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mindir_mode

This commit is contained in:
bai-yangfan 2020-09-23 14:32:36 +08:00
parent b993ea0288
commit 2b07d7ffb3
2 changed files with 19 additions and 33 deletions
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27
mindspore/train/quant/quant.py
View File

@ -342,6 +342,14 @@ class ExportToQuantInferNetwork:
network = self._convert_quant2deploy(network)
return network
def statistic_weight(self, weight):
out_nums = np.shape(weight)[0]
sta_metric = np.zeros((out_nums, 2), dtype=np.float32)
for num in range(out_nums):
sta_metric[num, 0] = np.min(weight[num])
sta_metric[num, 1] = np.max(weight[num])
return np.mean(sta_metric[:, 1]).tolist(), np.mean(sta_metric[:, 0]).tolist()
def _get_quant_block(self, cell_core, activation, fake_quant_a_out):
"""convet network's quant subcell to deploy subcell"""
# Calculate the scale and zero point
@ -357,14 +365,12 @@ class ExportToQuantInferNetwork:
param_dict["mean"] = self.mean
param_dict["std_dev"] = self.std_dev
param_dict["symmetric"] = fake_quant_a_out.symmetric
if self.is_mindir:
scale_w, zp_w, param_dict["filter_maxq"], param_dict["filter_minq"] = \
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"] = \
quant_utils.scale_zp_max_min_from_fake_quant_cell(fake_quant_a_out, np_type)
else:
scale_w, zp_w = quant_utils.scale_zp_from_fake_quant_cell(cell_core.fake_quant_weight, np_type)
scale_a_out, _ = quant_utils.scale_zp_from_fake_quant_cell(fake_quant_a_out, np_type)
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"] = \
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
@ -403,6 +409,8 @@ class ExportToQuantInferNetwork:
weight, bias = quant_utils.fold_batchnorm(weight, cell_core)
elif isinstance(cell_core, quant.Conv2dBnWithoutFoldQuant):
weight, bias = quant_utils.without_fold_batchnorm(weight, cell_core)
if self.is_mindir:
param_dict["filter_maxq"], param_dict["filter_minq"] = self.statistic_weight(weight)
weight_b = weight
bias_b = bias
# apply the quant
@ -467,6 +475,9 @@ class ExportToQuantInferNetwork:
elif isinstance(subcell, _AddFakeQuantAfterSubCell):
op = subcell.subcell
if op.name in ConvertToQuantNetwork.__quant_op_name__ and isinstance(op, ops.Primitive):
if self.is_mindir:
op.add_prim_attr('output_maxq', Tensor(subcell.fake_quant_act.maxq.data.asnumpy()))
op.add_prim_attr('output_minq', Tensor(subcell.fake_quant_act.minq.data.asnumpy()))
network.__delattr__(name)
network.__setattr__(name, op)
change = True

25
mindspore/train/quant/quant_utils.py
View File

@ -120,31 +120,6 @@ def weight2int(data, scale, zero_point):
return np.round((data / scale) + zero_point)
def scale_zp_from_fake_quant_cell(cell, data_type):
r"""
Get calculate quantization params for scale and zero point From `FakeQuantWithMinMax`.
Args:
cell (Cell): `mindspore.nn.layer.FakeQuantWithMinMax`
data_type (numpy type): Can ben `numpy.int8` or `numpy.uint8`.
Returns:
scale (numpy.ndarray): quantization param.
zero point (numpy.ndarray): quantization param.
"""
minq = cell.minq.data.asnumpy()
maxq = cell.maxq.data.asnumpy()
op = cell.fake_quant_infer
scale, zp = cal_quantization_params(
minq, maxq, data_type,
num_bits=op.num_bits,
symmetric=op.symmetric,
narrow_range=op.narrow_range)
return scale, zp
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`."""
minq = cell.minq.data.asnumpy()