# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from socket import has_dualstack_ipv6
import sys
import copy
import traceback
import math
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import torch
import torch.fft
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.cm
import dnnlib
from torch_utils.ops import upfirdn2d
import legacy # pylint: disable=import-error
#----------------------------------------------------------------------------
class CapturedException(Exception):
def __init__(self, msg=None):
if msg is None:
_type, value, _traceback = sys.exc_info()
assert value is not None
if isinstance(value, CapturedException):
msg = str(value)
else:
msg = traceback.format_exc()
assert isinstance(msg, str)
super().__init__(msg)
#----------------------------------------------------------------------------
class CaptureSuccess(Exception):
def __init__(self, out):
super().__init__()
self.out = out
#----------------------------------------------------------------------------
def add_watermark_np(input_image_array, watermark_text="AI Generated"):
image = Image.fromarray(np.uint8(input_image_array)).convert("RGBA")
# Initialize text image
txt = Image.new('RGBA', image.size, (255, 255, 255, 0))
font = ImageFont.truetype('arial.ttf', round(25/512*image.size[0]))
d = ImageDraw.Draw(txt)
text_width, text_height = font.getsize(watermark_text)
text_position = (image.size[0] - text_width - 10, image.size[1] - text_height - 10)
text_color = (255, 255, 255, 128) # white color with the alpha channel set to semi-transparent
# Draw the text onto the text canvas
d.text(text_position, watermark_text, font=font, fill=text_color)
# Combine the image with the watermark
watermarked = Image.alpha_composite(image, txt)
watermarked_array = np.array(watermarked)
return watermarked_array
#----------------------------------------------------------------------------
class Renderer:
def __init__(self, disable_timing=False):
self._device = torch.device('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
self._dtype = torch.float32 if self._device.type == 'mps' else torch.float64
self._pkl_data = dict() # {pkl: dict | CapturedException, ...}
self._networks = dict() # {cache_key: torch.nn.Module, ...}
self._pinned_bufs = dict() # {(shape, dtype): torch.Tensor, ...}
self._cmaps = dict() # {name: torch.Tensor, ...}
self._is_timing = False
if not disable_timing:
self._start_event = torch.cuda.Event(enable_timing=True)
self._end_event = torch.cuda.Event(enable_timing=True)
self._disable_timing = disable_timing
self._net_layers = dict() # {cache_key: [dnnlib.EasyDict, ...], ...}
def render(self, **args):
if self._disable_timing:
self._is_timing = False
else:
self._start_event.record(torch.cuda.current_stream(self._device))
self._is_timing = True
res = dnnlib.EasyDict()
try:
init_net = False
if not hasattr(self, 'G'):
init_net = True
if hasattr(self, 'pkl'):
if self.pkl != args['pkl']:
init_net = True
if hasattr(self, 'w_load'):
if self.w_load is not args['w_load']:
init_net = True
if hasattr(self, 'w0_seed'):
if self.w0_seed != args['w0_seed']:
init_net = True
if hasattr(self, 'w_plus'):
if self.w_plus != args['w_plus']:
init_net = True
if args['reset_w']:
init_net = True
res.init_net = init_net
if init_net:
self.init_network(res, **args)
self._render_drag_impl(res, **args)
except:
res.error = CapturedException()
if not self._disable_timing:
self._end_event.record(torch.cuda.current_stream(self._device))
if 'image' in res:
res.image = self.to_cpu(res.image).detach().numpy()
res.image = add_watermark_np(res.image, 'AI Generated')
if 'stats' in res:
res.stats = self.to_cpu(res.stats).detach().numpy()
if 'error' in res:
res.error = str(res.error)
# if 'stop' in res and res.stop:
if self._is_timing and not self._disable_timing:
self._end_event.synchronize()
res.render_time = self._start_event.elapsed_time(self._end_event) * 1e-3
self._is_timing = False
return res
def get_network(self, pkl, key, **tweak_kwargs):
data = self._pkl_data.get(pkl, None)
if data is None:
print(f'Loading "{pkl}"... ', end='', flush=True)
try:
with dnnlib.util.open_url(pkl, verbose=False) as f:
data = legacy.load_network_pkl(f)
print('Done.')
except:
data = CapturedException()
print('Failed!')
self._pkl_data[pkl] = data
self._ignore_timing()
if isinstance(data, CapturedException):
raise data
orig_net = data[key]
cache_key = (orig_net, self._device, tuple(sorted(tweak_kwargs.items())))
net = self._networks.get(cache_key, None)
if net is None:
try:
if 'stylegan2' in pkl:
from training.networks_stylegan2 import Generator
elif 'stylegan3' in pkl:
from training.networks_stylegan3 import Generator
elif 'stylegan_human' in pkl:
from stylegan_human.training_scripts.sg2.training.networks import Generator
else:
raise NameError('Cannot infer model type from pkl name!')
print(data[key].init_args)
print(data[key].init_kwargs)
if 'stylegan_human' in pkl:
net = Generator(*data[key].init_args, **data[key].init_kwargs, square=False, padding=True)
else:
net = Generator(*data[key].init_args, **data[key].init_kwargs)
net.load_state_dict(data[key].state_dict())
net.to(self._device)
except:
net = CapturedException()
self._networks[cache_key] = net
self._ignore_timing()
if isinstance(net, CapturedException):
raise net
return net
def _get_pinned_buf(self, ref):
key = (tuple(ref.shape), ref.dtype)
buf = self._pinned_bufs.get(key, None)
if buf is None:
buf = torch.empty(ref.shape, dtype=ref.dtype).pin_memory()
self._pinned_bufs[key] = buf
return buf
def to_device(self, buf):
return self._get_pinned_buf(buf).copy_(buf).to(self._device)
def to_cpu(self, buf):
return self._get_pinned_buf(buf).copy_(buf).clone()
def _ignore_timing(self):
self._is_timing = False
def _apply_cmap(self, x, name='viridis'):
cmap = self._cmaps.get(name, None)
if cmap is None:
cmap = matplotlib.cm.get_cmap(name)
cmap = cmap(np.linspace(0, 1, num=1024), bytes=True)[:, :3]
cmap = self.to_device(torch.from_numpy(cmap))
self._cmaps[name] = cmap
hi = cmap.shape[0] - 1
x = (x * hi + 0.5).clamp(0, hi).to(torch.int64)
x = torch.nn.functional.embedding(x, cmap)
return x
def init_network(self, res,
pkl = None,
w0_seed = 0,
w_load = None,
w_plus = True,
noise_mode = 'const',
trunc_psi = 0.7,
trunc_cutoff = None,
input_transform = None,
lr = 0.001,
**kwargs
):
# Dig up network details.
self.pkl = pkl
G = self.get_network(pkl, 'G_ema')
self.G = G
res.img_resolution = G.img_resolution
res.num_ws = G.num_ws
res.has_noise = any('noise_const' in name for name, _buf in G.synthesis.named_buffers())
res.has_input_transform = (hasattr(G.synthesis, 'input') and hasattr(G.synthesis.input, 'transform'))
# Set input transform.
if res.has_input_transform:
m = np.eye(3)
try:
if input_transform is not None:
m = np.linalg.inv(np.asarray(input_transform))
except np.linalg.LinAlgError:
res.error = CapturedException()
G.synthesis.input.transform.copy_(torch.from_numpy(m))
# Generate random latents.
self.w0_seed = w0_seed
self.w_load = w_load
if self.w_load is None:
# Generate random latents.
z = torch.from_numpy(np.random.RandomState(w0_seed).randn(1, 512)).to(self._device, dtype=self._dtype)
# Run mapping network.
label = torch.zeros([1, G.c_dim], device=self._device)
w = G.mapping(z, label, truncation_psi=trunc_psi, truncation_cutoff=trunc_cutoff)
else:
w = self.w_load.clone().to(self._device)
self.w0 = w.detach().clone()
self.w_plus = w_plus
if w_plus:
self.w = w.detach()
else:
self.w = w[:, 0, :].detach()
self.w.requires_grad = True
self.w_optim = torch.optim.Adam([self.w], lr=lr)
self.feat_refs = None
self.points0_pt = None
def update_lr(self, lr):
del self.w_optim
self.w_optim = torch.optim.Adam([self.w], lr=lr)
print(f'Rebuild optimizer with lr: {lr}')
print(' Remain feat_refs and points0_pt')
def _render_drag_impl(self, res,
points = [],
targets = [],
mask = None,
lambda_mask = 10,
reg = 0,
feature_idx = 5,
r1 = 3,
r2 = 12,
random_seed = 0,
noise_mode = 'const',
trunc_psi = 0.7,
force_fp32 = False,
layer_name = None,
sel_channels = 3,
base_channel = 0,
img_scale_db = 0,
img_normalize = False,
untransform = False,
is_drag = False,
reset = False,
to_pil = False,
**kwargs
):
G = self.G
ws = self.w
if ws.dim() == 2:
ws = ws.unsqueeze(1).repeat(1,6,1)
ws = torch.cat([ws[:,:6,:], self.w0[:,6:,:]], dim=1)
if hasattr(self, 'points'):
if len(points) != len(self.points):
reset = True
if reset:
self.feat_refs = None
self.points0_pt = None
self.points = points
# Run synthesis network.
label = torch.zeros([1, G.c_dim], device=self._device)
img, feat = G(ws, label, truncation_psi=trunc_psi, noise_mode=noise_mode, input_is_w=True, return_feature=True)
h, w = G.img_resolution, G.img_resolution
if is_drag:
X = torch.linspace(0, h, h)
Y = torch.linspace(0, w, w)
xx, yy = torch.meshgrid(X, Y)
feat_resize = F.interpolate(feat[feature_idx], [h, w], mode='bilinear')
if self.feat_refs is None:
self.feat0_resize = F.interpolate(feat[feature_idx].detach(), [h, w], mode='bilinear')
self.feat_refs = []
for point in points:
py, px = round(point[0]), round(point[1])
self.feat_refs.append(self.feat0_resize[:,:,py,px])
self.points0_pt = torch.Tensor(points).unsqueeze(0).to(self._device) # 1, N, 2
# Point tracking with feature matching
with torch.no_grad():
for j, point in enumerate(points):
r = round(r2 / 512 * h)
up = max(point[0] - r, 0)
down = min(point[0] + r + 1, h)
left = max(point[1] - r, 0)
right = min(point[1] + r + 1, w)
feat_patch = feat_resize[:,:,up:down,left:right]
L2 = torch.linalg.norm(feat_patch - self.feat_refs[j].reshape(1,-1,1,1), dim=1)
_, idx = torch.min(L2.view(1,-1), -1)
width = right - left
point = [idx.item() // width + up, idx.item() % width + left]
points[j] = point
res.points = [[point[0], point[1]] for point in points]
# Motion supervision
loss_motion = 0
res.stop = True
for j, point in enumerate(points):
direction = torch.Tensor([targets[j][1] - point[1], targets[j][0] - point[0]])
if torch.linalg.norm(direction) > max(2 / 512 * h, 2):
res.stop = False
if torch.linalg.norm(direction) > 1:
distance = ((xx.to(self._device) - point[0])**2 + (yy.to(self._device) - point[1])**2)**0.5
relis, reljs = torch.where(distance < round(r1 / 512 * h))
direction = direction / (torch.linalg.norm(direction) + 1e-7)
gridh = (relis-direction[1]) / (h-1) * 2 - 1
gridw = (reljs-direction[0]) / (w-1) * 2 - 1
grid = torch.stack([gridw,gridh], dim=-1).unsqueeze(0).unsqueeze(0)
target = F.grid_sample(feat_resize.float(), grid, align_corners=True).squeeze(2)
loss_motion += F.l1_loss(feat_resize[:,:,relis,reljs], target.detach())
loss = loss_motion
if mask is not None:
if mask.min() == 0 and mask.max() == 1:
mask_usq = mask.to(self._device).unsqueeze(0).unsqueeze(0)
loss_fix = F.l1_loss(feat_resize * mask_usq, self.feat0_resize * mask_usq)
loss += lambda_mask * loss_fix
loss += reg * F.l1_loss(ws, self.w0) # latent code regularization
if not res.stop:
self.w_optim.zero_grad()
loss.backward()
self.w_optim.step()
# Scale and convert to uint8.
img = img[0]
if img_normalize:
img = img / img.norm(float('inf'), dim=[1,2], keepdim=True).clip(1e-8, 1e8)
img = img * (10 ** (img_scale_db / 20))
img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8).permute(1, 2, 0)
if to_pil:
from PIL import Image
img = img.cpu().numpy()
img = Image.fromarray(img)
res.image = img
res.w = ws.detach().cpu().numpy()
#----------------------------------------------------------------------------