plot saved feature data, first version

visualize.py

+import argparse
+import matplotlib.pyplot as plt
+import torch
+import numpy as np
+
+
+def setup_parser():
+    out = argparse.ArgumentParser()
+    out.add_argument('--folder',
+                     default=r'C:/Users/cassi/OneDrive/Desktop/Master_Thesis/2702_test/',
+                     type=str, help="Path to load model parameter")
+    return out
+
+
+def plot(data, label, save_path):
+    fig = plt.figure()
+    data = np.array(data)
+    plt.plot(np.arange(0, len(data)), data, label=label)
+    plt.legend()
+    plt.savefig(save_path)
+
+
+if __name__ == '__main__':
+    parser = setup_parser()
+    args, unknown = parser.parse_known_args()
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    folder = args.folder
+
+
+    with open(f"{folder}avg_prob_non_empty_per_batch.txt", 'r') as fp:
+        avg_prob_non_empty_pre = fp.read()
+
+    avg_prob_non_empty_pre = avg_prob_non_empty_pre.split("\n")
+    avg_prob_non_empty = [float(t) for t in avg_prob_non_empty_pre[:-1]]
+
+    plot(avg_prob_non_empty, "avg_prob_non_empty", f"{folder}avg_prob_non_empty.png")
+
+    with open(f"{folder}ms_per_batch.txt", 'r') as fp:
+        ms_per_batch_all_pre = fp.read()
+
+    ms_per_batch_all_pre = ms_per_batch_all_pre.split("\n")
+    ms_per_batch_all = [float(t) for t in ms_per_batch_all_pre[:-1]]
+
+    plot(ms_per_batch_all, "ms_per_batch_all", f"{folder}ms_per_batch_all.png")
+
+    with open(f"{folder}ppl.txt", 'r') as fp:
+        ppl_all_pre = fp.read()
+
+    ppl_all_pre = ppl_all_pre.split("\n")
+    ppl_all = [float(t) for t in ppl_all_pre[:-1]]
+
+    plot(ppl_all[1:], "ppl_all", f"{folder}ppl_all.png")
+
+    with open(f"{folder}ppl_v_2.txt", 'r') as fp:
+        ppl_v_2_all_pre = fp.read()
+
+    ppl_v_2_all_pre = ppl_v_2_all_pre.split("\n")
+    ppl_v_2_all = [float(t) for t in ppl_v_2_all_pre[:-1]]
+
+    plot(ppl_v_2_all[7:], "ppl_v_2_all", f"{folder}ppl_v_2_all.png")
+
+    with open(f"{folder}loss.txt", 'r') as fp:
+        loss_all_pre = fp.read()
+
+    loss_all_pre = loss_all_pre.split("\n")
+    loss_all = [float(t) for t in loss_all_pre[:-1]]
+
+    plot(loss_all, "loss_all", f"{folder}loss_all.png")
+
+    try:
+        with open(f"{folder}greater_ratio_values.txt", 'r') as fp:
+            greater_ratio_values_all_pre = fp.read()
+
+        greater_ratio_values_all_pre = greater_ratio_values_all_pre.split("\n")
+        greater_ratio_values_all = [float(t) for t in greater_ratio_values_all_pre[:-1]]
+
+        plot(greater_ratio_values_all, "greater_ratio_values_all", f"{folder}greater_ratio_values_all.png")
+    except FileNotFoundError:
+        greater_ratio_values_all = []
+
+    with open(f"{folder}avg_prob_non_empty_per_batch.txt", 'r') as fp:
+        avg_prob_non_empty_per_batch_pre = fp.read()
+
+    avg_prob_non_empty_per_batch_pre = avg_prob_non_empty_per_batch_pre.split("\n")
+    avg_prob_non_empty_per_batch = [float(t) for t in avg_prob_non_empty_per_batch_pre[:-1]]
+
+    plot(avg_prob_non_empty_per_batch, "avg_prob_non_empty_per_batch", f"{folder}avg_prob_non_empty_per_batch.png")
+
+    with open(f"{folder}average_right_tokens.txt", 'r') as fp:
+        average_right_tokens_all_pre = fp.read()
+
+    average_right_tokens_all_pre = average_right_tokens_all_pre.split("\n")
+    average_right_tokens_all = [float(t) for t in average_right_tokens_all_pre[:-1]]
+
+    plot(average_right_tokens_all, "average_right_tokens_all", f"{folder}average_right_tokens_all.png")
+