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https://github.com/facebookresearch/pytorch3d.git
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2edb93d184
Summary: Make method for SDF's use of object mask more general, so that a renderer can be given per-pixel values. Reviewed By: shapovalov Differential Revision: D35247412 fbshipit-source-id: 6aeccb1d0b5f1265a3f692a1453407a07e51a33c
107 lines
3.7 KiB
Python
107 lines
3.7 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import unittest
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import torch
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from pytorch3d.implicitron.models.base import GenericModel
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from pytorch3d.implicitron.models.renderer.base import EvaluationMode
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from pytorch3d.implicitron.tools.config import expand_args_fields, get_default_args
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from pytorch3d.renderer.cameras import look_at_view_transform, PerspectiveCameras
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class TestGenericModel(unittest.TestCase):
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def test_gm(self):
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# Simple test of a forward and backward pass of the default GenericModel.
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device = torch.device("cuda:1")
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expand_args_fields(GenericModel)
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model = GenericModel()
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model.to(device)
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n_train_cameras = 2
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R, T = look_at_view_transform(azim=torch.rand(n_train_cameras) * 360)
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cameras = PerspectiveCameras(R=R, T=T, device=device)
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# TODO: make these default to None?
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defaulted_args = {
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"fg_probability": None,
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"depth_map": None,
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"mask_crop": None,
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"sequence_name": None,
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}
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with self.assertWarnsRegex(UserWarning, "No main objective found"):
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model(
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camera=cameras,
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evaluation_mode=EvaluationMode.TRAINING,
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**defaulted_args,
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image_rgb=None,
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)
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target_image_rgb = torch.rand(
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(n_train_cameras, 3, model.render_image_height, model.render_image_width),
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device=device,
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)
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train_preds = model(
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camera=cameras,
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evaluation_mode=EvaluationMode.TRAINING,
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image_rgb=target_image_rgb,
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**defaulted_args,
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)
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self.assertGreater(train_preds["objective"].item(), 0)
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train_preds["objective"].backward()
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model.eval()
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with torch.no_grad():
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# TODO: perhaps this warning should be skipped in eval mode?
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with self.assertWarnsRegex(UserWarning, "No main objective found"):
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eval_preds = model(
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camera=cameras[0],
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**defaulted_args,
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image_rgb=None,
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)
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self.assertEqual(
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eval_preds["images_render"].shape,
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(1, 3, model.render_image_height, model.render_image_width),
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)
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def test_idr(self):
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# Forward pass of GenericModel with IDR.
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device = torch.device("cuda:1")
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args = get_default_args(GenericModel)
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args.renderer_class_type = "SignedDistanceFunctionRenderer"
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args.implicit_function_class_type = "IdrFeatureField"
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args.implicit_function_IdrFeatureField_args.n_harmonic_functions_xyz = 6
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model = GenericModel(**args)
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model.to(device)
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n_train_cameras = 2
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R, T = look_at_view_transform(azim=torch.rand(n_train_cameras) * 360)
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cameras = PerspectiveCameras(R=R, T=T, device=device)
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defaulted_args = {
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"depth_map": None,
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"mask_crop": None,
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"sequence_name": None,
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}
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target_image_rgb = torch.rand(
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(n_train_cameras, 3, model.render_image_height, model.render_image_width),
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device=device,
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)
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fg_probability = torch.rand(
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(n_train_cameras, 1, model.render_image_height, model.render_image_width),
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device=device,
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)
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train_preds = model(
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camera=cameras,
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evaluation_mode=EvaluationMode.TRAINING,
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image_rgb=target_image_rgb,
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fg_probability=fg_probability,
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**defaulted_args,
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)
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self.assertGreater(train_preds["objective"].item(), 0)
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