Spaces:
Running
Running
File size: 10,053 Bytes
7088d16 |
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 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from collections import defaultdict
from dataclasses import dataclass
from itertools import product
import numpy as np
import torch
from pytorch3d.implicitron.dataset.data_loader_map_provider import (
DoublePoolBatchSampler,
)
from pytorch3d.implicitron.dataset.dataset_base import DatasetBase
from pytorch3d.implicitron.dataset.frame_data import FrameData
from pytorch3d.implicitron.dataset.scene_batch_sampler import SceneBatchSampler
@dataclass
class MockFrameAnnotation:
frame_number: int
sequence_name: str = "sequence"
frame_timestamp: float = 0.0
class MockDataset(DatasetBase):
def __init__(self, num_seq, max_frame_gap=1):
"""
Makes a gap of max_frame_gap frame numbers in the middle of each sequence
"""
self.seq_annots = {f"seq_{i}": None for i in range(num_seq)}
self._seq_to_idx = {
f"seq_{i}": list(range(i * 10, i * 10 + 10)) for i in range(num_seq)
}
# frame numbers within sequence: [0, ..., 4, n, ..., n+4]
# where n - 4 == max_frame_gap
frame_nos = list(range(5)) + list(range(4 + max_frame_gap, 9 + max_frame_gap))
self.frame_annots = [
{"frame_annotation": MockFrameAnnotation(no)} for no in frame_nos * num_seq
]
for seq_name, idx in self._seq_to_idx.items():
for i in idx:
self.frame_annots[i]["frame_annotation"].sequence_name = seq_name
def get_frame_numbers_and_timestamps(self, idxs, subset_filter=None):
assert subset_filter is None
out = []
for idx in idxs:
frame_annotation = self.frame_annots[idx]["frame_annotation"]
out.append(
(frame_annotation.frame_number, frame_annotation.frame_timestamp)
)
return out
def __getitem__(self, index: int):
fa = self.frame_annots[index]["frame_annotation"]
fd = FrameData(
sequence_name=fa.sequence_name,
sequence_category="default_category",
frame_number=torch.LongTensor([fa.frame_number]),
frame_timestamp=torch.LongTensor([fa.frame_timestamp]),
)
return fd
class TestSceneBatchSampler(unittest.TestCase):
def setUp(self):
np.random.seed(42)
self.dataset_overfit = MockDataset(1)
def test_overfit(self):
num_batches = 3
batch_size = 10
sampler = SceneBatchSampler(
self.dataset_overfit,
batch_size=batch_size,
num_batches=num_batches,
images_per_seq_options=[10], # will try to sample batch_size anyway
)
self.assertEqual(len(sampler), num_batches)
it = iter(sampler)
for _ in range(num_batches):
batch = next(it)
self.assertIsNotNone(batch)
self.assertEqual(len(batch), batch_size) # true for our examples
self.assertTrue(all(idx // 10 == 0 for idx in batch))
with self.assertRaises(StopIteration):
batch = next(it)
def test_multiseq(self):
for ips_options in [[10], [2], [3], [2, 3, 4]]:
for sample_consecutive_frames in [True, False]:
for consecutive_frames_max_gap in [0, 1, 3]:
self._test_multiseq_flavour(
ips_options,
sample_consecutive_frames,
consecutive_frames_max_gap,
)
def test_multiseq_gaps(self):
num_batches = 16
batch_size = 10
dataset_multiseq = MockDataset(5, max_frame_gap=3)
for ips_options in [[10], [2], [3], [2, 3, 4]]:
debug_info = f" Images per sequence: {ips_options}."
sampler = SceneBatchSampler(
dataset_multiseq,
batch_size=batch_size,
num_batches=num_batches,
images_per_seq_options=ips_options,
sample_consecutive_frames=True,
consecutive_frames_max_gap=1,
)
self.assertEqual(len(sampler), num_batches, msg=debug_info)
it = iter(sampler)
for _ in range(num_batches):
batch = next(it)
self.assertIsNotNone(batch, "batch is None in" + debug_info)
if max(ips_options) > 5:
# true for our examples
self.assertEqual(len(batch), 5, msg=debug_info)
else:
# true for our examples
self.assertEqual(len(batch), batch_size, msg=debug_info)
self._check_frames_are_consecutive(
batch, dataset_multiseq.frame_annots, debug_info
)
def _test_multiseq_flavour(
self,
ips_options,
sample_consecutive_frames,
consecutive_frames_max_gap,
num_batches=16,
batch_size=10,
):
debug_info = (
f" Images per sequence: {ips_options}, "
f"sample_consecutive_frames: {sample_consecutive_frames}, "
f"consecutive_frames_max_gap: {consecutive_frames_max_gap}, "
)
# in this test, either consecutive_frames_max_gap == max_frame_gap,
# or consecutive_frames_max_gap == 0, so segments consist of full sequences
frame_gap = consecutive_frames_max_gap if consecutive_frames_max_gap > 0 else 3
dataset_multiseq = MockDataset(5, max_frame_gap=frame_gap)
sampler = SceneBatchSampler(
dataset_multiseq,
batch_size=batch_size,
num_batches=num_batches,
images_per_seq_options=ips_options,
sample_consecutive_frames=sample_consecutive_frames,
consecutive_frames_max_gap=consecutive_frames_max_gap,
)
self.assertEqual(len(sampler), num_batches, msg=debug_info)
it = iter(sampler)
typical_counts = set()
for _ in range(num_batches):
batch = next(it)
self.assertIsNotNone(batch, "batch is None in" + debug_info)
# true for our examples
self.assertEqual(len(batch), batch_size, msg=debug_info)
# find distribution over sequences
counts = _count_by_quotient(batch, 10)
freqs = _count_by_quotient(counts.values(), 1)
self.assertLessEqual(
len(freqs),
2,
msg="We should have maximum of 2 different "
"frequences of sequences in the batch." + debug_info,
)
if len(freqs) == 2:
most_seq_count = max(*freqs.keys())
last_seq = min(*freqs.keys())
self.assertEqual(
freqs[last_seq],
1,
msg="Only one odd sequence allowed." + debug_info,
)
else:
self.assertEqual(len(freqs), 1)
most_seq_count = next(iter(freqs))
self.assertIn(most_seq_count, ips_options)
typical_counts.add(most_seq_count)
if sample_consecutive_frames:
self._check_frames_are_consecutive(
batch,
dataset_multiseq.frame_annots,
debug_info,
max_gap=consecutive_frames_max_gap,
)
self.assertTrue(
all(i in typical_counts for i in ips_options),
"Some of the frequency options did not occur among "
f"the {num_batches} batches (could be just bad luck)." + debug_info,
)
with self.assertRaises(StopIteration):
batch = next(it)
def _check_frames_are_consecutive(self, batch, annots, debug_info, max_gap=1):
# make sure that sampled frames are consecutive
for i in range(len(batch) - 1):
curr_idx, next_idx = batch[i : i + 2]
if curr_idx // 10 == next_idx // 10: # same sequence
if max_gap > 0:
curr_idx, next_idx = [
annots[idx]["frame_annotation"].frame_number
for idx in (curr_idx, next_idx)
]
gap = max_gap
else:
gap = 1 # we'll check that raw dataset indices are consecutive
self.assertLessEqual(next_idx - curr_idx, gap, msg=debug_info)
def _count_by_quotient(indices, divisor):
counter = defaultdict(int)
for i in indices:
counter[i // divisor] += 1
return counter
class TestRandomSampling(unittest.TestCase):
def test_double_pool_batch_sampler(self):
unknown_idxs = [2, 3, 4, 5, 8]
known_idxs = [2, 9, 10, 11, 12, 13, 14, 15, 16, 17]
for replacement, num_batches in product([True, False], [None, 4, 5, 6, 30]):
with self.subTest(f"{replacement}, {num_batches}"):
sampler = DoublePoolBatchSampler(
first_indices=unknown_idxs,
rest_indices=known_idxs,
batch_size=4,
replacement=replacement,
num_batches=num_batches,
)
for _ in range(6):
epoch = list(sampler)
self.assertEqual(len(epoch), num_batches or len(unknown_idxs))
for batch in epoch:
self.assertEqual(len(batch), 4)
self.assertIn(batch[0], unknown_idxs)
for i in batch[1:]:
self.assertIn(i, known_idxs)
if not replacement and 4 != num_batches:
self.assertEqual(
{batch[0] for batch in epoch}, set(unknown_idxs)
)
|