pytorch 其他深度框架使用tensorflow的tensorboard 可视化

拼搏现实的明天。 2021-06-24 15:59 491阅读 0赞

[代码来源][Link 1]  
首先你需要安装

tensorflow
    pytorch

**定义一个logger.py文件：**

import tensorflow as tf
    import numpy as np
    import scipy.misc 
    
    try:
        from StringIO import StringIO  # Python 2.7
    except ImportError:
        from io import BytesIO         # Python 3.x
    
    
    class Logger(object):
    
        def __init__(self, log_dir):
            """Create a summary writer logging to log_dir."""
            self.writer = tf.summary.FileWriter(log_dir)
    
        def scalar_summary(self, tag, value, step):
            """Log a scalar variable."""
            summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
            self.writer.add_summary(summary, step)
    
        def image_summary(self, tag, images, step):
            """Log a list of images."""
    
            img_summaries = []
            for i, img in enumerate(images):
                # Write the image to a string
                try:
                    s = StringIO()
                except:
                    s = BytesIO()
                scipy.misc.toimage(img).save(s, format="png")
    
                # Create an Image object
                img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
                                           height=img.shape[0],
                                           width=img.shape[1])
                # Create a Summary value
                img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum))
    
            # Create and write Summary
            summary = tf.Summary(value=img_summaries)
            self.writer.add_summary(summary, step)
    
        def histo_summary(self, tag, values, step, bins=1000):
            """Log a histogram of the tensor of values."""
    
            # Create a histogram using numpy
            counts, bin_edges = np.histogram(values, bins=bins)
    
            # Fill the fields of the histogram proto
            hist = tf.HistogramProto()
            hist.min = float(np.min(values))
            hist.max = float(np.max(values))
            hist.num = int(np.prod(values.shape))
            hist.sum = float(np.sum(values))
            hist.sum_squares = float(np.sum(values**2))
    
            # Drop the start of the first bin
            bin_edges = bin_edges[1:]
    
            # Add bin edges and counts
            for edge in bin_edges:
                hist.bucket_limit.append(edge)
            for c in counts:
                hist.bucket.append(c)
    
            # Create and write Summary
            summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
            self.writer.add_summary(summary, step)
            self.writer.flush()

**在你想要使用的地方使用：**

# 使用方法：
    from logger import Logger
    logger = Logger('./logs')
    
    # (1) Log the scalar values
    info = {
        'loss': loss.data[0],
        'accuracy': accuracy.data[0]
    }
    
    for tag, value in info.items():
        logger.scalar_summary(tag, value, step)
    
    # (2) Log values and gradients of the parameters (histogram)
    for tag, value in model.named_parameters():
        tag = tag.replace('.', '/')
        logger.histo_summary(tag, to_np(value), step)  # from Parameter to np.array
        logger.histo_summary(tag+'/grad', to_np(value.grad), step) # from Variable to np.array
    
    # (3) Log the images
    info = { # reshape (样本，h,w)
        'images': to_np(img.view(-1, 28, 28)[:10])
    }
    
    for tag, images in info.items():
        logger.image_summary(tag, images, step)

**打开终端开启tensorboard：**

# shell终端打开可视化
    tensorbard --logdir='./logs'

[Link 1]: https://github.com/EmbraceLife/shendusuipian/blob/8aca1da5af103fcd190e6240e4d16ab97483963d/pytorch/ready/pytorch_tensorboard/requirements.txt