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!2943 [quant]export bug fix

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Merge pull request !2943 from vlne-v1/quant_export_bugfix
pull/2943/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent d925c52bb1 b522368161
commit d3ec05d716
8 changed files with 47 additions and 272 deletions
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3
mindspore/ccsrc/pipeline/pipeline.cc
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@ -328,6 +328,9 @@ std::map<std::string, std::pair<PrimitivePyPtr, std::string>> ExecutorPy::FetchI
x = cnode->input(1);
count += 1;
}
if (x->isa<Parameter>()) {
fake_quant_table[weight_name] = std::make_pair(nullptr, "input");
}
// get the fakequant parameter minq's name
if (!is_quant_cnode(x)) {
continue;

4
mindspore/nn/layer/quant.py
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@ -1169,9 +1169,9 @@ class QuantBlock(Cell):
return x
def extend_repr(self):
str_info = f'quant={self.quant}, core_op={type(self.core_op)}'
str_info = f'quant={self.quant}, core_op={type(self.core_op)}, weight=shape[{self.weight.shape}]'
if self.has_bias:
str_info = str_info + f', bias={self.bias}'
str_info = str_info + f', bias=shape[{self.bias.shape}]'
if self.has_act:
str_info = str_info + f', activation={self.activation}'
str_info = str_info + f', dequant={self.dequant}'

22
mindspore/ops/primitive.py
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@ -237,12 +237,14 @@ class PrimitiveWithInfer(Primitive):
"""
Infer output shape based on input shape.
Args:
inputs (tuple(int)): dimensions of input tensors.
outputs (tuple(int)): dimensions of output tensors.
Note:
The shape of scalar is an empty tuple.
Args:
args (tuple(int)): shapes of input tensors.
Return:
`tuple(int)`, shapes of output tensors.
"""
return None
@ -251,8 +253,10 @@ class PrimitiveWithInfer(Primitive):
Infer output dtype based on input dtype.
Args:
inputs (mstype): data type of inputs.
outputs (mstype): data type of outputs.
args (:class:`mindspore.dtype`): data type of inputs.
Return:
:class:`mindspore.dtype`, data type of outputs.
"""
return None
@ -261,8 +265,10 @@ class PrimitiveWithInfer(Primitive):
Infer output value based on input value at compile time.
Args:
inputs (any): value of inputs.
outputs (any): value of outputs.
args (Any): value of inputs.
Return:
Value of outputs. Return `None` for, cat not infer the value at compile time.
"""
return None

3
mindspore/train/quant/quant.py
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@ -318,6 +318,9 @@ class ExportToQuantInferNetwork:
info = self.quant_info_table.get(w_minq_name, None)
if info:
fack_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:
maxq = self.all_parameters[minq_name[:-4] + "maxq"]
minq = self.all_parameters[minq_name]
scale_a_in, zp_a_in = quant_utils.scale_zp_from_data(fack_quant_a_in_op, maxq, minq, np_type)

3
mindspore/train/quant/quant_utils.py
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@ -104,7 +104,8 @@ def weight2int(data, scale, zero_point):
raise ValueError("`scale` and `zero_point` should have the same shape.")
if scale.shape[0] < 0:
raise ValueError("`scale` and `zero_point` shape should greater than zero.")
if len(scale.shape) > 1:
# for perchannel
if scale.shape[0] == data.shape[0]:
# `Conv2d` or `Dense` op weight
shape_list = [-1] + [1] * len(data.shape[1:])

115
tests/ut/python/train/quant/mobilenetv2.py
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@ -1,115 +0,0 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""MobileNetV2"""
from mindspore import nn
from mindspore.ops import operations as P
def make_divisible(input_x, div_by=8):
return int((input_x + div_by) // div_by)
def _conv_bn(in_channel,
out_channel,
ksize,
stride=1):
"""Get a conv2d batchnorm and relu layer."""
return nn.SequentialCell(
[nn.Conv2d(in_channel,
out_channel,
kernel_size=ksize,
stride=stride),
nn.BatchNorm2d(out_channel)])
class InvertedResidual(nn.Cell):
def __init__(self, inp, oup, stride, expend_ratio):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
hidden_dim = int(inp * expend_ratio)
self.use_res_connect = self.stride == 1 and inp == oup
if expend_ratio == 1:
self.conv = nn.SequentialCell([
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, group=hidden_dim),
nn.BatchNorm2d(hidden_dim),
nn.ReLU6(),
nn.Conv2d(hidden_dim, oup, 1, 1),
nn.BatchNorm2d(oup)
])
else:
self.conv = nn.SequentialCell([
nn.Conv2d(inp, hidden_dim, 1, 1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU6(),
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, group=hidden_dim),
nn.BatchNorm2d(hidden_dim),
nn.ReLU6(),
nn.Conv2d(hidden_dim, oup, 1, 1),
nn.BatchNorm2d(oup)
])
def construct(self, input_x):
out = self.conv(input_x)
if self.use_res_connect:
out = input_x + out
return out
class MobileNetV2(nn.Cell):
def __init__(self, num_class=1000, input_size=224, width_mul=1.):
super(MobileNetV2, self).__init__()
_ = input_size
block = InvertedResidual
input_channel = 32
last_channel = 1280
inverted_residual_setting = [
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
[6, 160, 3, 2],
[6, 230, 1, 1],
]
if width_mul > 1.0:
last_channel = make_divisible(last_channel * width_mul)
self.last_channel = last_channel
features = [_conv_bn(3, input_channel, 3, 2)]
for t, c, n, s in inverted_residual_setting:
out_channel = make_divisible(c * width_mul) if t > 1 else c
for i in range(n):
if i == 0:
features.append(block(input_channel, out_channel, s, t))
else:
features.append(block(input_channel, out_channel, 1, t))
input_channel = out_channel
features.append(_conv_bn(input_channel, self.last_channel, 1))
self.features = nn.SequentialCell(features)
self.mean = P.ReduceMean(keep_dims=False)
self.classifier = nn.Dense(self.last_channel, num_class)
def construct(self, input_x):
out = input_x
out = self.features(out)
out = self.mean(out, (2, 3))
out = self.classifier(out)
return out

122
tests/ut/python/train/quant/mobilenetv2_combined.py
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@ -1,122 +0,0 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""mobile net v2"""
from mindspore import nn
from mindspore.ops import operations as P
def make_divisible(input_x, div_by=8):
return int((input_x + div_by) // div_by)
def _conv_bn(in_channel,
out_channel,
ksize,
stride=1):
"""Get a conv2d batchnorm and relu layer."""
return nn.SequentialCell(
[nn.Conv2dBnAct(in_channel,
out_channel,
kernel_size=ksize,
stride=stride,
has_bn=True)])
class InvertedResidual(nn.Cell):
def __init__(self, inp, oup, stride, expend_ratio):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
hidden_dim = int(inp * expend_ratio)
self.use_res_connect = self.stride == 1 and inp == oup
if expend_ratio == 1:
self.conv = nn.SequentialCell([
nn.Conv2dBnAct(hidden_dim,
hidden_dim,
3,
stride,
group=hidden_dim,
has_bn=True,
activation='relu6'),
nn.Conv2dBnAct(hidden_dim, oup, 1, 1,
has_bn=True)
])
else:
self.conv = nn.SequentialCell([
nn.Conv2dBnAct(inp, hidden_dim, 1, 1,
has_bn=True,
activation='relu6'),
nn.Conv2dBnAct(hidden_dim,
hidden_dim,
3,
stride,
group=hidden_dim,
has_bn=True,
activation='relu6'),
nn.Conv2dBnAct(hidden_dim, oup, 1, 1,
has_bn=True)
])
self.add = P.TensorAdd()
def construct(self, input_x):
out = self.conv(input_x)
if self.use_res_connect:
out = self.add(input_x, out)
return out
class MobileNetV2(nn.Cell):
def __init__(self, num_class=1000, input_size=224, width_mul=1.):
super(MobileNetV2, self).__init__()
_ = input_size
block = InvertedResidual
input_channel = 32
last_channel = 1280
inverted_residual_setting = [
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
[6, 160, 3, 2],
[6, 230, 1, 1],
]
if width_mul > 1.0:
last_channel = make_divisible(last_channel * width_mul)
self.last_channel = last_channel
features = [_conv_bn(3, input_channel, 3, 2)]
for t, c, n, s in inverted_residual_setting:
out_channel = make_divisible(c * width_mul) if t > 1 else c
for i in range(n):
if i == 0:
features.append(block(input_channel, out_channel, s, t))
else:
features.append(block(input_channel, out_channel, 1, t))
input_channel = out_channel
features.append(_conv_bn(input_channel, self.last_channel, 1))
self.features = nn.SequentialCell(features)
self.mean = P.ReduceMean(keep_dims=False)
self.classifier = nn.DenseBnAct(self.last_channel, num_class)
def construct(self, input_x):
out = input_x
out = self.features(out)
out = self.mean(out, (2, 3))
out = self.classifier(out)
return out

17
tests/ut/python/train/quant/test_quant.py
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@ -20,7 +20,7 @@ import mindspore.context as context
from mindspore import Tensor
from mindspore import nn
from mindspore.train.quant import quant as qat
from mobilenetv2_combined import MobileNetV2
from model_zoo.mobilenetv2_quant.src.mobilenetV2 import mobilenetV2
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
@ -42,7 +42,7 @@ class LeNet5(nn.Cell):
def __init__(self, num_class=10):
super(LeNet5, self).__init__()
self.num_class = num_class
self.conv1 = nn.Conv2dBnAct(1, 6, kernel_size=5, has_bn=True, activation='relu6', pad_mode="valid")
self.conv1 = nn.Conv2dBnAct(1, 6, kernel_size=5, has_bn=True, activation='relu', pad_mode="valid")
self.conv2 = nn.Conv2dBnAct(6, 16, kernel_size=5, activation='relu', pad_mode="valid")
self.fc1 = nn.DenseBnAct(16 * 5 * 5, 120, activation='relu')
self.fc2 = nn.DenseBnAct(120, 84, activation='relu')
@ -67,20 +67,19 @@ def test_qat_lenet():
img = Tensor(np.ones((32, 1, 32, 32)).astype(np.float32))
net = LeNet5()
net = qat.convert_quant_network(
net, freeze_bn=10000, num_bits=8)
net, bn_fold=True, per_channel=[True, False], symmetric=[True, False])
# should load the checkpoint. mock here
for param in net.get_parameters():
param.init_data()
qat.export_geir(net, img, file_name="quant.pb")
qat.export(net, img, file_name="quant.pb")
@pytest.mark.skip(reason="no `te.lang.cce` in ut env")
def test_qat_mobile():
net = MobileNetV2()
network = mobilenetV2(num_classes=1000)
img = Tensor(np.ones((1, 3, 224, 224)).astype(np.float32))
net = qat.convert_quant_network(
net, quant_delay=0, bn_fold=True, freeze_bn=10000, num_bits=8)
network = qat.convert_quant_network(network, bn_fold=True, per_channel=[True, False], symmetric=[True, False])
# should load the checkpoint. mock here
for param in net.get_parameters():
for param in network.get_parameters():
param.init_data()
qat.export_geir(net, img, file_name="quant.pb")
qat.export(network, img, file_name="quant.pb")

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