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added missing file
Browse files- legacy.py +323 -0
- pages/1_Disentanglement.py +1 -0
legacy.py
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1 |
+
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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2 |
+
#
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3 |
+
# NVIDIA CORPORATION and its licensors retain all intellectual property
|
4 |
+
# and proprietary rights in and to this software, related documentation
|
5 |
+
# and any modifications thereto. Any use, reproduction, disclosure or
|
6 |
+
# distribution of this software and related documentation without an express
|
7 |
+
# license agreement from NVIDIA CORPORATION is strictly prohibited.
|
8 |
+
|
9 |
+
"""Converting legacy network pickle into the new format."""
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10 |
+
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11 |
+
import click
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12 |
+
import pickle
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13 |
+
import re
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14 |
+
import copy
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15 |
+
import numpy as np
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16 |
+
import torch
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17 |
+
import dnnlib
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18 |
+
from torch_utils import misc
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19 |
+
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20 |
+
#----------------------------------------------------------------------------
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21 |
+
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22 |
+
def load_network_pkl(f, force_fp16=False):
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23 |
+
data = _LegacyUnpickler(f).load()
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24 |
+
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25 |
+
# Legacy TensorFlow pickle => convert.
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26 |
+
if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data):
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27 |
+
tf_G, tf_D, tf_Gs = data
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28 |
+
G = convert_tf_generator(tf_G)
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29 |
+
D = convert_tf_discriminator(tf_D)
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30 |
+
G_ema = convert_tf_generator(tf_Gs)
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31 |
+
data = dict(G=G, D=D, G_ema=G_ema)
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32 |
+
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33 |
+
# Add missing fields.
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34 |
+
if 'training_set_kwargs' not in data:
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35 |
+
data['training_set_kwargs'] = None
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36 |
+
if 'augment_pipe' not in data:
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37 |
+
data['augment_pipe'] = None
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38 |
+
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39 |
+
# Validate contents.
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40 |
+
assert isinstance(data['G'], torch.nn.Module)
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41 |
+
assert isinstance(data['D'], torch.nn.Module)
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42 |
+
assert isinstance(data['G_ema'], torch.nn.Module)
|
43 |
+
assert isinstance(data['training_set_kwargs'], (dict, type(None)))
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44 |
+
assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None)))
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45 |
+
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46 |
+
# Force FP16.
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47 |
+
if force_fp16:
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48 |
+
for key in ['G', 'D', 'G_ema']:
|
49 |
+
old = data[key]
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50 |
+
kwargs = copy.deepcopy(old.init_kwargs)
|
51 |
+
fp16_kwargs = kwargs.get('synthesis_kwargs', kwargs)
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52 |
+
fp16_kwargs.num_fp16_res = 4
|
53 |
+
fp16_kwargs.conv_clamp = 256
|
54 |
+
if kwargs != old.init_kwargs:
|
55 |
+
new = type(old)(**kwargs).eval().requires_grad_(False)
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56 |
+
misc.copy_params_and_buffers(old, new, require_all=True)
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57 |
+
data[key] = new
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58 |
+
return data
|
59 |
+
|
60 |
+
#----------------------------------------------------------------------------
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61 |
+
|
62 |
+
class _TFNetworkStub(dnnlib.EasyDict):
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63 |
+
pass
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64 |
+
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65 |
+
class _LegacyUnpickler(pickle.Unpickler):
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66 |
+
def find_class(self, module, name):
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67 |
+
if module == 'dnnlib.tflib.network' and name == 'Network':
|
68 |
+
return _TFNetworkStub
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69 |
+
return super().find_class(module, name)
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70 |
+
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71 |
+
#----------------------------------------------------------------------------
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72 |
+
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73 |
+
def _collect_tf_params(tf_net):
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74 |
+
# pylint: disable=protected-access
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75 |
+
tf_params = dict()
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76 |
+
def recurse(prefix, tf_net):
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77 |
+
for name, value in tf_net.variables:
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78 |
+
tf_params[prefix + name] = value
|
79 |
+
for name, comp in tf_net.components.items():
|
80 |
+
recurse(prefix + name + '/', comp)
|
81 |
+
recurse('', tf_net)
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82 |
+
return tf_params
|
83 |
+
|
84 |
+
#----------------------------------------------------------------------------
|
85 |
+
|
86 |
+
def _populate_module_params(module, *patterns):
|
87 |
+
for name, tensor in misc.named_params_and_buffers(module):
|
88 |
+
found = False
|
89 |
+
value = None
|
90 |
+
for pattern, value_fn in zip(patterns[0::2], patterns[1::2]):
|
91 |
+
match = re.fullmatch(pattern, name)
|
92 |
+
if match:
|
93 |
+
found = True
|
94 |
+
if value_fn is not None:
|
95 |
+
value = value_fn(*match.groups())
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96 |
+
break
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97 |
+
try:
|
98 |
+
assert found
|
99 |
+
if value is not None:
|
100 |
+
tensor.copy_(torch.from_numpy(np.array(value)))
|
101 |
+
except:
|
102 |
+
print(name, list(tensor.shape))
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103 |
+
raise
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104 |
+
|
105 |
+
#----------------------------------------------------------------------------
|
106 |
+
|
107 |
+
def convert_tf_generator(tf_G):
|
108 |
+
if tf_G.version < 4:
|
109 |
+
raise ValueError('TensorFlow pickle version too low')
|
110 |
+
|
111 |
+
# Collect kwargs.
|
112 |
+
tf_kwargs = tf_G.static_kwargs
|
113 |
+
known_kwargs = set()
|
114 |
+
def kwarg(tf_name, default=None, none=None):
|
115 |
+
known_kwargs.add(tf_name)
|
116 |
+
val = tf_kwargs.get(tf_name, default)
|
117 |
+
return val if val is not None else none
|
118 |
+
|
119 |
+
# Convert kwargs.
|
120 |
+
from training import networks_stylegan2
|
121 |
+
network_class = networks_stylegan2.Generator
|
122 |
+
kwargs = dnnlib.EasyDict(
|
123 |
+
z_dim = kwarg('latent_size', 512),
|
124 |
+
c_dim = kwarg('label_size', 0),
|
125 |
+
w_dim = kwarg('dlatent_size', 512),
|
126 |
+
img_resolution = kwarg('resolution', 1024),
|
127 |
+
img_channels = kwarg('num_channels', 3),
|
128 |
+
channel_base = kwarg('fmap_base', 16384) * 2,
|
129 |
+
channel_max = kwarg('fmap_max', 512),
|
130 |
+
num_fp16_res = kwarg('num_fp16_res', 0),
|
131 |
+
conv_clamp = kwarg('conv_clamp', None),
|
132 |
+
architecture = kwarg('architecture', 'skip'),
|
133 |
+
resample_filter = kwarg('resample_kernel', [1,3,3,1]),
|
134 |
+
use_noise = kwarg('use_noise', True),
|
135 |
+
activation = kwarg('nonlinearity', 'lrelu'),
|
136 |
+
mapping_kwargs = dnnlib.EasyDict(
|
137 |
+
num_layers = kwarg('mapping_layers', 8),
|
138 |
+
embed_features = kwarg('label_fmaps', None),
|
139 |
+
layer_features = kwarg('mapping_fmaps', None),
|
140 |
+
activation = kwarg('mapping_nonlinearity', 'lrelu'),
|
141 |
+
lr_multiplier = kwarg('mapping_lrmul', 0.01),
|
142 |
+
w_avg_beta = kwarg('w_avg_beta', 0.995, none=1),
|
143 |
+
),
|
144 |
+
)
|
145 |
+
|
146 |
+
# Check for unknown kwargs.
|
147 |
+
kwarg('truncation_psi')
|
148 |
+
kwarg('truncation_cutoff')
|
149 |
+
kwarg('style_mixing_prob')
|
150 |
+
kwarg('structure')
|
151 |
+
kwarg('conditioning')
|
152 |
+
kwarg('fused_modconv')
|
153 |
+
unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
|
154 |
+
if len(unknown_kwargs) > 0:
|
155 |
+
raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
|
156 |
+
|
157 |
+
# Collect params.
|
158 |
+
tf_params = _collect_tf_params(tf_G)
|
159 |
+
for name, value in list(tf_params.items()):
|
160 |
+
match = re.fullmatch(r'ToRGB_lod(\d+)/(.*)', name)
|
161 |
+
if match:
|
162 |
+
r = kwargs.img_resolution // (2 ** int(match.group(1)))
|
163 |
+
tf_params[f'{r}x{r}/ToRGB/{match.group(2)}'] = value
|
164 |
+
kwargs.synthesis.kwargs.architecture = 'orig'
|
165 |
+
#for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
|
166 |
+
|
167 |
+
# Convert params.
|
168 |
+
G = network_class(**kwargs).eval().requires_grad_(False)
|
169 |
+
# pylint: disable=unnecessary-lambda
|
170 |
+
# pylint: disable=f-string-without-interpolation
|
171 |
+
_populate_module_params(G,
|
172 |
+
r'mapping\.w_avg', lambda: tf_params[f'dlatent_avg'],
|
173 |
+
r'mapping\.embed\.weight', lambda: tf_params[f'mapping/LabelEmbed/weight'].transpose(),
|
174 |
+
r'mapping\.embed\.bias', lambda: tf_params[f'mapping/LabelEmbed/bias'],
|
175 |
+
r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'mapping/Dense{i}/weight'].transpose(),
|
176 |
+
r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'mapping/Dense{i}/bias'],
|
177 |
+
r'synthesis\.b4\.const', lambda: tf_params[f'synthesis/4x4/Const/const'][0],
|
178 |
+
r'synthesis\.b4\.conv1\.weight', lambda: tf_params[f'synthesis/4x4/Conv/weight'].transpose(3, 2, 0, 1),
|
179 |
+
r'synthesis\.b4\.conv1\.bias', lambda: tf_params[f'synthesis/4x4/Conv/bias'],
|
180 |
+
r'synthesis\.b4\.conv1\.noise_const', lambda: tf_params[f'synthesis/noise0'][0, 0],
|
181 |
+
r'synthesis\.b4\.conv1\.noise_strength', lambda: tf_params[f'synthesis/4x4/Conv/noise_strength'],
|
182 |
+
r'synthesis\.b4\.conv1\.affine\.weight', lambda: tf_params[f'synthesis/4x4/Conv/mod_weight'].transpose(),
|
183 |
+
r'synthesis\.b4\.conv1\.affine\.bias', lambda: tf_params[f'synthesis/4x4/Conv/mod_bias'] + 1,
|
184 |
+
r'synthesis\.b(\d+)\.conv0\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
|
185 |
+
r'synthesis\.b(\d+)\.conv0\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/bias'],
|
186 |
+
r'synthesis\.b(\d+)\.conv0\.noise_const', lambda r: tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-5}'][0, 0],
|
187 |
+
r'synthesis\.b(\d+)\.conv0\.noise_strength', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/noise_strength'],
|
188 |
+
r'synthesis\.b(\d+)\.conv0\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_weight'].transpose(),
|
189 |
+
r'synthesis\.b(\d+)\.conv0\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_bias'] + 1,
|
190 |
+
r'synthesis\.b(\d+)\.conv1\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/weight'].transpose(3, 2, 0, 1),
|
191 |
+
r'synthesis\.b(\d+)\.conv1\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/bias'],
|
192 |
+
r'synthesis\.b(\d+)\.conv1\.noise_const', lambda r: tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-4}'][0, 0],
|
193 |
+
r'synthesis\.b(\d+)\.conv1\.noise_strength', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/noise_strength'],
|
194 |
+
r'synthesis\.b(\d+)\.conv1\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/mod_weight'].transpose(),
|
195 |
+
r'synthesis\.b(\d+)\.conv1\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/mod_bias'] + 1,
|
196 |
+
r'synthesis\.b(\d+)\.torgb\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/weight'].transpose(3, 2, 0, 1),
|
197 |
+
r'synthesis\.b(\d+)\.torgb\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/bias'],
|
198 |
+
r'synthesis\.b(\d+)\.torgb\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/mod_weight'].transpose(),
|
199 |
+
r'synthesis\.b(\d+)\.torgb\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/mod_bias'] + 1,
|
200 |
+
r'synthesis\.b(\d+)\.skip\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Skip/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
|
201 |
+
r'.*\.resample_filter', None,
|
202 |
+
r'.*\.act_filter', None,
|
203 |
+
)
|
204 |
+
return G
|
205 |
+
|
206 |
+
#----------------------------------------------------------------------------
|
207 |
+
|
208 |
+
def convert_tf_discriminator(tf_D):
|
209 |
+
if tf_D.version < 4:
|
210 |
+
raise ValueError('TensorFlow pickle version too low')
|
211 |
+
|
212 |
+
# Collect kwargs.
|
213 |
+
tf_kwargs = tf_D.static_kwargs
|
214 |
+
known_kwargs = set()
|
215 |
+
def kwarg(tf_name, default=None):
|
216 |
+
known_kwargs.add(tf_name)
|
217 |
+
return tf_kwargs.get(tf_name, default)
|
218 |
+
|
219 |
+
# Convert kwargs.
|
220 |
+
kwargs = dnnlib.EasyDict(
|
221 |
+
c_dim = kwarg('label_size', 0),
|
222 |
+
img_resolution = kwarg('resolution', 1024),
|
223 |
+
img_channels = kwarg('num_channels', 3),
|
224 |
+
architecture = kwarg('architecture', 'resnet'),
|
225 |
+
channel_base = kwarg('fmap_base', 16384) * 2,
|
226 |
+
channel_max = kwarg('fmap_max', 512),
|
227 |
+
num_fp16_res = kwarg('num_fp16_res', 0),
|
228 |
+
conv_clamp = kwarg('conv_clamp', None),
|
229 |
+
cmap_dim = kwarg('mapping_fmaps', None),
|
230 |
+
block_kwargs = dnnlib.EasyDict(
|
231 |
+
activation = kwarg('nonlinearity', 'lrelu'),
|
232 |
+
resample_filter = kwarg('resample_kernel', [1,3,3,1]),
|
233 |
+
freeze_layers = kwarg('freeze_layers', 0),
|
234 |
+
),
|
235 |
+
mapping_kwargs = dnnlib.EasyDict(
|
236 |
+
num_layers = kwarg('mapping_layers', 0),
|
237 |
+
embed_features = kwarg('mapping_fmaps', None),
|
238 |
+
layer_features = kwarg('mapping_fmaps', None),
|
239 |
+
activation = kwarg('nonlinearity', 'lrelu'),
|
240 |
+
lr_multiplier = kwarg('mapping_lrmul', 0.1),
|
241 |
+
),
|
242 |
+
epilogue_kwargs = dnnlib.EasyDict(
|
243 |
+
mbstd_group_size = kwarg('mbstd_group_size', None),
|
244 |
+
mbstd_num_channels = kwarg('mbstd_num_features', 1),
|
245 |
+
activation = kwarg('nonlinearity', 'lrelu'),
|
246 |
+
),
|
247 |
+
)
|
248 |
+
|
249 |
+
# Check for unknown kwargs.
|
250 |
+
kwarg('structure')
|
251 |
+
kwarg('conditioning')
|
252 |
+
unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
|
253 |
+
if len(unknown_kwargs) > 0:
|
254 |
+
raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
|
255 |
+
|
256 |
+
# Collect params.
|
257 |
+
tf_params = _collect_tf_params(tf_D)
|
258 |
+
for name, value in list(tf_params.items()):
|
259 |
+
match = re.fullmatch(r'FromRGB_lod(\d+)/(.*)', name)
|
260 |
+
if match:
|
261 |
+
r = kwargs.img_resolution // (2 ** int(match.group(1)))
|
262 |
+
tf_params[f'{r}x{r}/FromRGB/{match.group(2)}'] = value
|
263 |
+
kwargs.architecture = 'orig'
|
264 |
+
#for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
|
265 |
+
|
266 |
+
# Convert params.
|
267 |
+
from training import networks_stylegan2
|
268 |
+
D = networks_stylegan2.Discriminator(**kwargs).eval().requires_grad_(False)
|
269 |
+
# pylint: disable=unnecessary-lambda
|
270 |
+
# pylint: disable=f-string-without-interpolation
|
271 |
+
_populate_module_params(D,
|
272 |
+
r'b(\d+)\.fromrgb\.weight', lambda r: tf_params[f'{r}x{r}/FromRGB/weight'].transpose(3, 2, 0, 1),
|
273 |
+
r'b(\d+)\.fromrgb\.bias', lambda r: tf_params[f'{r}x{r}/FromRGB/bias'],
|
274 |
+
r'b(\d+)\.conv(\d+)\.weight', lambda r, i: tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/weight'].transpose(3, 2, 0, 1),
|
275 |
+
r'b(\d+)\.conv(\d+)\.bias', lambda r, i: tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/bias'],
|
276 |
+
r'b(\d+)\.skip\.weight', lambda r: tf_params[f'{r}x{r}/Skip/weight'].transpose(3, 2, 0, 1),
|
277 |
+
r'mapping\.embed\.weight', lambda: tf_params[f'LabelEmbed/weight'].transpose(),
|
278 |
+
r'mapping\.embed\.bias', lambda: tf_params[f'LabelEmbed/bias'],
|
279 |
+
r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'Mapping{i}/weight'].transpose(),
|
280 |
+
r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'Mapping{i}/bias'],
|
281 |
+
r'b4\.conv\.weight', lambda: tf_params[f'4x4/Conv/weight'].transpose(3, 2, 0, 1),
|
282 |
+
r'b4\.conv\.bias', lambda: tf_params[f'4x4/Conv/bias'],
|
283 |
+
r'b4\.fc\.weight', lambda: tf_params[f'4x4/Dense0/weight'].transpose(),
|
284 |
+
r'b4\.fc\.bias', lambda: tf_params[f'4x4/Dense0/bias'],
|
285 |
+
r'b4\.out\.weight', lambda: tf_params[f'Output/weight'].transpose(),
|
286 |
+
r'b4\.out\.bias', lambda: tf_params[f'Output/bias'],
|
287 |
+
r'.*\.resample_filter', None,
|
288 |
+
)
|
289 |
+
return D
|
290 |
+
|
291 |
+
#----------------------------------------------------------------------------
|
292 |
+
|
293 |
+
@click.command()
|
294 |
+
@click.option('--source', help='Input pickle', required=True, metavar='PATH')
|
295 |
+
@click.option('--dest', help='Output pickle', required=True, metavar='PATH')
|
296 |
+
@click.option('--force-fp16', help='Force the networks to use FP16', type=bool, default=False, metavar='BOOL', show_default=True)
|
297 |
+
def convert_network_pickle(source, dest, force_fp16):
|
298 |
+
"""Convert legacy network pickle into the native PyTorch format.
|
299 |
+
|
300 |
+
The tool is able to load the main network configurations exported using the TensorFlow version of StyleGAN2 or StyleGAN2-ADA.
|
301 |
+
It does not support e.g. StyleGAN2-ADA comparison methods, StyleGAN2 configs A-D, or StyleGAN1 networks.
|
302 |
+
|
303 |
+
Example:
|
304 |
+
|
305 |
+
\b
|
306 |
+
python legacy.py \\
|
307 |
+
--source=https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-cat-config-f.pkl \\
|
308 |
+
--dest=stylegan2-cat-config-f.pkl
|
309 |
+
"""
|
310 |
+
print(f'Loading "{source}"...')
|
311 |
+
with dnnlib.util.open_url(source) as f:
|
312 |
+
data = load_network_pkl(f, force_fp16=force_fp16)
|
313 |
+
print(f'Saving "{dest}"...')
|
314 |
+
with open(dest, 'wb') as f:
|
315 |
+
pickle.dump(data, f)
|
316 |
+
print('Done.')
|
317 |
+
|
318 |
+
#----------------------------------------------------------------------------
|
319 |
+
|
320 |
+
if __name__ == "__main__":
|
321 |
+
convert_network_pickle() # pylint: disable=no-value-for-parameter
|
322 |
+
|
323 |
+
#----------------------------------------------------------------------------
|
pages/1_Disentanglement.py
CHANGED
@@ -10,6 +10,7 @@ from matplotlib.backends.backend_agg import RendererAgg
|
|
10 |
from backend.disentangle_concepts import *
|
11 |
import torch_utils
|
12 |
import dnnlib
|
|
|
13 |
|
14 |
_lock = RendererAgg.lock
|
15 |
|
|
|
10 |
from backend.disentangle_concepts import *
|
11 |
import torch_utils
|
12 |
import dnnlib
|
13 |
+
import legacy
|
14 |
|
15 |
_lock = RendererAgg.lock
|
16 |
|