Rename smpl->smplpytorch.

.gitignore

@@ -3,8 +3,10 @@
 *_bak.py
 
 .cache/
+.egg-info
 __pycache__/
 build/
 dist/
 
-smpl/native/models/*.pkl
+image.png
+smplpytorch/native/models/*.pkl

README.md

@@ -7,18 +7,31 @@ It can be integrated into any architecture as a differentiable layer to predict
 The code is adapted from the [manopth](https://github.com/hassony2/manopth) repository by [Yana Hasson](https://github.com/hassony2).
 
 <p align="center">
-<img src="image.png" alt="smpl" width="300"/>
+<img src="assets/image.png" alt="smpl" width="300"/>
 </p>
 
-## Setting up
 
-* Dependencies:
-  * Install the dependencies listed in [environment.yml](environment.yml)
-    * In an existing conda environment, `conda env update -f environment.yml`
-    * In a new environment, `conda env create -f environment.yml`, will create a conda environment named `smplpytorch`
-* Download SMPL pickle files:
+## Setup
+
+### 1. The `smplpytorch` package
+* **Run without installing:** You will need to install the dependencies listed in [environment.yml](environment.yml):
+  * `conda env update -f environment.yml` in an existing environment, or
+  * `conda env create -f environment.yml`, for a new `smplpytorch` environment
+* **Install:** To import `SMPL_Layer` in another project with `from smplpytorch.pytorch.smpl_layer import SMPL_Layer` do one of the following.
+  * Option 1: This should automatically install the dependencies.
+    ``` bash
+    git clone https://github.com/gulvarol/smplpytorch.git
+    cd smplpytorch
+    pip install .
+    ```
+  * Option 2: You can install `smplpytorch` from [PyPI](https://pypi.org/project/smplpytorch/). Additionally, you might need to install [chumpy](https://github.com/hassony2/chumpy.git).
+    ``` bash
+    pip install smplpytorch
+    ```
+
+### 2. Download SMPL pickle files
   * Download the models from the [SMPL website](http://smpl.is.tue.mpg.de/) by choosing "SMPL for Python users". Note that you need to comply with the [SMPL model license](http://smpl.is.tue.mpg.de/license_model).
-  * Extract and copy the `models` folder into the `smpl/native/` folder.
+  * Extract and copy the `models` folder into the `smplpytorch/native/` folder (or set the `model_root` parameter accordingly).
 
 ## Demo
 

demo.py

@@ -1,6 +1,6 @@
 import torch
 
-from smpl.pytorch.smpl_layer import SMPL_Layer
+from smplpytorch.pytorch.smpl_layer import SMPL_Layer
 from display_utils import display_model
 
 
@@ -12,7 +12,7 @@ if __name__ == '__main__':
     smpl_layer = SMPL_Layer(
         center_idx=0,
         gender='neutral',
-        model_root='smpl/native/models')
+        model_root='smplpytorch/native/models')
 
     # Generate random pose and shape parameters
     pose_params = torch.rand(batch_size, 72) * 0.2

environment.yml

@@ -6,5 +6,6 @@ dependencies:
   - matplotlib
   - numpy
   - pytorch
+  - pip
   - pip:
     - git+https://github.com/hassony2/chumpy.git

setup.py

@@ -1,6 +1,5 @@
 import setuptools
 
-
 with open("README.md", "r") as fh:
     long_description = fh.read()
 
@@ -12,7 +11,7 @@ REQUIREMENTS = [
     "chumpy @ git+ssh://git@github.com/hassony2/chumpy"]
 
 setuptools.setup(
-    name="smpl-pytorch",
+    name="smplpytorch",
     version="0.0.1",
     author="Gul Varol",
     author_email="gulvarols@gmail.com",
@@ -21,7 +20,7 @@ setuptools.setup(
     description="SMPL human body layer for PyTorch is a differentiable PyTorch layer",
     long_description=long_description,
     long_description_content_type="text/markdown",
-    url="https://github.com/gulvarol/smpl-pytorch",
+    url="https://github.com/gulvarol/smplpytorch",
     packages=setuptools.find_packages(),
     classifiers=[
         "Programming Language :: Python :: 3",

smplpytorch/__init__.py

@@ -0,0 +1 @@
+name = "smplpytorch"

smplpytorch/native/webuser/serialization.py

@@ -3,7 +3,7 @@ def ready_arguments(fname_or_dict):
     import pickle
     import chumpy as ch
     from chumpy.ch import MatVecMult
-    from smpl.native.webuser.posemapper import posemap
+    from smplpytorch.native.webuser.posemapper import posemap
 
     if not isinstance(fname_or_dict, dict):
         dd = pickle.load(open(fname_or_dict, 'rb'), encoding='latin1')

smplpytorch/pytorch/smpl_layer.py

@@ -4,9 +4,9 @@ import numpy as np
 import torch
 from torch.nn import Module
 
-from smpl.native.webuser.serialization import ready_arguments
-from smpl.pytorch import rodrigues_layer
-from smpl.pytorch.tensutils import (th_posemap_axisang, th_with_zeros, th_pack, make_list, subtract_flat_id)
+from smplpytorch.native.webuser.serialization import ready_arguments
+from smplpytorch.pytorch import rodrigues_layer
+from smplpytorch.pytorch.tensutils import (th_posemap_axisang, th_with_zeros, th_pack, make_list, subtract_flat_id)
 
 
 class SMPL_Layer(Module):

smplpytorch/pytorch/tensutils.py

@@ -1,6 +1,6 @@
 import torch
 
-from smpl.pytorch import rodrigues_layer
+from smplpytorch.pytorch import rodrigues_layer
 
 
 def th_posemap_axisang(pose_vectors):