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import copy |
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import pytest |
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import torch |
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from mmengine.structures import InstanceData |
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from mmdet.models.utils import (empty_instances, filter_gt_instances, |
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rename_loss_dict, reweight_loss_dict, |
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unpack_gt_instances) |
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from mmdet.testing import demo_mm_inputs |
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def test_parse_gt_instance_info(): |
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packed_inputs = demo_mm_inputs()['data_samples'] |
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batch_gt_instances, batch_gt_instances_ignore, batch_img_metas \ |
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= unpack_gt_instances(packed_inputs) |
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assert len(batch_gt_instances) == len(packed_inputs) |
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assert len(batch_gt_instances_ignore) == len(packed_inputs) |
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assert len(batch_img_metas) == len(packed_inputs) |
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def test_process_empty_roi(): |
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batch_size = 2 |
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batch_img_metas = [{'ori_shape': (10, 12)}] * batch_size |
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device = torch.device('cpu') |
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results_list = empty_instances(batch_img_metas, device, task_type='bbox') |
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assert len(results_list) == batch_size |
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for results in results_list: |
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assert isinstance(results, InstanceData) |
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assert len(results) == 0 |
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assert torch.allclose(results.bboxes, torch.zeros(0, 4, device=device)) |
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results_list = empty_instances( |
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batch_img_metas, |
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device, |
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task_type='mask', |
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instance_results=results_list, |
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mask_thr_binary=0.5) |
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assert len(results_list) == batch_size |
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for results in results_list: |
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assert isinstance(results, InstanceData) |
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assert len(results) == 0 |
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assert results.masks.shape == (0, 10, 12) |
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with pytest.raises(AssertionError): |
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empty_instances( |
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batch_img_metas, |
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device, |
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task_type='mask', |
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instance_results=[results_list[0]] * 3) |
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def test_filter_gt_instances(): |
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packed_inputs = demo_mm_inputs()['data_samples'] |
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score_thr = 0.7 |
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with pytest.raises(AssertionError): |
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filter_gt_instances(packed_inputs, score_thr=score_thr) |
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for inputs in packed_inputs: |
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inputs.gt_instances.scores = torch.ones_like( |
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inputs.gt_instances.labels).float() |
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filtered_packed_inputs = filter_gt_instances( |
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copy.deepcopy(packed_inputs), score_thr=score_thr) |
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for filtered_inputs, inputs in zip(filtered_packed_inputs, packed_inputs): |
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assert len(filtered_inputs.gt_instances) == len(inputs.gt_instances) |
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for inputs in packed_inputs: |
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inputs.gt_instances.scores = torch.zeros_like( |
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inputs.gt_instances.labels).float() |
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filtered_packed_inputs = filter_gt_instances( |
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copy.deepcopy(packed_inputs), score_thr=score_thr) |
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for filtered_inputs in filtered_packed_inputs: |
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assert len(filtered_inputs.gt_instances) == 0 |
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packed_inputs = demo_mm_inputs()['data_samples'] |
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wh_thr = (0, 0) |
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filtered_packed_inputs = filter_gt_instances( |
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copy.deepcopy(packed_inputs), wh_thr=wh_thr) |
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for filtered_inputs, inputs in zip(filtered_packed_inputs, packed_inputs): |
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assert len(filtered_inputs.gt_instances) == len(inputs.gt_instances) |
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for inputs in packed_inputs: |
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img_shape = inputs.img_shape |
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wh_thr = (max(wh_thr[0], img_shape[0]), max(wh_thr[1], img_shape[1])) |
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filtered_packed_inputs = filter_gt_instances( |
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copy.deepcopy(packed_inputs), wh_thr=wh_thr) |
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for filtered_inputs in filtered_packed_inputs: |
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assert len(filtered_inputs.gt_instances) == 0 |
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def test_rename_loss_dict(): |
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prefix = 'sup_' |
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losses = {'cls_loss': torch.tensor(2.), 'reg_loss': torch.tensor(1.)} |
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sup_losses = rename_loss_dict(prefix, losses) |
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for name in losses.keys(): |
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assert sup_losses[prefix + name] == losses[name] |
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def test_reweight_loss_dict(): |
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weight = 4 |
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losses = {'cls_loss': torch.tensor(2.), 'reg_loss': torch.tensor(1.)} |
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weighted_losses = reweight_loss_dict(copy.deepcopy(losses), weight) |
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for name in losses.keys(): |
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assert weighted_losses[name] == losses[name] * weight |
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