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config.yaml
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config.yaml
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# Copyright 2023 Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac,
# Mickaël Chen, Alain Rakotomamonjy
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
model: discr_flow
model_params: # `DiscrFlowDict` in `gpm/models/discr_flow/base.py`
model: ipm
nb_steps: 25
eta: 2
p_std: 1
grad_penalty: 0.05
grad_penalty_center: 0
embedding: fourier
embedding_size: 128
discriminator: # `ResNetEncoderDict` in `gpm/networks/conv/resnet.py`
name: resnet
batch_norm: false
spectral_norm: false
width: 64
activation: # `gpm/networks/activations.py`
name: leaky_relu
init: # `gpm/networks/init.py`
name: normal
gain: 0.02
dataset: celeba
dataset_params: # `gpm/data/image/celeba.py`
image_size: 64
optim:
batch_size: 64
nb_steps: 200000
optimizers:
optimizer:
name: Adam # PyTorch parameters
settings:
lr: 0.0002
betas: [0.5, 0.999]
schedulers: ~
eval_freq: 1000
eval: # Can be is list of named evaluations
- config_name: 'standard'
batch_size: 100
nb_steps: 10
nb_quali: 20 # Number of qualitative outputs in the logs
tests: ['gen_quali', 'fid'] # List of outputs for this validation, cf. `gpm/eval/tests.py`
metric: 'fid' # Will select the best model w.r.t. FID
test_params: # `DiscrFlowInferenceDict` in `gpm/models/discr_flow/base.py`
nb_steps: 25
save_best: true
test:
- config_name: 'standard'
batch_size: 100
nb_steps: 0
nb_quali: 20
tests: ['gen_quali', 'fid']
test_params:
nb_steps: 25