utils/common.py

from typing import Mapping, Any, Tuple, Callable
import importlib
import os
from urllib.parse import urlparse

import torch
from torch import Tensor
from torch.nn import functional as F
import numpy as np

from torch.hub import download_url_to_file, get_dir


def get_obj_from_str(string: str, reload: bool=False) -> Any:
    module, cls = string.rsplit(".", 1)
    if reload:
        module_imp = importlib.import_module(module)
        importlib.reload(module_imp)
    return getattr(importlib.import_module(module, package=None), cls)


def instantiate_from_config(config: Mapping[str, Any]) -> Any:
    if not "target" in config:
        raise KeyError("Expected key `target` to instantiate.")
    # import ipdb; ipdb.set_trace()
    return get_obj_from_str(config["target"])(**config.get("params", dict()))


def wavelet_blur(image: Tensor, radius: int):
    """
    Apply wavelet blur to the input tensor.
    """
    # input shape: (1, 3, H, W)
    # convolution kernel
    kernel_vals = [
        [0.0625, 0.125, 0.0625],
        [0.125, 0.25, 0.125],
        [0.0625, 0.125, 0.0625],
    ]
    kernel = torch.tensor(kernel_vals, dtype=image.dtype, device=image.device)
    # add channel dimensions to the kernel to make it a 4D tensor
    kernel = kernel[None, None]
    # repeat the kernel across all input channels
    kernel = kernel.repeat(3, 1, 1, 1)
    image = F.pad(image, (radius, radius, radius, radius), mode='replicate')
    # apply convolution
    output = F.conv2d(image, kernel, groups=3, dilation=radius)
    return output


def wavelet_decomposition(image: Tensor, levels=5):
    """
    Apply wavelet decomposition to the input tensor.
    This function only returns the low frequency & the high frequency.
    """
    high_freq = torch.zeros_like(image)
    for i in range(levels):
        radius = 2 ** i
        low_freq = wavelet_blur(image, radius)
        high_freq += (image - low_freq)
        image = low_freq

    return high_freq, low_freq


def wavelet_reconstruction(content_feat:Tensor, style_feat:Tensor):
    """
    Apply wavelet decomposition, so that the content will have the same color as the style.
    """
    # calculate the wavelet decomposition of the content feature
    content_high_freq, content_low_freq = wavelet_decomposition(content_feat)
    del content_low_freq
    # calculate the wavelet decomposition of the style feature
    style_high_freq, style_low_freq = wavelet_decomposition(style_feat)
    del style_high_freq
    # reconstruct the content feature with the style's high frequency
    return content_high_freq + style_low_freq


# https://github.com/XPixelGroup/BasicSR/blob/master/basicsr/utils/download_util.py/
def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
    """Load file form http url, will download models if necessary.

    Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py

    Args:
        url (str): URL to be downloaded.
        model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir.
            Default: None.
        progress (bool): Whether to show the download progress. Default: True.
        file_name (str): The downloaded file name. If None, use the file name in the url. Default: None.

    Returns:
        str: The path to the downloaded file.
    """
    if model_dir is None:  # use the pytorch hub_dir
        hub_dir = get_dir()
        model_dir = os.path.join(hub_dir, 'checkpoints')

    os.makedirs(model_dir, exist_ok=True)

    parts = urlparse(url)
    filename = os.path.basename(parts.path)
    if file_name is not None:
        filename = file_name
    cached_file = os.path.abspath(os.path.join(model_dir, filename))
    if not os.path.exists(cached_file):
        print(f'Downloading: "{url}" to {cached_file}\n')
        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
    return cached_file


def sliding_windows(h: int, w: int, tile_size: int, tile_stride: int) -> Tuple[int, int, int, int]:
    hi_list = list(range(0, h - tile_size + 1, tile_stride))
    if (h - tile_size) % tile_stride != 0:
        hi_list.append(h - tile_size)
    
    wi_list = list(range(0, w - tile_size + 1, tile_stride))
    if (w - tile_size) % tile_stride != 0:
        wi_list.append(w - tile_size)
    
    coords = []
    for hi in hi_list:
        for wi in wi_list:
            coords.append((hi, hi + tile_size, wi, wi + tile_size))
    return coords


# https://github.com/csslc/CCSR/blob/main/model/q_sampler.py#L503
def gaussian_weights(tile_width: int, tile_height: int) -> np.ndarray:
    """Generates a gaussian mask of weights for tile contributions"""
    latent_width = tile_width
    latent_height = tile_height
    var = 0.01
    midpoint = (latent_width - 1) / 2  # -1 because index goes from 0 to latent_width - 1
    x_probs = [
        np.exp(-(x - midpoint) * (x - midpoint) / (latent_width * latent_width) / (2 * var)) / np.sqrt(2 * np.pi * var)
        for x in range(latent_width)]
    midpoint = latent_height / 2
    y_probs = [
        np.exp(-(y - midpoint) * (y - midpoint) / (latent_height * latent_height) / (2 * var)) / np.sqrt(2 * np.pi * var)
        for y in range(latent_height)]
    weights = np.outer(y_probs, x_probs)
    return weights


COUNT_VRAM = bool(os.environ.get("COUNT_VRAM", False))

def count_vram_usage(func: Callable) -> Callable:
    if not COUNT_VRAM:
        return func
    
    def wrapper(*args, **kwargs):
        peak_before = torch.cuda.max_memory_allocated() / (1024 ** 3)
        ret = func(*args, **kwargs)
        torch.cuda.synchronize()
        peak_after = torch.cuda.max_memory_allocated() / (1024 ** 3)
        print(f"VRAM peak before {func.__name__}: {peak_before:.5f} GB, after: {peak_after:.5f} GB")
        return ret
    return wrapper