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Recover DINO head unit tests
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@jaegukhyun
jaegukhyun committed Jul 18, 2023
1 parent 66e8a67 commit 16e2eb7
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4 changes: 4 additions & 0 deletions tests/unit/algorithms/detection/adapters/mmdet/models/heads/__init__.py
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"""Unit tests for src/otx/algorithms/detection/adapters/mmdet/models/heads."""
# Copyright (C) 2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
#
212 changes: 212 additions & 0 deletions tests/unit/algorithms/detection/adapters/mmdet/models/heads/test_custom_dino_head.py
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"""Unit tests for CustomDINOHead."""
# Copyright (C) 2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
#

import numpy as np
import pytest
import torch
from mmcv.utils import ConfigDict
from mmdet.core import build_assigner
from mmdet.models.builder import build_detector

from tests.test_suite.e2e_test_system import e2e_pytest_unit


class TestCustomDINOHead:
@pytest.fixture(autouse=True)
def setup(self):
torch.manual_seed(5)
cfg = ConfigDict(
dict(
type="CustomDINOHead",
num_query=900,
num_classes=80,
in_channels=2048,
sync_cls_avg_factor=True,
with_box_refine=True,
as_two_stage=True,
transformer=dict(
type="CustomDINOTransformer",
encoder=dict(
type="DetrTransformerEncoder",
num_layers=6,
transformerlayers=dict(
type="BaseTransformerLayer",
attn_cfgs=dict(type="MultiScaleDeformableAttention", embed_dims=256, dropout=0.0),
feedforward_channels=2048,
ffn_dropout=0.0,
operation_order=("self_attn", "norm", "ffn", "norm"),
),
),
decoder=dict(
type="DINOTransformerDecoder",
num_layers=6,
return_intermediate=True,
transformerlayers=dict(
type="DetrTransformerDecoderLayer",
attn_cfgs=[
dict(type="MultiheadAttention", embed_dims=256, num_heads=8, dropout=0.0),
dict(type="MultiScaleDeformableAttention", embed_dims=256, dropout=0.0),
],
feedforward_channels=2048,
ffn_dropout=0.0,
operation_order=("self_attn", "norm", "cross_attn", "norm", "ffn", "norm"),
),
),
),
positional_encoding=dict(
type="SinePositionalEncoding", num_feats=128, normalize=True, offset=0.0, temperature=20
),
loss_cls=dict(type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0),
loss_bbox=dict(type="L1Loss", loss_weight=5.0),
loss_iou=dict(type="GIoULoss", loss_weight=2.0),
dn_cfg=dict(
label_noise_scale=0.5,
box_noise_scale=1.0, # 0.4 for DN-DETR
group_cfg=dict(dynamic=True, num_groups=None, num_dn_queries=100),
),
),
)
self.bbox_head = build_detector(cfg)

assigner_cfg = ConfigDict(
type="HungarianAssigner",
cls_cost=dict(type="FocalLossCost", weight=1.0),
reg_cost=dict(type="BBoxL1Cost", weight=5.0, box_format="xywh"),
iou_cost=dict(type="IoUCost", iou_mode="giou", weight=2.0),
)
self.bbox_head.assigner = build_assigner(assigner_cfg)

test_cfg = dict(max_per_img=300)
self.bbox_head.test_cfg = test_cfg

@e2e_pytest_unit
def test_forward_train(self):
inputs = [
torch.zeros([2, 256, 92, 95]),
torch.zeros([2, 256, 46, 48]),
torch.zeros([2, 256, 23, 24]),
torch.zeros([2, 256, 12, 12]),
]
gt_bboxes = [
torch.Tensor(
[
[432.2500, 514.2661, 632.6323, 638.8889],
[361.2484, 294.9931, 558.4751, 466.9410],
[616.8542, 201.9204, 752.5462, 328.1207],
[591.6091, 386.4883, 733.6124, 571.0562],
[728.8790, 255.5556, 760.0000, 408.5734],
[713.1008, 397.5309, 760.0000, 541.0837],
[246.0680, 354.9383, 427.5165, 498.4911],
[113.5316, 361.2483, 309.1805, 517.4211],
[457.4950, 654.6639, 646.8326, 736.0000],
[132.4654, 631.0014, 187.6889, 684.6365],
[217.6673, 694.1015, 298.1358, 736.0000],
[0.0000, 583.6763, 56.7303, 672.0164],
[86.7088, 675.1714, 168.7551, 736.0000],
[173.4885, 93.0727, 253.9570, 151.4403],
[738.3458, 119.8903, 760.0000, 164.0603],
[683.1224, 522.1536, 760.0000, 736.0000],
]
),
torch.Tensor(
[
[442.0, 279.0, 544.0, 377.0],
[386.0, 1.0, 497.0, 108.0],
[288.0, 1.0, 399.0, 84.0],
[154.0, 1.0, 268.0, 77.0],
[530.0, 163.0, 625.0, 248.0],
[179.0, 298.0, 278.0, 398.0],
[275.0, 320.0, 374.0, 420.0],
[525.0, 394.0, 613.0, 480.0],
[332.0, 160.0, 463.0, 286.0],
[210.0, 395.0, 308.0, 480.0],
[141.0, 395.0, 239.0, 480.0],
[106.0, 225.0, 204.0, 310.0],
[12.0, 1.0, 148.0, 70.0],
[165.0, 79.0, 396.0, 247.0],
[483.0, 13.0, 518.0, 52.0],
],
),
]
gt_labels = [
torch.Tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 2]).long(),
torch.Tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0]).long(),
]
img_metas = [
{
"flip_direction": "horizontal",
"img_shape": (736, 760, 3),
"ori_shape": (480, 640, 3),
"img_norm_cfg": {
"mean": np.array([123.675, 116.28, 103.53], dtype=np.float32),
"std": np.array([58.395, 57.12, 57.375], dtype=np.float32),
"to_rgb": False,
},
"scale_factor": np.array([1.5139443, 1.5144033, 1.5139443, 1.5144033], dtype=np.float32),
"flip": True,
"pad_shape": (736, 760, 3),
"batch_input_shape": (736, 760),
},
{
"flip_direction": "horizontal",
"img_shape": (480, 640, 3),
"ori_shape": (480, 640, 3),
"img_norm_cfg": {
"mean": np.array([123.675, 116.28, 103.53], dtype=np.float32),
"std": np.array([58.395, 57.12, 57.375], dtype=np.float32),
"to_rgb": False,
},
"scale_factor": np.array([1.0, 1.0, 1.0, 1.0], dtype=np.float32),
"flip": True,
"pad_shape": (480, 640, 3),
"batch_input_shape": (736, 760),
},
]
losses = self.bbox_head.forward_train(inputs, img_metas, gt_bboxes, gt_labels)
assert len(losses) == 39

@e2e_pytest_unit
def test_simple_test_bboxes(self):
feats = [
torch.zeros([2, 256, 100, 134]),
torch.zeros([2, 256, 50, 67]),
torch.zeros([2, 256, 25, 34]),
torch.zeros([2, 256, 13, 17]),
]
img_metas = [
{
"ori_shape": (480, 640, 3),
"img_shape": (800, 1067, 3),
"pad_shape": (800, 1067, 3),
"scale_factor": np.array([1.6671875, 1.6666666, 1.6671875, 1.6666666], dtype=np.float32),
"flip": False,
"flip_direction": None,
"img_norm_cfg": {
"mean": np.array([123.675, 116.28, 103.53], dtype=np.float32),
"std": np.array([58.395, 57.12, 57.375], dtype=np.float32),
"to_rgb": False,
},
"batch_input_shape": (800, 1067),
},
{
"ori_shape": (480, 640, 3),
"img_shape": (800, 1067, 3),
"pad_shape": (800, 1067, 3),
"scale_factor": np.array([1.6671875, 1.6666666, 1.6671875, 1.6666666], dtype=np.float32),
"flip": False,
"flip_direction": None,
"img_norm_cfg": {
"mean": np.array([123.675, 116.28, 103.53], dtype=np.float32),
"std": np.array([58.395, 57.12, 57.375], dtype=np.float32),
"to_rgb": False,
},
"batch_input_shape": (800, 1067),
},
]
self.bbox_head.eval()
results = self.bbox_head.simple_test_bboxes(feats, img_metas)
assert len(results) == 2
assert results[0][0].shape == torch.Size([300, 5])
assert results[0][1].shape == torch.Size([300])

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