텐서보드로 파이토치 학습 모니터링 하기

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  1. 제갈식2017년 9월 12일 오전 2:18

    #============ TensorBoard logging ============#
    # (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+1)

    # (2) Log values and gradients of the parameters (histogram)
    for tag, value in net.named_parameters():
    tag = tag.replace('.', '/')
    logger.histo_summary(tag, to_np(value), step+1)
    logger.histo_summary(tag+'/grad', to_np(value.grad), step+1)

    # (3) Log the images
    info = {
    'images': to_np(images.view(-1, 28, 28)[:10])
    }

    for tag, images in info.items():
    logger.image_summary(tag, images, step+1)

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파이썬으로 Homomorphic Filtering 하기

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import cv2 # For OpenCV modules (For Image I/O and Contour Finding) import numpy as np # For general purpose array manipulation import scipy . fftpack # For FFT2 #### imclearborder definition def imclearborder ( imgBW , radius ): # Given a black and white image, first find all of its contours imgBWcopy = imgBW . copy () contours , hierarchy = cv2 . findContours ( imgBWcopy . copy (), cv2 . RETR_LIST , cv2 . CHAIN_APPROX_SIMPLE ) # Get dimensions of image imgRows = imgBW . shape [ 0 ] imgCols = imgBW . shape [ 1 ] contourList = [] # ID list of contours that touch the border # For each contour... for idx in np . arange ( len ( contours )): # Get the i'th contour cnt = contours [ idx ] # Look at each point in the contour for pt in cnt : rowCnt = pt [ 0 ][ 1 ] colCnt = pt [ 0 ][ 0 ] # If this is within the radius of th...
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파이썬으로 Histogram matching 하기.

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import numpy as np def hist_match ( source , template ): """ Adjust the pixel values of a grayscale image such that its histogram matches that of a target image Arguments: ----------- source: np.ndarray Image to transform; the histogram is computed over the flattened array template: np.ndarray Template image; can have different dimensions to source Returns: ----------- matched: np.ndarray The transformed output image """ oldshape = source . shape source = source . ravel () template = template . ravel () # get the set of unique pixel values and their corresponding indices and # counts s_values , bin_idx , s_counts = np . unique ( source , return_inverse = True , return_counts = True ) t_values , t_counts = np . unique ( template , return_counts = True ) ...
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