Fix bugs

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