More robust I/O tests, sample meshes, bug fixes and changes to USD importers

docs/notes/installation.rst

@@ -152,7 +152,7 @@ For an exhaustive check, install testing dependencies and run tests as follows:
     $ pip install -r tools/ci_requirements.txt
     $ export CI='true' # on Linux
     $ set CI='true' # on Windows
-    $ pytest tests/python/
+    $ pytest --import-mode=importlib -s tests/python/
 
 .. Note::
     These tests rely on CUDA operations and will fail if you installed on CPU only, where not all functionality is available.

kaolin/io/usd/utils.py

@@ -56,8 +56,8 @@ def get_scene_paths(file_path_or_stage, scene_path_regex=None, prim_types=None,
             Path to usd file (\*.usd, \*.usda) or :class:`Usd.Stage`.
         scene_path_regex (str, optional):
             Optional regular expression used to select returned scene paths.
-        prim_types (list of str, optional):
-            Optional list of valid USD Prim types used to select scene paths.
+        prim_types (list of str, str, optional):
+            Optional list of valid USD Prim types used to select scene paths, or a single USD Prim type string.
         conditional (function path: Bool): Custom conditionals to check
 
     Returns:
@@ -78,6 +78,8 @@ def get_scene_paths(file_path_or_stage, scene_path_regex=None, prim_types=None,
     if scene_path_regex is None:
         scene_path_regex = '.*'
     if prim_types is not None:
+        if isinstance(prim_types, str):
+            prim_types = [prim_types]
         prim_types = [pt.lower() for pt in prim_types]
 
     scene_paths = []

kaolin/utils/testing.py

@@ -13,8 +13,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import functools
+import copy
 import collections
+import functools
+import logging
 import numpy as np
 import torch
 
@@ -68,6 +70,10 @@ def check_tensor(tensor, shape=None, dtype=None, device=None, throw=True):
             if a dimension is set at ``None`` then it's not verified.
         dtype (torch.dtype, optional): the expected dtype.
         device (torch.device, optional): the expected device.
+        throw (bool): if true (default), will throw if checks fail
+
+    Return:
+        (bool) True if checks pass
     """
     if shape is not None:
         if len(shape) != tensor.ndim:
@@ -117,7 +123,7 @@ def check_packed_tensor(tensor, total_numel=None, last_dim=None, dtype=None, dev
         return False
     return True
 
-def check_padded_tensor(tensor, padding_value=None, shape_per_tensor=None, 
+def check_padded_tensor(tensor, padding_value=None, shape_per_tensor=None,
                         batch_size=None, max_shape=None, last_dim=None,
                         dtype=None, device=None, throw=True):
     """Check if :ref:`padded tensor<padded>` is valid given set of criteria.
@@ -135,7 +141,7 @@ def check_padded_tensor(tensor, padding_value=None, shape_per_tensor=None,
 
     Return:
         (bool): status of the check.
-    """ 
+    """
     if not check_tensor(tensor, dtype=dtype, device=device, throw=throw):
         return False
     if shape_per_tensor is not None:
@@ -274,48 +280,112 @@ def _get_details_str():
              (_get_stats_str() if print_stats else ''),
              (_get_details_str() if detailed else '')))
 
-def contained_torch_equal(elem, other):
-    """Check for equality of two objects potentially containing tensors.
+def contained_torch_equal(elem, other, approximate=False, **allclose_args):
+    """Check for equality (or allclose if approximate) of two objects potentially containing tensors.
 
     :func:`torch.equal` do not support data structure like dictionary / arrays
     and `==` is ambiguous on :class:`torch.Tensor`.
     This class will try to apply recursion through :class:`collections.abc.Mapping`,
     :class:`collections.abc.Sequence`, :func:`torch.equal` if the objects are `torch.Tensor`,
     of else `==` operator.
-    
+
     Args:
-        elem (object): The first object
-        other (object): The other object to compare to ``elem``
+        elem (object, dict, list, tuple): The first object
+        other (object, dict, list, tuple): The other object to compare to ``elem``
+        approximate (bool): if requested will use allclose for comparison instead (default=False)
+        allclose_args: arguments to `torch.allclose` if approximate comparison requested
 
     Return (bool): the comparison result
     """
     elem_type = type(elem)
     if elem_type != type(other):
         return False
 
+    def _tensor_compare(a, b):
+        if not approximate:
+            return torch.equal(a, b)
+        else:
+            return torch.allclose(a, b, **allclose_args)
+
+    def _number_compare(a, b):
+        return _tensor_compare(torch.tensor([a]), torch.tensor([b]))
+
+    recursive_args = copy.copy(allclose_args)
+    recursive_args['approximate'] = approximate
+
     if isinstance(elem, torch.Tensor):
-        return torch.equal(elem, other)
+        return _tensor_compare(elem, other)
     elif isinstance(elem, str):
         return elem == other
+    elif isinstance(elem, float):
+        return _number_compare(elem, other)
     elif isinstance(elem, collections.abc.Mapping):
         if elem.keys() != other.keys():
             return False
-        return all(contained_torch_equal(elem[key], other[key]) for key in elem)
+        return all(contained_torch_equal(elem[key], other[key], **recursive_args) for key in elem)
     elif isinstance(elem, tuple) and hasattr(elem, '_fields'):  # namedtuple
-        if set(elem._fields()) != set(other._fields()):
+        if set(elem._fields) != set(other._fields):
             return False
         return all(contained_torch_equal(
-            getattr(elem, f), getattr(other, f)) for f in elem._fields()
+            getattr(elem, f), getattr(other, f), **recursive_args) for f in elem._fields
         )
     elif isinstance(elem, collections.abc.Sequence):
         if len(elem) != len(other):
             return False
-        return all(contained_torch_equal(a, b) for a, b in zip(elem, other))
+        return all(contained_torch_equal(a, b, **recursive_args) for a, b in zip(elem, other))
     else:
         return elem == other
 
 def check_allclose(tensor, other, rtol=1e-5, atol=1e-8, equal_nan=False):
-    if not torch.allclose(tensor, other, atol, rtol, equal_nan):
-        diff_idx = torch.where(~torch.isclose(tensor, other, atol, rtol, equal_nan))
+    if not torch.allclose(tensor, other, atol=atol, rtol=rtol, equal_nan=equal_nan):
+        diff_idx = torch.where(~torch.isclose(tensor, other, atol=atol, rtol=rtol, equal_nan=equal_nan))
         raise ValueError(f"Tensors are not close on indices {diff_idx}:",
                          f"Example values: {tensor[diff_idx][:10]} vs {other[diff_idx][:10]}.")
+
+def check_tensor_attribute_shapes(container, throw=True, **attribute_info):
+    """Checks shape on all specified attributes of the container.
+
+    Args:
+        container (dict, tuple, object): container with named attributes to be tested
+        throw (bool): if true (default), will throw error on first check that fails
+        attribute_info: named attribute=shape values, where shape can be list or tuple (see `check_tensor`)
+
+    Return:
+        (bool) True if checks pass
+    """
+    def _get_item(container, attr):
+        if isinstance(container, collections.abc.Mapping):
+            return container[attr]
+        else:
+            return getattr(container, attr)
+
+    success = True
+    for k, shape in attribute_info.items():
+        val = _get_item(container, k)
+        if not check_tensor(val, shape=shape, throw=False):
+            success = False
+            message = f'Attribute {k} has shape {val.shape} (expected {shape})'
+            if throw:
+                raise ValueError(message)
+            else:
+                logging.error(message)
+    return success
+
+
+def print_namedtuple_attributes(ntuple, name='', **tensor_info_args):
+    print_dict_attributes(ntuple._asdict(), name=name, **tensor_info_args)
+
+
+def print_dict_attributes(in_dict, name='', **tensor_info_args):
+    name_info = '' if len(name) == 0 else f' of {name}'
+    print(f'\nAttributes{name_info}:')
+    for k, v in in_dict.items():
+        if torch.is_tensor(v):
+            tinfo = tensor_info(v, **tensor_info_args)
+        elif isinstance(v, (str, int, float)):
+            tinfo = v
+        elif isinstance(v, collections.abc.Sequence):
+            tinfo = f'{type(v)} of length {len(v)}'
+        else:
+            tinfo = type(v)
+        print(f'   {k}: {tinfo}')

sample_data/meshes/README.md

@@ -0,0 +1,89 @@
+# Sample Meshes
+
+This directory contains sample meshes that might be useful across the project, in both
+tests and examples. These are simple test cases for stress-testing and prototyping 
+and are not meant to be high-quality 3D examples.
+
+## Flat-shaded Ico Sphere
+
+**Filenames**: [ico_flat.obj](ico_flat.obj), [ico_flat.usda](ico_flat.usda).
+
+**Source**: this mesh is an ico sphere that was authored in Blender with flat shading.
+
+**Formats**: was exported as both `obj` and `usda` (ascii) from Blender.
+
+**Sanity checks**: displays correctly as `obj` and `usda` imported into Blender3.1 and as `usda` imported into [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse/). 
+
+<img width="30%" style="padding:0 20px 0 0; float: left" src="renders/ico_flat.jpg">
+
+**Attributes** of the single mesh in this file:
+* Vertices: 42
+* Faces: 80 triangles
+* Materials: one simple material applied to all faces
+* Normals: for flat shading (same normal for all vertices of a face)
+* UVs
+
+<div style="clear:both"></div>
+
+## Smooth-shaded Ico Sphere
+
+**Filenames**: [ico_smooth.obj](ico_smooth.obj), [ico_smooth.usda](ico_smooth.usda). 
+
+**Source**: this mesh is an ico sphere that was authored in Blender with smooth shading.
+
+**Formats**: was exported as both `obj` and `usda` (ascii) from Blender.
+
+**Sanity checks**: displays correctly as `obj` and `usda` imported into Blender3.1 and as `usda` imported into [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse/). 
+
+<img width="30%" style="padding:0 20px 0 0; float: left" src="renders/ico_smooth.jpg">
+
+**Attributes** of the single mesh in this file:
+* Vertices: 42
+* Faces: 80 triangles
+* Materials: one simple material applied to all faces
+* Normals: for smooth shading
+* UVs
+
+<div style="clear:both"></div>
+
+## Textured Fox
+
+**Filenames:** [fox.obj](fox.obj), [fox.usdc](fox.usdc).
+
+**Source:** this file is a simiplified version of TurboSquid ID 1073771, which NVIDIA has licensed with distribution rights. *By using this TurboSquid model, you agree that you will only use the content for research purposes only. You may not redistribute this model.*
+
+**Formats**: the original format is `obj`, which was converted to `usdc` (binary) using Blender exporter.
+
+**Sanity checks**: displays correctly as `obj` and `usdc` imported into Blender3.1 and as `usdc` imported into [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse/). 
+
+<img width="30%" style="padding:0 20px 0 0; float: left" src="renders/fox.jpg">
+
+**Attributes** of the single mesh in this file:
+* Vertices: 5002
+* Faces: 10000 triangles
+* Materials: one simple material with texture applied to all faces
+* Normals: for smooth shading
+* UVs.
+
+<div style="clear:both"></div>
+
+## Multi-Material Pizza
+
+**Filenames:** [pizza.obj](pizza.obj), [pizza.usda](pizza.usda).
+
+**Source:** this file was authored in Blender with pizza texture taken from a [royalty-free photo by Rene Strgar](https://www.pexels.com/photo/italian-style-pizza-13814644/).
+
+**Formats**: was exported as both `obj` and `usda` (ascii) from Blender.
+
+**Sanity checks**: displays correctly as `obj` (not usd) imported into Blender3.1 and as `usda` imported into [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse/). 
+
+<img width="30%" style="padding:0 20px 0 0; float: left" src="renders/pizza.jpg">
+
+**Attributes** of the single mesh in this file:
+* Vertices: 482
+* Faces: 960 triangles
+* Materials: one simple material and one texture material applied to groups of faces
+* Normals: for smooth shading
+* UVs
+
+<div style="clear:both"></div>

sample_data/meshes/fox.mtl

@@ -0,0 +1,13 @@
+# Blender MTL File: 'None'
+# Material Count: 1
+
+newmtl material_0
+Ns 0.000000
+Ka 1.000000 1.000000 1.000000
+Kd 0.800000 0.800000 0.800000
+Ks 1.000000 1.000000 1.000000
+Ke 0.000000 0.000000 0.000000
+Ni 1.450000
+d 1.000000
+illum 2
+map_Kd textures/fox.jpg