ldm/util.py

import importlib
from typing import List, Optional, Tuple, Union

import torch
import numpy as np
from tqdm import tqdm
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
import hashlib
import requests
import os

URL_MAP = {
    'vggishish_lpaps': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vggishish16.pt',
    'vggishish_mean_std_melspec_10s_22050hz': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/train_means_stds_melspec_10s_22050hz.txt',
    'melception': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/melception-21-05-10T09-28-40.pt',
}

CKPT_MAP = {
    'vggishish_lpaps': 'vggishish16.pt',
    'vggishish_mean_std_melspec_10s_22050hz': 'train_means_stds_melspec_10s_22050hz.txt',
    'melception': 'melception-21-05-10T09-28-40.pt',
}

MD5_MAP = {
    'vggishish_lpaps': '197040c524a07ccacf7715d7080a80bd',
    'vggishish_mean_std_melspec_10s_22050hz': 'f449c6fd0e248936c16f6d22492bb625',
    'melception': 'a71a41041e945b457c7d3d814bbcf72d',
}


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 log_txt_as_img(wh, xc, size=10):
    # wh a tuple of (width, height),xc a list of captions to plot
    b = len(xc)
    txts = list()
    for bi in range(b):
        txt = Image.new("RGB", wh, color="white")
        draw = ImageDraw.Draw(txt)
        font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
        nc = int(40 * (wh[0] / 256))
        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))

        try:
            draw.text((0, 0), lines, fill="black", font=font)
        except UnicodeEncodeError:
            print("Cant encode string for logging. Skipping.")

        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
        txts.append(txt)
    txts = np.stack(txts)
    txts = torch.tensor(txts)
    return txts


def ismap(x):
    if not isinstance(x, torch.Tensor):
        return False
    return (len(x.shape) == 4) and (x.shape[1] > 3)


def isimage(x):
    if not isinstance(x,torch.Tensor):
        return False
    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)


def exists(x):
    return x is not None


def default(val, d):
    if exists(val):
        return val
    return d() if isfunction(d) else d


def mean_flat(tensor):
    """

    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86

    Take the mean over all non-batch dimensions.

    """
    return tensor.mean(dim=list(range(1, len(tensor.shape))))


def count_params(model, verbose=False):
    total_params = sum(p.numel() for p in model.parameters())
    if verbose:
        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
    return total_params


def instantiate_from_config(config,reload=False):
    if not "target" in config:
        if config == '__is_first_stage__':
            return None
        elif config == "__is_unconditional__":
            return None
        raise KeyError("Expected key `target` to instantiate.")
    return get_obj_from_str(config["target"],reload=reload)(**config.get("params", dict()))


def get_obj_from_str(string, reload=False):
    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 get_ckpt_path(name, root, check=False):
    assert name in URL_MAP
    path = os.path.join(root, CKPT_MAP[name])
    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

def load_ckpt(cur_model, ckpt_base_dir, model_name='model', force=True, strict=True):
    if os.path.isfile(ckpt_base_dir):
        base_dir = os.path.dirname(ckpt_base_dir)
        ckpt_path = ckpt_base_dir
        checkpoint = torch.load(ckpt_base_dir, map_location='cpu')
    else:
        base_dir = ckpt_base_dir
        checkpoint, ckpt_path = get_last_checkpoint(ckpt_base_dir)
    if checkpoint is not None:
        state_dict = checkpoint["state_dict"]
        if len([k for k in state_dict.keys() if '.' in k]) > 0:
            state_dict = {k[len(model_name) + 1:]: v for k, v in state_dict.items()
                          if k.startswith(f'{model_name}.')}
        else:
            if '.' not in model_name:
                state_dict = state_dict[model_name]
            else:
                base_model_name = model_name.split('.')[0]
                rest_model_name = model_name[len(base_model_name) + 1:]
                state_dict = {
                    k[len(rest_model_name) + 1:]: v for k, v in state_dict[base_model_name].items()
                    if k.startswith(f'{rest_model_name}.')}
        if not strict:
            cur_model_state_dict = cur_model.state_dict()
            unmatched_keys = []
            for key, param in state_dict.items():
                if key in cur_model_state_dict:
                    new_param = cur_model_state_dict[key]
                    if new_param.shape != param.shape:
                        unmatched_keys.append(key)
                        print("| Unmatched keys: ", key, new_param.shape, param.shape)
            for key in unmatched_keys:
                del state_dict[key]
        cur_model.load_state_dict(state_dict, strict=strict)
        print(f"| load '{model_name}' from '{ckpt_path}'.")
    else:
        e_msg = f"| ckpt not found in {base_dir}."
        if force:
            assert False, e_msg
        else:
            print(e_msg)

def randn_tensor(

    shape: Union[Tuple, List],

    generator: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,

    device: Optional["torch.device"] = None,

    dtype: Optional["torch.dtype"] = None,

    layout: Optional["torch.layout"] = None,

):
    """A helper function to create random tensors on the desired `device` with the desired `dtype`. When

    passing a list of generators, you can seed each batch size individually. If CPU generators are passed, the tensor

    is always created on the CPU.

    """
    # device on which tensor is created defaults to device
    rand_device = device
    batch_size = shape[0]

    layout = layout or torch.strided
    device = device or torch.device("cpu")

    if generator is not None:
        gen_device_type = generator.device.type if not isinstance(generator, list) else generator[0].device.type
        if gen_device_type != device.type and gen_device_type == "cpu":
            rand_device = "cpu"
            if device != "mps":
                logger.info(
                    f"The passed generator was created on 'cpu' even though a tensor on {device} was expected."
                    f" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"
                    f" slighly speed up this function by passing a generator that was created on the {device} device."
                )
        elif gen_device_type != device.type and gen_device_type == "cuda":
            raise ValueError(f"Cannot generate a {device} tensor from a generator of type {gen_device_type}.")

    # make sure generator list of length 1 is treated like a non-list
    if isinstance(generator, list) and len(generator) == 1:
        generator = generator[0]

    if isinstance(generator, list):
        shape = (1,) + shape[1:]
        latents = [
            torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype, layout=layout)
            for i in range(batch_size)
        ]
        latents = torch.cat(latents, dim=0).to(device)
    else:
        latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype, layout=layout).to(device)

    return latents