File size: 8,459 Bytes
a0bcaae
 
 
 
 
 
 
 
bb0f5a9
a0bcaae
 
 
 
 
 
 
 
 
 
bb0f5a9
 
a0bcaae
 
 
bb0f5a9
 
 
a0bcaae
 
 
 
bb0f5a9
a0bcaae
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bb0f5a9
 
 
a0bcaae
 
 
 
bb0f5a9
 
a0bcaae
 
 
 
 
 
 
 
 
bb0f5a9
 
a0bcaae
 
 
 
bb0f5a9
a0bcaae
 
 
 
 
 
bb0f5a9
 
a0bcaae
 
 
 
 
 
 
 
 
 
 
 
bb0f5a9
 
a0bcaae
 
 
 
bb0f5a9
 
 
 
a0bcaae
 
 
 
 
 
 
 
 
bb0f5a9
a0bcaae
bb0f5a9
a0bcaae
 
bb0f5a9
a0bcaae
 
 
 
bb0f5a9
 
 
a0bcaae
 
 
bb0f5a9
 
 
 
a0bcaae
 
 
 
 
bb0f5a9
 
a0bcaae
 
 
bb0f5a9
 
a0bcaae
 
 
 
 
bb0f5a9
 
 
 
a0bcaae
 
 
 
bb0f5a9
 
a0bcaae
bb0f5a9
 
 
a0bcaae
 
 
 
 
 
 
bb0f5a9
a0bcaae
 
 
 
 
 
 
 
 
 
 
 
 
bb0f5a9
 
 
 
a0bcaae
bb0f5a9
 
a0bcaae
bb0f5a9
a0bcaae
bb0f5a9
 
 
 
 
 
 
 
a0bcaae
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
# Copyright (c) SenseTime Research. All rights reserved.

# 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.
#
import pickle
import dnnlib
import re
from typing import List, Optional
import torch
import copy
import numpy as np
from torch_utils import misc


# ----------------------------------------------------------------------------
# loading torch pkl
def load_network_pkl(f, force_fp16=False, G_only=False):
    data = _LegacyUnpickler(f).load()
    if G_only:
        f = open('ori_model_Gonly.txt', 'a+')
    else:
        f = open('ori_model.txt', 'a+')
    for key in data.keys():
        f.write(str(data[key]))
    f.close()

    # We comment out this part, if you want to convert TF pickle, you can use the original script from StyleGAN2-ada-pytorch
    # # Legacy TensorFlow pickle => convert.
    # if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data):
    #     tf_G, tf_D, tf_Gs = data
    #     G = convert_tf_generator(tf_G)
    #     D = convert_tf_discriminator(tf_D)
    #     G_ema = convert_tf_generator(tf_Gs)
    #     data = dict(G=G, D=D, G_ema=G_ema)

    # Add missing fields.
    if 'training_set_kwargs' not in data:
        data['training_set_kwargs'] = None
    if 'augment_pipe' not in data:
        data['augment_pipe'] = None

    # Validate contents.
    assert isinstance(data['G_ema'], torch.nn.Module)
    if not G_only:
        assert isinstance(data['D'], torch.nn.Module)
        assert isinstance(data['G'], torch.nn.Module)
        assert isinstance(data['training_set_kwargs'], (dict, type(None)))
        assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None)))

    # Force FP16.
    if force_fp16:
        if G_only:
            convert_list = ['G_ema']  # 'G'
        else:
            convert_list = ['G', 'D', 'G_ema']
        for key in convert_list:
            old = data[key]
            kwargs = copy.deepcopy(old.init_kwargs)
            if key.startswith('G'):
                kwargs.synthesis_kwargs = dnnlib.EasyDict(
                    kwargs.get('synthesis_kwargs', {}))
                kwargs.synthesis_kwargs.num_fp16_res = 4
                kwargs.synthesis_kwargs.conv_clamp = 256
            if key.startswith('D'):
                kwargs.num_fp16_res = 4
                kwargs.conv_clamp = 256
            if kwargs != old.init_kwargs:
                new = type(old)(**kwargs).eval().requires_grad_(False)
                misc.copy_params_and_buffers(old, new, require_all=True)
                data[key] = new
    return data


class _TFNetworkStub(dnnlib.EasyDict):
    pass


class _LegacyUnpickler(pickle.Unpickler):
    def find_class(self, module, name):
        if module == 'dnnlib.tflib.network' and name == 'Network':
            return _TFNetworkStub
        return super().find_class(module, name)

# ----------------------------------------------------------------------------


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]


# ----------------------------------------------------------------------------
# loading tf pkl
def load_pkl(file_or_url):
    with open(file_or_url, 'rb') as file:
        return pickle.load(file, encoding='latin1')

# ----------------------------------------------------------------------------

# For editing


def visual(output, out_path):
    import torch
    import cv2
    import numpy as np
    output = (output + 1)/2
    output = torch.clamp(output, 0, 1)
    if output.shape[1] == 1:
        output = torch.cat([output, output, output], 1)
    output = output[0].detach().cpu().permute(1, 2, 0).numpy()
    output = (output*255).astype(np.uint8)
    output = output[:, :, ::-1]
    cv2.imwrite(out_path, output)


def save_obj(obj, path):
    with open(path, 'wb+') as f:
        pickle.dump(obj, f, protocol=4)

# ----------------------------------------------------------------------------

# Converting pkl to pth, change dict info inside pickle


def convert_to_rgb(state_ros, state_nv, ros_name, nv_name):
    state_ros[f"{ros_name}.conv.weight"] = state_nv[f"{nv_name}.torgb.weight"].unsqueeze(
        0)
    state_ros[f"{ros_name}.bias"] = state_nv[f"{nv_name}.torgb.bias"].unsqueeze(
        0).unsqueeze(-1).unsqueeze(-1)
    state_ros[f"{ros_name}.conv.modulation.weight"] = state_nv[f"{nv_name}.torgb.affine.weight"]
    state_ros[f"{ros_name}.conv.modulation.bias"] = state_nv[f"{nv_name}.torgb.affine.bias"]


def convert_conv(state_ros, state_nv, ros_name, nv_name):
    state_ros[f"{ros_name}.conv.weight"] = state_nv[f"{nv_name}.weight"].unsqueeze(
        0)
    state_ros[f"{ros_name}.activate.bias"] = state_nv[f"{nv_name}.bias"]
    state_ros[f"{ros_name}.conv.modulation.weight"] = state_nv[f"{nv_name}.affine.weight"]
    state_ros[f"{ros_name}.conv.modulation.bias"] = state_nv[f"{nv_name}.affine.bias"]
    state_ros[f"{ros_name}.noise.weight"] = state_nv[f"{nv_name}.noise_strength"].unsqueeze(
        0)


def convert_blur_kernel(state_ros, state_nv, level):
    """Not quite sure why there is a factor of 4 here"""
    # They are all the same
    state_ros[f"convs.{2*level}.conv.blur.kernel"] = 4 * \
        state_nv["synthesis.b4.resample_filter"]
    state_ros[f"to_rgbs.{level}.upsample.kernel"] = 4 * \
        state_nv["synthesis.b4.resample_filter"]


def determine_config(state_nv):
    mapping_names = [name for name in state_nv.keys() if "mapping.fc" in name]
    sythesis_names = [
        name for name in state_nv.keys() if "synthesis.b" in name]

    n_mapping = max([int(re.findall("(\d+)", n)[0])
                    for n in mapping_names]) + 1
    resolution = max([int(re.findall("(\d+)", n)[0]) for n in sythesis_names])
    n_layers = np.log(resolution/2)/np.log(2)

    return n_mapping, n_layers


def convert(network_pkl, output_file, G_only=False):
    with dnnlib.util.open_url(network_pkl) as f:
        G_nvidia = load_network_pkl(f, G_only=G_only)['G_ema']

    state_nv = G_nvidia.state_dict()
    n_mapping, n_layers = determine_config(state_nv)

    state_ros = {}

    for i in range(n_mapping):
        state_ros[f"style.{i+1}.weight"] = state_nv[f"mapping.fc{i}.weight"]
        state_ros[f"style.{i+1}.bias"] = state_nv[f"mapping.fc{i}.bias"]

    for i in range(int(n_layers)):
        if i > 0:
            for conv_level in range(2):
                convert_conv(
                    state_ros, state_nv, f"convs.{2*i-2+conv_level}", f"synthesis.b{4*(2**i)}.conv{conv_level}")
                state_ros[f"noises.noise_{2*i-1+conv_level}"] = state_nv[f"synthesis.b{4*(2**i)}.conv{conv_level}.noise_const"].unsqueeze(
                    0).unsqueeze(0)

            convert_to_rgb(state_ros, state_nv,
                           f"to_rgbs.{i-1}", f"synthesis.b{4*(2**i)}")
            convert_blur_kernel(state_ros, state_nv, i-1)

        else:
            state_ros[f"input.input"] = state_nv[f"synthesis.b{4*(2**i)}.const"].unsqueeze(
                0)
            convert_conv(state_ros, state_nv, "conv1",
                         f"synthesis.b{4*(2**i)}.conv1")
            state_ros[f"noises.noise_{2*i}"] = state_nv[f"synthesis.b{4*(2**i)}.conv1.noise_const"].unsqueeze(
                0).unsqueeze(0)
            convert_to_rgb(state_ros, state_nv, "to_rgb1",
                           f"synthesis.b{4*(2**i)}")

    # https://github.com/yuval-alaluf/restyle-encoder/issues/1#issuecomment-828354736
    latent_avg = state_nv['mapping.w_avg']
    state_dict = {"g_ema": state_ros, "latent_avg": latent_avg}
    # if G_only:
    #     f = open('converted_model_Gonly.txt','a+')
    # else:
    #     f = open('converted_model.txt','a+')
    # for key in state_dict['g_ema'].keys():
    #     f.write(str(key)+': '+str(state_dict['g_ema'][key].shape)+'\n')
    # f.close()
    torch.save(state_dict, output_file)