tczhangzhi

from collections import OrderedDict

def load_parallel(model, state_dict):
    new_state_dict = OrderedDict()
    for k, v in state_dict.items():
        name = k[7:]
        new_state_dict[name] = v
    model.load_state_dict(new_state_dict)
    return model

tczhangzhi

import diff_match_patch

paper_a_path = 'a.tex'
paper_b_path = 'b.tex'

a_data = None
with open(icme_path, "r") as f:
    a_data = f.read()

b_data = None

tczhangzhi

import math
import torch

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

from matplotlib import cm
from matplotlib.lines import Line2D

tczhangzhi

import torch
import torch.nn
import torch.nn.functional as F


def one_hot_encode_truth(truth, num_class=4):
    # N, H, W --> N, C, H, W (C = num_class)
    one_hot = truth.repeat(1, num_class, 1, 1)
    arange = torch.arange(1, num_class + 1).view(1, num_class, 1, 1).to(truth.device)
    one_hot = (one_hot == arange).float()

tczhangzhi

Sub ResizePlotArea()
    Dim iHeight, iWidth As Integer
    iTop = 40
    iLeft = 0
    iHeight = 180
    iWidth = 280

    ActiveChart.PlotArea.Select
    Selection.Top = iTop
    Selection.Left = iLeft