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160 lines
4.9 KiB
160 lines
4.9 KiB
import os
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import torch
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import logging.config
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import shutil
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import pandas as pd
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from bokeh.io import output_file, save, show
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from bokeh.plotting import figure
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from bokeh.layouts import column
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#from bokeh.charts import Line, defaults
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#
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#defaults.width = 800
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#defaults.height = 400
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#defaults.tools = 'pan,box_zoom,wheel_zoom,box_select,hover,resize,reset,save'
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def setup_logging(log_file='log.txt'):
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"""Setup logging configuration
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"""
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logging.basicConfig(level=logging.DEBUG,
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format="%(asctime)s - %(levelname)s - %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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filename=log_file,
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filemode='w')
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console = logging.StreamHandler()
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console.setLevel(logging.INFO)
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formatter = logging.Formatter('%(message)s')
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console.setFormatter(formatter)
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logging.getLogger('').addHandler(console)
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class ResultsLog(object):
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def __init__(self, path='results.csv', plot_path=None):
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self.path = path
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self.plot_path = plot_path or (self.path + '.html')
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self.figures = []
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self.results = None
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def add(self, **kwargs):
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df = pd.DataFrame([kwargs.values()], columns=kwargs.keys())
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if self.results is None:
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self.results = df
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else:
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self.results = self.results.append(df, ignore_index=True)
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def save(self, title='Training Results'):
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if len(self.figures) > 0:
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if os.path.isfile(self.plot_path):
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os.remove(self.plot_path)
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output_file(self.plot_path, title=title)
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plot = column(*self.figures)
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save(plot)
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self.figures = []
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self.results.to_csv(self.path, index=False, index_label=False)
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def load(self, path=None):
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path = path or self.path
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if os.path.isfile(path):
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self.results.read_csv(path)
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def show(self):
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if len(self.figures) > 0:
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plot = column(*self.figures)
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show(plot)
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#def plot(self, *kargs, **kwargs):
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# line = Line(data=self.results, *kargs, **kwargs)
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# self.figures.append(line)
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def image(self, *kargs, **kwargs):
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fig = figure()
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fig.image(*kargs, **kwargs)
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self.figures.append(fig)
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def save_checkpoint(state, is_best, path='.', filename='checkpoint.pth.tar', save_all=False):
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filename = os.path.join(path, filename)
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torch.save(state, filename)
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if is_best:
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shutil.copyfile(filename, os.path.join(path, 'model_best.pth.tar'))
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if save_all:
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shutil.copyfile(filename, os.path.join(
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path, 'checkpoint_epoch_%s.pth.tar' % state['epoch']))
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class AverageMeter(object):
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"""Computes and stores the average and current value"""
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def __init__(self):
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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__optimizers = {
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'SGD': torch.optim.SGD,
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'ASGD': torch.optim.ASGD,
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'Adam': torch.optim.Adam,
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'Adamax': torch.optim.Adamax,
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'Adagrad': torch.optim.Adagrad,
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'Adadelta': torch.optim.Adadelta,
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'Rprop': torch.optim.Rprop,
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'RMSprop': torch.optim.RMSprop
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}
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def adjust_optimizer(optimizer, epoch, config):
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"""Reconfigures the optimizer according to epoch and config dict"""
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def modify_optimizer(optimizer, setting):
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if 'optimizer' in setting:
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optimizer = __optimizers[setting['optimizer']](
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optimizer.param_groups)
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logging.debug('OPTIMIZER - setting method = %s' %
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setting['optimizer'])
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for param_group in optimizer.param_groups:
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for key in param_group.keys():
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if key in setting:
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logging.debug('OPTIMIZER - setting %s = %s' %
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(key, setting[key]))
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param_group[key] = setting[key]
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return optimizer
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if callable(config):
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optimizer = modify_optimizer(optimizer, config(epoch))
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else:
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for e in range(epoch + 1): # run over all epochs - sticky setting
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if e in config:
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optimizer = modify_optimizer(optimizer, config[e])
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return optimizer
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def accuracy(output, target, topk=(1,)):
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"""Computes the precision@k for the specified values of k"""
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maxk = max(topk)
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batch_size = target.size(0)
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_, pred = output.float().topk(maxk, 1, True, True)
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pred = pred.t()
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correct = pred.eq(target.view(1, -1).expand_as(pred))
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res = []
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for k in topk:
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correct_k = correct[:k].view(-1).float().sum(0)
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res.append(correct_k.mul_(100.0 / batch_size))
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return res
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# kernel_img = model.features[0][0].kernel.data.clone()
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# kernel_img.add_(-kernel_img.min())
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# kernel_img.mul_(255 / kernel_img.max())
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# save_image(kernel_img, 'kernel%s.jpg' % epoch)
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