From de12a4aa6b0f7959e1a551ff928a6631f87a35b9 Mon Sep 17 00:00:00 2001
From: Andre Anjos <andre.dos.anjos@gmail.com>
Date: Mon, 20 Apr 2020 19:48:40 +0200
Subject: [PATCH] [engine.ssltrainer] Fix doc generation
---
bob/ip/binseg/engine/ssltrainer.py | 14 ++++++++------
1 file changed, 8 insertions(+), 6 deletions(-)
diff --git a/bob/ip/binseg/engine/ssltrainer.py b/bob/ip/binseg/engine/ssltrainer.py
index 27c0089f..d7310ed0 100644
--- a/bob/ip/binseg/engine/ssltrainer.py
+++ b/bob/ip/binseg/engine/ssltrainer.py
@@ -5,10 +5,12 @@ import os
import csv
import time
import datetime
+import distutils.version
+
+import numpy
+import pandas
import torch
-import pandas as pd
from tqdm import tqdm
-import numpy as np
from bob.ip.binseg.utils.metric import SmoothedValue
from bob.ip.binseg.utils.plot import loss_curve
@@ -48,7 +50,7 @@ def mix_up(alpha, input, target, unlabelled_input, unlabled_target):
"""
# TODO:
with torch.no_grad():
- l = np.random.beta(alpha, alpha) # Eq (8)
+ l = numpy.random.beta(alpha, alpha) # Eq (8)
l = max(l, 1 - l) # Eq (9)
# Shuffle and concat. Alg. 1 Line: 12
w_inputs = torch.cat([input, unlabelled_input], 0)
@@ -96,7 +98,7 @@ def square_rampup(current, rampup_length=16):
if rampup_length == 0:
return 1.0
else:
- current = np.clip((current / float(rampup_length)) ** 2, 0.0, 1.0)
+ current = numpy.clip((current / float(rampup_length)) ** 2, 0.0, 1.0)
return float(current)
@@ -121,7 +123,7 @@ def linear_rampup(current, rampup_length=16):
if rampup_length == 0:
return 1.0
else:
- current = np.clip(current / rampup_length, 0.0, 1.0)
+ current = numpy.clip(current / rampup_length, 0.0, 1.0)
return float(current)
@@ -340,7 +342,7 @@ def run(
)
# plots a version of the CSV trainlog into a PDF
- logdf = pd.read_csv(logfile_name, header=0, names=logfile_fields)
+ logdf = pandas.read_csv(logfile_name, header=0, names=logfile_fields)
fig = loss_curve(logdf)
figurefile_name = os.path.join(output_folder, "trainlog.pdf")
logger.info(f"Saving {figurefile_name}")
--
GitLab