add cpu rendder, see README.md

Author: walkoncross

README.md

@@ -94,16 +94,31 @@ conda env create -f environment.yml
 source activate deep3d_pytorch
 ```
 
-2. Install Nvdiffrast library (Only needed for training and testing with rendering/visualization):
-```
-git clone -b 0.3.0 https://github.com/NVlabs/nvdiffrast
-cd nvdiffrast    # ./Deep3DFaceRecon_pytorch/nvdiffrast
-pip install .
-```
+2. Install mesh renderer:
+   1.  Nvdiffrast library (necessary for training, optional for testing):
+    ```
+    git clone -b 0.3.0 https://github.com/NVlabs/nvdiffrast
+    cd nvdiffrast    # ./Deep3DFaceRecon_pytorch/nvdiffrast
+    pip install .
+    cd ..    # ./Deep3DFaceRecon_pytorch
+    ```
+   2.  Use a cpu renderer from 3DDFA-V3 instead for testing (which can work on MacOS):
+    ```
+    git clone --depth=1 https://github.com/wang-zidu/3DDFA-V3
+    cp 3DDFA-V3/utils/cpu_renderer.py ./utils/
+    cp -r 3DDFA-V3/utils/cython_renderer ./utils/
+
+    pip install Cython
+
+    cd util/cython_renderer/
+    python setup.py build_ext -i
+    cd ../..     # ./Deep3DFaceRecon_pytorch
+    ```
+    3. Skip this step for inference/test, but you need run test.py with "--renderer_type none --no_viz" options
+
 
 3. Install Arcface Pytorch:
 ```
-cd ..    # ./Deep3DFaceRecon_pytorch
 git clone https://github.com/deepinsight/insightface.git
 cp -r ./insightface/recognition/arcface_torch ./models/
 ```
@@ -183,19 +198,29 @@ On **MacOS**, you can run the test script with CPU or Apple Silicon (M1, M2, M3
 run with MPS:
 ```
 # get reconstruction results of your custom images
-python test.py --name=<model_name> --epoch=20 --img_folder=<folder_to_test_images> --renderer_type none --device cpu --no_viz
+python test.py --name=<model_name> --epoch=20 --img_folder=<folder_to_test_images> --renderer_type none --device mps
+
+# no visualization
+python test.py --name=<model_name> --epoch=20 --img_folder=<folder_to_test_images> --renderer_type none --device mps --no_viz
 
 # get reconstruction results of example images
-python test.py --name=<model_name> --epoch=20 --img_folder=./datasets/examples --renderer_type none --device cpu --no_viz
+python test.py --name=<model_name> --epoch=20 --img_folder=./datasets/examples --renderer_type none --device mps
+
+# no visualization
+python test.py --name=<model_name> --epoch=20 --img_folder=./datasets/examples --renderer_type none --device mps --no_viz
 ```
 
 or run with CPU:
 ```
 # get reconstruction results of your custom images
-python test.py --name=<model_name> --epoch=20 --img_folder=<folder_to_test_images> --renderer_type none --device mps --no_viz
+python test.py --name=<model_name> --epoch=20 --img_folder=<folder_to_test_images> --renderer_type none --device cpu
+
+python test.py --name=<model_name> --epoch=20 --img_folder=<folder_to_test_images> --renderer_type none --device cpu --no_viz
 
 # get reconstruction results of example images
-python test.py --name=<model_name> --epoch=20 --img_folder=./datasets/examples --renderer_type none --device mps --no_viz
+python test.py --name=<model_name> --epoch=20 --img_folder=./datasets/examples --renderer_type none --device cpu
+
+python test.py --name=<model_name> --epoch=20 --img_folder=./datasets/examples --renderer_type none --device cpu --no_viz
 ```
 
 **_Following [#108](https://github.com/sicxu/Deep3DFaceRecon_pytorch/issues/108), if you don't have OpenGL environment, you can simply add "--use_opengl False" to use CUDA context. Make sure you have updated the nvdiffrast to the latest version._**

models/facerecon_model.py

@@ -88,7 +88,7 @@ def __init__(self, opt):
 
         self.parallel_names = self.model_names
 
-        if opt.renderer_type != 'none':
+        if opt.renderer_type == "nvdiffrast":
             self.parallel_names.append('renderer')
 
         self.net_recon = networks.define_net_recon(
@@ -107,11 +107,20 @@ def __init__(self, opt):
             self.renderer = MeshRenderer(
                 rasterize_fov=fov, znear=opt.z_near, zfar=opt.z_far, rasterize_size=int(2 * opt.center), use_opengl=opt.use_opengl
             )
+        elif opt.renderer_type == "face3d" or opt.renderer_type == "cpu":
+            from util.cpu_renderer import MeshRenderer_cpu as MeshRenderer
+
+            fov = 2 * np.arctan(opt.center / opt.focal) * 180 / np.pi
+            self.renderer = MeshRenderer(
+                rasterize_fov=fov, znear=opt.z_near, zfar=opt.z_far, rasterize_size=int(2 * opt.center)
+            )
         else:
             self.renderer = None
 
+        self.output_vis = None
+
         if self.isTrain:
-            assert self.renderer is not None, f"{self.cls.__name__} should be initialized with opt.renderer_type='nvdiffrast'"
+            assert self.renderer_type == "nvdiffrast", f"{self.cls.__name__} should be initialized with opt.renderer_type='nvdiffrast'"
 
             self.loss_names = ['all', 'feat', 'color', 'lm', 'reg', 'gamma', 'reflc']
 
@@ -159,7 +168,7 @@ def forward(self, do_render=True):
 
         if do_render:
             self.pred_mask, _, self.pred_face = self.renderer(
-                self.pred_vertex, self.facemodel.face_buf, feat=self.pred_color)
+                self.pred_vertex, self.facemodel.face_buf, feat=self.pred_color)[:3]
 
         self.pred_coeffs_dict = self.facemodel.split_coeff(output_coeff)
 

options/base_options.py

@@ -43,7 +43,7 @@ def initialize(self, parser):
         parser.add_argument('--model', type=str, default='facerecon', help='chooses which model to use.')
 
         # renderer parameters
-        parser.add_argument('--renderer_type', type=str, default='nvdiffrast', help='chooses which renderer to use. [nvdiffrast | none]')
+        parser.add_argument('--renderer_type', type=str, default='nvdiffrast', help='chooses which renderer to use. [nvdiffrast | face3d | cpu | none], cpu=face3d')
 
         # additional parameters
         parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')