/
load_tf_weights.py
174 lines (148 loc) · 10.2 KB
/
load_tf_weights.py
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
import numpy as np
import tensorflow as tf
import torch
tf.compat.v1.disable_v2_behavior()
def load_param(checkpoint_file, conversion_table, model_name):
"""
Load parameters according to conversion_table.
Args:
checkpoint_file (string): pretrained checkpoint model file in tensorflow
conversion_table (dict): { pytorch tensor in a model : checkpoint variable name }
"""
for pyt_param, tf_param_name in conversion_table.items():
tf_param_name = str(model_name) + '/' + tf_param_name
tf_param = tf.train.load_variable(checkpoint_file, tf_param_name)
if 'conv' in tf_param_name and 'kernel' in tf_param_name:
tf_param = np.transpose(tf_param, (3, 2, 0, 1))
if 'depthwise' in tf_param_name:
tf_param = np.transpose(tf_param, (1, 0, 2, 3))
elif tf_param_name.endswith('kernel'): # for weight(kernel), we should do transpose
tf_param = np.transpose(tf_param)
assert pyt_param.size() == tf_param.shape, \
'Dim Mismatch: %s vs %s ; %s' % (tuple(pyt_param.size()), tf_param.shape, tf_param_name)
pyt_param.data = torch.from_numpy(tf_param)
def load_efficientnet(model, checkpoint_file, model_name):
"""
Load PyTorch EfficientNet from TensorFlow checkpoint file
"""
# This will store the enire conversion table
conversion_table = {}
merge = lambda dict1, dict2: {**dict1, **dict2}
# All the weights not in the conv blocks
conversion_table_for_weights_outside_blocks = {
model._conv_stem.weight: 'stem/conv2d/kernel', # [3, 3, 3, 32]),
model._bn0.bias: 'stem/tpu_batch_normalization/beta', # [32]),
model._bn0.weight: 'stem/tpu_batch_normalization/gamma', # [32]),
model._bn0.running_mean: 'stem/tpu_batch_normalization/moving_mean', # [32]),
model._bn0.running_var: 'stem/tpu_batch_normalization/moving_variance', # [32]),
model._conv_head.weight: 'head/conv2d/kernel', # [1, 1, 320, 1280]),
model._bn1.bias: 'head/tpu_batch_normalization/beta', # [1280]),
model._bn1.weight: 'head/tpu_batch_normalization/gamma', # [1280]),
model._bn1.running_mean: 'head/tpu_batch_normalization/moving_mean', # [32]),
model._bn1.running_var: 'head/tpu_batch_normalization/moving_variance', # [32]),
model._fc.bias: 'head/dense/bias', # [1000]),
model._fc.weight: 'head/dense/kernel', # [1280, 1000]),
}
conversion_table = merge(conversion_table, conversion_table_for_weights_outside_blocks)
# The first conv block is special because it does not have _expand_conv
conversion_table_for_first_block = {
model._blocks[0]._project_conv.weight: 'blocks_0/conv2d/kernel', # 1, 1, 32, 16]),
model._blocks[0]._depthwise_conv.weight: 'blocks_0/depthwise_conv2d/depthwise_kernel', # [3, 3, 32, 1]),
model._blocks[0]._se_reduce.bias: 'blocks_0/se/conv2d/bias', # , [8]),
model._blocks[0]._se_reduce.weight: 'blocks_0/se/conv2d/kernel', # , [1, 1, 32, 8]),
model._blocks[0]._se_expand.bias: 'blocks_0/se/conv2d_1/bias', # , [32]),
model._blocks[0]._se_expand.weight: 'blocks_0/se/conv2d_1/kernel', # , [1, 1, 8, 32]),
model._blocks[0]._bn1.bias: 'blocks_0/tpu_batch_normalization/beta', # [32]),
model._blocks[0]._bn1.weight: 'blocks_0/tpu_batch_normalization/gamma', # [32]),
model._blocks[0]._bn1.running_mean: 'blocks_0/tpu_batch_normalization/moving_mean',
model._blocks[0]._bn1.running_var: 'blocks_0/tpu_batch_normalization/moving_variance',
model._blocks[0]._bn2.bias: 'blocks_0/tpu_batch_normalization_1/beta', # [16]),
model._blocks[0]._bn2.weight: 'blocks_0/tpu_batch_normalization_1/gamma', # [16]),
model._blocks[0]._bn2.running_mean: 'blocks_0/tpu_batch_normalization_1/moving_mean',
model._blocks[0]._bn2.running_var: 'blocks_0/tpu_batch_normalization_1/moving_variance',
}
conversion_table = merge(conversion_table, conversion_table_for_first_block)
# Conv blocks
for i in range(len(model._blocks)):
is_first_block = '_expand_conv.weight' not in [n for n, p in model._blocks[i].named_parameters()]
if is_first_block:
conversion_table_block = {
model._blocks[i]._project_conv.weight: 'blocks_' + str(i) + '/conv2d/kernel', # 1, 1, 32, 16]),
model._blocks[i]._depthwise_conv.weight: 'blocks_' + str(i) + '/depthwise_conv2d/depthwise_kernel',
# [3, 3, 32, 1]),
model._blocks[i]._se_reduce.bias: 'blocks_' + str(i) + '/se/conv2d/bias', # , [8]),
model._blocks[i]._se_reduce.weight: 'blocks_' + str(i) + '/se/conv2d/kernel', # , [1, 1, 32, 8]),
model._blocks[i]._se_expand.bias: 'blocks_' + str(i) + '/se/conv2d_1/bias', # , [32]),
model._blocks[i]._se_expand.weight: 'blocks_' + str(i) + '/se/conv2d_1/kernel', # , [1, 1, 8, 32]),
model._blocks[i]._bn1.bias: 'blocks_' + str(i) + '/tpu_batch_normalization/beta', # [32]),
model._blocks[i]._bn1.weight: 'blocks_' + str(i) + '/tpu_batch_normalization/gamma', # [32]),
model._blocks[i]._bn1.running_mean: 'blocks_' + str(i) + '/tpu_batch_normalization/moving_mean',
model._blocks[i]._bn1.running_var: 'blocks_' + str(i) + '/tpu_batch_normalization/moving_variance',
model._blocks[i]._bn2.bias: 'blocks_' + str(i) + '/tpu_batch_normalization_1/beta', # [16]),
model._blocks[i]._bn2.weight: 'blocks_' + str(i) + '/tpu_batch_normalization_1/gamma', # [16]),
model._blocks[i]._bn2.running_mean: 'blocks_' + str(i) + '/tpu_batch_normalization_1/moving_mean',
model._blocks[i]._bn2.running_var: 'blocks_' + str(i) + '/tpu_batch_normalization_1/moving_variance',
}
else:
conversion_table_block = {
model._blocks[i]._expand_conv.weight: 'blocks_' + str(i) + '/conv2d/kernel',
model._blocks[i]._project_conv.weight: 'blocks_' + str(i) + '/conv2d_1/kernel',
model._blocks[i]._depthwise_conv.weight: 'blocks_' + str(i) + '/depthwise_conv2d/depthwise_kernel',
model._blocks[i]._se_reduce.bias: 'blocks_' + str(i) + '/se/conv2d/bias',
model._blocks[i]._se_reduce.weight: 'blocks_' + str(i) + '/se/conv2d/kernel',
model._blocks[i]._se_expand.bias: 'blocks_' + str(i) + '/se/conv2d_1/bias',
model._blocks[i]._se_expand.weight: 'blocks_' + str(i) + '/se/conv2d_1/kernel',
model._blocks[i]._bn0.bias: 'blocks_' + str(i) + '/tpu_batch_normalization/beta',
model._blocks[i]._bn0.weight: 'blocks_' + str(i) + '/tpu_batch_normalization/gamma',
model._blocks[i]._bn0.running_mean: 'blocks_' + str(i) + '/tpu_batch_normalization/moving_mean',
model._blocks[i]._bn0.running_var: 'blocks_' + str(i) + '/tpu_batch_normalization/moving_variance',
model._blocks[i]._bn1.bias: 'blocks_' + str(i) + '/tpu_batch_normalization_1/beta',
model._blocks[i]._bn1.weight: 'blocks_' + str(i) + '/tpu_batch_normalization_1/gamma',
model._blocks[i]._bn1.running_mean: 'blocks_' + str(i) + '/tpu_batch_normalization_1/moving_mean',
model._blocks[i]._bn1.running_var: 'blocks_' + str(i) + '/tpu_batch_normalization_1/moving_variance',
model._blocks[i]._bn2.bias: 'blocks_' + str(i) + '/tpu_batch_normalization_2/beta',
model._blocks[i]._bn2.weight: 'blocks_' + str(i) + '/tpu_batch_normalization_2/gamma',
model._blocks[i]._bn2.running_mean: 'blocks_' + str(i) + '/tpu_batch_normalization_2/moving_mean',
model._blocks[i]._bn2.running_var: 'blocks_' + str(i) + '/tpu_batch_normalization_2/moving_variance',
}
conversion_table = merge(conversion_table, conversion_table_block)
# Load TensorFlow parameters into PyTorch model
load_param(checkpoint_file, conversion_table, model_name)
return conversion_table
def load_and_save_temporary_tensorflow_model(model_name, model_ckpt, example_img= '../../example/img.jpg'):
""" Loads and saves a TensorFlow model. """
image_files = [example_img]
eval_ckpt_driver = eval_ckpt_main.EvalCkptDriver(model_name)
with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
images, labels = eval_ckpt_driver.build_dataset(image_files, [0] * len(image_files), False)
probs = eval_ckpt_driver.build_model(images, is_training=False)
sess.run(tf.compat.v1.global_variables_initializer())
print(model_ckpt)
eval_ckpt_driver.restore_model(sess, model_ckpt)
tf.compat.v1.train.Saver().save(sess, 'tmp/model.ckpt')
if __name__ == '__main__':
import sys
import argparse
sys.path.append('original_tf')
import eval_ckpt_main
from efficientnet_pytorch import EfficientNet
parser = argparse.ArgumentParser(
description='Convert TF model to PyTorch model and save for easier future loading')
parser.add_argument('--model_name', type=str, default='efficientnet-b0',
help='efficientnet-b{N}, where N is an integer 0 <= N <= 8')
parser.add_argument('--tf_checkpoint', type=str, default='pretrained_tensorflow/efficientnet-b0/',
help='checkpoint file path')
parser.add_argument('--output_file', type=str, default='pretrained_pytorch/efficientnet-b0.pth',
help='output PyTorch model file name')
args = parser.parse_args()
# Build model
model = EfficientNet.from_name(args.model_name)
# Load and save temporary TensorFlow file due to TF nuances
print(args.tf_checkpoint)
load_and_save_temporary_tensorflow_model(args.model_name, args.tf_checkpoint)
# Load weights
load_efficientnet(model, 'tmp/model.ckpt', model_name=args.model_name)
print('Loaded TF checkpoint weights')
# Save PyTorch file
torch.save(model.state_dict(), args.output_file)
print('Saved model to', args.output_file)