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loader.py
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loader.py
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'''
Load pretrained models,
and convert tf-model to torch-model.
'''
import click
import pickle
import re
import copy
import numpy as np
import torch
import dnnlib
from torch_utils import misc
gdrive_urls = {
"gdrive:clevr-snapshot.pkl": "https://drive.google.com/uc?id=1eRgwoasbDgUAA2tsD-LKghDiYTsfHWn3",
"gdrive:cityscapes-snapshot.pkl": "https://drive.google.com/uc?id=1Lrq3ga9N9ViH2KyvpCnSdG2xyZe_yDUA",
"gdrive:ffhq-snapshot.pkl": "https://drive.google.com/uc?id=1QvGFQfvPXsqsiQE5jWgRM9awxfaWnoqd",
"gdrive:bedrooms-snapshot.pkl": "https://drive.google.com/uc?id=1GkmnFqwUI0X5dOnSHOFDeWg_jJAc08Za"
}
def get_path_or_url(path_or_gdrive_path):
return gdrive_urls.get(path_or_gdrive_path, path_or_gdrive_path)
def load_network(filename):
filename = get_path_or_url(filename)
with dnnlib.util.open_url(filename) as f:
network = load_network_pkl(f)
return network
def load_network_pkl(f):
data = _LegacyUnpickler(f).load()
# 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)
Gs = convert_tf_generator(tf_Gs)
data = dict(G = G, D = D, Gs = Gs)
# Validate contents
assert isinstance(data["G"], torch.nn.Module)
assert isinstance(data["D"], torch.nn.Module)
assert isinstance(data["Gs"], torch.nn.Module)
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 _collect_tf_params(tf_net):
tf_params = dict()
def recurse(prefix, tf_net):
for name, value in tf_net.variables:
tf_params[prefix + name] = value
for name, comp in tf_net.components.items():
recurse(prefix + name + "/", comp)
recurse("", tf_net)
return tf_params
def _populate_module_params(module, *patterns):
for name, tensor in misc.named_params_and_buffers(module):
found = False
value = None
for pattern, value_fn in zip(patterns[0::2], patterns[1::2]):
match = re.fullmatch(pattern, name)
if match:
found = True
if value_fn is not None:
value = value_fn(*match.groups())
break
try:
assert found
if value is not None:
tensor.copy_(torch.from_numpy(np.array(value)))
except:
print(name, list(tensor.shape))
raise
#----------------------------------------------------------------------------
def convert_tf_generator(tf_G):
if tf_G.version < 4:
raise ValueError("TensorFlow pickle version too low")
# Collect kwargs
tf_kwargs = tf_G.static_kwargs
known_kwargs = set()
def kwarg(tf_name, default = None, none = None):
known_kwargs.add(tf_name)
val = tf_kwargs.get(tf_name, default)
return val if val is not None else none
# Convert kwargs
kwargs = dnnlib.EasyDict(
z_dim = kwarg("latent_size", 512),
c_dim = kwarg("label_size", 0),
w_dim = kwarg("dlatent_size", 512),
k = kwarg("components_num", 1) + int(tf_G.static_kwargs.get("transformer", False)),
img_resolution = kwarg("resolution", 1024),
img_channels = kwarg("num_channels", 3),
mapping_kwargs = dnnlib.EasyDict(
num_layers = kwarg("mapping_layersnum", 8),
layer_dim = kwarg("mapping_dim", None),
act = kwarg("mapping_nonlinearity", "lrelu"),
lrmul = kwarg("mapping_lrmul", 0.01),
w_avg_beta = kwarg("dlatent_avg_beta", 0.995, none = 1),
resnet = kwarg("mapping_resnet", False),
ltnt2ltnt = kwarg("mapping_ltnt2ltnt", False),
transformer = kwarg("transformer", False),
num_heads = kwarg("num_heads", 1),
attention_dropout = kwarg("attention_dropout", 0.12),
ltnt_gate = kwarg("ltnt_gate", False),
use_pos = kwarg("use_pos", False),
normalize_global = False,
),
synthesis_kwargs = dnnlib.EasyDict(
channel_base = kwarg("fmap_base", 16 << 10) * 2,
channel_max = kwarg("fmap_max", 512),
architecture = kwarg("architecture", "skip"),
resample_kernel = kwarg("resample_kernel", [1,3,3,1]),
local_noise = kwarg("local_noise", True),
act = kwarg("nonlinearity", "lrelu"),
latent_stem = kwarg("latent_stem", False),
style = kwarg("style", True),
transformer = kwarg("transformer", False),
start_res = kwarg("start_res", 0),
end_res = kwarg("end_res", 8),
num_heads = kwarg("num_heads", 1),
attention_dropout = kwarg("attention_dropout", 0.12),
ltnt_gate = kwarg("ltnt_gate", False),
img_gate = kwarg("img_gate", False),
integration = kwarg("integration", "add"),
norm = kwarg("norm", None),
kmeans = kwarg("kmeans", False),
kmeans_iters = kwarg("kmeans_iters", 1),
iterative = kwarg("iterative", False),
use_pos = kwarg("use_pos", False),
pos_dim = kwarg("pos_dim", None),
pos_type = kwarg("pos_type", "sinus"),
pos_init = kwarg("pos_init", "uniform"),
pos_directions_num = kwarg("pos_directions_num", 2),
),
)
# Check for unknown kwargs
# kwarg("truncation_psi")
# kwarg("truncation_cutoff")
# kwarg("style_mixing_prob")
# kwarg("structure")
# unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
# if len(unknown_kwargs) > 0:
# raise ValueError("Unknown TensorFlow kwarg", unknown_kwargs[0])
# Collect params
tf_params = _collect_tf_params(tf_G)
for name, value in list(tf_params.items()):
match = re.fullmatch(r"ToRGB_lod(\d+)/(.*)", name)
if match:
r = kwargs.img_resolution // (2 ** int(match.group(1)))
tf_params[f"{r}x{r}/ToRGB/{match.group(2)}"] = value
kwargs.synthesis.kwargs.architecture = "orig"
# for name, value in tf_params.items(): print(f"{name:<50s}{list(value.shape)}")
# Convert params
from training import networks
G = networks.Generator(**kwargs).eval().requires_grad_(False)
index = lambda r, i: "" if int(r) == 4 else f"{i}{['_up',''][int(i)]}"
plural = lambda s: {"queries": "query", "keys": "key", "values": "value"}[s]
global_fix = lambda s: "global/" if "global" in s else ""
_populate_module_params(G,
r"pos", lambda: tf_params["ltnt_emb/emb"],
# Mapping network
r"mapping\.w_avg", lambda: tf_params["dlatent_avg"],
r"mapping\.embed\.weight", lambda: tf_params["mapping/LabelConcat/weight"].transpose(),
r"mapping\.embed\.bias", lambda: np.zeros([tf_G.static_kwargs.get("latent_size", 512)]),
r"mapping\.([a-z_]+)\.l(\d+)\.fc(\d+)\.weight", lambda s, i, j: tf_params[f"mapping/{global_fix(s)}Dense{i}_{j}/weight"].transpose(),
r"mapping\.([a-z_]+)\.l(\d+)\.fc(\d+)\.bias", lambda s, i, j: tf_params[f"mapping/{global_fix(s)}Dense{i}_{j}/bias"],
r"mapping\.([a-z_]+)\.out_layer\.weight", lambda s: tf_params[f"mapping/{global_fix(s)}Dense3/weight"].transpose(),
r"mapping\.([a-z_]+)\.out_layer\.bias", lambda s: tf_params[f"mapping/{global_fix(s)}Dense3/bias"],
r"mapping\.mlp\.l(\d+)\.fc(\d+)\.weight", lambda i, j: tf_params[f"mapping/Dense{i}_{j}/weight"].transpose(),
r"mapping\.mlp\.l(\d+)\.fc(\d+)\.bias", lambda i, j: tf_params[f"mapping/Dense{i}_{j}/bias"],
r"mapping\.mlp\.out_layer\.weight", lambda: tf_params[f"mapping/Dense3/weight"].transpose(),
r"mapping\.mlp\.out_layer\.bias", lambda: tf_params[f"mapping/Dense3/bias"],
# Mapping ltnt2ltnt
r"mapping\.mlp\.sa(\d+)\.to_([a-z]+)\.weight", lambda i, s: tf_params[f"mapping/AttLayer_{i}/weight_{plural(s)}"].transpose(),
r"mapping\.mlp\.sa(\d+)\.to_([a-z]+)\.bias", lambda i, s: tf_params[f"mapping/AttLayer_{i}/bias_{plural(s)}"],
r"mapping\.mlp\.sa(\d+)\.([a-z]+)_pos_map\.weight", lambda i, s: tf_params[f"mapping/AttLayer_{i}/weight_{s}_pos"].transpose(),
r"mapping\.mlp\.sa(\d+)\.([a-z]+)_pos_map\.bias", lambda i, s: tf_params[f"mapping/AttLayer_{i}/bias_{s}_pos"],
r"mapping\.mlp\.sa(\d+)\.modulation\.weight", lambda i: tf_params[f"mapping/AttLayer_{i}/weight_out"].transpose(),
r"mapping\.mlp\.sa(\d+)\.modulation\.bias", lambda i: tf_params[f"mapping/AttLayer_{i}/bias_out"],
r"mapping\.mlp\.sa(\d+)\.centroids", lambda i: tf_params[f"mapping/AttLayer_{i}/toasgn_init"],
r"mapping\.mlp\.sa(\d+)\.queries2centroids", lambda i: tf_params[f"mapping/AttLayer_{i}/weight_key2"].transpose(),
r"mapping\.mlp\.sa(\d+)\.queries2centroids", lambda i: tf_params[f"mapping/AttLayer_{i}/bias_key2"],
r"mapping\.mlp\.sa(\d+)\.att_weight", lambda i: tf_params[f"mapping/AttLayer_{i}/iter_0/st_weights"],
# Synthesis Network
r"synthesis\.b4\.const", lambda: tf_params[f"synthesis/4x4/Const/const"][0],
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),
r"synthesis\.b(\d+)\.conv1\.weight", lambda r: tf_params[f"synthesis/{r}x{r}/Conv{index(r,1)}/weight"].transpose(3, 2, 0, 1),
r"synthesis\.b(\d+)\.conv(\d+)\.biasAct\.bias", lambda r, i: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/bias"],
r"synthesis\.b(\d+)\.conv(\d+)\.noise_const", lambda r, i: tf_params[f"synthesis/noise{int(np.log2(int(r)))*2-5+int(i)}"][0, 0],
r"synthesis\.b(\d+)\.conv(\d+)\.noise_strength", lambda r, i: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/noise_strength"],
r"synthesis\.b(\d+)\.conv(\d+)\.affine\.weight", lambda r, i: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/mod_weight"].transpose(),
r"synthesis\.b(\d+)\.conv(\d+)\.affine\.bias", lambda r, i: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/mod_bias"] + 1,
# Synthesis Network: Latents to Image
r"synthesis\.b(\d+)\.conv(\d+)\.transformer\.to_([a-z]+)\.weight", lambda r, i, s: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/AttLayer_l2n/weight_{plural(s)}"].transpose(),
r"synthesis\.b(\d+)\.conv(\d+)\.transformer\.to_([a-z]+)\.bias", lambda r, i, s: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/AttLayer_l2n/bias_{plural(s)}"],
r"synthesis\.b(\d+)\.conv(\d+)\.transformer\.([a-z]+)_pos_map\.weight", lambda r, i, s: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/AttLayer_l2n/weight_{s}_pos"].transpose(),
r"synthesis\.b(\d+)\.conv(\d+)\.transformer\.([a-z]+)_pos_map\.bias", lambda r, i, s: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/AttLayer_l2n/bias_{s}_pos"],
r"synthesis\.b(\d+)\.conv(\d+)\.transformer\.modulation\.weight", lambda r, i: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/AttLayer_l2n/weight_out"].transpose(),
r"synthesis\.b(\d+)\.conv(\d+)\.transformer\.modulation\.bias", lambda r, i: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/AttLayer_l2n/bias_out"],
r"synthesis\.b(\d+)\.conv(\d+)\.transformer\.centroids", lambda r, i: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/AttLayer_l2n/toasgn_init"],
r"synthesis\.b(\d+)\.conv(\d+)\.transformer\.queries2centroids", lambda r, i: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/AttLayer_l2n/weight_key2"].transpose(),
r"synthesis\.b(\d+)\.conv(\d+)\.transformer\.queries2centroids", lambda r, i: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/AttLayer_l2n/bias_key2"],
r"synthesis\.b(\d+)\.conv(\d+)\.transformer\.att_weight", lambda r, i: tf_params[f"synthesis/{r}x{r}/Conv{index(r,i)}/AttLayer_l2n/iter_0/st_weights"],
# Synthesis Network's RGB layer
r"synthesis\.b(\d+)\.torgb\.weight", lambda r: tf_params[f"synthesis/{r}x{r}/ToRGB/weight"].transpose(3, 2, 0, 1),
r"synthesis\.b(\d+)\.torgb\.biasAct\.bias", lambda r: tf_params[f"synthesis/{r}x{r}/ToRGB/bias"],
r"synthesis\.b(\d+)\.torgb\.affine\.weight", lambda r: tf_params[f"synthesis/{r}x{r}/ToRGB/mod_weight"].transpose(),
r"synthesis\.b(\d+)\.torgb\.affine\.bias", lambda r: tf_params[f"synthesis/{r}x{r}/ToRGB/mod_bias"] + 1,
r"synthesis\.b(\d+)\.skip\.weight", lambda r: tf_params[f"synthesis/{r}x{r}/Skip/weight"][::-1, ::-1].transpose(3, 2, 0, 1),
r"synthesis\.b256\.conv_last\.weight", lambda: tf_params[f"synthesis/256x256/ToRGB/extraLayer/weight"].transpose(3, 2, 0, 1),
r"synthesis\.b256\.conv_last\.affine\.weight", lambda: tf_params[f"synthesis/256x256/ToRGB/extraLayer/mod_weight"].transpose(),
r"synthesis\.b256\.conv_last\.affine\.bias", lambda: tf_params[f"synthesis/256x256/ToRGB/extraLayer/mod_bias"] + 1,
r"synthesis.b512.conv_last.weight", lambda: tf_params[f"synthesis/512x512/ToRGB/extraLayer/weight"].transpose(3, 2, 0, 1),
r"synthesis.b512.conv_last.affine.weight", lambda: tf_params[f"synthesis/512x512/ToRGB/extraLayer/mod_weight"].transpose(),
r"synthesis.b512.conv_last.affine.bias", lambda: tf_params[f"synthesis/512x512/ToRGB/extraLayer/mod_bias"] + 1,
r"synthesis.b1024.conv_last.weight", lambda: tf_params[f"synthesis/1024x1024/ToRGB/extraLayer/weight"].transpose(3, 2, 0, 1),
r"synthesis.b1024.conv_last.affine.weight", lambda: tf_params[f"synthesis/1024x1024/ToRGB/extraLayer/mod_weight"].transpose(),
r"synthesis.b1024.conv_last.affine.bias", lambda: tf_params[f"synthesis/1024x1024/ToRGB/extraLayer/mod_bias"] + 1,
r".*\.resample_kernel", None,
r".*\.grid_pos", None,
)
return G
#----------------------------------------------------------------------------
def convert_tf_discriminator(tf_D):
if tf_D.version < 4:
raise ValueError("TensorFlow pickle version too low")
# Collect kwargs
tf_kwargs = tf_D.static_kwargs
known_kwargs = set()
def kwarg(tf_name, default = None):
known_kwargs.add(tf_name)
return tf_kwargs.get(tf_name, default)
# Convert kwargs
kwargs = dnnlib.EasyDict(
c_dim = kwarg("label_size", 0),
img_resolution = kwarg("resolution", 1024),
img_channels = kwarg("num_channels", 3),
architecture = kwarg("architecture", "resnet"),
channel_base = kwarg("fmap_base", 16 << 10) * 2,
channel_max = kwarg("fmap_max", 512),
block_kwargs = dnnlib.EasyDict(
act = kwarg("nonlinearity", "lrelu"),
resample_kernel = kwarg("resample_kernel", [1,3,3,1]),
),
epilogue_kwargs = dnnlib.EasyDict(
act = kwarg("nonlinearity", "lrelu"),
mbstd_group_size = kwarg("mbstd_group_size", 4),
mbstd_num_channels = kwarg("mbstd_num_features", 1),
),
)
# Check for unknown kwargs
# kwarg("structure")
# unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
# if len(unknown_kwargs) > 0:
# raise ValueError("Unknown TensorFlow kwarg", unknown_kwargs[0])
# Collect params
tf_params = _collect_tf_params(tf_D)
for name, value in list(tf_params.items()):
match = re.fullmatch(r"FromRGB_lod(\d+)/(.*)", name)
if match:
r = kwargs.img_resolution // (2 ** int(match.group(1)))
tf_params[f"{r}x{r}/FromRGB/{match.group(2)}"] = value
kwargs.architecture = "orig"
#for name, value in tf_params.items(): print(f"{name:<50s}{list(value.shape)}")
# Convert params
from training import networks
D = networks.Discriminator(**kwargs).eval().requires_grad_(False)
_populate_module_params(D,
r"b(\d+)\.fromrgb\.weight", lambda r: tf_params[f"{r}x{r}/FromRGB/weight"].transpose(3, 2, 0, 1),
r"b(\d+)\.fromrgb\.biasAct\.bias", lambda r: tf_params[f"{r}x{r}/FromRGB/bias"],
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),
r"b(\d+)\.conv(\d+)\.biasAct\.bias", lambda r, i: tf_params[f"{r}x{r}/Conv{i}{['','_down'][int(i)]}/bias"],
r"b(\d+)\.skip\.weight", lambda r: tf_params[f"{r}x{r}/Skip/weight"].transpose(3, 2, 0, 1),
r"b4\.conv\.weight", lambda: tf_params[f"4x4/Conv/weight"].transpose(3, 2, 0, 1),
r"b4\.conv\.biasAct\.bias", lambda: tf_params[f"4x4/Conv/bias"],
r"b4\.fc\.weight", lambda: tf_params[f"4x4/Dense0/weight"].transpose(),
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_kernel", 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")
def convert_network_pickle(source, dest):
"""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 GANFormer.
Example: python loader.py --source=checkpoint-TF.pkl --dest=checkpoint.pkl
"""
print(f"Loading {source}...")
with dnnlib.util.open_url(source) as f:
data = load_network_pkl(f)
print(f"Saving {dest}...")
with open(dest, "wb") as f:
pickle.dump(data, f)
print("Done.")
if __name__ == "__main__":
ro = '/home/na/1_Face_morphing/1_code/2_morphing/5_gansformer-main_V2_256/'
source = ro + 'models/ffhq-snapshot-1024.pkl'
# 'cityscapes-snapshot-2048.pkl
dest = ro + 'pytorch_version/models/ffhq-snapshot-1024.pkl'
convert_network_pickle(source, dest)
# gdrive:clevr-snapshot.pkl":
# "gdrive:cityscapes-snapshot.pkl":
# "gdrive:ffhq-snapshot.pkl":
# "gdrive:bedrooms-snapshot.pkl":