324 lines
16 KiB
Python
324 lines
16 KiB
Python
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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION is strictly prohibited.
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"""Converting legacy network pickle into the new format."""
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import click
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import pickle
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import re
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import copy
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import numpy as np
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import torch
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import dnnlib
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from torch_utils import misc
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#----------------------------------------------------------------------------
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def load_network_pkl(f, force_fp16=False):
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data = _LegacyUnpickler(f).load()
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# Legacy TensorFlow pickle => convert.
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if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data):
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tf_G, tf_D, tf_Gs = data
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G = convert_tf_generator(tf_G)
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D = convert_tf_discriminator(tf_D)
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G_ema = convert_tf_generator(tf_Gs)
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data = dict(G=G, D=D, G_ema=G_ema)
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# Add missing fields.
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if 'training_set_kwargs' not in data:
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data['training_set_kwargs'] = None
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if 'augment_pipe' not in data:
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data['augment_pipe'] = None
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# Validate contents.
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assert isinstance(data['G'], torch.nn.Module)
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assert isinstance(data['D'], torch.nn.Module)
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assert isinstance(data['G_ema'], torch.nn.Module)
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assert isinstance(data['training_set_kwargs'], (dict, type(None)))
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assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None)))
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# Force FP16.
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if force_fp16:
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for key in ['G', 'D', 'G_ema']:
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old = data[key]
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kwargs = copy.deepcopy(old.init_kwargs)
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fp16_kwargs = kwargs.get('synthesis_kwargs', kwargs)
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fp16_kwargs.num_fp16_res = 4
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fp16_kwargs.conv_clamp = 256
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if kwargs != old.init_kwargs:
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new = type(old)(**kwargs).eval().requires_grad_(False)
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misc.copy_params_and_buffers(old, new, require_all=True)
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data[key] = new
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return data
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#----------------------------------------------------------------------------
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class _TFNetworkStub(dnnlib.EasyDict):
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pass
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class _LegacyUnpickler(pickle.Unpickler):
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def find_class(self, module, name):
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if module == 'dnnlib.tflib.network' and name == 'Network':
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return _TFNetworkStub
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return super().find_class(module, name)
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#----------------------------------------------------------------------------
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def _collect_tf_params(tf_net):
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# pylint: disable=protected-access
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tf_params = dict()
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def recurse(prefix, tf_net):
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for name, value in tf_net.variables:
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tf_params[prefix + name] = value
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for name, comp in tf_net.components.items():
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recurse(prefix + name + '/', comp)
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recurse('', tf_net)
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return tf_params
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#----------------------------------------------------------------------------
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def _populate_module_params(module, *patterns):
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for name, tensor in misc.named_params_and_buffers(module):
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found = False
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value = None
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for pattern, value_fn in zip(patterns[0::2], patterns[1::2]):
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match = re.fullmatch(pattern, name)
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if match:
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found = True
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if value_fn is not None:
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value = value_fn(*match.groups())
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break
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try:
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assert found
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if value is not None:
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tensor.copy_(torch.from_numpy(np.array(value)))
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except:
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print(name, list(tensor.shape))
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raise
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#----------------------------------------------------------------------------
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def convert_tf_generator(tf_G):
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if tf_G.version < 4:
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raise ValueError('TensorFlow pickle version too low')
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# Collect kwargs.
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tf_kwargs = tf_G.static_kwargs
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known_kwargs = set()
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def kwarg(tf_name, default=None, none=None):
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known_kwargs.add(tf_name)
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val = tf_kwargs.get(tf_name, default)
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return val if val is not None else none
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# Convert kwargs.
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from training import networks_stylegan2
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network_class = networks_stylegan2.Generator
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kwargs = dnnlib.EasyDict(
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z_dim = kwarg('latent_size', 512),
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c_dim = kwarg('label_size', 0),
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w_dim = kwarg('dlatent_size', 512),
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img_resolution = kwarg('resolution', 1024),
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img_channels = kwarg('num_channels', 3),
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channel_base = kwarg('fmap_base', 16384) * 2,
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channel_max = kwarg('fmap_max', 512),
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num_fp16_res = kwarg('num_fp16_res', 0),
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conv_clamp = kwarg('conv_clamp', None),
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architecture = kwarg('architecture', 'skip'),
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resample_filter = kwarg('resample_kernel', [1,3,3,1]),
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use_noise = kwarg('use_noise', True),
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activation = kwarg('nonlinearity', 'lrelu'),
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mapping_kwargs = dnnlib.EasyDict(
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num_layers = kwarg('mapping_layers', 8),
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embed_features = kwarg('label_fmaps', None),
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layer_features = kwarg('mapping_fmaps', None),
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activation = kwarg('mapping_nonlinearity', 'lrelu'),
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lr_multiplier = kwarg('mapping_lrmul', 0.01),
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w_avg_beta = kwarg('w_avg_beta', 0.995, none=1),
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),
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)
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# Check for unknown kwargs.
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kwarg('truncation_psi')
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kwarg('truncation_cutoff')
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kwarg('style_mixing_prob')
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kwarg('structure')
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kwarg('conditioning')
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kwarg('fused_modconv')
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unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
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if len(unknown_kwargs) > 0:
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raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
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# Collect params.
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tf_params = _collect_tf_params(tf_G)
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for name, value in list(tf_params.items()):
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match = re.fullmatch(r'ToRGB_lod(\d+)/(.*)', name)
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if match:
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r = kwargs.img_resolution // (2 ** int(match.group(1)))
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tf_params[f'{r}x{r}/ToRGB/{match.group(2)}'] = value
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kwargs.synthesis.kwargs.architecture = 'orig'
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#for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
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# Convert params.
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G = network_class(**kwargs).eval().requires_grad_(False)
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# pylint: disable=unnecessary-lambda
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# pylint: disable=f-string-without-interpolation
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_populate_module_params(G,
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r'mapping\.w_avg', lambda: tf_params[f'dlatent_avg'],
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r'mapping\.embed\.weight', lambda: tf_params[f'mapping/LabelEmbed/weight'].transpose(),
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r'mapping\.embed\.bias', lambda: tf_params[f'mapping/LabelEmbed/bias'],
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r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'mapping/Dense{i}/weight'].transpose(),
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r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'mapping/Dense{i}/bias'],
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r'synthesis\.b4\.const', lambda: tf_params[f'synthesis/4x4/Const/const'][0],
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r'synthesis\.b4\.conv1\.weight', lambda: tf_params[f'synthesis/4x4/Conv/weight'].transpose(3, 2, 0, 1),
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r'synthesis\.b4\.conv1\.bias', lambda: tf_params[f'synthesis/4x4/Conv/bias'],
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r'synthesis\.b4\.conv1\.noise_const', lambda: tf_params[f'synthesis/noise0'][0, 0],
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r'synthesis\.b4\.conv1\.noise_strength', lambda: tf_params[f'synthesis/4x4/Conv/noise_strength'],
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r'synthesis\.b4\.conv1\.affine\.weight', lambda: tf_params[f'synthesis/4x4/Conv/mod_weight'].transpose(),
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r'synthesis\.b4\.conv1\.affine\.bias', lambda: tf_params[f'synthesis/4x4/Conv/mod_bias'] + 1,
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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),
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r'synthesis\.b(\d+)\.conv0\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/bias'],
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r'synthesis\.b(\d+)\.conv0\.noise_const', lambda r: tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-5}'][0, 0],
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r'synthesis\.b(\d+)\.conv0\.noise_strength', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/noise_strength'],
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r'synthesis\.b(\d+)\.conv0\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_weight'].transpose(),
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r'synthesis\.b(\d+)\.conv0\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_bias'] + 1,
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r'synthesis\.b(\d+)\.conv1\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/weight'].transpose(3, 2, 0, 1),
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r'synthesis\.b(\d+)\.conv1\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/bias'],
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r'synthesis\.b(\d+)\.conv1\.noise_const', lambda r: tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-4}'][0, 0],
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r'synthesis\.b(\d+)\.conv1\.noise_strength', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/noise_strength'],
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r'synthesis\.b(\d+)\.conv1\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/mod_weight'].transpose(),
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r'synthesis\.b(\d+)\.conv1\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/mod_bias'] + 1,
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r'synthesis\.b(\d+)\.torgb\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/weight'].transpose(3, 2, 0, 1),
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r'synthesis\.b(\d+)\.torgb\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/bias'],
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r'synthesis\.b(\d+)\.torgb\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/mod_weight'].transpose(),
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r'synthesis\.b(\d+)\.torgb\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/mod_bias'] + 1,
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r'synthesis\.b(\d+)\.skip\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Skip/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
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r'.*\.resample_filter', None,
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r'.*\.act_filter', None,
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)
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return G
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#----------------------------------------------------------------------------
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def convert_tf_discriminator(tf_D):
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if tf_D.version < 4:
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raise ValueError('TensorFlow pickle version too low')
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# Collect kwargs.
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tf_kwargs = tf_D.static_kwargs
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known_kwargs = set()
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def kwarg(tf_name, default=None):
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known_kwargs.add(tf_name)
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return tf_kwargs.get(tf_name, default)
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# Convert kwargs.
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kwargs = dnnlib.EasyDict(
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c_dim = kwarg('label_size', 0),
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img_resolution = kwarg('resolution', 1024),
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img_channels = kwarg('num_channels', 3),
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architecture = kwarg('architecture', 'resnet'),
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channel_base = kwarg('fmap_base', 16384) * 2,
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channel_max = kwarg('fmap_max', 512),
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num_fp16_res = kwarg('num_fp16_res', 0),
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conv_clamp = kwarg('conv_clamp', None),
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cmap_dim = kwarg('mapping_fmaps', None),
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block_kwargs = dnnlib.EasyDict(
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activation = kwarg('nonlinearity', 'lrelu'),
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resample_filter = kwarg('resample_kernel', [1,3,3,1]),
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freeze_layers = kwarg('freeze_layers', 0),
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),
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mapping_kwargs = dnnlib.EasyDict(
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num_layers = kwarg('mapping_layers', 0),
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embed_features = kwarg('mapping_fmaps', None),
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layer_features = kwarg('mapping_fmaps', None),
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activation = kwarg('nonlinearity', 'lrelu'),
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lr_multiplier = kwarg('mapping_lrmul', 0.1),
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),
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epilogue_kwargs = dnnlib.EasyDict(
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mbstd_group_size = kwarg('mbstd_group_size', None),
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mbstd_num_channels = kwarg('mbstd_num_features', 1),
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activation = kwarg('nonlinearity', 'lrelu'),
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),
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)
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# Check for unknown kwargs.
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kwarg('structure')
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kwarg('conditioning')
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unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
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if len(unknown_kwargs) > 0:
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raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
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# Collect params.
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tf_params = _collect_tf_params(tf_D)
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for name, value in list(tf_params.items()):
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match = re.fullmatch(r'FromRGB_lod(\d+)/(.*)', name)
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if match:
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r = kwargs.img_resolution // (2 ** int(match.group(1)))
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tf_params[f'{r}x{r}/FromRGB/{match.group(2)}'] = value
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kwargs.architecture = 'orig'
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#for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
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# Convert params.
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from training import networks_stylegan2
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D = networks_stylegan2.Discriminator(**kwargs).eval().requires_grad_(False)
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# pylint: disable=unnecessary-lambda
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# pylint: disable=f-string-without-interpolation
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_populate_module_params(D,
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r'b(\d+)\.fromrgb\.weight', lambda r: tf_params[f'{r}x{r}/FromRGB/weight'].transpose(3, 2, 0, 1),
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r'b(\d+)\.fromrgb\.bias', lambda r: tf_params[f'{r}x{r}/FromRGB/bias'],
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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),
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r'b(\d+)\.conv(\d+)\.bias', lambda r, i: tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/bias'],
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r'b(\d+)\.skip\.weight', lambda r: tf_params[f'{r}x{r}/Skip/weight'].transpose(3, 2, 0, 1),
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r'mapping\.embed\.weight', lambda: tf_params[f'LabelEmbed/weight'].transpose(),
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r'mapping\.embed\.bias', lambda: tf_params[f'LabelEmbed/bias'],
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r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'Mapping{i}/weight'].transpose(),
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r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'Mapping{i}/bias'],
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r'b4\.conv\.weight', lambda: tf_params[f'4x4/Conv/weight'].transpose(3, 2, 0, 1),
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r'b4\.conv\.bias', lambda: tf_params[f'4x4/Conv/bias'],
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r'b4\.fc\.weight', lambda: tf_params[f'4x4/Dense0/weight'].transpose(),
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r'b4\.fc\.bias', lambda: tf_params[f'4x4/Dense0/bias'],
|
|||
|
|
r'b4\.out\.weight', lambda: tf_params[f'Output/weight'].transpose(),
|
|||
|
|
r'b4\.out\.bias', lambda: tf_params[f'Output/bias'],
|
|||
|
|
r'.*\.resample_filter', None,
|
|||
|
|
)
|
|||
|
|
return D
|
|||
|
|
|
|||
|
|
#----------------------------------------------------------------------------
|
|||
|
|
|
|||
|
|
@click.command()
|
|||
|
|
@click.option('--source', help='Input pickle', required=True, metavar='PATH')
|
|||
|
|
@click.option('--dest', help='Output pickle', required=True, metavar='PATH')
|
|||
|
|
@click.option('--force-fp16', help='Force the networks to use FP16', type=bool, default=False, metavar='BOOL', show_default=True)
|
|||
|
|
def convert_network_pickle(source, dest, force_fp16):
|
|||
|
|
"""Convert legacy network pickle into the native PyTorch format.
|
|||
|
|
|
|||
|
|
The tool is able to load the main network configurations exported using the TensorFlow version of StyleGAN2 or StyleGAN2-ADA.
|
|||
|
|
It does not support e.g. StyleGAN2-ADA comparison methods, StyleGAN2 configs A-D, or StyleGAN1 networks.
|
|||
|
|
|
|||
|
|
Example:
|
|||
|
|
|
|||
|
|
\b
|
|||
|
|
python legacy.py \\
|
|||
|
|
--source=https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-cat-config-f.pkl \\
|
|||
|
|
--dest=stylegan2-cat-config-f.pkl
|
|||
|
|
"""
|
|||
|
|
print(f'Loading "{source}"...')
|
|||
|
|
with dnnlib.util.open_url(source) as f:
|
|||
|
|
data = load_network_pkl(f, force_fp16=force_fp16)
|
|||
|
|
print(f'Saving "{dest}"...')
|
|||
|
|
with open(dest, 'wb') as f:
|
|||
|
|
pickle.dump(data, f)
|
|||
|
|
print('Done.')
|
|||
|
|
|
|||
|
|
#----------------------------------------------------------------------------
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
convert_network_pickle() # pylint: disable=no-value-for-parameter
|
|||
|
|
|
|||
|
|
#----------------------------------------------------------------------------
|