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mAP Update (#176) 

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This commit is contained in:
Glenn Jocher 2019-03-30 18:45:04 +01:00 committed by GitHub
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97
README.md
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@ -30,6 +30,7 @@ Python 3.7 or later with the following `pip3 install -U -r requirements.txt` pac
# Tutorials # Tutorials
* [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
* [Transfer Learning](https://github.com/ultralytics/yolov3/wiki/Example:-Transfer-Learning) * [Transfer Learning](https://github.com/ultralytics/yolov3/wiki/Example:-Transfer-Learning)
* [Train Single Image](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Image) * [Train Single Image](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Image)
* [Train Single Class](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class) * [Train Single Class](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class)
@ -67,13 +68,16 @@ HS**V** Intensity | +/- 50%
https://cloud.google.com/deep-learning-vm/ https://cloud.google.com/deep-learning-vm/
**Machine type:** n1-standard-8 (8 vCPUs, 30 GB memory) **Machine type:** n1-standard-8 (8 vCPUs, 30 GB memory)
**CPU platform:** Intel Skylake **CPU platform:** Intel Skylake
**GPUs:** 1-4x P100 ($0.493/hr), 1-8x V100 ($0.803/hr) **GPUs:** K80 ($0.198/hr), P4 ($0.279/hr), T4 ($0.353/hr), P100 ($0.493/hr), V100 ($0.803/hr)
**HDD:** 100 GB SSD **HDD:** 100 GB SSD
**Dataset:** COCO train 2014 **Dataset:** COCO train 2014
GPUs | `batch_size` | batch time | epoch time | epoch cost GPUs | `batch_size` | batch time | epoch time | epoch cost
--- |---| --- | --- | --- --- |---| --- | --- | ---
<i></i> | (images) | (s/batch) | | <i></i> | (images) | (s/batch) | |
1 K80 | 16 | 1.43s | 175min | $0.58
1 P4 | 8 | 0.51s | 125min | $0.58
1 T4 | 16 | 0.78s | 94min | $0.55
1 P100 | 16 | 0.39s | 48min | $0.39 1 P100 | 16 | 0.39s | 48min | $0.39
2 P100 | 32 | 0.48s | 29min | $0.47 2 P100 | 32 | 0.48s | 29min | $0.47
4 P100 | 64 | 0.65s | 20min | $0.65 4 P100 | 64 | 0.65s | 20min | $0.65
@ -108,13 +112,32 @@ Run `detect.py` with `webcam=True` to show a live webcam feed.
- Use `test.py --weights weights/yolov3.weights` to test the official YOLOv3 weights. - Use `test.py --weights weights/yolov3.weights` to test the official YOLOv3 weights.
- Use `test.py --weights weights/latest.pt` to test the latest training results. - Use `test.py --weights weights/latest.pt` to test the latest training results.
- Compare to official darknet results from https://arxiv.org/abs/1804.02767. - Compare to darknet published results https://arxiv.org/abs/1804.02767.
<i></i> | ultralytics/yolov3 | darknet <!---
--- | ---| --- %<i></i> | ultralytics/yolov3 fastest 5:52@416 (`pycocotools`) | darknet
YOLOv3-320 | 51.3 | 51.5 --- | --- | ---
YOLOv3-416 | 54.9 | 55.3 YOLOv3-320 | 51.9 (51.4) | 51.5
YOLOv3-608 | 57.9 | 57.9 YOLOv3-416 | 55.0 (54.9) | 55.3
YOLOv3-608 | 57.5 (57.8) | 57.9
<i></i> | ultralytics/yolov3 MERGE 7:15@416 (`pycocotools`) | darknet
--- | --- | ---
YOLOv3-320 | 52.3 (51.7) | 51.5
YOLOv3-416 | 55.4 (55.3) | 55.3
YOLOv3-608 | 57.9 (58.1) | 57.9
<i></i> | ultralytics/yolov3 MERGE+earlier_pred4 8:34@416 (`pycocotools`) | darknet
--- | --- | ---
YOLOv3-320 | 52.3 (51.8) | 51.5
YOLOv3-416 | 55.5 (55.4) | 55.3
YOLOv3-608 | 57.9 (58.2) | 57.9
--->
<i></i> | [ultralytics/yolov3](https://github.com/ultralytics/yolov3) with `pycocotools` | [darknet/yolov3](https://arxiv.org/abs/1804.02767)
--- | --- | ---
YOLOv3-320 | 51.8 | 51.5
YOLOv3-416 | 55.4 | 55.3
YOLOv3-608 | 58.2 | 57.9
``` bash ``` bash
sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3 sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
@ -123,34 +146,42 @@ sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd co
cd yolov3 cd yolov3
python3 test.py --save-json --conf-thres 0.001 --img-size 416 python3 test.py --save-json --conf-thres 0.001 --img-size 416
Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights') Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights')
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.308 Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.549 Image Total P R mAP
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.310 Calculating mAP: 100%|█████████████████████████████████| 157/157 [08:34<00:00, 2.53s/it]
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141 5000 5000 0.0896 0.756 0.555
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.312
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.454 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.554
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.267 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.317
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.403 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.343
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.452
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.268
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.585 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.411
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.435
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.244
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.477
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.587
python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16 python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16
Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights') Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights')
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.579 Image Total P R mAP
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.335 Calculating mAP: 100%|█████████████████████████████████| 313/313 [08:54<00:00, 1.55s/it]
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190 5000 5000 0.0966 0.786 0.579
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.582
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.279 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.344
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.429 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.198
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.362
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.299 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.427
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.483 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.281
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.572 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.437
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.463
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.309
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577
``` ```
# Contact # Contact

23
detect.py
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@ -14,7 +14,7 @@ def detect(
output='output', # output folder output='output', # output folder
img_size=416, img_size=416,
conf_thres=0.3, conf_thres=0.3,
nms_thres=0.45, nms_thres=0.5,
save_txt=False, save_txt=False,
save_images=True, save_images=True,
webcam=False webcam=False
@ -29,9 +29,6 @@ def detect(
# Load weights # Load weights
if weights.endswith('.pt'): # pytorch format if weights.endswith('.pt'): # pytorch format
if weights.endswith('yolov3.pt') and not os.path.exists(weights):
if platform in ('darwin', 'linux'): # linux/macos
os.system('wget https://storage.googleapis.com/ultralytics/yolov3.pt -O ' + weights)
model.load_state_dict(torch.load(weights, map_location=device)['model']) model.load_state_dict(torch.load(weights, map_location=device)['model'])
else: # darknet format else: # darknet format
_ = load_darknet_weights(model, weights) _ = load_darknet_weights(model, weights)
@ -63,26 +60,22 @@ def detect(
torch.onnx.export(model, img, 'weights/model.onnx', verbose=True) torch.onnx.export(model, img, 'weights/model.onnx', verbose=True)
return return
pred = model(img) pred = model(img)
pred = pred[pred[:, :, 4] > conf_thres] # remove boxes < threshold detections = non_max_suppression(pred, conf_thres, nms_thres)[0]
if len(pred) > 0:
# Run NMS on predictions
detections = non_max_suppression(pred.unsqueeze(0), conf_thres, nms_thres)[0]
if len(detections) > 0:
# Rescale boxes from 416 to true image size # Rescale boxes from 416 to true image size
scale_coords(img_size, detections[:, :4], im0.shape).round() scale_coords(img_size, detections[:, :4], im0.shape).round()
# Print results to screen # Print results to screen
unique_classes = detections[:, -1].cpu().unique() for c in detections[:, -1].unique():
for c in unique_classes: n = (detections[:, -1] == c).sum()
n = (detections[:, -1].cpu() == c).sum()
print('%g %ss' % (n, classes[int(c)]), end=', ') print('%g %ss' % (n, classes[int(c)]), end=', ')
# Draw bounding boxes and labels of detections # Draw bounding boxes and labels of detections
for *xyxy, conf, cls_conf, cls in detections: for *xyxy, conf, cls_conf, cls in detections:
if save_txt: # Write to file if save_txt: # Write to file
with open(save_path + '.txt', 'a') as file: with open(save_path + '.txt', 'a') as file:
file.write(('%g ' * 6 + '\n') % (*xyxy, cls, cls_conf * conf)) file.write(('%g ' * 6 + '\n') % (*xyxy, cls, conf))
# Add bbox to the image # Add bbox to the image
label = '%s %.2f' % (classes[int(cls)], conf) label = '%s %.2f' % (classes[int(cls)], conf)
@ -106,8 +99,8 @@ if __name__ == '__main__':
parser.add_argument('--weights', type=str, default='weights/yolov3.weights', help='path to weights file') parser.add_argument('--weights', type=str, default='weights/yolov3.weights', help='path to weights file')
parser.add_argument('--images', type=str, default='data/samples', help='path to images') parser.add_argument('--images', type=str, default='data/samples', help='path to images')
parser.add_argument('--img-size', type=int, default=32 * 13, help='size of each image dimension') parser.add_argument('--img-size', type=int, default=32 * 13, help='size of each image dimension')
parser.add_argument('--conf-thres', type=float, default=0.50, help='object confidence threshold') parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
parser.add_argument('--nms-thres', type=float, default=0.45, help='iou threshold for non-maximum suppression') parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
opt = parser.parse_args() opt = parser.parse_args()
print(opt) print(opt)

4
models.py
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@ -1,5 +1,7 @@
import os import os
import torch.nn.functional as F
from utils.parse_config import * from utils.parse_config import *
from utils.utils import * from utils.utils import *
@ -158,6 +160,8 @@ class YOLOLayer(nn.Module):
p[..., 2:4] = torch.exp(p[..., 2:4]) * self.anchor_wh # wh yolo method p[..., 2:4] = torch.exp(p[..., 2:4]) * self.anchor_wh # wh yolo method
# p[..., 2:4] = ((torch.sigmoid(p[..., 2:4]) * 2) ** 2) * self.anchor_wh # wh power method # p[..., 2:4] = ((torch.sigmoid(p[..., 2:4]) * 2) ** 2) * self.anchor_wh # wh power method
p[..., 4] = torch.sigmoid(p[..., 4]) # p_conf p[..., 4] = torch.sigmoid(p[..., 4]) # p_conf
p[..., 5:] = torch.sigmoid(p[..., 5:]) # p_class
# p[..., 5:] = F.softmax(p[..., 5:], dim=4) # p_class
p[..., :4] *= self.stride p[..., :4] *= self.stride
# reshape from [1, 3, 13, 13, 85] to [1, 507, 85] # reshape from [1, 3, 13, 13, 85] to [1, 507, 85]

131
test.py
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@ -1,6 +1,5 @@
import argparse import argparse
import json import json
import time
from torch.utils.data import DataLoader from torch.utils.data import DataLoader
@ -12,18 +11,18 @@ from utils.utils import *
def test( def test(
cfg, cfg,
data_cfg, data_cfg,
weights, weights=None,
batch_size=16, batch_size=16,
img_size=416, img_size=416,
iou_thres=0.5, iou_thres=0.5,
conf_thres=0.3, conf_thres=0.1,
nms_thres=0.45, nms_thres=0.5,
save_json=False, save_json=False,
model=None model=None
): ):
if model is None:
device = torch_utils.select_device() device = torch_utils.select_device()
if model is None:
# Initialize model # Initialize model
model = Darknet(cfg, img_size).to(device) model = Darknet(cfg, img_size).to(device)
@ -35,11 +34,14 @@ def test(
if torch.cuda.device_count() > 1: if torch.cuda.device_count() > 1:
model = nn.DataParallel(model) model = nn.DataParallel(model)
else:
device = next(model.parameters()).device
# Configure run # Configure run
data_cfg = parse_data_cfg(data_cfg) data_cfg = parse_data_cfg(data_cfg)
nC = int(data_cfg['classes']) # number of classes (80 for COCO)
test_path = data_cfg['valid'] test_path = data_cfg['valid']
if (os.sep + 'coco' + os.sep) in test_path: # COCO dataset probable
save_json = True # use pycocotools
# Dataloader # Dataloader
dataset = LoadImagesAndLabels(test_path, img_size=img_size) dataset = LoadImagesAndLabels(test_path, img_size=img_size)
@ -50,104 +52,111 @@ def test(
collate_fn=dataset.collate_fn) collate_fn=dataset.collate_fn)
model.eval() model.eval()
mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0 seen = 0
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP')) print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
mP, mR, mAPs, TP, jdict = [], [], [], [], [] mP, mR, mAP, mAPj = 0.0, 0.0, 0.0, 0.0
AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC) jdict, tdict, stats, AP, AP_class = [], [], [], [], []
coco91class = coco80_to_coco91_class() coco91class = coco80_to_coco91_class()
for imgs, targets, paths, shapes in tqdm(dataloader): for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader, desc='Calculating mAP')):
t = time.time()
targets = targets.to(device) targets = targets.to(device)
imgs = imgs.to(device) imgs = imgs.to(device)
output = model(imgs) output = model(imgs)
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)
# Compute average precision for each sample # Per image
for si, detections in enumerate(output): for si, pred in enumerate(output):
image_id = int(Path(paths[si]).stem.split('_')[-1])
labels = targets[targets[:, 0] == si, 1:] labels = targets[targets[:, 0] == si, 1:]
seen += 1 seen += 1
if detections is None: if pred is None:
# If there are labels but no detections mark as zero AP
if len(labels) != 0:
mP.append(0), mR.append(0), mAPs.append(0)
continue continue
# Get detections sorted by decreasing confidence scores
detections = detections[(-detections[:, 4]).argsort()]
if save_json: if save_json:
# add to json pred dictionary
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ... # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
box = detections[:, :4].clone() # xyxy box = pred[:, :4].clone() # xyxy
scale_coords(img_size, box, shapes[si]) # to original shape scale_coords(img_size, box, shapes[si]) # to original shape
box = xyxy2xywh(box) # xywh box = xyxy2xywh(box) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for di, d in enumerate(pred):
# add to json dictionary
for di, d in enumerate(detections):
jdict.append({ jdict.append({
'image_id': int(Path(paths[si]).stem.split('_')[-1]), 'image_id': image_id,
'category_id': coco91class[int(d[6])], 'category_id': coco91class[int(d[6])],
'bbox': [float3(x) for x in box[di]], 'bbox': [float3(x) for x in box[di]],
'score': float3(d[4] * d[5]) 'score': float(d[4])
}) })
# if len(labels) > 0:
# # add to json targets dictionary
# # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], ...
# box = labels[:, 1:].clone()
# box[:, [0, 2]] *= shapes[si][1] # scale width
# box[:, [1, 3]] *= shapes[si][0] # scale height
# box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
# for di, d in enumerate(labels):
# tdict.append({
# 'segmentation': [[]],
# 'iscrowd': 0,
# 'image_id': image_id,
# 'category_id': coco91class[int(d[0])],
# 'id': seen,
# 'bbox': [float3(x) for x in box[di]],
# 'area': float3(box[di][2:4].prod())
# })
# If no labels add number of detections as incorrect # If no labels add number of detections as incorrect
correct = [] correct = []
detected = []
if len(labels) == 0: if len(labels) == 0:
# correct.extend([0 for _ in range(len(detections))]) # correct.extend([0 for _ in range(len(detections))])
mP.append(0), mR.append(0), mAPs.append(0)
continue continue
else: else:
# Extract target boxes as (x1, y1, x2, y2) # Extract target boxes as (x1, y1, x2, y2)
target_box = xywh2xyxy(labels[:, 1:5]) * img_size target_box = xywh2xyxy(labels[:, 1:5]) * img_size
target_cls = labels[:, 0] target_cls = labels[:, 0]
detected = [] for *pred_box, conf, cls_conf, cls_pred in pred:
for *pred_box, conf, cls_conf, cls_pred in detections: if cls_pred not in target_cls:
correct.append(0)
continue
# Best iou, index between pred and targets # Best iou, index between pred and targets
iou, bi = bbox_iou(pred_box, target_box).max(0) iou, bi = bbox_iou(pred_box, target_box).max(0)
# If iou > threshold and class is correct mark as correct # If iou > threshold and class is correct mark as correct
if iou > iou_thres and cls_pred == target_cls[bi] and bi not in detected: if iou > iou_thres and bi not in detected:
correct.append(1) correct.append(1)
detected.append(bi) detected.append(bi)
else: else:
correct.append(0) correct.append(0)
# Compute Average Precision (AP) per class # Convert to Numpy
AP, AP_class, R, P = ap_per_class(tp=np.array(correct), tp = np.array(correct)
conf=detections[:, 4].cpu().numpy(), conf = pred[:, 4].cpu().numpy()
pred_cls=detections[:, 6].cpu().numpy(), pred_cls = pred[:, 6].cpu().numpy()
target_cls=target_cls.cpu().numpy()) target_cls = target_cls.cpu().numpy()
stats.append((tp, conf, pred_cls, target_cls))
# Accumulate AP per class # Compute means
AP_accum_count += np.bincount(AP_class, minlength=nC) stats_np = [np.concatenate(x, 0) for x in list(zip(*stats))]
AP_accum += np.bincount(AP_class, minlength=nC, weights=AP) if len(stats_np):
AP, AP_class, R, P = ap_per_class(*stats_np)
mP, mR, mAP = P.mean(), R.mean(), AP.mean()
# Compute mean AP across all classes in this image, and append to image list # Print P, R, mAP
mP.append(P.mean()) print(('%11s%11s' + '%11.3g' * 3) % (seen, len(dataset), mP, mR, mAP))
mR.append(R.mean())
mAPs.append(AP.mean())
# Means of all images
mean_P = np.mean(mP)
mean_R = np.mean(mR)
mean_mAP = np.mean(mAPs)
# Print image mAP and running mean mAP
print(('%11s%11s' + '%11.3g' * 4 + 's') %
(seen, len(dataset), mean_P, mean_R, mean_mAP, time.time() - t))
# Print mAP per class # Print mAP per class
if len(stats_np):
print('\nmAP Per Class:') print('\nmAP Per Class:')
for i, c in enumerate(load_classes(data_cfg['names'])): names = load_classes(data_cfg['names'])
if AP_accum_count[i]: for c, a in zip(AP_class, AP):
print('%15s: %-.4f' % (c, AP_accum[i] / (AP_accum_count[i]))) print('%15s: %-.4f' % (names[c], a))
# Save JSON # Save JSON
if save_json: if save_json and mAP and len(jdict):
imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataset.img_files] imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataset.img_files]
with open('results.json', 'w') as file: with open('results.json', 'w') as file:
json.dump(jdict, file) json.dump(jdict, file)
@ -157,16 +166,20 @@ def test(
# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
cocoGt = COCO('../coco/annotations/instances_val2014.json') # initialize COCO ground truth api cocoGt = COCO('../coco/annotations/instances_val2014.json') # initialize COCO ground truth api
cocoDt = cocoGt.loadRes('results.json') # initialize COCO detections api cocoDt = cocoGt.loadRes('results.json') # initialize COCO pred api
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
cocoEval.params.imgIds = imgIds # [:32] # only evaluate these images cocoEval.params.imgIds = imgIds # [:32] # only evaluate these images
cocoEval.evaluate() cocoEval.evaluate()
cocoEval.accumulate() cocoEval.accumulate()
cocoEval.summarize() cocoEval.summarize()
mAP = cocoEval.stats[1] # update mAP to pycocotools mAP
# F1 score = harmonic mean of precision and recall
# F1 = 2 * (mP * mR) / (mP + mR)
# Return mAP # Return mAP
return mean_P, mean_R, mean_mAP return mP, mR, mAP
if __name__ == '__main__': if __name__ == '__main__':
@ -176,8 +189,8 @@ if __name__ == '__main__':
parser.add_argument('--data-cfg', type=str, default='cfg/coco.data', help='coco.data file path') parser.add_argument('--data-cfg', type=str, default='cfg/coco.data', help='coco.data file path')
parser.add_argument('--weights', type=str, default='weights/yolov3.weights', help='path to weights file') parser.add_argument('--weights', type=str, default='weights/yolov3.weights', help='path to weights file')
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected') 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=0.3, help='object confidence threshold') parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
parser.add_argument('--nms-thres', type=float, default=0.45, help='iou threshold for non-maximum suppression') parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file') parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
parser.add_argument('--img-size', type=int, default=416, help='size of each image dimension') parser.add_argument('--img-size', type=int, default=416, help='size of each image dimension')
opt = parser.parse_args() opt = parser.parse_args()

12
train.py
View File

@ -40,7 +40,7 @@ def train(
# Optimizer # Optimizer
lr0 = 0.001 # initial learning rate lr0 = 0.001 # initial learning rate
optimizer = torch.optim.SGD(model.parameters(), lr=lr0, momentum=.9, weight_decay=0.0005) optimizer = torch.optim.SGD(model.parameters(), lr=lr0, momentum=0.9, weight_decay=0.0005)
cutoff = -1 # backbone reaches to cutoff layer cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0 start_epoch = 0
@ -119,9 +119,9 @@ def train(
if plot_images: if plot_images:
fig = plt.figure(figsize=(10, 10)) fig = plt.figure(figsize=(10, 10))
for ip in range(batch_size): for ip in range(batch_size):
labels = xywh2xyxy(targets[targets[:, 0] == ip, 2:6]).numpy() * img_size boxes = xywh2xyxy(targets[targets[:, 0] == ip, 2:6]).numpy().T * img_size
plt.subplot(4, 4, ip + 1).imshow(imgs[ip].numpy().transpose(1, 2, 0)) plt.subplot(4, 4, ip + 1).imshow(imgs[ip].numpy().transpose(1, 2, 0))
plt.plot(labels[:, [0, 2, 2, 0, 0]].T, labels[:, [1, 1, 3, 3, 1]].T, '.-') plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-')
plt.axis('off') plt.axis('off')
fig.tight_layout() fig.tight_layout()
fig.savefig('batch_%g.jpg' % i, dpi=fig.dpi) fig.savefig('batch_%g.jpg' % i, dpi=fig.dpi)
@ -170,7 +170,7 @@ def train(
best_loss = mloss['total'] best_loss = mloss['total']
# Save training results # Save training results
save = True save = False
if save: if save:
# Save latest checkpoint # Save latest checkpoint
checkpoint = {'epoch': epoch, checkpoint = {'epoch': epoch,
@ -190,11 +190,11 @@ def train(
# Calculate mAP # Calculate mAP
with torch.no_grad(): with torch.no_grad():
P, R, mAP = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size) results = test.test(cfg, data_cfg, batch_size=batch_size, img_size=img_size, model=model)
# Write epoch results # Write epoch results
with open('results.txt', 'a') as file: with open('results.txt', 'a') as file:
file.write(s + '%11.3g' * 3 % (P, R, mAP) + '\n') file.write(s + '%11.3g' * 3 % results + '\n') # append P, R, mAP
if __name__ == '__main__': if __name__ == '__main__':

24
utils/gcp.sh
View File

@ -10,8 +10,8 @@ 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 multi_gpu --depth 1 https://github.com/ultralytics/yolov3 # branch git clone -b map_update --depth 1 https://github.com/ultralytics/yolov3 yolov3 # 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
@ -26,11 +26,11 @@ python3 train.py --resume
python3 detect.py python3 detect.py
# Test # Test
python3 detect.py --save-json --conf-thres 0.001 --img-size 416 python3 test.py --save-json
# Git pull # Git pull
git pull https://github.com/ultralytics/yolov3 # master git pull https://github.com/ultralytics/yolov3 # master
git pull https://github.com/ultralytics/yolov3 multi_gpu # branch git pull https://github.com/ultralytics/yolov3 map_update # branch
# Test Darknet training # Test Darknet training
python3 test.py --weights ../darknet/backup/yolov3.backup python3 test.py --weights ../darknet/backup/yolov3.backup
@ -40,10 +40,16 @@ gsutil cp yolov3/weights/latest1gpu.pt gs://ultralytics
# Copy latest.pt FROM bucket # Copy latest.pt FROM bucket
gsutil cp gs://ultralytics/latest.pt yolov3/weights/latest.pt gsutil cp gs://ultralytics/latest.pt yolov3/weights/latest.pt
wget https://storage.googleapis.com/ultralytics/latest.pt -O weights/latest.pt wget https://storage.googleapis.com/ultralytics/yolov3/latest_v1_0.pt -O weights/latest_v1_0.pt
wget https://storage.googleapis.com/ultralytics/yolov3/best_v1_0.pt -O weights/best_v1_0.pt
# Trade Studies # Debug/Development
sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3 sudo rm -rf yolov3
# git clone https://github.com/ultralytics/yolov3 # master
git clone -b map_update --depth 1 https://github.com/ultralytics/yolov3 yolov3 # branch
cp -r weights yolov3 cp -r weights yolov3
cd yolov3 && python3 train.py --batch-size 16 --epochs 1 cp -r cocoapi/PythonAPI/pycocotools yolov3
sudo shutdown cd yolov3
#git pull https://github.com/ultralytics/yolov3 map_update # branch
python3 test.py --img-size 320

153
utils/utils.py
View File

@ -7,7 +7,6 @@ import matplotlib.pyplot as plt
import numpy as np import numpy as np
import torch import torch
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F
from utils import torch_utils from utils import torch_utils
@ -106,10 +105,10 @@ def xyxy2xywh(x):
def xywh2xyxy(x): def xywh2xyxy(x):
# Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2] # Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x) y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
y[:, 0] = (x[:, 0] - x[:, 2] / 2) y[:, 0] = x[:, 0] - x[:, 2] / 2
y[:, 1] = (x[:, 1] - x[:, 3] / 2) y[:, 1] = x[:, 1] - x[:, 3] / 2
y[:, 2] = (x[:, 0] + x[:, 2] / 2) y[:, 2] = x[:, 0] + x[:, 2] / 2
y[:, 3] = (x[:, 1] + x[:, 3] / 2) y[:, 3] = x[:, 1] + x[:, 3] / 2
return y return y
@ -142,25 +141,25 @@ def ap_per_class(tp, conf, pred_cls, target_cls):
tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
# Find unique classes # Find unique classes
unique_classes = np.unique(np.concatenate((pred_cls, target_cls), 0)) unique_classes = np.unique(target_cls)
# Create Precision-Recall curve and compute AP for each class # Create Precision-Recall curve and compute AP for each class
ap, p, r = [], [], [] ap, p, r = [], [], []
for c in unique_classes: for c in unique_classes:
i = pred_cls == c i = pred_cls == c
n_gt = sum(target_cls == c) # Number of ground truth objects n_gt = (target_cls == c).sum() # Number of ground truth objects
n_p = sum(i) # Number of predicted objects n_p = i.sum() # Number of predicted objects
if (n_p == 0) and (n_gt == 0): if n_p == 0 and n_gt == 0:
continue continue
elif (n_p == 0) or (n_gt == 0): elif n_p == 0 or n_gt == 0:
ap.append(0) ap.append(0)
r.append(0) r.append(0)
p.append(0) p.append(0)
else: else:
# Accumulate FPs and TPs # Accumulate FPs and TPs
fpc = np.cumsum(1 - tp[i]) fpc = (1 - tp[i]).cumsum()
tpc = np.cumsum(tp[i]) tpc = (tp[i]).cumsum()
# Recall # Recall
recall_curve = tpc / (n_gt + 1e-16) recall_curve = tpc / (n_gt + 1e-16)
@ -328,15 +327,18 @@ def build_targets(model, targets):
return txy, twh, tcls, indices return txy, twh, tcls, indices
# @profile
def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4): def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
""" """
Removes detections with lower object confidence score than 'conf_thres' Removes detections with lower object confidence score than 'conf_thres'
Non-Maximum Suppression to further filter detections. Non-Maximum Suppression to further filter detections.
Returns detections with shape: Returns detections with shape:
(x1, y1, x2, y2, object_conf, class_score, class_pred) (x1, y1, x2, y2, object_conf, class_conf, class)
""" """
output = [None for _ in range(len(prediction))] min_wh = 2 # (pixels) minimum box width and height
output = [None] * len(prediction)
for image_i, pred in enumerate(prediction): for image_i, pred in enumerate(prediction):
# Experiment: Prior class size rejection # Experiment: Prior class size rejection
# x, y, w, h = pred[:, 0], pred[:, 1], pred[:, 2], pred[:, 3] # x, y, w, h = pred[:, 0], pred[:, 1], pred[:, 2], pred[:, 3]
@ -352,56 +354,53 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
# multivariate_normal.pdf(x, mean=mat['class_mu'][c, :2], cov=mat['class_cov'][c, :2, :2]) # multivariate_normal.pdf(x, mean=mat['class_mu'][c, :2], cov=mat['class_cov'][c, :2, :2])
# Filter out confidence scores below threshold # Filter out confidence scores below threshold
class_prob, class_pred = torch.max(F.softmax(pred[:, 5:], 1), 1) class_conf, class_pred = pred[:, 5:].max(1)
v = pred[:, 4] > conf_thres # pred[:, 4] *= class_conf
v = v.nonzero().squeeze()
if len(v.shape) == 0:
v = v.unsqueeze(0)
pred = pred[v] i = (pred[:, 4] > conf_thres) & (pred[:, 2] > min_wh) & (pred[:, 3] > min_wh)
class_prob = class_prob[v] pred = pred[i]
class_pred = class_pred[v]
# If none are remaining => process next image # If none are remaining => process next image
nP = pred.shape[0] if len(pred) == 0:
if not nP:
continue continue
# From (center x, center y, width, height) to (x1, y1, x2, y2) # Select predicted classes
class_conf = class_conf[i]
class_pred = class_pred[i].unsqueeze(1).float()
# Box (center x, center y, width, height) to (x1, y1, x2, y2)
pred[:, :4] = xywh2xyxy(pred[:, :4]) pred[:, :4] = xywh2xyxy(pred[:, :4])
pred[:, 4] *= class_conf # improves mAP from 0.549 to 0.551
# Detections ordered as (x1, y1, x2, y2, obj_conf, class_prob, class_pred) # Detections ordered as (x1y1x2y2, obj_conf, class_conf, class_pred)
detections = torch.cat((pred[:, :5], class_prob.float().unsqueeze(1), class_pred.float().unsqueeze(1)), 1) pred = torch.cat((pred[:, :5], class_conf.unsqueeze(1), class_pred), 1)
# Iterate through all predicted classes
unique_labels = detections[:, -1].cpu().unique().to(prediction.device)
nms_style = 'OR' # 'OR' (default), 'AND', 'MERGE' (experimental) # Get detections sorted by decreasing confidence scores
for c in unique_labels: pred = pred[(-pred[:, 4]).argsort()]
# Get the detections with class c
dc = detections[detections[:, -1] == c] det_max = []
# Sort the detections by maximum object confidence nms_style = 'MERGE' # 'OR' (default), 'AND', 'MERGE' (experimental)
_, conf_sort_index = torch.sort(dc[:, 4] * dc[:, 5], descending=True) for c in pred[:, -1].unique():
dc = dc[conf_sort_index] dc = pred[pred[:, -1] == c] # select class c
dc = dc[:min(len(dc), 100)] # limit to first 100 boxes: https://github.com/ultralytics/yolov3/issues/117 dc = dc[:min(len(dc), 100)] # limit to first 100 boxes: https://github.com/ultralytics/yolov3/issues/117
# Non-maximum suppression # Non-maximum suppression
det_max = []
ind = list(range(len(dc)))
if nms_style == 'OR': # default if nms_style == 'OR': # default
while len(ind): # METHOD1
j = ind[0] # ind = list(range(len(dc)))
det_max.append(dc[j:j + 1]) # save highest conf detection # while len(ind):
reject = bbox_iou(dc[j], dc[ind]) > nms_thres # j = ind[0]
[ind.pop(i) for i in reversed(reject.nonzero())] # det_max.append(dc[j:j + 1]) # save highest conf detection
# while dc.shape[0]: # SLOWER METHOD # reject = (bbox_iou(dc[j], dc[ind]) > nms_thres).nonzero()
# det_max.append(dc[:1]) # save highest conf detection # [ind.pop(i) for i in reversed(reject)]
# if len(dc) == 1: # Stop if we're at the last detection
# break
# iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
# dc = dc[1:][iou < nms_thres] # remove ious > threshold
# Image Total P R mAP # METHOD2
# 4964 5000 0.629 0.594 0.586 while dc.shape[0]:
det_max.append(dc[:1]) # save highest conf detection
if len(dc) == 1: # Stop if we're at the last detection
break
iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
dc = dc[1:][iou < nms_thres] # remove ious > threshold
elif nms_style == 'AND': # requires overlap, single boxes erased elif nms_style == 'AND': # requires overlap, single boxes erased
while len(dc) > 1: while len(dc) > 1:
@ -411,22 +410,16 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
dc = dc[1:][iou < nms_thres] # remove ious > threshold dc = dc[1:][iou < nms_thres] # remove ious > threshold
elif nms_style == 'MERGE': # weighted mixture box elif nms_style == 'MERGE': # weighted mixture box
while len(dc) > 0: while len(dc):
iou = bbox_iou(dc[0], dc[0:]) # iou with other boxes i = bbox_iou(dc[0], dc) > nms_thres # iou with other boxes
i = iou > nms_thres weights = dc[i, 4:5]
weights = dc[i, 4:5] * dc[i, 5:6]
dc[0, :4] = (weights * dc[i, :4]).sum(0) / weights.sum() dc[0, :4] = (weights * dc[i, :4]).sum(0) / weights.sum()
det_max.append(dc[:1]) det_max.append(dc[:1])
dc = dc[iou < nms_thres] dc = dc[i == 0]
# Image Total P R mAP if len(det_max):
# 4964 5000 0.633 0.598 0.589 # normal det_max = torch.cat(det_max) # concatenate
output[image_i] = det_max[(-det_max[:, 4]).argsort()] # sort
if len(det_max) > 0:
det_max = torch.cat(det_max)
# Add max detections to outputs
output[image_i] = det_max if output[image_i] is None else torch.cat((output[image_i], det_max))
return output return output
@ -463,20 +456,42 @@ def coco_only_people(path='../coco/labels/val2014/'):
print(labels.shape[0], file) print(labels.shape[0], file)
def plot_results(start=0): def plot_wh_methods(): # from utils.utils import *; plot_wh_methods()
# Compares the two methods for width-height anchor multiplication
# https://github.com/ultralytics/yolov3/issues/168
x = np.arange(-4.0, 4.0, .1)
ya = np.exp(x)
yb = (torch.sigmoid(torch.from_numpy(x)).numpy() * 2)
fig = plt.figure(figsize=(6, 3), dpi=150)
plt.plot(x, ya, '.-', label='yolo method')
plt.plot(x, yb ** 2, '.-', label='^2 power method')
plt.plot(x, yb ** 2.5, '.-', label='^2.5 power method')
plt.xlim(left=-4, right=4)
plt.ylim(bottom=0, top=6)
plt.xlabel('input')
plt.ylabel('output')
plt.legend()
fig.tight_layout()
fig.savefig('comparison.jpg', dpi=fig.dpi)
def plot_results(start=0): # from utils.utils import *; plot_results()
# Plot YOLO training results file 'results.txt' # Plot YOLO training results file 'results.txt'
# import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v3.txt') # import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v3.txt')
# from utils.utils import *; plot_results()
fig = plt.figure(figsize=(14, 7)) fig = plt.figure(figsize=(14, 7))
s = ['X + Y', 'Width + Height', 'Confidence', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP'] s = ['X + Y', 'Width + Height', 'Confidence', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP']
for f in sorted(glob.glob('results*.txt')): for f in sorted(glob.glob('results*.txt')):
results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 9, 10, 11]).T # column 11 is mAP results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 9, 10, 11, 12]).T # column 11 is mAP
x = range(1, results.shape[1]) x = range(start, results.shape[1])
for i in range(8): for i in range(8):
plt.subplot(2, 4, i + 1) plt.subplot(2, 4, i + 1)
plt.plot(results[i, x[start:]], marker='.', label=f) plt.plot(x, results[i, x], marker='.', label=f)
plt.title(s[i]) plt.title(s[i])
if i == 0: if i == 0:
plt.legend() plt.legend()
if i == 7:
plt.plot(x, results[i + 1, x], marker='.', label=f)
fig.tight_layout() fig.tight_layout()
fig.savefig('results.jpg', dpi=fig.dpi)