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* Add ZMD * Add ImageNet data sample * Add a Dockerfile and other improvements * Update README --------- Co-authored-by: Maria Wyrzykowska <[email protected]>
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# Use an official lightweight image as a base | ||
FROM ubuntu:22.04 | ||
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# Set environment variables to avoid interactive prompts during installation | ||
ENV DEBIAN_FRONTEND=noninteractive | ||
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# Install system dependencies | ||
RUN apt-get update && apt-get install -y \ | ||
curl \ | ||
wget \ | ||
bzip2 \ | ||
ca-certificates \ | ||
libglib2.0-0 \ | ||
libxext6 \ | ||
libsm6 \ | ||
libxrender1 \ | ||
git \ | ||
&& apt-get clean \ | ||
&& rm -rf /var/lib/apt/lists/* | ||
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# Install micromamba | ||
RUN curl -L https://micro.mamba.pm/api/micromamba/linux-64/latest | tar -xvj -C /usr/local/bin --strip-components=1 bin/micromamba | ||
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# Set up working directory | ||
WORKDIR /app | ||
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# Copy current directory contents into the container | ||
COPY . . | ||
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# Set environment name | ||
ENV ENV_NAME=cvdm | ||
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ENV MAMBA_ROOT_PREFIX=/opt/micromamba | ||
ENV PATH=$MAMBA_ROOT_PREFIX/envs/$ENV_NAME/bin:$PATH | ||
ENV PYTHONPATH=$MAMBA_ROOT_PREFIX/envs/$ENV_NAME/lib/python3.10/site-packages:$PYTHONPATH | ||
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RUN apt-get update && apt-get install ffmpeg libsm6 libxext6 -y | ||
# Set up micromamba environment, install dependencies and pip packages | ||
RUN micromamba create -n $ENV_NAME -y && \ | ||
micromamba install -n $ENV_NAME \ | ||
tensorflow-gpu==2.15.0 \ | ||
keras==2.15.* \ | ||
matplotlib==3.8.0 \ | ||
tqdm==4.65.0 \ | ||
scikit-learn==1.4.2 \ | ||
scikit-image==0.22.0 \ | ||
einops==0.7.0 \ | ||
neptune==1.10.2 -y && \ | ||
/usr/local/bin/micromamba run -n $ENV_NAME pip3 install opencv-python==4.9.0.80 \ | ||
tensorflow-addons==0.23.0 \ | ||
cupy-cuda12x==13.3.0 && \ | ||
/usr/local/bin/micromamba run -n $ENV_NAME python -m pip install . | ||
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# Default command when the container starts (optional) | ||
CMD ["/bin/bash"] |
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task: "imagenet_phase" | ||
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model: | ||
noise_model_type: "unet" | ||
alpha: 0.001 | ||
load_weights: null | ||
load_mu_weights: null | ||
snr_expansion_n: 1 | ||
zmd: False | ||
diff_inp: False | ||
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training: | ||
lr: 0.0001 | ||
epochs: 100 | ||
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eval: | ||
output_path: "outputs/imagenet" | ||
generation_timesteps: 200 | ||
checkpoint_freq: 1000 | ||
log_freq: 10 | ||
image_freq: 100 | ||
val_freq: 200 | ||
val_len: 10 | ||
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data: | ||
dataset_path: "data/imnet_sample" | ||
n_samples: 100 | ||
batch_size: 2 | ||
im_size: 256 | ||
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neptune: | ||
name: "CVDM" | ||
project: "mlid/test" |
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task: "imagenet_sr" | ||
model: | ||
noise_model_type: "unet" | ||
alpha: 0.001 | ||
load_weights: null | ||
load_mu_weights: null | ||
snr_expansion_n: 1 | ||
zmd: False | ||
diff_inp: True | ||
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training: | ||
lr: 0.0001 | ||
epochs: 100 | ||
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eval: | ||
output_path: "outputs/imagenet" | ||
generation_timesteps: 200 | ||
checkpoint_freq: 1000 | ||
log_freq: 10 | ||
image_freq: 1000 | ||
val_freq: 2000 | ||
val_len: 10 | ||
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data: | ||
dataset_path: "data/imnet_sample" | ||
n_samples: 100 | ||
batch_size: 2 | ||
im_size: 256 | ||
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neptune: | ||
name: "CVDM" | ||
project: "mlid/test" |
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