Spaces:
No application file
No application file
File size: 5,942 Bytes
430de99 |
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 |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import numpy as np
import unittest
import torch
import detectron2.model_zoo as model_zoo
from detectron2.config import get_cfg
from detectron2.modeling import build_model
from detectron2.structures import BitMasks, Boxes, ImageList, Instances
from detectron2.utils.events import EventStorage
def get_model_zoo(config_path):
"""
Like model_zoo.get, but do not load any weights (even pretrained)
"""
cfg_file = model_zoo.get_config_file(config_path)
cfg = get_cfg()
cfg.merge_from_file(cfg_file)
if not torch.cuda.is_available():
cfg.MODEL.DEVICE = "cpu"
return build_model(cfg)
def create_model_input(img, inst=None):
if inst is not None:
return {"image": img, "instances": inst}
else:
return {"image": img}
def get_empty_instance(h, w):
inst = Instances((h, w))
inst.gt_boxes = Boxes(torch.rand(0, 4))
inst.gt_classes = torch.tensor([]).to(dtype=torch.int64)
inst.gt_masks = BitMasks(torch.rand(0, h, w))
return inst
def get_regular_bitmask_instances(h, w):
inst = Instances((h, w))
inst.gt_boxes = Boxes(torch.rand(3, 4))
inst.gt_boxes.tensor[:, 2:] += inst.gt_boxes.tensor[:, :2]
inst.gt_classes = torch.tensor([3, 4, 5]).to(dtype=torch.int64)
inst.gt_masks = BitMasks((torch.rand(3, h, w) > 0.5))
return inst
class ModelE2ETest:
def setUp(self):
torch.manual_seed(43)
self.model = get_model_zoo(self.CONFIG_PATH)
def _test_eval(self, input_sizes):
inputs = [create_model_input(torch.rand(3, s[0], s[1])) for s in input_sizes]
self.model.eval()
self.model(inputs)
def _test_train(self, input_sizes, instances):
assert len(input_sizes) == len(instances)
inputs = [
create_model_input(torch.rand(3, s[0], s[1]), inst)
for s, inst in zip(input_sizes, instances)
]
self.model.train()
with EventStorage():
losses = self.model(inputs)
sum(losses.values()).backward()
del losses
def _inf_tensor(self, *shape):
return 1.0 / torch.zeros(*shape, device=self.model.device)
def _nan_tensor(self, *shape):
return torch.zeros(*shape, device=self.model.device).fill_(float("nan"))
def test_empty_data(self):
instances = [get_empty_instance(200, 250), get_empty_instance(200, 249)]
self._test_eval([(200, 250), (200, 249)])
self._test_train([(200, 250), (200, 249)], instances)
@unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable")
def test_eval_tocpu(self):
model = get_model_zoo(self.CONFIG_PATH).cpu()
model.eval()
input_sizes = [(200, 250), (200, 249)]
inputs = [create_model_input(torch.rand(3, s[0], s[1])) for s in input_sizes]
model(inputs)
class MaskRCNNE2ETest(ModelE2ETest, unittest.TestCase):
CONFIG_PATH = "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
def test_half_empty_data(self):
instances = [get_empty_instance(200, 250), get_regular_bitmask_instances(200, 249)]
self._test_train([(200, 250), (200, 249)], instances)
# This test is flaky because in some environment the output features are zero due to relu
# def test_rpn_inf_nan_data(self):
# self.model.eval()
# for tensor in [self._inf_tensor, self._nan_tensor]:
# images = ImageList(tensor(1, 3, 512, 512), [(510, 510)])
# features = {
# "p2": tensor(1, 256, 256, 256),
# "p3": tensor(1, 256, 128, 128),
# "p4": tensor(1, 256, 64, 64),
# "p5": tensor(1, 256, 32, 32),
# "p6": tensor(1, 256, 16, 16),
# }
# props, _ = self.model.proposal_generator(images, features)
# self.assertEqual(len(props[0]), 0)
def test_roiheads_inf_nan_data(self):
self.model.eval()
for tensor in [self._inf_tensor, self._nan_tensor]:
images = ImageList(tensor(1, 3, 512, 512), [(510, 510)])
features = {
"p2": tensor(1, 256, 256, 256),
"p3": tensor(1, 256, 128, 128),
"p4": tensor(1, 256, 64, 64),
"p5": tensor(1, 256, 32, 32),
"p6": tensor(1, 256, 16, 16),
}
props = [Instances((510, 510))]
props[0].proposal_boxes = Boxes([[10, 10, 20, 20]]).to(device=self.model.device)
props[0].objectness_logits = torch.tensor([1.0]).reshape(1, 1)
det, _ = self.model.roi_heads(images, features, props)
self.assertEqual(len(det[0]), 0)
class RetinaNetE2ETest(ModelE2ETest, unittest.TestCase):
CONFIG_PATH = "COCO-Detection/retinanet_R_50_FPN_1x.yaml"
def test_inf_nan_data(self):
self.model.eval()
self.model.score_threshold = -999999999
for tensor in [self._inf_tensor, self._nan_tensor]:
images = ImageList(tensor(1, 3, 512, 512), [(510, 510)])
features = [
tensor(1, 256, 128, 128),
tensor(1, 256, 64, 64),
tensor(1, 256, 32, 32),
tensor(1, 256, 16, 16),
tensor(1, 256, 8, 8),
]
anchors = self.model.anchor_generator(features)
_, pred_anchor_deltas = self.model.head(features)
HWAs = [np.prod(x.shape[-3:]) // 4 for x in pred_anchor_deltas]
pred_logits = [tensor(1, HWA, self.model.num_classes) for HWA in HWAs]
pred_anchor_deltas = [tensor(1, HWA, 4) for HWA in HWAs]
det = self.model.inference(anchors, pred_logits, pred_anchor_deltas, images.image_sizes)
# all predictions (if any) are infinite or nan
if len(det[0]):
self.assertTrue(torch.isfinite(det[0].pred_boxes.tensor).sum() == 0)
|