convert_weight.py

import argparse
import os
import sys
import pickle
import math

import torch
import numpy as np
from torchvision import utils

from model import Generator, Discriminator


def convert_modconv(vars, source_name, target_name, flip=False):
    weight = vars[source_name + "/weight"].value().eval()
    mod_weight = vars[source_name + "/mod_weight"].value().eval()
    mod_bias = vars[source_name + "/mod_bias"].value().eval()
    noise = vars[source_name + "/noise_strength"].value().eval()
    bias = vars[source_name + "/bias"].value().eval()

    dic = {
        "conv.weight": np.expand_dims(weight.transpose((3, 2, 0, 1)), 0),
        "conv.modulation.weight": mod_weight.transpose((1, 0)),
        "conv.modulation.bias": mod_bias + 1,
        "noise.weight": np.array([noise]),
        "activate.bias": bias,
    }

    dic_torch = {}

    for k, v in dic.items():
        dic_torch[target_name + "." + k] = torch.from_numpy(v)

    if flip:
        dic_torch[target_name + ".conv.weight"] = torch.flip(
            dic_torch[target_name + ".conv.weight"], [3, 4]
        )

    return dic_torch


def convert_conv(vars, source_name, target_name, bias=True, start=0):
    weight = vars[source_name + "/weight"].value().eval()

    dic = {"weight": weight.transpose((3, 2, 0, 1))}

    if bias:
        dic["bias"] = vars[source_name + "/bias"].value().eval()

    dic_torch = {}

    dic_torch[target_name + f".{start}.weight"] = torch.from_numpy(dic["weight"])

    if bias:
        dic_torch[target_name + f".{start + 1}.bias"] = torch.from_numpy(dic["bias"])

    return dic_torch


def convert_torgb(vars, source_name, target_name):
    weight = vars[source_name + "/weight"].value().eval()
    mod_weight = vars[source_name + "/mod_weight"].value().eval()
    mod_bias = vars[source_name + "/mod_bias"].value().eval()
    bias = vars[source_name + "/bias"].value().eval()

    dic = {
        "conv.weight": np.expand_dims(weight.transpose((3, 2, 0, 1)), 0),
        "conv.modulation.weight": mod_weight.transpose((1, 0)),
        "conv.modulation.bias": mod_bias + 1,
        "bias": bias.reshape((1, 3, 1, 1)),
    }

    dic_torch = {}

    for k, v in dic.items():
        dic_torch[target_name + "." + k] = torch.from_numpy(v)

    return dic_torch


def convert_dense(vars, source_name, target_name):
    weight = vars[source_name + "/weight"].value().eval()
    bias = vars[source_name + "/bias"].value().eval()

    dic = {"weight": weight.transpose((1, 0)), "bias": bias}

    dic_torch = {}

    for k, v in dic.items():
        dic_torch[target_name + "." + k] = torch.from_numpy(v)

    return dic_torch


def update(state_dict, new):
    for k, v in new.items():
        if k not in state_dict:
            raise KeyError(k + " is not found")

        if v.shape != state_dict[k].shape:
            raise ValueError(f"Shape mismatch: {v.shape} vs {state_dict[k].shape}")

        state_dict[k] = v


def discriminator_fill_statedict(statedict, vars, size):
    log_size = int(math.log(size, 2))

    update(statedict, convert_conv(vars, f"{size}x{size}/FromRGB", "convs.0"))

    conv_i = 1

    for i in range(log_size - 2, 0, -1):
        reso = 4 * 2 ** i
        update(
            statedict,
            convert_conv(vars, f"{reso}x{reso}/Conv0", f"convs.{conv_i}.conv1"),
        )
        update(
            statedict,
            convert_conv(
                vars, f"{reso}x{reso}/Conv1_down", f"convs.{conv_i}.conv2", start=1
            ),
        )
        update(
            statedict,
            convert_conv(
                vars, f"{reso}x{reso}/Skip", f"convs.{conv_i}.skip", start=1, bias=False
            ),
        )
        conv_i += 1

    update(statedict, convert_conv(vars, f"4x4/Conv", "final_conv"))
    update(statedict, convert_dense(vars, f"4x4/Dense0", "final_linear.0"))
    update(statedict, convert_dense(vars, f"Output", "final_linear.1"))

    return statedict


def fill_statedict(state_dict, vars, size,n_mlp=8):
    log_size = int(math.log(size, 2))

    for i in range(n_mlp):
        update(state_dict, convert_dense(vars, f"G_mapping/Dense{i}", f"style.{i + 1}"))

    update(
        state_dict,
        {
            "input.input": torch.from_numpy(
                vars["G_synthesis/4x4/Const/const"].value().eval()
            )
        },
    )

    update(state_dict, convert_torgb(vars, "G_synthesis/4x4/ToRGB", "to_rgb1"))

    for i in range(log_size - 2):
        reso = 4 * 2 ** (i + 1)
        update(
            state_dict,
            convert_torgb(vars, f"G_synthesis/{reso}x{reso}/ToRGB", f"to_rgbs.{i}"),
        )

    update(state_dict, convert_modconv(vars, "G_synthesis/4x4/Conv", "conv1"))

    conv_i = 0

    for i in range(log_size - 2):
        reso = 4 * 2 ** (i + 1)
        update(
            state_dict,
            convert_modconv(
                vars,
                f"G_synthesis/{reso}x{reso}/Conv0_up",
                f"convs.{conv_i}",
                flip=True,
            ),
        )
        update(
            state_dict,
            convert_modconv(
                vars, f"G_synthesis/{reso}x{reso}/Conv1", f"convs.{conv_i + 1}"
            ),
        )
        conv_i += 2

    for i in range(0, (log_size - 2) * 2 + 1):
        update(
            state_dict,
            {
                f"noises.noise_{i}": torch.from_numpy(
                    vars[f"G_synthesis/noise{i}"].value().eval()
                )
            },
        )

    return state_dict


def convertStyleGan2(_G,_D,Gs,channel_multiplier = 4,style_dim=1024,n_mlp=4,max_channel_size=1024):
    generator, discriminator, g_ema = _G, _D, Gs

    size = g_ema.output_shape[2]

    g = Generator(size, style_dim, n_mlp, channel_multiplier=channel_multiplier,max_channel_size=max_channel_size)
    state_dict = g.state_dict()
    state_dict = fill_statedict(state_dict, g_ema.vars, size,n_mlp)

    g.load_state_dict(state_dict)

    latent_avg = torch.from_numpy(g_ema.vars["dlatent_avg"].value().eval())

    ckpt = {"g_ema": state_dict, "latent_avg": latent_avg}
    
    
    #convert _G
    g_train = Generator(size, style_dim, n_mlp, channel_multiplier=channel_multiplier,max_channel_size=max_channel_size)
    g_train_state = g_train.state_dict()
    g_train_state = fill_statedict(g_train_state, generator.vars, size,n_mlp)
    ckpt["g"] = g_train_state
    
    
    #convert discriminator
    channel_multiplier=2
    disc = Discriminator(size, 
                 channel_multiplier=channel_multiplier,
                 stddev_group = 32,
                 stddev_feat = 4)
    
    d_state = disc.state_dict()
    d_state = discriminator_fill_statedict(d_state, discriminator.vars, size)

    disc.load_state_dict(d_state)
    
    ckpt["d"] = d_state
    
    
    
    
    return ckpt, g, disc, g_train
    
    
    


if __name__ == "__main__":
    device = "cuda"

    parser = argparse.ArgumentParser(
        description="Tensorflow to pytorch model checkpoint converter"
    )
    parser.add_argument(
        "--repo",
        type=str,
        required=True,
        help="path to the offical StyleGAN2 repository with dnnlib/ folder",
    )
    parser.add_argument(
        "--gen", action="store_true", help="convert the generator weights"
    )
    parser.add_argument(
        "--disc", action="store_true", help="convert the discriminator weights"
    )
    parser.add_argument(
        "--channel_multiplier",
        type=int,
        default=2,
        help="channel multiplier factor. config-f = 2, else = 1",
    )
    parser.add_argument("path", metavar="PATH", help="path to the tensorflow weights")

    args = parser.parse_args()

    sys.path.append(args.repo)

    import dnnlib
    from dnnlib import tflib

    tflib.init_tf()

    with open(args.path, "rb") as f:
        generator, discriminator, g_ema = pickle.load(f)

    size = g_ema.output_shape[2]

    g = Generator(size, 512, 8, channel_multiplier=args.channel_multiplier)
    state_dict = g.state_dict()
    state_dict = fill_statedict(state_dict, g_ema.vars, size)

    g.load_state_dict(state_dict)

    latent_avg = torch.from_numpy(g_ema.vars["dlatent_avg"].value().eval())

    ckpt = {"g_ema": state_dict, "latent_avg": latent_avg}

    if args.gen:
        g_train = Generator(size, 512, 8, channel_multiplier=args.channel_multiplier)
        g_train_state = g_train.state_dict()
        g_train_state = fill_statedict(g_train_state, generator.vars, size)
        ckpt["g"] = g_train_state

    if args.disc:
        disc = Discriminator(size, channel_multiplier=args.channel_multiplier)
        d_state = disc.state_dict()
        d_state = discriminator_fill_statedict(d_state, discriminator.vars, size)
        ckpt["d"] = d_state

    name = os.path.splitext(os.path.basename(args.path))[0]
    torch.save(ckpt, name + ".pt")

    batch_size = {256: 16, 512: 9, 1024: 4}
    n_sample = batch_size.get(size, 25)

    g = g.to(device)

    z = np.random.RandomState(0).randn(n_sample, 512).astype("float32")

    with torch.no_grad():
        img_pt, _ = g(
            [torch.from_numpy(z).to(device)],
            truncation=0.5,
            truncation_latent=latent_avg.to(device),
            randomize_noise=False,
        )

    Gs_kwargs = dnnlib.EasyDict()
    Gs_kwargs.randomize_noise = False
    img_tf = g_ema.run(z, None, **Gs_kwargs)
    img_tf = torch.from_numpy(img_tf).to(device)

    img_diff = ((img_pt + 1) / 2).clamp(0.0, 1.0) - ((img_tf.to(device) + 1) / 2).clamp(
        0.0, 1.0
    )

    img_concat = torch.cat((img_tf, img_pt, img_diff), dim=0)

    print(img_diff.abs().max())

    utils.save_image(
        img_concat, name + ".png", nrow=n_sample, normalize=True, range=(-1, 1)
    )