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EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
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17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
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Program, unless a warranty or assumption of liability accompanies a
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END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
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(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.

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README.md Executable file
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<img src="https://storage.googleapis.com/ultralytics/UltralyticsLogoName1000×676.png" width="200">
# Introduction
This directory contains software developed by Ultralytics LLC. For more information on Ultralytics projects please visit:
http://www.ultralytics.com  
# Description
The https://github.com/ultralytics/yolov3 repo contains code to train YOLOv3 on the COCO dataset: https://cocodataset.org/#home. Credit to P.J. Reddie for YOLO (https://pjreddie.com/darknet/yolo/) and to Erik Lindernoren for the pytorch implementation this repo is based on (https://github.com/eriklindernoren/PyTorch-YOLOv3).
# Requirements
Python 3.6 or later with the following `pip3 install -U -r requirements.txt` packages:
- `numpy`
- `torch`
- `opencv-python`
# Running
Run `train.py` to begin training. Each epoch trains on 120,000 images from the train and validate sets, and validates on 5000 images in the validation set. An Nvidia GTX 1080 Ti will run about 16 epochs per day. Loss plots for the bounding boxes, objectness and class confidence should appear similar to results shown here.
![Alt](https://github.com/ultralytics/yolov3/blob/master/data/xview_training_loss.png "training loss")
Checkpoints will be saved in `/checkpoints` directory. Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here.
![Alt](https://github.com/ultralytics/yolov3/blob/master/data/zidane_result.jpg "example")
Run `test.py` to test the latest checkpoint on the 5000 validation images. Joseph Redmon's official YOLOv3 weights produce a mAP of .581 using this method, compared to .579 in his paper.
# Contact
For questions or comments please contact Glenn Jocher at glenn.jocher@ultralytics.com or visit us at http://www.ultralytics.com/contact

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classes=80
train=/Users/glennjocher/Downloads/DATA/coco/trainvalno5k.txt
valid=/Users/glennjocher/Downloads/DATA/coco/5k.txt
names=data/coco.names
backup=backup/
eval=coco

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[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=16
subdivisions=1
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 6,7,8
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 3,4,5
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 0,1,2
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1

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person
bicycle
car
motorbike
aeroplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
sofa
pottedplant
bed
diningtable
toilet
tvmonitor
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush

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#!/bin/bash
# CREDIT: https://github.com/pjreddie/darknet/tree/master/scripts/get_coco_dataset.sh
# Clone COCO API
git clone https://github.com/pdollar/coco
cd coco
mkdir images
cd images
# Download Images
wget -c https://pjreddie.com/media/files/train2014.zip
wget -c https://pjreddie.com/media/files/val2014.zip
# Unzip
unzip -q train2014.zip
unzip -q val2014.zip
cd ..
# Download COCO Metadata
wget -c https://pjreddie.com/media/files/instances_train-val2014.zip
wget -c https://pjreddie.com/media/files/coco/5k.part
wget -c https://pjreddie.com/media/files/coco/trainvalno5k.part
wget -c https://pjreddie.com/media/files/coco/labels.tgz
tar xzf labels.tgz
unzip -q instances_train-val2014.zip
# Set Up Image Lists
#paste <(awk "{print \"$PWD\"}" <5k.part) 5k.part | tr -d '\t' > 5k.txt
#paste <(awk "{print \"$PWD\"}" <trainvalno5k.part) trainvalno5k.part | tr -d '\t' > trainvalno5k.txt
sudo shutdown
# get xview training data
# wget -O train_images.tgz 'https://d307kc0mrhucc3.cloudfront.net/train_images.tgz?Expires=1530124049&Signature=JrQoxipmsETvb7eQHCfDFUO-QEHJGAayUv0i-ParmS-1hn7hl9D~bzGuHWG82imEbZSLUARTtm0wOJ7EmYMGmG5PtLKz9H5qi6DjoSUuFc13NQ-~6yUhE~NfPaTnehUdUMCa3On2wl1h1ZtRG~0Jq1P-AJbpe~oQxbyBrs1KccaMa7FK4F4oMM6sMnNgoXx8-3O77kYw~uOpTMFmTaQdHln6EztW0Lx17i57kK3ogbSUpXgaUTqjHCRA1dWIl7PY1ngQnLslkLhZqmKcaL-BvWf0ZGjHxCDQBpnUjIlvMu5NasegkwD9Jjc0ClgTxsttSkmbapVqaVC8peR0pO619Q__&Key-Pair-Id=APKAIKGDJB5C3XUL2DXQ'
# tar -xvzf train_images.tgz
# sudo rm -rf train_images/._*
# lastly convert each .tif to a .bmp for faster loading in cv2

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import argparse
import time
from models import *
from utils.datasets import *
from utils.utils import *
cuda = torch.cuda.is_available()
device = torch.device('cuda:0' if cuda else 'cpu')
parser = argparse.ArgumentParser()
# Get data configuration
# cd yolo && python3 detect.py -secondary_classifier 1
parser.add_argument('-image_folder', type=str, default='data/samples', help='path to images')
parser.add_argument('-output_folder', type=str, default='output', help='path to outputs')
parser.add_argument('-plot_flag', type=bool, default=True)
parser.add_argument('-txt_out', type=bool, default=False)
parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file')
parser.add_argument('-conf_thres', type=float, default=0.8, help='object confidence threshold')
parser.add_argument('-nms_thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
parser.add_argument('-batch_size', type=int, default=1, help='size of the batches')
parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension')
opt = parser.parse_args()
print(opt)
def detect(opt):
os.system('rm -rf ' + opt.output_folder)
os.makedirs(opt.output_folder, exist_ok=True)
# Load model
model = Darknet(opt.cfg, opt.img_size)
weights_path = 'checkpoints/yolov3.weights'
if weights_path.endswith('.weights'): # saved in darknet format
load_weights(model, weights_path)
else: # endswith('.pt'), saved in pytorch format
checkpoint = torch.load(weights_path, map_location='cpu')
model.load_state_dict(checkpoint['model'])
del checkpoint
# current = model.state_dict()
# saved = checkpoint['model']
# # 1. filter out unnecessary keys
# saved = {k: v for k, v in saved.items() if ((k in current) and (current[k].shape == v.shape))}
# # 2. overwrite entries in the existing state dict
# current.update(saved)
# # 3. load the new state dict
# model.load_state_dict(current)
# model.to(device).eval()
# del checkpoint, current, saved
model.to(device).eval()
# Set Dataloader
classes = load_classes(opt.class_path) # Extracts class labels from file
dataloader = ImageFolder(opt.image_folder, batch_size=opt.batch_size, img_size=opt.img_size)
imgs = [] # Stores image paths
img_detections = [] # Stores detections for each image index
prev_time = time.time()
detections = None
for batch_i, (img_paths, img) in enumerate(dataloader):
print(batch_i, img.shape, end=' ')
preds = []
# Get detections
with torch.no_grad():
# Normal orientation
chip = torch.from_numpy(img).unsqueeze(0).to(device)
pred = model(chip)
pred = pred[pred[:, :, 4] > opt.conf_thres]
if len(pred) > 0:
preds.append(pred.unsqueeze(0))
if len(preds) > 0:
detections = non_max_suppression(torch.cat(preds, 1), opt.conf_thres, opt.nms_thres)
img_detections.extend(detections)
imgs.extend(img_paths)
print('Batch %d... (Done %.3fs)' % (batch_i, time.time() - prev_time))
prev_time = time.time()
# Bounding-box colors
color_list = [[random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)] for _ in range(len(classes))]
if len(img_detections) == 0:
return
# Iterate through images and save plot of detections
for img_i, (path, detections) in enumerate(zip(imgs, img_detections)):
print("image %g: '%s'" % (img_i, path))
if opt.plot_flag:
img = cv2.imread(path)
# The amount of padding that was added
pad_x = max(img.shape[0] - img.shape[1], 0) * (opt.img_size / max(img.shape))
pad_y = max(img.shape[1] - img.shape[0], 0) * (opt.img_size / max(img.shape))
# Image height and width after padding is removed
unpad_h = opt.img_size - pad_y
unpad_w = opt.img_size - pad_x
# Draw bounding boxes and labels of detections
if detections is not None:
unique_classes = detections[:, -1].cpu().unique()
bbox_colors = random.sample(color_list, len(unique_classes))
# write results to .txt file
results_img_path = os.path.join(opt.output_folder, path.split('/')[-1])
results_txt_path = results_img_path + '.txt'
if os.path.isfile(results_txt_path):
os.remove(results_txt_path)
for i in unique_classes:
n = (detections[:, -1].cpu() == i).sum()
print('%g %ss' % (n, classes[int(i)]))
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
# Rescale coordinates to original dimensions
box_h = ((y2 - y1) / unpad_h) * img.shape[0]
box_w = ((x2 - x1) / unpad_w) * img.shape[1]
y1 = (((y1 - pad_y // 2) / unpad_h) * img.shape[0]).round().item()
x1 = (((x1 - pad_x // 2) / unpad_w) * img.shape[1]).round().item()
x2 = (x1 + box_w).round().item()
y2 = (y1 + box_h).round().item()
x1, y1, x2, y2 = max(x1, 0), max(y1, 0), max(x2, 0), max(y2, 0)
# write to file
if opt.txt_out:
with open(results_txt_path, 'a') as file:
file.write(('%g %g %g %g %g %g \n') % (x1, y1, x2, y2, cls_pred, cls_conf * conf))
if opt.plot_flag:
# Add the bbox to the plot
label = '%s %.2f' % (classes[int(cls_pred)], cls_conf) if cls_conf > 0.05 else None
color = bbox_colors[int(np.where(unique_classes == int(cls_pred))[0])]
plot_one_box([x1, y1, x2, y2], img, label=label, color=color, line_thickness=3)
if opt.plot_flag:
# Save generated image with detections
cv2.imwrite(results_img_path.replace('.bmp', '.jpg').replace('.tif', '.jpg'), img)
if __name__ == '__main__':
torch.cuda.empty_cache()
detect(opt)

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from collections import defaultdict
import torch.nn as nn
from utils.utils import *
from utils.parse_config import *
def create_modules(module_defs):
"""
Constructs module list of layer blocks from module configuration in module_defs
"""
hyperparams = module_defs.pop(0)
output_filters = [int(hyperparams['channels'])]
module_list = nn.ModuleList()
for i, module_def in enumerate(module_defs):
modules = nn.Sequential()
if module_def['type'] == 'convolutional':
bn = int(module_def['batch_normalize'])
filters = int(module_def['filters'])
kernel_size = int(module_def['size'])
pad = (kernel_size - 1) // 2 if int(module_def['pad']) else 0
modules.add_module('conv_%d' % i, nn.Conv2d(in_channels=output_filters[-1],
out_channels=filters,
kernel_size=kernel_size,
stride=int(module_def['stride']),
padding=pad,
bias=not bn))
if bn:
modules.add_module('batch_norm_%d' % i, nn.BatchNorm2d(filters))
if module_def['activation'] == 'leaky':
modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1))
elif module_def['type'] == 'upsample':
upsample = nn.Upsample(scale_factor=int(module_def['stride']), mode='nearest')
modules.add_module('upsample_%d' % i, upsample)
elif module_def['type'] == 'route':
layers = [int(x) for x in module_def["layers"].split(',')]
filters = sum([output_filters[layer_i] for layer_i in layers])
modules.add_module('route_%d' % i, EmptyLayer())
elif module_def['type'] == 'shortcut':
filters = output_filters[int(module_def['from'])]
modules.add_module("shortcut_%d" % i, EmptyLayer())
elif module_def["type"] == "yolo":
anchor_idxs = [int(x) for x in module_def["mask"].split(",")]
# Extract anchors
anchors = [float(x) for x in module_def["anchors"].split(",")]
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
anchors = [anchors[i] for i in anchor_idxs]
num_classes = int(module_def['classes'])
img_height = int(hyperparams['height'])
# Define detection layer
yolo_layer = YOLOLayer(anchors, num_classes, img_height, anchor_idxs)
modules.add_module('yolo_%d' % i, yolo_layer)
# Register module list and number of output filters
module_list.append(modules)
output_filters.append(filters)
return hyperparams, module_list
class EmptyLayer(nn.Module):
"""Placeholder for 'route' and 'shortcut' layers"""
def __init__(self):
super(EmptyLayer, self).__init__()
class YOLOLayer(nn.Module):
# YOLO Layer 0
def __init__(self, anchors, nC, img_dim, anchor_idxs):
super(YOLOLayer, self).__init__()
anchors = [(a_w, a_h) for a_w, a_h in anchors] # (pixels)
nA = len(anchors)
self.anchors = anchors
self.nA = nA # number of anchors (3)
self.nC = nC # number of classes (60)
self.bbox_attrs = 5 + nC
self.img_dim = img_dim # from hyperparams in cfg file, NOT from parser
if anchor_idxs[0] == (nA * 2): # 6
stride = 32
elif anchor_idxs[0] == nA: # 3
stride = 16
else:
stride = 8
# Build anchor grids
nG = int(self.img_dim / stride)
self.grid_x = torch.arange(nG).repeat(nG, 1).view([1, 1, nG, nG]).float()
self.grid_y = torch.arange(nG).repeat(nG, 1).t().view([1, 1, nG, nG]).float()
self.scaled_anchors = torch.FloatTensor([(a_w / stride, a_h / stride) for a_w, a_h in anchors])
self.anchor_w = self.scaled_anchors[:, 0:1].view((1, nA, 1, 1))
self.anchor_h = self.scaled_anchors[:, 1:2].view((1, nA, 1, 1))
def forward(self, p, targets=None, requestPrecision=False, epoch=None):
FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor
# device = torch.device('cuda:0' if p.is_cuda else 'cpu')
bs = p.shape[0]
nG = p.shape[2]
stride = self.img_dim / nG
if p.is_cuda and not self.grid_x.is_cuda:
self.grid_x, self.grid_y = self.grid_x.cuda(), self.grid_y.cuda()
self.anchor_w, self.anchor_h = self.anchor_w.cuda(), self.anchor_h.cuda()
# self.scaled_anchors = self.scaled_anchors.cuda()
# x.view(4, 650, 19, 19) -- > (4, 10, 19, 19, 65) # (bs, anchors, grid, grid, classes + xywh)
p = p.view(bs, self.nA, self.bbox_attrs, nG, nG).permute(0, 1, 3, 4, 2).contiguous() # prediction
# Get outputs
x = torch.sigmoid(p[..., 0]) # Center x
y = torch.sigmoid(p[..., 1]) # Center y
w = p[..., 2] # Width
h = p[..., 3] # Height
width = torch.exp(w.data) * self.anchor_w
height = torch.exp(h.data) * self.anchor_h
# Add offset and scale with anchors (in grid space, i.e. 0-13)
pred_boxes = FT(bs, self.nA, nG, nG, 4)
pred_conf = p[..., 4] # Conf
pred_cls = p[..., 5:] # Class
# Training
if targets is not None:
BCEWithLogitsLoss1 = nn.BCEWithLogitsLoss(size_average=False) # version 0.4.0
BCEWithLogitsLoss0 = nn.BCEWithLogitsLoss()
# BCEWithLogitsLoss2 = nn.BCEWithLogitsLoss(size_average=True)
MSELoss = nn.MSELoss(size_average=False) # version 0.4.0
CrossEntropyLoss = nn.CrossEntropyLoss()
if requestPrecision:
gx = self.grid_x[:, :, :nG, :nG]
gy = self.grid_y[:, :, :nG, :nG]
pred_boxes[..., 0] = x.data + gx - width / 2
pred_boxes[..., 1] = y.data + gy - height / 2
pred_boxes[..., 2] = x.data + gx + width / 2
pred_boxes[..., 3] = y.data + gy + height / 2
tx, ty, tw, th, mask, tcls, TP, FP, FN, TC = \
build_targets(pred_boxes, pred_conf, pred_cls, targets, self.scaled_anchors, self.nA, self.nC, nG,
requestPrecision)
tcls = tcls[mask]
if x.is_cuda:
tx, ty, tw, th, mask, tcls = tx.cuda(), ty.cuda(), tw.cuda(), th.cuda(), mask.cuda(), tcls.cuda()
# Mask outputs to ignore non-existing objects (but keep confidence predictions)
nM = mask.sum().float()
nGT = sum([len(x) for x in targets])
if nM > 0:
lx = 5 * MSELoss(x[mask], tx[mask])
ly = 5 * MSELoss(y[mask], ty[mask])
lw = 5 * MSELoss(w[mask], tw[mask])
lh = 5 * MSELoss(h[mask], th[mask])
lconf = 1.5 * BCEWithLogitsLoss1(pred_conf[mask], mask[mask].float())
lcls = nM * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
# lcls = BCEWithLogitsLoss1(pred_cls[mask], tcls.float())
else:
lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
lconf += nM * BCEWithLogitsLoss0(pred_conf[~mask], mask[~mask].float())
loss = lx + ly + lw + lh + lconf + lcls
i = torch.sigmoid(pred_conf[~mask]) > 0.99
FPe = torch.zeros(self.nC)
if i.sum() > 0:
FP_classes = torch.argmax(pred_cls[~mask][i], 1)
for c in FP_classes:
FPe[c] += 1
return loss, loss.item(), lx.item(), ly.item(), lw.item(), lh.item(), lconf.item(), lcls.item(), \
nGT, TP, FP, FPe, FN, TC
else:
pred_boxes[..., 0] = x.data + self.grid_x
pred_boxes[..., 1] = y.data + self.grid_y
pred_boxes[..., 2] = width
pred_boxes[..., 3] = height
# If not in training phase return predictions
output = torch.cat((pred_boxes.view(bs, -1, 4) * stride,
torch.sigmoid(pred_conf.view(bs, -1, 1)), pred_cls.view(bs, -1, self.nC)), -1)
return output.data
class Darknet(nn.Module):
"""YOLOv3 object detection model"""
def __init__(self, config_path, img_size=416):
super(Darknet, self).__init__()
self.module_defs = parse_model_config(config_path)
self.module_defs[0]['height'] = img_size
self.hyperparams, self.module_list = create_modules(self.module_defs)
self.img_size = img_size
self.loss_names = ['loss', 'x', 'y', 'w', 'h', 'conf', 'cls', 'nGT', 'TP', 'FP', 'FPe', 'FN', 'TC']
def forward(self, x, targets=None, requestPrecision=False, epoch=None):
is_training = targets is not None
output = []
self.losses = defaultdict(float)
layer_outputs = []
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
if module_def['type'] in ['convolutional', 'upsample']:
x = module(x)
elif module_def['type'] == 'route':
layer_i = [int(x) for x in module_def['layers'].split(',')]
x = torch.cat([layer_outputs[i] for i in layer_i], 1)
elif module_def['type'] == 'shortcut':
layer_i = int(module_def['from'])
x = layer_outputs[-1] + layer_outputs[layer_i]
elif module_def['type'] == 'yolo':
# Train phase: get loss
if is_training:
x, *losses = module[0](x, targets, requestPrecision, epoch)
for name, loss in zip(self.loss_names, losses):
self.losses[name] += loss
# Test phase: Get detections
else:
x = module(x)
output.append(x)
layer_outputs.append(x)
if is_training:
self.losses['nGT'] /= 3
self.losses['TC'] /= 3
metrics = torch.zeros(4, len(self.losses['FPe'])) # TP, FP, FN, target_count
ui = np.unique(self.losses['TC'])[1:]
for i in ui:
j = self.losses['TC'] == float(i)
metrics[0, i] = (self.losses['TP'][j] > 0).sum().float() # TP
metrics[1, i] = (self.losses['FP'][j] > 0).sum().float() # FP
metrics[2, i] = (self.losses['FN'][j] == 3).sum().float() # FN
metrics[3] = metrics.sum(0)
metrics[1] += self.losses['FPe']
self.losses['TP'] = metrics[0].sum()
self.losses['FP'] = metrics[1].sum()
self.losses['FN'] = metrics[2].sum()
self.losses['TC'] = 0
self.losses['metrics'] = metrics
return sum(output) if is_training else torch.cat(output, 1)
def load_weights(self, weights_path):
"""Parses and loads the weights stored in 'weights_path'"""
# Open the weights file
fp = open(weights_path, "rb")
header = np.fromfile(fp, dtype=np.int32, count=5) # First five are header values
# Needed to write header when saving weights
self.header_info = header
self.seen = header[3]
weights = np.fromfile(fp, dtype=np.float32) # The rest are weights
fp.close()
ptr = 0
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
if module_def['type'] == 'convolutional':
conv_layer = module[0]
if module_def['batch_normalize']:
# Load BN bias, weights, running mean and running variance
bn_layer = module[1]
num_b = bn_layer.bias.numel() # Number of biases
# Bias
bn_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.bias)
bn_layer.bias.data.copy_(bn_b)
ptr += num_b
# Weight
bn_w = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.weight)
bn_layer.weight.data.copy_(bn_w)
ptr += num_b
# Running Mean
bn_rm = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_mean)
bn_layer.running_mean.data.copy_(bn_rm)
ptr += num_b
# Running Var
bn_rv = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_var)
bn_layer.running_var.data.copy_(bn_rv)
ptr += num_b
else:
# Load conv. bias
num_b = conv_layer.bias.numel()
conv_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(conv_layer.bias)
conv_layer.bias.data.copy_(conv_b)
ptr += num_b
# Load conv. weights
num_w = conv_layer.weight.numel()
conv_w = torch.from_numpy(weights[ptr:ptr + num_w]).view_as(conv_layer.weight)
conv_layer.weight.data.copy_(conv_w)
ptr += num_w
"""
@:param path - path of the new weights file
@:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
"""
def save_weights(self, path, cutoff=-1):
fp = open(path, 'wb')
self.header_info[3] = self.seen
self.header_info.tofile(fp)
# Iterate through layers
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
if module_def['type'] == 'convolutional':
conv_layer = module[0]
# If batch norm, load bn first
if module_def['batch_normalize']:
bn_layer = module[1]
bn_layer.bias.data.cpu().numpy().tofile(fp)
bn_layer.weight.data.cpu().numpy().tofile(fp)
bn_layer.running_mean.data.cpu().numpy().tofile(fp)
bn_layer.running_var.data.cpu().numpy().tofile(fp)
# Load conv bias
else:
conv_layer.bias.data.cpu().numpy().tofile(fp)
# Load conv weights
conv_layer.weight.data.cpu().numpy().tofile(fp)
fp.close()

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requirements.txt Executable file
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# pip3 install -U -r requirements.txt
numpy
scipy
opencv-python
torch
matplotlib
tqdm
h5py

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import argparse
from models import *
from utils.datasets import *
from utils.utils import *
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=200, help='number of epochs')
parser.add_argument('--batch_size', type=int, default=32, help='size of each image batch')
parser.add_argument('--model_config_path', type=str, default='cfg/yolov3.cfg', help='path to model config file')
parser.add_argument('--data_config_path', type=str, default='cfg/coco.data', help='path to data config file')
parser.add_argument('--weights_path', type=str, default='checkpoints/yolov3.weights', help='path to weights file')
parser.add_argument('--class_path', type=str, default='data/coco.names', help='path to class label file')
parser.add_argument('--iou_thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
parser.add_argument('--conf_thres', type=float, default=0.5, help='object confidence threshold')
parser.add_argument('--nms_thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
parser.add_argument('--n_cpu', type=int, default=0, help='number of cpu threads to use during batch generation')
parser.add_argument('--img_size', type=int, default=416, help='size of each image dimension')
parser.add_argument('--use_cuda', type=bool, default=True, help='whether to use cuda if available')
opt = parser.parse_args()
print(opt)
cuda = torch.cuda.is_available() and opt.use_cuda
device = torch.device('cuda:0' if cuda else 'cpu')
# Get data configuration
data_config = parse_data_config(opt.data_config_path)
test_path = data_config['valid']
num_classes = int(data_config['classes'])
# Initiate model
model = Darknet(opt.model_config_path, opt.img_size)
# Load weights
weights_path = 'checkpoints/yolov3.pt'
if weights_path.endswith('.weights'): # darknet format
load_weights(model, weights_path)
elif weights_path.endswith('.pt'): # pytorch format
checkpoint = torch.load(weights_path, map_location='cpu')
model.load_state_dict(checkpoint['model'])
del checkpoint
model.to(device).eval()
# Get dataloader
# dataset = ListDataset(test_path)
# dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
dataloader = ListDataset(test_path, batch_size=opt.batch_size, img_size=opt.img_size)
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
n_gt = 0
correct = 0
print('Compute mAP...')
outputs = []
targets = None
APs = []
for batch_i, (imgs, targets) in enumerate(dataloader):
imgs = imgs.to(device)
with torch.no_grad():
output = model(imgs)
output = non_max_suppression(output, conf_thres=opt.conf_thres, nms_thres=opt.nms_thres)
# Compute average precision for each sample
for sample_i in range(len(targets)):
correct = []
# Get labels for sample where width is not zero (dummies)
annotations = targets[sample_i]
# Extract detections
detections = output[sample_i]
if detections is None:
# If there are no detections but there are annotations mask as zero AP
if annotations.size(0) != 0:
APs.append(0)
continue
# Get detections sorted by decreasing confidence scores
detections = detections[np.argsort(-detections[:, 4])]
# If no annotations add number of detections as incorrect
if annotations.size(0) == 0:
correct.extend([0 for _ in range(len(detections))])
else:
# Extract target boxes as (x1, y1, x2, y2)
target_boxes = torch.FloatTensor(annotations[:, 1:].shape)
target_boxes[:, 0] = (annotations[:, 1] - annotations[:, 3] / 2)
target_boxes[:, 1] = (annotations[:, 2] - annotations[:, 4] / 2)
target_boxes[:, 2] = (annotations[:, 1] + annotations[:, 3] / 2)
target_boxes[:, 3] = (annotations[:, 2] + annotations[:, 4] / 2)
target_boxes *= opt.img_size
detected = []
for *pred_bbox, conf, obj_conf, obj_pred in detections:
pred_bbox = torch.FloatTensor(pred_bbox).view(1, -1)
# Compute iou with target boxes
iou = bbox_iou(pred_bbox, target_boxes)
# Extract index of largest overlap
best_i = np.argmax(iou)
# If overlap exceeds threshold and classification is correct mark as correct
if iou[best_i] > opt.iou_thres and obj_pred == annotations[best_i, 0] and best_i not in detected:
correct.append(1)
detected.append(best_i)
else:
correct.append(0)
# Extract true and false positives
true_positives = np.array(correct)
false_positives = 1 - true_positives
# Compute cumulative false positives and true positives
false_positives = np.cumsum(false_positives)
true_positives = np.cumsum(true_positives)
# Compute recall and precision at all ranks
recall = true_positives / annotations.size(0) if annotations.size(0) else true_positives
precision = true_positives / np.maximum(true_positives + false_positives, np.finfo(np.float64).eps)
# Compute average precision
AP = compute_ap(recall, precision)
APs.append(AP)
print("+ Sample [%d/%d] AP: %.4f (%.4f)" % (len(APs), len(dataloader) * opt.batch_size, AP, np.mean(APs)))
print("Mean Average Precision: %.4f" % np.mean(APs))

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import argparse
import time
from sys import platform
from models import *
from utils.datasets import *
from utils.utils import *
parser = argparse.ArgumentParser()
parser.add_argument('-epochs', type=int, default=999, help='number of epochs')
parser.add_argument('-batch_size', type=int, default=12, help='size of each image batch')
parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='data config file path')
parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension')
parser.add_argument('-resume', default=False, help='resume training flag')
opt = parser.parse_args()
print(opt)
cuda = torch.cuda.is_available()
device = torch.device('cuda:0' if cuda else 'cpu')
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
if cuda:
torch.cuda.manual_seed(0)
torch.cuda.manual_seed_all(0)
torch.backends.cudnn.benchmark = True
def main(opt):
os.makedirs('checkpoints', exist_ok=True)
# Configure run
data_config = parse_data_config(opt.data_config_path)
num_classes = int(data_config['classes'])
if platform == 'darwin': # macos
train_path = data_config['valid']
else: # linux (gcp cloud)
train_path = '../coco/trainvalno5k.txt'
# Initialize model
model = Darknet(opt.cfg, opt.img_size)
# Get dataloader
dataloader = ListDataset(train_path, batch_size=opt.batch_size, img_size=opt.img_size)
# reload saved optimizer state
start_epoch = 0
best_loss = float('inf')
if opt.resume:
checkpoint = torch.load('checkpoints/latest.pt', map_location='cpu')
model.load_state_dict(checkpoint['model'])
if torch.cuda.device_count() > 1:
print('Using ', torch.cuda.device_count(), ' GPUs')
model = nn.DataParallel(model)
model.to(device).train()
# # Transfer learning
# for i, (name, p) in enumerate(model.named_parameters()):
# #name = name.replace('module_list.', '')
# #print('%4g %70s %9s %12g %20s %12g %12g' % (
# # i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
# if p.shape[0] != 650: # not YOLO layer
# p.requires_grad = False
# Set optimizer
# optimizer = torch.optim.SGD(model.parameters(), lr=.001, momentum=.9, weight_decay=0.0005 * 0, nesterov=True)
# optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()))
optimizer = torch.optim.Adam(model.parameters())
optimizer.load_state_dict(checkpoint['optimizer'])
start_epoch = checkpoint['epoch'] + 1
best_loss = checkpoint['best_loss']
del checkpoint # current, saved
else:
if torch.cuda.device_count() > 1:
print('Using ', torch.cuda.device_count(), ' GPUs')
model = nn.DataParallel(model)
model.to(device).train()
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-4, weight_decay=5e-4)
# Set scheduler
# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 24, eta_min=0.00001, last_epoch=-1)
# y = 0.001 * exp(-0.00921 * x) # 1e-4 @ 250, 1e-5 @ 500
# scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.99082, last_epoch=start_epoch - 1)
modelinfo(model)
t0, t1 = time.time(), time.time()
print('%10s' * 16 % (
'Epoch', 'Batch', 'x', 'y', 'w', 'h', 'conf', 'cls', 'total', 'P', 'R', 'nGT', 'TP', 'FP', 'FN', 'time'))
for epoch in range(opt.epochs):
epoch += start_epoch
# img_size = random.choice([19, 20, 21, 22, 23, 24, 25]) * 32
# dataloader = ListDataset(train_path, batch_size=opt.batch_size, img_size=img_size, targets_path=targets_path)
# print('Running image size %g' % img_size)
# Update scheduler
# if epoch % 25 == 0:
# scheduler.last_epoch = -1 # for cosine annealing, restart every 25 epochs
# scheduler.step()
# if epoch <= 100:
# for g in optimizer.param_groups:
# g['lr'] = 0.0005 * (0.992 ** epoch) # 1/10 th every 250 epochs
# g['lr'] = 0.001 * (0.9773 ** epoch) # 1/10 th every 100 epochs
# g['lr'] = 0.0005 * (0.955 ** epoch) # 1/10 th every 50 epochs
# g['lr'] = 0.0005 * (0.926 ** epoch) # 1/10 th every 30 epochs
ui = -1
rloss = defaultdict(float) # running loss
metrics = torch.zeros(4, num_classes)
for i, (imgs, targets) in enumerate(dataloader):
n = opt.batch_size # number of pictures at a time
for j in range(int(len(imgs) / n)):
targets_j = targets[j * n:j * n + n]
nGT = sum([len(x) for x in targets_j])
if nGT < 1:
continue
loss = model(imgs[j * n:j * n + n].to(device), targets_j, requestPrecision=True, epoch=epoch)
optimizer.zero_grad()
loss.backward()
optimizer.step()
ui += 1
metrics += model.losses['metrics']
for key, val in model.losses.items():
rloss[key] = (rloss[key] * ui + val) / (ui + 1)
# Precision
precision = metrics[0] / (metrics[0] + metrics[1] + 1e-16)
k = (metrics[0] + metrics[1]) > 0
if k.sum() > 0:
mean_precision = precision[k].mean()
else:
mean_precision = 0
# Recall
recall = metrics[0] / (metrics[0] + metrics[2] + 1e-16)
k = (metrics[0] + metrics[2]) > 0
if k.sum() > 0:
mean_recall = recall[k].mean()
else:
mean_recall = 0
s = ('%10s%10s' + '%10.3g' * 14) % (
'%g/%g' % (epoch, opt.epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['x'],
rloss['y'], rloss['w'], rloss['h'], rloss['conf'], rloss['cls'],
rloss['loss'], mean_precision, mean_recall, model.losses['nGT'], model.losses['TP'],
model.losses['FP'], model.losses['FN'], time.time() - t1)
t1 = time.time()
print(s)
# if i == 1:
# return
# Write epoch results
with open('results.txt', 'a') as file:
file.write(s + '\n')
# Update best loss
loss_per_target = rloss['loss'] / rloss['nGT']
if loss_per_target < best_loss:
best_loss = loss_per_target
# Save latest checkpoint
checkpoint = {'epoch': epoch,
'best_loss': best_loss,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()}
torch.save(checkpoint, 'checkpoints/latest.pt')
# Save best checkpoint
if best_loss == loss_per_target:
os.system('cp checkpoints/latest.pt checkpoints/best.pt')
# Save backup checkpoint
if (epoch > 0) & (epoch % 100 == 0):
os.system('cp checkpoints/latest.pt checkpoints/backup' + str(epoch) + '.pt')
# Save final model
dt = time.time() - t0
print('Finished %g epochs in %.2fs (%.2fs/epoch)' % (epoch, dt, dt / (epoch + 1)))
if __name__ == '__main__':
torch.cuda.empty_cache()
main(opt)
torch.cuda.empty_cache()

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import glob
import math
import os
import random
import cv2
import numpy as np
import torch
# from torch.utils.data import Dataset
from utils.utils import xyxy2xywh
class ImageFolder(): # for eval-only
def __init__(self, path, batch_size=1, img_size=416):
if os.path.isdir(path):
self.files = sorted(glob.glob('%s/*.*' % path))
elif os.path.isfile(path):
self.files = [path]
self.nF = len(self.files) # number of image files
self.nB = math.ceil(self.nF / batch_size) # number of batches
self.batch_size = batch_size
self.height = img_size
assert self.nF > 0, 'No images found in path %s' % path
# RGB normalization values
# self.rgb_mean = np.array([60.134, 49.697, 40.746], dtype=np.float32).reshape((3, 1, 1))
# self.rgb_std = np.array([29.99, 24.498, 22.046], dtype=np.float32).reshape((3, 1, 1))
def __iter__(self):
self.count = -1
return self
def __next__(self):
self.count += 1
if self.count == self.nB:
raise StopIteration
img_path = self.files[self.count]
# Read image
img = cv2.imread(img_path) # BGR
# Padded resize
img, _, _, _ = resize_square(img, height=self.height, color=(127.5, 127.5, 127.5))
# Normalize RGB
img = img[:, :, ::-1].transpose(2, 0, 1)
img = np.ascontiguousarray(img, dtype=np.float32)
# img -= self.rgb_mean
# img /= self.rgb_std
img /= 255.0
return [img_path], img
def __len__(self):
return self.nB # number of batches
class ListDataset(): # for training
def __init__(self, path, batch_size=1, img_size=608):
self.path = path
#self.img_files = sorted(glob.glob('%s/*.*' % path))
with open(path, 'r') as file:
self.img_files = file.readlines()
self.img_files = [path.replace('\n', '').replace('/images','/Users/glennjocher/Downloads/DATA/coco/images') for path in self.img_files]
self.label_files = [path.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt') for path in
self.img_files]
self.nF = len(self.img_files) # number of image files
self.nB = math.ceil(self.nF / batch_size) # number of batches
self.batch_size = batch_size
#assert self.nB > 0, 'No images found in path %s' % path
self.height = img_size
# RGB normalization values
# self.rgb_mean = np.array([60.134, 49.697, 40.746], dtype=np.float32).reshape((1, 3, 1, 1))
# self.rgb_std = np.array([29.99, 24.498, 22.046], dtype=np.float32).reshape((1, 3, 1, 1))
def __iter__(self):
self.count = -1
# self.shuffled_vector = np.random.permutation(self.nF) # shuffled vector
self.shuffled_vector = np.arange(self.nF)
return self
def __next__(self):
self.count += 1
if self.count == self.nB:
raise StopIteration
ia = self.count * self.batch_size
ib = min((self.count + 1) * self.batch_size, self.nF)
height = self.height
img_all = []
labels_all = []
for index, files_index in enumerate(range(ia, ib)):
img_path = self.img_files[self.shuffled_vector[files_index]]
label_path = self.label_files[self.shuffled_vector[files_index]]
img = cv2.imread(img_path) # BGR
if img is None:
continue
augment_hsv = False
if augment_hsv:
# SV augmentation by 50%
fraction = 0.50
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
S = img_hsv[:, :, 1].astype(np.float32)
V = img_hsv[:, :, 2].astype(np.float32)
a = (random.random() * 2 - 1) * fraction + 1
S *= a
if a > 1:
np.clip(S, a_min=0, a_max=255, out=S)
a = (random.random() * 2 - 1) * fraction + 1
V *= a
if a > 1:
np.clip(V, a_min=0, a_max=255, out=V)
img_hsv[:, :, 1] = S.astype(np.uint8)
img_hsv[:, :, 2] = V.astype(np.uint8)
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
h, w, _ = img.shape
img, ratio, padw, padh = resize_square(img, height=height, color=(127.5, 127.5, 127.5))
# Load labels
if os.path.isfile(label_path):
labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 5)
# Normalized xywh to pixel xyxy format
labels = labels0.copy()
labels[:, 1] = ratio * w * (labels0[:, 1] - labels0[:, 3] / 2) + padw
labels[:, 2] = ratio * h * (labels0[:, 2] - labels0[:, 4] / 2) + padh
labels[:, 3] = ratio * w * (labels0[:, 1] + labels0[:, 3] / 2) + padw
labels[:, 4] = ratio * h * (labels0[:, 2] + labels0[:, 4] / 2) + padh
else:
labels = np.array([])
# Augment image and labels
# img, labels, M = random_affine(img, targets=labels, degrees=(-5, 5), translate=(0.1, 0.1), scale=(0.8, 1.2)) # RGB
plotFlag = False
if plotFlag:
import matplotlib.pyplot as plt
plt.subplot(4, 4, index + 1).imshow(img[:, :, ::-1])
plt.plot(labels[:, [1, 3, 3, 1, 1]].T, labels[:, [2, 2, 4, 4, 2]].T, '.-')
nL = len(labels)
if nL > 0:
# convert xyxy to xywh
labels[:, 1:5] = xyxy2xywh(labels[:, 1:5].copy()) / height
# random left-right flip
lr_flip = False
if lr_flip & (random.random() > 0.5):
img = np.fliplr(img)
if nL > 0:
labels[:, 1] = 1 - labels[:, 1]
# random up-down flip
ud_flip = False
if ud_flip & (random.random() > 0.5):
img = np.flipud(img)
if nL > 0:
labels[:, 2] = 1 - labels[:, 2]
img_all.append(img)
labels_all.append(torch.from_numpy(labels))
# Normalize
img_all = np.stack(img_all)[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB and cv2 to pytorch
img_all = np.ascontiguousarray(img_all, dtype=np.float32)
# img_all -= self.rgb_mean
# img_all /= self.rgb_std
img_all /= 255.0
return torch.from_numpy(img_all), labels_all
def __len__(self):
return self.nB # number of batches
def resize_square(img, height=416, color=(0, 0, 0)): # resize a rectangular image to a padded square
shape = img.shape[:2] # shape = [height, width]
ratio = float(height) / max(shape)
new_shape = [round(shape[0] * ratio), round(shape[1] * ratio)]
dw = height - new_shape[1] # width padding
dh = height - new_shape[0] # height padding
top, bottom = dh // 2, dh - (dh // 2)
left, right = dw // 2, dw - (dw // 2)
img = cv2.resize(img, (new_shape[1], new_shape[0]), interpolation=cv2.INTER_AREA)
return cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color), ratio, dw // 2, dh // 2
def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-3, 3),
borderValue=(0, 0, 0)):
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
# https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
border = 0 # width of added border (optional)
height = max(img.shape[0], img.shape[1]) + border * 2
# Rotation and Scale
R = np.eye(3)
a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
# a += random.choice([-180, -90, 0, 90]) # random 90deg rotations added to small rotations
s = random.random() * (scale[1] - scale[0]) + scale[0]
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
# Translation
T = np.eye(3)
T[0, 2] = (random.random() * 2 - 1) * translate[0] * img.shape[0] + border # x translation (pixels)
T[1, 2] = (random.random() * 2 - 1) * translate[1] * img.shape[1] + border # y translation (pixels)
# Shear
S = np.eye(3)
S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # y shear (deg)
M = S @ T @ R # ORDER IS IMPORTANT HERE!!
imw = cv2.warpPerspective(img, M, dsize=(height, height), flags=cv2.INTER_LINEAR,
borderValue=borderValue) # BGR order (YUV-equalized BGR means)
# Return warped points also
if targets is not None:
if len(targets) > 0:
n = targets.shape[0]
points = targets[:, 1:5].copy()
area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1])
# warp points
xy = np.ones((n * 4, 3))
xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
xy = (xy @ M.T)[:, :2].reshape(n, 8)
# create new boxes
x = xy[:, [0, 2, 4, 6]]
y = xy[:, [1, 3, 5, 7]]
xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
# apply angle-based reduction
radians = a * math.pi / 180
reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
x = (xy[:, 2] + xy[:, 0]) / 2
y = (xy[:, 3] + xy[:, 1]) / 2
w = (xy[:, 2] - xy[:, 0]) * reduction
h = (xy[:, 3] - xy[:, 1]) * reduction
xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
# reject warped points outside of image
np.clip(xy, 0, height, out=xy)
w = xy[:, 2] - xy[:, 0]
h = xy[:, 3] - xy[:, 1]
area = w * h
ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
i = (w > 4) & (h > 4) & (area / area0 > 0.1) & (ar < 10)
targets = targets[i]
targets[:, 1:5] = xy[i]
return imw, targets, M
else:
return imw
def convert_tif2bmp(p='/Users/glennjocher/Downloads/DATA/xview/val_images_bmp'):
import glob
import cv2
files = sorted(glob.glob('%s/*.tif' % p))
for i, f in enumerate(files):
print('%g/%g' % (i + 1, len(files)))
img = cv2.imread(f)
cv2.imwrite(f.replace('.tif', '.bmp'), img)
os.system('rm -rf ' + f)

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#!/usr/bin/env bash
# Start
sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3 && cd yolov3 && python3 train.py -img_size 416 -epochs 999
# Resume
cd yolov3 && python3 train.py -img_size 416 -resume 1
# Detect
gsutil cp gs://ultralytics/fresh9_5_e201.pt yolov3/checkpoints
cd yolov3 && python3 detect.py

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def parse_model_config(path):
"""Parses the yolo-v3 layer configuration file and returns module definitions"""
file = open(path, 'r')
lines = file.read().split('\n')
lines = [x for x in lines if x and not x.startswith('#')]
lines = [x.rstrip().lstrip() for x in lines] # get rid of fringe whitespaces
module_defs = []
for line in lines:
if line.startswith('['): # This marks the start of a new block
module_defs.append({})
module_defs[-1]['type'] = line[1:-1].rstrip()
if module_defs[-1]['type'] == 'convolutional':
module_defs[-1]['batch_normalize'] = 0
else:
key, value = line.split("=")
value = value.strip()
module_defs[-1][key.rstrip()] = value.strip()
return module_defs
def parse_data_config(path):
"""Parses the data configuration file"""
options = dict()
options['gpus'] = '0,1,2,3'
options['num_workers'] = '10'
with open(path, 'r') as fp:
lines = fp.readlines()
for line in lines:
line = line.strip()
if line == '' or line.startswith('#'):
continue
key, value = line.split('=')
options[key.strip()] = value.strip()
return options

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utils/utils.py Executable file
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import random
import cv2
import numpy as np
import torch
import torch.nn.functional as F
# set printoptions
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
def load_classes(path):
"""
Loads class labels at 'path'
"""
fp = open(path, "r")
names = fp.read().split("\n")[:-1]
return names
def modelinfo(model):
nparams = sum(x.numel() for x in model.parameters())
ngradients = sum(x.numel() for x in model.parameters() if x.requires_grad)
print('\n%4s %70s %9s %12s %20s %12s %12s' % ('', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
for i, (name, p) in enumerate(model.named_parameters()):
name = name.replace('module_list.', '')
print('%4g %70s %9s %12g %20s %12g %12g' % (
i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
print('\n%g layers, %g parameters, %g gradients' % (i + 1, nparams, ngradients))
def xview_class_weights(indices): # weights of each class in the training set, normalized to mu = 1
weights = 1 / torch.FloatTensor(
[74, 364, 713, 71, 2925, 209767, 6925, 1101, 3612, 12134, 5871, 3640, 860, 4062, 895, 149, 174, 17, 1624, 1846,
125, 122, 124, 662, 1452, 697, 222, 190, 786, 200, 450, 295, 79, 205, 156, 181, 70, 64, 337, 1352, 336, 78,
628, 841, 287, 83, 702, 1177, 313865, 195, 1081, 882, 1059, 4175, 123, 1700, 2317, 1579, 368, 85])
weights /= weights.sum()
return weights[indices]
def plot_one_box(x, im, color=None, label=None, line_thickness=None):
tl = line_thickness or round(0.003 * max(im.shape[0:2])) # line thickness
color = color or [random.randint(0, 255) for _ in range(3)]
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
cv2.rectangle(im, c1, c2, color, thickness=tl)
if label:
tf = max(tl - 1, 1) # font thickness
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
cv2.rectangle(im, c1, c2, color, -1) # filled
cv2.putText(im, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.03)
elif classname.find('BatchNorm2d') != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.03)
torch.nn.init.constant_(m.bias.data, 0.0)
def xyxy2xywh(box):
xywh = np.zeros(box.shape)
xywh[:, 0] = (box[:, 0] + box[:, 2]) / 2
xywh[:, 1] = (box[:, 1] + box[:, 3]) / 2
xywh[:, 2] = box[:, 2] - box[:, 0]
xywh[:, 3] = box[:, 3] - box[:, 1]
return xywh
def compute_ap(recall, precision):
""" Compute the average precision, given the recall and precision curves.
Code originally from https://github.com/rbgirshick/py-faster-rcnn.
# Arguments
recall: The recall curve (list).
precision: The precision curve (list).
# Returns
The average precision as computed in py-faster-rcnn.
"""
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.], recall, [1.]))
mpre = np.concatenate(([0.], precision, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
def bbox_iou(box1, box2, x1y1x2y2=True):
# if len(box1.shape) == 1:
# box1 = box1.reshape(1, 4)
"""
Returns the IoU of two bounding boxes
"""
if x1y1x2y2:
# Get the coordinates of bounding boxes
b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]
else:
# Transform from center and width to exact coordinates
b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
# get the corrdinates of the intersection rectangle
inter_rect_x1 = torch.max(b1_x1, b2_x1)
inter_rect_y1 = torch.max(b1_y1, b2_y1)
inter_rect_x2 = torch.min(b1_x2, b2_x2)
inter_rect_y2 = torch.min(b1_y2, b2_y2)
# Intersection area
inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1, 0) * torch.clamp(inter_rect_y2 - inter_rect_y1, 0)
# Union Area
b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
return inter_area / (b1_area + b2_area - inter_area + 1e-16)
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
"""
nB = len(target) # target.shape[0]
nT = [len(x) for x in target] # torch.argmin(target[:, :, 4], 1) # targets per image
tx = torch.zeros(nB, nA, nG, nG) # batch size (4), number of anchors (3), number of grid points (13)
ty = torch.zeros(nB, nA, nG, nG)
tw = torch.zeros(nB, nA, nG, nG)
th = torch.zeros(nB, nA, nG, nG)
tconf = torch.ByteTensor(nB, nA, nG, nG).fill_(0)
tcls = torch.ByteTensor(nB, nA, nG, nG, nC).fill_(0) # nC = number of classes
TP = torch.ByteTensor(nB, max(nT)).fill_(0)
FP = torch.ByteTensor(nB, max(nT)).fill_(0)
FN = torch.ByteTensor(nB, max(nT)).fill_(0)
TC = torch.ShortTensor(nB, max(nT)).fill_(-1) # target category
for b in range(nB):
nTb = nT[b] # number of targets
if nTb == 0:
continue
t = target[b]
FN[b, :nTb] = 1
# Convert to position relative to box
TC[b, :nTb], gx, gy, gw, gh = t[:, 0].long(), t[:, 1] * nG, t[:, 2] * nG, t[:, 3] * nG, t[:, 4] * nG
# Get grid box indices and prevent overflows (i.e. 13.01 on 13 anchors)
gi = torch.clamp(gx.long(), min=0, max=nG - 1)
gj = torch.clamp(gy.long(), min=0, max=nG - 1)
# iou of targets-anchors (using wh only)
box1 = t[:, 3:5] * nG
# box2 = anchor_grid_wh[:, gj, gi]
box2 = anchor_wh.unsqueeze(1).repeat(1, nTb, 1)
inter_area = torch.min(box1, box2).prod(2)
iou_anch = inter_area / (gw * gh + box2.prod(2) - inter_area + 1e-16)
# Select best iou_pred and anchor
iou_anch_best, a = iou_anch.max(0) # best anchor [0-2] for each target
# Two targets can not claim the same anchor
if nTb > 1:
iou_order = np.argsort(-iou_anch_best) # best to worst
# u = torch.cat((gi, gj, a), 0).view(3, -1).numpy()
# _, first_unique = np.unique(u[:, iou_order], axis=1, return_index=True) # first unique indices
u = gi.float() * 0.4361538773074043 + gj.float() * 0.28012496588736746 + a.float() * 0.6627147212460307
_, first_unique = np.unique(u[iou_order], return_index=True) # first unique indices
# print(((np.sort(first_unique) - np.sort(first_unique2)) ** 2).sum())
i = iou_order[first_unique]
# best anchor must share significant commonality (iou) with target
i = i[iou_anch_best[i] > 0.10]
if len(i) == 0:
continue
a, gj, gi, t = a[i], gj[i], gi[i], t[i]
if len(t.shape) == 1:
t = t.view(1, 5)
else:
if iou_anch_best < 0.10:
continue
i = 0
tc, gx, gy, gw, gh = t[:, 0].long(), t[:, 1] * nG, t[:, 2] * nG, t[:, 3] * nG, t[:, 4] * nG
# Coordinates
tx[b, a, gj, gi] = gx - gi.float()
ty[b, a, gj, gi] = gy - gj.float()
# Width and height (sqrt method)
# tw[b, a, gj, gi] = torch.sqrt(gw / anchor_wh[a, 0]) / 2
# th[b, a, gj, gi] = torch.sqrt(gh / anchor_wh[a, 1]) / 2
# Width and height (yolov3 method)
tw[b, a, gj, gi] = torch.log(gw / anchor_wh[a, 0] + 1e-16)
th[b, a, gj, gi] = torch.log(gh / anchor_wh[a, 1] + 1e-16)
# One-hot encoding of label
tcls[b, a, gj, gi, tc] = 1
tconf[b, a, gj, gi] = 1
if requestPrecision:
# predicted classes and confidence
tb = torch.cat((gx - gw / 2, gy - gh / 2, gx + gw / 2, gy + gh / 2)).view(4, -1).t() # target boxes
pcls = torch.argmax(pred_cls[b, a, gj, gi], 1).cpu()
pconf = torch.sigmoid(pred_conf[b, a, gj, gi]).cpu()
iou_pred = bbox_iou(tb, pred_boxes[b, a, gj, gi].cpu())
TP[b, i] = (pconf > 0.99) & (iou_pred > 0.5) & (pcls == tc)
FP[b, i] = (pconf > 0.99) & (TP[b, i] == 0) # coordinates or class are wrong
FN[b, i] = pconf <= 0.99 # confidence score is too low (set to zero)
return tx, ty, tw, th, tconf, tcls, TP, FP, FN, TC
def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
prediction = prediction.cpu()
"""
Removes detections with lower object confidence score than 'conf_thres' and performs
Non-Maximum Suppression to further filter detections.
Returns detections with shape:
(x1, y1, x2, y2, object_conf, class_score, class_pred)
"""
output = [None for _ in range(len(prediction))]
for image_i, pred in enumerate(prediction):
# Filter out confidence scores below threshold
# Get score and class with highest confidence
# cross-class NMS
cross_class_nms = False
if cross_class_nms:
thresh = 0.85
a = pred.clone()
a = a[np.argsort(-a[:, 4])] # sort best to worst
radius = 30 # area to search for cross-class ious
for i in range(len(a)):
if i >= len(a) - 1:
break
close = (np.abs(a[i, 0] - a[i + 1:, 0]) < radius) & (np.abs(a[i, 1] - a[i + 1:, 1]) < radius)
close = close.nonzero()
if len(close) > 0:
close = close + i + 1
iou = bbox_iou(a[i:i + 1, :4], a[close.squeeze(), :4].reshape(-1, 4), x1y1x2y2=False)
bad = close[iou > thresh]
if len(bad) > 0:
mask = torch.ones(len(a)).type(torch.ByteTensor)
mask[bad] = 0
a = a[mask]
pred = a
x, y, w, h = pred[:, 0].numpy(), pred[:, 1].numpy(), pred[:, 2].numpy(), pred[:, 3].numpy()
a = w * h # area
ar = w / (h + 1e-16) # aspect ratio
log_w, log_h, log_a, log_ar = np.log(w), np.log(h), np.log(a), np.log(ar)
# n = len(w)
# shape_likelihood = np.zeros((n, 60), dtype=np.float32)
# x = np.concatenate((log_w.reshape(-1, 1), log_h.reshape(-1, 1)), 1)
# from scipy.stats import multivariate_normal
# for c in range(60):
# shape_likelihood[:, c] = multivariate_normal.pdf(x, mean=mat['class_mu'][c, :2], cov=mat['class_cov'][c, :2, :2])
class_prob, class_pred = torch.max(F.softmax(pred[:, 5:], 1), 1)
v = ((pred[:, 4] > conf_thres) & (class_prob > .3)).numpy()
v = v.nonzero()
pred = pred[v]
class_prob = class_prob[v]
class_pred = class_pred[v]
# If none are remaining => process next image
nP = pred.shape[0]
if not nP:
continue
# From (center x, center y, width, height) to (x1, y1, x2, y2)
box_corner = pred.new(nP, 4)
xy = pred[:, 0:2]
wh = pred[:, 2:4] / 2
box_corner[:, 0:2] = xy - wh
box_corner[:, 2:4] = xy + wh
pred[:, :4] = box_corner
# Detections ordered as (x1, y1, x2, y2, obj_conf, class_prob, class_pred)
detections = torch.cat((pred[:, :5], class_prob.float().unsqueeze(1), class_pred.float().unsqueeze(1)), 1)
# Iterate through all predicted classes
unique_labels = detections[:, -1].cpu().unique()
if prediction.is_cuda:
unique_labels = unique_labels.cuda()
nms_style = 'OR' # 'AND' or 'OR' (classical)
for c in unique_labels:
# Get the detections with the particular class
detections_class = detections[detections[:, -1] == c]
# Sort the detections by maximum objectness confidence
_, conf_sort_index = torch.sort(detections_class[:, 4], descending=True)
detections_class = detections_class[conf_sort_index]
# Perform non-maximum suppression
max_detections = []
if nms_style == 'OR': # Classical NMS
while detections_class.shape[0]:
# Get detection with highest confidence and save as max detection
max_detections.append(detections_class[0].unsqueeze(0))
# Stop if we're at the last detection
if len(detections_class) == 1:
break
# Get the IOUs for all boxes with lower confidence
ious = bbox_iou(max_detections[-1], detections_class[1:])
# Remove detections with IoU >= NMS threshold
detections_class = detections_class[1:][ious < nms_thres]
elif nms_style == 'AND': # 'AND'-style NMS, at least two boxes must share commonality to pass, single boxes erased
while detections_class.shape[0]:
if len(detections_class) == 1:
break
ious = bbox_iou(detections_class[:1], detections_class[1:])
if ious.max() > 0.5:
max_detections.append(detections_class[0].unsqueeze(0))
# Remove detections with IoU >= NMS threshold
detections_class = detections_class[1:][ious < nms_thres]
if len(max_detections) > 0:
max_detections = torch.cat(max_detections).data
# Add max detections to outputs
output[image_i] = max_detections if output[image_i] is None else torch.cat(
(output[image_i], max_detections))
return output
def strip_optimizer_from_checkpoint(filename='checkpoints/best.pt'):
# Strip optimizer from *.pt files for lighter files (reduced by 2/3 size)
import torch
a = torch.load(filename, map_location='cpu')
a['optimizer'] = []
torch.save(a, filename.replace('.pt', '_lite.pt'))
def plotResults():
# Plot YOLO training results file "results.txt"
import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(18, 9))
s = ['x', 'y', 'w', 'h', 'conf', 'cls', 'loss', 'prec', 'recall']
for f in ('results.txt',):
results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10]).T
for i in range(9):
plt.subplot(2, 5, i + 1)
plt.plot(results[i, :3000], marker='.', label=f)
plt.title(s[i])
plt.legend()