You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
180 lines
7.4 KiB
180 lines
7.4 KiB
# 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 turn on mix_precision."""
|
|
|
|
import os
|
|
import re
|
|
import pytest
|
|
import numpy as np
|
|
from mindspore.common import dtype
|
|
from mindspore import nn
|
|
from mindspore import ops
|
|
from mindspore import amp
|
|
from mindspore import Tensor
|
|
from mindspore import context
|
|
from mindspore.train.loss_scale_manager import FixedLossScaleManager
|
|
from mindspore.train.model import Model
|
|
from utils import FakeData
|
|
from utils import allclose_nparray
|
|
from utils import FakeDataInitMode
|
|
from utils import find_newest_validateir_file
|
|
from utils import clean_all_ir_files
|
|
|
|
|
|
def read_validateir_file(path_folder):
|
|
filename = find_newest_validateir_file(path_folder)
|
|
with open(os.path.join(filename), 'r') as f:
|
|
contend = f.read()
|
|
return contend
|
|
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self, in_c, out_c):
|
|
super().__init__()
|
|
self.relu = nn.ReLU()
|
|
self.bn1 = nn.BatchNorm2d(num_features=in_c,
|
|
gamma_init='ones',
|
|
beta_init='zeros',
|
|
moving_mean_init='zeros',
|
|
moving_var_init='ones')
|
|
self.bn2 = nn.BatchNorm2d(num_features=out_c,
|
|
gamma_init='ones',
|
|
beta_init='zeros',
|
|
moving_mean_init='zeros',
|
|
moving_var_init='ones')
|
|
self.conv = nn.Conv2d(in_channels=in_c,
|
|
out_channels=out_c,
|
|
kernel_size=3,
|
|
stride=1,
|
|
has_bias=True,
|
|
pad_mode='same',
|
|
weight_init='ones',
|
|
bias_init='ones')
|
|
self.mean = ops.ReduceMean(keep_dims=False)
|
|
|
|
def construct(self, x):
|
|
x = self.relu(x)
|
|
x = self.bn1(x)
|
|
x = self.conv(x)
|
|
x = self.bn2(x)
|
|
x = self.relu(x)
|
|
x = self.mean(x, (2, 3))
|
|
return x
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_sit_auto_mix_precision_train_o3():
|
|
input_data = np.random.randn(32, 3, 224, 224).astype(np.float64)
|
|
label_data = np.random.randn(32, 10).astype(np.float32)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
net = Net(3, 10)
|
|
opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.001, momentum=0.0009, weight_decay=0.001,
|
|
loss_scale=0.0001)
|
|
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
|
|
train_network = amp.build_train_network(net, opt, loss, level="O3",
|
|
loss_scale_manager=FixedLossScaleManager(drop_overflow_update=False))
|
|
out = train_network(Tensor(input_data), Tensor(label_data))
|
|
|
|
# pynative mode
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
net_pynative = Net(3, 10)
|
|
opt_pynative = nn.Momentum(params=net_pynative.trainable_params(), learning_rate=0.001, momentum=0.0009,
|
|
weight_decay=0.001,
|
|
loss_scale=0.0001)
|
|
loss_pynative = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
|
|
train_network_pynative = amp.build_train_network(net_pynative, opt_pynative, loss_pynative, level="O3",
|
|
loss_scale_manager=FixedLossScaleManager(
|
|
drop_overflow_update=False))
|
|
out_pynative = train_network_pynative(Tensor(input_data), Tensor(label_data))
|
|
assert np.allclose(out.asnumpy(), out_pynative.asnumpy(), 0.001, 0.001)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_sit_auto_mix_precision_model_o0():
|
|
input_data = np.random.randn(32, 3, 224, 224).astype(np.float32)
|
|
dataset1 = FakeData(size=32,
|
|
batch_size=32,
|
|
image_size=(3, 224, 224),
|
|
num_classes=10,
|
|
fakedata_mode=FakeDataInitMode.OnesInit)
|
|
dataset1.set_label_data_type(np.float16)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
context.set_context(save_graphs=True, save_graphs_path='./test_amp_o0')
|
|
net = Net(3, 10)
|
|
net.to_float(dtype.float16)
|
|
opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.001, momentum=0.0009)
|
|
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
|
|
model = Model(net, loss, opt, amp_level="O0")
|
|
model.train(1, dataset1, dataset_sink_mode=False)
|
|
contend = read_validateir_file('./test_amp_o0')
|
|
castnum = re.findall("Cast", contend)
|
|
assert len(castnum) == 5
|
|
clean_all_ir_files('./test_amp_o0')
|
|
model.predict(Tensor(input_data))
|
|
contend = read_validateir_file('./test_amp_o0')
|
|
castnum = re.findall("Cast", contend)
|
|
assert len(castnum) == 11
|
|
clean_all_ir_files('./test_amp_o0')
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_sit_auto_mix_precision_model_o2():
|
|
input_data = np.random.randn(32, 3, 224, 224).astype(np.float32)
|
|
dataset1 = FakeData(size=32,
|
|
batch_size=32,
|
|
image_size=(3, 224, 224),
|
|
num_classes=10,
|
|
fakedata_mode=FakeDataInitMode.OnesInit)
|
|
dataset2 = FakeData(size=32,
|
|
batch_size=32,
|
|
image_size=(3, 224, 224),
|
|
num_classes=10,
|
|
fakedata_mode=FakeDataInitMode.OnesInit)
|
|
# graph mode
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
context.set_context(save_graphs=True, save_graphs_path='./test_amp_o2')
|
|
net = Net(3, 10)
|
|
opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.001, momentum=0.0009)
|
|
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
|
|
model = Model(net, loss, opt, amp_level="O2")
|
|
model.train(1, dataset1, dataset_sink_mode=False)
|
|
contend = read_validateir_file('./test_amp_o2')
|
|
castnum = re.findall("Cast", contend)
|
|
assert len(castnum) == 14
|
|
clean_all_ir_files('./test_amp_o2')
|
|
out_graph = model.predict(Tensor(input_data))
|
|
|
|
# pynative mode
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
net_pynative = Net(3, 10)
|
|
opt_pynative = nn.Momentum(params=net_pynative.trainable_params(), learning_rate=0.001, momentum=0.0009)
|
|
loss_pynative = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
|
|
model_pynative = Model(net_pynative, loss_pynative, opt_pynative, amp_level="O2")
|
|
model_pynative.train(1, dataset2, dataset_sink_mode=False)
|
|
out_pynative = model_pynative.predict(Tensor(input_data))
|
|
allclose_nparray(out_graph.asnumpy(), out_pynative.asnumpy(), 0.001, 0.001)
|