Fix bugs

exps/LFNA/vis-synthetic.py

@@ -66,23 +66,24 @@ def draw_multi_fig(save_dir, timestamp, scatter_list, wh, fig_title=None):
 
 def find_min(cur, others):
     if cur is None:
-        return float(others.min())
+        return float(others)
     else:
-        return float(min(cur, others.min()))
+        return float(min(cur, others))
 
 
 def find_max(cur, others):
     if cur is None:
         return float(others.max())
     else:
-        return float(max(cur, others.max()))
+        return float(max(cur, others))
 
 
 def compare_cl(save_dir):
     save_dir = Path(str(save_dir))
     save_dir.mkdir(parents=True, exist_ok=True)
     dynamic_env, function = create_example_v1(
-        timestamp_config=dict(num=200, min_timestamp=-1, max_timestamp=1.0),
+        # timestamp_config=dict(num=200, min_timestamp=-1, max_timestamp=1.0),
+        timestamp_config=None,
         num_per_task=1000,
     )
 
@@ -104,13 +105,11 @@ def compare_cl(save_dir):
         current_data["lfna_xaxis_all"] = xaxis_all
         current_data["lfna_yaxis_all"] = yaxis_all
 
-        import pdb
-
-        pdb.set_trace()
-
         # compute cl-min
-        cl_xaxis_min = find_min(cl_xaxis_min, xaxis_all)
+        cl_xaxis_min = find_min(cl_xaxis_min, xaxis_all.mean() - xaxis_all.std())
-        cl_xaxis_max = find_max(cl_xaxis_max, xaxis_all) + idx * 0.1
+        cl_xaxis_max = (
+            find_max(cl_xaxis_max, xaxis_all.mean() + xaxis_all.std()) + idx * 0.1
+        )
         cl_xaxis_all = np.arange(cl_xaxis_min, cl_xaxis_max, step=0.05)
 
         cl_yaxis_all = cl_function.noise_call(cl_xaxis_all)
@@ -142,8 +141,8 @@ def compare_cl(save_dir):
                 "yaxis": cl_yaxis_all,
                 "color": "r",
                 "s": 10,
-                "xlim": (-6, 6 + timestamp * 0.2),
+                "xlim": (round(cl_xaxis_all.min(), 1), round(cl_xaxis_all.max(), 1)),
-                "ylim": (-40, 40),
+                "ylim": (round(cl_xaxis_all.min(), 1), round(cl_yaxis_all.max(), 1)),
                 "alpha": 0.99,
                 "label": "Continual Learning",
             }
@@ -151,10 +150,10 @@ def compare_cl(save_dir):
 
         draw_multi_fig(
             save_dir,
-            timestamp,
+            idx,
             scatter_list,
             wh=(2000, 1300),
-            fig_title="Timestamp={:03d}".format(timestamp),
+            fig_title="Timestamp={:03d}".format(idx),
         )
     print("Save all figures into {:}".format(save_dir))
     save_dir = save_dir.resolve()

lib/datasets/synthetic_env.py

@@ -63,7 +63,7 @@ class SyntheticDEnv(data.Dataset):
         dataset = np.random.multivariate_normal(
             mean_list, cov_matrix, size=self._num_per_task
         )
-        return index, torch.Tensor(dataset)
+        return timestamp, torch.Tensor(dataset)
 
     def __len__(self):
         return len(self._timestamp_generator)

lib/datasets/synthetic_example.py

@@ -8,9 +8,10 @@ from .synthetic_env import SyntheticDEnv
 
 
 def create_example_v1(
-    timestamp_config=dict(num=100, min_timestamp=0.0, max_timestamp=1.0),
+    timestamp_config=None,
     num_per_task=5000,
 ):
+    # timestamp_config=dict(num=100, min_timestamp=0.0, max_timestamp=1.0),
     mean_generator = ComposedSinFunc()
     std_generator = ComposedSinFunc(min_amplitude=0.5, max_amplitude=0.5)
 

lib/procedures/advanced_main.py

@@ -1,8 +1,10 @@
 #####################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.04 #
 #####################################################
+# To be finished.
+#
 import os, sys, time, torch
-from typing import import Optional, Text, Callable
+from typing import Optional, Text, Callable
 
 # modules in AutoDL
 from log_utils import AverageMeter
@@ -60,9 +62,10 @@ def procedure(
     network,
     criterion,
     optimizer,
+    eval_metric,
     mode: Text,
     print_freq: int = 100,
-    logger_fn: Callable = None
+    logger_fn: Callable = None,
 ):
     data_time, batch_time, losses = AverageMeter(), AverageMeter(), AverageMeter()
     if mode.lower() == "train":
@@ -90,7 +93,7 @@ def procedure(
             optimizer.step()
 
         # record
-        metrics = 
+        metrics = eval_metric(logits.data, targets.data)
         prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
         losses.update(loss.item(), inputs.size(0))
         top1.update(prec1.item(), inputs.size(0))

lib/procedures/eval_funcs.py

@@ -3,6 +3,7 @@
 #####################################################
 import abc
 
+
 def obtain_accuracy(output, target, topk=(1,)):
     """Computes the precision@k for the specified values of k"""
     maxk = max(topk)
@@ -20,7 +21,6 @@ def obtain_accuracy(output, target, topk=(1,)):
 
 
 class EvaluationMetric(abc.ABC):
-    
     def __init__(self):
         self._total_metrics = 0