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How to use fake_outputs method in avocado

Best Python code snippet using avocado_python

test_bottom_up_eval.py

Source:test_bottom_up_eval.py

1# Copyright (c) OpenMMLab. All rights reserved.2import numpy as np3import torch4from mmpose.core import (aggregate_scale, aggregate_stage_flip,5                         flip_feature_maps, get_group_preds, split_ae_outputs)6def test_split_ae_outputs():7    fake_outputs = [torch.zeros((1, 4, 2, 2))]8    heatmaps, tags = split_ae_outputs(9        fake_outputs,10        num_joints=4,11        with_heatmaps=[False],12        with_ae=[True],13        select_output_index=[0])14def test_flip_feature_maps():15    fake_outputs = [torch.zeros((1, 4, 2, 2))]16    _ = flip_feature_maps(fake_outputs, None)17    _ = flip_feature_maps(fake_outputs, flip_index=[1, 0])18def test_aggregate_stage_flip():19    fake_outputs = [torch.zeros((1, 4, 2, 2))]20    fake_flip_outputs = [torch.ones((1, 4, 2, 2))]21    output = aggregate_stage_flip(22        fake_outputs,23        fake_flip_outputs,24        index=-1,25        project2image=True,26        size_projected=(4, 4),27        align_corners=False,28        aggregate_stage='concat',29        aggregate_flip='average')30    assert isinstance(output, list)31    output = aggregate_stage_flip(32        fake_outputs,33        fake_flip_outputs,34        index=-1,35        project2image=True,36        size_projected=(4, 4),37        align_corners=False,38        aggregate_stage='average',39        aggregate_flip='average')40    assert isinstance(output, list)41    output = aggregate_stage_flip(42        fake_outputs,43        fake_flip_outputs,44        index=-1,45        project2image=True,46        size_projected=(4, 4),47        align_corners=False,48        aggregate_stage='average',49        aggregate_flip='concat')50    assert isinstance(output, list)51    output = aggregate_stage_flip(52        fake_outputs,53        fake_flip_outputs,54        index=-1,55        project2image=True,56        size_projected=(4, 4),57        align_corners=False,58        aggregate_stage='concat',59        aggregate_flip='concat')60    assert isinstance(output, list)61def test_aggregate_scale():62    fake_outputs = [torch.zeros((1, 4, 2, 2)), torch.zeros((1, 4, 2, 2))]63    output = aggregate_scale(64        fake_outputs, align_corners=False, aggregate_scale='average')65    assert isinstance(output, torch.Tensor)66    assert output.shape == fake_outputs[0].shape67    output = aggregate_scale(68        fake_outputs, align_corners=False, aggregate_scale='unsqueeze_concat')69    assert isinstance(output, torch.Tensor)70    assert len(output.shape) == len(fake_outputs[0].shape) + 171def test_get_group_preds():72    fake_grouped_joints = [np.array([[[0, 0], [1, 1]]])]73    results = get_group_preds(74        fake_grouped_joints,75        center=np.array([0, 0]),76        scale=np.array([1, 1]),77        heatmap_size=np.array([2, 2]))78    assert not results == []79    results = get_group_preds(80        fake_grouped_joints,81        center=np.array([0, 0]),82        scale=np.array([1, 1]),83        heatmap_size=np.array([2, 2]),84        use_udp=True)...

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