Add at new repo again

vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/tests/common.py

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+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+
+import os
+import torch
+
+from detectron2.config import get_cfg
+from detectron2.engine import default_setup
+from detectron2.modeling import build_model
+
+from densepose import add_dataset_category_config, add_densepose_config
+
+_BASE_CONFIG_DIR = "configs"
+_EVOLUTION_CONFIG_SUB_DIR = "evolution"
+_QUICK_SCHEDULES_CONFIG_SUB_DIR = "quick_schedules"
+_BASE_CONFIG_FILE_PREFIX = "Base-"
+_CONFIG_FILE_EXT = ".yaml"
+
+
+def _get_base_config_dir():
+    """
+    Return the base directory for configurations
+    """
+    return os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", _BASE_CONFIG_DIR)
+
+
+def _get_evolution_config_dir():
+    """
+    Return the base directory for evolution configurations
+    """
+    return os.path.join(_get_base_config_dir(), _EVOLUTION_CONFIG_SUB_DIR)
+
+
+def _get_quick_schedules_config_dir():
+    """
+    Return the base directory for quick schedules configurations
+    """
+    return os.path.join(_get_base_config_dir(), _QUICK_SCHEDULES_CONFIG_SUB_DIR)
+
+
+def _collect_config_files(config_dir):
+    """
+    Collect all configuration files (i.e. densepose_*.yaml) directly in the specified directory
+    """
+    start = _get_base_config_dir()
+    results = []
+    for entry in os.listdir(config_dir):
+        path = os.path.join(config_dir, entry)
+        if not os.path.isfile(path):
+            continue
+        _, ext = os.path.splitext(entry)
+        if ext != _CONFIG_FILE_EXT:
+            continue
+        if entry.startswith(_BASE_CONFIG_FILE_PREFIX):
+            continue
+        config_file = os.path.relpath(path, start)
+        results.append(config_file)
+    return results
+
+
+def get_config_files():
+    """
+    Get all the configuration files (relative to the base configuration directory)
+    """
+    return _collect_config_files(_get_base_config_dir())
+
+
+def get_evolution_config_files():
+    """
+    Get all the evolution configuration files (relative to the base configuration directory)
+    """
+    return _collect_config_files(_get_evolution_config_dir())
+
+
+def get_quick_schedules_config_files():
+    """
+    Get all the quick schedules configuration files (relative to the base configuration directory)
+    """
+    return _collect_config_files(_get_quick_schedules_config_dir())
+
+
+def _get_model_config(config_file):
+    """
+    Load and return the configuration from the specified file (relative to the base configuration
+    directory)
+    """
+    cfg = get_cfg()
+    add_dataset_category_config(cfg)
+    add_densepose_config(cfg)
+    path = os.path.join(_get_base_config_dir(), config_file)
+    cfg.merge_from_file(path)
+    if not torch.cuda.is_available():
+        cfg.MODEL_DEVICE = "cpu"
+    return cfg
+
+
+def get_model(config_file):
+    """
+    Get the model from the specified file (relative to the base configuration directory)
+    """
+    cfg = _get_model_config(config_file)
+    return build_model(cfg)
+
+
+def setup(config_file):
+    """
+    Setup the configuration from the specified file (relative to the base configuration directory)
+    """
+    cfg = _get_model_config(config_file)
+    cfg.freeze()
+    default_setup(cfg, {})

vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/tests/test_model_e2e.py

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+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+
+import unittest
+import torch
+
+from detectron2.structures import BitMasks, Boxes, Instances
+
+from .common import get_model
+
+
+# TODO(plabatut): Modularize detectron2 tests and re-use
+def make_model_inputs(image, instances=None):
+    if instances is None:
+        return {"image": image}
+
+    return {"image": image, "instances": instances}
+
+
+def make_empty_instances(h, w):
+    instances = Instances((h, w))
+    instances.gt_boxes = Boxes(torch.rand(0, 4))
+    instances.gt_classes = torch.tensor([]).to(dtype=torch.int64)
+    instances.gt_masks = BitMasks(torch.rand(0, h, w))
+    return instances
+
+
+class ModelE2ETest(unittest.TestCase):
+    CONFIG_PATH = ""
+
+    def setUp(self):
+        self.model = get_model(self.CONFIG_PATH)
+
+    def _test_eval(self, sizes):
+        inputs = [make_model_inputs(torch.rand(3, size[0], size[1])) for size in sizes]
+        self.model.eval()
+        self.model(inputs)
+
+
+class DensePoseRCNNE2ETest(ModelE2ETest):
+    CONFIG_PATH = "densepose_rcnn_R_101_FPN_s1x.yaml"
+
+    def test_empty_data(self):
+        self._test_eval([(200, 250), (200, 249)])

vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/tests/test_setup.py

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+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+
+import unittest
+
+from .common import (
+    get_config_files,
+    get_evolution_config_files,
+    get_quick_schedules_config_files,
+    setup,
+)
+
+
+class TestSetup(unittest.TestCase):
+    def _test_setup(self, config_file):
+        setup(config_file)
+
+    def test_setup_configs(self):
+        config_files = get_config_files()
+        for config_file in config_files:
+            self._test_setup(config_file)
+
+    def test_setup_evolution_configs(self):
+        config_files = get_evolution_config_files()
+        for config_file in config_files:
+            self._test_setup(config_file)
+
+    def test_setup_quick_schedules_configs(self):
+        config_files = get_quick_schedules_config_files()
+        for config_file in config_files:
+            self._test_setup(config_file)

vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/tests/test_structures.py

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+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+
+import unittest
+
+from densepose.data.structures import normalized_coords_transform
+
+
+class TestStructures(unittest.TestCase):
+    def test_normalized_coords_transform(self):
+        bbox = (32, 24, 288, 216)
+        x0, y0, w, h = bbox
+        xmin, ymin, xmax, ymax = x0, y0, x0 + w, y0 + h
+        f = normalized_coords_transform(*bbox)
+        # Top-left
+        expected_p, actual_p = (-1, -1), f((xmin, ymin))
+        self.assertEqual(expected_p, actual_p)
+        # Top-right
+        expected_p, actual_p = (1, -1), f((xmax, ymin))
+        self.assertEqual(expected_p, actual_p)
+        # Bottom-left
+        expected_p, actual_p = (-1, 1), f((xmin, ymax))
+        self.assertEqual(expected_p, actual_p)
+        # Bottom-right
+        expected_p, actual_p = (1, 1), f((xmax, ymax))
+        self.assertEqual(expected_p, actual_p)