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# 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 logging
import os
import unittest
from collections import Counter
import pkg_resources
import torch
from pytorch3d.implicitron.dataset.sql_dataset import SqlIndexDataset
NO_BLOBS_KWARGS = {
"dataset_root": "",
"load_images": False,
"load_depths": False,
"load_masks": False,
"load_depth_masks": False,
"box_crop": False,
}
logger = logging.getLogger("pytorch3d.implicitron.dataset.sql_dataset")
sh = logging.StreamHandler()
logger.addHandler(sh)
logger.setLevel(logging.DEBUG)
DATASET_ROOT = pkg_resources.resource_filename(__name__, "data/sql_dataset")
METADATA_FILE = os.path.join(DATASET_ROOT, "sql_dataset_100.sqlite")
SET_LIST_FILE = os.path.join(DATASET_ROOT, "set_lists_100.json")
class TestSqlDataset(unittest.TestCase):
def test_basic(self, sequence="cat1_seq2", frame_number=4):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), 100)
# check the items are consecutive
past_sequences = set()
last_frame_number = -1
last_sequence = ""
for i in range(len(dataset)):
item = dataset[i]
if item.frame_number == 0:
self.assertNotIn(item.sequence_name, past_sequences)
past_sequences.add(item.sequence_name)
last_sequence = item.sequence_name
else:
self.assertEqual(item.sequence_name, last_sequence)
self.assertEqual(item.frame_number, last_frame_number + 1)
last_frame_number = item.frame_number
# test indexing
with self.assertRaises(IndexError):
dataset[len(dataset) + 1]
# test sequence-frame indexing
item = dataset[sequence, frame_number]
self.assertEqual(item.sequence_name, sequence)
self.assertEqual(item.frame_number, frame_number)
with self.assertRaises(IndexError):
dataset[sequence, 13]
def test_filter_empty_masks(self):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=True,
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), 78)
def test_pick_frames_sql_clause(self):
dataset_no_empty_masks = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=True,
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
pick_frames_sql_clause="_mask_mass IS NULL OR _mask_mass > 0",
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
# check the datasets are equal
self.assertEqual(len(dataset), len(dataset_no_empty_masks))
for i in range(len(dataset)):
item_nem = dataset_no_empty_masks[i]
item = dataset[i]
self.assertEqual(item_nem.image_path, item.image_path)
# remove_empty_masks together with the custom criterion
dataset_ts = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=True,
pick_frames_sql_clause="frame_timestamp < 0.15",
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset_ts), 19)
def test_limit_categories(self, category="cat0"):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
pick_categories=[category],
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), 50)
for i in range(len(dataset)):
self.assertEqual(dataset[i].sequence_category, category)
def test_limit_sequences(self, num_sequences=3):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
limit_sequences_to=num_sequences,
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), 10 * num_sequences)
def delist(sequence_name):
return sequence_name if isinstance(sequence_name, str) else sequence_name[0]
unique_seqs = {delist(dataset[i].sequence_name) for i in range(len(dataset))}
self.assertEqual(len(unique_seqs), num_sequences)
def test_pick_exclude_sequencess(self, sequence="cat1_seq2"):
# pick sequence
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
pick_sequences=[sequence],
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), 10)
unique_seqs = {dataset[i].sequence_name for i in range(len(dataset))}
self.assertCountEqual(unique_seqs, {sequence})
item = dataset[sequence, 0]
self.assertEqual(item.sequence_name, sequence)
self.assertEqual(item.frame_number, 0)
# exclude sequence
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
exclude_sequences=[sequence],
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), 90)
unique_seqs = {dataset[i].sequence_name for i in range(len(dataset))}
self.assertNotIn(sequence, unique_seqs)
with self.assertRaises(IndexError):
dataset[sequence, 0]
def test_limit_frames(self, num_frames=13):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
limit_to=num_frames,
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), num_frames)
unique_seqs = {dataset[i].sequence_name for i in range(len(dataset))}
self.assertEqual(len(unique_seqs), 2)
# test when the limit is not binding
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
limit_to=1000,
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), 100)
def test_limit_frames_per_sequence(self, num_frames=2):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
n_frames_per_sequence=num_frames,
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), num_frames * 10)
seq_counts = Counter(dataset[i].sequence_name for i in range(len(dataset)))
self.assertEqual(len(seq_counts), 10)
self.assertCountEqual(
set(seq_counts.values()), {2}
) # all counts are num_frames
with self.assertRaises(IndexError):
dataset[next(iter(seq_counts)), num_frames + 1]
# test when the limit is not binding
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
n_frames_per_sequence=13,
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), 100)
def test_limit_sequence_per_category(self, num_sequences=2):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
limit_sequences_per_category_to=num_sequences,
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), num_sequences * 10 * 2)
seq_names = list(dataset.sequence_names())
self.assertEqual(len(seq_names), num_sequences * 2)
# check that we respect the row order
for seq_name in seq_names:
self.assertLess(int(seq_name[-1]), num_sequences)
# test when the limit is not binding
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
limit_sequences_per_category_to=13,
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), 100)
def test_filter_medley(self):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=True,
pick_categories=["cat1"],
exclude_sequences=["cat1_seq0"], # retaining "cat1_seq1" and on
limit_sequences_to=2, # retaining "cat1_seq1" and "cat1_seq2"
limit_to=14, # retaining full "cat1_seq1" and 4 from "cat1_seq2"
n_frames_per_sequence=6, # cutting "cat1_seq1" to 6 frames
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
# result: preserved 6 frames from cat1_seq1 and 4 from cat1_seq2
seq_counts = Counter(dataset[i].sequence_name for i in range(len(dataset)))
self.assertCountEqual(seq_counts.keys(), ["cat1_seq1", "cat1_seq2"])
self.assertEqual(seq_counts["cat1_seq1"], 6)
self.assertEqual(seq_counts["cat1_seq2"], 4)
def test_subsets_trivial(self):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
subset_lists_file=SET_LIST_FILE,
limit_to=100, # force sorting
subsets=["train", "test"],
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), 100)
# check the items are consecutive
past_sequences = set()
last_frame_number = -1
last_sequence = ""
for i in range(len(dataset)):
item = dataset[i]
if item.frame_number == 0:
self.assertNotIn(item.sequence_name, past_sequences)
past_sequences.add(item.sequence_name)
last_sequence = item.sequence_name
else:
self.assertEqual(item.sequence_name, last_sequence)
self.assertEqual(item.frame_number, last_frame_number + 1)
last_frame_number = item.frame_number
def test_subsets_filter_empty_masks(self):
# we need to test this case as it uses quite different logic with `df.drop()`
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=True,
subset_lists_file=SET_LIST_FILE,
subsets=["train", "test"],
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), 78)
def test_subsets_pick_frames_sql_clause(self):
dataset_no_empty_masks = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=True,
subset_lists_file=SET_LIST_FILE,
subsets=["train", "test"],
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
pick_frames_sql_clause="_mask_mass IS NULL OR _mask_mass > 0",
subset_lists_file=SET_LIST_FILE,
subsets=["train", "test"],
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
# check the datasets are equal
self.assertEqual(len(dataset), len(dataset_no_empty_masks))
for i in range(len(dataset)):
item_nem = dataset_no_empty_masks[i]
item = dataset[i]
self.assertEqual(item_nem.image_path, item.image_path)
# remove_empty_masks together with the custom criterion
dataset_ts = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=True,
pick_frames_sql_clause="frame_timestamp < 0.15",
subset_lists_file=SET_LIST_FILE,
subsets=["train", "test"],
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset_ts), 19)
def test_single_subset(self):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
subset_lists_file=SET_LIST_FILE,
subsets=["train"],
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), 50)
with self.assertRaises(IndexError):
dataset[51]
# check the items are consecutive
past_sequences = set()
last_frame_number = -1
last_sequence = ""
for i in range(len(dataset)):
item = dataset[i]
if item.frame_number < 2:
self.assertNotIn(item.sequence_name, past_sequences)
past_sequences.add(item.sequence_name)
last_sequence = item.sequence_name
else:
self.assertEqual(item.sequence_name, last_sequence)
self.assertEqual(item.frame_number, last_frame_number + 2)
last_frame_number = item.frame_number
item = dataset[last_sequence, 0]
self.assertEqual(item.sequence_name, last_sequence)
with self.assertRaises(IndexError):
dataset[last_sequence, 1]
def test_subset_with_filters(self):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=True,
subset_lists_file=SET_LIST_FILE,
subsets=["train"],
pick_categories=["cat1"],
exclude_sequences=["cat1_seq0"], # retaining "cat1_seq1" and on
limit_sequences_to=2, # retaining "cat1_seq1" and "cat1_seq2"
limit_to=7, # retaining full train set of "cat1_seq1" and 2 from "cat1_seq2"
n_frames_per_sequence=3, # cutting "cat1_seq1" to 3 frames
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
# result: preserved 6 frames from cat1_seq1 and 4 from cat1_seq2
seq_counts = Counter(dataset[i].sequence_name for i in range(len(dataset)))
self.assertCountEqual(seq_counts.keys(), ["cat1_seq1", "cat1_seq2"])
self.assertEqual(seq_counts["cat1_seq1"], 3)
self.assertEqual(seq_counts["cat1_seq2"], 2)
def test_visitor(self):
dataset_sorted = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
sequences = dataset_sorted.sequence_names()
i = 0
for seq in sequences:
last_ts = float("-Inf")
for ts, _, idx in dataset_sorted.sequence_frames_in_order(seq):
self.assertEqual(i, idx)
i += 1
self.assertGreaterEqual(ts, last_ts)
last_ts = ts
# test legacy visitor
old_indices = None
for seq in sequences:
last_ts = float("-Inf")
rows = dataset_sorted._index.index.get_loc(seq)
indices = list(range(rows.start or 0, rows.stop, rows.step or 1))
fn_ts_list = dataset_sorted.get_frame_numbers_and_timestamps(indices)
self.assertEqual(len(fn_ts_list), len(indices))
if old_indices:
# check raising if we ask for multiple sequences
with self.assertRaises(ValueError):
dataset_sorted.get_frame_numbers_and_timestamps(
indices + old_indices
)
old_indices = indices
def test_visitor_subsets(self):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
limit_to=100, # force sorting
subset_lists_file=SET_LIST_FILE,
subsets=["train", "test"],
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
sequences = dataset.sequence_names()
i = 0
for seq in sequences:
last_ts = float("-Inf")
seq_frames = list(dataset.sequence_frames_in_order(seq))
self.assertEqual(len(seq_frames), 10)
for ts, _, idx in seq_frames:
self.assertEqual(i, idx)
i += 1
self.assertGreaterEqual(ts, last_ts)
last_ts = ts
last_ts = float("-Inf")
train_frames = list(dataset.sequence_frames_in_order(seq, "train"))
self.assertEqual(len(train_frames), 5)
for ts, _, _ in train_frames:
self.assertGreaterEqual(ts, last_ts)
last_ts = ts
def test_category_to_sequence_names(self):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
subset_lists_file=SET_LIST_FILE,
subsets=["train", "test"],
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
cat_to_seqs = dataset.category_to_sequence_names()
self.assertEqual(len(cat_to_seqs), 2)
self.assertIn("cat1", cat_to_seqs)
self.assertEqual(len(cat_to_seqs["cat1"]), 5)
# check that override preserves the behavior
cat_to_seqs_base = super(SqlIndexDataset, dataset).category_to_sequence_names()
self.assertDictEqual(cat_to_seqs, cat_to_seqs_base)
def test_category_to_sequence_names_filters(self):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=True,
subset_lists_file=SET_LIST_FILE,
exclude_sequences=["cat1_seq0"],
subsets=["train", "test"],
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
cat_to_seqs = dataset.category_to_sequence_names()
self.assertEqual(len(cat_to_seqs), 2)
self.assertIn("cat1", cat_to_seqs)
self.assertEqual(len(cat_to_seqs["cat1"]), 4) # minus one
# check that override preserves the behavior
cat_to_seqs_base = super(SqlIndexDataset, dataset).category_to_sequence_names()
self.assertDictEqual(cat_to_seqs, cat_to_seqs_base)
def test_meta_access(self):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
subset_lists_file=SET_LIST_FILE,
subsets=["train"],
frame_data_builder_FrameDataBuilder_args=NO_BLOBS_KWARGS,
)
self.assertEqual(len(dataset), 50)
for idx in [10, ("cat0_seq2", 2)]:
example_meta = dataset.meta[idx]
example = dataset[idx]
self.assertEqual(example_meta.sequence_name, example.sequence_name)
self.assertEqual(example_meta.frame_number, example.frame_number)
self.assertEqual(example_meta.frame_timestamp, example.frame_timestamp)
self.assertEqual(example_meta.sequence_category, example.sequence_category)
torch.testing.assert_close(example_meta.camera.R, example.camera.R)
torch.testing.assert_close(example_meta.camera.T, example.camera.T)
torch.testing.assert_close(
example_meta.camera.focal_length, example.camera.focal_length
)
torch.testing.assert_close(
example_meta.camera.principal_point, example.camera.principal_point
)
def test_meta_access_no_blobs(self):
dataset = SqlIndexDataset(
sqlite_metadata_file=METADATA_FILE,
remove_empty_masks=False,
subset_lists_file=SET_LIST_FILE,
subsets=["train"],
frame_data_builder_FrameDataBuilder_args={
"dataset_root": ".",
"box_crop": False, # required by blob-less accessor
},
)
self.assertIsNone(dataset.meta[0].image_rgb)
self.assertIsNone(dataset.meta[0].fg_probability)
self.assertIsNone(dataset.meta[0].depth_map)
self.assertIsNone(dataset.meta[0].sequence_point_cloud)
self.assertIsNotNone(dataset.meta[0].camera)
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