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import numpy as np
import torch
from detectron2 import model_zoo
from detectron2.data import DatasetCatalog
from detectron2.data.detection_utils import read_image
from detectron2.modeling import build_model
from detectron2.structures import Boxes
from detectron2.utils.file_io import PathManager
"""
Internal utilities for tests. Don't use except for writing tests.
"""
def get_model_no_weights(config_path):
"""
Like model_zoo.get, but do not load any weights (even pretrained)
"""
cfg = model_zoo.get_config(config_path)
if not torch.cuda.is_available():
cfg.MODEL.DEVICE = "cpu"
return build_model(cfg)
def random_boxes(num_boxes, max_coord=100, device="cpu"):
"""
Create a random Nx4 boxes tensor, with coordinates < max_coord.
"""
boxes = torch.rand(num_boxes, 4, device=device) * (max_coord * 0.5)
boxes.clamp_(min=1.0) # tiny boxes cause numerical instability in box regression
# Note: the implementation of this function in torchvision is:
# boxes[:, 2:] += torch.rand(N, 2) * 100
# but it does not guarantee non-negative widths/heights constraints:
# boxes[:, 2] >= boxes[:, 0] and boxes[:, 3] >= boxes[:, 1]:
boxes[:, 2:] += boxes[:, :2]
return boxes
def get_sample_coco_image(tensor=True):
"""
Args:
tensor (bool): if True, returns 3xHxW tensor.
else, returns a HxWx3 numpy array.
Returns:
an image, in BGR color.
"""
try:
file_name = DatasetCatalog.get("coco_2017_train")[0]["file_name"]
if not PathManager.exists(file_name):
raise FileNotFoundError()
except IOError:
# for public CI to run
file_name = "http://images.cocodataset.org/train2017/000000000009.jpg"
ret = read_image(file_name, format="BGR")
if tensor:
ret = torch.from_numpy(np.ascontiguousarray(ret.transpose(2, 0, 1)))
return ret
def assert_instances_allclose(input, other, rtol=1e-5, msg=""):
"""
Args:
input, other (Instances):
"""
if not msg:
msg = "Two Instances are different! "
else:
msg = msg.rstrip() + " "
assert input.image_size == other.image_size, (
msg + f"image_size is {input.image_size} vs. {other.image_size}!"
)
fields = sorted(input.get_fields().keys())
fields_other = sorted(other.get_fields().keys())
assert fields == fields_other, msg + f"Fields are {fields} vs {fields_other}!"
for f in fields:
val1, val2 = input.get(f), other.get(f)
if isinstance(val1, Boxes):
# boxes in the range of O(100) and can have a larger tolerance
assert torch.allclose(val1.tensor, val2.tensor, atol=100 * rtol), (
msg + f"Field {f} differs too much!"
)
elif isinstance(val1, torch.Tensor):
if val1.dtype.is_floating_point:
mag = torch.abs(val1).max().cpu().item()
assert torch.allclose(val1, val2, atol=mag * rtol), (
msg + f"Field {f} differs too much!"
)
else:
assert torch.equal(val1, val2), msg + f"Field {f} is different!"
else:
raise ValueError(f"Don't know how to compare type {type(val1)}")
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