import os
from src.train import train
from src.utils import get_experiments_dir, get_data_dir
opts = {
# ---folders---
"data_path": os.path.join(get_data_dir(), 'SCM', 'free-field'),
"logs_path": os.path.join(get_experiments_dir(), 'fc'),
"experiment_name": 'fc_freefield_full',
# ---network structure---
"model_name": 'fc',
"input_sh_order": 3,
"rank":
None, # None -> output is full matrix, Int -> output is low rank matrix transformed into full matrix
"hidden_layers": 1,
"hidden_sizes": [3500],
"residual_flag": False,
"residual_only": False,
# ---data---
"batch_size": 25,
"num_workers": 15,
# ---optimization---
"lr": 1e-3,
"lr_sched_thresh": 0.01,
"lr_sched_patience": 5,
"max_epochs": 1000,
"gpus": -1
}
train(opts)
from abc import abstractmethod
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from src.data.sig2scm_dataset import Dataset
from src.models.base_model import BaseModel
from src.utils import get_data_dir, get_experiments_dir
import src.utils.complex_torch as ctorch
default_opts = {
# ---folders---
"data_path": os.path.join(get_data_dir(), 'whitenoise_10_reflections'),
"logs_path": get_experiments_dir(),
"experiment_name": 'rnn_QA',
# ---data options---
"center_frequency": 2500.,
"bandwidth": 400,
# ---network structure---
"model_name": 'rnn',
"loss": 'mse', # 'mse'
"sh_order_sig": float("inf"),
"sh_order_scm": float("inf"),
"time_len_sig": float("inf"),
# ---data---
# "dtype": torch.float32, # TODO: implement (does errors in saving hyperparameters)
"transform": None,
"batch_size": 25,
"num_workers": 0,
import os
from src.train import train
from src.utils import get_experiments_dir, get_data_dir
opts = {
# ---folders---
"data_path": os.path.join(get_data_dir(), 'SCM', 'image-method'),
"logs_path": os.path.join(get_experiments_dir(), 'fc', 'image_method'),
"experiment_name": 'fc_imagemethod_full',
# ---network structure---
"model_name": 'fc',
"input_sh_order": 3,
"rank":
None, # None -> output is full matrix, Int -> output is low rank matrix transformed into full matrix
"hidden_layers": 1,
"hidden_sizes": [3000],
"residual_flag": True,
"residual_only": False,
# ---data---
"batch_size": 50,
"num_workers": 15,
# ---optimization---
"lr": 1e-3,
"lr_sched_thresh": 0.01,
"lr_sched_patience": 10,
"max_epochs": 1000,
"gpus": -1
}
train(opts)