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import importlib
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
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