Docker

README.md

@@ -30,6 +30,10 @@ python -m pip install matplotlib opencv-python notebook tqdm
 
 conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.0 -c pytorch
 
+## Run with docker file
+- Build dockerfile `docker build -t cvlab/cvcount .`
+- Run image `docker run --rm --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0,1 -e NVIDIA_DRIVER_CAPABILITIES=compute,utility -ti -v $PWD:/workdir cvlab/cvcount`
+- Type `bash --login` in docker terminal
 
 ## Quick demo
 

dockerfile

@@ -0,0 +1,29 @@
+FROM continuumio/miniconda3
+
+
+WORKDIR /app
+
+# RUN conda create -n fscount python=3.7 -y
+# SHELL ["conda", "run", "-n", "fscount", "/bin/bash", "-c"]
+# RUN pip install -r requirements.txt
+# RUN conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.0 -c pytorch
+
+
+# Make RUN commands use `bash --login`:
+SHELL ["/bin/bash", "--login", "-c"]
+
+# Create the environment:
+COPY environment.yml .
+RUN conda env create -f environment.yml
+
+# Initialize conda in bash config fiiles:
+RUN conda init bash
+
+# Fix libGL.so.1: cannot open shared object file
+RUN apt-get update
+RUN apt-get install ffmpeg libsm6 libxext6  -y
+
+# Activate the environment, and make sure it's activated:
+RUN echo "conda activate fscount" > ~/.bashrc
+
+WORKDIR /workdir

environment.yml

@@ -0,0 +1,113 @@
+name: fscount
+channels:
+  - pytorch
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=4.5=1_gnu
+  - blas=1.0=mkl
+  - ca-certificates=2021.10.26=h06a4308_2
+  - certifi=2021.10.8=py37h06a4308_0
+  - cudatoolkit=10.0.130=0
+  - freetype=2.11.0=h70c0345_0
+  - giflib=5.2.1=h7b6447c_0
+  - intel-openmp=2021.4.0=h06a4308_3561
+  - jpeg=9d=h7f8727e_0
+  - lcms2=2.12=h3be6417_0
+  - ld_impl_linux-64=2.35.1=h7274673_9
+  - libffi=3.3=he6710b0_2
+  - libgcc-ng=9.3.0=h5101ec6_17
+  - libgomp=9.3.0=h5101ec6_17
+  - libpng=1.6.37=hbc83047_0
+  - libstdcxx-ng=9.3.0=hd4cf53a_17
+  - libtiff=4.2.0=h85742a9_0
+  - libwebp=1.2.0=h89dd481_0
+  - libwebp-base=1.2.0=h27cfd23_0
+  - lz4-c=1.9.3=h295c915_1
+  - mkl=2021.4.0=h06a4308_640
+  - mkl-service=2.4.0=py37h7f8727e_0
+  - mkl_fft=1.3.1=py37hd3c417c_0
+  - mkl_random=1.2.2=py37h51133e4_0
+  - ncurses=6.3=h7f8727e_2
+  - ninja=1.10.2=py37hd09550d_3
+  - numpy-base=1.21.2=py37h79a1101_0
+  - olefile=0.46=py37_0
+  - openssl=1.1.1l=h7f8727e_0
+  - pip=21.0.1=py37h06a4308_0
+  - python=3.7.11=h12debd9_0
+  - pytorch=1.4.0=py3.7_cuda10.0.130_cudnn7.6.3_0
+  - readline=8.1=h27cfd23_0
+  - setuptools=58.0.4=py37h06a4308_0
+  - six=1.16.0=pyhd3eb1b0_0
+  - sqlite=3.36.0=hc218d9a_0
+  - tk=8.6.11=h1ccaba5_0
+  - torchvision=0.5.0=py37_cu100
+  - wheel=0.37.0=pyhd3eb1b0_1
+  - xz=5.2.5=h7b6447c_0
+  - zlib=1.2.11=h7b6447c_3
+  - zstd=1.4.9=haebb681_0
+  - pip:
+    - argcomplete==1.12.3
+    - argon2-cffi==21.1.0
+    - attrs==21.2.0
+    - backcall==0.2.0
+    - bleach==4.1.0
+    - cffi==1.15.0
+    - cycler==0.11.0
+    - debugpy==1.5.1
+    - decorator==5.1.0
+    - defusedxml==0.7.1
+    - entrypoints==0.3
+    - fonttools==4.28.1
+    - importlib-metadata==4.8.2
+    - importlib-resources==5.4.0
+    - ipykernel==6.5.0
+    - ipython==7.29.0
+    - ipython-genutils==0.2.0
+    - jedi==0.18.0
+    - jinja2==3.0.3
+    - jsonschema==4.2.1
+    - jupyter-client==7.0.6
+    - jupyter-core==4.9.1
+    - jupyterlab-pygments==0.1.2
+    - kiwisolver==1.3.2
+    - markupsafe==2.0.1
+    - matplotlib==3.5.0
+    - matplotlib-inline==0.1.3
+    - mistune==0.8.4
+    - nbclient==0.5.8
+    - nbconvert==6.3.0
+    - nbformat==5.1.3
+    - nest-asyncio==1.5.1
+    - notebook==6.4.5
+    - numpy==1.21.4
+    - opencv-python==4.5.4.58
+    - packaging==21.2
+    - pandocfilters==1.5.0
+    - parso==0.8.2
+    - pexpect==4.8.0
+    - pickleshare==0.7.5
+    - pillow==8.4.0
+    - prometheus-client==0.12.0
+    - prompt-toolkit==3.0.22
+    - ptyprocess==0.7.0
+    - pycparser==2.21
+    - pygments==2.10.0
+    - pyparsing==2.4.7
+    - pyrsistent==0.18.0
+    - python-dateutil==2.8.2
+    - pyzmq==22.3.0
+    - send2trash==1.8.0
+    - setuptools-scm==6.3.2
+    - terminado==0.12.1
+    - testpath==0.5.0
+    - tomli==1.2.2
+    - tornado==6.1
+    - tqdm==4.62.3
+    - traitlets==5.1.1
+    - typing-extensions==4.0.0
+    - wcwidth==0.2.5
+    - webencodings==0.5.1
+    - zipp==3.6.0
+    - wandb
+prefix: /opt/conda/envs/fscount

train.py

@@ -19,10 +19,13 @@
 import random
 import torch.optim as optim
 import torch.nn.functional as F
+import wandb
+
+wandb.init(project="learning-to-count-every-thing")
 
 
 parser = argparse.ArgumentParser(description="Few Shot Counting Evaluation code")
-parser.add_argument("-dp", "--data_path", type=str, default='/home/hoai/DataSets/AgnosticCounting/FSC147_384_V2/', help="Path to the FSC147 dataset")
+parser.add_argument("-dp", "--data_path", type=str, default='./data/', help="Path to the FSC147 dataset")
 parser.add_argument("-o", "--output_dir", type=str,default="./logsSave", help="/Path/to/output/logs/")
 parser.add_argument("-ts", "--test-split", type=str, default='val', choices=["train", "test", "val"], help="what data split to evaluate on on")
 parser.add_argument("-ep", "--epochs", type=int,default=1500, help="number of training epochs")
@@ -184,7 +187,15 @@ def eval():
         best_rmse = val_rmse
         model_name = args.output_dir + '/' + "FamNet.pth"
         torch.save(regressor.state_dict(), model_name)
-
+    # Log loss
+    wandb.log({"Epoch": epoch+1,
+                "Avg. Epoch Loss:": stats[-1][0],
+                "Train MAE": stats[-1][1],
+                "Train RMSE": stats[-1][2],
+                "Val MAE": stats[-1][3],
+                "Val RMSE": stats[-1][4]
+                })
+    print("")
     print("Epoch {}, Avg. Epoch Loss: {} Train MAE: {} Train RMSE: {} Val MAE: {} Val RMSE: {} Best Val MAE: {} Best Val RMSE: {} ".format(
               epoch+1,  stats[-1][0], stats[-1][1], stats[-1][2], stats[-1][3], stats[-1][4], best_mae, best_rmse))