Merge branch 'develop' into baker

examples/tacotron2/train.py

@@ -86,15 +86,18 @@ class Experiment(ExperimentBase):
                 valid_losses[k].append(float(v))
 
             attention_weights = outputs["alignments"]
-            display.add_attention_plots(self.visualizer,
-                                        f"valid_sentence_{i}_alignments",
-                                        attention_weights[0], self.iteration)
-            display.add_spectrogram_plots(
-                self.visualizer, f"valid_sentence_{i}_target_spectrogram",
-                mels[0], self.iteration)
-            display.add_spectrogram_plots(
-                self.visualizer, f"valid_sentence_{i}_predicted_spectrogram",
-                outputs['mel_outputs_postnet'][0], self.iteration)
+            self.visualizer.add_figure(
+                f"valid_sentence_{i}_alignments",
+                display.plot_alignment(attention_weights[0].numpy()),
+                self.iteration)
+            self.visualizer.add_figure(
+                f"valid_sentence_{i}_target_spectrogram",
+                display.plot_spectrogram(mels[0].numpy().T),
+                self.iteration)
+            self.visualizer.add_figure(
+                f"valid_sentence_{i}_predicted_spectrogram",
+                display.plot_spectrogram(outputs['mel_outputs_postnet'][0].numpy().T),
+                self.iteration)
 
         # write visual log
         valid_losses = {k: np.mean(v) for k, v in valid_losses.items()}

parakeet/training/experiment.py

@@ -19,7 +19,7 @@ import numpy as np
 import paddle
 from paddle import distributed as dist
 from paddle.io import DataLoader, DistributedBatchSampler
-from tensorboardX import SummaryWriter
+from visualdl import LogWriter
 from collections import defaultdict
 import time
 

parakeet/utils/display.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+
 import numpy as np
 import matplotlib
 import librosa
@@ -21,44 +22,11 @@ import matplotlib.pylab as plt
 from matplotlib import cm, pyplot
 
 __all__ = [
-    "pack_attention_images",
-    "add_attention_plots",
     "plot_alignment",
-    "min_max_normalize",
-    "add_spectrogram_plots",
+    "plot_spectrogram",
 ]
 
 
-def pack_attention_images(attention_weights, rotate=False):
-    # add a box
-    attention_weights = np.pad(attention_weights, [(0, 0), (1, 1), (1, 1)],
-                               mode="constant",
-                               constant_values=1.)
-    if rotate:
-        attention_weights = np.rot90(attention_weights, axes=(1, 2))
-    n, h, w = attention_weights.shape
-
-    ratio = h / w
-    if ratio < 1:
-        cols = max(int(np.sqrt(n / ratio)), 1)
-        rows = int(np.ceil(n / cols))
-    else:
-        rows = max(int(np.sqrt(n / ratio)), 1)
-        cols = int(np.ceil(n / rows))
-    extras = rows * cols - n
-    #print(rows, cols, extras)
-    total = np.append(attention_weights, np.zeros([extras, h, w]), axis=0)
-    total = np.reshape(total, [rows, cols, h, w])
-    img = np.block([[total[i, j] for j in range(cols)] for i in range(rows)])
-    return img
-
-
-def save_figure_to_numpy(fig):
-    # save it to a numpy array.
-    data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3, ))
-    return data
-
 
 def plot_alignment(alignment, title=None):
     fig, ax = plt.subplots(figsize=(6, 4))
@@ -73,42 +41,13 @@ def plot_alignment(alignment, title=None):
     plt.tight_layout()
     return fig
 
-    fig.canvas.draw()
-    data = save_figure_to_numpy(fig)
-    plt.close()
-    return data
 
-
-def add_attention_plots(writer, tag, attention_weights, global_step):
-    img = plot_alignment(attention_weights.numpy().T)
-    writer.add_image(tag, img, global_step, dataformats="HWC")
-
-
-def add_multi_attention_plots(writer, tag, attention_weights, global_step):
-    attns = [attn[0].numpy() for attn in attention_weights]
-    for i, attn in enumerate(attns):
-        img = pack_attention_images(attn)
-        writer.add_image(
-            f"{tag}/{i}",
-            cm.plasma(img),
-            global_step=global_step,
-            dataformats="HWC")
-
-
-def add_spectrogram_plots(writer, tag, spec, global_step):
-    spec = spec.numpy().T
+def plot_spectrogram(spec):
+    # spec: [C, T] librosa convention
     fig, ax = plt.subplots(figsize=(12, 3))
     im = ax.imshow(spec, aspect="auto", origin="lower", interpolation='none')
     plt.colorbar(im, ax=ax)
     plt.xlabel("Frames")
     plt.ylabel("Channels")
     plt.tight_layout()
-
-    fig.canvas.draw()
-    data = save_figure_to_numpy(fig)
-    plt.close()
-    writer.add_image(tag, data, global_step, dataformats="HWC")
-
-
-def min_max_normalize(v):
-    return (v - v.min()) / (v.max() - v.min())
+    return fig

tools/copyright.hook

@@ -9,7 +9,7 @@ import subprocess
 import platform
 
 COPYRIGHT = '''
-Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.