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test.py
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test.py
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import argparse
import json
import os
from pathlib import Path
from threading import Thread
import numpy as np
import torch
import yaml
from tqdm import tqdm
from matplotlib.figure import Figure
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from pylab import *
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.cbook import get_sample_data
import cv2
from matplotlib._png import read_png
from models.experimental import attempt_load
from utils.datasets import create_dataloader
from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \
box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr
from utils.metrics import ap_per_class, ConfusionMatrix
from utils.plots import plot_images, output_to_target, plot_study_txt
from utils.torch_utils import select_device, time_synchronized
from MR_2 import validate
from torchvision import *
import cv2
from torch.nn import functional as F
import json
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
unloader = transforms.ToPILImage()
def test(data,
weights=None,
batch_size=32,
imgsz=640,#640
conf_thres=0.001,
iou_thres=0.6, # for NMS
save_json=False,
single_cls=False,
augment=False,
verbose=False,
model=None,
dataloader=None,
save_dir=Path(''), # for saving images
save_txt=False, # for auto-labelling
save_hybrid=False, # for hybrid auto-labelling
save_conf=False, # save auto-label confidences
plots=True,
wandb_logger=None,
compute_loss=None,
half_precision=True,
is_coco=False,
MR=True,
Datasets = 'citytocaltech', #'citytocaltech',#'foggycity'
vision_show_feature = False,
iter = 0, epoch = 0, in_iter = False, img_size = 640,
):#use MR-2 instead of MAP
# Initialize/load model and set device
if Datasets == 'cityperson' or Datasets == 'caltechtocity':
dict_path = 'city_val_dict_reverse.json'
if Datasets == 'foggycity' or Datasets == 'citytofoggy':
dict_path = 'foggy_city_dict.json'
if Datasets == 'caltech' or Datasets == 'citytocaltech':
dict_path = 'caltech_dict.json'
if Datasets == 'bdd_day' or Datasets == 'citytobdd_day':
dict_path = 'bdd_day_dict.json'
if Datasets == 'bdd_night' or Datasets == 'citytobdd_night':
dict_path = 'bdd_night_dict.json'
if Datasets == 'bdd10k':
dict_path = 'bdd_10k_dict.json'
with open(dict_path, 'r', encoding='utf8') as fp1:
img_dict = json.load(fp1)
training = model is not None#False
if training: # called by train.py
device = next(model.parameters()).device # get model device
else: # called directly
set_logging()
device = select_device(opt.device, batch_size=batch_size)
# Directories
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run#runs/test/exp14
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
gs = max(int(model.stride.max()), 32) # grid size (max stride)
imgsz = check_img_size(imgsz, s=gs) # check img_size
# Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
# if device.type != 'cpu' and torch.cuda.device_count() > 1:
# model = nn.DataParallel(model)
# Half
half = device.type != 'cpu' and half_precision # half precision only supported on CUDA
if half:
model.half()
# Configure
model.eval()
if isinstance(data, str):
is_coco = data.endswith('coco.yaml')
with open(data) as f:
data = yaml.load(f, Loader=yaml.SafeLoader)
check_dataset(data) # check
nc = 1 if single_cls else int(data['nc']) # number of classes
iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
niou = iouv.numel()#10
# Logging
log_imgs = 0
if wandb_logger and wandb_logger.wandb:
log_imgs = min(wandb_logger.log_imgs, 100)
# Dataloader
if not training:
if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
task = opt.task if opt.task in ('train', 'val', 'test') else 'val' # path to train/val/test images
dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True,
prefix=colorstr(f'{task}: '))[0]
seen = 0#初始化测试的图片数量
confusion_matrix = ConfusionMatrix(nc=nc)
names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
coco91class = coco80_to_coco91_class()
#s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')#cyc
#if MR:
#s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'MR_2', 'mAP@.5', 'mAP@.5:.95')
#else:
s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []#初始化json文件的字典
for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
W = 0
img = img.to(device, non_blocking=True)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
nb, _, height, width = img.shape # batch size, channels, height, width
with torch.no_grad():
# Run model
t = time_synchronized()
out, train_out, feature, feature_afterconv, feature_after_backbone = model(img, augment=augment) # inference and training outputs#, vision_feature=True
# else:
# out, train_out = model(img, augment=augment)
t0 += time_synchronized() - t
# for NUM_i in range(len(paths)):
# if paths[NUM_i].split('/')[-1] == 'set07_V000_179.png':
# W = 1
# print("find it")
# continue
if vision_show_feature and W:#paths[0].split('/')[-1] == 'frankfurt_000001_013016_leftImg8bit.png': #and paths[0].split('/')[-1] == 'set07_V000_179.png':
only_first = True
# feature_first = torch.max(feature[0],dim=1)[0]
# feature_first = 255*torch.sum(feature[0], dim=1, keepdim=True)/feature[0].max()
# feature_first = F.interpolate(feature_first, (1024, 2048), mode='bilinear',
# align_corners=True)
# feature_show_first = unloader(feature_first.squeeze(0))
# # feature_second = torch.max(feature[1], dim=1)[0]
# feature_second = 255*torch.sum(feature[1], dim=1, keepdim=True)/feature[1].max()
# feature_second = F.interpolate(feature_second, (1024, 2048), mode='bilinear',
# align_corners=True)
# feature_show_second = unloader(feature_second.squeeze(0))
# # feature_third = torch.max(feature[2], dim=1)[0]
# feature_third = 255*torch.sum(feature[2], dim=1, keepdim=True)/feature[2].max()
# feature_third = F.interpolate(feature_third, (1024, 2048), mode='bilinear',
# align_corners=True)
# feature_show_third = unloader(feature_third.squeeze(0))
# # feature_forth = torch.max(feature[3], dim=1)[0]
# feature_forth = 255*torch.sum(feature[3], dim=1, keepdim=True)/feature[3].max()
# feature_forth = F.interpolate(feature_forth, (1024, 2048), mode='bilinear',
# align_corners=True)
# feature_show_forth = unloader(feature_forth.squeeze(0))
# cv2.imwrite(str(save_dir) + '/feature_first'+str(batch_i)+'.png', np.array(feature_show_first))
# cv2.imwrite(str(save_dir) + '/feature_second'+str(batch_i)+'.png', np.array(feature_show_second))
# cv2.imwrite(str(save_dir) + '/feature_third'+str(batch_i)+'.png', np.array(feature_show_third))
# cv2.imwrite(str(save_dir) + '/feature_forth'+str(batch_i)+'.png', np.array(feature_show_forth))
#############################################after_conv
if img_size == 640:
k = (480,640)
if img_size == 2048:
k = (1024,2048)
A = False
feature_1_afterconv = torch.sum(feature_afterconv[0][NUM_i].unsqueeze(0), dim=1, keepdim=True)# / 5.#feature_afterconv[0].max() *255.
feature_1_afterconv = F.interpolate(feature_1_afterconv, k, mode='bilinear',#(1024,2048)(480, 640)
align_corners=A)
feature_show_1_afterconv = unloader(feature_1_afterconv.squeeze(0))
# feature_2 = torch.max(feature[1], dim=1)[0]
if not only_first:
feature_2_afterconv = torch.sum(feature_afterconv[1][NUM_i].unsqueeze(0), dim=1,
keepdim=True) # / 5.#feature_afterconv[1].max() *255.
feature_2_afterconv = F.interpolate(feature_2_afterconv, k, mode='bilinear',
align_corners=A)
feature_show_2_afterconv = unloader(feature_2_afterconv.squeeze(0))
# feature_third = torch.max(feature[2], dim=1)[0]
feature_3_afterconv = torch.sum(feature_afterconv[2][NUM_i].unsqueeze(0), dim=1,
keepdim=True) # / 5.#feature_afterconv[2].max() *255.
feature_3_afterconv = F.interpolate(feature_3_afterconv, k, mode='bilinear',
align_corners=A)
feature_show_3_afterconv = unloader(feature_3_afterconv.squeeze(0))
# feature_forth = torch.max(feature[3], dim=1)[0]
feature_4_afterconv = torch.sum(feature_afterconv[3][NUM_i].unsqueeze(0), dim=1,
keepdim=True) # / 5.#feature_afterconv[3].max() *255.
feature_4_afterconv = F.interpolate(feature_4_afterconv, k, mode='bilinear',
align_corners=A)
feature_show_4_afterconv = unloader(feature_4_afterconv.squeeze(0))
# feature_for_mat_show = {feature_first_afterconv,feature_second_afterconv,feature_third_afterconv,feature_forth_afterconv}
# mat_dir = str(save_dir) + '/mat_show.json'
# with open(mat_dir,'w') as fp:
# json.dump(feature_for_mat_show,fp)
###feature_third_afterconv.clone().cpu().numpy()[0][0]
if img_size == 640:
XX = 640
YY = 480
Ww = 2
if img_size == 2048:
XX = 2048
YY = 1024
Ww = 8
if only_first:
ir = 1
else:
ir = 4
for i in range(ir):
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
X = np.arange(0,XX,Ww)#(0,640,4)
Y = np.arange(0,YY,Ww)#(0,480,4)
X, Y = np.meshgrid(X, Y)
if i == 0:
feature_1_afterconv = F.interpolate(feature_1_afterconv.clone(), (int(YY/Ww), int(XX/Ww)), mode='bilinear',#(128, 256)
align_corners=A)
Z = feature_1_afterconv.clone().cpu().numpy()[0][0]
if i == 1:
feature_2_afterconv = F.interpolate(feature_2_afterconv.clone(), (int(YY/Ww), int(XX/Ww)), mode='bilinear',#(120, 160)
align_corners=A)
Z = feature_2_afterconv.clone().cpu().numpy()[0][0]
if i == 2:
feature_3_afterconv = F.interpolate(feature_3_afterconv.clone(), (int(YY/Ww), int(XX/Ww)), mode='bilinear',
align_corners=A)
Z = feature_3_afterconv.clone().cpu().numpy()[0][0]
if i == 3:
feature_4_afterconv = F.interpolate(feature_4_afterconv.clone(), (int(YY/Ww), int(XX/Ww)), mode='bilinear',
align_corners=A)
Z = feature_4_afterconv.clone().cpu().numpy()[0][0]
# Z = Z - Z.min() #(更好看)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'), alpha = 0.8)
ax.set_zlim(Z.min(), Z.max())
ax.zaxis.set_major_locator(LinearLocator(10))#设置Z轴间隔
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
img = cv2.imread("/remote-home/share/Caltech/images/test_debug/set07/set07_V000_179.png")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = img.astype('float32') / 255
img = np.einsum('wlc->lwc', img)
# img = cv2.flip(img, 1)
img = cv2.flip(img, 1)
x, y = np.ogrid[0:img.shape[0], 0:img.shape[1]]
ax.plot_surface(x, y, np.atleast_2d(Z.min()), rstride=2, cstride=2, facecolors=img)
font = {'family': 'serif',
'color': 'red',
'weight': 'normal',
'size': 16,}
# ax.set_xlabel('length(L)',fontdict=font)
# ax.set_ylabel('width(W)',fontdict=font)
# ax.set_zlabel('feature value(V)',fontdict=font)
# ax.set_xlabel('L', fontdict=font)
# ax.set_ylabel('W', fontdict=font)
# ax.set_zlabel('V', fontdict=font)
# fig.colorbar(surf, shrink=0.5, aspect=5)
if not training:
plt.savefig(str(save_dir) + "/height_B" + str(batch_i) + "_" + str(i + 1) + ".png", bbox_inches='tight',dpi=200)
plt.close()
# if training and paths[0].split('/')[-1] == 'set07_V000_179.png':
# plt.savefig(str(save_dir) + "/height_B" + str(batch_i) + "_" + str(i + 1) + "_epoch"+ str(epoch) +"_iter"+ str(iter) + ".png", bbox_inches='tight')
# plt.close()
if not training:
cv2.imwrite(str(save_dir) + '/feature_first_afterconv' + str(batch_i) + '.png',
np.array(feature_show_1_afterconv))
cv2.imwrite(str(save_dir) + '/feature_second_afterconv' + str(batch_i) + '.png',
np.array(feature_show_2_afterconv))
cv2.imwrite(str(save_dir) + '/feature_third_afterconv' + str(batch_i) + '.png',
np.array(feature_show_3_afterconv))
cv2.imwrite(str(save_dir) + '/feature_forth_afterconv' + str(batch_i) + '.png',
np.array(feature_show_4_afterconv))
# if training and batch_i == 1:
# cv2.imwrite(str(save_dir) + '/feature_first_afterconv' + str(batch_i) + "_" + str(i + 1) + "_epoch"+ epoch +"_iter"+ iter + '.png',
# np.array(feature_show_1_afterconv))
# cv2.imwrite(str(save_dir) + '/feature_second_afterconv' + str(batch_i) + "_" + str(i + 1) + "_epoch"+ epoch +"_iter"+ iter + '.png',
# np.array(feature_show_2_afterconv))
# cv2.imwrite(str(save_dir) + '/feature_third_afterconv' + str(batch_i) + "_" + str(i + 1) + "_epoch"+ epoch +"_iter"+ iter + '.png',
# np.array(feature_show_3_afterconv))
# cv2.imwrite(str(save_dir) + '/feature_forth_afterconv' + str(batch_i) + "_" + str(i + 1) + "_epoch"+ epoch +"_iter"+ iter + '.png',
# np.array(feature_show_4_afterconv))
# Compute loss
if compute_loss:
loss += compute_loss([x.float() for x in train_out], targets)[1][:3] # box, obj, cls
# Run NMS
targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
t = time_synchronized()
out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True)
t1 += time_synchronized() - t
# Statistics per image
for si, pred in enumerate(out):
labels = targets[targets[:, 0] == si, 1:]
nl = len(labels)
tcls = labels[:, 0].tolist() if nl else [] # target class
path = Path(paths[si])
seen += 1
if len(pred) == 0:
if nl:
stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
continue
# Predictions
predn = pred.clone()
scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred
# Append to text file
if save_txt:
gn = torch.tensor(shapes[si][0])[[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(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
# W&B logging - Media Panel Plots
if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation
if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0:
box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
"class_id": int(cls),
"box_caption": "%s %.3f" % (names[cls], conf),
"scores": {"class_score": conf},
"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))
wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None
# Append to pycocotools JSON dictionary
if MR:
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(pred.tolist(), box.tolist()):
new_id = img_dict[image_id]
jdict.append({'image_id': int(new_id),
# 'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),#cyc(为了弥补yolov5和cityperson的groundtruth不合)
'category_id': 1,
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5)})
if save_json:
# [{"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(pred.tolist(), box.tolist()):
jdict.append({'image_id': image_id,
'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5)})
# Assign all predictions as incorrect
correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
if nl:
detected = [] # target indices
tcls_tensor = labels[:, 0]
# target boxes
tbox = xywh2xyxy(labels[:, 1:5])
scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels
if plots:
confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1))
# Per target class
for cls in torch.unique(tcls_tensor):
ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices
pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices
# Search for detections
if pi.shape[0]:
# Prediction to target ious
ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1) # best ious, indices
# Append detections
detected_set = set()
for j in (ious > iouv[0]).nonzero(as_tuple=False):
d = ti[i[j]] # detected target
if d.item() not in detected_set:
detected_set.add(d.item())
detected.append(d)
correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
if len(detected) == nl: # all targets already located in image
break
# Append statistics (correct, conf, pcls, tcls)
stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
# Plot images
if plots and batch_i < 3:
f = save_dir / f'test_batch{batch_i}_labels.jpg' # labels
Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
f = save_dir / f'test_batch{batch_i}_pred.jpg' # predictions
Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()
# Compute statistics
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
if len(stats) and stats[0].any():
p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
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(np.int64), minlength=nc) # number of targets per class
else:
nt = torch.zeros(1)
# Print results
if not in_iter:
pf = '%20s' + '%12i' * 2 + '%12.3g' * 4 # print format
print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
# 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):
print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
# Print speeds
t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple
if not training:
print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
# Plots
if plots:
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
if wandb_logger and wandb_logger.wandb:
val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]
wandb_logger.log({"Validation": val_batches})
if wandb_images:
wandb_logger.log({"Bounding Box Debugger/Images": wandb_images})
# save MR
if MR:
w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''
pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
with open(pred_json, 'w') as f:
json.dump(jdict, f)
if Datasets == 'cityperson' or Datasets == 'caltechtocity':
MRs = validate('./val_gt.json', pred_json)#cyc
if Datasets == 'caltech' or Datasets == 'citytocaltech':
MRs = validate('./val_caltech.json',pred_json)
if Datasets == 'foggycity' or Datasets == 'citytofoggy':
MRs = validate('./val_foggy_city.json', pred_json)
if Datasets == 'bdd_day' or Datasets == 'citytobdd_day':
MRs = validate('./val_bdd100.json', pred_json)
if Datasets == 'bdd_night' or Datasets == 'citytobdd_night':
MRs = validate('./val_bdd100_night.json', pred_json)
if Datasets == 'bdd10k':
MRs = validate('./val_bdd10.json', pred_json)
# if Datasets == 'citytocaltech':
# MRs = validate('./val_caltech_new.json', pred_json)
# if Datasets == 'caltechtocity':
# MRs = validate('./val_city_new.json', pred_json)
if not in_iter:
print('Summarize: [Reasonable: %.2f%%], [Bare: %.2f%%], [Partial: %.2f%%], [Heavy: %.2f%%]'
% (MRs[0] * 100, MRs[1] * 100, MRs[2] * 100, MRs[3] * 100))
# 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 = '../coco/annotations/instances_val2017.json' # annotations json
pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
if not in_iter:
print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
with open(pred_json, 'w') as f:
json.dump(jdict, f)
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
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.img_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:
print(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 ''
print(f"Results saved to {save_dir}{s}")
maps = np.zeros(nc) + map
for i, c in enumerate(ap_class):
maps[c] = ap[i]
if MR:
return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t, MRs
return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
if __name__ == '__main__':
parser = argparse.ArgumentParser(prog='test.py')#./runs/trainforpaper_crossdomain/exp24/weights/best_mr.pt#./runs/train_forpaper/exp17/weights/best_mr.pt
# parser.add_argument('--weights', nargs='+', type=str, default='./runs/trainforpaper_crossdomain_transformer/exp34/weights/best_mr.pt', help='model.pt path(s)')#_mr
# parser.add_argument('--weights', nargs='+', type=str,
# default='./runs/trainforpaper_cycle_baseradar/exp9/weights/best_mr.pt',
# help='model.pt path(s)')
# parser.add_argument('--weights', nargs='+', type=str,
# default='./runs/train_forpaper/exp62/weights/best_mr.pt', #28
# help='model.pt path(s)')
# parser.add_argument('--weights', nargs='+', type=str, default='./runs/trainforpaper_crossdomain_transformer/exp32/weights/best_mr.pt', help='model.pt path(s)')
# parser.add_argument('--weights', nargs='+', type=str,
# default='./runs/train_forpaper/exp28/weights/best_mr.pt',
# help='model.pt path(s)')
parser.add_argument('--weights', nargs='+', type=str,
default='./runs/trainforpaper_ctf_hr_twodis/exp2/weights/best_mr.pt',
help='model.pt path(s)')
# parser.add_argument('--weights', nargs='+', type=str,
# default='./runs/trainforpaper_crossdomain_onlycvt_ctob/exp/weights/best_mr.pt',
# help='model.pt path(s)')
parser.add_argument('--data', type=str, default='data/citytocaltech.yaml', help='*.data path')#citytocaltech#cityperson#citytobdd_day#foggycity
parser.add_argument('--batch-size', type=int, default=128, help='size of each image batch')
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
parser.add_argument('--task', default='val', help='train, val, test, speed or study')
parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--single-cls', action='store_true', default=False, 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 cocoapi-compatible JSON results file')
parser.add_argument('--project', default='runs/test', 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('--pattern', type=str, default='citytocaltech', help='use what dict')#citytocaltech#foggycity
parser.add_argument('--vision_feature', action='store_true', default=False, help='use what dict')
opt = parser.parse_args()
opt.save_json |= opt.data.endswith('coco.yaml')
opt.data = check_file(opt.data) # check file
print(opt)
check_requirements()
if opt.task in ('train', 'val', 'test'): # run normally
test(opt.data,
opt.weights,
opt.batch_size,
opt.img_size,
opt.conf_thres,
opt.iou_thres,
opt.save_json,
opt.single_cls,
opt.augment,
opt.verbose,
save_txt=opt.save_txt | opt.save_hybrid,
save_hybrid=opt.save_hybrid,
save_conf=opt.save_conf,
Datasets=opt.pattern,
vision_show_feature=opt.vision_feature,
img_size= opt.img_size
)
elif opt.task == 'speed': # speed benchmarks
for w in opt.weights:
test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False)
elif opt.task == 'study': # run over a range of settings and save/plot
# python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt
x = list(range(256, 1536 + 128, 128)) # x axis (image sizes)
for w in opt.weights:
f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt' # filename to save to
y = [] # y axis
for i in x: # img-size
print(f'\nRunning {f} point {i}...')
r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json,
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_study_txt(x=x) # plot