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修复st门禁概率失败

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王程浩 2022-04-02 09:29:14 +00:00 committed by cheng-hao-wang
parent a49b9877a9
commit feb060dfa5
3 changed files with 13 additions and 514 deletions
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246
tests/st/networks/models/resnet50/test_resnet50_imagenet.py
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@ -1,246 +0,0 @@
# Copyright 2020-2021 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 evaluate resnet50 network on imagenet dataset"""
import os
import time
from multiprocessing import Process, Queue
import pytest
import numpy as np
from mindspore import context
from mindspore.common.tensor import Tensor
from mindspore.communication.management import init
from mindspore.train.model import Model
from mindspore.context import ParallelMode
from mindspore.train.callback import Callback
from mindspore.train.loss_scale_manager import FixedLossScaleManager
import mindspore.nn as nn
import mindspore.dataset as ds
from tests.st.networks.models.resnet50.src.resnet import resnet50
from tests.st.networks.models.resnet50.src.dataset import create_dataset
from tests.st.networks.models.resnet50.src.lr_generator import get_learning_rate
from tests.st.networks.models.resnet50.src.config import config
from tests.st.networks.models.resnet50.src.metric import DistAccuracy, ClassifyCorrectCell
from tests.st.networks.models.resnet50.src.CrossEntropySmooth import CrossEntropySmooth
MINDSPORE_HCCL_CONFIG_PATH = "/home/workspace/mindspore_config/hccl/rank_table_8p.json"
dataset_path = "/home/workspace/mindspore_dataset/imagenet/imagenet_original/train"
eval_path = "/home/workspace/mindspore_dataset/imagenet/imagenet_original/val"
np.random.seed(1)
ds.config.set_seed(1)
os.environ['GLOG_v'] = str(2)
class LossGet(Callback):
def __init__(self, per_print_times, data_size):
super(LossGet, self).__init__()
if not isinstance(per_print_times, int) or per_print_times < 0:
raise ValueError("print_step must be int and >= 0.")
self._per_print_times = per_print_times
self._loss = 0.0
self.data_size = data_size
def step_end(self, run_context):
cb_params = run_context.original_args()
loss = cb_params.net_outputs
if isinstance(loss, (tuple, list)):
if isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray):
loss = loss[0]
if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray):
loss = np.mean(loss.asnumpy())
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
if isinstance(loss, float) and (np.isnan(loss) or np.isinf(loss)):
raise ValueError("epoch: {} step: {}. Invalid loss, terminating training."
.format(cb_params.cur_epoch_num, cur_step_in_epoch))
if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0:
self._loss = loss
def epoch_begin(self, run_context):
self.epoch_time = time.time()
def epoch_end(self, run_context):
epoch_mseconds = (time.time() - self.epoch_time) * 1000
self._per_step_mseconds = epoch_mseconds / self.data_size
def get_loss(self):
return self._loss
def get_per_step_time(self):
return self._per_step_mseconds
def train_process(q, device_id, epoch_size, device_num, enable_hccl):
os.system("mkdir " + str(device_id))
os.chdir(str(device_id))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
context.set_context(device_id=device_id)
os.environ['MINDSPORE_HCCL_CONFIG_PATH'] = MINDSPORE_HCCL_CONFIG_PATH
os.environ['RANK_ID'] = str(device_id)
os.environ['RANK_SIZE'] = str(device_num)
if enable_hccl:
context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True, all_reduce_fusion_config=[107, 160])
init()
# network
net = resnet50(class_num=config.class_num)
# evaluation network
dist_eval_network = ClassifyCorrectCell(net)
if not config.use_label_smooth:
config.label_smooth_factor = 0.0
# loss
loss = CrossEntropySmooth(sparse=True, reduction="mean", smooth_factor=config.label_smooth_factor,
num_classes=config.class_num)
# train dataset
dataset = create_dataset(dataset_path=dataset_path, do_train=True,
repeat_num=1, batch_size=config.batch_size)
step_size = dataset.get_dataset_size()
eval_interval = config.eval_interval
dataset.__loop_size__ = step_size * eval_interval
# evaluation dataset
eval_dataset = create_dataset(dataset_path=eval_path, do_train=False,
repeat_num=1, batch_size=config.eval_batch_size)
# loss scale
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
# learning rate
lr = Tensor(get_learning_rate(lr_init=config.lr_init, lr_end=0.0, lr_max=config.lr_max,
warmup_epochs=config.warmup_epochs, total_epochs=config.epoch_size,
steps_per_epoch=step_size, lr_decay_mode=config.lr_decay_mode))
# optimizer
decayed_params = []
no_decayed_params = []
for param in net.trainable_params():
if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
decayed_params.append(param)
else:
no_decayed_params.append(param)
group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
{'params': no_decayed_params, 'weight_decay': 0.0},
{'order_params': net.trainable_params()}]
if config.use_lars:
momentum = nn.Momentum(group_params, lr, config.momentum,
loss_scale=config.loss_scale, use_nesterov=config.use_nesterov)
opt = nn.LARS(momentum, epsilon=config.lars_epsilon, coefficient=config.lars_coefficient,
lars_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'bias' not in x.name)
else:
opt = nn.Momentum(group_params, lr, config.momentum,
loss_scale=config.loss_scale, use_nesterov=config.use_nesterov)
# model
model = Model(net, loss_fn=loss, optimizer=opt,
loss_scale_manager=loss_scale, amp_level="O2", keep_batchnorm_fp32=False,
metrics={'acc': DistAccuracy(batch_size=config.eval_batch_size, device_num=device_num)},
eval_network=dist_eval_network)
# callbacks
loss_cb = LossGet(1, step_size)
# train and eval
print("run_start", device_id)
acc = 0.0
time_cost = 0.0
for epoch_idx in range(0, int(epoch_size / eval_interval)):
model.train(1, dataset, callbacks=loss_cb)
eval_start = time.time()
output = model.eval(eval_dataset)
eval_cost = (time.time() - eval_start) * 1000
acc = float(output["acc"])
time_cost = loss_cb.get_per_step_time()
loss = loss_cb.get_loss()
print("the {} epoch's resnet result:\n "
"device{}, training loss {}, acc {}, "
"training per step cost {:.2f} ms, eval cost {:.2f} ms, total_cost {:.2f} ms".format(
epoch_idx, device_id, loss, acc, time_cost, eval_cost, time_cost * step_size + eval_cost))
q.put({'acc': acc, 'cost': time_cost})
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_single
def test_resnet_imagenet_8p():
"""
Feature: Resnet50 network.
Description: Train and evaluate resnet50 network on imagenet dataset.
Expectation: accuracy > 0.1, time cost < 26.
"""
context.set_context(enable_graph_kernel=False, enable_sparse=False)
context.reset_auto_parallel_context()
context.reset_ps_context()
q = Queue()
device_num = 8
epoch_size = 2
enable_hccl = True
process = []
for i in range(device_num):
device_id = i
process.append(Process(target=train_process,
args=(q, device_id, epoch_size, device_num, enable_hccl)))
cpu_count = os.cpu_count()
each_cpu_count = cpu_count // device_num
for i in range(device_num):
process[i].start()
if each_cpu_count > 1:
cpu_start = each_cpu_count * i
cpu_end = each_cpu_count * (i + 1)
process_cpu = [x for x in range(cpu_start, cpu_end)]
pid1 = process[i].pid
os.sched_setaffinity(pid1, set(process_cpu))
print("Waiting for all subprocesses done...")
for i in range(device_num):
process[i].join()
# resnet
acc = 0.0
cost = 0.0
for i in range(device_num):
assert not q.empty()
output = q.get()
acc += output['acc']
cost += output['cost']
acc = acc / device_num
cost = cost / device_num
for i in range(device_num):
os.system("rm -rf " + str(i))
print("End training...")
assert acc > 0.1
assert cost < 26

23
tests/st/networks/models/resnet50/test_resnet50_imagenet_and_thor.py
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@ -99,9 +99,9 @@ class LossGet(Callback):
cb_params = run_context.original_args()
loss = cb_params.net_outputs
self._epoch = cb_params.cur_epoch_num
if isinstance(loss,
(tuple, list) and isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray)):
loss = loss[0]
if isinstance(loss, (tuple, list)):
if isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray):
loss = loss[0]
if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray):
loss = np.mean(loss.asnumpy())
@ -114,7 +114,8 @@ class LossGet(Callback):
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0:
self._loss = loss
print("epoch: %s step: %s, loss is %s" % (cb_params.cur_epoch_num, cur_step_in_epoch, loss), flush=True)
print("epoch: %s step: %s, loss is %s" % (cb_params.cur_epoch_num,
cur_step_in_epoch, loss), flush=True)
def epoch_begin(self, run_context):
self.epoch_time = time.time()
@ -186,6 +187,7 @@ def train_process(q, device_id, epoch_size, device_num, enable_hccl):
# train dataset
dataset = create_dataset(dataset_path=dataset_path, do_train=True, repeat_num=1, batch_size=config.batch_size)
step_size = dataset.get_dataset_size()
# evaluation dataset
eval_dataset = create_dataset(dataset_path=eval_path, do_train=False,
repeat_num=1, batch_size=config.eval_batch_size)
@ -302,10 +304,6 @@ def train_process_thor(q, device_id, epoch_size, device_num, enable_hccl):
q.put({'loss': loss, 'cost': time_cost})
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_single
def resnet_end(device_num, q):
acc = 0.0
cost = 0.0
@ -334,13 +332,17 @@ def thor_end(device_num, q):
thor_loss = thor_loss / device_num
thor_cost = thor_cost / device_num
for i in range(0, device_num):
for i in range(4, device_num + 4):
os.system("rm -rf " + str(i))
print("End training...")
assert thor_loss < 7
assert thor_cost < 30
@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_single
def test_resnet_imagenet_and_thor_4p():
"""
Feature: Resnet50 network.
@ -367,7 +369,7 @@ def test_resnet_imagenet_and_thor_4p():
args=(q2, device_id + 4, epoch_size_2, device_num, enable_hccl)))
cpu_count = os.cpu_count()
half_cpu_count = cpu_count // 2
each_cpu_count = cpu_count // device_num
each_cpu_count = half_cpu_count // device_num
for i in range(device_num):
process[i].start()
process2[i].start()
@ -389,3 +391,4 @@ def test_resnet_imagenet_and_thor_4p():
resnet_end(device_num, q)
# thor
thor_end(device_num, q2)

258
tests/st/networks/models/resnet50/test_resnet50_thor_imagenet.py
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@ -1,258 +0,0 @@
# Copyright 2020-2021 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 evaluate resnet50 network on imagenet dataset"""
import os
import time
from multiprocessing import Process, Queue
import pytest
import numpy as np
from mindspore import context
from mindspore.common.tensor import Tensor
from mindspore.communication.management import init
from mindspore.context import ParallelMode
from mindspore.train.callback import Callback
from mindspore.train.model import Model
from mindspore.train.train_thor import ConvertModelUtils
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from mindspore.nn.optim import thor
import mindspore.dataset as ds
from tests.st.networks.models.resnet50.src.metric import DistAccuracy, ClassifyCorrectCell
from tests.st.networks.models.resnet50.src.CrossEntropySmooth import CrossEntropySmooth
from tests.st.networks.models.resnet50.src_thor.config import config as thor_config
from tests.st.networks.models.resnet50.src_thor.dataset import create_dataset2 as create_dataset_thor
from tests.st.networks.models.resnet50.src.resnet import resnet50
MINDSPORE_HCCL_CONFIG_PATH = "/home/workspace/mindspore_config/hccl/rank_table_8p.json"
dataset_path = "/home/workspace/mindspore_dataset/imagenet/imagenet_original/train"
eval_path = "/home/workspace/mindspore_dataset/imagenet/imagenet_original/val"
np.random.seed(1)
ds.config.set_seed(1)
os.environ['GLOG_v'] = str(2)
def get_thor_lr(global_step, lr_init, decay, total_epochs, steps_per_epoch, decay_epochs=100):
"""get_model_lr"""
lr_each_step = []
total_steps = steps_per_epoch * total_epochs
for i in range(total_steps):
epoch = (i + 1) / steps_per_epoch
base = (1.0 - float(epoch) / total_epochs) ** decay
lr_local = lr_init * base
if epoch >= decay_epochs:
lr_local = lr_local * 0.5
if epoch >= decay_epochs + 1:
lr_local = lr_local * 0.5
lr_each_step.append(lr_local)
current_step = global_step
lr_each_step = np.array(lr_each_step).astype(np.float32)
learning_rate = lr_each_step[current_step:]
return learning_rate
def get_thor_damping(global_step, damping_init, decay_rate, total_epochs, steps_per_epoch):
"""get_model_damping"""
damping_each_step = []
total_steps = steps_per_epoch * total_epochs
for step in range(total_steps):
epoch = (step + 1) / steps_per_epoch
damping_here = damping_init * (decay_rate ** (epoch / 10))
damping_each_step.append(damping_here)
current_step = global_step
damping_each_step = np.array(damping_each_step).astype(np.float32)
damping_now = damping_each_step[current_step:]
return damping_now
class LossGet(Callback):
def __init__(self, per_print_times, data_size):
super(LossGet, self).__init__()
if not isinstance(per_print_times, int) or per_print_times < 0:
raise ValueError("print_step must be int and >= 0.")
self._per_print_times = per_print_times
self._loss = 0.0
self.data_size = data_size
self._epoch = 0
def step_end(self, run_context):
cb_params = run_context.original_args()
loss = cb_params.net_outputs
self._epoch = cb_params.cur_epoch_num
if isinstance(loss, (tuple, list)):
if isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray):
loss = loss[0]
if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray):
loss = np.mean(loss.asnumpy())
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
if isinstance(loss, float) and (np.isnan(loss) or np.isinf(loss)):
raise ValueError("epoch: {} step: {}. Invalid loss, terminating training."
.format(cb_params.cur_epoch_num, cur_step_in_epoch))
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0:
self._loss = loss
print("epoch: %s step: %s, loss is %s" % (cb_params.cur_epoch_num,
cur_step_in_epoch, loss), flush=True)
def epoch_begin(self, run_context):
self.epoch_time = time.time()
def epoch_end(self, run_context):
epoch_mseconds = (time.time() - self.epoch_time) * 1000
self._per_step_mseconds = epoch_mseconds / self.data_size
def get_loss(self):
return self._loss
def get_per_step_time(self):
return self._per_step_mseconds
def get_epoch(self):
return self._epoch
def train_process_thor(q, device_id, epoch_size, device_num, enable_hccl):
os.system("mkdir " + str(device_id))
os.chdir(str(device_id))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
context.set_context(device_id=device_id)
os.environ['MINDSPORE_HCCL_CONFIG_PATH'] = MINDSPORE_HCCL_CONFIG_PATH
os.environ['RANK_ID'] = str(device_id)
os.environ['RANK_SIZE'] = str(device_num)
if enable_hccl:
context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True, all_reduce_fusion_config=[85, 160])
init()
# network
net = resnet50(thor_config.class_num)
if not thor_config.label_smooth:
thor_config.label_smooth_factor = 0.0
# loss
loss = CrossEntropySmooth(sparse=True, reduction="mean", smooth_factor=thor_config.label_smooth_factor,
num_classes=thor_config.class_num)
# train dataset
dataset = create_dataset_thor(dataset_path=dataset_path, do_train=True,
batch_size=thor_config.batch_size, train_image_size=thor_config.train_image_size,
eval_image_size=thor_config.eval_image_size, target="Ascend",
distribute=True)
step_size = dataset.get_dataset_size()
# loss scale
loss_scale = FixedLossScaleManager(thor_config.loss_scale, drop_overflow_update=False)
# learning rate
lr = get_thor_lr(0, 0.05803, 4.04839, 53, 5004, decay_epochs=39)
damping = get_thor_damping(0, 0.02714, 0.50036, 70, 5004)
# optimizer
split_indices = [26, 53]
opt = thor(net, Tensor(lr), Tensor(damping), thor_config.momentum, thor_config.weight_decay, thor_config.loss_scale,
thor_config.batch_size, split_indices=split_indices, frequency=thor_config.frequency)
# evaluation network
dist_eval_network = ClassifyCorrectCell(net)
# model
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale,
metrics={'acc': DistAccuracy(batch_size=thor_config.eval_batch_size, device_num=device_num)},
amp_level="O2", keep_batchnorm_fp32=False,
eval_network=dist_eval_network)
model = ConvertModelUtils().convert_to_thor_model(model=model, network=net, loss_fn=loss, optimizer=opt,
loss_scale_manager=loss_scale, metrics={'acc'},
amp_level="O2", keep_batchnorm_fp32=False)
# callbacks
loss_cb = LossGet(1, step_size)
# train and eval
print("run_start", device_id)
model.train(2, dataset, callbacks=loss_cb,
sink_size=dataset.get_dataset_size(), dataset_sink_mode=True)
time_cost = loss_cb.get_per_step_time()
loss = loss_cb.get_loss()
epoch_idx = loss_cb.get_epoch()
print("the {} epoch's resnet result:\n "
"device{}, training loss {}, "
"training per step cost {:.2f} ms, total_cost {:.2f} ms".format(epoch_idx, device_id,
loss, time_cost, time_cost * step_size))
q.put({'loss': loss, 'cost': time_cost})
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_single
def test_resnet_thor_imagenet_8p_1():
"""
Feature: Resnet50 thor network
Description: Train and evaluate resnet50 thor network on imagenet dataset
Expectation: accuracy > 0.28, time cost < 25.
"""
context.set_context(enable_graph_kernel=False, enable_sparse=False)
context.reset_auto_parallel_context()
context.reset_ps_context()
q = Queue()
# resnet50_thor
device_num = 8
epoch_size = 2
enable_hccl = True
process = []
for i in range(device_num):
device_id = i
process.append(Process(target=train_process_thor,
args=(q, device_id, epoch_size, device_num, enable_hccl)))
cpu_count = os.cpu_count()
each_cpu_count = cpu_count // device_num
for i in range(device_num):
process[i].start()
if each_cpu_count > 1:
cpu_start = each_cpu_count * i
cpu_end = each_cpu_count * (i + 1)
process_cpu = [x for x in range(cpu_start, cpu_end)]
pid = process[i].pid
os.sched_setaffinity(pid, set(process_cpu))
print("Waiting for all subprocesses done...")
for i in range(device_num):
process[i].join()
# THOR
thor_loss = 0.0
thor_cost = 0.0
for i in range(device_num):
output = q.get()
thor_loss += output['loss']
thor_cost += output['cost']
thor_loss = thor_loss / device_num
thor_cost = thor_cost / device_num
for i in range(0, device_num):
os.system("rm -rf " + str(i))
print("End training...")
assert thor_loss < 7
assert thor_cost < 30