mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-12-17 20:28:01 +01:00
Reformat and rewrite _get_name_params (#57)
* Reformat
* rewrite _get_name_params
* Add workflow for automatic formatting
* Revert "Add workflow for automatic formatting"
This reverts commit 9111c5dbc1.
* revert Retrieval_based_Voice_Conversion_WebUI.ipynb
---------
Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com>
This commit is contained in:
Ftps
642
train/utils.py
642
train/utils.py
@@ -1,4 +1,4 @@
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import os,traceback
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import os, traceback
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import glob
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import sys
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import argparse
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@@ -14,44 +14,53 @@ MATPLOTLIB_FLAG = False
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logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
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logger = logging
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def load_checkpoint_d(checkpoint_path, combd,sbd, optimizer=None,load_opt=1):
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assert os.path.isfile(checkpoint_path)
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checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
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##################
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def go(model,bkey):
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saved_state_dict = checkpoint_dict[bkey]
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if hasattr(model, 'module'):state_dict = model.module.state_dict()
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else:state_dict = model.state_dict()
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new_state_dict= {}
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for k, v in state_dict.items():#模型需要的shape
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try:
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new_state_dict[k] = saved_state_dict[k]
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if(saved_state_dict[k].shape!=state_dict[k].shape):
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print("shape-%s-mismatch|need-%s|get-%s"%(k,state_dict[k].shape,saved_state_dict[k].shape))#
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raise KeyError
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except:
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# logger.info(traceback.format_exc())
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logger.info("%s is not in the checkpoint" % k)#pretrain缺失的
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new_state_dict[k] = v#模型自带的随机值
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if hasattr(model, 'module'):
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model.module.load_state_dict(new_state_dict,strict=False)
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else:
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model.load_state_dict(new_state_dict,strict=False)
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go(combd,"combd")
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go(sbd,"sbd")
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#############
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logger.info("Loaded model weights")
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def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
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assert os.path.isfile(checkpoint_path)
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checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
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iteration = checkpoint_dict['iteration']
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learning_rate = checkpoint_dict['learning_rate']
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if optimizer is not None and load_opt==1:###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
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# try:
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optimizer.load_state_dict(checkpoint_dict['optimizer'])
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# except:
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# traceback.print_exc()
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logger.info("Loaded checkpoint '{}' (epoch {})" .format(checkpoint_path, iteration))
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return model, optimizer, learning_rate, iteration
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##################
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def go(model, bkey):
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saved_state_dict = checkpoint_dict[bkey]
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if hasattr(model, "module"):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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new_state_dict = {}
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for k, v in state_dict.items(): # 模型需要的shape
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try:
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new_state_dict[k] = saved_state_dict[k]
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if saved_state_dict[k].shape != state_dict[k].shape:
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print(
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"shape-%s-mismatch|need-%s|get-%s"
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% (k, state_dict[k].shape, saved_state_dict[k].shape)
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) #
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raise KeyError
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except:
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# logger.info(traceback.format_exc())
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logger.info("%s is not in the checkpoint" % k) # pretrain缺失的
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new_state_dict[k] = v # 模型自带的随机值
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if hasattr(model, "module"):
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model.module.load_state_dict(new_state_dict, strict=False)
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else:
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model.load_state_dict(new_state_dict, strict=False)
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go(combd, "combd")
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go(sbd, "sbd")
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#############
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logger.info("Loaded model weights")
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iteration = checkpoint_dict["iteration"]
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learning_rate = checkpoint_dict["learning_rate"]
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if (
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optimizer is not None and load_opt == 1
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): ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
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# try:
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optimizer.load_state_dict(checkpoint_dict["optimizer"])
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# except:
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# traceback.print_exc()
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logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
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return model, optimizer, learning_rate, iteration
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# def load_checkpoint(checkpoint_path, model, optimizer=None):
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@@ -83,303 +92,380 @@ def load_checkpoint_d(checkpoint_path, combd,sbd, optimizer=None,load_opt=1):
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# logger.info("Loaded checkpoint '{}' (epoch {})" .format(
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# checkpoint_path, iteration))
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# return model, optimizer, learning_rate, iteration
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def load_checkpoint(checkpoint_path, model, optimizer=None,load_opt=1):
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assert os.path.isfile(checkpoint_path)
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checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
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def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
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assert os.path.isfile(checkpoint_path)
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checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
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saved_state_dict = checkpoint_dict['model']
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if hasattr(model, 'module'):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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new_state_dict= {}
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for k, v in state_dict.items():#模型需要的shape
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try:
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new_state_dict[k] = saved_state_dict[k]
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if(saved_state_dict[k].shape!=state_dict[k].shape):
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print("shape-%s-mismatch|need-%s|get-%s"%(k,state_dict[k].shape,saved_state_dict[k].shape))#
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raise KeyError
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except:
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# logger.info(traceback.format_exc())
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logger.info("%s is not in the checkpoint" % k)#pretrain缺失的
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new_state_dict[k] = v#模型自带的随机值
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if hasattr(model, 'module'):
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model.module.load_state_dict(new_state_dict,strict=False)
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else:
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model.load_state_dict(new_state_dict,strict=False)
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logger.info("Loaded model weights")
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saved_state_dict = checkpoint_dict["model"]
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if hasattr(model, "module"):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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new_state_dict = {}
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for k, v in state_dict.items(): # 模型需要的shape
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try:
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new_state_dict[k] = saved_state_dict[k]
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if saved_state_dict[k].shape != state_dict[k].shape:
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print(
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"shape-%s-mismatch|need-%s|get-%s"
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% (k, state_dict[k].shape, saved_state_dict[k].shape)
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) #
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raise KeyError
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except:
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# logger.info(traceback.format_exc())
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logger.info("%s is not in the checkpoint" % k) # pretrain缺失的
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new_state_dict[k] = v # 模型自带的随机值
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if hasattr(model, "module"):
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model.module.load_state_dict(new_state_dict, strict=False)
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else:
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model.load_state_dict(new_state_dict, strict=False)
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logger.info("Loaded model weights")
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iteration = checkpoint_dict['iteration']
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learning_rate = checkpoint_dict['learning_rate']
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if optimizer is not None and load_opt==1:###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
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# try:
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optimizer.load_state_dict(checkpoint_dict['optimizer'])
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# except:
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# traceback.print_exc()
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logger.info("Loaded checkpoint '{}' (epoch {})" .format(checkpoint_path, iteration))
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return model, optimizer, learning_rate, iteration
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iteration = checkpoint_dict["iteration"]
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learning_rate = checkpoint_dict["learning_rate"]
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if (
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optimizer is not None and load_opt == 1
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): ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
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# try:
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optimizer.load_state_dict(checkpoint_dict["optimizer"])
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# except:
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# traceback.print_exc()
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logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
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return model, optimizer, learning_rate, iteration
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def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
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logger.info("Saving model and optimizer state at epoch {} to {}".format(
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iteration, checkpoint_path))
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if hasattr(model, 'module'):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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torch.save({'model': state_dict,
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'iteration': iteration,
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'optimizer': optimizer.state_dict(),
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'learning_rate': learning_rate}, checkpoint_path)
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logger.info(
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"Saving model and optimizer state at epoch {} to {}".format(
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iteration, checkpoint_path
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)
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)
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if hasattr(model, "module"):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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torch.save(
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{
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"model": state_dict,
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"iteration": iteration,
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"optimizer": optimizer.state_dict(),
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"learning_rate": learning_rate,
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},
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checkpoint_path,
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)
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def save_checkpoint_d(combd, sbd, optimizer, learning_rate, iteration, checkpoint_path):
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logger.info("Saving model and optimizer state at epoch {} to {}".format(
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iteration, checkpoint_path))
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if hasattr(combd, 'module'): state_dict_combd = combd.module.state_dict()
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else:state_dict_combd = combd.state_dict()
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if hasattr(sbd, 'module'): state_dict_sbd = sbd.module.state_dict()
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else:state_dict_sbd = sbd.state_dict()
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torch.save({
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'combd': state_dict_combd,
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'sbd': state_dict_sbd,
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'iteration': iteration,
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'optimizer': optimizer.state_dict(),
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'learning_rate': learning_rate}, checkpoint_path)
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logger.info(
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"Saving model and optimizer state at epoch {} to {}".format(
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iteration, checkpoint_path
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)
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)
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if hasattr(combd, "module"):
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state_dict_combd = combd.module.state_dict()
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else:
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state_dict_combd = combd.state_dict()
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if hasattr(sbd, "module"):
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state_dict_sbd = sbd.module.state_dict()
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else:
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state_dict_sbd = sbd.state_dict()
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torch.save(
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{
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"combd": state_dict_combd,
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"sbd": state_dict_sbd,
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"iteration": iteration,
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"optimizer": optimizer.state_dict(),
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"learning_rate": learning_rate,
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},
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checkpoint_path,
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)
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def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
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for k, v in scalars.items():
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writer.add_scalar(k, v, global_step)
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for k, v in histograms.items():
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writer.add_histogram(k, v, global_step)
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for k, v in images.items():
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writer.add_image(k, v, global_step, dataformats='HWC')
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for k, v in audios.items():
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writer.add_audio(k, v, global_step, audio_sampling_rate)
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def summarize(
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writer,
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global_step,
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scalars={},
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histograms={},
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images={},
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audios={},
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audio_sampling_rate=22050,
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):
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for k, v in scalars.items():
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writer.add_scalar(k, v, global_step)
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for k, v in histograms.items():
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writer.add_histogram(k, v, global_step)
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for k, v in images.items():
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writer.add_image(k, v, global_step, dataformats="HWC")
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for k, v in audios.items():
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writer.add_audio(k, v, global_step, audio_sampling_rate)
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def latest_checkpoint_path(dir_path, regex="G_*.pth"):
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f_list = glob.glob(os.path.join(dir_path, regex))
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f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
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x = f_list[-1]
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print(x)
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return x
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f_list = glob.glob(os.path.join(dir_path, regex))
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f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
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x = f_list[-1]
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print(x)
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return x
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def plot_spectrogram_to_numpy(spectrogram):
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global MATPLOTLIB_FLAG
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if not MATPLOTLIB_FLAG:
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import matplotlib
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matplotlib.use("Agg")
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MATPLOTLIB_FLAG = True
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mpl_logger = logging.getLogger('matplotlib')
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mpl_logger.setLevel(logging.WARNING)
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import matplotlib.pylab as plt
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import numpy as np
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fig, ax = plt.subplots(figsize=(10,2))
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im = ax.imshow(spectrogram, aspect="auto", origin="lower",
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interpolation='none')
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plt.colorbar(im, ax=ax)
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plt.xlabel("Frames")
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plt.ylabel("Channels")
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plt.tight_layout()
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global MATPLOTLIB_FLAG
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if not MATPLOTLIB_FLAG:
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import matplotlib
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fig.canvas.draw()
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data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
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data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
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plt.close()
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return data
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matplotlib.use("Agg")
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MATPLOTLIB_FLAG = True
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mpl_logger = logging.getLogger("matplotlib")
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mpl_logger.setLevel(logging.WARNING)
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import matplotlib.pylab as plt
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import numpy as np
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fig, ax = plt.subplots(figsize=(10, 2))
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im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
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plt.colorbar(im, ax=ax)
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plt.xlabel("Frames")
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plt.ylabel("Channels")
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plt.tight_layout()
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fig.canvas.draw()
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data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
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data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
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plt.close()
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return data
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def plot_alignment_to_numpy(alignment, info=None):
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global MATPLOTLIB_FLAG
|
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if not MATPLOTLIB_FLAG:
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import matplotlib
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matplotlib.use("Agg")
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MATPLOTLIB_FLAG = True
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mpl_logger = logging.getLogger('matplotlib')
|
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mpl_logger.setLevel(logging.WARNING)
|
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import matplotlib.pylab as plt
|
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import numpy as np
|
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global MATPLOTLIB_FLAG
|
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if not MATPLOTLIB_FLAG:
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import matplotlib
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fig, ax = plt.subplots(figsize=(6, 4))
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im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
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interpolation='none')
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fig.colorbar(im, ax=ax)
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xlabel = 'Decoder timestep'
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if info is not None:
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xlabel += '\n\n' + info
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plt.xlabel(xlabel)
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plt.ylabel('Encoder timestep')
|
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plt.tight_layout()
|
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matplotlib.use("Agg")
|
||||
MATPLOTLIB_FLAG = True
|
||||
mpl_logger = logging.getLogger("matplotlib")
|
||||
mpl_logger.setLevel(logging.WARNING)
|
||||
import matplotlib.pylab as plt
|
||||
import numpy as np
|
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|
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fig.canvas.draw()
|
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data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
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data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
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plt.close()
|
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return data
|
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fig, ax = plt.subplots(figsize=(6, 4))
|
||||
im = ax.imshow(
|
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alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
|
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)
|
||||
fig.colorbar(im, ax=ax)
|
||||
xlabel = "Decoder timestep"
|
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if info is not None:
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xlabel += "\n\n" + info
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plt.xlabel(xlabel)
|
||||
plt.ylabel("Encoder timestep")
|
||||
plt.tight_layout()
|
||||
|
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fig.canvas.draw()
|
||||
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
|
||||
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
||||
plt.close()
|
||||
return data
|
||||
|
||||
|
||||
def load_wav_to_torch(full_path):
|
||||
sampling_rate, data = read(full_path)
|
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return torch.FloatTensor(data.astype(np.float32)), sampling_rate
|
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sampling_rate, data = read(full_path)
|
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return torch.FloatTensor(data.astype(np.float32)), sampling_rate
|
||||
|
||||
|
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def load_filepaths_and_text(filename, split="|"):
|
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with open(filename, encoding='utf-8') as f:
|
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filepaths_and_text = [line.strip().split(split) for line in f]
|
||||
return filepaths_and_text
|
||||
with open(filename, encoding="utf-8") as f:
|
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filepaths_and_text = [line.strip().split(split) for line in f]
|
||||
return filepaths_and_text
|
||||
|
||||
|
||||
def get_hparams(init=True):
|
||||
'''
|
||||
todo:
|
||||
结尾七人组：
|
||||
保存频率、总epoch done
|
||||
bs done
|
||||
pretrainG、pretrainD done
|
||||
卡号：os.en["CUDA_VISIBLE_DEVICES"] done
|
||||
if_latest todo
|
||||
模型：if_f0 todo
|
||||
采样率：自动选择config done
|
||||
是否缓存数据集进GPU:if_cache_data_in_gpu done
|
||||
"""
|
||||
todo:
|
||||
结尾七人组：
|
||||
保存频率、总epoch done
|
||||
bs done
|
||||
pretrainG、pretrainD done
|
||||
卡号：os.en["CUDA_VISIBLE_DEVICES"] done
|
||||
if_latest todo
|
||||
模型：if_f0 todo
|
||||
采样率：自动选择config done
|
||||
是否缓存数据集进GPU:if_cache_data_in_gpu done
|
||||
|
||||
-m:
|
||||
自动决定training_files路径,改掉train_nsf_load_pretrain.py里的hps.data.training_files done
|
||||
-c不要了
|
||||
'''
|
||||
parser = argparse.ArgumentParser()
|
||||
# parser.add_argument('-c', '--config', type=str, default="configs/40k.json",help='JSON file for configuration')
|
||||
parser.add_argument('-se', '--save_every_epoch', type=int, required=True,help='checkpoint save frequency (epoch)')
|
||||
parser.add_argument('-te', '--total_epoch', type=int, required=True,help='total_epoch')
|
||||
parser.add_argument('-pg', '--pretrainG', type=str, default="",help='Pretrained Discriminator path')
|
||||
parser.add_argument('-pd', '--pretrainD', type=str, default="",help='Pretrained Generator path')
|
||||
parser.add_argument('-g', '--gpus', type=str, default="0",help='split by -')
|
||||
parser.add_argument('-bs', '--batch_size', type=int, required=True,help='batch size')
|
||||
parser.add_argument('-e', '--experiment_dir', type=str, required=True,help='experiment dir')#-m
|
||||
parser.add_argument('-sr', '--sample_rate', type=str, required=True,help='sample rate, 32k/40k/48k')
|
||||
parser.add_argument('-f0', '--if_f0', type=int, required=True,help='use f0 as one of the inputs of the model, 1 or 0')
|
||||
parser.add_argument('-l', '--if_latest', type=int, required=True,help='if only save the latest G/D pth file, 1 or 0')
|
||||
parser.add_argument('-c', '--if_cache_data_in_gpu', type=int, required=True,help='if caching the dataset in GPU memory, 1 or 0')
|
||||
-m:
|
||||
自动决定training_files路径,改掉train_nsf_load_pretrain.py里的hps.data.training_files done
|
||||
-c不要了
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
# parser.add_argument('-c', '--config', type=str, default="configs/40k.json",help='JSON file for configuration')
|
||||
parser.add_argument(
|
||||
"-se",
|
||||
"--save_every_epoch",
|
||||
type=int,
|
||||
required=True,
|
||||
help="checkpoint save frequency (epoch)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-te", "--total_epoch", type=int, required=True, help="total_epoch"
|
||||
)
|
||||
parser.add_argument(
|
||||
"-pg", "--pretrainG", type=str, default="", help="Pretrained Discriminator path"
|
||||
)
|
||||
parser.add_argument(
|
||||
"-pd", "--pretrainD", type=str, default="", help="Pretrained Generator path"
|
||||
)
|
||||
parser.add_argument("-g", "--gpus", type=str, default="0", help="split by -")
|
||||
parser.add_argument(
|
||||
"-bs", "--batch_size", type=int, required=True, help="batch size"
|
||||
)
|
||||
parser.add_argument(
|
||||
"-e", "--experiment_dir", type=str, required=True, help="experiment dir"
|
||||
) # -m
|
||||
parser.add_argument(
|
||||
"-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
|
||||
)
|
||||
parser.add_argument(
|
||||
"-f0",
|
||||
"--if_f0",
|
||||
type=int,
|
||||
required=True,
|
||||
help="use f0 as one of the inputs of the model, 1 or 0",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-l",
|
||||
"--if_latest",
|
||||
type=int,
|
||||
required=True,
|
||||
help="if only save the latest G/D pth file, 1 or 0",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-c",
|
||||
"--if_cache_data_in_gpu",
|
||||
type=int,
|
||||
required=True,
|
||||
help="if caching the dataset in GPU memory, 1 or 0",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
name = args.experiment_dir
|
||||
experiment_dir = os.path.join("./logs", args.experiment_dir)
|
||||
args = parser.parse_args()
|
||||
name = args.experiment_dir
|
||||
experiment_dir = os.path.join("./logs", args.experiment_dir)
|
||||
|
||||
if not os.path.exists(experiment_dir):
|
||||
os.makedirs(experiment_dir)
|
||||
if not os.path.exists(experiment_dir):
|
||||
os.makedirs(experiment_dir)
|
||||
|
||||
config_path = "configs/%s.json"%args.sample_rate
|
||||
config_save_path = os.path.join(experiment_dir, "config.json")
|
||||
if init:
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
with open(config_save_path, "w") as f:
|
||||
f.write(data)
|
||||
else:
|
||||
with open(config_save_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
config_path = "configs/%s.json" % args.sample_rate
|
||||
config_save_path = os.path.join(experiment_dir, "config.json")
|
||||
if init:
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
with open(config_save_path, "w") as f:
|
||||
f.write(data)
|
||||
else:
|
||||
with open(config_save_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
|
||||
hparams = HParams(**config)
|
||||
hparams.model_dir = hparams.experiment_dir = experiment_dir
|
||||
hparams.save_every_epoch = args.save_every_epoch
|
||||
hparams.name = name
|
||||
hparams.total_epoch = args.total_epoch
|
||||
hparams.pretrainG = args.pretrainG
|
||||
hparams.pretrainD = args.pretrainD
|
||||
hparams.gpus = args.gpus
|
||||
hparams.train.batch_size = args.batch_size
|
||||
hparams.sample_rate = args.sample_rate
|
||||
hparams.if_f0 = args.if_f0
|
||||
hparams.if_latest = args.if_latest
|
||||
hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
|
||||
hparams.data.training_files = "%s/filelist.txt"%experiment_dir
|
||||
return hparams
|
||||
hparams = HParams(**config)
|
||||
hparams.model_dir = hparams.experiment_dir = experiment_dir
|
||||
hparams.save_every_epoch = args.save_every_epoch
|
||||
hparams.name = name
|
||||
hparams.total_epoch = args.total_epoch
|
||||
hparams.pretrainG = args.pretrainG
|
||||
hparams.pretrainD = args.pretrainD
|
||||
hparams.gpus = args.gpus
|
||||
hparams.train.batch_size = args.batch_size
|
||||
hparams.sample_rate = args.sample_rate
|
||||
hparams.if_f0 = args.if_f0
|
||||
hparams.if_latest = args.if_latest
|
||||
hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
|
||||
hparams.data.training_files = "%s/filelist.txt" % experiment_dir
|
||||
return hparams
|
||||
|
||||
|
||||
def get_hparams_from_dir(model_dir):
|
||||
config_save_path = os.path.join(model_dir, "config.json")
|
||||
with open(config_save_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
config_save_path = os.path.join(model_dir, "config.json")
|
||||
with open(config_save_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
|
||||
hparams =HParams(**config)
|
||||
hparams.model_dir = model_dir
|
||||
return hparams
|
||||
hparams = HParams(**config)
|
||||
hparams.model_dir = model_dir
|
||||
return hparams
|
||||
|
||||
|
||||
def get_hparams_from_file(config_path):
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
|
||||
hparams =HParams(**config)
|
||||
return hparams
|
||||
hparams = HParams(**config)
|
||||
return hparams
|
||||
|
||||
|
||||
def check_git_hash(model_dir):
|
||||
source_dir = os.path.dirname(os.path.realpath(__file__))
|
||||
if not os.path.exists(os.path.join(source_dir, ".git")):
|
||||
logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
|
||||
source_dir
|
||||
))
|
||||
return
|
||||
source_dir = os.path.dirname(os.path.realpath(__file__))
|
||||
if not os.path.exists(os.path.join(source_dir, ".git")):
|
||||
logger.warn(
|
||||
"{} is not a git repository, therefore hash value comparison will be ignored.".format(
|
||||
source_dir
|
||||
)
|
||||
)
|
||||
return
|
||||
|
||||
cur_hash = subprocess.getoutput("git rev-parse HEAD")
|
||||
cur_hash = subprocess.getoutput("git rev-parse HEAD")
|
||||
|
||||
path = os.path.join(model_dir, "githash")
|
||||
if os.path.exists(path):
|
||||
saved_hash = open(path).read()
|
||||
if saved_hash != cur_hash:
|
||||
logger.warn("git hash values are different. {}(saved) != {}(current)".format(
|
||||
saved_hash[:8], cur_hash[:8]))
|
||||
else:
|
||||
open(path, "w").write(cur_hash)
|
||||
path = os.path.join(model_dir, "githash")
|
||||
if os.path.exists(path):
|
||||
saved_hash = open(path).read()
|
||||
if saved_hash != cur_hash:
|
||||
logger.warn(
|
||||
"git hash values are different. {}(saved) != {}(current)".format(
|
||||
saved_hash[:8], cur_hash[:8]
|
||||
)
|
||||
)
|
||||
else:
|
||||
open(path, "w").write(cur_hash)
|
||||
|
||||
|
||||
def get_logger(model_dir, filename="train.log"):
|
||||
global logger
|
||||
logger = logging.getLogger(os.path.basename(model_dir))
|
||||
logger.setLevel(logging.DEBUG)
|
||||
|
||||
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
|
||||
if not os.path.exists(model_dir):
|
||||
os.makedirs(model_dir)
|
||||
h = logging.FileHandler(os.path.join(model_dir, filename))
|
||||
h.setLevel(logging.DEBUG)
|
||||
h.setFormatter(formatter)
|
||||
logger.addHandler(h)
|
||||
return logger
|
||||
global logger
|
||||
logger = logging.getLogger(os.path.basename(model_dir))
|
||||
logger.setLevel(logging.DEBUG)
|
||||
|
||||
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
|
||||
if not os.path.exists(model_dir):
|
||||
os.makedirs(model_dir)
|
||||
h = logging.FileHandler(os.path.join(model_dir, filename))
|
||||
h.setLevel(logging.DEBUG)
|
||||
h.setFormatter(formatter)
|
||||
logger.addHandler(h)
|
||||
return logger
|
||||
|
||||
|
||||
class HParams():
|
||||
def __init__(self, **kwargs):
|
||||
for k, v in kwargs.items():
|
||||
if type(v) == dict:
|
||||
v = HParams(**v)
|
||||
self[k] = v
|
||||
|
||||
def keys(self):
|
||||
return self.__dict__.keys()
|
||||
class HParams:
|
||||
def __init__(self, **kwargs):
|
||||
for k, v in kwargs.items():
|
||||
if type(v) == dict:
|
||||
v = HParams(**v)
|
||||
self[k] = v
|
||||
|
||||
def items(self):
|
||||
return self.__dict__.items()
|
||||
def keys(self):
|
||||
return self.__dict__.keys()
|
||||
|
||||
def values(self):
|
||||
return self.__dict__.values()
|
||||
def items(self):
|
||||
return self.__dict__.items()
|
||||
|
||||
def __len__(self):
|
||||
return len(self.__dict__)
|
||||
def values(self):
|
||||
return self.__dict__.values()
|
||||
|
||||
def __getitem__(self, key):
|
||||
return getattr(self, key)
|
||||
def __len__(self):
|
||||
return len(self.__dict__)
|
||||
|
||||
def __setitem__(self, key, value):
|
||||
return setattr(self, key, value)
|
||||
def __getitem__(self, key):
|
||||
return getattr(self, key)
|
||||
|
||||
def __contains__(self, key):
|
||||
return key in self.__dict__
|
||||
def __setitem__(self, key, value):
|
||||
return setattr(self, key, value)
|
||||
|
||||
def __repr__(self):
|
||||
return self.__dict__.__repr__()
|
||||
def __contains__(self, key):
|
||||
return key in self.__dict__
|
||||
|
||||
def __repr__(self):
|
||||
return self.__dict__.__repr__()
|
||||
|
||||
Reference in New Issue
Block a user