def test_model(state,
reference_tsv_path,
reduced_number_of_data=None,
strore_predicitions_fname=None):
dataset = DatasetDcase2019Task4(os.path.join(cfg.workspace),
base_feature_dir=os.path.join(
cfg.workspace, "dataset", "features"),
save_log_feature=False)
crnn_kwargs = state["model"]["kwargs"]
crnn = CRNN(**crnn_kwargs)
crnn.load(parameters=state["model"]["state_dict"])
LOG.info("Model loaded at epoch: {}".format(state["epoch"]))
pooling_time_ratio = state["pooling_time_ratio"]
crnn.load(parameters=state["model"]["state_dict"])
scaler = Scaler()
scaler.load_state_dict(state["scaler"])
classes = cfg.classes
many_hot_encoder = ManyHotEncoder.load_state_dict(
state["many_hot_encoder"])
crnn = crnn.eval()
[crnn] = to_cuda_if_available([crnn])
transforms_valid = get_transforms(cfg.max_frames, scaler=scaler)
LOG.info(reference_tsv_path)
df = dataset.initialize_and_get_df(reference_tsv_path,
reduced_number_of_data)
strong_dataload = DataLoadDf(df,
dataset.get_feature_file,
many_hot_encoder.encode_strong_df,
transform=transforms_valid)
predictions = get_predictions(crnn,
strong_dataload,
many_hot_encoder.decode_strong,
pooling_time_ratio,
save_predictions=strore_predicitions_fname)
compute_strong_metrics(predictions, df)
weak_dataload = DataLoadDf(df,
dataset.get_feature_file,
many_hot_encoder.encode_weak,
transform=transforms_valid)
weak_metric = get_f_measure_by_class(
crnn, len(classes), DataLoader(weak_dataload,
batch_size=cfg.batch_size))
LOG.info("Weak F1-score per class: \n {}".format(
pd.DataFrame(weak_metric * 100, many_hot_encoder.labels)))
LOG.info("Weak F1-score macro averaged: {}".format(np.mean(weak_metric)))
dataset = DesedSynthetic(cfg.relative_data_path,
base_feature_dir=cfg.base_feature_dir,
save_log_feature=False)
dfs = get_dfs(dataset, weak_path, test_path, eval_path, subpart_data, valid_list=val_list,
frames_in_sec=frames_in_sec,
segment=segment,
dropna=f_args.dropna, unique_fr=f_args.unique_fr,
fixed_segment=f_args.fixed_segment
)
if resume_training is None:
classes = dataset.classes
many_hot_encoder = ManyHotEncoder(classes)
else:
many_hot_encoder = ManyHotEncoder.load_state_dict(state["many_hot_encoder"])
classes = many_hot_encoder.labels
encode_function_label = many_hot_encoder.encode_weak
# Datasets
trans_fr = [ApplyLog(), ToTensor(), Unsqueeze(0)]
train_weak_df_fr = dfs["train"]
train_weak_dl_fr = DataLoadDf(train_weak_df_fr, encode_function_label, transform=Compose(trans_fr))
if type_positive != "label" or type_negative != "label":
unlabel_df_fr = dataset.get_df_feat_dir(cfg.unlabel, subpart_data=subpart_data, frames_in_sec=frames_in_sec)
unlabel_dl_fr = DataLoadDf(unlabel_df_fr, encode_function_label, transform=Compose(trans_fr))
datasets_mean = [train_weak_dl_fr, unlabel_dl_fr]
else:
datasets_mean = [train_weak_dl_fr]
def test_model(state, reduced_number_of_data, strore_predicitions_fname=None):
crnn_kwargs = state["model"]["kwargs"]
crnn = CRNN(**crnn_kwargs)
crnn.load(parameters=state["model"]["state_dict"])
LOG.info("Model loaded at epoch: {}".format(state["epoch"]))
pooling_time_ratio = state["pooling_time_ratio"]
crnn.load(parameters=state["model"]["state_dict"])
scaler = Scaler()
scaler.load_state_dict(state["scaler"])
classes = cfg.classes
many_hot_encoder = ManyHotEncoder.load_state_dict(
state["many_hot_encoder"])
# ##############
# Validation
# ##############
crnn = crnn.eval()
[crnn] = to_cuda_if_available([crnn])
transforms_valid = get_transforms(cfg.max_frames, scaler=scaler)
# # 2018
# LOG.info("Eval 2018")
# eval_2018_df = dataset.initialize_and_get_df(cfg.eval2018, reduced_number_of_data)
# # Strong
# eval_2018_strong = DataLoadDf(eval_2018_df, dataset.get_feature_file, many_hot_encoder.encode_strong_df,
# transform=transforms_valid)
# predictions = get_predictions(crnn, eval_2018_strong, many_hot_encoder.decode_strong)
# compute_strong_metrics(predictions, eval_2018_df, pooling_time_ratio)
# # Weak
# eval_2018_weak = DataLoadDf(eval_2018_df, dataset.get_feature_file, many_hot_encoder.encode_weak,
# transform=transforms_valid)
# weak_metric = get_f_measure_by_class(crnn, len(classes), DataLoader(eval_2018_weak, batch_size=cfg.batch_size))
# LOG.info("Weak F1-score per class: \n {}".format(pd.DataFrame(weak_metric * 100, many_hot_encoder.labels)))
# LOG.info("Weak F1-score macro averaged: {}".format(np.mean(weak_metric)))
# Validation 2019
# LOG.info("Validation 2019 (original code)")
# b_dataset = B_DatasetDcase2019Task4(cfg.workspace,
# base_feature_dir=os.path.join(cfg.workspace, 'dataset', 'features'),
# save_log_feature=False)
# b_validation_df = b_dataset.initialize_and_get_df(cfg.validation, reduced_number_of_data)
# b_validation_df.to_csv('old.csv')
# b_validation_strong = B_DataLoadDf(b_validation_df,
# b_dataset.get_feature_file, many_hot_encoder.encode_strong_df,
# transform=transforms_valid)
# predictions2 = get_predictions(crnn, b_validation_strong, many_hot_encoder.decode_strong,
# save_predictions=strore_predicitions_fname)
# compute_strong_metrics(predictions2, b_validation_df, pooling_time_ratio)
# b_validation_weak = B_DataLoadDf(b_validation_df, b_dataset.get_feature_file, many_hot_encoder.encode_weak,
# transform=transforms_valid)
# weak_metric = get_f_measure_by_class(crnn, len(classes), DataLoader(b_validation_weak, batch_size=cfg.batch_size))
# LOG.info("Weak F1-score per class: \n {}".format(pd.DataFrame(weak_metric * 100, many_hot_encoder.labels)))
# LOG.info("Weak F1-score macro averaged: {}".format(np.mean(weak_metric)))
# ============================================================================================
# ============================================================================================
# ============================================================================================
dataset = DatasetDcase2019Task4(feature_dir=cfg.feature_dir,
local_path=cfg.workspace,
exp_tag=cfg.exp_tag,
save_log_feature=False)
# Validation 2019
LOG.info("Validation 2019")
validation_df = dataset.initialize_and_get_df(cfg.validation,
reduced_number_of_data)
validation_strong = DataLoadDf(validation_df,
dataset.get_feature_file,
many_hot_encoder.encode_strong_df,
transform=transforms_valid)
predictions = get_predictions(crnn,
validation_strong,
many_hot_encoder.decode_strong,
save_predictions=strore_predicitions_fname)
vdf = validation_df.copy()
vdf.filename = vdf.filename.str.replace('.npy', '.wav')
pdf = predictions.copy()
pdf.filename = pdf.filename.str.replace('.npy', '.wav')
compute_strong_metrics(pdf, vdf, pooling_time_ratio)
validation_weak = DataLoadDf(validation_df,
dataset.get_feature_file,
many_hot_encoder.encode_weak,
transform=transforms_valid)
weak_metric = get_f_measure_by_class(
crnn, len(classes),
DataLoader(validation_weak, batch_size=cfg.batch_size))
LOG.info("Weak F1-score per class: \n {}".format(
pd.DataFrame(weak_metric * 100, many_hot_encoder.labels)))
LOG.info("Weak F1-score macro averaged: {}".format(np.mean(weak_metric)))
embed_name = model_path.split("/")[-2]
############
# Parameters experiences
###########
subpart_data = f_args.subpart_data
dataset = DesedSynthetic("../dcase2019",
base_feature_dir="../dcase2019/features",
save_log_feature=False)
emb_model, state = load_model(model_path, return_state=True)
epoch_model = state["epoch"]
LOG.info("model loaded at epoch: {}".format(epoch_model))
if torch.cuda.is_available():
emb_model = emb_model.cuda()
emb_model.eval()
many_hot_encoder = ManyHotEncoder.load_state_dict(state['many_hot_encoder'])
encode_function_label = many_hot_encoder.encode_weak
scaler = ScalerSum.load_state_dict(state['scaler'])
frames_in_sec = cfg.frames_in_sec
transf = Compose([ApplyLog(), PadOrTrunc(nb_frames=cfg.frames), ToTensor(), Unsqueeze(0),
Normalize(scaler), Unsqueeze(1)])
test_fr = dataset.get_df_feat_dir(cfg.test2018, frames_in_sec=frames_in_sec, subpart_data=subpart_data)
print(len(test_fr))
test_dataset = DataLoadDf(test_fr, many_hot_encoder.encode_weak, transform=transf)
embed_set = "embedding"
embed_dir = "stored_data/embeddings"
embed_dir = os.path.join(embed_dir, embed_name, "embeddings")