From a71868f1e5877e19a39f73bb5a7e8727614d1942 Mon Sep 17 00:00:00 2001
From: chenfei <chefei52@huawei.com>
Date: Thu, 20 Aug 2020 19:36:52 +0800
Subject: [PATCH] add ci for quant
add ci for quant
---
.../official/cv/lenet_quant/eval_quant.py | 1 -
model_zoo/official/cv/resnet50_quant/eval.py | 15 +-
.../official/cv/resnet50_quant/src/dataset.py | 4 +-
tests/st/quantization/lenet_quant/config.py | 44 ++++++
tests/st/quantization/lenet_quant/dataset.py | 60 ++++++++
tests/st/quantization/lenet_quant/lenet.py | 79 ++++++++++
.../quantization/lenet_quant/lenet_fusion.py | 58 ++++++++
.../lenet_quant/test_lenet_quant.py | 136 ++++++++++++++++++
8 files changed, 386 insertions(+), 11 deletions(-)
create mode 100644 tests/st/quantization/lenet_quant/config.py
create mode 100644 tests/st/quantization/lenet_quant/dataset.py
create mode 100644 tests/st/quantization/lenet_quant/lenet.py
create mode 100644 tests/st/quantization/lenet_quant/lenet_fusion.py
create mode 100644 tests/st/quantization/lenet_quant/test_lenet_quant.py
diff --git a/model_zoo/official/cv/lenet_quant/eval_quant.py b/model_zoo/official/cv/lenet_quant/eval_quant.py
index 4849d01daf9..f545a8a23a8 100644
--- a/model_zoo/official/cv/lenet_quant/eval_quant.py
+++ b/model_zoo/official/cv/lenet_quant/eval_quant.py
@@ -45,7 +45,6 @@ args = parser.parse_args()
if __name__ == "__main__":
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
ds_eval = create_dataset(os.path.join(args.data_path, "test"), cfg.batch_size, 1)
- step_size = ds_eval.get_dataset_size()
# define fusion network
network = LeNet5Fusion(cfg.num_classes)
diff --git a/model_zoo/official/cv/resnet50_quant/eval.py b/model_zoo/official/cv/resnet50_quant/eval.py
index 0395e38b601..9eb3ce3520a 100755
--- a/model_zoo/official/cv/resnet50_quant/eval.py
+++ b/model_zoo/official/cv/resnet50_quant/eval.py
@@ -17,7 +17,7 @@
import os
import argparse
-from src.config import quant_set, config_quant, config_noquant
+from src.config import config_quant
from src.dataset import create_dataset
from src.crossentropy import CrossEntropy
from models.resnet_quant import resnet50_quant
@@ -34,7 +34,7 @@ parser.add_argument('--device_target', type=str, default='Ascend', help='Device
args_opt = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, save_graphs=False)
-config = config_quant if quant_set.quantization_aware else config_noquant
+config = config_quant
if args_opt.device_target == "Ascend":
device_id = int(os.getenv('DEVICE_ID'))
@@ -43,12 +43,11 @@ if args_opt.device_target == "Ascend":
if __name__ == '__main__':
# define fusion network
net = resnet50_quant(class_num=config.class_num)
- if quant_set.quantization_aware:
- # convert fusion network to quantization aware network
- net = quant.convert_quant_network(net,
- bn_fold=True,
- per_channel=[True, False],
- symmetric=[True, False])
+ # convert fusion network to quantization aware network
+ net = quant.convert_quant_network(net,
+ bn_fold=True,
+ per_channel=[True, False],
+ symmetric=[True, False])
# define network loss
if not config.use_label_smooth:
config.label_smooth_factor = 0.0
diff --git a/model_zoo/official/cv/resnet50_quant/src/dataset.py b/model_zoo/official/cv/resnet50_quant/src/dataset.py
index 73c07800900..3c35adeaaa0 100755
--- a/model_zoo/official/cv/resnet50_quant/src/dataset.py
+++ b/model_zoo/official/cv/resnet50_quant/src/dataset.py
@@ -23,9 +23,9 @@ import mindspore.dataset.transforms.vision.c_transforms as C
import mindspore.dataset.transforms.c_transforms as C2
import mindspore.dataset.transforms.vision.py_transforms as P
from mindspore.communication.management import init, get_rank, get_group_size
-from src.config import quant_set, config_quant, config_noquant
+from src.config import config_quant
-config = config_quant if quant_set.quantization_aware else config_noquant
+config = config_quant
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
diff --git a/tests/st/quantization/lenet_quant/config.py b/tests/st/quantization/lenet_quant/config.py
new file mode 100644
index 00000000000..7c4f5a54b7e
--- /dev/null
+++ b/tests/st/quantization/lenet_quant/config.py
@@ -0,0 +1,44 @@
+# 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.
+# ============================================================================
+"""
+network config setting, will be used in test_lenet_quant.py
+"""
+
+from easydict import EasyDict as edict
+
+nonquant_cfg = edict({
+ 'num_classes': 10,
+ 'lr': 0.01,
+ 'momentum': 0.9,
+ 'epoch_size': 10,
+ 'batch_size': 32,
+ 'buffer_size': 1000,
+ 'image_height': 32,
+ 'image_width': 32,
+ 'save_checkpoint_steps': 1875,
+ 'keep_checkpoint_max': 10,
+})
+
+quant_cfg = edict({
+ 'num_classes': 10,
+ 'lr': 0.01,
+ 'momentum': 0.9,
+ 'epoch_size': 10,
+ 'batch_size': 64,
+ 'buffer_size': 1000,
+ 'image_height': 32,
+ 'image_width': 32,
+ 'keep_checkpoint_max': 10,
+})
diff --git a/tests/st/quantization/lenet_quant/dataset.py b/tests/st/quantization/lenet_quant/dataset.py
new file mode 100644
index 00000000000..cef69734839
--- /dev/null
+++ b/tests/st/quantization/lenet_quant/dataset.py
@@ -0,0 +1,60 @@
+# 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.
+# ============================================================================
+"""
+Produce the dataset
+"""
+
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as CV
+import mindspore.dataset.transforms.c_transforms as C
+from mindspore.dataset.transforms.vision import Inter
+from mindspore.common import dtype as mstype
+
+
+def create_dataset(data_path, batch_size=32, repeat_size=1,
+ num_parallel_workers=1):
+ """
+ create dataset for train or test
+ """
+ # define dataset
+ mnist_ds = ds.MnistDataset(data_path)
+
+ resize_height, resize_width = 32, 32
+ rescale = 1.0 / 255.0
+ shift = 0.0
+ rescale_nml = 1 / 0.3081
+ shift_nml = -1 * 0.1307 / 0.3081
+
+ # define map operations
+ resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
+ rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
+ rescale_op = CV.Rescale(rescale, shift)
+ hwc2chw_op = CV.HWC2CHW()
+ type_cast_op = C.TypeCast(mstype.int32)
+
+ # apply map operations on images
+ mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
+ mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
+ mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
+ mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
+ mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
+
+ # apply DatasetOps
+ buffer_size = 10000
+ mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script
+ mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
+ mnist_ds = mnist_ds.repeat(repeat_size)
+
+ return mnist_ds
diff --git a/tests/st/quantization/lenet_quant/lenet.py b/tests/st/quantization/lenet_quant/lenet.py
new file mode 100644
index 00000000000..42444100073
--- /dev/null
+++ b/tests/st/quantization/lenet_quant/lenet.py
@@ -0,0 +1,79 @@
+# 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.
+# ============================================================================
+"""LeNet."""
+import mindspore.nn as nn
+from mindspore.common.initializer import TruncatedNormal
+
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
+ """weight initial for conv layer"""
+ weight = weight_variable()
+ return nn.Conv2d(in_channels, out_channels,
+ kernel_size=kernel_size, stride=stride, padding=padding,
+ weight_init=weight, has_bias=False, pad_mode="valid")
+
+
+def fc_with_initialize(input_channels, out_channels):
+ """weight initial for fc layer"""
+ weight = weight_variable()
+ bias = weight_variable()
+ return nn.Dense(input_channels, out_channels, weight, bias)
+
+
+def weight_variable():
+ """weight initial"""
+ return TruncatedNormal(0.02)
+
+
+class LeNet5(nn.Cell):
+ """
+ Lenet network
+
+ Args:
+ num_class (int): Num classes. Default: 10.
+
+ Returns:
+ Tensor, output tensor
+ Examples:
+ >>> LeNet(num_class=10)
+
+ """
+
+ def __init__(self, num_class=10, channel=1):
+ super(LeNet5, self).__init__()
+ self.num_class = num_class
+ self.conv1 = conv(channel, 6, 5)
+ self.conv2 = conv(6, 16, 5)
+ self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
+ self.fc2 = fc_with_initialize(120, 84)
+ self.fc3 = fc_with_initialize(84, self.num_class)
+ self.relu = nn.ReLU()
+ self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+ self.flatten = nn.Flatten()
+
+ def construct(self, x):
+ x = self.conv1(x)
+ x = self.relu(x)
+ x = self.max_pool2d(x)
+ x = self.conv2(x)
+ x = self.relu(x)
+ x = self.max_pool2d(x)
+ x = self.flatten(x)
+ x = self.fc1(x)
+ x = self.relu(x)
+ x = self.fc2(x)
+ x = self.relu(x)
+ x = self.fc3(x)
+ return x
diff --git a/tests/st/quantization/lenet_quant/lenet_fusion.py b/tests/st/quantization/lenet_quant/lenet_fusion.py
new file mode 100644
index 00000000000..88b35935027
--- /dev/null
+++ b/tests/st/quantization/lenet_quant/lenet_fusion.py
@@ -0,0 +1,58 @@
+# 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.
+# ============================================================================
+"""LeNet."""
+import mindspore.nn as nn
+
+
+class LeNet5(nn.Cell):
+ """
+ Lenet network
+
+ Args:
+ num_class (int): Num classes. Default: 10.
+
+ Returns:
+ Tensor, output tensor
+ Examples:
+ >>> LeNet(num_class=10)
+
+ """
+
+ def __init__(self, num_class=10, channel=1):
+ super(LeNet5, self).__init__()
+ self.type = "fusion"
+ self.num_class = num_class
+
+ # change `nn.Conv2d` to `nn.Conv2dBnAct`
+ self.conv1 = nn.Conv2dBnAct(channel, 6, 5, pad_mode='valid', activation='relu')
+ self.conv2 = nn.Conv2dBnAct(6, 16, 5, pad_mode='valid', activation='relu')
+ # change `nn.Dense` to `nn.DenseBnAct`
+ self.fc1 = nn.DenseBnAct(16 * 5 * 5, 120, activation='relu')
+ self.fc2 = nn.DenseBnAct(120, 84, activation='relu')
+ self.fc3 = nn.DenseBnAct(84, self.num_class)
+
+ self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+ self.flatten = nn.Flatten()
+
+ def construct(self, x):
+ x = self.conv1(x)
+ x = self.max_pool2d(x)
+ x = self.conv2(x)
+ x = self.max_pool2d(x)
+ x = self.flatten(x)
+ x = self.fc1(x)
+ x = self.fc2(x)
+ x = self.fc3(x)
+ return x
diff --git a/tests/st/quantization/lenet_quant/test_lenet_quant.py b/tests/st/quantization/lenet_quant/test_lenet_quant.py
new file mode 100644
index 00000000000..361aa1abf62
--- /dev/null
+++ b/tests/st/quantization/lenet_quant/test_lenet_quant.py
@@ -0,0 +1,136 @@
+# 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.
+# ============================================================================
+"""
+train and infer lenet quantization network
+"""
+
+import os
+import pytest
+from mindspore import context
+import mindspore.nn as nn
+from mindspore.nn.metrics import Accuracy
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train import Model
+from mindspore.train.quant import quant
+from mindspore.train.quant.quant_utils import load_nonquant_param_into_quant_net
+from dataset import create_dataset
+from config import nonquant_cfg, quant_cfg
+from lenet import LeNet5
+from lenet_fusion import LeNet5 as LeNet5Fusion
+
+device_target = 'GPU'
+data_path = "/home/workspace/mindspore_dataset/mnist"
+
+
+def train_lenet():
+ context.set_context(mode=context.GRAPH_MODE, device_target=device_target)
+ cfg = nonquant_cfg
+ ds_train = create_dataset(os.path.join(data_path, "train"),
+ cfg.batch_size)
+
+ network = LeNet5(cfg.num_classes)
+ net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+ net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+ time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
+ config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
+ keep_checkpoint_max=cfg.keep_checkpoint_max)
+ ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
+ model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+ print("============== Starting Training Lenet==============")
+ model.train(cfg['epoch_size'], ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()],
+ dataset_sink_mode=True)
+
+
+def train_lenet_quant():
+ context.set_context(mode=context.GRAPH_MODE, device_target=device_target)
+ cfg = quant_cfg
+ ckpt_path = './checkpoint_lenet-10_1875.ckpt'
+ ds_train = create_dataset(os.path.join(data_path, "train"), cfg.batch_size, 1)
+ step_size = ds_train.get_dataset_size()
+
+ # define fusion network
+ network = LeNet5Fusion(cfg.num_classes)
+
+ # load quantization aware network checkpoint
+ param_dict = load_checkpoint(ckpt_path)
+ load_nonquant_param_into_quant_net(network, param_dict)
+
+ # 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])
+
+ # 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
+ 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)
+
+ # define model
+ model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+ print("============== Starting Training ==============")
+ model.train(cfg['epoch_size'], ds_train, callbacks=[ckpt_callback, LossMonitor()],
+ dataset_sink_mode=True)
+ print("============== End Training ==============")
+
+
+def eval_quant():
+ context.set_context(mode=context.GRAPH_MODE, device_target=device_target)
+ cfg = quant_cfg
+ ds_eval = create_dataset(os.path.join(data_path, "test"), cfg.batch_size, 1)
+ ckpt_path = './checkpoint_lenet_1-10_937.ckpt'
+ # 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,
+ per_channel=[True, False])
+
+ # define 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
+ model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+ # load quantization aware network checkpoint
+ param_dict = load_checkpoint(ckpt_path)
+ not_load_param = load_param_into_net(network, param_dict)
+ if not_load_param:
+ raise ValueError("Load param into net fail!")
+
+ print("============== Starting Testing ==============")
+ acc = model.eval(ds_eval, dataset_sink_mode=True)
+ print("============== {} ==============".format(acc))
+ assert acc['Accuracy'] > 0.98
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_lenet_quant():
+ train_lenet()
+ train_lenet_quant()
+ eval_quant()
+
+
+if __name__ == "__main__":
+ train_lenet_quant()