def evaluate(model, data_loader, device, mAP_list=None):
n_threads = torch.get_num_threads()
# FIXME remove this and make paste_masks_in_image run on the GPU
torch.set_num_threads(1)
cpu_device = torch.device("cpu")
model.eval()
metric_logger = MetricLogger(delimiter=" ")
header = "Test: "
coco = get_coco_api_from_dataset(data_loader.dataset)
iou_types = _get_iou_types(model)
coco_evaluator = CocoEvaluator(coco, iou_types)
for image, targets in metric_logger.log_every(data_loader, 100, header):
image = list(img.to(device) for img in image)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
# 当使用CPU时，跳过GPU相关指令
if device != torch.device("cpu"):
torch.cuda.synchronize(device)
model_time = time.time()
outputs = model(image)
outputs = [{k: v.to(cpu_device)
for k, v in t.items()} for t in outputs]
model_time = time.time() - model_time
res = {
target["image_id"].item(): output
for target, output in zip(targets, outputs)
}
evaluator_time = time.time()
coco_evaluator.update(res)
evaluator_time = time.time() - evaluator_time
metric_logger.update(model_time=model_time,
evaluator_time=evaluator_time)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
coco_evaluator.synchronize_between_processes()
# accumulate predictions from all images
coco_evaluator.accumulate()
coco_evaluator.summarize()
torch.set_num_threads(n_threads)
print_txt = coco_evaluator.coco_eval[iou_types[0]].stats
coco_mAP = print_txt[0]
voc_mAP = print_txt[1]
if isinstance(mAP_list, list):
mAP_list.append(voc_mAP)
return coco_evaluator
def evaluate(model, data_loader, device):
n_threads = torch.get_num_threads()
# FIXME remove this and make paste_masks_in_image run on the GPU
torch.set_num_threads(1)
cpu_device = torch.device("cpu")
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
coco = get_coco_api_from_dataset(data_loader.dataset)
iou_types = _get_iou_types(model)
coco_evaluator = CocoEvaluator(coco, iou_types)
for images, targets in metric_logger.log_every(data_loader, 100, header):
images = list(img.to(device) for img in images)
torch.cuda.synchronize()
model_time = time.time()
outputs = model(images)
outputs = [{k: v.to(cpu_device) for k, v in t.items()} for t in outputs]
model_time = time.time() - model_time
res = {target["image_id"].item(): output for target, output in zip(targets, outputs)}
evaluator_time = time.time()
coco_evaluator.update(res)
evaluator_time = time.time() - evaluator_time
metric_logger.update(model_time=model_time, evaluator_time=evaluator_time)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
coco_evaluator.synchronize_between_processes()
# accumulate predictions from all images
coco_evaluator.accumulate()
coco_evaluator.summarize()
torch.set_num_threads(n_threads)
return coco_evaluator
def evaluate(model, data_loader, device, data_set=None, mAP_list=None):
n_threads = torch.get_num_threads()
# FIXME remove this and make paste_masks_in_image run on the GPU
torch.set_num_threads(1)
cpu_device = torch.device("cpu")
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = "Test: "
if data_set is None:
data_set = get_coco_api_from_dataset(data_loader.dataset)
iou_types = _get_iou_types(model)
coco_evaluator = CocoEvaluator(data_set, iou_types)
for images, targets in metric_logger.log_every(data_loader, 100, header):
images = torch.stack(images, dim=0)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
images = images.to(device)
# targets = {k: v.to(device) for k, v in targets.items()}
if device != torch.device("cpu"):
torch.cuda.synchronize(device)
model_time = time.time()
# list((bboxes_out, labels_out, scores_out), ...)
results = model(images, targets)
outputs = []
for index, (bboxes_out, labels_out, scores_out) in enumerate(results):
# 将box的相对坐标信息（0-1）转为绝对值坐标(xmin, ymin, xmax, ymax)
height_width = targets[index]["height_width"]
# height_width = [300, 300]
bboxes_out[:, [0, 2]] = bboxes_out[:, [0, 2]] * height_width[1]
bboxes_out[:, [1, 3]] = bboxes_out[:, [1, 3]] * height_width[0]
info = {"boxes": bboxes_out.to(cpu_device),
"labels": labels_out.to(cpu_device),
"scores": scores_out.to(cpu_device)}
outputs.append(info)
# outputs = [{k: v.to(cpu_device) for k, v in t.items()} for t in outputs]
model_time = time.time() - model_time
res = dict()
for index in range(len(outputs)):
info = {targets[index]["image_id"].item(): outputs[index]}
res.update(info)
# res = {target["image_id"].item(): output for target, output in zip(targets, outputs)}
evaluator_time = time.time()
coco_evaluator.update(res)
evaluator_time = time.time() - evaluator_time
metric_logger.update(model_time=model_time, evaluator_time=evaluator_time)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
coco_evaluator.synchronize_between_processes()
# accumulate predictions from all images
coco_evaluator.accumulate()
coco_evaluator.summarize()
torch.set_num_threads(n_threads)
print_txt = coco_evaluator.coco_eval[iou_types[0]].stats
coco_mAP = print_txt[0]
voc_mAP = print_txt[1]
if isinstance(mAP_list, list):
mAP_list.append(voc_mAP)
def main(parser_data):
device = torch.device(
parser_data.device if torch.cuda.is_available() else "cpu")
print("Using {} device training.".format(device.type))
data_transform = {"val": transforms.Compose([transforms.ToTensor()])}
# read class_indict
label_json_path = './pascal_voc_classes.json'
assert os.path.exists(
label_json_path), "json file {} dose not exist.".format(
label_json_path)
json_file = open(label_json_path, 'r')
class_dict = json.load(json_file)
category_index = {v: k for k, v in class_dict.items()}
VOC_root = parser_data.data_path
# check voc root
if os.path.exists(os.path.join(VOC_root, "VOCdevkit")) is False:
raise FileNotFoundError(
"VOCdevkit dose not in path:'{}'.".format(VOC_root))
# 注意这里的collate_fn是自定义的，因为读取的数据包括image和targets，不能直接使用默认的方法合成batch
batch_size = parser_data.batch_size
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0,
8]) # number of workers
print('Using %g dataloader workers' % nw)
# load validation data set
val_data_set = VOC2007DataSet(VOC_root, data_transform["val"], "val.txt")
val_data_set_loader = torch.utils.data.DataLoader(
val_data_set,
batch_size=batch_size,
shuffle=False,
num_workers=nw,
collate_fn=val_data_set.collate_fn)
# create model num_classes equal background + 20 classes
backbone = resnet50_fpn_backbone()
model = FasterRCNN(backbone=backbone,
num_classes=parser_data.num_classes + 1)
# 载入你自己训练好的模型权重
weights_path = parser_data.weights
assert os.path.exists(weights_path), "not found {} file.".format(
weights_path)
weights_dict = torch.load(weights_path, map_location=device)
model.load_state_dict(weights_dict['model'])
# print(model)
model.to(device)
# evaluate on the test dataset
coco = get_coco_api_from_dataset(val_data_set)
iou_types = ["bbox"]
coco_evaluator = CocoEvaluator(coco, iou_types)
cpu_device = torch.device("cpu")
model.eval()
with torch.no_grad():
for image, targets in tqdm(val_data_set_loader, desc="validation..."):
# 将图片传入指定设备device
image = list(img.to(device) for img in image)
# inference
outputs = model(image)
outputs = [{k: v.to(cpu_device)
for k, v in t.items()} for t in outputs]
res = {
target["image_id"].item(): output
for target, output in zip(targets, outputs)
}
coco_evaluator.update(res)
coco_evaluator.synchronize_between_processes()
# accumulate predictions from all images
coco_evaluator.accumulate()
coco_evaluator.summarize()
coco_eval = coco_evaluator.coco_eval["bbox"]
# calculate COCO info for all classes
coco_stats, print_coco = summarize(coco_eval)
# calculate voc info for every classes(IoU=0.5)
voc_map_info_list = []
for i in range(len(category_index)):
stats, _ = summarize(coco_eval, catId=i)
voc_map_info_list.append(" {:15}: {}".format(category_index[i + 1],
stats[1]))
print_voc = "\n".join(voc_map_info_list)
print(print_voc)
# 将验证结果保存至txt文件中
with open("record_mAP.txt", "w") as f:
record_lines = [
"COCO results:", print_coco, "", "mAP(IoU=0.5) for each category:",
print_voc
]
f.write("\n".join(record_lines))
def evaluate(model, data_loader, coco=None, device=None):
n_threads = torch.get_num_threads()
# FIXME remove this and make paste_masks_in_image run on the GPU
torch.set_num_threads(1)
cpu_device = torch.device("cpu")
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = "Test: "
if coco is None:
coco = get_coco_api_from_dataset(data_loader.dataset)
iou_types = _get_iou_types(model)
coco_evaluator = CocoEvaluator(coco, iou_types)
for imgs, targets, paths, _, img_index in metric_logger.log_every(data_loader, 100, header):
imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
# targets = targets.to(device)
# 当使用CPU时，跳过GPU相关指令
if device != torch.device("cpu"):
torch.cuda.synchronize(device)
model_time = time.time()
pred = model(imgs)[0] # only get inference result
pred = non_max_suppression(pred, conf_thres=0.001, iou_thres=0.6, multi_label=False)
outputs = []
for index, p in enumerate(pred):
if p is None:
p = torch.empty((0, 6), device=cpu_device)
boxes = torch.empty((0, 4), device=cpu_device)
else:
# xmin, ymin, xmax, ymax
boxes = p[:, :4]
# 注意这里传入的boxes格式必须是xmin, ymin, xmax, ymax，且为绝对坐标
info = {"boxes": boxes.to(cpu_device),
"labels": p[:, 5].to(device=cpu_device, dtype=torch.int64),
"scores": p[:, 4].to(cpu_device)}
outputs.append(info)
model_time = time.time() - model_time
res = {img_id: output for img_id, output in zip(img_index, outputs)}
evaluator_time = time.time()
coco_evaluator.update(res)
evaluator_time = time.time() - evaluator_time
metric_logger.update(model_time=model_time, evaluator_time=evaluator_time)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
coco_evaluator.synchronize_between_processes()
# accumulate predictions from all images
coco_evaluator.accumulate()
coco_evaluator.summarize()
torch.set_num_threads(n_threads)
result_info = coco_evaluator.coco_eval[iou_types[0]].stats
return result_info
def evaluate(self, data_loader, coco=None, device=None):
n_threads = torch.get_num_threads()
# FIXME remove this and make paste_masks_in_image run on the GPU
torch.set_num_threads(1)
if not device:
device = torch.device("cpu")
self.model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = "Test: "
if coco is None:
coco = get_coco_api_from_dataset(data_loader.dataset)
iou_types = _get_iou_types(self.model)
coco_evaluator = CocoEvaluator(coco, iou_types)
log_every = metric_logger.log_every(data_loader, 100, header)
for images, targets, paths, shapes, img_index in log_every:
images = images.to(device).float(
) / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
# 当使用CPU时，跳过GPU相关指令
if device != torch.device("cpu"):
torch.cuda.synchronize(device)
model_time = time.time()
pred = self.model(images)[0] # only get inference result
pred = non_max_suppression(pred,
conf_thres=0.001,
iou_thres=0.6,
multi_label=False)
outputs = []
for index, pred_i in enumerate(pred):
if pred_i is None:
pred_i = torch.empty((0, 6), device=device)
boxes = torch.empty((0, 4), device=device)
else:
boxes = pred_i[:, :4] # l, t, r, b
# shapes: (h0, w0), ((h / h0, w / w0), pad)
# 将boxes信息还原回原图尺度，这样计算的mAP才是准确的
boxes = scale_coordinates(boxes, images[index].shape[1:],
shapes[index]).round()
image = images[index]
self.img_show(image, boxes)
# 注意这里传入的boxes格式必须是 l_abs, t_abs, r_abs, b_abs，且为绝对坐标
info = {
"boxes": boxes.to(device),
"labels": pred_i[:, 5].to(device=device,
dtype=torch.int64),
"scores": pred_i[:, 4].to(device)
}
outputs.append(info)
model_time = time.time() - model_time
res = {
img_id: output
for img_id, output in zip(img_index, outputs)
}
evaluator_time = time.time()
coco_evaluator.update(res)
evaluator_time = time.time() - evaluator_time
metric_logger.update(model_time=model_time,
evaluator_time=evaluator_time)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
coco_evaluator.synchronize_between_processes()
# accumulate predictions from all images
coco_evaluator.accumulate()
coco_evaluator.summarize()
torch.set_num_threads(n_threads)
result_info = coco_evaluator.coco_eval[
iou_types[0]].stats.tolist() # numpy to list
return result_info
