exp_code/1_benchmark/Pyramid-Flow/utils.py

import os
import torch
import PIL.Image
import numpy as np
from torch import nn
import torch.distributed as dist
import timm.models as timm_hub

"""Modified from https://github.com/CompVis/taming-transformers.git"""

import hashlib
import requests
from tqdm import tqdm
try:
    import piq
except:
    pass

_CONTEXT_PARALLEL_GROUP = None
_CONTEXT_PARALLEL_SIZE = None


def is_dist_avail_and_initialized():
    if not dist.is_available():
        return False
    if not dist.is_initialized():
        return False
    return True


def get_world_size():
    if not is_dist_avail_and_initialized():
        return 1
    return dist.get_world_size()


def get_rank():
    if not is_dist_avail_and_initialized():
        return 0
    return dist.get_rank()


def is_main_process():
    return get_rank() == 0


def is_context_parallel_initialized():
    if _CONTEXT_PARALLEL_GROUP is None:
        return False
    else:
        return True


def set_context_parallel_group(size, group):
    global _CONTEXT_PARALLEL_GROUP
    global _CONTEXT_PARALLEL_SIZE
    _CONTEXT_PARALLEL_GROUP = group
    _CONTEXT_PARALLEL_SIZE = size


def initialize_context_parallel(context_parallel_size):
    global _CONTEXT_PARALLEL_GROUP
    global _CONTEXT_PARALLEL_SIZE

    assert _CONTEXT_PARALLEL_GROUP is None, "context parallel group is already initialized"
    _CONTEXT_PARALLEL_SIZE = context_parallel_size

    rank = torch.distributed.get_rank()
    world_size = torch.distributed.get_world_size()

    for i in range(0, world_size, context_parallel_size):
        ranks = range(i, i + context_parallel_size)
        group = torch.distributed.new_group(ranks)
        if rank in ranks:
            _CONTEXT_PARALLEL_GROUP = group
            break


def get_context_parallel_group():
    assert _CONTEXT_PARALLEL_GROUP is not None, "context parallel group is not initialized"

    return _CONTEXT_PARALLEL_GROUP


def get_context_parallel_world_size():
    assert _CONTEXT_PARALLEL_SIZE is not None, "context parallel size is not initialized"

    return _CONTEXT_PARALLEL_SIZE


def get_context_parallel_rank():
    assert _CONTEXT_PARALLEL_SIZE is not None, "context parallel size is not initialized"

    rank = get_rank()
    cp_rank = rank % _CONTEXT_PARALLEL_SIZE
    return cp_rank


def get_context_parallel_group_rank():
    assert _CONTEXT_PARALLEL_SIZE is not None, "context parallel size is not initialized"

    rank = get_rank()
    cp_group_rank = rank // _CONTEXT_PARALLEL_SIZE

    return cp_group_rank


def download_cached_file(url, check_hash=True, progress=False):
    """
    Download a file from a URL and cache it locally. If the file already exists, it is not downloaded again.
    If distributed, only the main process downloads the file, and the other processes wait for the file to be downloaded.
    """

    def get_cached_file_path():
        # a hack to sync the file path across processes
        parts = torch.hub.urlparse(url)
        filename = os.path.basename(parts.path)
        cached_file = os.path.join(timm_hub.get_cache_dir(), filename)

        return cached_file

    if is_main_process():
        timm_hub.download_cached_file(url, check_hash, progress)

    if is_dist_avail_and_initialized():
        dist.barrier()

    return get_cached_file_path()


def convert_weights_to_fp16(model: nn.Module):
    """Convert applicable model parameters to fp16"""

    def _convert_weights_to_fp16(l):
        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d, nn.Linear)):
            l.weight.data = l.weight.data.to(torch.float16)
            if l.bias is not None:
                l.bias.data = l.bias.data.to(torch.float16)

    model.apply(_convert_weights_to_fp16)


def convert_weights_to_bf16(model: nn.Module):
    """Convert applicable model parameters to fp16"""

    def _convert_weights_to_bf16(l):
        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d, nn.Linear)):
            l.weight.data = l.weight.data.to(torch.bfloat16)
            if l.bias is not None:
                l.bias.data = l.bias.data.to(torch.bfloat16)

    model.apply(_convert_weights_to_bf16)


def save_result(result, result_dir, filename, remove_duplicate="", save_format='json'):
    import json
    import jsonlines
    print("Dump result")

    # Make the temp dir for saving results
    if not os.path.exists(result_dir):
        if is_main_process():
            os.makedirs(result_dir)
        if is_dist_avail_and_initialized():
            torch.distributed.barrier()

    result_file = os.path.join(
        result_dir, "%s_rank%d.json" % (filename, get_rank())
    )
    
    final_result_file = os.path.join(result_dir, f"{filename}.{save_format}")

    json.dump(result, open(result_file, "w"))

    if is_dist_avail_and_initialized():
        torch.distributed.barrier()

    if is_main_process():
        # print("rank %d starts merging results." % get_rank())
        # combine results from all processes
        result = []

        for rank in range(get_world_size()):
            result_file = os.path.join(result_dir, "%s_rank%d.json" % (filename, rank))
            res = json.load(open(result_file, "r"))
            result += res

        # print("Remove duplicate")
        if remove_duplicate:
            result_new = []
            id_set = set()
            for res in result:
                if res[remove_duplicate] not in id_set:
                    id_set.add(res[remove_duplicate])
                    result_new.append(res)
            result = result_new

        if save_format == 'json':
            json.dump(result, open(final_result_file, "w"))
        else:
            assert save_format == 'jsonl', "Only support json adn jsonl format"
            with jsonlines.open(final_result_file, "w") as writer:
                writer.write_all(result)

        # print("result file saved to %s" % final_result_file)

    return final_result_file


# resizing utils
# TODO: clean up later
def _resize_with_antialiasing(input, size, interpolation="bicubic", align_corners=True):
    h, w = input.shape[-2:]
    factors = (h / size[0], w / size[1])

    # First, we have to determine sigma
    # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171
    sigmas = (
        max((factors[0] - 1.0) / 2.0, 0.001),
        max((factors[1] - 1.0) / 2.0, 0.001),
    )

    # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma
    # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206
    # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now
    ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3))

    # Make sure it is odd
    if (ks[0] % 2) == 0:
        ks = ks[0] + 1, ks[1]

    if (ks[1] % 2) == 0:
        ks = ks[0], ks[1] + 1

    input = _gaussian_blur2d(input, ks, sigmas)

    output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners)
    return output


def _compute_padding(kernel_size):
    """Compute padding tuple."""
    # 4 or 6 ints:  (padding_left, padding_right,padding_top,padding_bottom)
    # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad
    if len(kernel_size) < 2:
        raise AssertionError(kernel_size)
    computed = [k - 1 for k in kernel_size]

    # for even kernels we need to do asymmetric padding :(
    out_padding = 2 * len(kernel_size) * [0]

    for i in range(len(kernel_size)):
        computed_tmp = computed[-(i + 1)]

        pad_front = computed_tmp // 2
        pad_rear = computed_tmp - pad_front

        out_padding[2 * i + 0] = pad_front
        out_padding[2 * i + 1] = pad_rear

    return out_padding


def _filter2d(input, kernel):
    # prepare kernel
    b, c, h, w = input.shape
    tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype)

    tmp_kernel = tmp_kernel.expand(-1, c, -1, -1)

    height, width = tmp_kernel.shape[-2:]

    padding_shape: list[int] = _compute_padding([height, width])
    input = torch.nn.functional.pad(input, padding_shape, mode="reflect")

    # kernel and input tensor reshape to align element-wise or batch-wise params
    tmp_kernel = tmp_kernel.reshape(-1, 1, height, width)
    input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1))

    # convolve the tensor with the kernel.
    output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1)

    out = output.view(b, c, h, w)
    return out


def _gaussian(window_size: int, sigma):
    if isinstance(sigma, float):
        sigma = torch.tensor([[sigma]])

    batch_size = sigma.shape[0]

    x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1)

    if window_size % 2 == 0:
        x = x + 0.5

    gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0)))

    return gauss / gauss.sum(-1, keepdim=True)


def _gaussian_blur2d(input, kernel_size, sigma):
    if isinstance(sigma, tuple):
        sigma = torch.tensor([sigma], dtype=input.dtype)
    else:
        sigma = sigma.to(dtype=input.dtype)

    ky, kx = int(kernel_size[0]), int(kernel_size[1])
    bs = sigma.shape[0]
    kernel_x = _gaussian(kx, sigma[:, 1].view(bs, 1))
    kernel_y = _gaussian(ky, sigma[:, 0].view(bs, 1))
    out_x = _filter2d(input, kernel_x[..., None, :])
    out = _filter2d(out_x, kernel_y[..., None])

    return out


URL_MAP = {
    "vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"
}

CKPT_MAP = {
    "vgg_lpips": "vgg.pth"
}

MD5_MAP = {
    "vgg_lpips": "d507d7349b931f0638a25a48a722f98a"
}


def download(url, local_path, chunk_size=1024):
    os.makedirs(os.path.split(local_path)[0], exist_ok=True)
    with requests.get(url, stream=True) as r:
        total_size = int(r.headers.get("content-length", 0))
        with tqdm(total=total_size, unit="B", unit_scale=True) as pbar:
            with open(local_path, "wb") as f:
                for data in r.iter_content(chunk_size=chunk_size):
                    if data:
                        f.write(data)
                        pbar.update(chunk_size)


def md5_hash(path):
    with open(path, "rb") as f:
        content = f.read()
    return hashlib.md5(content).hexdigest()


def get_ckpt_path(name, root, check=False):
    assert name in URL_MAP
    path = os.path.join(root, CKPT_MAP[name])
    print(md5_hash(path))
    if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):
        print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path))
        download(URL_MAP[name], path)
        md5 = md5_hash(path)
        assert md5 == MD5_MAP[name], md5
    return path


class KeyNotFoundError(Exception):
    def __init__(self, cause, keys=None, visited=None):
        self.cause = cause
        self.keys = keys
        self.visited = visited
        messages = list()
        if keys is not None:
            messages.append("Key not found: {}".format(keys))
        if visited is not None:
            messages.append("Visited: {}".format(visited))
        messages.append("Cause:\n{}".format(cause))
        message = "\n".join(messages)
        super().__init__(message)


def retrieve(
    list_or_dict, key, splitval="/", default=None, expand=True, pass_success=False
):
    """Given a nested list or dict return the desired value at key expanding
    callable nodes if necessary and :attr:`expand` is ``True``. The expansion
    is done in-place.

    Parameters
    ----------
        list_or_dict : list or dict
            Possibly nested list or dictionary.
        key : str
            key/to/value, path like string describing all keys necessary to
            consider to get to the desired value. List indices can also be
            passed here.
        splitval : str
            String that defines the delimiter between keys of the
            different depth levels in `key`.
        default : obj
            Value returned if :attr:`key` is not found.
        expand : bool
            Whether to expand callable nodes on the path or not.

    Returns
    -------
        The desired value or if :attr:`default` is not ``None`` and the
        :attr:`key` is not found returns ``default``.

    Raises
    ------
        Exception if ``key`` not in ``list_or_dict`` and :attr:`default` is
        ``None``.
    """

    keys = key.split(splitval)

    success = True
    try:
        visited = []
        parent = None
        last_key = None
        for key in keys:
            if callable(list_or_dict):
                if not expand:
                    raise KeyNotFoundError(
                        ValueError(
                            "Trying to get past callable node with expand=False."
                        ),
                        keys=keys,
                        visited=visited,
                    )
                list_or_dict = list_or_dict()
                parent[last_key] = list_or_dict

            last_key = key
            parent = list_or_dict

            try:
                if isinstance(list_or_dict, dict):
                    list_or_dict = list_or_dict[key]
                else:
                    list_or_dict = list_or_dict[int(key)]
            except (KeyError, IndexError, ValueError) as e:
                raise KeyNotFoundError(e, keys=keys, visited=visited)

            visited += [key]
        # final expansion of retrieved value
        if expand and callable(list_or_dict):
            list_or_dict = list_or_dict()
            parent[last_key] = list_or_dict
    except KeyNotFoundError as e:
        if default is None:
            raise e
        else:
            list_or_dict = default
            success = False

    if not pass_success:
        return list_or_dict
    else:
        return list_or_dict, success