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loss_test.py
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loss_test.py
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import argparse
import json
import os
import sys
from pathlib import Path
import torchvision
import numpy as np
import torch
from tqdm import tqdm
import kornia
import cv2
from matplotlib import pyplot as plt
import random
FILE = Path(__file__).resolve()
ROOT = Path(os.getcwd()) # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from models.common import DetectMultiBackend
from utils.callbacks import Callbacks
from utils.dataloaders import create_dataloader
from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, check_requirements,
check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression,
print_args, scale_boxes, xywh2xyxy, xyxy2xywh)
from utils.metrics import ConfusionMatrix, ap_per_class
from utils.plots import output_to_target, plot_images, plot_val_study
from utils.torch_utils import select_device, smart_inference_mode
def parse_opt(arguments):
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default='data/coco128.yaml', help='dataset.yaml path')
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model path(s)')
parser.add_argument('--batch-size', type=int, default=8, help='batch size')
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.6, help='confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold')
parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image')
parser.add_argument('--task', default='val', help='train, val, test, speed or study')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--verbose', action='store_true', help='report mAP by class')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')
parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name')
parser.add_argument('--name', default='exp', help='save to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
opt = parser.parse_args(arguments)
opt.data = check_yaml(opt.data) # check YAML
opt.save_json |= opt.data.endswith('coco.yaml')
opt.save_txt |= opt.save_hybrid
print_args(vars(opt))
return opt
def box_iou(box1, box2, eps=1e-7):
# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
"""
Return intersection-over-union (Jaccard index) of boxes.
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
Arguments:
box1 (Tensor[N, 4])
box2 (Tensor[M, 4])
Returns:
iou (Tensor[N, M]): the NxM matrix containing the pairwise
IoU values for every element in boxes1 and boxes2
"""
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
xd1 = box1.unsqueeze(1).chunk(2, 2)
xd2 = box2.unsqueeze(0).chunk(2, 2)
gt1, gt2 = box1.unsqueeze(1).chunk(2,2)
p1, p2 = box2.unsqueeze(0).chunk(2,2)
(a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
hehe=torch.min(a2,b2)
haha = torch.max(a1,b1)
inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
# IoU = inter / (area1 + area2 - inter)
return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
def save_one_txt(predn, save_conf, shape, file):
# Save one txt result
gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(file, 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
def save_one_json(predn, jdict, path, class_map):
# Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
box = xyxy2xywh(predn[:, :4]) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for p, b in zip(predn.tolist(), box.tolist()):
jdict.append({
'image_id': image_id,
'category_id': class_map[int(p[5])],
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5)})
def inverse_normalize(tensor, mean, std):
for t, m, s in zip(tensor, mean, std):
t.mul_(s).add_(m)
return tensor
def bboxes_to_lines(bboxes):
"""
bboxes - [N,4] N is amount of bboxes defined by xyxy annotation (:
returns:
lines - [N,4] a line defined by 2 points it passes through - both are on
the vertical edge of the given boudning box (at the half of it exactly)
"""
bboxes = bboxes.to(torch.int32)
lines = torch.empty((bboxes.shape[0], 4), dtype=torch.int32)
for i, box in enumerate(bboxes):
assert box[1] <= box[3], f"y2 must be bigger than y1 in predicted bounding boxes gotten: y1={box[1]} y2={box[3]}" #i want to be sure that y2 > y1
bbox_height = box[3] - box[1]
x1, y1 = box[0], box[1] + int(bbox_height/2)
x2, y2 = box[2], box[3] - int(bbox_height/2)
lines[i] = torch.tensor([x1, y1, x2, y2], dtype=torch.int32)
return lines
def keypoints_to_lines(lines):
"""
lines = [N,4] lines in x1,y1,x2,y2 format
returns:
keypoints = []
"""
for line in lines:
pass
def _draw_pixel(image: torch.Tensor, x: int, y: int, color: torch.Tensor) -> None:
r"""Draws a pixel into an image.
Args:
image: the input image to where to draw the lines with shape :math`(C,H,W)`.
x: the x coordinate of the pixel.
y: the y coordinate of the pixel.
color: the color of the pixel with :math`(C)` where :math`C` is the number of channels of the image.
Return:
Nothing is returned.
"""
image[:, y, x] = color
def draw_line(image: torch.Tensor, p1: torch.Tensor, p2: torch.Tensor, color: torch.Tensor) -> torch.Tensor:
r"""Draw a single line into an image.
Args:
image: the input image to where to draw the lines with shape :math`(C,H,W)`.
p1: the start point [x y] of the line with shape (2).
p2: the end point [x y] of the line with shape (2).
color: the color of the line with shape :math`(C)` where :math`C` is the number of channels of the image.
Return:
the image with containing the line.
Examples:
>>> image = torch.zeros(1, 8, 8)
>>> draw_line(image, torch.tensor([6, 4]), torch.tensor([1, 4]), torch.tensor([255]))
tensor([[[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 255., 255., 255., 255., 255., 255., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.]]])
"""
if (len(p1) != 2) or (len(p2) != 2):
raise ValueError("p1 and p2 must have length 2.")
if len(image.size()) != 3:
raise ValueError("image must have 3 dimensions (C,H,W).")
if color.size(0) != image.size(0):
raise ValueError("color must have the same number of channels as the image.")
#IF MY LINE IS OUT OF BOUND IDC JUST DRAW IT TO THE EDGE OF THE IMG
if (p1[0] >= image.size(2)) or (p1[1] >= image.size(1) or (p1[0] < 0) or (p1[1] < 0)):
if (p1[0] >= image.size(2)):
p1[0] = image.size(2) - 1
if (p1[1] >= image.size(1)):
p1[1] = image.size(1) - 1
#raise ValueError("p1 is out of bounds.")
#IF MY LINE IS OUT OF BOUND IDC JUST DRAW IT TO THE EDGE OF THE IMG
if (p2[0] >= image.size(2)) or (p2[1] >= image.size(1) or (p2[0] < 0) or (p2[1] < 0)):
if (p2[0] >= image.size(2)):
p2[0] = image.size(2) - 1
if (p2[1] >= image.size(1)):
p2[1] = image.size(1) - 1
#raise ValueError("p2 is out of bounds.")
# move p1 and p2 to the same device as the input image
# move color to the same device and dtype as the input image
p1 = p1.to(image.device).to(torch.int64)
p2 = p2.to(image.device).to(torch.int64)
color = color.to(image)
# assign points
x1, y1 = p1
x2, y2 = p2
# calcullate coefficients A,B,C of line
# from equation Ax + By + C = 0
A = y2 - y1
B = x1 - x2
C = x2 * y1 - x1 * y2
if A == 0:
y2 = y2 + 1
y1 = y1 - 1
A = y2-y1
C = x2 * y1 - x1 * y2
# make sure A is positive to utilize the function properly
if A < 0:
A = -A
B = -B
C = -C
# calculate the slope of the line
# check for division by zero
if B != 0:
m = -A / B
# make sure you start drawing in the right direction
x1, x2 = min(x1, x2).long(), max(x1, x2).long()
y1, y2 = min(y1, y2).long(), max(y1, y2).long()
# line equation that determines the distance away from the line
def line_equation(x, y):
return A * x + B * y + C
# vertical line
if B == 0:
image[:, y1 : y2 + 1, x1] = color
# horizontal line
elif A == 0:
pass
#A = 0.01
#y2 = y2+1
#image[:, y1, x1 : x2 + 1] = color
# slope between 0 and 1
elif 0 < m < 1:
for i in range(x1, x2 + 1):
_draw_pixel(image, i, y1, color)
if line_equation(i + 1, y1 + 0.5) > 0:
y1 += 1
# slope greater than or equal to 1
elif m >= 1:
for j in range(y1, y2 + 1):
_draw_pixel(image, x1, j, color)
if line_equation(x1 + 0.5, j + 1) < 0:
x1 += 1
# slope less then -1
elif m <= -1:
for j in range(y1, y2 + 1):
_draw_pixel(image, x2, j, color)
if line_equation(x2 - 0.5, j + 1) > 0:
x2 -= 1
# slope between -1 and 0
elif -1 < m < 0:
for i in range(x1, x2 + 1):
_draw_pixel(image, i, y2, color)
if line_equation(i + 1, y2 - 0.5) > 0:
y2 -= 1
return image
def visualise_detections_labels(detections, labels, im):
"""
im - image
detections - predicted bounding boxes you wanna draw (red) (x1,y1,x2,y2) - exact pixels on image
labels - GT bboxes u wanna draw (green) (x1,y1,x2,y2) - exact pixels on image
only works for one class
"""
im = im.to(torch.uint8)
lbls = labels.to(torch.int32)
dets = detections.to(torch.int32)
im_drawn = torchvision.utils.draw_bounding_boxes(im, boxes=lbls, labels=[f"{number}" for number in range(len(lbls))], width=6, colors='green', fill=True, font_size=55)
im_drawn = torchvision.utils.draw_bounding_boxes(im_drawn, boxes=dets, labels=[f"{number}" for number in range(len(dets))], width=2, colors='red', fill=False, font_size=55)
lines = bboxes_to_lines(dets)
for line in lines:
#im_drawn[:, y1, x1 : x2+1] =
im_drawn = draw_line(im_drawn,
torch.tensor([line[0],line[1]]),
torch.tensor([line[2], line[3]]),
color=torch.tensor([255,255,2], dtype=torch.uint8))
a=random.randint(1, 200)
torchvision.io.write_png(im_drawn, f"slike/test{a}.png")
torchvision.io.write_png(im.cpu(), f"slike/test{a}_clean .png")
#kornia.save_image(im_drawn, "test.png")
return im_drawn
def LoGT_loss(detections, labels, im):
"""
returns a LoGT matrix whether a label has a line going across it
Args:
detections (torch.tensor): [Mx4] output of yolo predictions
labels (torch.tensor): [Nx4] ground truths in xyxy format
im (torch.tensor): img CxHxW
Returns:
LoGT (torch.tensor): LoGT matrix (LoGT = Line over Ground Truth [N]
"""
iou = box_iou(labels[:, 1:], detections[:, :4])
#jiou = jere_iou(best_box_per_label[:, :4], labels[:, 1:], im)
detections = detections[:,:4]
labels = labels[:,1:]
#TODO: best box per label se ne ponasa dobro kada neki GT box nije predictan uopce ?!
best_box_per_label = detections[torch.argmax(iou, dim=1)]
#TODO: MAKNI DUPLIKATE IZ BEST BOX PER LABEL
im_drawn = visualise_detections_labels(best_box_per_label, labels, im)
lines = bboxes_to_lines(best_box_per_label)
#assert(detections.shape[0] == lines.shape[0])
if labels.shape[0] != best_box_per_label.shape[0]:
LOGGER.info("alo alo falija si jednu ili vise kutija")
LoGT = torch.zeros(labels.shape[0], dtype=torch.float32, device=labels.device)
for i in range(lines.shape[0]):
line_y = (lines[i][1] + lines[i][3]) / 2
if labels[i][1] < line_y < labels[i][3]:
#if line_y > best_box_per_label[i][1] and line_y < best_box_per_label[i][3]:
LoGT[i] = torch.tensor(1)
#print("LINE Y IS IN DETECTION")
else:
LoGT[i] = torch.tensor(0)
pass
#TODO:implementiraj gubitak iou ako je line_y izvan GT bboxa
a=1
return LoGT
def calculate_logt_on_dataset(logt_on_dataset):
#TODO: make it better not just 0s and 1s
pass
correct_predictions = torch.count_nonzero(logt_on_dataset)
return (correct_predictions / logt_on_dataset.shape[0]).cpu().item()
def process_batch(detections, labels, iouv, im):
"""
Return correct prediction matrix
Arguments:
detections (array[N, 6]), x1, y1, x2, y2, conf, class
labels (array[M, 5]), class, x1, y1, x2, y2
Returns:
correct (array[N, 10]), for 10 IoU levels
"""
correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
iou = box_iou(labels[:, 1:], detections[:, :4])
#UZET BOX SA NAJVECIN IOU IZ ZA SVAKI LABEL I ONDA NA NJIH PRIMJENIT MOJ ALGO
#TODO: MOJA METRIKA POKLAPANJA
#best_box_per_label = detections[torch.argmax(iou, dim=1)]
#jere_iou = box_iou(labels[:, 1:], detections[:, :4])
correct_class = labels[:, 0:1] == detections[:, 5]
for i in range(len(iouv)):
x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
#matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
correct[matches[:, 1].astype(int), i] = True
return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
@smart_inference_mode()
def run(
data,
weights=None, # model.pt path(s)
batch_size=32, # batch size
imgsz=640, # inference size (pixels)
conf_thres=0.001, # confidence threshold
iou_thres=0.6, # NMS IoU threshold
max_det=300, # maximum detections per image
task='val', # train, val, test, speed or study
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
workers=8, # max dataloader workers (per RANK in DDP mode)
single_cls=False, # treat as single-class dataset
augment=False, # augmented inference
verbose=False, # verbose output
save_txt=False, # save results to *.txt
save_hybrid=False, # save label+prediction hybrid results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_json=False, # save a COCO-JSON results file
project=ROOT / 'runs/val', # save to project/name
name='jupytertest', # save to project/name
exist_ok=False, # existing project/name ok, do not increment
half=True, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
model=None,
dataloader=None,
save_dir=Path(''),
plots=True,
callbacks=Callbacks(),
compute_loss=None,
):
# Initialize/load model and set device
training = model is not None
if training: # called by train.py
device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
half &= device.type != 'cpu' # half precision only supported on CUDA
model.half() if half else model.float()
else: # called directly
device = select_device(device, batch_size=batch_size)
# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Load model
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
imgsz = check_img_size(imgsz, s=stride) # check image size
half = model.fp16 # FP16 supported on limited backends with CUDA
if engine:
batch_size = model.batch_size
else:
device = model.device
if not (pt or jit):
batch_size = 1 # export.py models default to batch-size 1
LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
# Data
data = check_dataset(data) # check
# Configure
model.eval()
cuda = device.type != 'cpu'
is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset
nc = 1 if single_cls else int(data['nc']) # number of classes
iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95
niou = iouv.numel()
# Dataloader
if not training:
if pt and not single_cls: # check --weights are trained on --data
ncm = model.model.nc
assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \
f'classes). Pass correct combination of --weights and --data that are trained together.'
model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks
task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
dataloader = create_dataloader(data[task],
imgsz,
batch_size,
stride,
single_cls,
pad=pad,
rect=rect,
workers=workers,
prefix=colorstr(f'{task}: '))[0]
seen = 0
confusion_matrix = ConfusionMatrix(nc=nc)
names = model.names if hasattr(model, 'names') else model.module.names # get class names
if isinstance(names, (list, tuple)): # old format
names = dict(enumerate(names))
class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
s = ('%22s' + '%11s' * 7) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP50', 'mAP50-95', 'LoGT')
tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
dt = Profile(), Profile(), Profile() # profiling times
loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class, logt_list = [], [], [], [], []
callbacks.run('on_val_start')
pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
#anchors = model.model.model[-1].anchors
#print(model.model.model[-1].anchors)
#LOGGER.info(f'anchori su mi {model.model.model[-1].anchors}')
#exit()
for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
callbacks.run('on_val_batch_start')
with dt[0]:
if cuda:
im = im.to(device, non_blocking=True)
targets = targets.to(device)
im = im.half() if half else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
nb, _, height, width = im.shape # batch size, channels, height, width
# Inference
with dt[1]:
preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None)
# Loss
if compute_loss:
loss += compute_loss(train_out, targets)[1] # box, obj, cls
# NMS
targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
with dt[2]:
preds = non_max_suppression(preds,
conf_thres,
iou_thres,
labels=lb,
multi_label=True,
agnostic=single_cls,
max_det=max_det)
# Metrics
for si, pred in enumerate(preds):
labels = targets[targets[:, 0] == si, 1:]
nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
path, shape = Path(paths[si]), shapes[si][0]
correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
seen += 1
if npr == 0:
if nl:
stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0]))
if plots:
confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
continue
# Predictions
if single_cls:
pred[:, 5] = 0
predn = pred.clone()
#scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
# Evaluate
if nl:
tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
#scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
correct = process_batch(predn, labelsn, iouv, im[si]*255)
logt = LoGT_loss(predn.clone(), labelsn.clone(), im[si]*255)
#logt.to(device)
altim = im
if plots:
confusion_matrix.process_batch(predn, labelsn)
logt_list.append(logt)
stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # (correct, conf, pcls, tcls, LoGT tensor)
# Save/log
if save_txt:
save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
if save_json:
save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary
callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
# Plot images
if plots and batch_i < 100:
plot_images(im, targets, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) # labels
plot_images(im, output_to_target(preds), paths, save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred
callbacks.run('on_val_batch_end', batch_i, im, targets, paths, shapes, preds)
#------------END ENTIRE DATALOADER
# Compute metrics
pass
a=1
for x in zip(*stats):
a=11
stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
a=2
if len(stats) and stats[0].any():
tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
logt_on_dataset = calculate_logt_on_dataset(torch.cat(logt_list))
ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95
mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class
# Print results
pf = '%22s' + '%11i' * 2 + '%11.3g' * 5 # print format
LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map, logt_on_dataset))
if nt.sum() == 0:
LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels')
# Print results per class
if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
for i, c in enumerate(ap_class):
LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
# Print speeds
t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
if not training:
shape = (batch_size, 3, imgsz, imgsz)
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
# Plots
if plots:
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
callbacks.run('on_val_end', nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
# Save JSON
if save_json and len(jdict):
w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations
pred_json = str(save_dir / f"{w}_predictions.json") # predictions
LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
with open(pred_json, 'w') as f:
json.dump(jdict, f)
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
check_requirements('pycocotools')
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
anno = COCO(anno_json) # init annotations api
pred = anno.loadRes(pred_json) # init predictions api
eval = COCOeval(anno, pred, 'bbox')
if is_coco:
eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate
eval.evaluate()
eval.accumulate()
eval.summarize()
map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5)
except Exception as e:
LOGGER.info(f'pycocotools unable to run: {e}')
# Return results
model.float() # for training
if not training:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
maps = np.zeros(nc) + map
for i, c in enumerate(ap_class):
maps[c] = ap[i]
return (mp, mr, map50, map, logt_on_dataset, *(loss.cpu() / len(dataloader)).tolist()), maps, t
def main(opt):
check_requirements(exclude=('tensorboard', 'thop'))
if opt.task in ('train', 'val', 'test'): # run normally
if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466
LOGGER.info(f'WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results')
if opt.save_hybrid:
LOGGER.info('WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone')
run(**vars(opt))
else:
weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
opt.half = torch.cuda.is_available() and opt.device != 'cpu' # FP16 for fastest results
if opt.task == 'speed': # speed benchmarks
# python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
for opt.weights in weights:
run(**vars(opt), plots=True)
elif opt.task == 'study': # speed vs mAP benchmarks
# python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
for opt.weights in weights:
f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to
x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis
for opt.imgsz in x: # img-size
LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...')
r, _, t = run(**vars(opt), plots=False)
y.append(r + t) # results and times
np.savetxt(f, y, fmt='%10.4g') # save
os.system('zip -r study.zip study_*.txt')
plot_val_study(x=x) # plot
if __name__ =="__main__":
opt = parse_opt(["--data", "data/police1.yaml", "--weights", "runs/train/yolo5L500ep_25_anchorahardcoded_sgd_finetunepokusaj22/weights/best.pt", "--imgsz", "1024", "--task", "val"])
main(opt)