def auditConfig(self):
for name in self._mandatory_member_name:
if name not in self._mandatory_member:
LOG.logE(
"Error! self.{} must be definded in your subclass.".format(
name),
exit=True)
def loadStateDict(self):
if not self.state_dict:
LOG.logI("self.state_dict not initialized, omit loadStateDict()")
return
self.net.load_state_dict(self.state_dict, strict=False)
self.net.eval()
self.net = self.net.to(self.device)
def process(self):
self.iter = 0
for epoch in range(self.epoch, self.conf.epoch_num):
self.epoch = epoch
LOG.logI('Epoch {} started...'.format(self.epoch))
self.processTrain()
self.processVal()
self.processAccept()
def initOutputDir(self):
if self.conf.output_dir != 'output':
LOG.logW("According deepvac standard, you should save model files to output directory.")
self.output_dir = '{}/{}'.format(self.conf.output_dir, self.branch)
LOG.logI('model save dir: {}'.format(self.output_dir))
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
def exportONNX(self, img):
if not self.conf.onnx_output_model_path:
return
torch.onnx._export(self.net,
img,
self.conf.onnx_output_model_path,
export_params=True)
LOG.logI(
"Pytorch model convert to ONNX model succeed, save model in {}".
format(self.conf.onnx_output_model_path))
def initDDP(self):
parser = argparse.ArgumentParser(description='DeepvacDDP')
parser.add_argument("--gpu", default=-1, type=int, help="gpu")
parser.add_argument('--rank', default=-1, type=int, help='node rank for distributed training')
self.args = parser.parse_args()
self.map_location = {'cuda:%d' % 0: 'cuda:%d' % self.args.rank}
LOG.logI("Start dist.init_process_group {} {}@{} on {}".format(self.conf.dist_url, self.args.rank, self.conf.world_size - 1, self.args.gpu))
dist.init_process_group(backend='nccl', init_method=self.conf.dist_url, world_size=self.conf.world_size, rank=self.args.rank)
torch.cuda.set_device(self.args.gpu)
def initCheckpoint(self, map_location=None):
if not self.conf.checkpoint_suffix or self.conf.checkpoint_suffix == "":
LOG.logI('Omit the checkpoint file since not specified...')
return
LOG.logI('Load checkpoint from {} folder'.format(self.output_dir))
self.net.load_state_dict(
torch.load(self.output_dir +
'/model:{}'.format(conf.checkpoint_suffix),
map_location=map_location))
self.optimizer.load_state_dict(
torch.load(self.output_dir +
'/optimizer:{}'.format(conf.checkpoint_suffix),
map_location=map_location))
def initStateDict(self):
LOG.logI('Loading State Dict from {}'.format(self.model_path))
device = torch.cuda.current_device()
self.state_dict = torch.load(
self.model_path,
map_location=lambda storage, loc: storage.cuda(device))
#remove prefix begin
prefix = 'module.'
f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
if "state_dict" in self.state_dict.keys():
self.state_dict = {
f(key): value
for key, value in self.state_dict['state_dict'].items()
}
else:
self.state_dict = {
f(key): value
for key, value in self.state_dict.items()
}
#remove prefix end
# just do audit on model file
state_dict_keys = set(self.state_dict.keys())
code_net_keys = set(self.net.state_dict().keys())
used_keys = code_net_keys & state_dict_keys
unused_keys = state_dict_keys - code_net_keys
missing_keys = code_net_keys - state_dict_keys
LOG.logI('Missing keys:{}'.format(len(missing_keys)))
LOG.logI('Unused checkpoint keys:{}'.format(len(unused_keys)))
LOG.logI('Used keys:{}'.format(len(used_keys)))
assert len(used_keys) > 0, 'load NONE from pretrained model'
assert len(missing_keys) == 0, 'Net mismatched with pretrained model'
def process(self):
label_list = config.label_list
for image_path in self.dataset():
self.getTask(image_path)
self.request.set_content(
HttpContentHelper.toValue({
"tasks": [self.task],
"scenes": [config.scenes]
}))
response = self.clt.do_action_with_exception(self.request)
result = json.loads(response)
if result['code'] != 200:
LOG.logE(
f"got request error! please checkout your AccessKeyId and Secret"
)
continue
if result['data'][0]['code'] != 200:
LOG.logE(
f"got error! [file: {image_path}, msg: {result['data'][0]['msg']}]"
)
continue
pred = result['data'][0]['results'][0]['label']
label = image_path.split('/')[-2]
if label not in label_list:
LOG.logE(f"got error! test file structure not standard")
continue
LOG.logI(
f"got Success! [file: {image_path}, label: {label}, pred: {pred}]"
)
self.report.add(label_list.index(label), label_list.index(pred))
def processVal(self):
self.setValContext()
LOG.logI('Phase {} started...'.format(self.phase))
with torch.no_grad():
self.preEpoch()
for i, (img, idx) in enumerate(self.loader):
self.idx = idx
self.img = img
self.preIter()
self.doForward()
self.doLoss()
LOG.logI('{}: [{}][{}/{}]'.format(self.phase, self.epoch, i, len(self.loader)))
self.postIter()
self.postEpoch()
self.saveState(self.getTime())
def __init__(self, deepvac_config):
self.path_prefix = deepvac_config.fileline_data_path_prefix
self.fileline_path = deepvac_config.fileline_path
self.transform = deepvac_config.transform
self.samples = []
mark = []
with open(self.fileline_path) as f:
for line in f:
label = self._buildLabelFromLine(line)
self.samples.append(label)
mark.append(label[1])
self.len = len(self.samples)
self.class_num = len(np.unique(mark))
LOG.logI('FileLineDataset size: {} / {}'.format(
self.len, self.class_num))
def __init__(self, deepvac_config):
self.path_prefix = deepvac_config.dataset.fileline_data_path_prefix
self.fileline_path = deepvac_config.dataset.fileline_path
self.transform = deepvac_config.dataset.transform
self.samples = []
mark = []
with open(self.fileline_path) as f:
for line in f:
line = line.strip().split(" ")
label = [line[0], int(line[1])]
self.samples.append(label)
mark.append(label[1])
self.len = len(self.samples)
self.class_num = len(np.unique(mark))
LOG.logI('FileLineDataset size: {} / {}'.format(
self.len, self.class_num))
def separateBN4OptimizerPG(self, modules):
paras_only_bn = []
paras_wo_bn = []
memo = set()
gemfield_set = set()
gemfield_set.update(set(modules.parameters()))
LOG.logI("separateBN4OptimizerPG set len: {}".format(
len(gemfield_set)))
named_modules = modules.named_modules(prefix='')
for module_prefix, module in named_modules:
if "module" not in module_prefix:
LOG.logI(
"separateBN4OptimizerPG skip {}".format(module_prefix))
continue
members = module._parameters.items()
for k, v in members:
name = module_prefix + ('.' if module_prefix else '') + k
if v is None:
continue
if v in memo:
continue
memo.add(v)
if "batchnorm" in str(module.__class__):
paras_only_bn.append(v)
else:
paras_wo_bn.append(v)
LOG.logI("separateBN4OptimizerPG param len: {} - {}".format(
len(paras_wo_bn), len(paras_only_bn)))
return paras_only_bn, paras_wo_bn
def assertInGit(self):
if os.environ.get("disable_git"):
self.branch = "sevice"
return
if self.conf.disable_git:
self.branch = "disable_git"
return
self.branch = getCurrentGitBranch()
if self.branch is None:
LOG.logE(
'According to deepvac standard, you must working in a git repo.',
exit=True)
if len(self.branch) < 6:
LOG.logE(
'According to deepvac standard, your git branch name is too short: {}'
.format(self.branch),
exit=True)
if self.branch.startswith('LTS_'):
return
if self.branch.startswith('PROTO_'):
return
LOG.logE(
'According to deepvac standard, git branch name should start from LTS_ or PROTO_: {}'
.format(self.branch),
exit=True)
def processTrain(self):
self.setTrainContext()
self.step = 0
LOG.logI('Phase {} started...'.format(self.phase))
self.preEpoch()
loader_len = len(self.loader)
save_every = loader_len // self.conf.save_num
save_list = list(range(0, loader_len + 1, save_every))
self.save_list = save_list[1:-1]
LOG.logI('SAVE LIST: {}'.format(self.save_list))
for i, (img, idx) in enumerate(self.loader):
self.step = i
self.iter += 1
self.idx = idx
self.img = img
self.preIter()
self.doForward()
self.doLoss()
self.doBackward()
self.doOptimize()
if i % self.conf.log_every == 0:
LOG.logI('{}: [{}][{}/{}] [Loss:{} Lr:{}]'.format(
self.phase, self.epoch, self.step, loader_len,
self.loss.item(), self.optimizer.param_groups[0]['lr']))
self.postIter()
if self.step in self.save_list:
self.processVal()
self.setTrainContext()
self.postEpoch()
def __call__(self):
img_names = os.listdir(self.test_dataset)
ocr_report = OcrReport(ds_name='deepvac_ocr1.0_test_plate',
total_num=len(img_names))
for img_name in img_names:
image = self.get_file_content(
os.path.join(self.test_dataset, img_name))
# 高精度版通用ocr api
res = self.client.basicAccurate(image)
# 通用ocr api
#res = self.client.basicGeneral(image)
pred = ''
if 'error_code' in res.keys():
ocr_report.add(self.labels[img_name], pred)
LOG.logI('error code:{}'.format(res))
continue
for dic in res['words_result']:
pred += dic['words'].strip()
ocr_report.add(self.labels[img_name], pred)
LOG.logI('{}: {}'.format(img_name, res))
time.sleep(1)
ocr_report()
def initNetWithCode(self):
self.net = None
LOG.logE(
"You must reimplement initNetWithCode() to initialize self.net",
exit=True)
def process(self):
LOG.logE(
"You must reimplement process() to process self.input_output['input']",
exit=True)
def initOptimizer(self):
self.initSgdOptimizer()
LOG.logW(
"You should reimplement initOptimizer() to initialize self.optimizer, unless SGD is exactly what you need"
)
def initValLoader(self):
self.val_loader = None
LOG.logE(
"You must reimplement initTrainLoader() to initialize self.val_loader",
exit=True)
def initScheduler(self):
self.scheduler = torch.optim.lr_scheduler.StepLR(
self.optimizer, self.conf.lr_step, self.conf.lr_factor)
LOG.logW(
"You should reimplement initScheduler() to initialize self.scheduler, unless lr_scheduler.StepLR() is exactly what you need"
)
def initCriterion(self):
self.criterion = torch.nn.CrossEntropyLoss()
LOG.logW(
"You should reimplement initCriterion() to initialize self.criterion, unless CrossEntropyLoss() is exactly what you need"
)
def exportNCNN(self, img):
if not self.conf.ncnn_param_output_path or not self.conf.ncnn_bin_output_path:
return
if not self.conf.onnx2ncnn:
LOG.logE(
"You must set the onnx2ncnn executable program path in config file. If you want to compile onnx2ncnn tools, reference https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-linux-x86 ",
exit=True)
import onnx
import subprocess
import tempfile
from onnxsim import simplify
if not self.conf.onnx_output_model_path:
f = tempfile.NamedTemporaryFile()
self.conf.onnx_output_model_path = f.name
self.exportONNX(img)
cmd = self.conf.onnx2ncnn + " " + self.conf.onnx_output_model_path + " " + self.conf.ncnn_param_output_path + " " + self.conf.ncnn_bin_output_path
pd = subprocess.Popen(cmd,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
if pd.stderr.read() != b"":
LOG.logE(
pd.stderr.read() +
b". Error occured when export ncnn model. We try to simplify the model first"
)
model_op, check_ok = simplify(self.conf.onnx_output_model_path,
check_n=3,
perform_optimization=True,
skip_fuse_bn=True,
skip_shape_inference=False)
onnx.save(model_op, self.conf.onnx_output_model_path)
if not check_ok:
LOG.logE(
"Maybe something wrong when simplify the model, we can't guarantee generate model is right"
)
else:
LOG.logI("Simplify model succeed")
subprocess.Popen(cmd,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
if pd.stderr.read() != b"":
LOG.logE(pd.stderr.read() +
b". we can't guarantee generate model is right")
LOG.logI(
"Pytorch model convert to NCNN model succeed, save ncnn param file in {}, save ncnn bin file in {}"
.format(self.conf.ncnn_param_output_path,
self.conf.ncnn_bin_output_path))
