Function plot_input

slowfast/utils/misc.py:224–251

Plot the input tensor with the optional bounding box and save it to disk. Args: tensor (tensor): a tensor with shape of `NxCxHxW`. bboxes (tuple): bounding boxes with format of [[x, y, h, w]]. texts (tuple): a tuple of string to plot. path (str): path to the

(tensor, bboxes=(), texts=(), path="./tmp_vis.png")

def plot_input(tensor, bboxes=(), texts=(), path="./tmp_vis.png"):
    """
    Plot the input tensor with the optional bounding box and save it to disk.
    Args:
        tensor (tensor): a tensor with shape of `NxCxHxW`.
        bboxes (tuple): bounding boxes with format of [[x, y, h, w]].
        texts (tuple): a tuple of string to plot.
        path (str): path to the image to save to.
    """
    tensor = tensor.float()
    tensor = tensor - tensor.min()
    tensor = tensor / tensor.max()
    f, ax = plt.subplots(nrows=1, ncols=tensor.shape[0], figsize=(50, 20))
    for i in range(tensor.shape[0]):
        ax[i].axis("off")
        ax[i].imshow(tensor[i].permute(1, 2, 0))
        # ax[1][0].axis('off')
        if bboxes is not None and len(bboxes) > i:
            for box in bboxes[i]:
                x1, y1, x2, y2 = box
                ax[i].vlines(x1, y1, y2, colors="g", linestyles="solid")
                ax[i].vlines(x2, y1, y2, colors="g", linestyles="solid")
                ax[i].hlines(y1, x1, x2, colors="g", linestyles="solid")
                ax[i].hlines(y2, x1, x2, colors="g", linestyles="solid")

        if texts is not None and len(texts) > i:
            ax[i].text(0, 0, texts[i])
    f.savefig(path)


def frozen_bn_stats(model):