/
data.py
198 lines (174 loc) · 8.41 KB
/
data.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
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from options import FLAGS as opts
import functools
import glob
import numpy as np
import os.path as osp
import random
import tensorflow as tf
def provide_data(dataset_name='', parent_dir='', batch_size=8, subset=None,
max_examples=None, crop_flag=False, crop_size=512, seeds=None,
use_appearance=True, shuffle=128):
# Parsing function for each tfrecord example.
record_parse_fn = functools.partial(
_parser_rendered_dataset, crop_flag=crop_flag, crop_size=crop_size,
use_alpha=opts.use_alpha, use_depth=opts.use_depth,
use_semantics=opts.use_semantic, seeds=seeds,
use_appearance=use_appearance, use_normal=opts.use_normal,
use_wc=opts.use_wc)
input_dict_var = multi_input_fn_record(
record_parse_fn, parent_dir, dataset_name, batch_size,
subset=subset, max_examples=max_examples, shuffle=shuffle)
return input_dict_var
def _parser_rendered_dataset(
serialized_example, crop_flag, crop_size, seeds, use_alpha, use_depth,
use_semantics, use_appearance, use_normal, use_wc):
"""
Parses a single tf.Example into a features dictionary with input tensors.
"""
# Structure of features_dict need to match the dictionary structure that was
# serialized to a tf.Example
features_dict = {'height': tf.FixedLenFeature([], tf.int64),
'width': tf.FixedLenFeature([], tf.int64),
'rendered': tf.FixedLenFeature([], tf.string),
'depth': tf.FixedLenFeature([], tf.string),
'real': tf.FixedLenFeature([], tf.string),
'normal': tf.FixedLenFeature([], tf.string),
'wc': tf.FixedLenFeature([], tf.string),
'point': tf.FixedLenFeature([], tf.string),
'seg': tf.FixedLenFeature([], tf.string)}
features = tf.compat.v1.parse_single_example(serialized_example, features=features_dict)
height = tf.compat.v1.cast(features['height'], tf.int32)
width = tf.compat.v1.cast(features['width'], tf.int32)
# Parse the rendered image.
rendered = tf.decode_raw(features['rendered'], tf.uint8)
rendered = tf.compat.v1.cast(rendered, tf.float32) * (2.0 / 255) - 1.0
rendered = tf.compat.v1.reshape(rendered, [height, width, 3])
if not use_alpha:
rendered = tf.compat.v1.slice(rendered, [0, 0, 0], [height, width, 3])
conditional_input = rendered
# Parse the depth image.
if use_depth:
depth = tf.decode_raw(features['depth'], tf.uint16)
depth = tf.compat.v1.reshape(depth, [height, width, 1])
depth = tf.compat.v1.cast(depth, tf.float32) * (2.0 / 255) - 1.0
conditional_input = tf.compat.v1.concat([conditional_input, depth], axis=-1)
# Parse the semantic map.
if use_semantics:
seg_img = tf.decode_raw(features['seg'], tf.uint8)
seg_img = tf.compat.v1.reshape(seg_img, [height, width, 3])
seg_img = tf.compat.v1.cast(seg_img, tf.float32) * (2.0 / 255) - 1
conditional_input = tf.compat.v1.concat([conditional_input, seg_img], axis=-1)
# Parse the normal image
if use_normal:
normal_img = tf.decode_raw(features['normal'], tf.uint8)
normal_img = tf.compat.v1.reshape(normal_img, [height, width, 3])
normal_img = tf.compat.v1.cast(normal_img, tf.float32) * (2.0 / 255) - 1
conditional_input = tf.compat.v1.concat([conditional_input, normal_img], axis=-1)
# Parse the wc image
if use_wc:
wc_img = tf.decode_raw(features['wc'], tf.uint8)
wc_img = tf.compat.v1.reshape(wc_img, [height, width, 3])
wc_img = tf.compat.v1.cast(wc_img, tf.float32) * (2.0 / 255) - 1
conditional_input = tf.compat.v1.concat([conditional_input, wc_img], axis=-1)
# Verify that the parsed input has the correct number of channels.
assert conditional_input.shape[-1] == (opts.deep_buffer_nc), ('num channels '
'in the parsed input doesn\'t match num input channels specified in '
'opts.deep_buffer_nc!')
# Parse the ground truth image.
real = tf.decode_raw(features['real'], tf.uint8)
real = tf.compat.v1.cast(real, tf.float32) * (2.0 / 255) - 1.0
real = tf.compat.v1.reshape(real, [height, width, 3])
# Parse the appearance image (if any).
appearance_input = []
if use_appearance:
# Concatenate the deep buffer to the real image.
appearance_input = tf.compat.v1.concat([real, conditional_input], axis=-1)
# Verify that the parsed input has the correct number of channels.
assert appearance_input.shape[-1] == (opts.appearance_nc), ('num channels '
'in the parsed appearance input doesn\'t match num input channels '
'specified in opts.appearance_nc!')
# Parse the point "image"
point_img = tf.decode_raw(features['point'], tf.uint8)
point_img = tf.compat.v1.reshape(point_img, [height, width, 1])
point_img = tf.compat.v1.cast(point_img, tf.float32)
conditional_input = tf.compat.v1.concat([conditional_input, point_img], axis=-1)
# Crop conditional_input and real images, but keep the appearance input
# uncropped (learn a one-to-many mapping from appearance to output)
if crop_flag:
assert crop_size is not None, 'crop_size is not provided!'
if isinstance(crop_size, int):
crop_size = [crop_size, crop_size]
assert len(crop_size) == 2, 'crop_size is either an int or a 2-tuple!'
# Central crop
if seeds is not None and len(seeds) <= 1:
conditional_input = tf.image.resize_image_with_crop_or_pad(
conditional_input, crop_size[0], crop_size[1])
real = tf.image.resize_image_with_crop_or_pad(real, crop_size[0],
crop_size[1])
else:
if not seeds: # random crops
seed = random.randint(0, (1 << 31) - 1)
else: # fixed crops
seed_idx = random.randint(0, len(seeds) - 1)
seed = seeds[seed_idx]
conditional_input = tf.random_crop(
conditional_input, crop_size + [opts.deep_buffer_nc + 1], seed=seed)
real = tf.random_crop(real, crop_size + [3], seed=seed)
print('==================')
print('x shape is: ')
print(conditional_input.shape)
features = {'conditional_input': conditional_input,
'expected_output': real,
'peek_input': appearance_input}
return features
def multi_input_fn_record(
record_parse_fn, parent_dir, tfrecord_basename, batch_size, subset=None,
max_examples=None, shuffle=128):
"""Creates a Dataset pipeline for tfrecord files.
Returns:
Dataset iterator.
"""
subset_suffix = '*_%s.tfrecord' % subset if subset else '*.tfrecord'
input_pattern = osp.join(parent_dir, tfrecord_basename + subset_suffix)
filenames = sorted(glob.glob(input_pattern))
assert len(filenames) > 0, ('Error! input pattern "%s" didn\'t match any '
'files' % input_pattern)
dataset = tf.data.TFRecordDataset(filenames)
if shuffle == 0: # keep input deterministic
# use one thread to get deterministic results
dataset = dataset.map(record_parse_fn, num_parallel_calls=None)
else:
dataset = dataset.repeat() # Repeat indefinitely.
dataset = dataset.map(record_parse_fn,
num_parallel_calls=max(4, batch_size // 4))
if opts.training_pipeline == 'drit':
dataset1 = dataset.shuffle(shuffle)
dataset2 = dataset.shuffle(shuffle)
paired_dataset = tf.data.Dataset.zip((dataset1, dataset2))
def _join_paired_dataset(features_a, features_b):
features_a['conditional_input_2'] = features_b['conditional_input']
features_a['expected_output_2'] = features_b['expected_output']
return features_a
joined_dataset = paired_dataset.map(_join_paired_dataset)
dataset = joined_dataset
else:
dataset = dataset.shuffle(shuffle)
if max_examples is not None:
dataset = dataset.take(max_examples)
dataset = dataset.batch(batch_size)
if shuffle > 0: # input is not deterministic
dataset = dataset.prefetch(4) # Prefetch a few batches.
return dataset.make_one_shot_iterator().get_next()