Merge pull request #4 from PPierzc/main

HRNet Update

Dockerfile

@@ -35,7 +35,10 @@ RUN python -m pip install --no-cache-dir nflows\
     imageio-ffmpeg\
     brax\
     wandb\
-    neuralpredictors
+    neuralpredictors\
+    yacs
+
+RUN pip install --upgrade pillow
 
 RUN pip install git+https://github.com/sinzlab/neuralpredictors.git
 RUN pip install torch-scatter -f https://data.pyg.org/whl/torch-1.9.0+cu111.html

README.md

@@ -20,8 +20,22 @@ from propose.models.flows import CondGraphFlow
 flow = CondGraphFlow.from_pretrained('ppierzc/cgnf/cgnf_human36m:best')
 ```
 
-## Reproducing results
+#### HRNet Loading
+You can also load a pretrained HRNet model.
+```python
+from propose.models.detectors import HRNet
+
+hrnet = HRNet.from_pretrained('ppierzc/cgnf/hrnet:v0')
+```
+This will load the HRNet model provided in the [repo](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch).
+The model loaded here is the `pose_hrnet_w32_256x256` trained on the MPII dataset.
+
 ### Requirements
+#### Requirements for the package
+The requirements for the package can be found in the [requirements.txt](/requirements.txt).
+
+#### Docker
+Alternatively, you can use [Docker](https://www.docker.com/) to run the package.
 This project requires that you have the following installed:
 - `docker`
 - `docker-compose`
@@ -40,15 +54,10 @@ docker pull sinzlab/pytorch:v3.9-torch1.9.0-cuda11.1-dj0.12.7
 5. You can now open JupyterLab in your browser at [`http://localhost:10101`](http://localhost:10101).
 
 #### Available Models
-| Model Name | description                                                        | Artifact path                   |
-| --- |--------------------------------------------------------------------|---------------------------------|
-| cGNF Human 3.6m | Model trained on the Human 3.6M dataset with MPII input keypoints. | ```ppierzc/cgnf/cgnf_human36m:best``` |
-
-### Run Evaluation
-You can run the evaluation script with the following command:
-```
-docker-compose run eval --human36m --experiment=cgnf_human36m 
-```
+| Model Name | description                                                                                                                                             | Artifact path                   | Import Code                      |
+| --- |---------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------|----------------------------------|
+| cGNF Human 3.6m | Model trained on the Human 3.6M dataset with MPII input keypoints.                                                                                      | ```ppierzc/cgnf/cgnf_human36m:best``` | ```from propose.models.flows import CondGraphFlow``` |
+ | HRNet | Instance of the [official](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch) HRNet model trained on the MPII dataset with w32 and 256x256 | ```ppierzc/cgnf/hrnet:v0``` | ```from propose.models.detectors import HRNet``` |
 
 ### Run Tests
 To run the tests, from the root directory call:
@@ -61,17 +70,7 @@ docker-compose run pytest tests
 ## Data
 ### Rat7m
 You can download the Rat 7M dataset from [here](https://figshare.com/collections/Rat_7M/5295370).
-To preprocess the dataset run the following command.
-```
-docker-compose run preprocess --rat7m
-```
 
 ### Human3.6M dataset
 Due to license restrictions, the dataset is not included in the repository.
 You can download it from the official [website](http://vision.imar.ro/human3.6m).
-
-Download the *D3 Positions mono* by subject and place them into the `data/human36m/raw` directory.
-Then run the following command.
-```
-docker-compose run preprocess --human36m
-```

docker-compose.yml

@@ -27,52 +27,6 @@ services:
       - ./scripts:/scripts
       - ./data:/data
 
-  python:
-    &python
-    image: propose
-    entrypoint: [ "python" ]
-
-  train:
-    image: propose
-    volumes:
-      - .:/src/propose
-      - ./scripts:/scripts
-      - ./data:/data
-      - ./experiments:/experiments
-    env_file:
-      - .env
-    entrypoint: [ "python", "/scripts/train.py" ]
-
-  eval:
-    image: propose
-    volumes:
-      - .:/src/propose
-      - ./scripts:/scripts
-      - ./data:/data
-      - ./experiments:/experiments
-    env_file:
-      - .env
-    entrypoint: [ "python", "/scripts/eval.py" ]
-
-  sweep:
-    image: propose
-    volumes:
-      - .:/src/propose
-      - ./scripts:/scripts
-      - ./data:/data
-      - ./sweeps:/sweeps
-    env_file:
-      - .env
-    entrypoint: [ "python", "/scripts/sweep.py" ]
-
-  preprocess:
-    image: propose
-    volumes:
-      - .:/src/propose
-      - ./scripts:/scripts
-      - ./data:/data
-    entrypoint: [ "python", "/scripts/preprocess.py" ]
-
   pytest:
     <<: *common
     volumes: