Iterate over multiple logits plots

related to: #5

penn/plot/logits/__main__.py

@@ -28,6 +28,10 @@ def parse_args():
         '--gpu',
         type=int,
         help='The index of the GPU to use for inference')
+    parser.add_argument(
+        '--iters',
+        type=int,
+        help='Number of dummy iterations on the loader before extracting the data')
     return parser.parse_known_args()[0]
 
 

penn/plot/logits/core.py

@@ -84,7 +84,7 @@ def logits_matplotlib(logits, bins=None, voiced=None, stem=None):
         predicted_bins, pitch, periodicity = penn.postprocess(logits)
 
     # Change font size
-    matplotlib.rcParams.update({'font.size': 5})
+    matplotlib.rcParams.update({'font.size': 10})
 
     # Setup figure
     figure, axis = plt.subplots(figsize=figsize)
@@ -101,14 +101,14 @@ def logits_matplotlib(logits, bins=None, voiced=None, stem=None):
 
     yticks = torch.linspace(0, penn.PITCH_BINS - 1, 5)
     ylabels = penn.convert.bins_to_frequency(yticks)
-    ylabels_chunk = [ylabels for _ in range(penn.PITCH_CATS)]
-    ylabels = torch.cat(ylabels_chunk)
-    yticks = torch.linspace(0, 
-                            penn.PITCH_BINS * penn.PITCH_CATS - 1,
-                            5 * penn.PITCH_CATS)
-
-    ylabels = ylabels.round().int().tolist()
-    axis.get_yaxis().set_ticks(yticks, ylabels)
+    # ylabels_chunk = [ylabels for _ in range(penn.PITCH_CATS)]
+    # ylabels = torch.cat(ylabels_chunk)
+    # yticks = torch.linspace(0, 
+    #                         penn.PITCH_BINS * penn.PITCH_CATS - 1,
+    #                         5 * penn.PITCH_CATS)
+
+    # ylabels = ylabels.round().int().tolist()
+    # axis.get_yaxis().set_ticks(yticks, ylabels)
     axis.set_xlabel('Time (seconds)')
     axis.set_ylabel('Frequency (Hz)')
     axis.set_title(f"track: {stem}")
@@ -127,7 +127,7 @@ def logits_matplotlib(logits, bins=None, voiced=None, stem=None):
         nbins_masked = np.ma.MaskedArray(nbins, np.logical_not(nvoiced))
 
         for nbins_row in range(penn.PITCH_CATS):
-            axis.plot(nbins_masked[:, nbins_row], 'r--', linewidth=1)
+            axis.plot(nbins_masked[:, nbins_row], 'r--', marker='o', linewidth=1)
 
         # if predicted_bins is not None:
         #     npredicted_bins = predicted_bins.detach().cpu().numpy()
@@ -177,12 +177,15 @@ def from_audio(
 ###############################################################################
 
 
-def from_model_and_testset(model, loader, gpu=None):
+def from_model_and_testset(model, loader, gpu=None, iters=0):
     device = torch.device('cpu' if gpu is None else f'cuda:{gpu}')
 
     # Prepare model for inference
     with penn.inference_context(model):
 
+        for iter in range(iters):
+            next(loader)
+
         # Iterate over test set
         audio, bins, _, voiced, stem = next(loader)
 
@@ -213,7 +216,7 @@ def from_model_and_testset(model, loader, gpu=None):
         return logits_matplotlib(logits, bins, voiced, stem)
 
 
-def from_testset(checkpoint=None, gpu=None):
+def from_testset(checkpoint=None, gpu=None, iters=0):
     # Initialize model
     model = penn.Model()
 
@@ -229,7 +232,7 @@ def from_testset(checkpoint=None, gpu=None):
     loader = penn.data.loader(penn.EVALUATION_DATASETS, 'test')
     loader = iter(loader)
 
-    return from_model_and_testset(model, loader)
+    return from_model_and_testset(model, loader, iters=iters)
 
 
 def from_file(audio_file, checkpoint=None, gpu=None):
@@ -241,13 +244,15 @@ def from_file(audio_file, checkpoint=None, gpu=None):
     return from_audio(audio, penn.SAMPLE_RATE, checkpoint, gpu)
 
 
-def from_file_to_file(audio_file=None, output_file=None, checkpoint=None, gpu=None):
+def from_file_to_file(audio_file=None, output_file=None, checkpoint=None, gpu=None, iters=0):
     """Plot pitch and periodicity and save to disk"""
     # Plot
     if audio_file is not None:
         figure = from_file(audio_file, checkpoint, gpu)
     else:
-        figure = from_testset(checkpoint, gpu)
+        figure = from_testset(checkpoint, gpu, iters=iters)
+
+    breakpoint()
 
     # Save to disk
     if output_file is not None: