generator.py

# 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 numpy as np
import torch

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
from copy import deepcopy


import click

import PIL.Image
from torch import linalg as LA
import torch.nn.functional as F


def block_forward(self, x, img, ws, shapes, force_fp32=True, 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 block_forward_from_style(self, x, img, ws, shapes, force_fp32=True, 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 w_to_s(
    GIn,
    wsIn:np.ndarray,
    outdir: str ="s_out",
    truncation_psi: float = 0.7,
    noise_mode: str = "const",
):
    G=deepcopy(GIn)
    
    # Use GPU if available
    if torch.cuda.is_available():
        device = torch.device("cuda")
    else:
        device = torch.device("cpu")

    os.makedirs(outdir, exist_ok=True)

    # Generate images.
    for i in G.parameters():
        i.requires_grad = True

    # ws = np.load(projected_w)['w']
    ws = torch.tensor(wsIn, device=device)

    block_ws = []
    with torch.autograd.profiler.record_function('split_ws'):
        misc.assert_shape(ws, [None, G.synthesis.num_ws, G.synthesis.w_dim])
        ws = ws.to(torch.float32)

        w_idx = 0
        for res in G.synthesis.block_resolutions:
            block = getattr(G.synthesis, f'b{res}')
            block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
            w_idx += block.num_conv

    styles = torch.zeros(1,26,512, device=device)
    styles_idx = 0
    temp_shapes = []
    for res, cur_ws in zip(G.synthesis.block_resolutions, block_ws):
        block = getattr(G.synthesis, f'b{res}')

        if res == 4:
            temp_shape = (block.conv1.affine.weight.shape[0], block.conv1.affine.weight.shape[0], block.torgb.affine.weight.shape[0])
            styles[0,:1,:] = block.conv1.affine(cur_ws[0,:1,:])
            styles[0,1:2,:] = block.torgb.affine(cur_ws[0,1:2,:])

            block.conv1.affine = torch.nn.Identity()
            block.torgb.affine = torch.nn.Identity()
            styles_idx += 2
        else:
            temp_shape = (block.conv0.affine.weight.shape[0], block.conv1.affine.weight.shape[0], block.torgb.affine.weight.shape[0])
            styles[0,styles_idx:styles_idx+1,:temp_shape[0]] = block.conv0.affine(cur_ws[0,:1,:])
            styles[0,styles_idx+1:styles_idx+2,:temp_shape[1]] = block.conv1.affine(cur_ws[0,1:2,:])
            styles[0,styles_idx+2:styles_idx+3,:temp_shape[2]] = block.torgb.affine(cur_ws[0,2:3,:])

            block.conv0.affine = torch.nn.Identity()
            block.conv1.affine = torch.nn.Identity()
            block.torgb.affine = torch.nn.Identity()
            styles_idx += 3
        temp_shapes.append(temp_shape)

    styles = styles.detach()
    np.savez(f'{outdir}/input.npz', s=styles.cpu().numpy())
    return styles.cpu().numpy()

def generate_from_style(
    GIn,
    styles: np.ndarray,
    styles_direction: np.ndarray,
    outdir: str,
    change_power: int,
    truncation_psi: float = 0.7,
    noise_mode: str = "const",
):
    G=deepcopy(GIn)
    # Use GPU if available
    if torch.cuda.is_available():
        device = torch.device("cuda")
    else:
        device = torch.device("cpu")

    os.makedirs(outdir, exist_ok=True)

    # Generate images
    for i in G.parameters():
      i.requires_grad = False

    temp_shapes = []
    for res in G.synthesis.block_resolutions:
        block = getattr(G.synthesis, f'b{res}')
        if res == 4:
            temp_shape = (block.conv1.affine.weight.shape[0], block.conv1.affine.weight.shape[0], block.torgb.affine.weight.shape[0])
            block.conv1.affine = torch.nn.Identity()
            block.torgb.affine = torch.nn.Identity()

        else:
            temp_shape = (block.conv0.affine.weight.shape[0], block.conv1.affine.weight.shape[0], block.torgb.affine.weight.shape[0])
            block.conv0.affine = torch.nn.Identity()
            block.conv1.affine = torch.nn.Identity()
            block.torgb.affine = torch.nn.Identity()

        temp_shapes.append(temp_shape)

    styles_direction = torch.tensor(styles_direction, device=device)
    styles = torch.tensor(styles, device=device)

    with torch.no_grad():
        imgs = []
        grad_changes = [change_power]

        for grad_change in grad_changes:
            styles += styles_direction*grad_change

            styles_idx = 0
            x = img = None
            for k , res in enumerate(G.synthesis.block_resolutions):
                block = getattr(G.synthesis, f'b{res}')

                if res == 4:
                    x, img = block_forward_from_style(block, x, img, styles[:, styles_idx:styles_idx+2, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True)
                    styles_idx += 2
                else:
                    x, img = block_forward_from_style(block, x, img, styles[:, styles_idx:styles_idx+3, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True)
                    styles_idx += 3

            img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255)
            imgs.append(img[0].to(torch.uint8).cpu().numpy())

            styles -= styles_direction*grad_change

        output_image = PIL.Image.fromarray(np.concatenate(imgs, axis=1), 'RGB')
        output_image.save(os.path.join(outdir, 'final_out.png'), quality=95)
        return output_image