Merge pull request #31 from ShenYuhan/add_vdl

add visualdl for parakeet

examples/clarinet/synthesis.py

@@ -21,7 +21,6 @@ import random
 from tqdm import tqdm
 import pickle
 import numpy as np
-from tensorboardX import SummaryWriter
 
 import paddle.fluid.dygraph as dg
 from paddle import fluid

examples/clarinet/train.py

@@ -21,7 +21,7 @@ import random
 from tqdm import tqdm
 import pickle
 import numpy as np
-from tensorboardX import SummaryWriter
+from visualdl import LogWriter
 
 import paddle.fluid.dygraph as dg
 from paddle import fluid
@@ -179,7 +179,7 @@ if __name__ == "__main__":
     checkpoint_dir = os.path.join(args.output, "checkpoints")
     state_dir = os.path.join(args.output, "states")
     log_dir = os.path.join(args.output, "log")
-    writer = SummaryWriter(log_dir)
+    writer = LogWriter(log_dir)
 
     if args.checkpoint is not None:
         iteration = io.load_parameters(

examples/clarinet/utils.py

@@ -15,7 +15,6 @@
 from __future__ import division
 import os
 import soundfile as sf
-from tensorboardX import SummaryWriter
 from collections import OrderedDict
 
 from paddle import fluid

examples/deepvoice3/synthesize.py

@@ -11,7 +11,6 @@ from paddle import fluid
 from paddle.fluid import layers as F
 from paddle.fluid import dygraph as dg
 from paddle.fluid.io import DataLoader
-from tensorboardX import SummaryWriter
 import soundfile as sf
 
 from parakeet.data import SliceDataset, DataCargo, PartialyRandomizedSimilarTimeLengthSampler, SequentialSampler

examples/deepvoice3/train.py

@@ -10,7 +10,7 @@ from paddle.fluid import layers as F
 from paddle.fluid import initializer as I
 from paddle.fluid import dygraph as dg
 from paddle.fluid.io import DataLoader
-from tensorboardX import SummaryWriter
+from visualdl import LogWriter
 
 from parakeet.models.deepvoice3 import Encoder, Decoder, PostNet, SpectraNet
 from parakeet.data import SliceDataset, DataCargo, SequentialSampler, RandomSampler
@@ -181,7 +181,7 @@ if __name__ == "__main__":
     global global_step
     global_step = 1
     global writer
-    writer = SummaryWriter()
+    writer = LogWriter()
     print("[Training] tensorboard log and checkpoints are save in {}".format(
         writer.logdir))
     train(args, config)

examples/fastspeech/synthesis.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
-from tensorboardX import SummaryWriter
+from visualdl import LogWriter
 from scipy.io.wavfile import write
 from collections import OrderedDict
 import argparse
@@ -78,7 +78,7 @@ def synthesis(text_input, args):
     if not os.path.exists(args.output):
         os.mkdir(args.output)
 
-    writer = SummaryWriter(os.path.join(args.output, 'log'))
+    writer = LogWriter(os.path.join(args.output, 'log'))
 
     model = FastSpeech(cfg['network'], num_mels=cfg['audio']['num_mels'])
     # Load parameters.

examples/fastspeech/train.py

@@ -22,7 +22,7 @@ from ruamel import yaml
 from tqdm import tqdm
 from matplotlib import cm
 from collections import OrderedDict
-from tensorboardX import SummaryWriter
+from visualdl import LogWriter
 import paddle.fluid.dygraph as dg
 import paddle.fluid.layers as layers
 import paddle.fluid as fluid
@@ -69,8 +69,8 @@ def main(args):
     if not os.path.exists(args.output):
         os.mkdir(args.output)
 
-    writer = SummaryWriter(os.path.join(args.output,
+    writer = LogWriter(os.path.join(args.output,
-                                        'log')) if local_rank == 0 else None
+                                    'log')) if local_rank == 0 else None
 
     model = FastSpeech(cfg['network'], num_mels=cfg['audio']['num_mels'])
     model.train()

examples/transformer_tts/synthesis.py

@@ -16,7 +16,7 @@ from scipy.io.wavfile import write
 import numpy as np
 from tqdm import tqdm
 from matplotlib import cm
-from tensorboardX import SummaryWriter
+from visualdl import LogWriter
 from ruamel import yaml
 from pathlib import Path
 import argparse
@@ -81,7 +81,7 @@ def synthesis(text_input, args):
     if not os.path.exists(args.output):
         os.mkdir(args.output)
 
-    writer = SummaryWriter(os.path.join(args.output, 'log'))
+    writer = LogWriter(os.path.join(args.output, 'log'))
 
     fluid.enable_dygraph(place)
     with fluid.unique_name.guard():
@@ -121,8 +121,7 @@ def synthesis(text_input, args):
             writer.add_image(
                 'Attention_%d_0' % global_step,
                 x,
-                i * 4 + j,
+                i * 4 + j)
-                dataformats="HWC")
 
     if args.vocoder == 'griffin-lim':
         #synthesis use griffin-lim

examples/transformer_tts/train_transformer.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 import os
 from tqdm import tqdm
-from tensorboardX import SummaryWriter
+from visualdl import LogWriter
 from collections import OrderedDict
 import argparse
 from pprint import pprint
@@ -62,8 +62,8 @@ def main(args):
     if not os.path.exists(args.output):
         os.mkdir(args.output)
 
-    writer = SummaryWriter(os.path.join(args.output,
+    writer = LogWriter(os.path.join(args.output,
-                                        'log')) if local_rank == 0 else None
+                                    'log')) if local_rank == 0 else None
 
     fluid.enable_dygraph(place)
     network_cfg = cfg['network']
@@ -131,23 +131,28 @@ def main(args):
         loss = loss + stop_loss
 
         if local_rank == 0:
-            writer.add_scalars('training_loss', {
+            writer.add_scalar('training_loss/mel_loss',
-                'mel_loss': mel_loss.numpy(),
+                              mel_loss.numpy(),
-                'post_mel_loss': post_mel_loss.numpy()
+                              global_step)
-            }, global_step)
+            writer.add_scalar('training_loss/post_mel_loss',
-
+                              post_mel_loss.numpy(),
+                              global_step)
             writer.add_scalar('stop_loss', stop_loss.numpy(), global_step)
 
             if parallel:
-                writer.add_scalars('alphas', {
+                writer.add_scalar('alphas/encoder_alpha',
-                    'encoder_alpha': model._layers.encoder.alpha.numpy(),
+                                   model._layers.encoder.alpha.numpy(),
-                    'decoder_alpha': model._layers.decoder.alpha.numpy(),
+                                   global_step)
-                }, global_step)
+                writer.add_scalar('alphas/decoder_alpha',
+                                   model._layers.decoder.alpha.numpy(),
+                                   global_step)
             else:
-                writer.add_scalars('alphas', {
+                writer.add_scalar('alphas/encoder_alpha',
-                    'encoder_alpha': model.encoder.alpha.numpy(),
+                                   model.encoder.alpha.numpy(),
-                    'decoder_alpha': model.decoder.alpha.numpy(),
+                                   global_step)
-                }, global_step)
+                writer.add_scalar('alphas/decoder_alpha',
+                                   model.decoder.alpha.numpy(),
+                                   global_step)
 
             writer.add_scalar('learning_rate',
                               optimizer._learning_rate.step().numpy(),
@@ -162,8 +167,7 @@ def main(args):
                         writer.add_image(
                             'Attention_%d_0' % global_step,
                             x,
-                            i * 4 + j,
+                            i * 4 + j)
-                            dataformats="HWC")
 
                 for i, prob in enumerate(attn_enc):
                     for j in range(cfg['network']['encoder_num_head']):
@@ -173,8 +177,7 @@ def main(args):
                         writer.add_image(
                             'Attention_enc_%d_0' % global_step,
                             x,
-                            i * 4 + j,
+                            i * 4 + j)
-                            dataformats="HWC")
 
                 for i, prob in enumerate(attn_dec):
                     for j in range(cfg['network']['decoder_num_head']):
@@ -184,8 +187,7 @@ def main(args):
                         writer.add_image(
                             'Attention_dec_%d_0' % global_step,
                             x,
-                            i * 4 + j,
+                            i * 4 + j)
-                            dataformats="HWC")
 
         if parallel:
             loss = model.scale_loss(loss)

examples/transformer_tts/train_vocoder.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from tensorboardX import SummaryWriter
+from visualdl import LogWriter
 import os
 from tqdm import tqdm
 from pathlib import Path
@@ -60,8 +60,8 @@ def main(args):
     if not os.path.exists(args.output):
         os.mkdir(args.output)
 
-    writer = SummaryWriter(os.path.join(args.output,
+    writer = LogWriter(os.path.join(args.output,
-                                        'log')) if local_rank == 0 else None
+                                    'log')) if local_rank == 0 else None
 
     fluid.enable_dygraph(place)
     model = Vocoder(cfg['train']['batch_size'], cfg['vocoder']['hidden_size'],
@@ -121,7 +121,7 @@ def main(args):
             model.clear_gradients()
 
             if local_rank == 0:
-                writer.add_scalars('training_loss', {'loss': loss.numpy(), },
+                writer.add_scalar('training_loss/loss', loss.numpy(),
                                    global_step)
 
             # save checkpoint

examples/waveflow/train.py

@@ -22,7 +22,8 @@ import argparse
 import numpy as np
 import paddle.fluid.dygraph as dg
 from paddle import fluid
-from tensorboardX import SummaryWriter
+from visualdl import LogWriter
+
 
 import utils
 from parakeet.utils import io
@@ -78,8 +79,8 @@ def train(config):
         os.makedirs(checkpoint_dir)
 
     # Create tensorboard logger.
-    tb = SummaryWriter(os.path.join(run_dir, "logs")) \
+    vdl = LogWriter(os.path.join(run_dir, "logs")) \
-        if rank == 0 else None
+          if rank == 0 else None
 
     # Configurate device
     place = fluid.CUDAPlace(rank) if use_gpu else fluid.CPUPlace()
@@ -94,7 +95,7 @@ def train(config):
         print("Random Seed: ", seed)
 
         # Build model.
-        model = WaveFlow(config, checkpoint_dir, parallel, rank, nranks, tb)
+        model = WaveFlow(config, checkpoint_dir, parallel, rank, nranks, vdl)
         iteration = model.build()
 
         while iteration < config.max_iterations:
@@ -113,7 +114,7 @@ def train(config):
 
     # Close TensorBoard.
     if rank == 0:
-        tb.close()
+        vdl.close()
 
 
 if __name__ == "__main__":

examples/waveflow/waveflow.py

@@ -42,7 +42,7 @@ class WaveFlow():
         rank (int, optional): the rank of the process in a multi-process
             scenario. Defaults to 0.
         nranks (int, optional): the total number of processes. Defaults to 1.
-        tb_logger (obj, optional): logger to visualize metrics.
+        vdl_logger (obj, optional): logger to visualize metrics.
             Defaults to None.
 
     Returns:
@@ -55,13 +55,13 @@ class WaveFlow():
                  parallel=False,
                  rank=0,
                  nranks=1,
-                 tb_logger=None):
+                 vdl_logger=None):
         self.config = config
         self.checkpoint_dir = checkpoint_dir
         self.parallel = parallel
         self.rank = rank
         self.nranks = nranks
-        self.tb_logger = tb_logger
+        self.vdl_logger = vdl_logger
         self.dtype = "float16" if config.use_fp16 else "float32"
 
     def build(self, training=True):
@@ -161,8 +161,8 @@ class WaveFlow():
                   load_time - start_time, graph_time - load_time)
             print(log)
 
-            tb = self.tb_logger
+            vdl_writer = self.vdl_logger
-            tb.add_scalar("Train-Loss-Rank-0", loss_val, iteration)
+            vdl_writer.add_scalar("Train-Loss-Rank-0", loss_val, iteration)
 
     @dg.no_grad
     def valid_step(self, iteration):
@@ -175,7 +175,7 @@ class WaveFlow():
             None
         """
         self.waveflow.eval()
-        tb = self.tb_logger
+        vdl_writer = self.vdl_logger
 
         total_loss = []
         sample_audios = []
@@ -188,10 +188,12 @@ class WaveFlow():
 
             # Visualize latent z and scale log_s.
             if self.rank == 0 and i == 0:
-                tb.add_histogram("Valid-Latent_z", valid_z.numpy(), iteration)
+                vdl_writer.add_histogram("Valid-Latent_z", valid_z.numpy(),
+                                         iteration)
                 for j, valid_log_s in enumerate(valid_log_s_list):
                     hist_name = "Valid-{}th-Flow-Log_s".format(j)
-                    tb.add_histogram(hist_name, valid_log_s.numpy(), iteration)
+                    vdl_writer.add_histogram(hist_name, valid_log_s.numpy(),
+                                             iteration)
 
             valid_loss = self.criterion(valid_outputs)
             total_loss.append(float(valid_loss.numpy()))
@@ -202,7 +204,7 @@ class WaveFlow():
             log = "Test | Rank: {} AvgLoss: {:<8.3f} Time {:<8.3f}".format(
                 self.rank, loss_val, total_time)
             print(log)
-            tb.add_scalar("Valid-Avg-Loss", loss_val, iteration)
+            vdl_writer.add_scalar("Valid-Avg-Loss", loss_val, iteration)
 
     @dg.no_grad
     def infer(self, iteration):

examples/wavenet/synthesis.py

@@ -17,7 +17,6 @@ import os
 import ruamel.yaml
 import argparse
 from tqdm import tqdm
-from tensorboardX import SummaryWriter
 from paddle import fluid
 fluid.require_version('1.8.0')
 import paddle.fluid.dygraph as dg

examples/wavenet/train.py

@@ -17,7 +17,7 @@ import os
 import ruamel.yaml
 import argparse
 import tqdm
-from tensorboardX import SummaryWriter
+from visualdl import LogWriter
 from paddle import fluid
 fluid.require_version('1.8.0')
 import paddle.fluid.dygraph as dg
@@ -154,7 +154,7 @@ if __name__ == "__main__":
     eval_interval = train_config["eval_interval"]
     checkpoint_dir = os.path.join(args.output, "checkpoints")
     log_dir = os.path.join(args.output, "log")
-    writer = SummaryWriter(log_dir)
+    writer = LogWriter(log_dir)
 
     # load parameters and optimizer, and update iterations done so far
     if args.checkpoint is not None:

setup.py

@@ -57,8 +57,7 @@ setup_info = dict(
         'numba==0.47.0',
         'tqdm==4.19.8',
         'matplotlib',
-        'tensorboardX',
+        'visualdl>=2.0.1',
-        'tensorboard',
         'scipy',
         'ruamel.yaml',
         'pandas',