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#@title Gradio demo (used in space: )
from matplotlib import pyplot as plt
from huggingface_hub import PyTorchModelHubMixin
import numpy as np
import gradio as gr
#@title Defining Generator and associated code ourselves without the GPU requirements
import os
import json
import multiprocessing
from random import random
import math
from math import log2, floor
from functools import partial
from contextlib import contextmanager, ExitStack
from pathlib import Path
from shutil import rmtree
import torch
from torch.cuda.amp import autocast, GradScaler
from torch.optim import Adam
from torch import nn, einsum
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torch.autograd import grad as torch_grad
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
from PIL import Image
import torchvision
from torchvision import transforms
from kornia.filters import filter2d
from tqdm import tqdm
from einops import rearrange, reduce, repeat
from adabelief_pytorch import AdaBelief
# helpers
def DiffAugment(x, types=[]):
for p in types:
for f in AUGMENT_FNS[p]:
x = f(x)
return x.contiguous()
def exists(val):
return val is not None
@contextmanager
def null_context():
yield
def combine_contexts(contexts):
@contextmanager
def multi_contexts():
with ExitStack() as stack:
yield [stack.enter_context(ctx()) for ctx in contexts]
return multi_contexts
def is_power_of_two(val):
return log2(val).is_integer()
def default(val, d):
return val if exists(val) else d
def set_requires_grad(model, bool):
for p in model.parameters():
p.requires_grad = bool
def cycle(iterable):
while True:
for i in iterable:
yield i
def raise_if_nan(t):
if torch.isnan(t):
raise NanException
def gradient_accumulate_contexts(gradient_accumulate_every, is_ddp, ddps):
if is_ddp:
num_no_syncs = gradient_accumulate_every - 1
head = [combine_contexts(map(lambda ddp: ddp.no_sync, ddps))] * num_no_syncs
tail = [null_context]
contexts = head + tail
else:
contexts = [null_context] * gradient_accumulate_every
for context in contexts:
with context():
yield
def evaluate_in_chunks(max_batch_size, model, *args):
split_args = list(zip(*list(map(lambda x: x.split(max_batch_size, dim=0), args))))
chunked_outputs = [model(*i) for i in split_args]
if len(chunked_outputs) == 1:
return chunked_outputs[0]
return torch.cat(chunked_outputs, dim=0)
def slerp(val, low, high):
low_norm = low / torch.norm(low, dim=1, keepdim=True)
high_norm = high / torch.norm(high, dim=1, keepdim=True)
omega = torch.acos((low_norm * high_norm).sum(1))
so = torch.sin(omega)
res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
return res
def safe_div(n, d):
try:
res = n / d
except ZeroDivisionError:
prefix = '' if int(n >= 0) else '-'
res = float(f'{prefix}inf')
return res
# loss functions
def gen_hinge_loss(fake, real):
return fake.mean()
def hinge_loss(real, fake):
return (F.relu(1 + real) + F.relu(1 - fake)).mean()
def dual_contrastive_loss(real_logits, fake_logits):
device = real_logits.device
real_logits, fake_logits = map(lambda t: rearrange(t, '... -> (...)'), (real_logits, fake_logits))
def loss_half(t1, t2):
t1 = rearrange(t1, 'i -> i ()')
t2 = repeat(t2, 'j -> i j', i = t1.shape[0])
t = torch.cat((t1, t2), dim = -1)
return F.cross_entropy(t, torch.zeros(t1.shape[0], device = device, dtype = torch.long))
return loss_half(real_logits, fake_logits) + loss_half(-fake_logits, -real_logits)
# helper classes
class NanException(Exception):
pass
class EMA():
def __init__(self, beta):
super().__init__()
self.beta = beta
def update_average(self, old, new):
if not exists(old):
return new
return old * self.beta + (1 - self.beta) * new
class RandomApply(nn.Module):
def __init__(self, prob, fn, fn_else = lambda x: x):
super().__init__()
self.fn = fn
self.fn_else = fn_else
self.prob = prob
def forward(self, x):
fn = self.fn if random() < self.prob else self.fn_else
return fn(x)
class ChanNorm(nn.Module):
def __init__(self, dim, eps = 1e-5):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
mean = torch.mean(x, dim = 1, keepdim = True)
return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.fn = fn
self.norm = ChanNorm(dim)
def forward(self, x):
return self.fn(self.norm(x))
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x):
return self.fn(x) + x
class SumBranches(nn.Module):
def __init__(self, branches):
super().__init__()
self.branches = nn.ModuleList(branches)
def forward(self, x):
return sum(map(lambda fn: fn(x), self.branches))
class Blur(nn.Module):
def __init__(self):
super().__init__()
f = torch.Tensor([1, 2, 1])
self.register_buffer('f', f)
def forward(self, x):
f = self.f
f = f[None, None, :] * f [None, :, None]
return filter2d(x, f, normalized=True)
class Noise(nn.Module):
def __init__(self):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1))
def forward(self, x, noise = None):
b, _, h, w, device = *x.shape, x.device
if not exists(noise):
noise = torch.randn(b, 1, h, w, device = device)
return x + self.weight * noise
def Conv2dSame(dim_in, dim_out, kernel_size, bias = True):
pad_left = kernel_size // 2
pad_right = (pad_left - 1) if (kernel_size % 2) == 0 else pad_left
return nn.Sequential(
nn.ZeroPad2d((pad_left, pad_right, pad_left, pad_right)),
nn.Conv2d(dim_in, dim_out, kernel_size, bias = bias)
)
# attention
class DepthWiseConv2d(nn.Module):
def __init__(self, dim_in, dim_out, kernel_size, padding = 0, stride = 1, bias = True):
super().__init__()
self.net = nn.Sequential(
nn.Conv2d(dim_in, dim_in, kernel_size = kernel_size, padding = padding, groups = dim_in, stride = stride, bias = bias),
nn.Conv2d(dim_in, dim_out, kernel_size = 1, bias = bias)
)
def forward(self, x):
return self.net(x)
class LinearAttention(nn.Module):
def __init__(self, dim, dim_head = 64, heads = 8, kernel_size = 3):
super().__init__()
self.scale = dim_head ** -0.5
self.heads = heads
self.dim_head = dim_head
inner_dim = dim_head * heads
self.kernel_size = kernel_size
self.nonlin = nn.GELU()
self.to_lin_q = nn.Conv2d(dim, inner_dim, 1, bias = False)
self.to_lin_kv = DepthWiseConv2d(dim, inner_dim * 2, 3, padding = 1, bias = False)
self.to_q = nn.Conv2d(dim, inner_dim, 1, bias = False)
self.to_kv = nn.Conv2d(dim, inner_dim * 2, 1, bias = False)
self.to_out = nn.Conv2d(inner_dim * 2, dim, 1)
def forward(self, fmap):
h, x, y = self.heads, *fmap.shape[-2:]
# linear attention
lin_q, lin_k, lin_v = (self.to_lin_q(fmap), *self.to_lin_kv(fmap).chunk(2, dim = 1))
lin_q, lin_k, lin_v = map(lambda t: rearrange(t, 'b (h c) x y -> (b h) (x y) c', h = h), (lin_q, lin_k, lin_v))
lin_q = lin_q.softmax(dim = -1)
lin_k = lin_k.softmax(dim = -2)
lin_q = lin_q * self.scale
context = einsum('b n d, b n e -> b d e', lin_k, lin_v)
lin_out = einsum('b n d, b d e -> b n e', lin_q, context)
lin_out = rearrange(lin_out, '(b h) (x y) d -> b (h d) x y', h = h, x = x, y = y)
# conv-like full attention
q, k, v = (self.to_q(fmap), *self.to_kv(fmap).chunk(2, dim = 1))
q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> (b h) c x y', h = h), (q, k, v))
k = F.unfold(k, kernel_size = self.kernel_size, padding = self.kernel_size // 2)
v = F.unfold(v, kernel_size = self.kernel_size, padding = self.kernel_size // 2)
k, v = map(lambda t: rearrange(t, 'b (d j) n -> b n j d', d = self.dim_head), (k, v))
q = rearrange(q, 'b c ... -> b (...) c') * self.scale
sim = einsum('b i d, b i j d -> b i j', q, k)
sim = sim - sim.amax(dim = -1, keepdim = True).detach()
attn = sim.softmax(dim = -1)
full_out = einsum('b i j, b i j d -> b i d', attn, v)
full_out = rearrange(full_out, '(b h) (x y) d -> b (h d) x y', h = h, x = x, y = y)
# add outputs of linear attention + conv like full attention
lin_out = self.nonlin(lin_out)
out = torch.cat((lin_out, full_out), dim = 1)
return self.to_out(out)
# dataset
def convert_image_to(img_type, image):
if image.mode != img_type:
return image.convert(img_type)
return image
class identity(object):
def __call__(self, tensor):
return tensor
class expand_greyscale(object):
def __init__(self, transparent):
self.transparent = transparent
def __call__(self, tensor):
channels = tensor.shape[0]
num_target_channels = 4 if self.transparent else 3
if channels == num_target_channels:
return tensor
alpha = None
if channels == 1:
color = tensor.expand(3, -1, -1)
elif channels == 2:
color = tensor[:1].expand(3, -1, -1)
alpha = tensor[1:]
else:
raise Exception(f'image with invalid number of channels given {channels}')
if not exists(alpha) and self.transparent:
alpha = torch.ones(1, *tensor.shape[1:], device=tensor.device)
return color if not self.transparent else torch.cat((color, alpha))
def resize_to_minimum_size(min_size, image):
if max(*image.size) < min_size:
return torchvision.transforms.functional.resize(image, min_size)
return image
class ImageDataset(Dataset):
def __init__(
self,
folder,
image_size,
transparent = False,
greyscale = False,
aug_prob = 0.
):
super().__init__()
self.folder = folder
self.image_size = image_size
self.paths = [p for ext in EXTS for p in Path(f'{folder}').glob(f'**/*.{ext}')]
assert len(self.paths) > 0, f'No images were found in {folder} for training'
if transparent:
num_channels = 4
pillow_mode = 'RGBA'
expand_fn = expand_greyscale(transparent)
elif greyscale:
num_channels = 1
pillow_mode = 'L'
expand_fn = identity()
else:
num_channels = 3
pillow_mode = 'RGB'
expand_fn = expand_greyscale(transparent)
convert_image_fn = partial(convert_image_to, pillow_mode)
self.transform = transforms.Compose([
transforms.Lambda(convert_image_fn),
transforms.Lambda(partial(resize_to_minimum_size, image_size)),
transforms.Resize(image_size),
RandomApply(aug_prob, transforms.RandomResizedCrop(image_size, scale=(0.5, 1.0), ratio=(0.98, 1.02)), transforms.CenterCrop(image_size)),
transforms.ToTensor(),
transforms.Lambda(expand_fn)
])
def __len__(self):
return len(self.paths)
def __getitem__(self, index):
path = self.paths[index]
img = Image.open(path)
return self.transform(img)
# augmentations
def random_hflip(tensor, prob):
if prob > random():
return tensor
return torch.flip(tensor, dims=(3,))
class AugWrapper(nn.Module):
def __init__(self, D, image_size):
super().__init__()
self.D = D
def forward(self, images, prob = 0., types = [], detach = False, **kwargs):
context = torch.no_grad if detach else null_context
with context():
if random() < prob:
images = random_hflip(images, prob=0.5)
images = DiffAugment(images, types=types)
return self.D(images, **kwargs)
# modifiable global variables
norm_class = nn.BatchNorm2d
def upsample(scale_factor = 2):
return nn.Upsample(scale_factor = scale_factor)
# squeeze excitation classes
# global context network
# https://arxiv.org/abs/2012.13375
# similar to squeeze-excite, but with a simplified attention pooling and a subsequent layer norm
class GlobalContext(nn.Module):
def __init__(
self,
*,
chan_in,
chan_out
):
super().__init__()
self.to_k = nn.Conv2d(chan_in, 1, 1)
chan_intermediate = max(3, chan_out // 2)
self.net = nn.Sequential(
nn.Conv2d(chan_in, chan_intermediate, 1),
nn.LeakyReLU(0.1),
nn.Conv2d(chan_intermediate, chan_out, 1),
nn.Sigmoid()
)
def forward(self, x):
context = self.to_k(x)
context = context.flatten(2).softmax(dim = -1)
out = einsum('b i n, b c n -> b c i', context, x.flatten(2))
out = out.unsqueeze(-1)
return self.net(out)
# frequency channel attention
# https://arxiv.org/abs/2012.11879
def get_1d_dct(i, freq, L):
result = math.cos(math.pi * freq * (i + 0.5) / L) / math.sqrt(L)
return result * (1 if freq == 0 else math.sqrt(2))
def get_dct_weights(width, channel, fidx_u, fidx_v):
dct_weights = torch.zeros(1, channel, width, width)
c_part = channel // len(fidx_u)
for i, (u_x, v_y) in enumerate(zip(fidx_u, fidx_v)):
for x in range(width):
for y in range(width):
coor_value = get_1d_dct(x, u_x, width) * get_1d_dct(y, v_y, width)
dct_weights[:, i * c_part: (i + 1) * c_part, x, y] = coor_value
return dct_weights
class FCANet(nn.Module):
def __init__(
self,
*,
chan_in,
chan_out,
reduction = 4,
width
):
super().__init__()
freq_w, freq_h = ([0] * 8), list(range(8)) # in paper, it seems 16 frequencies was ideal
dct_weights = get_dct_weights(width, chan_in, [*freq_w, *freq_h], [*freq_h, *freq_w])
self.register_buffer('dct_weights', dct_weights)
chan_intermediate = max(3, chan_out // reduction)
self.net = nn.Sequential(
nn.Conv2d(chan_in, chan_intermediate, 1),
nn.LeakyReLU(0.1),
nn.Conv2d(chan_intermediate, chan_out, 1),
nn.Sigmoid()
)
def forward(self, x):
x = reduce(x * self.dct_weights, 'b c (h h1) (w w1) -> b c h1 w1', 'sum', h1 = 1, w1 = 1)
return self.net(x)
# generative adversarial network
class Generator(nn.Module):
def __init__(
self,
*,
image_size,
latent_dim = 256,
fmap_max = 512,
fmap_inverse_coef = 12,
transparent = False,
greyscale = False,
attn_res_layers = [],
freq_chan_attn = False
):
super().__init__()
resolution = log2(image_size)
assert is_power_of_two(image_size), 'image size must be a power of 2'
if transparent:
init_channel = 4
elif greyscale:
init_channel = 1
else:
init_channel = 3
fmap_max = default(fmap_max, latent_dim)
self.initial_conv = nn.Sequential(
nn.ConvTranspose2d(latent_dim, latent_dim * 2, 4),
norm_class(latent_dim * 2),
nn.GLU(dim = 1)
)
num_layers = int(resolution) - 2
features = list(map(lambda n: (n, 2 ** (fmap_inverse_coef - n)), range(2, num_layers + 2)))
features = list(map(lambda n: (n[0], min(n[1], fmap_max)), features))
features = list(map(lambda n: 3 if n[0] >= 8 else n[1], features))
features = [latent_dim, *features]
in_out_features = list(zip(features[:-1], features[1:]))
self.res_layers = range(2, num_layers + 2)
self.layers = nn.ModuleList([])
self.res_to_feature_map = dict(zip(self.res_layers, in_out_features))
self.sle_map = ((3, 7), (4, 8), (5, 9), (6, 10))
self.sle_map = list(filter(lambda t: t[0] <= resolution and t[1] <= resolution, self.sle_map))
self.sle_map = dict(self.sle_map)
self.num_layers_spatial_res = 1
for (res, (chan_in, chan_out)) in zip(self.res_layers, in_out_features):
image_width = 2 ** res
attn = None
if image_width in attn_res_layers:
attn = PreNorm(chan_in, LinearAttention(chan_in))
sle = None
if res in self.sle_map:
residual_layer = self.sle_map[res]
sle_chan_out = self.res_to_feature_map[residual_layer - 1][-1]
if freq_chan_attn:
sle = FCANet(
chan_in = chan_out,
chan_out = sle_chan_out,
width = 2 ** (res + 1)
)
else:
sle = GlobalContext(
chan_in = chan_out,
chan_out = sle_chan_out
)
layer = nn.ModuleList([
nn.Sequential(
upsample(),
Blur(),
Conv2dSame(chan_in, chan_out * 2, 4),
Noise(),
norm_class(chan_out * 2),
nn.GLU(dim = 1)
),
sle,
attn
])
self.layers.append(layer)
self.out_conv = nn.Conv2d(features[-1], init_channel, 3, padding = 1)
def forward(self, x):
x = rearrange(x, 'b c -> b c () ()')
x = self.initial_conv(x)
x = F.normalize(x, dim = 1)
residuals = dict()
for (res, (up, sle, attn)) in zip(self.res_layers, self.layers):
if exists(attn):
x = attn(x) + x
x = up(x)
if exists(sle):
out_res = self.sle_map[res]
residual = sle(x)
residuals[out_res] = residual
next_res = res + 1
if next_res in residuals:
x = x * residuals[next_res]
return self.out_conv(x)
# Initialize a generator model
gan_new = Generator(latent_dim=256, image_size=256, attn_res_layers = [32])
# Load from local saved state dict
# gan_new.load_state_dict(torch.load('/content/orbgan_e3_state_dict.pt'))
# Load from model hub:
class GeneratorWithPyTorchModelHubMixin(gan_new.__class__, PyTorchModelHubMixin):
pass
gan_new.__class__ = GeneratorWithPyTorchModelHubMixin
gan_new = gan_new.from_pretrained('johnowhitaker/colorb_gan', latent_dim=256, image_size=256, attn_res_layers = [32])
def gen_ims(n_rows):
ims = gan_new(torch.randn(int(n_rows)**2, 256)).clamp_(0., 1.)
grid = torchvision.utils.make_grid(ims, nrow=int(n_rows)).permute(1, 2, 0).detach().cpu().numpy()
return (grid*255).astype(np.uint8)
iface = gr.Interface(fn=gen_ims,
inputs=[gr.inputs.Slider(minimum=1, maximum=6, step=1, default=3,label="N rows")],
outputs=[gr.outputs.Image(type="numpy", label="Generated Images")],
title='Demo for Colorbgan model',
article = 'A lightweight-gans trained on johnowhitaker/colorbs. See https://huggingface.co/johnowhitaker/orbgan_e1 for training and inference scripts'
)
iface.launch() |