Spaces:
Runtime error
Runtime error
File size: 14,091 Bytes
e0c7c25 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 |
# coding: utf-8
"""
Convert a TF Hub model for BigGAN in a PT one.
"""
from __future__ import (absolute_import, division, print_function, unicode_literals)
from itertools import chain
import os
import argparse
import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.functional import normalize
from .model import BigGAN, WEIGHTS_NAME, CONFIG_NAME
from .config import BigGANConfig
logger = logging.getLogger(__name__)
def extract_batch_norm_stats(tf_model_path, batch_norm_stats_path=None):
try:
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
except ImportError:
raise ImportError("Loading a TensorFlow models in PyTorch, requires TensorFlow and TF Hub to be installed. "
"Please see https://www.tensorflow.org/install/ for installation instructions for TensorFlow. "
"And see https://github.com/tensorflow/hub for installing Hub. "
"Probably pip install tensorflow tensorflow-hub")
tf.reset_default_graph()
logger.info('Loading BigGAN module from: {}'.format(tf_model_path))
module = hub.Module(tf_model_path)
inputs = {k: tf.placeholder(v.dtype, v.get_shape().as_list(), k)
for k, v in module.get_input_info_dict().items()}
output = module(inputs)
initializer = tf.global_variables_initializer()
sess = tf.Session()
stacks = sum(((i*10 + 1, i*10 + 3, i*10 + 6, i*10 + 8) for i in range(50)), ())
numpy_stacks = []
for i in stacks:
logger.info("Retrieving module_apply_default/stack_{}".format(i))
try:
stack_var = tf.get_default_graph().get_tensor_by_name("module_apply_default/stack_%d:0" % i)
except KeyError:
break # We have all the stats
numpy_stacks.append(sess.run(stack_var))
if batch_norm_stats_path is not None:
torch.save(numpy_stacks, batch_norm_stats_path)
else:
return numpy_stacks
def build_tf_to_pytorch_map(model, config):
""" Build a map from TF variables to PyTorch modules. """
tf_to_pt_map = {}
# Embeddings and GenZ
tf_to_pt_map.update({'linear/w/ema_0.9999': model.embeddings.weight,
'Generator/GenZ/G_linear/b/ema_0.9999': model.generator.gen_z.bias,
'Generator/GenZ/G_linear/w/ema_0.9999': model.generator.gen_z.weight_orig,
'Generator/GenZ/G_linear/u0': model.generator.gen_z.weight_u})
# GBlock blocks
model_layer_idx = 0
for i, (up, in_channels, out_channels) in enumerate(config.layers):
if i == config.attention_layer_position:
model_layer_idx += 1
layer_str = "Generator/GBlock_%d/" % i if i > 0 else "Generator/GBlock/"
layer_pnt = model.generator.layers[model_layer_idx]
for i in range(4): # Batchnorms
batch_str = layer_str + ("BatchNorm_%d/" % i if i > 0 else "BatchNorm/")
batch_pnt = getattr(layer_pnt, 'bn_%d' % i)
for name in ('offset', 'scale'):
sub_module_str = batch_str + name + "/"
sub_module_pnt = getattr(batch_pnt, name)
tf_to_pt_map.update({sub_module_str + "w/ema_0.9999": sub_module_pnt.weight_orig,
sub_module_str + "u0": sub_module_pnt.weight_u})
for i in range(4): # Convolutions
conv_str = layer_str + "conv%d/" % i
conv_pnt = getattr(layer_pnt, 'conv_%d' % i)
tf_to_pt_map.update({conv_str + "b/ema_0.9999": conv_pnt.bias,
conv_str + "w/ema_0.9999": conv_pnt.weight_orig,
conv_str + "u0": conv_pnt.weight_u})
model_layer_idx += 1
# Attention block
layer_str = "Generator/attention/"
layer_pnt = model.generator.layers[config.attention_layer_position]
tf_to_pt_map.update({layer_str + "gamma/ema_0.9999": layer_pnt.gamma})
for pt_name, tf_name in zip(['snconv1x1_g', 'snconv1x1_o_conv', 'snconv1x1_phi', 'snconv1x1_theta'],
['g/', 'o_conv/', 'phi/', 'theta/']):
sub_module_str = layer_str + tf_name
sub_module_pnt = getattr(layer_pnt, pt_name)
tf_to_pt_map.update({sub_module_str + "w/ema_0.9999": sub_module_pnt.weight_orig,
sub_module_str + "u0": sub_module_pnt.weight_u})
# final batch norm and conv to rgb
layer_str = "Generator/BatchNorm/"
layer_pnt = model.generator.bn
tf_to_pt_map.update({layer_str + "offset/ema_0.9999": layer_pnt.bias,
layer_str + "scale/ema_0.9999": layer_pnt.weight})
layer_str = "Generator/conv_to_rgb/"
layer_pnt = model.generator.conv_to_rgb
tf_to_pt_map.update({layer_str + "b/ema_0.9999": layer_pnt.bias,
layer_str + "w/ema_0.9999": layer_pnt.weight_orig,
layer_str + "u0": layer_pnt.weight_u})
return tf_to_pt_map
def load_tf_weights_in_biggan(model, config, tf_model_path, batch_norm_stats_path=None):
""" Load tf checkpoints and standing statistics in a pytorch model
"""
try:
import numpy as np
import tensorflow as tf
except ImportError:
raise ImportError("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.")
# Load weights from TF model
checkpoint_path = tf_model_path + "/variables/variables"
init_vars = tf.train.list_variables(checkpoint_path)
from pprint import pprint
pprint(init_vars)
# Extract batch norm statistics from model if needed
if batch_norm_stats_path:
stats = torch.load(batch_norm_stats_path)
else:
logger.info("Extracting batch norm stats")
stats = extract_batch_norm_stats(tf_model_path)
# Build TF to PyTorch weights loading map
tf_to_pt_map = build_tf_to_pytorch_map(model, config)
tf_weights = {}
for name in tf_to_pt_map.keys():
array = tf.train.load_variable(checkpoint_path, name)
tf_weights[name] = array
# logger.info("Loading TF weight {} with shape {}".format(name, array.shape))
# Load parameters
with torch.no_grad():
pt_params_pnt = set()
for name, pointer in tf_to_pt_map.items():
array = tf_weights[name]
if pointer.dim() == 1:
if pointer.dim() < array.ndim:
array = np.squeeze(array)
elif pointer.dim() == 2: # Weights
array = np.transpose(array)
elif pointer.dim() == 4: # Convolutions
array = np.transpose(array, (3, 2, 0, 1))
else:
raise "Wrong dimensions to adjust: " + str((pointer.shape, array.shape))
if pointer.shape != array.shape:
raise ValueError("Wrong dimensions: " + str((pointer.shape, array.shape)))
logger.info("Initialize PyTorch weight {} with shape {}".format(name, pointer.shape))
pointer.data = torch.from_numpy(array) if isinstance(array, np.ndarray) else torch.tensor(array)
tf_weights.pop(name, None)
pt_params_pnt.add(pointer.data_ptr())
# Prepare SpectralNorm buffers by running one step of Spectral Norm (no need to train the model):
for module in model.modules():
for n, buffer in module.named_buffers():
if n == 'weight_v':
weight_mat = module.weight_orig
weight_mat = weight_mat.reshape(weight_mat.size(0), -1)
u = module.weight_u
v = normalize(torch.mv(weight_mat.t(), u), dim=0, eps=config.eps)
buffer.data = v
pt_params_pnt.add(buffer.data_ptr())
u = normalize(torch.mv(weight_mat, v), dim=0, eps=config.eps)
module.weight_u.data = u
pt_params_pnt.add(module.weight_u.data_ptr())
# Load batch norm statistics
index = 0
for layer in model.generator.layers:
if not hasattr(layer, 'bn_0'):
continue
for i in range(4): # Batchnorms
bn_pointer = getattr(layer, 'bn_%d' % i)
pointer = bn_pointer.running_means
if pointer.shape != stats[index].shape:
raise "Wrong dimensions: " + str((pointer.shape, stats[index].shape))
pointer.data = torch.from_numpy(stats[index])
pt_params_pnt.add(pointer.data_ptr())
pointer = bn_pointer.running_vars
if pointer.shape != stats[index+1].shape:
raise "Wrong dimensions: " + str((pointer.shape, stats[index].shape))
pointer.data = torch.from_numpy(stats[index+1])
pt_params_pnt.add(pointer.data_ptr())
index += 2
bn_pointer = model.generator.bn
pointer = bn_pointer.running_means
if pointer.shape != stats[index].shape:
raise "Wrong dimensions: " + str((pointer.shape, stats[index].shape))
pointer.data = torch.from_numpy(stats[index])
pt_params_pnt.add(pointer.data_ptr())
pointer = bn_pointer.running_vars
if pointer.shape != stats[index+1].shape:
raise "Wrong dimensions: " + str((pointer.shape, stats[index].shape))
pointer.data = torch.from_numpy(stats[index+1])
pt_params_pnt.add(pointer.data_ptr())
remaining_params = list(n for n, t in chain(model.named_parameters(), model.named_buffers()) \
if t.data_ptr() not in pt_params_pnt)
logger.info("TF Weights not copied to PyTorch model: {} -".format(', '.join(tf_weights.keys())))
logger.info("Remanining parameters/buffers from PyTorch model: {} -".format(', '.join(remaining_params)))
return model
BigGAN128 = BigGANConfig(output_dim=128, z_dim=128, class_embed_dim=128, channel_width=128, num_classes=1000,
layers=[(False, 16, 16),
(True, 16, 16),
(False, 16, 16),
(True, 16, 8),
(False, 8, 8),
(True, 8, 4),
(False, 4, 4),
(True, 4, 2),
(False, 2, 2),
(True, 2, 1)],
attention_layer_position=8, eps=1e-4, n_stats=51)
BigGAN256 = BigGANConfig(output_dim=256, z_dim=128, class_embed_dim=128, channel_width=128, num_classes=1000,
layers=[(False, 16, 16),
(True, 16, 16),
(False, 16, 16),
(True, 16, 8),
(False, 8, 8),
(True, 8, 8),
(False, 8, 8),
(True, 8, 4),
(False, 4, 4),
(True, 4, 2),
(False, 2, 2),
(True, 2, 1)],
attention_layer_position=8, eps=1e-4, n_stats=51)
BigGAN512 = BigGANConfig(output_dim=512, z_dim=128, class_embed_dim=128, channel_width=128, num_classes=1000,
layers=[(False, 16, 16),
(True, 16, 16),
(False, 16, 16),
(True, 16, 8),
(False, 8, 8),
(True, 8, 8),
(False, 8, 8),
(True, 8, 4),
(False, 4, 4),
(True, 4, 2),
(False, 2, 2),
(True, 2, 1),
(False, 1, 1),
(True, 1, 1)],
attention_layer_position=8, eps=1e-4, n_stats=51)
def main():
parser = argparse.ArgumentParser(description="Convert a BigGAN TF Hub model in a PyTorch model")
parser.add_argument("--model_type", type=str, default="", required=True,
help="BigGAN model type (128, 256, 512)")
parser.add_argument("--tf_model_path", type=str, default="", required=True,
help="Path of the downloaded TF Hub model")
parser.add_argument("--pt_save_path", type=str, default="",
help="Folder to save the PyTorch model (default: Folder of the TF Hub model)")
parser.add_argument("--batch_norm_stats_path", type=str, default="",
help="Path of previously extracted batch norm statistics")
args = parser.parse_args()
logging.basicConfig(level=logging.INFO)
if not args.pt_save_path:
args.pt_save_path = args.tf_model_path
if args.model_type == "128":
config = BigGAN128
elif args.model_type == "256":
config = BigGAN256
elif args.model_type == "512":
config = BigGAN512
else:
raise ValueError("model_type should be one of 128, 256 or 512")
model = BigGAN(config)
model = load_tf_weights_in_biggan(model, config, args.tf_model_path, args.batch_norm_stats_path)
model_save_path = os.path.join(args.pt_save_path, WEIGHTS_NAME)
config_save_path = os.path.join(args.pt_save_path, CONFIG_NAME)
logger.info("Save model dump to {}".format(model_save_path))
torch.save(model.state_dict(), model_save_path)
logger.info("Save configuration file to {}".format(config_save_path))
with open(config_save_path, "w", encoding="utf-8") as f:
f.write(config.to_json_string())
if __name__ == "__main__":
main()
|