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bug fix in auto create quant graph in master

This commit is contained in:
chenzomi 2020-07-03 10:42:34 +08:00
parent cf6dd99ed7
commit 9b7a426c6b
9 changed files with 98 additions and 34 deletions
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4
mindspore/nn/layer/quant.py
View File

@ -1193,9 +1193,9 @@ class QuantBlock(Cell):
self.dequant = dequant_op
self.dequant_scale = dequant_scale
self.bias = bias
self.has_bias = bias is None
self.has_bias = bias is not None
self.activation = activation
self.has_act = activation is None
self.has_act = activation is not None
self.bias_add = P.BiasAdd()
def construct(self, x):

2
mindspore/train/callback/_loss_monitor.py
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@ -86,7 +86,7 @@ class LossMonitor(Callback):
if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0:
print("Epoch: [{:3d}/{:3d}], step: [{:5d}/{:5d}], "
"loss: [{:5.4f}], avg los: [{:5.4f}], time: [{:5.4f}]".format(
"loss: [{:5.4f}], avg los: [{:5.4f}], time: [{:5.4f}ms]".format(
cb_params.cur_epoch_num, cb_params.epoch_num,
cur_step_in_epoch, int(cb_params.batch_num),
step_loss, np.mean(self.losses),

43
mindspore/train/quant/quant.py
View File

@ -33,7 +33,6 @@ from ...ops.operations import _inner_ops as inner
from ...train import serialization
from . import quant_utils
_ACTIVATION_MAP = {nn.ReLU: quant.ReLUQuant,
nn.ReLU6: quant.ReLU6Quant,
nn.HSigmoid: quant.HSigmoidQuant,
@ -178,7 +177,6 @@ class ConvertToQuantNetwork:
dilation=conv_inner.dilation,
group=conv_inner.group,
eps=bn_inner.eps,
momentum=1 - bn_inner.momentum,
quant_delay=self.weight_qdelay,
freeze_bn=self.freeze_bn,
per_channel=self.weight_channel,
@ -268,16 +266,16 @@ class ConvertToQuantNetwork:
narrow_range=self.act_range)
class ExportQuantNetworkDeploy:
class ExportToQuantInferNetwork:
"""
Convert quantization aware network to deploy network.
Convert quantization aware network to infer network.
Args:
network (Cell): MindSpore network produced by `convert_quant_network`.
inputs (Tensor): Inputs of the `network`.
network (Cell): MindSpore network API `convert_quant_network`.
inputs (Tensor): Input tensors of the `quantization aware training network`.
Returns:
Cell, converted network.
Cell, GEIR backend Infer network.
"""
__quant_op_name__ = ["TensorAdd", "Sub", "Mul", "RealDiv"]
@ -287,7 +285,7 @@ class ExportQuantNetworkDeploy:
network = validator.check_isinstance('network', network, (nn.Cell,))
self.data_type = mstype.int8
self.network = copy.deepcopy(network)
self.all_paramters = {p.name: p for p in self.network.get_parameters()}
self.all_parameters = {p.name: p for p in self.network.get_parameters()}
self.get_inputs_table(inputs)
def get_inputs_table(self, inputs):
@ -315,8 +313,8 @@ class ExportQuantNetworkDeploy:
info = self.quant_info_table.get(w_minq_name, None)
if info:
fack_quant_a_in_op, minq_name = info
maxq = self.all_paramters[minq_name[:-4] + "maxq"]
minq = self.all_paramters[minq_name]
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)
else:
logger.warning(f"Do not find `fake_quant` from input with `fack_quant.minq` {w_minq_name}")
@ -357,7 +355,7 @@ class ExportQuantNetworkDeploy:
return block
def _convert_quant2deploy(self, network):
"""Convet network's all quant subcell to deploy subcell."""
"""Convert network's all quant subcell to deploy subcell."""
cells = network.name_cells()
change = False
for name in cells:
@ -395,18 +393,26 @@ class ExportQuantNetworkDeploy:
return network
def export_geir(network, *inputs, file_name):
def export(network, *inputs, file_name, file_format='GEIR'):
"""
Exports MindSpore quant predict model to deploy with GEIR.
Exports MindSpore quantization predict model to deploy with GEIR.
Args:
network (Cell): MindSpore network produced by `convert_quant_network`.
inputs (Tensor): Inputs of the `network`.
inputs (Tensor): Inputs of the `quantization aware training network`.
file_name (str): File name of model to export.
file_format (str): MindSpore currently supports 'GEIR' format for exported quantization aware model.
- GEIR: Graph Engine Intermediate Representation. An Intermediate representation format of Ascend model.
"""
exporter = ExportQuantNetworkDeploy(network, *inputs)
deploy_net = exporter.run()
serialization.export(deploy_net, *inputs, file_name=file_name, file_format="GEIR")
supported_formats = ['GEIR']
if file_format not in supported_formats:
raise ValueError('Illegal file format {}.'.format(file_format))
if file_format == 'GEIR':
exporter = ExportToQuantInferNetwork(network, *inputs)
deploy_net = exporter.run()
serialization.export(deploy_net, *inputs, file_name=file_name, file_format=file_format)
def convert_quant_network(network,
@ -443,6 +449,7 @@ def convert_quant_network(network,
Cell, Network which has change to quantization aware training network cell.
"""
support_device = ["Ascend", "GPU"]
def convert2list(name, value):
if not isinstance(value, list) and not isinstance(value, tuple):
value = [value]
@ -457,7 +464,7 @@ def convert_quant_network(network,
narrow_range = convert2list("narrow range", narrow_range)
if context.get_context('device_target') not in support_device:
raise KeyError("Not support {} backend.".format(context.get_context('device_target')))
raise KeyError("Unsupported {} device target.".format(context.get_context('device_target')))
net = ConvertToQuantNetwork(network=network,
quant_delay=quant_delay,

5
mindspore/train/serialization.py
View File

@ -160,7 +160,10 @@ def load_checkpoint(ckpt_file_name, net=None):
if not isinstance(ckpt_file_name, str):
raise ValueError("The ckpt_file_name must be string.")
if not os.path.exists(ckpt_file_name) or ckpt_file_name[-5:] != ".ckpt":
if not os.path.exists(ckpt_file_name):
raise ValueError("The checkpoint file is not exist.")
if ckpt_file_name[-5:] != ".ckpt":
raise ValueError("Please input the correct checkpoint file name.")
if os.path.getsize(ckpt_file_name) == 0:

2
model_zoo/lenet_quant/eval.py
View File

@ -57,7 +57,7 @@ if __name__ == "__main__":
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
# load check point into network
param_dict = load_checkpoint(args.ckpt_path, network.type)
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)
print("============== Starting Testing ==============")

2
model_zoo/lenet_quant/eval_quant.py
View File

@ -49,7 +49,7 @@ if __name__ == "__main__":
# define fusion network
network = LeNet5Fusion(cfg.num_classes)
# convert fusion netwrok to quantization aware network
# convert fusion network to quantization aware network
network = quant.convert_quant_network(network, quant_delay=0, bn_fold=False, freeze_bn=10000)
# define loss

56
model_zoo/lenet_quant/export.py Normal file
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@ -0,0 +1,56 @@
# 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.
# ============================================================================
"""
export quantization aware training network to infer `GEIR` backend.
"""
import argparse
import numpy as np
import mindspore
from mindspore import Tensor
from mindspore import context
from mindspore.train.quant import quant
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from src.config import mnist_cfg as cfg
from src.lenet_fusion import LeNet5 as LeNet5Fusion
parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
parser.add_argument('--device_target', type=str, default="Ascend",
choices=['Ascend', 'GPU'],
help='device where the code will be implemented (default: Ascend)')
parser.add_argument('--data_path', type=str, default="./MNIST_Data",
help='path where the dataset is saved')
parser.add_argument('--ckpt_path', type=str, default="",
help='if mode is test, must provide path where the trained ckpt file')
parser.add_argument('--dataset_sink_mode', type=bool, default=True,
help='dataset_sink_mode is False or True')
args = parser.parse_args()
if __name__ == "__main__":
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
# 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)
# load quantization aware network checkpoint
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)
# export network
inputs = Tensor(np.ones([1, 1, cfg.image_height, cfg.image_width]), mindspore.float32)
quant.export(network, inputs, file_name="lenet_quant", file_format='GEIR')

5
model_zoo/lenet_quant/train.py
View File

@ -22,7 +22,7 @@ import os
import argparse
import mindspore.nn as nn
from mindspore import context
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train import Model
from mindspore.nn.metrics import Accuracy
from src.dataset import create_dataset
@ -54,7 +54,6 @@ if __name__ == "__main__":
net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
# call back and monitor
time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
config_ckpt = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpt_callback = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ckpt)
@ -63,6 +62,6 @@ if __name__ == "__main__":
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
print("============== Starting Training ==============")
model.train(cfg['epoch_size'], ds_train, callbacks=[time_cb, ckpt_callback, LossMonitor()],
model.train(cfg['epoch_size'], ds_train, callbacks=[ckpt_callback, LossMonitor()],
dataset_sink_mode=args.dataset_sink_mode)
print("============== End Training ==============")

13
model_zoo/lenet_quant/train_quant.py
View File

@ -23,7 +23,7 @@ import argparse
import mindspore.nn as nn
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train import Model
from mindspore.nn.metrics import Accuracy
from mindspore.train.quant import quant
@ -51,20 +51,19 @@ if __name__ == "__main__":
# define fusion network
network = LeNet5Fusion(cfg.num_classes)
# load quantization aware network checkpoint
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)
# convert fusion network to quantization aware network
network = quant.convert_quant_network(network, quant_delay=0, bn_fold=False, freeze_bn=10000)
# load quantization aware network checkpoint
param_dict = load_checkpoint(args.ckpt_path, network.type)
load_param_into_net(network, param_dict)
# define network loss
net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
# define network optimization
net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
# call back and monitor
time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
config_ckpt = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpt_callback = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ckpt)
@ -73,6 +72,6 @@ if __name__ == "__main__":
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
print("============== Starting Training ==============")
model.train(cfg['epoch_size'], ds_train, callbacks=[time_cb, ckpt_callback, LossMonitor()],
model.train(cfg['epoch_size'], ds_train, callbacks=[ckpt_callback, LossMonitor()],
dataset_sink_mode=args.dataset_sink_mode)
print("============== End Training ==============")