forked from apache/mxnet
-
Notifications
You must be signed in to change notification settings - Fork 34
/
Copy pathdata.py
158 lines (134 loc) · 6.24 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
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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
#
# http://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.
import os
import random
import numpy as np
import mxnet as mx
from mxnet import nd
def transform(data, target_wd, target_ht, is_train, box):
"""Crop and normnalize an image nd array."""
if box is not None:
x, y, w, h = box
data = data[y:min(y+h, data.shape[0]), x:min(x+w, data.shape[1])]
# Resize to target_wd * target_ht.
data = mx.image.imresize(data, target_wd, target_ht)
# Normalize in the same way as the pre-trained model.
data = data.astype(np.float32) / 255.0
data = (data - mx.nd.array([0.485, 0.456, 0.406])) / mx.nd.array([0.229, 0.224, 0.225])
if is_train:
if random.random() < 0.5:
data = nd.flip(data, axis=1)
data, _ = mx.image.random_crop(data, (224, 224))
else:
data, _ = mx.image.center_crop(data, (224, 224))
# Transpose from (target_wd, target_ht, 3)
# to (3, target_wd, target_ht).
data = nd.transpose(data, (2, 0, 1))
# If image is greyscale, repeat 3 times to get RGB image.
if data.shape[0] == 1:
data = nd.tile(data, (3, 1, 1))
return data.reshape((1,) + data.shape)
class CUB200Iter(mx.io.DataIter):
"""Iterator for the CUB200-2011 dataset.
Parameters
----------
data_path : str,
The path to dataset directory.
batch_k : int,
Number of images per class in a batch.
batch_size : int,
Batch size.
batch_size : tupple,
Data shape. E.g. (3, 224, 224).
is_train : bool,
Training data or testig data. Training batches are randomly sampled.
Testing batches are loaded sequentially until reaching the end.
"""
def __init__(self, data_path, batch_k, batch_size, data_shape, is_train):
super(CUB200Iter, self).__init__(batch_size)
self.data_shape = (batch_size,) + data_shape
self.batch_size = batch_size
self.provide_data = [('data', self.data_shape)]
self.batch_k = batch_k
self.is_train = is_train
self.train_image_files = [[] for _ in range(100)]
self.test_image_files = []
self.test_labels = []
self.boxes = {}
self.test_count = 0
with open(os.path.join(data_path, 'images.txt'), 'r') as f_img, \
open(os.path.join(data_path, 'image_class_labels.txt'), 'r') as f_label, \
open(os.path.join(data_path, 'bounding_boxes.txt'), 'r') as f_box:
for line_img, line_label, line_box in zip(f_img, f_label, f_box):
fname = os.path.join(data_path, 'images', line_img.strip().split()[-1])
label = int(line_label.strip().split()[-1]) - 1
box = [int(float(v)) for v in line_box.split()[-4:]]
self.boxes[fname] = box
# Following "Deep Metric Learning via Lifted Structured Feature Embedding" paper,
# we use the first 100 classes for training, and the remaining for testing.
if label < 100:
self.train_image_files[label].append(fname)
else:
self.test_labels.append(label)
self.test_image_files.append(fname)
self.n_test = len(self.test_image_files)
def get_image(self, img, is_train):
"""Load and transform an image."""
img_arr = mx.image.imread(img)
img_arr = transform(img_arr, 256, 256, is_train, self.boxes[img])
return img_arr
def sample_train_batch(self):
"""Sample a training batch (data and label)."""
batch = []
labels = []
num_groups = self.batch_size // self.batch_k
# For CUB200, we use the first 100 classes for training.
sampled_classes = np.random.choice(100, num_groups, replace=False)
for i in range(num_groups):
img_fnames = np.random.choice(self.train_image_files[sampled_classes[i]],
self.batch_k, replace=False)
batch += [self.get_image(img_fname, is_train=True) for img_fname in img_fnames]
labels += [sampled_classes[i] for _ in range(self.batch_k)]
return nd.concatenate(batch, axis=0), labels
def get_test_batch(self):
"""Sample a testing batch (data and label)."""
batch_size = self.batch_size
batch = [self.get_image(self.test_image_files[(self.test_count*batch_size + i)
% len(self.test_image_files)],
is_train=False) for i in range(batch_size)]
labels = [self.test_labels[(self.test_count*batch_size + i)
% len(self.test_image_files)] for i in range(batch_size)]
return nd.concatenate(batch, axis=0), labels
def reset(self):
"""Reset an iterator."""
self.test_count = 0
def next(self):
"""Return a batch."""
if self.is_train:
data, labels = self.sample_train_batch()
else:
if self.test_count * self.batch_size < len(self.test_image_files):
data, labels = self.get_test_batch()
self.test_count += 1
else:
self.test_count = 0
raise StopIteration
return mx.io.DataBatch(data=[data], label=[labels])
def cub200_iterator(data_path, batch_k, batch_size, data_shape):
"""Return training and testing iterator for the CUB200-2011 dataset."""
return (CUB200Iter(data_path, batch_k, batch_size, data_shape, is_train=True),
CUB200Iter(data_path, batch_k, batch_size, data_shape, is_train=False))