if zp_from_min_error < zp_from_max_error else zp_from_max
if zp_double < quant_min:
zp = quant_min
elif zp_double > quant_max:
zp = quant_max
else:
zp = np.floor(zp_double + 0.5)
zp_double = quant_min - input_min / scale
zp = np.floor(zp_double + 0.5)
return scale, zp
def weight2int(data, scale, zero_point):
def weight2int(data, scale, zero_point, data_type, num_bits=8, narrow_range=False):
r"""
Calculate int8/uint8 weight from fp32. the formula is defined as:
@ -95,6 +88,9 @@ def weight2int(data, scale, zero_point):
data (numpy.ndarray): The dimension of channel or 1. Should be NCHW.
scale (numpy.ndarray): The dimension of channel or 1.
zero_point (numpy.ndarray): The dimension of channel or 1.
data_type (numpy type) : Can be numpy int8, numpy uint8.
num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
narrow_range (bool): Whether the quantization algorithm uses narrow range or not. Default: False.
Returns:
weight (numpy.ndarray): The dimension of channel or 1.
@ -118,7 +114,21 @@ def weight2int(data, scale, zero_point):
else:
raise ValueError("Unsupported weight shape({})".format(data.shape))
return np.round((data / scale) + zero_point)
if data_type == np.int8:
quant_min = 0 - 2 ** (num_bits - 1)
quant_max = 2 ** (num_bits - 1) - 1
elif data_type == np.uint8:
quant_min = 0
quant_max = 2 ** num_bits - 1
else:
raise ValueError("Unsupported weight datatype({})".format(data_type))
if narrow_range:
quant_min = quant_min + 1
weight_int = np.round((data / scale) + zero_point)
weight_int[weight_int > quant_max] = quant_max
weight_int[weight_int < quant_min] = quant_min
return weight_int
def scale_zp_max_min_from_fake_quant_cell(cell, data_type):
"""Get calculate quantization params for scale, zero point, max and min from `FakeQuantWithMinMax`."""
@ -145,7 +155,7 @@ def scale_zp_from_data(op, minq, maxq, data_type):
`mindspore.ops.operation.FakeQuantPerChannel`
minq (Parameter): Parameter `minq` of `mindspore.nn.layer.FakeQuantWithMinMax`
maxq (Parameter): Parameter `maxq` of `mindspore.nn.layer.FakeQuantWithMinMax`
data_type (numpy type): Can ben `numpy.int8` or `numpy.uint8`.
data_type (numpy type): Can be `numpy.int8` or `numpy.uint8`.
Returns:
scale (numpy.ndarray): quantization param.

2
model_zoo/official/cv/lenet_quant/eval_quant.py
View File

@ -48,7 +48,7 @@ if __name__ == "__main__":
network = LeNet5Fusion(cfg.num_classes)
# convert fusion network to quantization aware network
network = quant.convert_quant_network(network, quant_delay=0, bn_fold=False, freeze_bn=10000,
per_channel=[True, False])
per_channel=[True, False], symmetric=[True, False])
# define loss
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")

3
model_zoo/official/cv/lenet_quant/export.py
View File

@ -44,7 +44,8 @@ if __name__ == "__main__":
# define fusion network
network = LeNet5Fusion(cfg.num_classes)
# convert fusion network to quantization aware network
network = quant.convert_quant_network(network, quant_delay=0, bn_fold=False, freeze_bn=10000)
network = quant.convert_quant_network(network, quant_delay=0, bn_fold=False, freeze_bn=10000,
per_channel=[True, False], symmetric=[True, False])
# load quantization aware network checkpoint
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)

2
model_zoo/official/cv/lenet_quant/train_quant.py
View File

@ -60,7 +60,7 @@ if __name__ == "__main__":
# convert fusion network to quantization aware network
network = quant.convert_quant_network(network, quant_delay=900, bn_fold=False, per_channel=[True, False],
symmetric=[False, False])
symmetric=[True, False])
# define network loss
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")