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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-04-06 16:13:11 +02:00
parent a34a760d0f
commit 112f061f4e
2 changed files with 9 additions and 6 deletions
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2
utils/gcp.sh
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@ -11,7 +11,7 @@ sudo reboot now
# Re-clone # Re-clone
sudo rm -rf yolov3 sudo rm -rf yolov3
# git clone https://github.com/ultralytics/yolov3 # master # git clone https://github.com/ultralytics/yolov3 # master
git clone -b map_update --depth 1 https://github.com/ultralytics/yolov3 yolov3 # branch git clone -b test --depth 1 https://github.com/ultralytics/yolov3 yolov3_test # branch
cp -r weights yolov3 cp -r weights yolov3
cp -r cocoapi/PythonAPI/pycocotools yolov3 cp -r cocoapi/PythonAPI/pycocotools yolov3
cd yolov3 cd yolov3

13
utils/utils.py
View File

@ -3,6 +3,7 @@ import random
from collections import defaultdict from collections import defaultdict
import cv2 import cv2
import matplotlib
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
import torch import torch
@ -10,6 +11,8 @@ import torch.nn as nn
from utils import torch_utils from utils import torch_utils
matplotlib.rc('font', **{'family': 'normal', 'size': 11})
# Set printoptions # Set printoptions
torch.set_printoptions(linewidth=1320, precision=5, profile='long') torch.set_printoptions(linewidth=1320, precision=5, profile='long')
np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
@ -286,8 +289,8 @@ def compute_loss(p, targets): # predictions, targets
# Add to dictionary # Add to dictionary
d = defaultdict(float) d = defaultdict(float)
losses = [loss.item(), lxy.item(), lwh.item(), lconf.item(), lcls.item()] losses = [loss.item(), lxy.item(), lwh.item(), lconf.item(), lcls.item()]
for name, x in zip(['total', 'xy', 'wh', 'conf', 'cls'], losses): for k, v in zip(['total', 'xy', 'wh', 'conf', 'cls'], losses):
d[name] = x d[k] = v
return loss, d return loss, d
@ -325,7 +328,7 @@ def build_targets(model, targets):
# Width and height # Width and height
twh.append(torch.log(gwh / layer.anchor_vec[a])) # yolo method twh.append(torch.log(gwh / layer.anchor_vec[a])) # yolo method
# twh.append(torch.sqrt(gwh / layer.anchor_vec[a]) / 2) # power method # twh.append((gwh / layer.anchor_vec[a]) ** (1 / 3) / 2) # power method
# Class # Class
tcls.append(c) tcls.append(c)
@ -468,7 +471,7 @@ def plot_wh_methods(): # from utils.utils import *; plot_wh_methods()
# https://github.com/ultralytics/yolov3/issues/168 # https://github.com/ultralytics/yolov3/issues/168
x = np.arange(-4.0, 4.0, .1) x = np.arange(-4.0, 4.0, .1)
ya = np.exp(x) ya = np.exp(x)
yb = (torch.sigmoid(torch.from_numpy(x)).numpy() * 2) yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2
fig = plt.figure(figsize=(6, 3), dpi=150) fig = plt.figure(figsize=(6, 3), dpi=150)
plt.plot(x, ya, '.-', label='yolo method') plt.plot(x, ya, '.-', label='yolo method')
@ -495,7 +498,7 @@ def plot_results(start=0, stop=0): # from utils.utils import *; plot_results()
x = range(start, stop if stop else results.shape[1]) x = range(start, stop if stop else results.shape[1])
for i in range(10): for i in range(10):
plt.subplot(2, 5, i + 1) plt.subplot(2, 5, i + 1)
plt.plot(x, results[i, x], marker='.', label=f) plt.plot(x, results[i, x].clip(max=500), marker='.', label=f)
plt.title(s[i]) plt.title(s[i])
if i == 0: if i == 0:
plt.legend() plt.legend()