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# Copyright (c) OpenMMLab. All rights reserved.
"""pytest tests/test_forward.py."""
import copy
from os.path import dirname, exists, join
import numpy as np
import pytest
import torch
def _get_config_directory():
"""Find the predefined detector config directory."""
try:
# Assume we are running in the source mmdetection repo
repo_dpath = dirname(dirname(dirname(__file__)))
except NameError:
# For IPython development when this __file__ is not defined
import mmdet
repo_dpath = dirname(dirname(mmdet.__file__))
config_dpath = join(repo_dpath, 'configs')
if not exists(config_dpath):
raise Exception('Cannot find config path')
return config_dpath
def _get_config_module(fname):
"""Load a configuration as a python module."""
from mmcv import Config
config_dpath = _get_config_directory()
config_fpath = join(config_dpath, fname)
config_mod = Config.fromfile(config_fpath)
return config_mod
def _get_detector_cfg(fname):
"""Grab configs necessary to create a detector.
These are deep copied to allow for safe modification of parameters without
influencing other tests.
"""
config = _get_config_module(fname)
model = copy.deepcopy(config.model)
return model
def _replace_r50_with_r18(model):
"""Replace ResNet50 with ResNet18 in config."""
model = copy.deepcopy(model)
if model.backbone.type == 'ResNet':
model.backbone.depth = 18
model.backbone.base_channels = 2
model.neck.in_channels = [2, 4, 8, 16]
return model
def test_sparse_rcnn_forward():
config_path = 'sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py'
model = _get_detector_cfg(config_path)
model = _replace_r50_with_r18(model)
model.backbone.init_cfg = None
from mmdet.models import build_detector
detector = build_detector(model)
detector.init_weights()
input_shape = (1, 3, 100, 100)
mm_inputs = _demo_mm_inputs(input_shape, num_items=[5])
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
# Test forward train with non-empty truth batch
detector.train()
gt_bboxes = mm_inputs['gt_bboxes']
gt_bboxes = [item for item in gt_bboxes]
gt_labels = mm_inputs['gt_labels']
gt_labels = [item for item in gt_labels]
losses = detector.forward(
imgs,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
return_loss=True)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
assert float(loss.item()) > 0
detector.forward_dummy(imgs)
# Test forward train with an empty truth batch
mm_inputs = _demo_mm_inputs(input_shape, num_items=[0])
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
gt_bboxes = mm_inputs['gt_bboxes']
gt_bboxes = [item for item in gt_bboxes]
gt_labels = mm_inputs['gt_labels']
gt_labels = [item for item in gt_labels]
losses = detector.forward(
imgs,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
return_loss=True)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
assert float(loss.item()) > 0
# Test forward test
detector.eval()
with torch.no_grad():
img_list = [g[None, :] for g in imgs]
batch_results = []
for one_img, one_meta in zip(img_list, img_metas):
result = detector.forward([one_img], [[one_meta]],
rescale=True,
return_loss=False)
batch_results.append(result)
# test empty proposal in roi_head
with torch.no_grad():
# test no proposal in the whole batch
detector.roi_head.simple_test([imgs[0][None, :]], torch.empty(
(1, 0, 4)), torch.empty((1, 100, 4)), [img_metas[0]],
torch.ones((1, 4)))
def test_rpn_forward():
model = _get_detector_cfg('rpn/rpn_r50_fpn_1x_coco.py')
model = _replace_r50_with_r18(model)
model.backbone.init_cfg = None
from mmdet.models import build_detector
detector = build_detector(model)
input_shape = (1, 3, 100, 100)
mm_inputs = _demo_mm_inputs(input_shape)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
# Test forward train
gt_bboxes = mm_inputs['gt_bboxes']
losses = detector.forward(
imgs, img_metas, gt_bboxes=gt_bboxes, return_loss=True)
assert isinstance(losses, dict)
# Test forward test
with torch.no_grad():
img_list = [g[None, :] for g in imgs]
batch_results = []
for one_img, one_meta in zip(img_list, img_metas):
result = detector.forward([one_img], [[one_meta]],
return_loss=False)
batch_results.append(result)
@pytest.mark.parametrize(
'cfg_file',
[
'reppoints/reppoints_moment_r50_fpn_1x_coco.py',
'retinanet/retinanet_r50_fpn_1x_coco.py',
'guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py',
'ghm/retinanet_ghm_r50_fpn_1x_coco.py',
'fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py',
'foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py',
# 'free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py',
# 'atss/atss_r50_fpn_1x_coco.py', # not ready for topk
'yolo/yolov3_mobilenetv2_320_300e_coco.py',
'yolox/yolox_tiny_8x8_300e_coco.py'
])
def test_single_stage_forward_gpu(cfg_file):
if not torch.cuda.is_available():
import pytest
pytest.skip('test requires GPU and torch+cuda')
model = _get_detector_cfg(cfg_file)
model = _replace_r50_with_r18(model)
model.backbone.init_cfg = None
from mmdet.models import build_detector
detector = build_detector(model)
input_shape = (2, 3, 128, 128)
mm_inputs = _demo_mm_inputs(input_shape)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
detector = detector.cuda()
imgs = imgs.cuda()
# Test forward train
gt_bboxes = [b.cuda() for b in mm_inputs['gt_bboxes']]
gt_labels = [g.cuda() for g in mm_inputs['gt_labels']]
losses = detector.forward(
imgs,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
return_loss=True)
assert isinstance(losses, dict)
# Test forward test
detector.eval()
with torch.no_grad():
img_list = [g[None, :] for g in imgs]
batch_results = []
for one_img, one_meta in zip(img_list, img_metas):
result = detector.forward([one_img], [[one_meta]],
return_loss=False)
batch_results.append(result)
def test_faster_rcnn_ohem_forward():
model = _get_detector_cfg(
'faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py')
model = _replace_r50_with_r18(model)
model.backbone.init_cfg = None
from mmdet.models import build_detector
detector = build_detector(model)
input_shape = (1, 3, 100, 100)
# Test forward train with a non-empty truth batch
mm_inputs = _demo_mm_inputs(input_shape, num_items=[10])
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
gt_bboxes = mm_inputs['gt_bboxes']
gt_labels = mm_inputs['gt_labels']
losses = detector.forward(
imgs,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
return_loss=True)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
assert float(loss.item()) > 0
# Test forward train with an empty truth batch
mm_inputs = _demo_mm_inputs(input_shape, num_items=[0])
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
gt_bboxes = mm_inputs['gt_bboxes']
gt_labels = mm_inputs['gt_labels']
losses = detector.forward(
imgs,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
return_loss=True)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
assert float(loss.item()) > 0
# Test RoI forward train with an empty proposals
feature = detector.extract_feat(imgs[0][None, :])
losses = detector.roi_head.forward_train(
feature,
img_metas, [torch.empty((0, 5))],
gt_bboxes=gt_bboxes,
gt_labels=gt_labels)
assert isinstance(losses, dict)
@pytest.mark.parametrize(
'cfg_file',
[
# 'cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py',
'mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py',
# 'grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py',
# 'ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py',
# 'htc/htc_r50_fpn_1x_coco.py',
# 'panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py',
# 'scnet/scnet_r50_fpn_20e_coco.py',
# 'seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501
])
def test_two_stage_forward(cfg_file):
models_with_semantic = [
'htc/htc_r50_fpn_1x_coco.py',
'panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py',
'scnet/scnet_r50_fpn_20e_coco.py',
]
if cfg_file in models_with_semantic:
with_semantic = True
else:
with_semantic = False
model = _get_detector_cfg(cfg_file)
model = _replace_r50_with_r18(model)
model.backbone.init_cfg = None
# Save cost
if cfg_file in [
'seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501
]:
model.roi_head.bbox_head.num_classes = 80
model.roi_head.bbox_head.loss_cls.num_classes = 80
model.roi_head.mask_head.num_classes = 80
model.test_cfg.rcnn.score_thr = 0.05
model.test_cfg.rcnn.max_per_img = 100
from mmdet.models import build_detector
detector = build_detector(model)
input_shape = (1, 3, 128, 128)
# Test forward train with a non-empty truth batch
mm_inputs = _demo_mm_inputs(
input_shape, num_items=[10], with_semantic=with_semantic)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
losses = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
loss.requires_grad_(True)
assert float(loss.item()) > 0
loss.backward()
# Test forward train with an empty truth batch
mm_inputs = _demo_mm_inputs(
input_shape, num_items=[0], with_semantic=with_semantic)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
losses = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
loss.requires_grad_(True)
assert float(loss.item()) > 0
loss.backward()
# Test RoI forward train with an empty proposals
if cfg_file in [
'panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py' # noqa: E501
]:
mm_inputs.pop('gt_semantic_seg')
feature = detector.extract_feat(imgs[0][None, :])
losses = detector.roi_head.forward_train(feature, img_metas,
[torch.empty(
(0, 5))], **mm_inputs)
assert isinstance(losses, dict)
# Test forward test
with torch.no_grad():
img_list = [g[None, :] for g in imgs]
batch_results = []
for one_img, one_meta in zip(img_list, img_metas):
result = detector.forward([one_img], [[one_meta]],
return_loss=False)
batch_results.append(result)
cascade_models = [
'cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py',
'htc/htc_r50_fpn_1x_coco.py',
'scnet/scnet_r50_fpn_20e_coco.py',
]
# test empty proposal in roi_head
with torch.no_grad():
# test no proposal in the whole batch
detector.simple_test(
imgs[0][None, :], [img_metas[0]], proposals=[torch.empty((0, 4))])
# test no proposal of aug
features = detector.extract_feats([imgs[0][None, :]] * 2)
detector.roi_head.aug_test(features, [torch.empty((0, 4))] * 2,
[[img_metas[0]]] * 2)
# test rcnn_test_cfg is None
if cfg_file not in cascade_models:
feature = detector.extract_feat(imgs[0][None, :])
bboxes, scores = detector.roi_head.simple_test_bboxes(
feature, [img_metas[0]], [torch.empty((0, 4))], None)
assert all([bbox.shape == torch.Size((0, 4)) for bbox in bboxes])
assert all([
score.shape == torch.Size(
(0, detector.roi_head.bbox_head.fc_cls.out_features))
for score in scores
])
# test no proposal in the some image
x1y1 = torch.randint(1, 100, (10, 2)).float()
# x2y2 must be greater than x1y1
x2y2 = x1y1 + torch.randint(1, 100, (10, 2))
detector.simple_test(
imgs[0][None, :].repeat(2, 1, 1, 1), [img_metas[0]] * 2,
proposals=[torch.empty((0, 4)),
torch.cat([x1y1, x2y2], dim=-1)])
# test no proposal of aug
detector.roi_head.aug_test(
features, [torch.cat([x1y1, x2y2], dim=-1),
torch.empty((0, 4))], [[img_metas[0]]] * 2)
# test rcnn_test_cfg is None
if cfg_file not in cascade_models:
feature = detector.extract_feat(imgs[0][None, :].repeat(
2, 1, 1, 1))
bboxes, scores = detector.roi_head.simple_test_bboxes(
feature, [img_metas[0]] * 2,
[torch.empty((0, 4)),
torch.cat([x1y1, x2y2], dim=-1)], None)
assert bboxes[0].shape == torch.Size((0, 4))
assert scores[0].shape == torch.Size(
(0, detector.roi_head.bbox_head.fc_cls.out_features))
@pytest.mark.parametrize(
'cfg_file', ['ghm/retinanet_ghm_r50_fpn_1x_coco.py', 'ssd/ssd300_coco.py'])
def test_single_stage_forward_cpu(cfg_file):
model = _get_detector_cfg(cfg_file)
model = _replace_r50_with_r18(model)
model.backbone.init_cfg = None
from mmdet.models import build_detector
detector = build_detector(model)
input_shape = (1, 3, 300, 300)
mm_inputs = _demo_mm_inputs(input_shape)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
# Test forward train
gt_bboxes = mm_inputs['gt_bboxes']
gt_labels = mm_inputs['gt_labels']
losses = detector.forward(
imgs,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
return_loss=True)
assert isinstance(losses, dict)
# Test forward test
detector.eval()
with torch.no_grad():
img_list = [g[None, :] for g in imgs]
batch_results = []
for one_img, one_meta in zip(img_list, img_metas):
result = detector.forward([one_img], [[one_meta]],
return_loss=False)
batch_results.append(result)
def _demo_mm_inputs(input_shape=(1, 3, 300, 300),
num_items=None, num_classes=10,
with_semantic=False): # yapf: disable
"""Create a superset of inputs needed to run test or train batches.
Args:
input_shape (tuple):
input batch dimensions
num_items (None | List[int]):
specifies the number of boxes in each batch item
num_classes (int):
number of different labels a box might have
"""
from mmdet.core import BitmapMasks
(N, C, H, W) = input_shape
rng = np.random.RandomState(0)
imgs = rng.rand(*input_shape)
img_metas = [{
'img_shape': (H, W, C),
'ori_shape': (H, W, C),
'pad_shape': (H, W, C),
'filename': '<demo>.png',
'scale_factor': np.array([1.1, 1.2, 1.1, 1.2]),
'flip': False,
'flip_direction': None,
} for _ in range(N)]
gt_bboxes = []
gt_labels = []
gt_masks = []
for batch_idx in range(N):
if num_items is None:
num_boxes = rng.randint(1, 10)
else:
num_boxes = num_items[batch_idx]
cx, cy, bw, bh = rng.rand(num_boxes, 4).T
tl_x = ((cx * W) - (W * bw / 2)).clip(0, W)
tl_y = ((cy * H) - (H * bh / 2)).clip(0, H)
br_x = ((cx * W) + (W * bw / 2)).clip(0, W)
br_y = ((cy * H) + (H * bh / 2)).clip(0, H)
boxes = np.vstack([tl_x, tl_y, br_x, br_y]).T
class_idxs = rng.randint(1, num_classes, size=num_boxes)
gt_bboxes.append(torch.FloatTensor(boxes))
gt_labels.append(torch.LongTensor(class_idxs))
mask = np.random.randint(0, 2, (len(boxes), H, W), dtype=np.uint8)
gt_masks.append(BitmapMasks(mask, H, W))
mm_inputs = {
'imgs': torch.FloatTensor(imgs).requires_grad_(True),
'img_metas': img_metas,
'gt_bboxes': gt_bboxes,
'gt_labels': gt_labels,
'gt_bboxes_ignore': None,
'gt_masks': gt_masks,
}
if with_semantic:
# assume gt_semantic_seg using scale 1/8 of the img
gt_semantic_seg = np.random.randint(
0, num_classes, (1, 1, H // 8, W // 8), dtype=np.uint8)
mm_inputs.update(
{'gt_semantic_seg': torch.ByteTensor(gt_semantic_seg)})
return mm_inputs
def test_yolact_forward():
model = _get_detector_cfg('yolact/yolact_r50_1x8_coco.py')
model = _replace_r50_with_r18(model)
model.backbone.init_cfg = None
from mmdet.models import build_detector
detector = build_detector(model)
input_shape = (1, 3, 100, 100)
mm_inputs = _demo_mm_inputs(input_shape)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
# Test forward train
detector.train()
gt_bboxes = mm_inputs['gt_bboxes']
gt_labels = mm_inputs['gt_labels']
gt_masks = mm_inputs['gt_masks']
losses = detector.forward(
imgs,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
gt_masks=gt_masks,
return_loss=True)
assert isinstance(losses, dict)
# Test forward dummy for get_flops
detector.forward_dummy(imgs)
# Test forward test
detector.eval()
with torch.no_grad():
img_list = [g[None, :] for g in imgs]
batch_results = []
for one_img, one_meta in zip(img_list, img_metas):
result = detector.forward([one_img], [[one_meta]],
rescale=True,
return_loss=False)
batch_results.append(result)
def test_detr_forward():
model = _get_detector_cfg('detr/detr_r50_8x2_150e_coco.py')
model.backbone.depth = 18
model.bbox_head.in_channels = 512
model.backbone.init_cfg = None
from mmdet.models import build_detector
detector = build_detector(model)
input_shape = (1, 3, 100, 100)
mm_inputs = _demo_mm_inputs(input_shape)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
# Test forward train with non-empty truth batch
detector.train()
gt_bboxes = mm_inputs['gt_bboxes']
gt_labels = mm_inputs['gt_labels']
losses = detector.forward(
imgs,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
return_loss=True)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
assert float(loss.item()) > 0
# Test forward train with an empty truth batch
mm_inputs = _demo_mm_inputs(input_shape, num_items=[0])
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
gt_bboxes = mm_inputs['gt_bboxes']
gt_labels = mm_inputs['gt_labels']
losses = detector.forward(
imgs,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
return_loss=True)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
assert float(loss.item()) > 0
# Test forward test
detector.eval()
with torch.no_grad():
img_list = [g[None, :] for g in imgs]
batch_results = []
for one_img, one_meta in zip(img_list, img_metas):
result = detector.forward([one_img], [[one_meta]],
rescale=True,
return_loss=False)
batch_results.append(result)
def test_inference_detector():
from mmcv import ConfigDict
from mmdet.apis import inference_detector
from mmdet.models import build_detector
# small RetinaNet
num_class = 3
model_dict = dict(
type='RetinaNet',
backbone=dict(
type='ResNet',
depth=18,
num_stages=4,
out_indices=(3, ),
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='pytorch'),
neck=None,
bbox_head=dict(
type='RetinaHead',
num_classes=num_class,
in_channels=512,
stacked_convs=1,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5],
strides=[32]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100))
rng = np.random.RandomState(0)
img1 = rng.rand(100, 100, 3)
img2 = rng.rand(100, 100, 3)
model = build_detector(ConfigDict(model_dict))
config = _get_config_module('retinanet/retinanet_r50_fpn_1x_coco.py')
model.cfg = config
# test single image
result = inference_detector(model, img1)
assert len(result) == num_class
# test multiple image
result = inference_detector(model, [img1, img2])
assert len(result) == 2 and len(result[0]) == num_class
def test_yolox_random_size():
from mmdet.models import build_detector
model = _get_detector_cfg('yolox/yolox_tiny_8x8_300e_coco.py')
model.random_size_range = (2, 2)
model.input_size = (64, 96)
model.random_size_interval = 1
detector = build_detector(model)
input_shape = (1, 3, 64, 64)
mm_inputs = _demo_mm_inputs(input_shape)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
# Test forward train with non-empty truth batch
detector.train()
gt_bboxes = mm_inputs['gt_bboxes']
gt_labels = mm_inputs['gt_labels']
detector.forward(
imgs,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
return_loss=True)
assert detector._input_size == (64, 96)
def test_maskformer_forward():
model_cfg = _get_detector_cfg(
'maskformer/maskformer_r50_mstrain_16x1_75e_coco.py')
base_channels = 32
model_cfg.backbone.depth = 18
model_cfg.backbone.init_cfg = None
model_cfg.backbone.base_channels = base_channels
model_cfg.panoptic_head.in_channels = [
base_channels * 2**i for i in range(4)
]
model_cfg.panoptic_head.feat_channels = base_channels
model_cfg.panoptic_head.out_channels = base_channels
model_cfg.panoptic_head.pixel_decoder.encoder.\
transformerlayers.attn_cfgs.embed_dims = base_channels
model_cfg.panoptic_head.pixel_decoder.encoder.\
transformerlayers.ffn_cfgs.embed_dims = base_channels
model_cfg.panoptic_head.pixel_decoder.encoder.\
transformerlayers.ffn_cfgs.feedforward_channels = base_channels * 8
model_cfg.panoptic_head.pixel_decoder.\
positional_encoding.num_feats = base_channels // 2
model_cfg.panoptic_head.positional_encoding.\
num_feats = base_channels // 2
model_cfg.panoptic_head.transformer_decoder.\
transformerlayers.attn_cfgs.embed_dims = base_channels
model_cfg.panoptic_head.transformer_decoder.\
transformerlayers.ffn_cfgs.embed_dims = base_channels
model_cfg.panoptic_head.transformer_decoder.\
transformerlayers.ffn_cfgs.feedforward_channels = base_channels * 8
model_cfg.panoptic_head.transformer_decoder.\
transformerlayers.feedforward_channels = base_channels * 8
from mmdet.core import BitmapMasks
from mmdet.models import build_detector
detector = build_detector(model_cfg)
# Test forward train with non-empty truth batch
detector.train()
img_metas = [
{
'batch_input_shape': (128, 160),
'img_shape': (126, 160, 3),
'ori_shape': (63, 80, 3),
'pad_shape': (128, 160, 3)
},
]
img = torch.rand((1, 3, 128, 160))
gt_bboxes = None
gt_labels = [
torch.tensor([10]).long(),
]
thing_mask1 = np.zeros((1, 128, 160), dtype=np.int32)
thing_mask1[0, :50] = 1
gt_masks = [
BitmapMasks(thing_mask1, 128, 160),
]
stuff_mask1 = torch.zeros((1, 128, 160)).long()
stuff_mask1[0, :50] = 10
stuff_mask1[0, 50:] = 100
gt_semantic_seg = [
stuff_mask1,
]
losses = detector.forward(
img=img,
img_metas=img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
gt_masks=gt_masks,
gt_semantic_seg=gt_semantic_seg,
return_loss=True)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
assert float(loss.item()) > 0
# Test forward train with an empty truth batch
gt_bboxes = [
torch.empty((0, 4)).float(),
]
gt_labels = [
torch.empty((0, )).long(),
]
mask = np.zeros((0, 128, 160), dtype=np.uint8)
gt_masks = [
BitmapMasks(mask, 128, 160),
]
gt_semantic_seg = [
torch.randint(0, 133, (0, 128, 160)),
]
losses = detector.forward(
img,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
gt_masks=gt_masks,
gt_semantic_seg=gt_semantic_seg,
return_loss=True)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
assert float(loss.item()) > 0
# Test forward test
detector.eval()
with torch.no_grad():
img_list = [g[None, :] for g in img]
batch_results = []
for one_img, one_meta in zip(img_list, img_metas):
result = detector.forward([one_img], [[one_meta]],
rescale=True,
return_loss=False)
batch_results.append(result)
@pytest.mark.parametrize('cfg_file', [
'mask2former/mask2former_r50_lsj_8x2_50e_coco.py',
'mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py'
])
def test_mask2former_forward(cfg_file):
# Test Panoptic Segmentation and Instance Segmentation
model_cfg = _get_detector_cfg(cfg_file)
base_channels = 32
model_cfg.backbone.depth = 18
model_cfg.backbone.init_cfg = None
model_cfg.backbone.base_channels = base_channels
model_cfg.panoptic_head.in_channels = [
base_channels * 2**i for i in range(4)
]
model_cfg.panoptic_head.feat_channels = base_channels
model_cfg.panoptic_head.out_channels = base_channels
model_cfg.panoptic_head.pixel_decoder.encoder.\
transformerlayers.attn_cfgs.embed_dims = base_channels
model_cfg.panoptic_head.pixel_decoder.encoder.\
transformerlayers.ffn_cfgs.embed_dims = base_channels
model_cfg.panoptic_head.pixel_decoder.encoder.\
transformerlayers.ffn_cfgs.feedforward_channels = base_channels * 4
model_cfg.panoptic_head.pixel_decoder.\
positional_encoding.num_feats = base_channels // 2
model_cfg.panoptic_head.positional_encoding.\
num_feats = base_channels // 2
model_cfg.panoptic_head.transformer_decoder.\
transformerlayers.attn_cfgs.embed_dims = base_channels
model_cfg.panoptic_head.transformer_decoder.\
transformerlayers.ffn_cfgs.embed_dims = base_channels
model_cfg.panoptic_head.transformer_decoder.\
transformerlayers.ffn_cfgs.feedforward_channels = base_channels * 8
model_cfg.panoptic_head.transformer_decoder.\
transformerlayers.feedforward_channels = base_channels * 8
num_stuff_classes = model_cfg.panoptic_head.num_stuff_classes
from mmdet.core import BitmapMasks
from mmdet.models import build_detector
detector = build_detector(model_cfg)
def _forward_train():
losses = detector.forward(
img,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
gt_masks=gt_masks,
gt_semantic_seg=gt_semantic_seg,
return_loss=True)
assert isinstance(losses, dict)
loss, _ = detector._parse_losses(losses)
assert float(loss.item()) > 0
# Test forward train with non-empty truth batch
detector.train()
img_metas = [
{
'batch_input_shape': (128, 160),
'img_shape': (126, 160, 3),
'ori_shape': (63, 80, 3),
'pad_shape': (128, 160, 3)
},
]
img = torch.rand((1, 3, 128, 160))
gt_bboxes = None
gt_labels = [
torch.tensor([10]).long(),
]
thing_mask1 = np.zeros((1, 128, 160), dtype=np.int32)
thing_mask1[0, :50] = 1
gt_masks = [
BitmapMasks(thing_mask1, 128, 160),
]
stuff_mask1 = torch.zeros((1, 128, 160)).long()
stuff_mask1[0, :50] = 10
stuff_mask1[0, 50:] = 100
gt_semantic_seg = [
stuff_mask1,
]
_forward_train()
# Test forward train with non-empty truth batch and gt_semantic_seg=None
gt_semantic_seg = None
_forward_train()
# Test forward train with an empty truth batch
gt_bboxes = [
torch.empty((0, 4)).float(),
]
gt_labels = [
torch.empty((0, )).long(),
]
mask = np.zeros((0, 128, 160), dtype=np.uint8)
gt_masks = [
BitmapMasks(mask, 128, 160),
]
gt_semantic_seg = [
torch.randint(0, 133, (0, 128, 160)),
]
_forward_train()
# Test forward train with an empty truth batch and gt_semantic_seg=None
gt_semantic_seg = None
_forward_train()
# Test forward test
detector.eval()
with torch.no_grad():
img_list = [g[None, :] for g in img]
batch_results = []
for one_img, one_meta in zip(img_list, img_metas):
result = detector.forward([one_img], [[one_meta]],
rescale=True,
return_loss=False)
if num_stuff_classes > 0:
assert isinstance(result[0], dict)
else:
assert isinstance(result[0], tuple)
batch_results.append(result)
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