forked from huawei/mindspore2022
add exoirt air test
This commit is contained in:
changzherui
parent
8d6c780f93
commit
ffeacf13e4
|
|
@ -36,7 +36,7 @@ class TimeMonitor(Callback):
|
|||
self.epoch_time = time.time()
|
||||
|
||||
def epoch_end(self, run_context):
|
||||
epoch_seconds = (time.time() - self.epoch_time) * 1000
|
||||
epoch_seconds = time.time() - self.epoch_time
|
||||
step_size = self.data_size
|
||||
cb_params = run_context.original_args()
|
||||
if hasattr(cb_params, "batch_num"):
|
||||
|
|
@ -49,4 +49,4 @@ class TimeMonitor(Callback):
|
|||
return
|
||||
|
||||
step_seconds = epoch_seconds / step_size
|
||||
print("Epoch time: {:5.3f}, per step time: {:5.3f}".format(epoch_seconds, step_seconds), flush=True)
|
||||
print("Epoch time: {:5.3f}s, per step time: {:5.3f}s".format(epoch_seconds, step_seconds), flush=True)
|
||||
|
|
|
|||
|
|
@ -378,7 +378,8 @@ def load_param_into_net(net, parameter_dict, strict_load=False):
|
|||
logger.debug("%s", param_name)
|
||||
|
||||
logger.info("Load parameter into net finish.")
|
||||
logger.warning("{} parameters in the net are not loaded.".format(len(param_not_load)))
|
||||
if param_not_load:
|
||||
logger.warning("{} parameters in the net are not loaded.".format(len(param_not_load)))
|
||||
return param_not_load
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,335 @@
|
|||
# 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.
|
||||
"""Test network export."""
|
||||
import os
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import mindspore.context as context
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn import Dense
|
||||
from mindspore.nn.cell import Cell
|
||||
from mindspore.nn.layer.basic import Flatten
|
||||
from mindspore.nn.layer.conv import Conv2d
|
||||
from mindspore.nn.layer.normalization import BatchNorm2d
|
||||
from mindspore.nn.layer.pooling import MaxPool2d
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.ops.operations import TensorAdd
|
||||
from mindspore.train.serialization import export
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
|
||||
|
||||
def random_normal_init(shape, mean=0.0, stddev=0.01, seed=None):
|
||||
init_value = np.ones(shape).astype(np.float32) * 0.01
|
||||
return Tensor(init_value)
|
||||
|
||||
|
||||
def variance_scaling_raw(shape):
|
||||
variance_scaling_value = np.ones(shape).astype(np.float32) * 0.01
|
||||
return Tensor(variance_scaling_value)
|
||||
|
||||
|
||||
def weight_variable_0(shape):
|
||||
zeros = np.zeros(shape).astype(np.float32)
|
||||
return Tensor(zeros)
|
||||
|
||||
|
||||
def weight_variable_1(shape):
|
||||
ones = np.ones(shape).astype(np.float32)
|
||||
return Tensor(ones)
|
||||
|
||||
|
||||
def conv3x3(in_channels, out_channels, stride=1, padding=1):
|
||||
"""3x3 convolution """
|
||||
weight_shape = (out_channels, in_channels, 3, 3)
|
||||
weight = variance_scaling_raw(weight_shape)
|
||||
return Conv2d(in_channels, out_channels,
|
||||
kernel_size=3, stride=stride, weight_init=weight, has_bias=False, pad_mode="same")
|
||||
|
||||
|
||||
def conv1x1(in_channels, out_channels, stride=1, padding=0):
|
||||
"""1x1 convolution"""
|
||||
weight_shape = (out_channels, in_channels, 1, 1)
|
||||
weight = variance_scaling_raw(weight_shape)
|
||||
return Conv2d(in_channels, out_channels,
|
||||
kernel_size=1, stride=stride, weight_init=weight, has_bias=False, pad_mode="same")
|
||||
|
||||
|
||||
def conv7x7(in_channels, out_channels, stride=1, padding=0):
|
||||
"""1x1 convolution"""
|
||||
weight_shape = (out_channels, in_channels, 7, 7)
|
||||
weight = variance_scaling_raw(weight_shape)
|
||||
return Conv2d(in_channels, out_channels,
|
||||
kernel_size=7, stride=stride, weight_init=weight, has_bias=False, pad_mode="same")
|
||||
|
||||
|
||||
def bn_with_initialize(out_channels):
|
||||
shape = (out_channels)
|
||||
mean = weight_variable_0(shape)
|
||||
var = weight_variable_1(shape)
|
||||
beta = weight_variable_0(shape)
|
||||
gamma = weight_variable_1(shape)
|
||||
bn = BatchNorm2d(out_channels, momentum=0.1, eps=0.0001, gamma_init=gamma,
|
||||
beta_init=beta, moving_mean_init=mean, moving_var_init=var)
|
||||
return bn
|
||||
|
||||
|
||||
def bn_with_initialize_last(out_channels):
|
||||
shape = (out_channels)
|
||||
mean = weight_variable_0(shape)
|
||||
var = weight_variable_1(shape)
|
||||
beta = weight_variable_0(shape)
|
||||
gamma = weight_variable_0(shape)
|
||||
bn = BatchNorm2d(out_channels, momentum=0.1, eps=0.0001, gamma_init=gamma,
|
||||
beta_init=beta, moving_mean_init=mean, moving_var_init=var)
|
||||
return bn
|
||||
|
||||
|
||||
def fc_with_initialize(input_channels, out_channels):
|
||||
weight_shape = (out_channels, input_channels)
|
||||
bias_shape = (out_channels)
|
||||
weight = random_normal_init(weight_shape)
|
||||
bias = weight_variable_0(bias_shape)
|
||||
|
||||
return Dense(input_channels, out_channels, weight, bias)
|
||||
|
||||
|
||||
class ResidualBlock(Cell):
|
||||
expansion = 4
|
||||
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
stride=1,
|
||||
down_sample=False):
|
||||
super(ResidualBlock, self).__init__()
|
||||
|
||||
out_chls = out_channels // self.expansion
|
||||
self.conv1 = conv1x1(in_channels, out_chls, stride=1, padding=0)
|
||||
self.bn1 = bn_with_initialize(out_chls)
|
||||
|
||||
self.conv2 = conv3x3(out_chls, out_chls, stride=stride, padding=1)
|
||||
self.bn2 = bn_with_initialize(out_chls)
|
||||
|
||||
self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
|
||||
self.bn3 = bn_with_initialize_last(out_channels)
|
||||
|
||||
self.relu = P.ReLU()
|
||||
self.add = TensorAdd()
|
||||
|
||||
def construct(self, x):
|
||||
identity = x
|
||||
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv3(out)
|
||||
out = self.bn3(out)
|
||||
|
||||
out = self.add(out, identity)
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class ResidualBlockWithDown(Cell):
|
||||
expansion = 4
|
||||
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
stride=1,
|
||||
down_sample=False):
|
||||
super(ResidualBlockWithDown, self).__init__()
|
||||
|
||||
out_chls = out_channels // self.expansion
|
||||
self.conv1 = conv1x1(in_channels, out_chls, stride=1, padding=0)
|
||||
self.bn1 = bn_with_initialize(out_chls)
|
||||
|
||||
self.conv2 = conv3x3(out_chls, out_chls, stride=stride, padding=1)
|
||||
self.bn2 = bn_with_initialize(out_chls)
|
||||
|
||||
self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
|
||||
self.bn3 = bn_with_initialize_last(out_channels)
|
||||
|
||||
self.relu = P.ReLU()
|
||||
self.downSample = down_sample
|
||||
|
||||
self.conv_down_sample = conv1x1(
|
||||
in_channels, out_channels, stride=stride, padding=0)
|
||||
self.bn_down_sample = bn_with_initialize(out_channels)
|
||||
self.add = TensorAdd()
|
||||
|
||||
def construct(self, x):
|
||||
identity = x
|
||||
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv3(out)
|
||||
out = self.bn3(out)
|
||||
|
||||
identity = self.conv_down_sample(identity)
|
||||
identity = self.bn_down_sample(identity)
|
||||
|
||||
out = self.add(out, identity)
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class MakeLayer0(Cell):
|
||||
|
||||
def __init__(self, block, layer_num, in_channels, out_channels, stride):
|
||||
super(MakeLayer0, self).__init__()
|
||||
self.a = ResidualBlockWithDown(
|
||||
in_channels, out_channels, stride=1, down_sample=True)
|
||||
self.b = block(out_channels, out_channels, stride=stride)
|
||||
self.c = block(out_channels, out_channels, stride=1)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.a(x)
|
||||
x = self.b(x)
|
||||
x = self.c(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class MakeLayer1(Cell):
|
||||
|
||||
def __init__(self, block, layer_num, in_channels, out_channels, stride):
|
||||
super(MakeLayer1, self).__init__()
|
||||
self.a = ResidualBlockWithDown(
|
||||
in_channels, out_channels, stride=stride, down_sample=True)
|
||||
self.b = block(out_channels, out_channels, stride=1)
|
||||
self.c = block(out_channels, out_channels, stride=1)
|
||||
self.d = block(out_channels, out_channels, stride=1)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.a(x)
|
||||
x = self.b(x)
|
||||
x = self.c(x)
|
||||
x = self.d(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class MakeLayer2(Cell):
|
||||
|
||||
def __init__(self, block, layer_num, in_channels, out_channels, stride):
|
||||
super(MakeLayer2, self).__init__()
|
||||
self.a = ResidualBlockWithDown(
|
||||
in_channels, out_channels, stride=stride, down_sample=True)
|
||||
self.b = block(out_channels, out_channels, stride=1)
|
||||
self.c = block(out_channels, out_channels, stride=1)
|
||||
self.d = block(out_channels, out_channels, stride=1)
|
||||
self.e = block(out_channels, out_channels, stride=1)
|
||||
self.f = block(out_channels, out_channels, stride=1)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.a(x)
|
||||
x = self.b(x)
|
||||
x = self.c(x)
|
||||
x = self.d(x)
|
||||
x = self.e(x)
|
||||
x = self.f(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class MakeLayer3(Cell):
|
||||
|
||||
def __init__(self, block, layer_num, in_channels, out_channels, stride):
|
||||
super(MakeLayer3, self).__init__()
|
||||
self.a = ResidualBlockWithDown(
|
||||
in_channels, out_channels, stride=stride, down_sample=True)
|
||||
self.b = block(out_channels, out_channels, stride=1)
|
||||
self.c = block(out_channels, out_channels, stride=1)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.a(x)
|
||||
x = self.b(x)
|
||||
x = self.c(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class ResNet(Cell):
|
||||
|
||||
def __init__(self, block, layer_num, num_classes=100):
|
||||
super(ResNet, self).__init__()
|
||||
|
||||
self.conv1 = conv7x7(3, 64, stride=2, padding=3)
|
||||
|
||||
self.bn1 = bn_with_initialize(64)
|
||||
self.relu = P.ReLU()
|
||||
self.maxpool = MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
|
||||
|
||||
self.layer1 = MakeLayer0(
|
||||
block, layer_num[0], in_channels=64, out_channels=256, stride=1)
|
||||
self.layer2 = MakeLayer1(
|
||||
block, layer_num[1], in_channels=256, out_channels=512, stride=2)
|
||||
self.layer3 = MakeLayer2(
|
||||
block, layer_num[2], in_channels=512, out_channels=1024, stride=2)
|
||||
self.layer4 = MakeLayer3(
|
||||
block, layer_num[3], in_channels=1024, out_channels=2048, stride=2)
|
||||
|
||||
self.pool = nn.AvgPool2d(7, 1)
|
||||
self.fc = fc_with_initialize(512 * block.expansion, num_classes)
|
||||
self.flatten = Flatten()
|
||||
|
||||
def construct(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
x = self.maxpool(x)
|
||||
|
||||
x = self.layer1(x)
|
||||
x = self.layer2(x)
|
||||
x = self.layer3(x)
|
||||
x = self.layer4(x)
|
||||
|
||||
x = self.pool(x)
|
||||
x = self.flatten(x)
|
||||
x = self.fc(x)
|
||||
return x
|
||||
|
||||
|
||||
def resnet50(num_classes):
|
||||
return ResNet(ResidualBlock, [3, 4, 6, 3], num_classes)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_export_resnet_air():
|
||||
net = resnet50(10)
|
||||
inputs = Tensor(np.ones([1, 3, 224, 224]).astype(np.float32) * 0.01)
|
||||
file_name = "resnet.air"
|
||||
export(net, inputs, file_name=file_name, file_format='AIR')
|
||||
assert os.path.exists(file_name)
|
||||
os.remove(file_name)
|
||||
Loading…
Reference in New Issue