-
Notifications
You must be signed in to change notification settings - Fork 130
/
Copy pathfmow_dataset.py
233 lines (199 loc) · 11.6 KB
/
fmow_dataset.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
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
from pathlib import Path
import shutil
import pandas as pd
import torch
from torch.utils.data import Dataset
import pickle
import numpy as np
import torchvision.transforms.functional as F
from torchvision import transforms
import tarfile
import datetime
import pytz
from PIL import Image
from tqdm import tqdm
from wilds.common.utils import subsample_idxs
from wilds.common.metrics.all_metrics import Accuracy
from wilds.common.grouper import CombinatorialGrouper
from wilds.datasets.wilds_dataset import WILDSDataset
Image.MAX_IMAGE_PIXELS = 10000000000
categories = ["airport", "airport_hangar", "airport_terminal", "amusement_park", "aquaculture", "archaeological_site", "barn", "border_checkpoint", "burial_site", "car_dealership", "construction_site", "crop_field", "dam", "debris_or_rubble", "educational_institution", "electric_substation", "factory_or_powerplant", "fire_station", "flooded_road", "fountain", "gas_station", "golf_course", "ground_transportation_station", "helipad", "hospital", "impoverished_settlement", "interchange", "lake_or_pond", "lighthouse", "military_facility", "multi-unit_residential", "nuclear_powerplant", "office_building", "oil_or_gas_facility", "park", "parking_lot_or_garage", "place_of_worship", "police_station", "port", "prison", "race_track", "railway_bridge", "recreational_facility", "road_bridge", "runway", "shipyard", "shopping_mall", "single-unit_residential", "smokestack", "solar_farm", "space_facility", "stadium", "storage_tank", "surface_mine", "swimming_pool", "toll_booth", "tower", "tunnel_opening", "waste_disposal", "water_treatment_facility", "wind_farm", "zoo"]
class FMoWDataset(WILDSDataset):
"""
The Functional Map of the World land use / building classification dataset.
This is a processed version of the Functional Map of the World dataset originally sourced from https://github.com/fMoW/dataset.
Supported `split_scheme`:
- 'official': official split, which is equivalent to 'time_after_2016'
- 'mixed-to-test'
- 'time_after_{YEAR}' for YEAR between 2002--2018
Input (x):
224 x 224 x 3 RGB satellite image.
Label (y):
y is one of 62 land use / building classes
Metadata:
each image is annotated with a location coordinate, timestamp, country code. This dataset computes region as a derivative of country code.
Website: https://github.com/fMoW/dataset
Original publication:
@inproceedings{fmow2018,
title={Functional Map of the World},
author={Christie, Gordon and Fendley, Neil and Wilson, James and Mukherjee, Ryan},
booktitle={CVPR},
year={2018}
}
License:
Distributed under the FMoW Challenge Public License.
https://github.com/fMoW/dataset/blob/master/LICENSE
"""
_dataset_name = 'fmow'
_versions_dict = {
'1.1': {
'download_url': 'https://worksheets.codalab.org/rest/bundles/0xaec91eb7c9d548ebb15e1b5e60f966ab/contents/blob/',
'compressed_size': 53_893_324_800}
}
def __init__(self, version=None, root_dir='data', download=False, split_scheme='official', seed=111, use_ood_val=True):
self._version = version
self._data_dir = self.initialize_data_dir(root_dir, download)
self._split_dict = {'train': 0, 'id_val': 1, 'id_test': 2, 'val': 3, 'test': 4}
self._split_names = {'train': 'Train', 'id_val': 'ID Val', 'id_test': 'ID Test', 'val': 'OOD Val', 'test': 'OOD Test'}
self._source_domain_splits = [0, 1, 2]
self.oracle_training_set = False
if split_scheme == 'official':
split_scheme = 'time_after_2016'
elif split_scheme == 'mixed-to-test':
split_scheme = 'time_after_2016'
self.oracle_training_set = True
self._split_scheme = split_scheme
self.root = Path(self._data_dir)
self.seed = int(seed)
self._original_resolution = (224, 224)
self.category_to_idx = {cat: i for i, cat in enumerate(categories)}
self.metadata = pd.read_csv(self.root / 'rgb_metadata.csv')
country_codes_df = pd.read_csv(self.root / 'country_code_mapping.csv')
countrycode_to_region = {k: v for k, v in zip(country_codes_df['alpha-3'], country_codes_df['region'])}
regions = [countrycode_to_region.get(code, 'Other') for code in self.metadata['country_code'].to_list()]
self.metadata['region'] = regions
all_countries = self.metadata['country_code']
self.num_chunks = 101
self.chunk_size = len(self.metadata) // (self.num_chunks - 1)
if self._split_scheme.startswith('time_after'):
year = int(self._split_scheme.split('_')[2])
year_dt = datetime.datetime(year, 1, 1, tzinfo=pytz.UTC)
self.test_ood_mask = np.asarray(pd.to_datetime(self.metadata['timestamp']) >= year_dt)
# use 3 years of the training set as validation
year_minus_3_dt = datetime.datetime(year-3, 1, 1, tzinfo=pytz.UTC)
self.val_ood_mask = np.asarray(pd.to_datetime(self.metadata['timestamp']) >= year_minus_3_dt) & ~self.test_ood_mask
self.ood_mask = self.test_ood_mask | self.val_ood_mask
else:
raise ValueError(f"Not supported: self._split_scheme = {self._split_scheme}")
self._split_array = -1 * np.ones(len(self.metadata))
for split in self._split_dict.keys():
idxs = np.arange(len(self.metadata))
if split == 'test':
test_mask = np.asarray(self.metadata['split'] == 'test')
idxs = idxs[self.test_ood_mask & test_mask]
elif split == 'val':
val_mask = np.asarray(self.metadata['split'] == 'val')
idxs = idxs[self.val_ood_mask & val_mask]
elif split == 'id_test':
test_mask = np.asarray(self.metadata['split'] == 'test')
idxs = idxs[~self.ood_mask & test_mask]
elif split == 'id_val':
val_mask = np.asarray(self.metadata['split'] == 'val')
idxs = idxs[~self.ood_mask & val_mask]
else:
split_mask = np.asarray(self.metadata['split'] == split)
idxs = idxs[~self.ood_mask & split_mask]
if self.oracle_training_set and split == 'train':
test_mask = np.asarray(self.metadata['split'] == 'test')
unused_ood_idxs = np.arange(len(self.metadata))[self.ood_mask & ~test_mask]
subsample_unused_ood_idxs = subsample_idxs(unused_ood_idxs, num=len(idxs)//2, seed=self.seed+2)
subsample_train_idxs = subsample_idxs(idxs.copy(), num=len(idxs) // 2, seed=self.seed+3)
idxs = np.concatenate([subsample_unused_ood_idxs, subsample_train_idxs])
self._split_array[idxs] = self._split_dict[split]
if not use_ood_val:
self._split_dict = {'train': 0, 'val': 1, 'id_test': 2, 'ood_val': 3, 'test': 4}
self._split_names = {'train': 'Train', 'val': 'ID Val', 'id_test': 'ID Test', 'ood_val': 'OOD Val', 'test': 'OOD Test'}
# filter out sequestered images from full dataset
seq_mask = np.asarray(self.metadata['split'] == 'seq')
# take out the sequestered images
self._split_array = self._split_array[~seq_mask]
self.full_idxs = np.arange(len(self.metadata))[~seq_mask]
self._y_array = np.asarray([self.category_to_idx[y] for y in list(self.metadata['category'])])
self.metadata['y'] = self._y_array
self._y_array = torch.from_numpy(self._y_array).long()[~seq_mask]
self._y_size = 1
self._n_classes = 62
# convert region to idxs
all_regions = list(self.metadata['region'].unique())
region_to_region_idx = {region: i for i, region in enumerate(all_regions)}
self._metadata_map = {'region': all_regions}
region_idxs = [region_to_region_idx[region] for region in self.metadata['region'].tolist()]
self.metadata['region'] = region_idxs
# make a year column in metadata
year_array = -1 * np.ones(len(self.metadata))
ts = pd.to_datetime(self.metadata['timestamp'])
for year in range(2002, 2018):
year_mask = np.asarray(ts >= datetime.datetime(year, 1, 1, tzinfo=pytz.UTC)) \
& np.asarray(ts < datetime.datetime(year+1, 1, 1, tzinfo=pytz.UTC))
year_array[year_mask] = year - 2002
self.metadata['year'] = year_array
self._metadata_map['year'] = list(range(2002, 2018))
self._metadata_fields = ['region', 'year', 'y']
self._metadata_array = torch.from_numpy(self.metadata[self._metadata_fields].astype(int).to_numpy()).long()[~seq_mask]
self._eval_groupers = {
'year': CombinatorialGrouper(dataset=self, groupby_fields=['year']),
'region': CombinatorialGrouper(dataset=self, groupby_fields=['region']),
}
super().__init__(root_dir, download, split_scheme)
def get_input(self, idx):
"""
Returns x for a given idx.
"""
idx = self.full_idxs[idx]
img = Image.open(self.root / 'images' / f'rgb_img_{idx}.png').convert('RGB')
return img
def eval(self, y_pred, y_true, metadata, prediction_fn=None):
"""
Computes all evaluation metrics.
Args:
- y_pred (Tensor): Predictions from a model. By default, they are predicted labels (LongTensor).
But they can also be other model outputs such that prediction_fn(y_pred)
are predicted labels.
- y_true (LongTensor): Ground-truth labels
- metadata (Tensor): Metadata
- prediction_fn (function): A function that turns y_pred into predicted labels
Output:
- results (dictionary): Dictionary of evaluation metrics
- results_str (str): String summarizing the evaluation metrics
"""
metric = Accuracy(prediction_fn=prediction_fn)
# Overall evaluation + evaluate by year
all_results, all_results_str = self.standard_group_eval(
metric,
self._eval_groupers['year'],
y_pred, y_true, metadata)
# Evaluate by region and ignore the "Other" region
region_grouper = self._eval_groupers['region']
region_results = metric.compute_group_wise(
y_pred,
y_true,
region_grouper.metadata_to_group(metadata),
region_grouper.n_groups)
all_results[f'{metric.name}_worst_year'] = all_results.pop(metric.worst_group_metric_field)
region_metric_list = []
for group_idx in range(region_grouper.n_groups):
group_str = region_grouper.group_field_str(group_idx)
group_metric = region_results[metric.group_metric_field(group_idx)]
group_counts = region_results[metric.group_count_field(group_idx)]
all_results[f'{metric.name}_{group_str}'] = group_metric
all_results[f'count_{group_str}'] = group_counts
if region_results[metric.group_count_field(group_idx)] == 0 or "Other" in group_str:
continue
all_results_str += (
f' {region_grouper.group_str(group_idx)} '
f"[n = {region_results[metric.group_count_field(group_idx)]:6.0f}]:\t"
f"{metric.name} = {region_results[metric.group_metric_field(group_idx)]:5.3f}\n")
region_metric_list.append(region_results[metric.group_metric_field(group_idx)])
all_results[f'{metric.name}_worst_region'] = metric.worst(region_metric_list)
all_results_str += f"Worst-group {metric.name}: {all_results[f'{metric.name}_worst_region']:.3f}\n"
return all_results, all_results_str