diaoenmao

# Resume
git clone https://github.com/Collaborative-AI/colda.git
cd colda

# Init
git commit --allow-empty -m "Initial dummy commit"

# Merge for package
git remote add --fetch package https://github.com/Collaborative-AI/Package.git
git merge package/main --allow-unrelated-histories

diaoenmao

from PIL import Image
from argparse import ArgumentParser
import os
from multiprocessing import Pool

alg_dict = {
    'lanczos': Image.LANCZOS,
    'nearest': Image.NEAREST,
    'bilinear': Image.BILINEAR,
    'bicubic': Image.BICUBIC,

diaoenmao

# Instructions can be found at https://github.com/willylulu/celeba-hq-modified and https://zhuanlan.zhihu.com/p/52188519

import os
import sys
import io
import glob
import pickle
import argparse
import threading
import queue

diaoenmao

if __name__ == '__main__':
    aligned_cmumosi_highlevel = mmdatasdk.mmdataset('./data/MOSI/deployed')
    data_keys = mmdatasdk.dataset.standard_datasets.CMU_MOSI.cmu_mosi_std_folds.standard_train_fold + \
                mmdatasdk.dataset.standard_datasets.CMU_MOSI.cmu_mosi_std_folds.standard_valid_fold + \
                mmdatasdk.dataset.standard_datasets.CMU_MOSI.cmu_mosi_std_folds.standard_test_fold
    supported_feature_names = {'covarep':'COVAREP','opensmile':'OpenSmile-emobase2010','facet':'FACET 4.1','glove':'glove_vectors','bert':'BERT embeddings','label':'Opinion Segment Labels'}
    data = {k:{} for k in supported_feature_names}
    id = {k:[] for k in supported_feature_names}
    print('multiple labels keys')
    for k in supported_feature_names:

diaoenmao

def extract_patches_2d(img,patch_shape,step=[1.0,1.0],batch_first=False):
    patch_H, patch_W = patch_shape[0], patch_shape[1]
    if(img.size(2)<patch_H):
        num_padded_H_Top = (patch_H - img.size(2))//2
        num_padded_H_Bottom = patch_H - img.size(2) - num_padded_H_Top
        padding_H = nn.ConstantPad2d((0,0,num_padded_H_Top,num_padded_H_Bottom),0)
        img = padding_H(img)
    if(img.size(3)<patch_W):
        num_padded_W_Left = (patch_W - img.size(3))//2
        num_padded_W_Right = patch_W - img.size(3) - num_padded_W_Left

diaoenmao

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


torch.manual_seed(2809)


def check_params(modelA, modelB):