Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-02-27 12:32:25 +01:00
parent 9a27339e04
commit 358f34afa8
2 changed files with 6 additions and 4 deletions
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8
test.py
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@ -1,5 +1,6 @@
import argparse import argparse
import json import json
import time
from pathlib import Path from pathlib import Path
from models import * from models import *
@ -47,6 +48,7 @@ def test(
AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC) AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
coco91class = coco80_to_coco91_class() coco91class = coco80_to_coco91_class()
for batch_i, (imgs, targets, paths, shapes) in enumerate(dataloader): for batch_i, (imgs, targets, paths, shapes) in enumerate(dataloader):
t = time.time()
output = model(imgs.to(device)) output = model(imgs.to(device))
output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres) output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres)
@ -128,13 +130,13 @@ def test(
mean_P = np.mean(mP) mean_P = np.mean(mP)
# Print image mAP and running mean mAP # Print image mAP and running mean mAP
print(('%11s%11s' + '%11.3g' * 3) % (seen, dataloader.nF, mean_P, mean_R, mean_mAP)) print(('%11s%11s' + '%11.3g' * 4 + 's') %
(seen, dataloader.nF, mean_P, mean_R, mean_mAP, time.time() - t))
# Print mAP per class # Print mAP per class
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP') + '\n\nmAP Per Class:') print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP') + '\n\nmAP Per Class:')
classes = load_classes(data_cfg_dict['names']) # Extracts class labels from file for i, c in enumerate(load_classes(data_cfg_dict['names'])):
for i, c in enumerate(classes):
print('%15s: %-.4f' % (c, AP_accum[i] / (AP_accum_count[i] + 1E-16))) print('%15s: %-.4f' % (c, AP_accum[i] / (AP_accum_count[i] + 1E-16)))
# Save JSON # Save JSON

2
utils/utils.py
View File

@ -374,7 +374,7 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
if prediction.is_cuda: if prediction.is_cuda:
unique_labels = unique_labels.cuda(prediction.device) unique_labels = unique_labels.cuda(prediction.device)
nms_style = 'MERGE' # 'OR' (default), 'AND', 'MERGE' (experimental) nms_style = 'OR' # 'OR' (default), 'AND', 'MERGE' (experimental)
for c in unique_labels: for c in unique_labels:
# Get the detections with class c # Get the detections with class c
dc = detections[detections[:, -1] == c] dc = detections[detections[:, -1] == c]