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| # Copyright (c) 2021, NVIDIA CORPORATION. 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. | |
| """Generate images using pretrained network pickle.""" | |
| import os | |
| import re | |
| from typing import List, Optional | |
| import torchvision | |
| from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize | |
| import click | |
| import dnnlib | |
| import numpy as np | |
| import PIL.Image | |
| import torch | |
| from torch import linalg as LA | |
| import clip | |
| from PIL import Image | |
| import legacy | |
| import torch.nn.functional as F | |
| import cv2 | |
| import matplotlib.pyplot as plt | |
| from torch_utils import misc | |
| from torch_utils import persistence | |
| from torch_utils.ops import conv2d_resample | |
| from torch_utils.ops import upfirdn2d | |
| from torch_utils.ops import bias_act | |
| from torch_utils.ops import fma | |
| import random | |
| import math | |
| import time | |
| import id_loss | |
| def block_forward(self, x, img, ws, shapes, force_fp32=False, fused_modconv=None, **layer_kwargs): | |
| misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim]) | |
| w_iter = iter(ws.unbind(dim=1)) | |
| dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 | |
| memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format | |
| if fused_modconv is None: | |
| with misc.suppress_tracer_warnings(): # this value will be treated as a constant | |
| fused_modconv = (not self.training) and (dtype == torch.float32 or int(x.shape[0]) == 1) | |
| # Input. | |
| if self.in_channels == 0: | |
| x = self.const.to(dtype=dtype, memory_format=memory_format) | |
| x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1]) | |
| else: | |
| misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) | |
| x = x.to(dtype=dtype, memory_format=memory_format) | |
| # Main layers. | |
| if self.in_channels == 0: | |
| x = self.conv1(x, next(w_iter)[...,:shapes[0]], fused_modconv=fused_modconv, **layer_kwargs) | |
| elif self.architecture == 'resnet': | |
| y = self.skip(x, gain=np.sqrt(0.5)) | |
| x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) | |
| x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs) | |
| x = y.add_(x) | |
| else: | |
| x = self.conv0(x, next(w_iter)[...,:shapes[0]], fused_modconv=fused_modconv, **layer_kwargs) | |
| x = self.conv1(x, next(w_iter)[...,:shapes[1]], fused_modconv=fused_modconv, **layer_kwargs) | |
| # ToRGB. | |
| if img is not None: | |
| misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) | |
| img = upfirdn2d.upsample2d(img, self.resample_filter) | |
| if self.is_last or self.architecture == 'skip': | |
| y = self.torgb(x, next(w_iter)[...,:shapes[2]], fused_modconv=fused_modconv) | |
| y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format) | |
| img = img.add_(y) if img is not None else y | |
| assert x.dtype == dtype | |
| assert img is None or img.dtype == torch.float32 | |
| return x, img | |
| def unravel_index(index, shape): | |
| out = [] | |
| for dim in reversed(shape): | |
| out.append(index % dim) | |
| index = index // dim | |
| return tuple(reversed(out)) | |
| def num_range(s: str) -> List[int]: | |
| '''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.''' | |
| range_re = re.compile(r'^(\d+)-(\d+)$') | |
| m = range_re.match(s) | |
| if m: | |
| return list(range(int(m.group(1)), int(m.group(2))+1)) | |
| vals = s.split(',') | |
| return [int(x) for x in vals] | |
| def generate_images( | |
| ctx: click.Context, | |
| network_pkl: str, | |
| seeds: Optional[List[int]], | |
| truncation_psi: float, | |
| noise_mode: str, | |
| class_idx: Optional[int], | |
| projected_w: Optional[str], | |
| projected_s: Optional[str] | |
| ): | |
| print('Loading networks from "%s"...' % network_pkl) | |
| # Use GPU if available | |
| if torch.cuda.is_available(): | |
| device = torch.device("cuda") | |
| else: | |
| device = torch.device("cpu") | |
| with dnnlib.util.open_url(network_pkl) as f: | |
| G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore | |
| if seeds is None: | |
| ctx.fail('--seeds option is required when not using --projected-w') | |
| # Labels. | |
| label = torch.zeros([1, G.c_dim], device=device).requires_grad_() | |
| if G.c_dim != 0: | |
| if class_idx is None: | |
| ctx.fail('Must specify class label with --class when using a conditional network') | |
| label[:, class_idx] = 1 | |
| else: | |
| if class_idx is not None: | |
| print ('warn: --class=lbl ignored when running on an unconditional network') | |
| z = torch.from_numpy(np.random.RandomState(seeds[0]).randn(1, G.z_dim)).to(device) | |
| ws = G.mapping(z, label, truncation_psi=truncation_psi) | |
| np.savez(f'encoder4editing/projected_w.npz', w=ws.detach().cpu().numpy()) | |
| if __name__ == "__main__": | |
| generate_images() | |