Add script to plot learning curve

benchmark/paddle/image/plotlog.py

+#coding=utf-8
+
+import sys
+import argparse
+import matplotlib.pyplot as plt
+
+
+def parse_args():
+    parser = argparse.ArgumentParser('Parse Log')
+    parser.add_argument(
+        '--file_path', '-f', type=str, help='the path of the log file')
+    parser.add_argument(
+        '--sample_rate',
+        '-s',
+        type=float,
+        default=1.0,
+        help='the rate to take samples from log')
+    parser.add_argument(
+        '--log_period', '-p', type=int, default=1, help='the period of log')
+
+    args = parser.parse_args()
+    return args
+
+
+def parse_file(file_name):
+    loss = []
+    error = []
+    with open(file_name) as f:
+        for i, line in enumerate(f):
+            line = line.strip()
+            if not line.startswith('pass'):
+                continue
+            line_split = line.split(' ')
+            if len(line_split) != 5:
+                continue
+
+            loss_str = line_split[2][:-1]
+            cur_loss = float(loss_str.split('=')[-1])
+            loss.append(cur_loss)
+
+            err_str = line_split[3][:-1]
+            cur_err = float(err_str.split('=')[-1])
+            error.append(cur_err)
+
+    accuracy = [1.0 - err for err in error]
+
+    return loss, accuracy
+
+
+def sample(metric, sample_rate):
+    interval = int(1.0 / sample_rate)
+    if interval > len(metric):
+        return metric[:1]
+
+    num = len(metric) / interval
+    idx = [interval * i for i in range(num)]
+    metric_sample = [metric[id] for id in idx]
+    return metric_sample
+
+
+def plot_metric(metric, batch_id, graph_title):
+    plt.figure()
+    plt.title(graph_title)
+    plt.plot(batch_id, metric)
+    plt.xlabel('batch')
+    plt.ylabel(graph_title)
+    plt.savefig(graph_title + '.jpg')
+    plt.close()
+
+
+def main():
+    args = parse_args()
+    assert args.sample_rate > 0. and args.sample_rate <= 1.0, "The sample rate should in the range (0, 1]."
+
+    loss, accuracy = parse_file(args.file_path)
+    batch = [args.log_period * i for i in range(len(loss))]
+
+    batch_sample = sample(batch, args.sample_rate)
+    loss_sample = sample(loss, args.sample_rate)
+    accuracy_sample = sample(accuracy, args.sample_rate)
+
+    plot_metric(loss_sample, batch_sample, 'loss')
+    plot_metric(accuracy_sample, batch_sample, 'accuracy')
+
+
+if __name__ == '__main__':
+    main()