[Fix] Fix potential circular import in core, data and utils, disconnect unnecessary pytorch3d dependency

demo/estimate_smpl.py

@@ -15,9 +15,8 @@
     inference_video_based_model,
     init_model,
 )
-from mmhuman3d.core.visualization import visualize_smpl_hmr
+from mmhuman3d.core.visualization.visualize_smpl import visualize_smpl_hmr
 from mmhuman3d.data.data_structures.human_data import HumanData
-from mmhuman3d.utils import array_to_images
 from mmhuman3d.utils.demo_utils import (
     extract_feature_sequence,
     get_speed_up_interval,
@@ -28,6 +27,7 @@
     speed_up_interpolate,
     speed_up_process,
 )
+from mmhuman3d.utils.ffmpeg_utils import array_to_images
 from mmhuman3d.utils.transforms import rotmat_to_aa
 
 try:

docs/conf.py

@@ -45,9 +45,8 @@ def get_version():
 ]
 
 autodoc_mock_imports = [
-    'mmhuman3d.version',
-    'mmhuman3d.core.visualization.renderer.torch3d_renderer', 'mmcv.ops',
-    'pytorch3d'
+    'mmhuman3d.version', 'mmhuman3d.core.renderer.torch3d_renderer',
+    'mmcv.ops', 'pytorch3d'
 ]
 
 # Ignore >>> when copying code

docs/render.md

@@ -37,7 +37,7 @@ renderer = MeshRenderer(
     shader=SoftPhongShader(device=device, cameras=cameras, lights=lights))
 
 ### initialized by mmhuman3d
-from mmhuman3d.core.visualization.renderer import MeshRenderer
+from mmhuman3d.core.renderer.torch3d_renderer.builder import MeshRenderer
 # rasterizer could be passed by nn.Module or dict
 rasterizer = dict(
     image_size=128,
@@ -60,7 +60,7 @@ shader = dict(type='SoftPhongShader')
 These two methods are equal.
 ```python
 import torch.nn as nn
-from mmhuman3d.core.visualization.renderer import MeshRenderer, build_renderer
+from mmhuman3d.core.renderer.torch3d_renderer.builder import MeshRenderer, build_renderer
 
 renderer = MeshRenderer(shader=shader, device=device, rasterizer=rasterizer, resolution=resolution)
 renderer = build_renderer(dict(type='mesh', device=device, shader=shader, rasterizer=rasterizer, resolution=resolution))
@@ -77,7 +77,7 @@ We provide `tensor2rgba` function for visualization, the returned tensor will be
  The operation is simple:
  ```python
  import torch
- from mmhuman3d.core.visualization.renderer import build_renderer
+ from mmhuman3d.core.renderer.torch3d_renderer.builder import build_renderer
 
  renderer = build_renderer(dict(type='mesh', device=device, resolution=resolution))
  rendered_tensor = renderer(meshes=meshes, cameras=cameras, lights=lights)
@@ -107,7 +107,7 @@ You could pass your data by `render_runner` to render a series batch of render.
 
 ```python
 import torch
-from mmhuman3d.core.visualization.renderer import render_runner
+from mmhuman3d.core.renderer.torch3d_renderer import render_runner
 
 render_data = dict(cameras=cameras, lights=lights, meshes=meshes, backgrounds=backgrounds)
 # no_grad=True for non-differentiable render
@@ -130,7 +130,7 @@ Warp a gray texture image to smpl_mesh.
 import torch
 from mmhuman3d.models.body_models.builder import build_body_model
 from pytorch3d.structures import meshes
-from mmhuman3d.core.visualization.renderer import build_renderer
+from mmhuman3d.core.renderer.torch3d_renderer.builder import build_renderer
 body_model = build_body_model(dict(type='smpl', model_path=model_path)).to(device)
 pose_dict = body_model.tensor2dict(torch.zeros(1, 72))
 verts = body_model(**pose_dict)['vertices']

docs/visualize_smpl.md

@@ -3,7 +3,7 @@
 
     You have smpl pose tensor or array shape of which is (frame, 72)
     ```python
-    from mmhuman3d.core.visualization import visualize_smpl_pose
+    from mmhuman3d.core.visualization.visualize_smpl import visualize_smpl_pose
     body_model_config = dict(
         type='smpl', model_path=model_path)
     visualize_smpl_pose(
@@ -29,7 +29,7 @@
     If you want to visualize a T-pose smpl or your poses do not have global_orient, you can do:
     ```python
     import torch
-    from mmhuman3d.core.visualization import visualize_T_pose
+    from mmhuman3d.core.visualization.visualize_smpl import visualize_T_pose
     body_model_config = dict(
         type='smpl', model_path=model_path)
     visualize_T_pose(
@@ -45,7 +45,7 @@
     E.g., we use vibe sample_video.mp4 as an example.
     ```python
     import pickle
-    from mmhuman3d.core.visualization import visualize_smpl_vibe
+    from mmhuman3d.core.visualization.visualize_smpl import visualize_smpl_vibe
     with open('vibe_output.pkl', 'rb') as f:
         d = pickle.load(f, encoding='latin1')
     poses = d[1]['pose']
@@ -86,7 +86,7 @@
     E.g., we use vibe sample_video.mp4 as an example.
     ```python
     import pickle
-    from mmhuman3d.core.visualization import visualize_smpl_hmr
+    from mmhuman3d.core.visualization.visualize_smpl import visualize_smpl_hmr
     gender = 'female'
     focal_length = 5000
     det_width = 224
@@ -142,7 +142,7 @@
 - **visualize smpl with opencv camera:**
     You should pass the opencv defined intrinsic matrix K and extrinsic matrix R, T.
     ```python
-    from mmhuman3d.core.visualization import visualize_smpl_calibration
+    from mmhuman3d.core.visualization.visualize_smpl import visualize_smpl_calibration
     body_model_config = dict(
         type='smpl', model_path=model_path, gender=gender)
     visualize_smpl_calibration(

mmhuman3d/apis/train.py

@@ -15,7 +15,7 @@
 from mmhuman3d.core.evaluation import DistEvalHook, EvalHook
 from mmhuman3d.core.optimizer import build_optimizers
 from mmhuman3d.data.datasets import build_dataloader, build_dataset
-from mmhuman3d.utils import get_root_logger
+from mmhuman3d.utils.logger import get_root_logger
 
 
 def set_random_seed(seed, deterministic=False):

mmhuman3d/core/visualization/renderer/matplotlib3d_renderer.py → mmhuman3d/core/renderer/matplotlib3d_renderer.py

@@ -13,7 +13,7 @@
 
 from mmhuman3d.core.conventions.cameras.convert_convention import \
     enc_camera_convention  # prevent yapf isort conflict
-from mmhuman3d.utils import get_different_colors
+from mmhuman3d.utils.demo_utils import get_different_colors
 from mmhuman3d.utils.ffmpeg_utils import images_to_video
 from mmhuman3d.utils.path_utils import check_path_suffix
 

mmhuman3d/core/visualization/renderer/torch3d_renderer/base_renderer.py → mmhuman3d/core/renderer/torch3d_renderer/base_renderer.py

@@ -20,12 +20,11 @@
 from mmhuman3d.core.cameras import MMCamerasBase
 from mmhuman3d.utils.ffmpeg_utils import images_to_gif, images_to_video
 from mmhuman3d.utils.path_utils import check_path_suffix
-from .builder import RENDERER, build_lights, build_shader
+from .lights import build_lights
+from .shader import build_shader
 from .utils import normalize, rgb2bgr, tensor2array
 
 
-@RENDERER.register_module(
-    name=['base', 'Base', 'base_renderer', 'BaseRenderer'])
 class BaseRenderer(nn.Module):
 
     def __init__(self,

mmhuman3d/core/renderer/torch3d_renderer/builder.py

@@ -0,0 +1,48 @@
+from mmcv.utils import Registry
+
+from .base_renderer import BaseRenderer
+from .depth_renderer import DepthRenderer
+from .mesh_renderer import MeshRenderer
+from .normal_renderer import NormalRenderer
+from .pointcloud_renderer import PointCloudRenderer
+from .segmentation_renderer import SegmentationRenderer
+from .silhouette_renderer import SilhouetteRenderer
+from .uv_renderer import UVRenderer
+
+RENDERER = Registry('renderer')
+RENDERER.register_module(
+    name=['base', 'Base', 'base_renderer', 'BaseRenderer'],
+    module=BaseRenderer)
+RENDERER.register_module(
+    name=['Depth', 'depth', 'depth_renderer', 'DepthRenderer'],
+    module=DepthRenderer)
+RENDERER.register_module(
+    name=['Mesh', 'mesh', 'mesh_renderer', 'MeshRenderer'],
+    module=MeshRenderer)
+RENDERER.register_module(
+    name=['Normal', 'normal', 'normal_renderer', 'NormalRenderer'],
+    module=NormalRenderer)
+RENDERER.register_module(
+    name=[
+        'PointCloud', 'pointcloud', 'point_cloud', 'pointcloud_renderer',
+        'PointCloudRenderer'
+    ],
+    module=PointCloudRenderer)
+RENDERER.register_module(
+    name=[
+        'segmentation', 'segmentation_renderer', 'Segmentation',
+        'SegmentationRenderer'
+    ],
+    module=SegmentationRenderer)
+RENDERER.register_module(
+    name=[
+        'silhouette', 'silhouette_renderer', 'Silhouette', 'SilhouetteRenderer'
+    ],
+    module=SilhouetteRenderer)
+RENDERER.register_module(
+    name=['uv_renderer', 'uv', 'UV', 'UVRenderer'], module=UVRenderer)
+
+
+def build_renderer(cfg):
+    """Build renderers."""
+    return RENDERER.build(cfg)

mmhuman3d/core/visualization/renderer/torch3d_renderer/depth_renderer.py → mmhuman3d/core/renderer/torch3d_renderer/depth_renderer.py

@@ -5,12 +5,10 @@
 
 from mmhuman3d.core.cameras import MMCamerasBase
 from .base_renderer import BaseRenderer
-from .builder import RENDERER, build_shader
+from .shader import build_shader
 from .utils import normalize
 
 
-@RENDERER.register_module(
-    name=['Depth', 'depth', 'depth_renderer', 'DepthRenderer'])
 class DepthRenderer(BaseRenderer):
     """Render depth map with the help of camera system."""
     shader_type = 'DepthShader'

mmhuman3d/core/renderer/torch3d_renderer/lights/__init__.py

@@ -0,0 +1,10 @@
+# yapf: disable
+from .builder import (  # noqa: F401
+    AmbientLights,
+    DirectionalLights,
+    PointLights,
+    build_lights,
+)
+from .lights import MMLights  # noqa: F401
+
+# yapf: enable

mmhuman3d/core/renderer/torch3d_renderer/lights/builder.py

@@ -0,0 +1,17 @@
+from mmcv.utils import Registry
+
+from .lights import AmbientLights, DirectionalLights, PointLights  # noqa:E401
+
+LIGHTS = Registry('lights')
+LIGHTS.register_module(
+    name=['directional', 'directional_lights', 'DirectionalLights'],
+    module=DirectionalLights)
+LIGHTS.register_module(
+    name=['point', 'point_lights', 'PointLights'], module=PointLights)
+LIGHTS.register_module(
+    name=['ambient', 'ambient_lights', 'AmbientLights'], module=AmbientLights)
+
+
+def build_lights(cfg):
+    """Build lights."""
+    return LIGHTS.build(cfg)

mmhuman3d/core/visualization/renderer/torch3d_renderer/mesh_renderer.py → mmhuman3d/core/renderer/torch3d_renderer/mesh_renderer.py

@@ -4,14 +4,10 @@
 from pytorch3d.structures import Meshes
 
 from mmhuman3d.core.cameras import MMCamerasBase
-from mmhuman3d.core.visualization.renderer.torch3d_renderer.lights import \
-    MMLights  # noqa: E501
 from .base_renderer import BaseRenderer
-from .builder import RENDERER
+from .lights import MMLights
 
 
-@RENDERER.register_module(
-    name=['Mesh', 'mesh', 'mesh_renderer', 'MeshRenderer'])
 class MeshRenderer(BaseRenderer):
     """Render RGBA image with the help of camera system."""
     shader_type = 'SoftPhongShader'

mmhuman3d/core/visualization/renderer/torch3d_renderer/meshes.py → mmhuman3d/core/renderer/torch3d_renderer/meshes.py

@@ -11,7 +11,7 @@
 from mmhuman3d.utils.mesh_utils import \
     join_meshes_as_batch as _join_meshes_as_batch
 from .builder import build_renderer
-from .textures import TexturesNearest
+from .textures.textures import TexturesNearest
 from .utils import align_input_to_padded
 
 

mmhuman3d/core/visualization/renderer/torch3d_renderer/normal_renderer.py → mmhuman3d/core/renderer/torch3d_renderer/normal_renderer.py

@@ -5,12 +5,9 @@
 
 from mmhuman3d.core.cameras import MMCamerasBase
 from .base_renderer import BaseRenderer
-from .builder import RENDERER
 from .utils import normalize
 
 
-@RENDERER.register_module(
-    name=['Normal', 'normal', 'normal_renderer', 'NormalRenderer'])
 class NormalRenderer(BaseRenderer):
     """Render normal map with the help of camera system."""
     shader_type = 'NormalShader'

mmhuman3d/core/visualization/renderer/torch3d_renderer/pointcloud_renderer.py → mmhuman3d/core/renderer/torch3d_renderer/pointcloud_renderer.py

@@ -13,13 +13,8 @@
 from mmhuman3d.core.cameras import MMCamerasBase
 from mmhuman3d.utils.mesh_utils import mesh_to_pointcloud_vc
 from .base_renderer import BaseRenderer
-from .builder import RENDERER
 
 
-@RENDERER.register_module(name=[
-    'PointCloud', 'pointcloud', 'point_cloud', 'pointcloud_renderer',
-    'PointCloudRenderer'
-])
 class PointCloudRenderer(BaseRenderer):
 
     def __init__(self,

mmhuman3d/core/visualization/renderer/torch3d_renderer/render_runner.py → mmhuman3d/core/renderer/torch3d_renderer/render_runner.py

@@ -13,8 +13,8 @@
 from mmhuman3d.core.cameras import MMCamerasBase
 from mmhuman3d.core.cameras.builder import build_cameras
 from .base_renderer import BaseRenderer
-from .builder import build_lights, build_renderer
-from .lights import AmbientLights, MMLights
+from .builder import build_renderer
+from .lights import AmbientLights, MMLights, build_lights
 
 osj = os.path.join
 

mmhuman3d/core/visualization/renderer/torch3d_renderer/segmentation_renderer.py → mmhuman3d/core/renderer/torch3d_renderer/segmentation_renderer.py

@@ -4,16 +4,11 @@
 from pytorch3d.structures import Meshes
 
 from mmhuman3d.core.cameras import MMCamerasBase
-from mmhuman3d.utils import get_different_colors
+from mmhuman3d.utils.demo_utils import get_different_colors
 from .base_renderer import BaseRenderer
-from .builder import RENDERER
 from .utils import normalize
 
 
-@RENDERER.register_module(name=[
-    'segmentation', 'segmentation_renderer', 'Segmentation',
-    'SegmentationRenderer'
-])
 class SegmentationRenderer(BaseRenderer):
     """Render segmentation map into a segmentation index tensor."""
     shader_type = 'SegmentationShader'

mmhuman3d/core/renderer/torch3d_renderer/shader/__init__.py

@@ -0,0 +1,16 @@
+# yapf: disable
+from .builder import (  # noqa: F401
+    DepthShader,
+    HardFlatShader,
+    HardGouraudShader,
+    HardPhongShader,
+    NoLightShader,
+    NormalShader,
+    SegmentationShader,
+    SilhouetteShader,
+    SoftGouraudShader,
+    SoftPhongShader,
+    build_shader,
+)
+
+# yapf: enable