state = {
'model': {
"name": crnn.__class__.__name__,
'args': '',
"kwargs": crnn_kwargs,
'state_dict': crnn.state_dict()
},
'optimizer': {
"name": optimizer.__class__.__name__,
'args': '',
"kwargs": optim_kwargs,
'state_dict': optimizer.state_dict()
},
"pooling_time_ratio": pooling_time_ratio,
'scaler': scaler.state_dict(),
"many_hot_encoder": many_hot_encoder.state_dict()
}
save_best_cb = SaveBest("sup")
# Eval 2018
eval_2018_df = dataset.initialize_and_get_df(cfg.eval2018,
reduced_number_of_data)
eval_2018 = DataLoadDf(eval_2018_df,
dataset.get_feature_file,
many_hot_encoder.encode_strong_df,
transform=transforms_valid)
[crnn] = to_cuda_if_available([crnn])
for epoch in range(cfg.n_epoch):
'state_dict': crnn.state_dict()
},
'model_ema': {
"name": crnn_ema.__class__.__name__,
'args': '',
"kwargs": crnn_kwargs,
'state_dict': crnn_ema.state_dict()
},
'optimizer': {
"name": optimizer.__class__.__name__,
'args': '',
"kwargs": optim_kwargs,
'state_dict': optimizer.state_dict()
},
"pooling_time_ratio": pooling_time_ratio,
"scaler": scaler.state_dict(),
"many_hot_encoder": many_hot_encoder.state_dict()
}
save_best_cb = SaveBest("sup")
# ##############
# Train
# ##############
for epoch in range(cfg.n_epoch):
crnn = crnn.train()
crnn_ema = crnn_ema.train()
[crnn, crnn_ema] = to_cuda_if_available([crnn, crnn_ema])
train(cfg,