From 4d1d4867f1fe829929105f789e90ba1ba10138f8 Mon Sep 17 00:00:00 2001
From: Andre Anjos <andre.anjos@idiap.ch>
Date: Thu, 14 Nov 2019 15:36:14 +0100
Subject: [PATCH] [engine/predicter] spaces
---
bob/ip/binseg/engine/predicter.py | 26 +++++++++++++-------------
1 file changed, 13 insertions(+), 13 deletions(-)
diff --git a/bob/ip/binseg/engine/predicter.py b/bob/ip/binseg/engine/predicter.py
index b6e8ad06..ebd09ac5 100644
--- a/bob/ip/binseg/engine/predicter.py
+++ b/bob/ip/binseg/engine/predicter.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
-import os
+import os
import logging
import time
import datetime
@@ -24,7 +24,7 @@ def do_predict(
"""
Run inference and calculate metrics
-
+
Parameters
---------
model : :py:class:`torch.nn.Module`
@@ -37,12 +37,12 @@ def do_predict(
logger = logging.getLogger("bob.ip.binseg.engine.inference")
logger.info("Start evaluation")
logger.info("Output folder: {}, Device: {}".format(output_folder, device))
- results_subfolder = os.path.join(output_folder,'results')
+ results_subfolder = os.path.join(output_folder,'results')
os.makedirs(results_subfolder,exist_ok=True)
-
+
model.eval().to(device)
- # Sigmoid for probabilities
- sigmoid = torch.nn.Sigmoid()
+ # Sigmoid for probabilities
+ sigmoid = torch.nn.Sigmoid()
# Setup timers
start_total_time = time.time()
@@ -55,24 +55,24 @@ def do_predict(
start_time = time.perf_counter()
outputs = model(images)
-
- # necessary check for hed architecture that uses several outputs
+
+ # necessary check for hed architecture that uses several outputs
# for loss calculation instead of just the last concatfuse block
if isinstance(outputs,list):
outputs = outputs[-1]
-
+
probabilities = sigmoid(outputs)
-
+
batch_time = time.perf_counter() - start_time
times.append(batch_time)
logger.info("Batch time: {:.5f} s".format(batch_time))
-
+
# Create probability images
save_probability_images(probabilities, names, output_folder, logger)
# Save hdf5
save_hdf(probabilities, names, output_folder, logger)
-
+
# Report times
total_inference_time = str(datetime.timedelta(seconds=int(sum(times))))
average_batch_inference_time = np.mean(times)
@@ -82,7 +82,7 @@ def do_predict(
times_file = "Times.txt"
logger.info("saving {}".format(times_file))
-
+
with open (os.path.join(results_subfolder,times_file), "w+") as outfile:
date = datetime.datetime.now()
outfile.write("Date: {} \n".format(date.strftime("%Y-%m-%d %H:%M:%S")))
--
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