utils.py

from __future__ import division
import shutil
import numpy as np
import torch
from matplotlib import cm
from matplotlib.colors import ListedColormap, LinearSegmentedColormap

def high_res_colormap(low_res_cmap, resolution=1000, max_value=1):
    # Construct the list colormap, with interpolated values for higer resolution
    # For a linear segmented colormap, you can just specify the number of point in
    # cm.get_cmap(name, lutsize) with the parameter lutsize
    x = np.linspace(0,1,low_res_cmap.N)
    low_res = low_res_cmap(x)
    new_x = np.linspace(0,max_value,resolution)
    high_res = np.stack([np.interp(new_x, x, low_res[:,i]) for i in range(low_res.shape[1])], axis=1)
    return ListedColormap(high_res)


def opencv_rainbow(resolution=1000):
    # Construct the opencv equivalent of Rainbow
    opencv_rainbow_data = (
        (0.000, (1.00, 0.00, 0.00)),
        (0.400, (1.00, 1.00, 0.00)),
        (0.600, (0.00, 1.00, 0.00)),
        (0.800, (0.00, 0.00, 1.00)),
        (1.000, (0.60, 0.00, 1.00))
    )

    return LinearSegmentedColormap.from_list('opencv_rainbow', opencv_rainbow_data, resolution)


COLORMAPS = {'rainbow': opencv_rainbow(),
             'magma': high_res_colormap(cm.get_cmap('magma')),
             'bone': cm.get_cmap('bone', 10000)}


def tensor2array(tensor, max_value=None, colormap='rainbow'):
    tensor = tensor.detach().cpu()
    if max_value is None:
        max_value = tensor.max().item()
    if tensor.ndimension() == 2 or tensor.size(0) == 1:
        norm_array = tensor.squeeze().numpy()/max_value
        array = COLORMAPS[colormap](norm_array).astype(np.float32)
        array = array[:,:,:3]
        array = array.transpose(2, 0, 1)

    elif tensor.ndimension() == 3:
        if (tensor.size(0) == 3):
            array = 0.5 + tensor.numpy()*0.5
        elif (tensor.size(0) == 2):
            array = tensor.numpy()

    return array

def save_checkpoint(save_path, dispnet_state, posenet_state, masknet_state, flownet_state, optimizer_state, is_best, filename='checkpoint.pth.tar'):
    file_prefixes = ['dispnet', 'posenet', 'masknet', 'flownet', 'optimizer']
    states = [dispnet_state, posenet_state, masknet_state, flownet_state, optimizer_state]
    for (prefix, state) in zip(file_prefixes, states):
        torch.save(state, save_path/'{}_{}'.format(prefix,filename))

    if is_best:
        for prefix in file_prefixes:
            shutil.copyfile(save_path/'{}_{}'.format(prefix,filename), save_path/'{}_model_best.pth.tar'.format(prefix))