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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)}") | |