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car-detection-bayes/utils/utils.py

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import glob
import os
import random
import shutil
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
from . import torch_utils # , google_utils
matplotlib.rc('font', **{'size': 11})
# 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
# Prevent OpenCV from multithreading (to use PyTorch DataLoader)
cv2.setNumThreads(0)
def floatn(x, n=3): # format floats to n decimals
return float(format(x, '.%gf' % n))
def init_seeds(seed=0):
random.seed(seed)
np.random.seed(seed)
torch_utils.init_seeds(seed=seed)
def load_classes(path):
# Loads *.names file at 'path'
with open(path, 'r') as f:
names = f.read().split('\n')
return list(filter(None, names)) # filter removes empty strings (such as last line)
def model_info(model, report='summary'):
# Plots a line-by-line description of a PyTorch model
n_p = sum(x.numel() for x in model.parameters()) # number parameters
n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients
if report is 'full':
print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
for i, (name, p) in enumerate(model.named_parameters()):
name = name.replace('module_list.', '')
print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
print('Model Summary: %g layers, %g parameters, %g gradients' % (len(list(model.parameters())), n_p, n_g))
def labels_to_class_weights(labels, nc=80):
# Get class weights (inverse frequency) from training labels
labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO
classes = labels[:, 0].astype(np.int) # labels = [class xywh]
weights = np.bincount(classes, minlength=nc) # occurences per class
weights[weights == 0] = 1 # replace empty bins with 1
weights = 1 / weights # number of targets per class
weights /= weights.sum() # normalize
return torch.Tensor(weights)
def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
# Produces image weights based on class mAPs
n = len(labels)
class_counts = np.array([np.bincount(labels[i][:, 0].astype(np.int), minlength=nc) for i in range(n)])
image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
# index = random.choices(range(n), weights=image_weights, k=1) # weight image sample
return image_weights
def coco_class_weights(): # frequency of each class in coco train2014
n = [187437, 4955, 30920, 6033, 3838, 4332, 3160, 7051, 7677, 9167, 1316, 1372, 833, 6757, 7355, 3302, 3776, 4671,
6769, 5706, 3908, 903, 3686, 3596, 6200, 7920, 8779, 4505, 4272, 1862, 4698, 1962, 4403, 6659, 2402, 2689,
4012, 4175, 3411, 17048, 5637, 14553, 3923, 5539, 4289, 10084, 7018, 4314, 3099, 4638, 4939, 5543, 2038, 4004,
5053, 4578, 27292, 4113, 5931, 2905, 11174, 2873, 4036, 3415, 1517, 4122, 1980, 4464, 1190, 2302, 156, 3933,
1877, 17630, 4337, 4624, 1075, 3468, 135, 1380]
weights = 1 / torch.Tensor(n)
weights /= weights.sum()
return weights
def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)
# https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
# a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
# b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
# x = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
return x
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(x):
# Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]
y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
y[:, 0] = (x[:, 0] + x[:, 2]) / 2
y[:, 1] = (x[:, 1] + x[:, 3]) / 2
y[:, 2] = x[:, 2] - x[:, 0]
y[:, 3] = x[:, 3] - x[:, 1]
return y
def xywh2xyxy(x):
# 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[:, 0] = x[:, 0] - x[:, 2] / 2
y[:, 1] = x[:, 1] - x[:, 3] / 2
y[:, 2] = x[:, 0] + x[:, 2] / 2
y[:, 3] = x[:, 1] + x[:, 3] / 2
return y
def scale_coords(img1_shape, coords, img0_shape):
# Rescale coords (xyxy) from img1_shape to img0_shape
gain = max(img1_shape) / max(img0_shape) # gain = old / new
coords[:, [0, 2]] -= (img1_shape[1] - img0_shape[1] * gain) / 2 # x padding
coords[:, [1, 3]] -= (img1_shape[0] - img0_shape[0] * gain) / 2 # y padding
coords[:, :4] /= gain
clip_coords(coords, img0_shape)
return coords
def clip_coords(boxes, img_shape):
# Clip bounding xyxy bounding boxes to image shape (height, width)
boxes[:, [0, 2]] = boxes[:, [0, 2]].clamp(min=0, max=img_shape[1]) # clip x
boxes[:, [1, 3]] = boxes[:, [1, 3]].clamp(min=0, max=img_shape[0]) # clip y
def ap_per_class(tp, conf, pred_cls, target_cls):
""" Compute the average precision, given the recall and precision curves.
Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
# Arguments
tp: True positives (list).
conf: Objectness value from 0-1 (list).
pred_cls: Predicted object classes (list).
target_cls: True object classes (list).
# Returns
The average precision as computed in py-faster-rcnn.
"""
# Sort by objectness
i = np.argsort(-conf)
tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
# Find unique classes
unique_classes = np.unique(target_cls)
# Create Precision-Recall curve and compute AP for each class
ap, p, r = [], [], []
for c in unique_classes:
i = pred_cls == c
n_gt = (target_cls == c).sum() # Number of ground truth objects
n_p = i.sum() # Number of predicted objects
if n_p == 0 and n_gt == 0:
continue
elif n_p == 0 or n_gt == 0:
ap.append(0)
r.append(0)
p.append(0)
else:
# Accumulate FPs and TPs
fpc = (1 - tp[i]).cumsum()
tpc = (tp[i]).cumsum()
# Recall
recall = tpc / (n_gt + 1e-16) # recall curve
r.append(recall[-1])
# Precision
precision = tpc / (tpc + fpc) # precision curve
p.append(precision[-1])
# AP from recall-precision curve
ap.append(compute_ap(recall, precision))
# Plot
# fig, ax = plt.subplots(1, 1, figsize=(4, 4))
# ax.plot(np.concatenate(([0.], recall)), np.concatenate(([0.], precision)))
# ax.set_xlabel('YOLOv3-SPP')
# ax.set_xlabel('Recall')
# ax.set_ylabel('Precision')
# ax.set_xlim(0, 1)
# fig.tight_layout()
# fig.savefig('PR_curve.png', dpi=300)
# Compute F1 score (harmonic mean of precision and recall)
p, r, ap = np.array(p), np.array(r), np.array(ap)
f1 = 2 * p * r / (p + r + 1e-16)
return p, r, ap, f1, unique_classes.astype('int32')
def compute_ap(recall, precision):
""" Compute the average precision, given the recall and precision curves.
Source: 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.
"""
# Append sentinel values to beginning and 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])
# Calculate area under PR curve, looking for points where x axis (recall) changes
i = np.where(mrec[1:] != mrec[:-1])[0]
# Sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False):
# Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
box2 = box2.t()
# Get the coordinates of bounding boxes
if x1y1x2y2:
# x1, y1, x2, y2 = box1
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:
# x, y, w, h = box1
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
# Intersection area
inter_area = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
(torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
# Union Area
union_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1) + 1e-16) + \
(b2_x2 - b2_x1) * (b2_y2 - b2_y1) - inter_area
iou = inter_area / union_area # iou
if GIoU: # Generalized IoU https://arxiv.org/pdf/1902.09630.pdf
c_x1, c_x2 = torch.min(b1_x1, b2_x1), torch.max(b1_x2, b2_x2)
c_y1, c_y2 = torch.min(b1_y1, b2_y1), torch.max(b1_y2, b2_y2)
c_area = (c_x2 - c_x1) * (c_y2 - c_y1) # convex area
return iou - (c_area - union_area) / c_area # GIoU
return iou
def wh_iou(box1, box2):
# Returns the IoU of wh1 to wh2. wh1 is 2, wh2 is nx2
box2 = box2.t()
# w, h = box1
w1, h1 = box1[0], box1[1]
w2, h2 = box2[0], box2[1]
# Intersection area
inter_area = torch.min(w1, w2) * torch.min(h1, h2)
# Union Area
union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area
return inter_area / union_area # iou
def compute_loss(p, targets, model, giou_loss=True): # predictions, targets, model
ft = torch.cuda.FloatTensor if p[0].is_cuda else torch.Tensor
lxy, lwh, lcls, lobj = ft([0]), ft([0]), ft([0]), ft([0])
txy, twh, tcls, tbox, indices, anchor_vec = build_targets(model, targets)
h = model.hyp # hyperparameters
# Define criteria
MSE = nn.MSELoss()
BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]))
BCEobj = nn.BCEWithLogitsLoss(pos_weight=ft([h['obj_pw']]))
# CE = nn.CrossEntropyLoss() # (weight=model.class_weights)
# Compute losses
bs = p[0].shape[0] # batch size
k = bs / 64 # loss gain
for i, pi0 in enumerate(p): # layer i predictions, i
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
tobj = torch.zeros_like(pi0[..., 0]) # target obj
# Compute losses
nb = len(b)
if nb: # number of targets
pi = pi0[b, a, gj, gi] # predictions closest to anchors
tobj[b, a, gj, gi] = 1.0 # obj
# pi[..., 2:4] = torch.sigmoid(pi[..., 2:4]) # wh power loss (uncomment)
# s = 1.5 # scale_xy
pxy = torch.sigmoid(pi[..., 0:2]) # * s - (s - 1) / 2
if giou_loss:
pbox = torch.cat((pxy, torch.exp(pi[..., 2:4]) * anchor_vec[i]), 1) # predicted
giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True) # giou computation
lxy += (k * h['giou']) * (1.0 - giou).mean() # giou loss
else:
lxy += (k * h['xy']) * MSE(pxy, txy[i]) # xy loss
lwh += (k * h['wh']) * MSE(pi[..., 2:4], twh[i]) # wh yolo loss
if model.nc > 1: # cls loss (only if multiple classes)
tclsm = torch.zeros_like(pi[..., 5:])
tclsm[range(nb), tcls[i]] = 1.0
lcls += (k * h['cls']) * BCEcls(pi[..., 5:], tclsm) # BCE
# lcls += (k * h['cls']) * CE(pi[..., 5:], tcls[i]) # CE
# Append targets to text file
# with open('targets.txt', 'a') as file:
# [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
lobj += (k * h['obj']) * BCEobj(pi0[..., 4], tobj) # obj loss
loss = lxy + lwh + lobj + lcls
return loss, torch.cat((lxy, lwh, lobj, lcls, loss)).detach()
def build_targets(model, targets):
# targets = [image, class, x, y, w, h]
nt = len(targets)
txy, twh, tcls, tbox, indices, av = [], [], [], [], [], []
multi_gpu = type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
for i in model.yolo_layers:
# get number of grid points and anchor vec for this yolo layer
if multi_gpu:
ng, anchor_vec = model.module.module_list[i].ng, model.module.module_list[i].anchor_vec
else:
ng, anchor_vec = model.module_list[i].ng, model.module_list[i].anchor_vec
# iou of targets-anchors
t, a = targets, []
gwh = t[:, 4:6] * ng
if nt:
iou = torch.stack([wh_iou(x, gwh) for x in anchor_vec], 0)
use_best_anchor = False
if use_best_anchor:
iou, a = iou.max(0) # best iou and anchor
else: # use all anchors
na = len(anchor_vec) # number of anchors
a = torch.arange(na).view((-1, 1)).repeat([1, nt]).view(-1)
t = targets.repeat([na, 1])
gwh = gwh.repeat([na, 1])
iou = iou.view(-1) # use all ious
# reject anchors below iou_thres (OPTIONAL, increases P, lowers R)
reject = True
if reject:
j = iou > model.hyp['iou_t'] # iou threshold hyperparameter
t, a, gwh = t[j], a[j], gwh[j]
# Indices
b, c = t[:, :2].long().t() # target image, class
gxy = t[:, 2:4] * ng # grid x, y
gi, gj = gxy.long().t() # grid x, y indices
indices.append((b, a, gj, gi))
# XY coordinates
gxy -= gxy.floor()
txy.append(gxy)
# GIoU
tbox.append(torch.cat((gxy, gwh), 1)) # xywh (grids)
av.append(anchor_vec[a]) # anchor vec
# Width and height
twh.append(torch.log(gwh / anchor_vec[a])) # wh yolo method
# twh.append((gwh / anchor_vec[a]) ** (1 / 3) / 2) # wh power method
# Class
tcls.append(c)
if c.shape[0]: # if any targets
assert c.max() <= model.nc, 'Target classes exceed model classes'
return txy, twh, tcls, tbox, indices, av
def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.5):
"""
Removes detections with lower object confidence score than 'conf_thres'
Non-Maximum Suppression to further filter detections.
Returns detections with shape:
(x1, y1, x2, y2, object_conf, class_conf, class)
"""
min_wh = 2 # (pixels) minimum box width and height
output = [None] * len(prediction)
for image_i, pred in enumerate(prediction):
# Experiment: Prior class size rejection
# x, y, w, h = pred[:, 0], pred[:, 1], pred[:, 2], pred[:, 3]
# a = w * h # area
# ar = w / (h + 1e-16) # aspect ratio
# n = len(w)
# log_w, log_h, log_a, log_ar = torch.log(w), torch.log(h), torch.log(a), torch.log(ar)
# 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])
# Multiply conf by class conf to get combined confidence
class_conf, class_pred = pred[:, 5:].max(1)
pred[:, 4] *= class_conf
# Select only suitable predictions
i = (pred[:, 4] > conf_thres) & (pred[:, 2:4] > min_wh).all(1) & torch.isfinite(pred).all(1)
pred = pred[i]
# If none are remaining => process next image
if len(pred) == 0:
continue
# 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] *= class_conf # improves mAP from 0.549 to 0.551
# Detections ordered as (x1y1x2y2, obj_conf, class_conf, class_pred)
pred = torch.cat((pred[:, :5], class_conf.unsqueeze(1), class_pred), 1)
# Get detections sorted by decreasing confidence scores
pred = pred[(-pred[:, 4]).argsort()]
det_max = []
nms_style = 'MERGE' # 'OR' (default), 'AND', 'MERGE' (experimental)
for c in pred[:, -1].unique():
dc = pred[pred[:, -1] == c] # select class c
n = len(dc)
if n == 1:
det_max.append(dc) # No NMS required if only 1 prediction
continue
elif n > 100:
dc = dc[:100] # limit to first 100 boxes: https://github.com/ultralytics/yolov3/issues/117
# Non-maximum suppression
if nms_style == 'OR': # default
# METHOD1
# ind = list(range(len(dc)))
# while len(ind):
# j = ind[0]
# det_max.append(dc[j:j + 1]) # save highest conf detection
# reject = (bbox_iou(dc[j], dc[ind]) > nms_thres).nonzero()
# [ind.pop(i) for i in reversed(reject)]
# METHOD2
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
while len(dc) > 1:
iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
if iou.max() > 0.5:
det_max.append(dc[:1])
dc = dc[1:][iou < nms_thres] # remove ious > threshold
elif nms_style == 'MERGE': # weighted mixture box
while len(dc):
if len(dc) == 1:
det_max.append(dc)
break
i = bbox_iou(dc[0], dc) > nms_thres # iou with other boxes
weights = dc[i, 4:5]
dc[0, :4] = (weights * dc[i, :4]).sum(0) / weights.sum()
det_max.append(dc[:1])
dc = dc[i == 0]
elif nms_style == 'SOFT': # soft-NMS https://arxiv.org/abs/1704.04503
sigma = 0.5 # soft-nms sigma parameter
while len(dc):
if len(dc) == 1:
det_max.append(dc)
break
det_max.append(dc[:1])
iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
dc = dc[1:]
dc[:, 4] *= torch.exp(-iou ** 2 / sigma) # decay confidences
# dc = dc[dc[:, 4] > nms_thres] # new line per https://github.com/ultralytics/yolov3/issues/362
if len(det_max):
det_max = torch.cat(det_max) # concatenate
output[image_i] = det_max[(-det_max[:, 4]).argsort()] # sort
return output
def get_yolo_layers(model):
bool_vec = [x['type'] == 'yolo' for x in model.module_defs]
return [i for i, x in enumerate(bool_vec) if x] # [82, 94, 106] for yolov3
def strip_optimizer_from_checkpoint(filename='weights/best.pt'):
# Strip optimizer from *.pt files for lighter files (reduced by 2/3 size)
a = torch.load(filename, map_location='cpu')
a['optimizer'] = []
torch.save(a, filename.replace('.pt', '_lite.pt'))
def coco_class_count(path='../coco/labels/train2014/'):
# Histogram of occurrences per class
nc = 80 # number classes
x = np.zeros(nc, dtype='int32')
files = sorted(glob.glob('%s/*.*' % path))
for i, file in enumerate(files):
labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5)
x += np.bincount(labels[:, 0].astype('int32'), minlength=nc)
print(i, len(files))
def coco_only_people(path='../coco/labels/val2014/'):
# Find images with only people
files = sorted(glob.glob('%s/*.*' % path))
for i, file in enumerate(files):
labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5)
if all(labels[:, 0] == 0):
print(labels.shape[0], file)
def select_best_evolve(path='evolve*.txt'): # from utils.utils import *; select_best_evolve()
# Find best evolved mutation
for file in sorted(glob.glob(path)):
x = np.loadtxt(file, dtype=np.float32)
fitness = x[:, 2] * 0.5 + x[:, 3] * 0.5 # weighted mAP and F1 combination
print(file, x[fitness.argmax()])
def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43):
# Makes single-class coco datasets. from utils.utils import *; coco_single_class_labels()
if os.path.exists('new/'):
shutil.rmtree('new/') # delete output folder
os.makedirs('new/') # make new output folder
os.makedirs('new/labels/')
os.makedirs('new/images/')
for file in tqdm(sorted(glob.glob('%s/*.*' % path))):
with open(file, 'r') as f:
labels = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
i = labels[:, 0] == label_class
if any(i):
img_file = file.replace('labels', 'images').replace('txt', 'jpg')
labels[:, 0] = 0 # reset class to 0
with open('new/images.txt', 'a') as f: # add image to dataset list
f.write(img_file + '\n')
with open('new/labels/' + Path(file).name, 'a') as f: # write label
for l in labels[i]:
f.write('%g %.6f %.6f %.6f %.6f\n' % tuple(l))
shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg')) # copy images
def kmeans_targets(path='data/coco_64img.txt', n=9, img_size=416): # from utils.utils import *; kmeans_targets()
# Produces a list of target kmeans suitable for use in *.cfg files
from utils.datasets import LoadImagesAndLabels
from scipy import cluster
# Get label wh
dataset = LoadImagesAndLabels(path, augment=True, rect=True)
for s, l in zip(dataset.shapes, dataset.labels):
l[:, [1, 3]] *= s[0] # normalized to pixels
l[:, [2, 4]] *= s[1]
l[:, 1:] *= img_size / max(s) # nominal img_size for training
wh = np.concatenate(dataset.labels, 0)[:, 3:5] # wh from cxywh
# Kmeans calculation
k = cluster.vq.kmeans(wh, n)[0]
k = k[np.argsort(k.prod(1))] # sort small to large
# Measure IoUs
iou = torch.stack([wh_iou(torch.Tensor(wh).T, torch.Tensor(x).T) for x in k], 0)
biou = iou.max(0)[0] # closest anchor IoU
print((biou < 0.2635).float().mean())
# Print
print('kmeans anchors (n=%g, img_size=%g, IoU=%.2f/%.2f/%.2f-min/mean/best): ' %
(n, img_size, biou.min(), iou.mean(), biou.mean()), end='')
for i, x in enumerate(k):
print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg
# Plot
# plt.hist(biou.numpy().ravel(), 100)
def print_mutation(hyp, results, bucket=''):
# Print mutation results to evolve.txt (for use with train.py --evolve)
a = '%11s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
b = '%11.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values
c = '%11.3g' * len(results) % results # results (P, R, mAP, F1, test_loss)
print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
if bucket:
os.system('gsutil cp gs://%s/evolve.txt .' % bucket) # download evolve.txt
with open('evolve.txt', 'a') as f: # append result
f.write(c + b + '\n')
x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows
np.savetxt('evolve.txt', x[np.argsort(-fitness(x))], '%11.3g') # save sort by fitness
if bucket:
os.system('gsutil cp evolve.txt gs://%s' % bucket) # upload evolve.txt
def fitness(x):
# Returns fitness (for use with results.txt or evolve.txt)
return 0.50 * x[:, 2] + 0.50 * x[:, 3] # fitness = 0.9 * mAP + 0.1 * F1
# Plotting functions ---------------------------------------------------------------------------------------------------
def plot_one_box(x, img, color=None, label=None, line_thickness=None):
# Plots one bounding box on image img
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # 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(img, 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(img, c1, c2, color, -1) # filled
cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
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.png', dpi=200)
def plot_images(imgs, targets, paths=None, fname='images.jpg'):
# Plots training images overlaid with targets
imgs = imgs.cpu().numpy()
targets = targets.cpu().numpy()
# targets = targets[targets[:, 1] == 21] # plot only one class
fig = plt.figure(figsize=(10, 10))
bs, _, h, w = imgs.shape # batch size, _, height, width
bs = min(bs, 16) # limit plot to 16 images
ns = np.ceil(bs ** 0.5) # number of subplots
for i in range(bs):
boxes = xywh2xyxy(targets[targets[:, 0] == i, 2:6]).T
boxes[[0, 2]] *= w
boxes[[1, 3]] *= h
plt.subplot(ns, ns, i + 1).imshow(imgs[i].transpose(1, 2, 0))
plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-')
plt.axis('off')
if paths is not None:
s = Path(paths[i]).name
plt.title(s[:min(len(s), 40)], fontdict={'size': 8}) # limit to 40 characters
fig.tight_layout()
fig.savefig(fname, dpi=200)
plt.close()
def plot_test_txt(): # from utils.utils import *; plot_test()
# Plot test.txt histograms
x = np.loadtxt('test.txt', dtype=np.float32)
box = xyxy2xywh(x[:, :4])
cx, cy = box[:, 0], box[:, 1]
fig, ax = plt.subplots(1, 1, figsize=(6, 6))
ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
ax.set_aspect('equal')
fig.tight_layout()
plt.savefig('hist2d.jpg', dpi=300)
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax[0].hist(cx, bins=600)
ax[1].hist(cy, bins=600)
fig.tight_layout()
plt.savefig('hist1d.jpg', dpi=200)
def plot_targets_txt(): # from utils.utils import *; plot_targets_txt()
# Plot test.txt histograms
x = np.loadtxt('targets.txt', dtype=np.float32)
x = x.T
s = ['x targets', 'y targets', 'width targets', 'height targets']
fig, ax = plt.subplots(2, 2, figsize=(8, 8))
ax = ax.ravel()
for i in range(4):
ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))
ax[i].legend()
ax[i].set_title(s[i])
fig.tight_layout()
plt.savefig('targets.jpg', dpi=200)
def plot_evolution_results(hyp): # from utils.utils import *; plot_evolution_results(hyp)
# Plot hyperparameter evolution results in evolve.txt
x = np.loadtxt('evolve.txt')
f = fitness(x)
weights = (f - f.min()) ** 2 # for weighted results
fig = plt.figure(figsize=(12, 10))
matplotlib.rc('font', **{'size': 8})
for i, (k, v) in enumerate(hyp.items()):
y = x[:, i + 5]
# mu = (y * weights).sum() / weights.sum() # best weighted result
mu = y[f.argmax()] # best single result
plt.subplot(4, 5, i + 1)
plt.plot(mu, f.max(), 'o', markersize=10)
plt.plot(y, f, '.')
plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters
print('%15s: %.3g' % (k, mu))
fig.tight_layout()
plt.savefig('evolve.png', dpi=200)
def plot_results(start=0, stop=0): # from utils.utils import *; plot_results()
# Plot training results files 'results*.txt'
fig, ax = plt.subplots(2, 5, figsize=(14, 7))
ax = ax.ravel()
s = ['GIoU', 'Confidence', 'Classification', 'Precision', 'Recall',
'val GIoU', 'val Confidence', 'val Classification', 'mAP', 'F1']
for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
results = np.loadtxt(f, usecols=[2, 4, 5, 9, 10, 13, 14, 15, 11, 12]).T
n = results.shape[1] # number of rows
x = range(start, min(stop, n) if stop else n)
for i in range(10):
y = results[i, x]
if i in [0, 1, 2, 5, 6, 7]:
y[y == 0] = np.nan # dont show zero loss values
ax[i].plot(x, y, marker='.', label=f.replace('.txt', ''))
ax[i].set_title(s[i])
if i in [5, 6, 7]: # share train and val loss y axes
ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
fig.tight_layout()
ax[0].legend()
fig.savefig('results.png', dpi=200)
def plot_results_overlay(start=1, stop=0): # from utils.utils import *; plot_results_overlay()
# Plot training results files 'results*.txt', overlaying train and val losses
s = ['train', 'train', 'train', 'Precision', 'mAP', 'val', 'val', 'val', 'Recall', 'F1'] # legends
t = ['GIoU', 'Confidence', 'Classification', 'P-R', 'mAP-F1'] # titles
for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
results = np.loadtxt(f, usecols=[2, 4, 5, 9, 11, 13, 14, 15, 10, 12]).T
n = results.shape[1] # number of rows
x = range(start, min(stop, n) if stop else n)
fig, ax = plt.subplots(1, 5, figsize=(14, 3.5))
ax = ax.ravel()
for i in range(5):
for j in [i, i + 5]:
y = results[j, x]
if i in [0, 1, 2]:
y[y == 0] = np.nan # dont show zero loss values
ax[i].plot(x, y, marker='.', label=s[j])
ax[i].set_title(t[i])
ax[i].legend()
ax[i].set_ylabel(f) if i == 0 else None # add filename
fig.tight_layout()
fig.savefig(f.replace('.txt', '.png'), dpi=200)
def version_to_tuple(version):
# Used to compare versions of library
return tuple(map(int, (version.split("."))))
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