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car-detection-bayes
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nGT to nT

This commit is contained in:
Glenn Jocher 2018-09-19 04:32:16 +02:00
parent 29fbcb059f
commit 1cfde4aba8
3 changed files with 7 additions and 7 deletions
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10
models.py
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@ -2,8 +2,8 @@ from collections import defaultdict
import torch.nn as nn import torch.nn as nn
from utils.utils import *
from utils.parse_config import * from utils.parse_config import *
from utils.utils import *
def create_modules(module_defs): def create_modules(module_defs):
@ -151,7 +151,7 @@ class YOLOLayer(nn.Module):
# Mask outputs to ignore non-existing objects (but keep confidence predictions) # Mask outputs to ignore non-existing objects (but keep confidence predictions)
nM = mask.sum().float() nM = mask.sum().float()
nGT = sum([len(x) for x in targets]) nT = sum([len(x) for x in targets])
if nM > 0: if nM > 0:
lx = 5 * MSELoss(x[mask], tx[mask]) lx = 5 * MSELoss(x[mask], tx[mask])
ly = 5 * MSELoss(y[mask], ty[mask]) ly = 5 * MSELoss(y[mask], ty[mask])
@ -177,7 +177,7 @@ class YOLOLayer(nn.Module):
FPe[c] += 1 FPe[c] += 1
return loss, loss.item(), lx.item(), ly.item(), lw.item(), lh.item(), lconf.item(), lcls.item(), \ return loss, loss.item(), lx.item(), ly.item(), lw.item(), lh.item(), lconf.item(), lcls.item(), \
nGT, TP, FP, FPe, FN, TC nT, TP, FP, FPe, FN, TC
else: else:
pred_boxes[..., 0] = x.data + self.grid_x pred_boxes[..., 0] = x.data + self.grid_x
@ -200,7 +200,7 @@ class Darknet(nn.Module):
self.module_defs[0]['height'] = img_size self.module_defs[0]['height'] = img_size
self.hyperparams, self.module_list = create_modules(self.module_defs) self.hyperparams, self.module_list = create_modules(self.module_defs)
self.img_size = img_size self.img_size = img_size
self.loss_names = ['loss', 'x', 'y', 'w', 'h', 'conf', 'cls', 'nGT', 'TP', 'FP', 'FPe', 'FN', 'TC'] self.loss_names = ['loss', 'x', 'y', 'w', 'h', 'conf', 'cls', 'nT', 'TP', 'FP', 'FPe', 'FN', 'TC']
def forward(self, x, targets=None, requestPrecision=False, epoch=None): def forward(self, x, targets=None, requestPrecision=False, epoch=None):
is_training = targets is not None is_training = targets is not None
@ -230,7 +230,7 @@ class Darknet(nn.Module):
layer_outputs.append(x) layer_outputs.append(x)
if is_training: if is_training:
self.losses['nGT'] /= 3 self.losses['nT'] /= 3
self.losses['TC'] /= 3 self.losses['TC'] /= 3
metrics = torch.zeros(4, len(self.losses['FPe'])) # TP, FP, FN, target_count metrics = torch.zeros(4, len(self.losses['FPe'])) # TP, FP, FN, target_count

2
utils/utils.py
View File

@ -190,7 +190,7 @@ def bbox_iou(box1, box2, x1y1x2y2=True):
def build_targets(pred_boxes, pred_conf, pred_cls, target, anchor_wh, nA, nC, nG, requestPrecision): def build_targets(pred_boxes, pred_conf, pred_cls, target, anchor_wh, nA, nC, nG, requestPrecision):
""" """
returns nGT, nCorrect, tx, ty, tw, th, tconf, tcls returns nT, nCorrect, tx, ty, tw, th, tconf, tcls
""" """
nB = len(target) # target.shape[0] nB = len(target) # target.shape[0]
nT = [len(x) for x in target] # torch.argmin(target[:, :, 4], 1) # targets per image nT = [len(x) for x in target] # torch.argmin(target[:, :, 4], 1) # targets per image