utils/common.py

import torch
import gc
import os
import torch.nn as nn
import urllib.request
import cv2
from tqdm import tqdm

HTTP_PREFIXES = [
    'http',
    'data:image/jpeg',
]


RELEASED_WEIGHTS = {
    "hayao:v1": (
        "v1",
        "https://github.com/ptran1203/pytorch-animeGAN/releases/download/v1.0/generator_hayao.pth"
    ),
    "hayao": (
        "v1",
        "https://github.com/ptran1203/pytorch-animeGAN/releases/download/v1.0/generator_hayao.pth"
    ),
    "shinkai:v1": (
        "v1",
        "https://github.com/ptran1203/pytorch-animeGAN/releases/download/v1.0/generator_shinkai.pth"
    ),
    "shinkai": (
        "v1",
        "https://github.com/ptran1203/pytorch-animeGAN/releases/download/v1.0/generator_shinkai.pth"
    ),
    
    ## VER 2 ##
    "hayao:v2": (
        # Dataset trained on Google Landmark micro as training real photo
        "v2",
        "https://github.com/ptran1203/pytorch-animeGAN/releases/download/v1.2/GeneratorV2_gldv2_Hayao.pt"
    ),
    "shinkai:v2": (
        # Dataset trained on Google Landmark micro as training real photo
        "v2",
        "https://github.com/ptran1203/pytorch-animeGAN/releases/download/v1.2/GeneratorV2_gldv2_Shinkai.pt"
    ),
    ## Face portrait
    "arcane:v2": (
        "v2",
        "https://github.com/ptran1203/pytorch-animeGAN/releases/download/v1.2/GeneratorV2_ffhq_Arcane_210624_e350.pt"
    )
}

def is_image_file(path):
    _, ext = os.path.splitext(path)
    return ext.lower() in (".png", ".jpg", ".jpeg", ".webp")

def is_video_file(path):
    # https://moviepy-tburrows13.readthedocs.io/en/improve-docs/ref/VideoClip/VideoFileClip.html
    _, ext = os.path.splitext(path)
    return ext.lower() in (".mp4", ".mov", ".ogv", ".avi", ".mpeg")


def read_image(path):
    """
    Read image from given path
    """

    if any(path.startswith(p) for p in HTTP_PREFIXES):
        urllib.request.urlretrieve(path, "temp.jpg")
        path = "temp.jpg"

    img = cv2.imread(path)
    if img.shape[-1] == 4:
        # 4 channels image
        img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)
    else:
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    return img


def save_checkpoint(model, path, optimizer=None, epoch=None):
    checkpoint = {
        'model_state_dict': model.state_dict(),
        'epoch': epoch,
    }
    if optimizer is  not None:
        checkpoint['optimizer_state_dict'] = optimizer.state_dict()

    torch.save(checkpoint, path)

def maybe_remove_module(state_dict):
    # Remove added module ins state_dict in ddp training
    # https://discuss.pytorch.org/t/why-are-state-dict-keys-getting-prepended-with-the-string-module/104627/3
    new_state_dict = {}
    module_str = 'module.'
    for k, v in state_dict.items():

        if k.startswith(module_str):
            k = k[len(module_str):]
        new_state_dict[k] = v
    return new_state_dict


def load_checkpoint(model, path, optimizer=None, strip_optimizer=False, map_location=None) -> int:
    state_dict, path = load_state_dict(path, map_location)
    model_state_dict = maybe_remove_module(state_dict['model_state_dict'])
    model.load_state_dict(
        model_state_dict,
        strict=True
    )
    if 'optimizer_state_dict' in state_dict:
        if optimizer is not None:
            optimizer.load_state_dict(state_dict['optimizer_state_dict'])
        if strip_optimizer:
            del state_dict["optimizer_state_dict"]
            torch.save(state_dict, path)
            print(f"Optimizer stripped and saved to {path}")

    epoch = state_dict.get('epoch', 0)
    return epoch


def load_state_dict(weight, map_location) -> dict:
    if weight.lower() in RELEASED_WEIGHTS:
        weight = _download_weight(weight.lower())

    if map_location is None:
        # auto select
        map_location = 'cuda' if torch.cuda.is_available() else 'cpu'
    state_dict = torch.load(weight, map_location=map_location)

    return state_dict, weight


def initialize_weights(net):
    for m in net.modules():
        try:
            if isinstance(m, nn.Conv2d):
                # m.weight.data.normal_(0, 0.02)
                torch.nn.init.xavier_uniform_(m.weight)
                m.bias.data.zero_()
            elif isinstance(m, nn.ConvTranspose2d):
                # m.weight.data.normal_(0, 0.02)
                torch.nn.init.xavier_uniform_(m.weight)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                # m.weight.data.normal_(0, 0.02)
                torch.nn.init.xavier_uniform_(m.weight)
                m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
        except Exception as e:
            # print(f'SKip layer {m}, {e}')
            pass


def set_lr(optimizer, lr):
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


class DownloadProgressBar(tqdm):
    '''
    https://stackoverflow.com/questions/15644964/python-progress-bar-and-downloads
    '''
    def update_to(self, b=1, bsize=1, tsize=None):
        if tsize is not None:
            self.total = tsize
        self.update(b * bsize - self.n)


def _download_weight(weight):
    '''
    Download weight and save to local file
    '''
    os.makedirs('.cache', exist_ok=True)
    url = RELEASED_WEIGHTS[weight][1]
    filename = os.path.basename(url)
    save_path = f'.cache/{filename}'

    if os.path.isfile(save_path):
        return save_path

    desc = f'Downloading {url} to {save_path}'
    with DownloadProgressBar(unit='B', unit_scale=True, miniters=1, desc=desc) as t:
        urllib.request.urlretrieve(url, save_path, reporthook=t.update_to)

    return save_path