[SPLIT] evaluateDCASE into simpler modules

NINAnor · Feb 20, 2024 · e5a8517 · e5a8517
1 parent 70cb5da
commit e5a8517
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Showing 4 changed files with 25 additions and 287 deletions.
diff --git a/evaluate/_utils_compute.py b/evaluate/_utils_compute.py
diff --git a/evaluate/_utils_writing.py b/evaluate/_utils_writing.py
@@ -89,30 +89,25 @@ def write_wav(
         ),
     )
 
-    arr = np.pad(
-            arr, (0, len(gt_labels) - len(arr)), "constant", constant_values=(0,)
-        )
-
-
     # pad with zeros
-    #gt_labels = np.pad(
-    #    gt_labels, (0, len(gt_labels) - len(arr)), "constant", constant_values=(0,)
-    #)
-    #pred_labels = np.pad(
-    #    pred_labels, (0, len(pred_labels) - len(arr) ), "constant", constant_values=(0,)
-    #)
-    #distances_to_pos = np.pad(
-    #    distances_to_pos,
-    #    (0, len(distances_to_pos) - len(arr)),
-    #    "constant",
-    #    constant_values=(0,),
-    #)
-    #z_scores_pos = np.pad(
-    #    z_scores_pos,
-    #  (0, len(z_scores_pos) - len(arr)),
-    #    "constant",
-    #    constant_values=(0,),
-    #)
+    gt_labels = np.pad(
+        gt_labels, (0, len(gt_labels) - len(arr)), "constant", constant_values=(0,)
+    )
+    pred_labels = np.pad(
+        pred_labels, (0, len(pred_labels) - len(arr) ), "constant", constant_values=(0,)
+    )
+    distances_to_pos = np.pad(
+        distances_to_pos,
+        (0, len(distances_to_pos) - len(arr)),
+        "constant",
+        constant_values=(0,),
+    )
+    z_scores_pos = np.pad(
+        z_scores_pos,
+      (0, len(z_scores_pos) - len(arr)),
+        "constant",
+        constant_values=(0,),
+    )
 
     # Write the results
     result_wav = np.vstack(

diff --git a/evaluate/evaluateDCASE.py b/evaluate/evaluateDCASE.py
@@ -27,6 +27,8 @@
 
 from callbacks.callbacks import MilestonesFinetuning
 
+from evaluate._utils_writing import write_wav, write_results
+
 
 def to_dataframe(features, labels):
     # Load the saved array and map the features and labels into a single dataframe
@@ -39,7 +41,7 @@ def to_dataframe(features, labels):
 
 
 def train_model(
-    model_type="pann",
+    model_type=None,
     datamodule_class=DCASEDataModule,
     max_epochs=1,
     enable_model_summary=False,
@@ -68,17 +70,9 @@ def train_model(
     )
 
     # create the model object
-    model = ProtoBEATsModel(model_type=model_type)
-
-    if pretrained_model:
-        # Load the pretrained model
-        try:
-            pretrained_model = ProtoBEATsModel.load_from_checkpoint(pretrained_model)
-        except KeyError:
-            print(
-                "Failed to load the pretrained model. Please check the checkpoint file."
-            )
-            return None
+    model = ProtoBEATsModel(model_type=model_type, 
+                            state=state, 
+                            model_path=pretrained_model)
 
     # train the model
     trainer.fit(model, datamodule=datamodule_class)
@@ -478,103 +472,7 @@ def compute(
     )
 
 
-def write_wav(
-    files,
-    cfg,
-    gt_labels,
-    pred_labels,
-    distances_to_pos,
-    z_scores_pos,
-    target_fs=16000,
-    target_path=None,
-    frame_shift=1,
-    support_spectrograms=None
-):
-    from scipy.io import wavfile
-
-    # Some path management
 
-    filename = (
-        os.path.basename(support_spectrograms).split("data_")[1].split(".")[0] + ".wav"
-    )
-    # Return the final product
-    output = os.path.join(target_path, filename)
-
-    # Find the filepath for the file being analysed
-    for f in files:
-        if os.path.basename(f) == filename:
-            print(os.path.basename(f))
-            print(filename)
-            arr, _ = librosa.load(f, sr=target_fs, mono=True)
-            break
-
-    # Expand the dimensions
-    gt_labels = np.repeat(
-        np.squeeze(gt_labels, axis=1).T,
-        int(
-            cfg["data"]["tensor_length"]
-            * cfg["data"]["overlap"]
-            * target_fs
-            * frame_shift
-            / 1000
-        ),
-    )
-    pred_labels = np.repeat(
-        pred_labels.T,
-        int(
-            cfg["data"]["tensor_length"]
-            * cfg["data"]["overlap"]
-            * target_fs
-            * frame_shift
-            / 1000
-        ),
-    )
-    distances_to_pos = np.repeat(
-        distances_to_pos.T,
-        int(
-            cfg["data"]["tensor_length"]
-            * cfg["data"]["overlap"]
-            * target_fs
-            * frame_shift
-            / 1000
-        ),
-    )
-    z_scores_pos = np.repeat(
-        z_scores_pos.T,
-        int(
-            cfg["data"]["tensor_length"]
-            * cfg["data"]["overlap"]
-            * target_fs
-            * frame_shift
-            / 1000
-        ),
-    )
-
-    # pad with zeros
-    gt_labels = np.pad(
-        gt_labels, (0, len(arr) - len(gt_labels)), "constant", constant_values=(0,)
-    )
-    pred_labels = np.pad(
-        pred_labels, (0, len(arr) - len(pred_labels)), "constant", constant_values=(0,)
-    )
-    distances_to_pos = np.pad(
-        distances_to_pos,
-        (0, len(arr) - len(distances_to_pos)),
-        "constant",
-        constant_values=(0,),
-    )
-    z_scores_pos = np.pad(
-        z_scores_pos,
-        (0, len(arr) - len(z_scores_pos)),
-        "constant",
-        constant_values=(0,),
-    )
-
-    # Write the results
-    result_wav = np.vstack(
-        (arr, gt_labels, pred_labels, distances_to_pos / 10, z_scores_pos)
-    )
-    wavfile.write(output, target_fs, result_wav.T)
 
 import hydra
 from omegaconf import DictConfig, OmegaConf

diff --git a/evaluate/evaluation_metrics/evaluation.py b/evaluate/evaluation_metrics/evaluation.py
@@ -319,8 +319,7 @@ def evaluate(pred_file_path, ref_file_path, team_name, dataset, savepath, metada
     parser.add_argument('-dataset', type=str, help="which set to evaluate: EVAL or VAL")
     parser.add_argument('-savepath', type=str, help="path where to save the report to")
     args = parser.parse_args()
-    # print(args)
 
     evaluate( args.pred_file, args.ref_files_path, args.team_name, args.dataset, args.savepath)
 
-    # docker run -v $PWD:/app -v /data/Prosjekter3/823001_19_metodesats_analyse_23_36_cretois/:/data --gpus all dcase poetry run python evaluate/evaluation_metrics/evaluation.py -pred_file /data/eval_out.csv -ref_files_path /data/DCASE/Development_Set_annotations/Validation_Set -team_name BEATs -dataset VAL -savepath /data/.
+    # docker run -v $PWD:/app -v /data/Prosjekter3/823001_19_metodesats_analyse_23_36_cretois/:/data --gpus all beats poetry run python evaluate/evaluation_metrics/evaluation.py -pred_file /data/eval_out.csv -ref_files_path /data/DCASE/Development_Set_annotations/Validation_Set -team_name BEATs -dataset VAL -savepath /data/.