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# 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()
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