#!/usr/bin/env python
-import math
+import math, re
import torch, torchvision
return s, variables
+def extract_results(seq):
+ f = lambda a: (a[0], -1 if a[1] == "" else int(a[1]))
+ results = [
+ dict([f(tuple(x.split(":"))) for x in re.findall("[A-Z]:[0-9]*", s)])
+ for s in seq
+ ]
+ return results
+
+
def generate_sequences(nb, nb_variables=5, length=20, randomize_length=False):
sequences = []
for n in range(nb):
import time
start_time = time.perf_counter()
- sequences = generate_sequences(1000, randomize_length=True)
+ sequences = generate_sequences(1000)
end_time = time.perf_counter()
for s in sequences[:10]:
print(s)
print(f"{len(sequences) / (end_time - start_time):.02f} samples per second")
+
+ print(extract_results(sequences[:10]))