File size: 13,458 Bytes
3ef1661 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 |
import numpy as np
import logging
import torch.distributed as dist
import math
import os
from mono.utils.comm import get_func, main_process
from torch.utils.data import ConcatDataset, DataLoader
import random
import copy
import torch
import logging
def build_dataset_n_sampler_with_cfg(cfg, phase):
# build data array, similar datasets are organized in the same group
datasets_array = build_data_array(cfg, phase)
# concatenate datasets with torch.utils.data.ConcatDataset methods
dataset_merge = concatenate_datasets(datasets_array)
# customerize sampler
custom_sampler = CustomerMultiDataSampler(cfg, dataset_merge, phase)
return dataset_merge, custom_sampler
class CustomerMultiDataSampler(torch.utils.data.Sampler):
"""
Customerize a sampler method. During this process, the size of some datasets will be tailored or expanded.
Such process aims to ensure each group has the same data size.
e.g. dataset_list: [[A, B, C], [E, F], M], then group 'A,B,C' (Size(A) + Size(B) + Size(C)) has the same size
as to group 'E,F' (Size(E) + Size(F)), so as to 'M'.
args:
@ cfg: configs for each dataset.
@ dataset_merge: merged multiple datasets with the torch.utils.data.ConcatDataset method.
@ phase: train/val/test phase.
"""
def __init__(self, cfg, dataset_merge, phase):
self.cfg = cfg
self.world_size = int(os.environ['WORLD_SIZE'])
self.phase = phase
self.global_rank = cfg.dist_params.global_rank
self.dataset_merge = dataset_merge
self.logger = logging.getLogger()
if main_process():
self.logger.info(f'Initilized CustomerMultiDataSampler for {phase}.')
self.random_seed = 136
self.random_seed_cp = 639
def __iter__(self):
self.create_samplers()
self.logger.info("Sample list of {} in rank {} is: {}".format(self.phase, self.global_rank, ' '.join(map(str, self.sample_indices_array[-20: -10]))))
# subsample, each rank sample a subset for training.
rank_offset = self.each_gpu_size * self.global_rank
rank_indices = self.sample_indices_array[rank_offset : rank_offset + self.each_gpu_size]
assert rank_indices.size == self.each_gpu_size
for id in rank_indices:
yield id
def __len__(self):
return self.total_dist_size
def create_samplers(self):
# sample idx for each dataset, idx value should not exceed the size of data,
# i.e. 0 <= idx < len(data_size)
#self.samples_mat = []
self.indices_mat = []
# size expanded, idx cumulative aggregrated for calling
self.indices_expand_mat = []
# max group size, each group may consists of multiple similar datasets
max_group_size = max([len(i) for i in self.dataset_merge.datasets])
dataset_cumulative_sizes = [0] + self.dataset_merge.cumulative_sizes
for gi, dataset_group in enumerate(self.dataset_merge.datasets):
# the merged dataset consists of multiple grouped datasets
samples_group = []
indices_expand_group = []
indices_group = []
# to ensure each group has the same size, group with less data has to duplicate its sample list for 'cp_times' times
cp_times = max_group_size / len(dataset_group)
# adjust each group to ensure they have the same data size
group_cumulative_sizes = [0] + dataset_group.cumulative_sizes
expand_indices_sizes = (np.array(group_cumulative_sizes) * cp_times).astype(np.int)
expand_indices_sizes[-1] = max_group_size
# datasets in the same group have to expand its sample list
expand_indices_sizes = expand_indices_sizes[1:] - expand_indices_sizes[:-1]
for di, dataset_i in enumerate(dataset_group.datasets):
# datasets residing in each group may have similar features
# samples indices list
dataset_i_ori_sample_list = self.dataset_merge.datasets[gi].datasets[di].sample_list
if self.phase == 'train':
#sample_list_i = random.sample(dataset_i_ori_sample_list, len(dataset_i_ori_sample_list))
sample_list_i = dataset_i_ori_sample_list
else:
# no shuffle in val or test
sample_list_i = dataset_i_ori_sample_list
#samples_group.append(sample_list_i)
# expand the sample list for each dataset
expand_size_i = expand_indices_sizes[di]
indices_expand_list = copy.deepcopy(sample_list_i)
for i in range(int(cp_times)-1):
#indices_expand_list += random.sample(sample_list_i, len(dataset_i))
indices_expand_list += sample_list_i
random.seed(self.random_seed_cp)
indices_expand_list += random.sample(sample_list_i, len(dataset_i))[:expand_size_i % len(dataset_i)]
# adjust indices value
indices_expand_list = np.array(indices_expand_list) + dataset_cumulative_sizes[gi] + group_cumulative_sizes[di]
indices_list = np.array(sample_list_i) + dataset_cumulative_sizes[gi] + group_cumulative_sizes[di]
# the expanded sample list for dataset_i
indices_expand_group.append(indices_expand_list)
# the original sample list for the dataset_i
indices_group.append(indices_list)
if main_process():
self.logger.info(f'"{dataset_i.data_name}", {self.phase} set in group {gi}: ' +
f'expand size {len(sample_list_i)} --->>>---, {expand_size_i}')
concat_group = np.concatenate(indices_expand_group)
# shuffle the grouped datasets samples, e.g. each group data is [a1, a2, a3, b1, b2, b3, b4, c1, c2], the shuffled one, maybe, is [a3, b1, b2, b3, b4, c1,...]
np.random.seed(self.random_seed)
if self.phase == 'train':
np.random.shuffle(concat_group)
self.indices_expand_mat.append(concat_group)
self.indices_mat.append(np.concatenate(indices_group))
# create sample list
if "train" in self.phase:
# data groups are cross sorted, i.e. [A, B, C, A, B, C....]
self.sample_indices_array = np.array(self.indices_expand_mat).transpose(1, 0).reshape(-1)
self.total_indices_size = max_group_size * len(self.dataset_merge.datasets)
else:
self.sample_indices_array = np.concatenate(self.indices_mat[:])
self.total_indices_size = self.sample_indices_array.size
self.total_sample_size = len(self.dataset_merge)
self.each_gpu_size = int(np.ceil(self.total_indices_size * 1.0 / self.world_size)) # ignore some residual samples
self.total_dist_size = self.each_gpu_size * self.world_size
# add extra samples to make it evenly divisible
diff_size = int(self.total_dist_size - self.total_indices_size) # int(self.total_dist_size - self.total_sample_size)
if diff_size > 0:
self.sample_indices_array = np.append(self.sample_indices_array, self.sample_indices_array[:diff_size])
#if main_process():
self.logger.info(f'Expanded data size in merged dataset: {self.total_sample_size}, adjusted data size for distributed running: {self.total_dist_size}')
self.random_seed += 413
self.random_seed_cp += 377
def build_data_array(cfg, phase):
"""
Construct data repo with cfg. In cfg, there is a data name array, which encloses the name of each data.
Each data name links to a data config file. With this config file, dataset can be constructed.
e.g. [['A', 'B', 'C'], ['E', 'F'], 'M']. Each letter indicates a dataset.
"""
datasets_array = []
data_array_names_for_log = []
dataname_array = cfg.data_array
for group_i in dataname_array:
dataset_group_i = []
data_group_i_names_for_log = []
if not isinstance(group_i, list):
group_i = [group_i, ]
for data_i in group_i:
if not isinstance(data_i, dict):
raise TypeError(f'data name must be a dict, but got {type(data_i)}')
# each data only can employ a single dataset config
assert len(data_i.values()) == 1
if list(data_i.values())[0] not in cfg:
raise RuntimeError(f'cannot find the data config for {data_i}')
# dataset configure for data i
#data_i_cfg = cfg[data_i]
args = copy.deepcopy(cfg) #data_i_cfg.copy()
data_i_cfg_name = list(data_i.values())[0]
data_i_db_info_name = list(data_i.keys())[0]
data_i_db_info = cfg.db_info[data_i_db_info_name]
# Online evaluation using only metric datasets
# if phase == 'val' and 'exclude' in cfg.evaluation \
# and data_i_db_info_name in cfg.evaluation.exclude:
# continue
# dataset lib name
obj_name = cfg[data_i_cfg_name]['lib']
obj_path = os.path.dirname(__file__).split(os.getcwd() + '/')[-1].replace('/', '.') + '.' + obj_name
obj_cls = get_func(obj_path)
if obj_cls is None:
raise KeyError(f'{obj_name} is not in .data')
dataset_i = obj_cls(
args[data_i_cfg_name],
phase,
db_info=data_i_db_info,
**cfg.data_basic)
# if 'Taskonomy' not in data_i:
# print('>>>>>>>>>>ditributed_sampler LN189', dataset_i.data_name, dataset_i.annotations['files'][0]['rgb'].split('/')[-1],
# dataset_i.annotations['files'][1000]['rgb'].split('/')[-1], dataset_i.annotations['files'][3000]['rgb'].split('/')[-1])
# else:
# print('>>>>>>>>>>ditributed_sampler LN189', dataset_i.data_name, dataset_i.annotations['files'][0]['meta_data'].split('/')[-1],
# dataset_i.annotations['files'][1000]['meta_data'].split('/')[-1], dataset_i.annotations['files'][3000]['meta_data'].split('/')[-1])
dataset_group_i.append(dataset_i)
# get data name for log
data_group_i_names_for_log.append(data_i_db_info_name)
datasets_array.append(dataset_group_i)
data_array_names_for_log.append(data_group_i_names_for_log)
if main_process():
logger = logging.getLogger()
logger.info(f'{phase}: data array ({data_array_names_for_log}) has been constructed.')
return datasets_array
def concatenate_datasets(datasets_array):
"""
Merge grouped datasets to a single one.
args:
@ dataset_list: the list of constructed dataset.
"""
#max_size = 0
dataset_merge = []
for group in datasets_array:
group_dataset = ConcatDataset(group)
group_size = len(group_dataset)
#max_size = max_size if group_size < max_size else group_size
dataset_merge.append(group_dataset)
return ConcatDataset(dataset_merge)
def log_canonical_transfer_info(cfg):
logger = logging.getLogger()
data = []
canonical_focal_length = cfg.data_basic.canonical_space.focal_length
canonical_size = cfg.data_basic.canonical_space.img_size
for group_i in cfg.data_array:
if not isinstance(group_i, list):
group_i = [group_i, ]
for data_i in group_i:
if not isinstance(data_i, dict):
raise TypeError(f'data name must be a dict, but got {type(data_i)}')
assert len(data_i.values()) == 1
if list(data_i.values())[0] not in cfg:
raise RuntimeError(f'cannot find the data config for {data_i.values()}')
if list(data_i.values())[0] not in data:
data.append(list(data_i.values())[0])
logger.info('>>>>>>>>>>>>>>Some data transfer details during augmentation.>>>>>>>>>>>>>>')
for data_i in data:
data_i_cfg = cfg[data_i]
if type(data_i_cfg.original_focal_length) != tuple:
ori_focal = (data_i_cfg.original_focal_length, )
else:
ori_focal = data_i_cfg.original_focal_length
log_str = '%s transfer details: \n' % data_i
for ori_f in ori_focal:
# to canonical space
scalor = canonical_focal_length / ori_f
img_size = (data_i_cfg.original_size[0]*scalor, data_i_cfg.original_size[1]*scalor)
log_str += 'To canonical space: focal length, %f -> %f; size, %s -> %s\n' %(ori_f, canonical_focal_length, data_i_cfg.original_size, img_size)
# random resize in augmentaiton
resize_range = data_i_cfg.data.train.pipeline[1].ratio_range
resize_low = (img_size[0]*resize_range[0], img_size[1]*resize_range[0])
resize_up = (img_size[0]*resize_range[1], img_size[1]*resize_range[1])
log_str += 'Random resize bound: %s ~ %s; \n' %(resize_low, resize_up)
logger.info(log_str) |