Delete scripts/start_tensorboard.py

scripts/start_tensorboard.py

@@ -1,89 +0,0 @@
-import re
-import os
-import numpy as np
-from tensorboardX import SummaryWriter
-
-writer = SummaryWriter('runs/metrics_visualization')
-
-epoch_pattern = re.compile(r'Train epoch: (\d+) Learning rate: ([\d.eE+-]+)')
-training_loss_pattern = re.compile(r'Training loss: ([\d.]+)')
-metric_pattern = re.compile(r'(\w+ \w+ \w+): ([\d.]+) \(Std: ([\d.]+)\)')
-avg_metric_pattern = re.compile(r'Metric avg (\w+)\s+: ([\d.]+)')
-
-data = {
-    'common': {
-        'learning_rate': [],
-        'training_loss': []
-    },
-    'dry': {
-        'Instr dry sdr': [],
-        'Instr dry l1_freq': [],
-        'Instr dry si_sdr': []
-    },
-    'other': {
-        'Instr other sdr': [],
-        'Instr other l1_freq': [],
-        'Instr other si_sdr': []
-    },
-    'avg': {
-        'Metric avg sdr': [],
-        'Metric avg l1_freq': [],
-        'Metric avg si_sdr': []
-    }
-}
-
-std_data = {
-    'dry': {key: [] for key in data['dry'].keys()},
-    'other': {key: [] for key in data['other'].keys()}
-}
-
-with open(r'E:\AI\datasets\msst\train.log', 'r') as f:
-    epoch = -1
-    for line in f:
-        epoch_match = epoch_pattern.match(line)
-        if epoch_match:
-            epoch = int(epoch_match.group(1))
-            learning_rate = float(epoch_match.group(2))
-            data['common']['learning_rate'].append((epoch, learning_rate))
-            continue
-        
-        training_loss_match = training_loss_pattern.match(line)
-        if training_loss_match:
-            training_loss = float(training_loss_match.group(1))
-            data['common']['training_loss'].append((epoch, training_loss))
-            continue
-        
-        metric_match = metric_pattern.match(line)
-        if metric_match:
-            metric_name = metric_match.group(1)
-            metric_value = float(metric_match.group(2))
-            std_value = float(metric_match.group(3))
-
-            if metric_name in data['dry']:
-                data['dry'][metric_name].append((epoch, metric_value))
-                std_data['dry'][metric_name].append((epoch, std_value))
-            elif metric_name in data['other']:
-                data['other'][metric_name].append((epoch, metric_value))
-                std_data['other'][metric_name].append((epoch, std_value))
-            continue
-        
-        avg_metric_match = avg_metric_pattern.match(line)
-        if avg_metric_match:
-            avg_metric_name = f'Metric avg {avg_metric_match.group(1)}'
-            avg_metric_value = float(avg_metric_match.group(2))
-            data['avg'][avg_metric_name].append((epoch, avg_metric_value))
-
-for category, metrics in data.items():
-    for key, values in metrics.items():
-        category_path = f'{category}/{key.replace(" ", "_").lower()}'
-        for epoch, value in values:
-            writer.add_scalar(f'{category_path}', value, epoch)
-
-for category, metrics in std_data.items():
-    for key, values in metrics.items():
-        category_path = f'{category}/{key.replace(" ", "_").lower()}_std'
-        for epoch, std in values:
-            writer.add_scalar(f'{category_path}', std, epoch)
-
-writer.close()
-os.system('tensorboard --logdir=runs')