def client_handler(c, addr, config, pid):
message = {"code": 1}
send_msg(c, json.dumps(message).encode("utf-8"))
yolo_losses = []
complex_yolo_416 = []
response = []
net = None
try:
classes = open(config["classes_names_path"], "r").read().split("\n")[:-1]
for i in range(3):
yolo_losses.append(YOLOLoss(config["yolo"]["anchors"][i], config["yolo"]["classes"], (config["img_w"], config["img_h"])))
except Exception as e:
print(e.__str__())
return
while True:
try:
res_start = time.time()
data = recv_msg(c)
start = time.time()
info = json.loads(str(data.decode('utf-8')))
if info["code"] == 1 and info["name"] is not None:
if info["name"] is not None:
with open(config["images_path"] + info["name"], 'wb') as file:
file.write(base64.b64decode(info["data"]))
if info["app"] == "yolo3":
if net is None:
net = initial_yolo_model(config, info["size"])
compute_time, cpu, complex_yolo_416= detect_image(info["name"], response, config, net, yolo_losses, classes, complex_yolo_416, pid)
elif info["app"] == "yolo2":
if net is None:
net = initial_yolo_model_2()
start_yolo_2 = time.time()
img = cv2.imread(os.path.join(config["images_path"], info["name"]))
net.return_predict(img)
compute_time = time.time() - start_yolo_2
cpu = psutil.cpu_percent()/100
complex_yolo_416.append(compute_time * cpu * 2.8)
print("\tyolo2} finished in {}s, system response in {} s, cpu in {} cycles(10^9)"
.format(round(cpu, 3), round(compute_time, 4)
, round(np.average(response), 4)
, round(np.average(complex_yolo_416), 3)))
else:
if net is None:
net = initial_pose_model(config)
compute_time, cpu, complex_yolo_416 = detect_pose(info["name"], net, response, config, complex_yolo_416, pid)
message = {"code": 2, "time": time.time() - start, "inx": info["inx"], "cpu": cpu,
"compute_time": compute_time, "path": info["name"], "next": True, "timestamp": info["timestamp"]}
send_msg(c, json.dumps(message).encode("utf-8"))
response.append(round(time.time() - res_start, 3))
if info["code"] == -1:
c.close()
print(addr, "close")
except Exception as e:
print("1." + traceback.format_exc())
return
print(list(response))
def __init__(self, config, is_training):
self.config = config
self.is_training = is_training
use_gpu = config.use_gpu
self.net = Model(self.config, is_training=self.is_training)
if self.is_training:
self.net.train(is_training)
else:
self.net.eval()
self.net.init_weights(gpu=use_gpu)
if self.is_training:
self.optimizer = self._get_optimizer()
if len(self.config.parallels) > 0:
self.net = nn.DataParallel(self.net)
if use_gpu:
self.net = self.net.cuda()
self.yolo_loss = []
for i in range(3):
self.yolo_loss.append(
YOLOLoss(config.anchors[i], config.image_size,
config.num_classes))
#if is_refine:
# self.refine_loss = RefineLoss(config.anchors, config.num_classes, (config.image_size, config.image_size))
if config.pretrained_weights:
logging.info("Load pretrained weights from {}".format(
config.pretrained_weights))
if use_gpu:
checkpoint = torch.load(config.pretrained_weights)
else:
checkpoint = torch.load(config.pretrained_weights,
map_location=torch.device('cpu'))
state_dict = checkpoint['state_dict']
self.net.load_state_dict(state_dict)
self.epoch = checkpoint["epoch"] + 1
self.global_step = checkpoint['global step'] + 1
else:
self.epoch = 0
self.global_step = 0
if config.official_weights:
logging.info("Loading official weights from {}".format(
config.official_weights))
self.net.load_state_dict(torch.load(config.official_weights))
self.global_step = 20000
#self.pre_prune_weights()
#self.prune_weights_in_training_perc()
self.prune_weights_in_training_thresh()
def detect(config):
is_training = False
# Load and initialize network
net = ModelMain(config, is_training=is_training)
net.train(is_training)
# Set data parallel
net = nn.DataParallel(net)
net = net.cuda()
# Restore pretrain model
if config["pretrain_snapshot"]:
state_dict = torch.load(config["pretrain_snapshot"])
net.load_state_dict(state_dict)
else:
logging.warning("missing pretrain_snapshot!!!")
# YOLO loss with 3 scales
yolo_losses = []
for i in range(3):
yolo_losses.append(
YOLOLoss(config["yolo"]["anchors"][i], config["yolo"]["classes"],
(config["img_w"], config["img_h"])))
# Load tested img
imgfile = config["img_path"]
img = Image.open(imgfile).convert('RGB')
resized = img.resize((config["img_w"], config["img_h"]))
input = image2torch(resized)
input = input.to(torch.device("cuda"))
start = time.time()
outputs = net(input)
output_list = []
for i in range(3):
output_list.append(yolo_losses[i](outputs[i]))
output = torch.cat(output_list, 1)
output = non_max_suppression(output,
config["yolo"]["classes"],
conf_thres=0.5,
nms_thres=0.4)
finish = time.time()
print('%s: Predicted in %f seconds.' % (imgfile, (finish - start)))
namefile = config["classname_path"]
class_names = load_class_names(namefile)
plot_boxes(img, output, 'predictions.jpg', class_names)
def main(video_fn):
logging.basicConfig(level=logging.DEBUG,
format="[%(asctime)s %(filename)s] %(message)s")
if len(sys.argv) != 2:
logging.error("Usage: python video.py params.py")
sys.exit()
params_path = sys.argv[1]
if not os.path.isfile(params_path):
logging.error("no params file found! path: {}".format(params_path))
sys.exit()
config = importlib.import_module(params_path[:-3]).TRAINING_PARAMS
config["batch_size"] *= len(config["parallels"])
is_training = False
# Load and initialize network
net = ModelMain(config, is_training=is_training)
net.train(is_training)
# Set data parallel
net = nn.DataParallel(net)
net = net.cuda()
# load pretrained model
if config["pretrain_snapshot"]:
logging.info("load checkpoint from {}".format(
config["pretrain_snapshot"]))
state_dict = torch.load(config["pretrain_snapshot"])
net.load_state_dict(state_dict)
else:
raise Exception("missing pretrain_snapshot!!!")
# YOLO loss with 3 scales
yolo_losses = []
for i in range(3):
yolo_losses.append(
YOLOLoss(config["yolo"]["anchors"][i], config["yolo"]["classes"],
(config["img_w"], config["img_h"])))
# load class names
classes = open(config["classes_names_path"], "r").read().split("\n")[:-1]
cap = cv2.VideoCapture(video_fn)
# Check if camera opened successfully
if (cap.isOpened() == False):
print("Error opening video stream or file")
# Read until video is completed
while (cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
frame = cv2.resize(frame, (0, 0), fx=0.5, fy=0.5)
if ret == True:
# 1. pre-process image
logging.info("processing frame")
image_tensor = prep_image(frame, config)
with torch.no_grad():
outputs = net(image_tensor)
output_list = []
for i in range(3):
output_list.append(yolo_losses[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(
output,
config["yolo"]["classes"],
conf_thres=config["confidence_threshold"],
nms_thres=0.45)
for idx, detections in enumerate(batch_detections):
if detections is not None:
unique_labels = detections[:, -1].cpu().unique()
n_cls_preds = len(unique_labels)
bbox_colors = random.sample(colors, n_cls_preds)
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
color = bbox_colors[int(
np.where(unique_labels == int(cls_pred))[0])]
# Rescale coordinates to original dimensions
x1, y1, box_w, box_h = get_rescaled_coords(
frame.shape[0], frame.shape[1], config["img_h"],
config["img_w"], x1, y1, x2, y2)
cv2.rectangle(frame, (x1, y1),
(x1 + box_w, y1 + box_h), color, 2)
cv2.putText(frame, classes[int(cls_pred)], (x1, y1),
cv2.FONT_HERSHEY_SIMPLEX, 1, color, 1,
cv2.LINE_AA)
cv2.imshow('Frame', frame)
# Press Q on keyboard to exit
if cv2.waitKey(25) & 0xFF == ord('q'):
break
# Break the loop
else:
break
# When everything done, release the video capture object
cap.release()
# Closes all the frames
cv2.destroyAllWindows()
def evaluate(config):
is_training = False
# Load and initialize network
net = ModelMain(config, is_training=is_training)
net.train(is_training)
# Set data parallel
net = nn.DataParallel(net)
net = net.cuda()
# Restore pretrain model
if config["pretrain_snapshot"]:
state_dict = torch.load(config["pretrain_snapshot"])
net.load_state_dict(state_dict)
else:
logging.warning("missing pretrain_snapshot!!!")
# YOLO loss with 3 scales
yolo_losses = []
for i in range(3):
yolo_losses.append(
YOLOLoss(config["yolo"]["anchors"][i], config["yolo"]["classes"],
(config["img_w"], config["img_h"])))
# DataLoader
dataloader = torch.utils.data.DataLoader(COCODataset(
config["val_path"], (config["img_w"], config["img_h"]),
is_training=False),
batch_size=config["batch_size"],
shuffle=False,
num_workers=16,
pin_memory=False)
# Start the eval loop
logging.info("Start eval.")
n_gt = 0
correct = 0
for step, samples in enumerate(dataloader):
images, labels = samples["image"], samples["label"]
labels = labels.cuda()
with torch.no_grad():
outputs = net(images)
output_list = []
for i in range(3):
output_list.append(yolo_losses[i](outputs[i]))
output = torch.cat(output_list, 1)
output = non_max_suppression(output, 80, conf_thres=0.2)
# calculate
for sample_i in range(labels.size(0)):
# Get labels for sample where width is not zero (dummies)
target_sample = labels[sample_i, labels[sample_i, :, 3] != 0]
for obj_cls, tx, ty, tw, th in target_sample:
# Get rescaled gt coordinates
tx1, tx2 = config["img_w"] * (
tx - tw / 2), config["img_w"] * (tx + tw / 2)
ty1, ty2 = config["img_h"] * (
ty - th / 2), config["img_h"] * (ty + th / 2)
n_gt += 1
box_gt = torch.cat([
coord.unsqueeze(0) for coord in [tx1, ty1, tx2, ty2]
]).view(1, -1)
sample_pred = output[sample_i]
if sample_pred is not None:
# Iterate through predictions where the class predicted is same as gt
for x1, y1, x2, y2, conf, obj_conf, obj_pred in sample_pred[
sample_pred[:, 6] == obj_cls]:
box_pred = torch.cat([
coord.unsqueeze(0)
for coord in [x1, y1, x2, y2]
]).view(1, -1)
iou = bbox_iou(box_pred, box_gt)
if iou >= config["iou_thres"]:
correct += 1
break
if n_gt:
logging.info('Batch [%d/%d] mAP: %.5f' %
(step, len(dataloader), float(correct / n_gt)))
logging.info('Mean Average Precision: %.5f' % float(correct / n_gt))
def Evaluate(net, config):
# logger = GetLogger(output_file=config['output_file'])
print('Start evaluating.')
net.eval()
device = config['device']
with open(config['class_names'], 'r') as f:
class_names = f.read().split('\n')[:-1]
dataset = DAC_SDC_2020_Dataset(dataset_path=config['valid_path'],
class_names=class_names,
img_w=config['img_w'],
img_h=config['img_h'],
is_training=False)
yolo_losses = []
for i in range(3):
yolo_losses.append(
YOLOLoss(config["yolo"]["anchors"][i], config["yolo"]["classes"],
(config["img_w"], config["img_h"]), config['device']))
batch_size = config['valid_batch_size']
results = []
right = 0
for step in range(0, len(dataset), batch_size):
# print('step:', step)
samples = []
for ii in range(step, min(len(dataset), step + batch_size)):
samples.append(dataset[ii])
images = torch.cat(
[sample['image'].unsqueeze(0) for sample in samples], 0)
images = images.to(device)
with torch.no_grad():
outputs = net(images)
tt = []
for i in range(3):
tt.append(yolo_losses[i](outputs[i]))
tt = torch.cat(tt, 1)
for kk in range(len(samples)):
t = tt[kk]
best = t[:, 4].argmax()
qq = t[best]
cc = qq[5:].argmax()
x = qq[0] / samples[kk]['image'].size(
2) * samples[kk]['original_image'].shape[1]
y = qq[1] / samples[kk]['image'].size(
1) * samples[kk]['original_image'].shape[0]
w = qq[2] / samples[kk]['image'].size(
2) * samples[kk]['original_image'].shape[1]
h = qq[3] / samples[kk]['image'].size(
1) * samples[kk]['original_image'].shape[0]
x1 = int(x - w / 2)
y1 = int(y - h / 2)
x2 = int(x + w / 2)
y2 = int(y + h / 2)
bbox = [x1, y1, x2, y2]
iou = GetIOU(bbox, samples[kk]['original_bbox'])
if samples[kk]['original_label'] == class_names[cc]:
right += 1
# logger.info('file: ' + samples[kk]['jpg_path'])
# logger.info('pred: ' + str(bbox) + ' ' + class_names[cc])
# logger.info('gt: ' + str(samples[kk]['original_bbox']) + ' ' + samples[kk]['original_label'])
# logger.info(f'iou: {iou}\n')
result = {
'jpg_path': samples[kk]['jpg_path'],
'pred': {
'bbox': bbox,
'class': class_names[cc],
},
'gt': {
'bbox': samples[kk]['original_bbox'],
'class': samples[kk]['original_label'],
},
'iou': iou,
}
results.append(result)
mean_iou = np.array([res['iou'] for res in results]).mean()
# logger.info('Mean IOU: ' + str(ious.mean()))
info = {
'mean_iou': mean_iou,
'accuracy': right / len(results),
'results': results,
'config': config,
}
return info
def test(config):
is_training = False
# Load and initialize network
net = ModelMain(config, is_training=is_training)
net.train(is_training)
# Set data parallel
net = nn.DataParallel(net)
net = net.cuda()
# Restore pretrain model
if config["pretrain_snapshot"]:
logging.info("load checkpoint from {}".format(
config["pretrain_snapshot"]))
state_dict = torch.load(config["pretrain_snapshot"])
net.load_state_dict(state_dict)
else:
raise Exception("missing pretrain_snapshot!!!")
# YOLO loss with 3 scales
yolo_losses = []
for i in range(3):
yolo_losses.append(
YOLOLoss(config["yolo"]["anchors"][i], config["yolo"]["classes"],
(config["img_w"], config["img_h"])))
# prepare images path
images_name = os.listdir(config["images_path"])
images_path = [
os.path.join(config["images_path"], name) for name in images_name
]
if len(images_path) == 0:
raise Exception("no image found in {}".format(config["images_path"]))
# Start testing FPS of different batch size
for batch_size in range(1, 10):
# preprocess
images = []
for path in images_path[:batch_size]:
image = cv2.imread(path, cv2.IMREAD_COLOR)
if image is None:
logging.error("read path error: {}. skip it.".format(path))
continue
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (config["img_w"], config["img_h"]),
interpolation=cv2.INTER_LINEAR)
image = image.astype(np.float32)
image /= 255.0
image = np.transpose(image, (2, 0, 1))
image = image.astype(np.float32)
images.append(image)
for i in range(batch_size - len(images)):
images.append(images[0]) # fill len to batch_sze
images = np.asarray(images)
images = torch.from_numpy(images).cuda()
# inference in 30 times and calculate average
inference_times = []
for i in range(30):
start_time = time.time()
with torch.no_grad():
outputs = net(images)
output_list = []
for i in range(3):
output_list.append(yolo_losses[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(
output,
config["yolo"]["classes"],
conf_thres=config["confidence_threshold"])
torch.cuda.synchronize() # wait all done.
end_time = time.time()
inference_times.append(end_time - start_time)
inference_time = sum(inference_times) / len(
inference_times) / batch_size
fps = 1.0 / inference_time
logging.info(
"Batch_Size: {}, Inference_Time: {:.5f} s/image, FPS: {}".format(
batch_size, inference_time, fps))
def train(config):
# Hyper-parameters
config["global_step"] = config.get("start_step", 0)
is_training = True
# Net & Loss & Optimizer
## Net Main
net = ModelMain(config, is_training=is_training)
net.train(is_training)
## YOLO Loss with 3 scales
yolo_losses = []
for i in range(3):
yolo_loss = YOLOLoss(config["yolo"]["anchors"][i],
config["yolo"]["classes"], (config["img_w"], config["img_h"]))
yolo_losses.append(yolo_loss)
## Optimizer and LR scheduler
optimizer = _get_optimizer(config, net)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=config["lr"]["decay_step"], gamma=config["lr"]["decay_gamma"])
net = nn.DataParallel(net)
net = net.cuda()
# Load checkpoint
if config["pretrain_snapshot"]:
logging.info("Load pretrained weights from {}".format(config["pretrain_snapshot"]))
state_dict = torch.load(config["pretrain_snapshot"])
net.load_state_dict(state_dict)
# DataLoader
dataloader = torch.utils.data.DataLoader(AIPrimeDataset(config["train_path"]),
batch_size=config["batch_size"],
shuffle=True, num_workers=16, pin_memory=False)
# Start the training
logging.info("Start training.")
for epoch in range(config["start_epoch"], config["epochs"]):
for step, (images, labels) in enumerate(dataloader):
start_time = time.time()
config["global_step"] += 1
# Forward
outputs = net(images)
# Loss
losses_name = ["total_loss", "x", "y", "w", "h", "conf", "cls"]
losses = [[]] * len(losses_name)
for i in range(3):
_loss_item = yolo_losses[i](outputs[i], labels)
for j, l in enumerate(_loss_item):
losses[j].append(l)
losses = [sum(l) for l in losses]
loss = losses[0]
# Zero & Backward & Step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Logging
if step > 0 and step % 10 == 0:
_loss = loss.item()
duration = float(time.time() - start_time)
example_per_second = config["batch_size"] / duration
lr = optimizer.param_groups[0]['lr']
logging.info(
"epoch [%.3d] iter = %d loss = %.2f example/sec = %.3f lr = %.5f " %
(epoch, step, _loss, example_per_second, lr)
)
# Things to be done for every epoch
## LR schedule
lr_scheduler.step()
## Save checkpoint
_save_checkpoint(net.state_dict(), config, epoch)
# Finish training
logging.info("QiaJiaBa~ BeiBei")
def evaluate(config):
is_training = False
# Load and initialize network
net = ModelMain(config, is_training=is_training)
net.train(is_training)
# Set data parallel
net = nn.DataParallel(net)
net = net.cuda()
# Restore pretrain model
if config["pretrain_snapshot"]:
logging.info("Load checkpoint: {}".format(config["pretrain_snapshot"]))
state_dict = torch.load(config["pretrain_snapshot"])
net.load_state_dict(state_dict)
else:
logging.warning("missing pretrain_snapshot!!!")
# YOLO loss with 3 scales
yolo_losses = []
for i in range(3):
yolo_losses.append(
YOLOLoss(config["yolo"]["anchors"][i], config["yolo"]["classes"],
(config["img_w"], config["img_h"])))
# DataLoader.
dataloader = torch.utils.data.DataLoader(COCODataset(
config["val_path"], (config["img_w"], config["img_h"]),
is_training=False),
batch_size=config["batch_size"],
shuffle=False,
num_workers=8,
pin_memory=False)
# Coco Prepare.
index2category = json.load(open("coco_index2category.json"))
# Start the eval loop
logging.info("Start eval.")
coco_results = []
coco_img_ids = set([])
for step, samples in enumerate(dataloader):
images, labels = samples["image"], samples["label"]
image_paths, origin_sizes = samples["image_path"], samples[
"origin_size"]
with torch.no_grad():
outputs = net(images)
output_list = []
for i in range(3):
output_list.append(yolo_losses[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output,
config["yolo"]["classes"],
conf_thres=0.01,
nms_thres=0.45)
for idx, detections in enumerate(batch_detections):
image_id = int(os.path.basename(image_paths[idx])[-16:-4])
coco_img_ids.add(image_id)
if detections is not None:
origin_size = eval(origin_sizes[idx])
detections = detections.cpu().numpy()
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
x1 = x1 / config["img_w"] * origin_size[0]
x2 = x2 / config["img_w"] * origin_size[0]
y1 = y1 / config["img_h"] * origin_size[1]
y2 = y2 / config["img_h"] * origin_size[1]
w = x2 - x1
h = y2 - y1
coco_results.append({
"image_id":
image_id,
"category_id":
index2category[str(int(cls_pred.item()))],
"bbox": (float(x1), float(y1), float(w), float(h)),
"score":
float(conf),
})
logging.info("Now {}/{}".format(step, len(dataloader)))
save_results_path = "coco_results.json"
with open(save_results_path, "w") as f:
json.dump(coco_results,
f,
sort_keys=True,
indent=4,
separators=(',', ':'))
logging.info("Save coco format results to {}".format(save_results_path))
# COCO api
logging.info("Using coco-evaluate tools to evaluate.")
cocoGt = COCO(config["annotation_path"])
cocoDt = cocoGt.loadRes(save_results_path)
cocoEval = COCOeval(cocoGt, cocoDt, "bbox")
cocoEval.params.imgIds = list(coco_img_ids) # real imgIds
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
}
}
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = YoloHead(config)
model.load_state_dict(torch.load('weights/yolov3_orig.pth'))
model = model.to(device)
model.train()
losses = []
cc_res = []
confidence = 0.1
iou_threshold = 0.6
for i in range(3):
losses.append(
YOLOLoss(config['yolo']['anchors'][i], 80, [416, 416]))
forw_sum = 0
with torch.no_grad():
for batch_idx, (images, targets) in enumerate(test_dataloader):
# measure data loading time
images = images.to(device)
targets2 = []
for t in targets:
dd = {}
for k, v in t.items():
if (k != 'img_size'):
dd[k] = v.to(device)
else:
def evaluate(config):
is_training = False
# Load and initialize network
net = ModelMain(config, is_training=is_training)
net.train(is_training)
# Set data parallel
net = nn.DataParallel(net)
net = net.cuda()
# Restore pretrain model
if config["pretrain_snapshot"]:
state_dict = torch.load(config["pretrain_snapshot"])
net.load_state_dict(state_dict)
else:
logging.warning("missing pretrain_snapshot!!!")
# YOLO loss with 3 scales
yolo_losses = []
for i in range(3):
yolo_losses.append(YOLOLoss(config["yolo"]["anchors"][i],
config["yolo"]["classes"], (config["img_w"], config["img_h"])))
# DataLoader
dataloader = torch.utils.data.DataLoader(dataset=COCODataset(config["val_path"], config["img_w"]),
batch_size=config["batch_size"],
shuffle=True, num_workers=1, pin_memory=False)
# Start the eval loop
logging.info("Start eval.")
n_gt = 0
correct = 0
logging.info('%s' % str(dataloader))
gt_histro={}
pred_histro = {}
correct_histro = {}
for i in range(config["yolo"]["classes"]):
gt_histro[i] = 1
pred_histro[i] = 1
correct_histro[i] = 0
# images 是一个batch里的全部图片，labels是一个batch里面的全部标签
for step, (images, labels) in enumerate(dataloader):
labels = labels.cuda()
with torch.no_grad():
outputs = net(images)
output_list = []
for i in range(3):
output_list.append(yolo_losses[i](outputs[i]))
# 把三个尺度上的预测结果在第1维度(第0维度是batch里的照片，第1维度是一张照片里面的各个预测框，第2维度是各个预测数值)上拼接起来
batch_output = torch.cat(output_list, dim=1)
logging.info('%s' % str(batch_output.shape))
# 进行NMS抑制
batch_output = non_max_suppression(prediction=batch_output, num_classes=config["yolo"]["classes"], conf_thres=config["conf_thresh"], nms_thres=config["nms_thresh"])
# calculate
for sample_index_in_batch in range(labels.size(0)):
# fetched img sample in tensor( C(RxGxB) x H x W ), transform to cv2 format in H x W x C(BxGxR)
sample_image = images[sample_index_in_batch].numpy()
sample_image = np.transpose(sample_image, (1, 2, 0))
sample_image = cv2.cvtColor(sample_image, cv2.COLOR_RGB2BGR)
logging.debug("fetched img %d size %s" % (sample_index_in_batch, sample_image.shape))
# Get labels for sample where width is not zero (dummies)(init all labels to zeros in array)
target_sample = labels[sample_index_in_batch, labels[sample_index_in_batch, :, 3] != 0]
# get prediction for this sample
sample_pred = batch_output[sample_index_in_batch]
if sample_pred is not None:
for x1, y1, x2, y2, conf, obj_conf, obj_pred in sample_pred: # for each prediction box
# logging.info("%d" % obj_cls)
box_pred = torch.cat([coord.unsqueeze(0) for coord in [x1, y1, x2, y2]]).view(1, -1)
sample_image = draw_prediction(sample_image,conf, obj_conf, int(obj_pred), (x1, y1, x2, y2), config)
# 每一个ground truth的 分类编号obj_cls、相对中心x、相对中心y、相对宽w、相对高h
for obj_cls, tx, ty, tw, th in target_sample:
# Get rescaled gt coordinates
# 转化为输入像素尺寸的 左上角像素tx1 ty1，右下角像素tx2 ty2
tx1, tx2 = config["img_w"] * (tx - tw / 2), config["img_w"] * (tx + tw / 2)
ty1, ty2 = config["img_h"] * (ty - th / 2), config["img_h"] * (ty + th / 2)
# 计算ground truth数量，用于统计信息
n_gt += 1
gt_histro[int(obj_cls)] += 1
# 转化为 shape(1,4)的tensor，用来计算IoU
box_gt = torch.cat([coord.unsqueeze(0) for coord in [tx1, ty1, tx2, ty2]]).view(1, -1)
# logging.info('%s' % str(box_gt.shape))
sample_pred = batch_output[sample_index_in_batch]
if sample_pred is not None:
# Iterate through predictions where the class predicted is same as gt
# 对于每一个ground truth，遍历预测结果
for x1, y1, x2, y2, conf, obj_conf, obj_pred in sample_pred[sample_pred[:, 6] == obj_cls]: # 如果当前预测分类 == 当前真实分类
#logging.info("%d" % obj_cls)
box_pred = torch.cat([coord.unsqueeze(0) for coord in [x1, y1, x2, y2]]).view(1, -1)
pred_histro[int(obj_pred)] += 1
iou = bbox_iou(box_pred, box_gt)
if iou >= config["iou_thresh"]:
correct += 1
correct_histro[int(obj_pred)] += 1
break
if n_gt:
types = config["types"]
reverse_types = {} # 建立一个反向的types
for key in types.keys():
reverse_types[types[key]] = key
logging.info('Batch [%d/%d] mAP: %.5f' % (step, len(dataloader), float(correct / n_gt)))
logging.info('mAP Histro:%s' % str([ reverse_types[i] +':'+ str(int(100 * correct_histro[i] / gt_histro[i])) for i in range(config["yolo"]["classes"] ) ]))
logging.info('Recall His:%s' % str([ reverse_types[i] +':'+ str(int(100 * correct_histro[i] / pred_histro[i])) for i in range(config["yolo"]["classes"]) ]))
logging.info('Mean Average Precision: %.5f' % float(correct / n_gt))
def evaluate(config):
is_training = False
# Load and initialize network
net = ModelMain(config, is_training=is_training)
net.train(is_training)
# Set data parallel
net = nn.DataParallel(net)
net = net.cuda()
# Restore pretrain model
if config["pretrain_snapshot"]:
logging.info("Load checkpoint: {}".format(config["pretrain_snapshot"]))
state_dict = torch.load(config["pretrain_snapshot"])
net.load_state_dict(state_dict)
else:
logging.warning("missing pretrain_snapshot!!!")
# YOLO loss with 3 scales
yolo_losses = []
for i in range(3):
yolo_losses.append(
YOLOLoss(config["yolo"]["anchors"][i], config["yolo"]["classes"],
(config["img_w"], config["img_h"])))
# DataLoader.
dataloader = torch.utils.data.DataLoader(COCODataset(
config["val_path"], (config["img_w"], config["img_h"]),
is_training=False),
batch_size=config["batch_size"],
shuffle=False,
num_workers=8,
pin_memory=False)
# Coco Prepare.
index2category = json.load(open("coco_index2category.json"))
# Start the eval loop
logging.info("Start eval.")
coco_results = []
coco_img_ids = set([])
APs = []
for step, samples in enumerate(dataloader):
images, labels = samples["image"], samples["label"]
image_paths, origin_sizes = samples["image_path"], samples[
"origin_size"]
with torch.no_grad():
outputs = net(images)
output_list = []
for i in range(3):
output_list.append(yolo_losses[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output,
config["yolo"]["classes"],
conf_thres=0.0001,
nms_thres=0.45)
for idx, detections in enumerate(batch_detections):
correct = []
annotations = labels[idx, labels[idx, :, 3] != 0]
image_id = int(os.path.basename(image_paths[idx])[-16:-4])
coco_img_ids.add(image_id)
if detections is None:
if annotations.size(0) != 0:
APs.append(0)
continue
detections = detections[np.argsort(-detections[:, 4])]
origin_size = eval(origin_sizes[idx])
detections = detections.cpu().numpy()
# ===========================================================================================================================
# The amount of padding that was added
pad_x = max(origin_size[1] - origin_size[0],
0) * (config["img_w"] / max(origin_size))
pad_y = max(origin_size[0] - origin_size[1],
0) * (config["img_w"] / max(origin_size))
# Image height and width after padding is removed
unpad_h = config["img_w"] - pad_y
unpad_w = config["img_w"] - pad_x
# ===========================================================================================================================
if annotations.size(0) == 0:
correct.extend([0 for _ in range(len(detections))])
else:
target_boxes = torch.FloatTensor(annotations[:, 1:].shape)
target_boxes[:,
0] = (annotations[:, 1] - annotations[:, 3] / 2)
target_boxes[:,
1] = (annotations[:, 2] - annotations[:, 4] / 2)
target_boxes[:,
2] = (annotations[:, 1] + annotations[:, 3] / 2)
target_boxes[:,
3] = (annotations[:, 2] + annotations[:, 4] / 2)
target_boxes *= config["img_w"]
detected = []
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
pred_bbox = (x1, y1, x2, y2)
#x1 = x1 / config["img_w"] * origin_size[0]
#x2 = x2 / config["img_w"] * origin_size[0]
#y1 = y1 / config["img_h"] * origin_size[1]
#y2 = y2 / config["img_h"] * origin_size[1]
#w = x2 - x1
#h = y2 - y1
h = ((y2 - y1) / unpad_h) * origin_size[1]
w = ((x2 - x1) / unpad_w) * origin_size[0]
y1 = ((y1 - pad_y // 2) / unpad_h) * origin_size[1]
x1 = ((x1 - pad_x // 2) / unpad_w) * origin_size[0]
coco_results.append({
"image_id":
image_id,
"category_id":
index2category[str(int(cls_pred.item()))],
"bbox": (float(x1), float(y1), float(w), float(h)),
"score":
float(conf),
})
pred_bbox = torch.FloatTensor(pred_bbox).view(1, -1)
# Compute iou with target boxes
iou = bbox_iou(pred_bbox, target_boxes)
# Extract index of largest overlap
best_i = np.argmax(iou)
# If overlap exceeds threshold and classification is correct mark as correct
if iou[best_i] > config[
'iou_thres'] and cls_pred == annotations[
best_i, 0] and best_i not in detected:
correct.append(1)
detected.append(best_i)
else:
correct.append(0)
true_positives = np.array(correct)
false_positives = 1 - true_positives
# Compute cumulative false positives and true positives
false_positives = np.cumsum(false_positives)
true_positives = np.cumsum(true_positives)
# Compute recall and precision at all ranks
recall = true_positives / annotations.size(0) if annotations.size(
0) else true_positives
precision = true_positives / np.maximum(
true_positives + false_positives,
np.finfo(np.float64).eps)
# Compute average precision
AP = compute_ap(recall, precision)
APs.append(AP)
print("+ Sample [%d/%d] AP: %.4f (%.4f)" %
(len(APs), 5000, AP, np.mean(APs)))
logging.info("Now {}/{}".format(step, len(dataloader)))
print("Mean Average Precision: %.4f" % np.mean(APs))
save_results_path = "coco_results.json"
with open(save_results_path, "w") as f:
json.dump(coco_results,
f,
sort_keys=True,
indent=4,
separators=(',', ':'))
logging.info("Save coco format results to {}".format(save_results_path))
# COCO api
logging.info("Using coco-evaluate tools to evaluate.")
cocoGt = COCO(config["annotation_path"])
cocoDt = cocoGt.loadRes(save_results_path)
cocoEval = COCOeval(cocoGt, cocoDt, "bbox")
cocoEval.params.imgIds = list(coco_img_ids) # real imgIds
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
def train(imgs, labels, checkpoint_path, config):
config["global_step"] = config.get("start_step", 0)
is_training = False if config.get("export_onnx") else True
# Load and initialize network
net = ModelMain(config, is_training=is_training)
net.train(is_training)
# Optimizer and learning rate
optimizer = _get_optimizer(config, net)
lr_scheduler = optim.lr_scheduler.StepLR(
optimizer,
step_size=config["lr"]["decay_step"],
gamma=config["lr"]["decay_gamma"])
# Set data parallel
net = nn.DataParallel(net)
net = net.cuda()
# Restore pretrain model
if checkpoint_path:
logging.info("Load pretrained weights from {}".format(checkpoint_path))
state_dict = torch.load(checkpoint_path)
net.load_state_dict(state_dict)
# YOLO loss with 3 scales
yolo_losses = []
for i in range(3):
yolo_losses.append(
YOLOLoss(config["yolo"]["anchors"][i], config["yolo"]["classes"],
(config["img_w"], config["img_h"])))
# DataLoader
dataloader = torch.utils.data.DataLoader(SatDataset(
imgs, labels, (config["img_w"], config["img_h"]), is_training=True),
batch_size=config["batch_size"],
shuffle=True,
num_workers=1,
pin_memory=True)
# Start the training loop
logging.info("Start training.")
for epoch in range(config["epochs"]):
for step, samples in enumerate(dataloader):
images, labels = samples["image"], samples["label"]
start_time = time.time()
config["global_step"] += 1
# Forward and backward
optimizer.zero_grad()
outputs = net(images)
losses_name = ["total_loss", "x", "y", "w", "h", "conf", "cls"]
losses = [[]] * len(losses_name)
for i in range(3):
_loss_item = yolo_losses[i](outputs[i], labels)
for j, l in enumerate(_loss_item):
losses[j].append(l)
losses = [sum(l) for l in losses]
loss = losses[0]
loss.backward()
optimizer.step()
if step > 0 and step % 10 == 0:
_loss = loss.item()
duration = float(time.time() - start_time)
example_per_second = config["batch_size"] / duration
lr = optimizer.param_groups[0]['lr']
logging.info(
"epoch [%.3d] iter = %d loss = %.2f example/sec = %.3f lr = %.5f "
% (epoch, step, _loss, example_per_second, lr))
config["tensorboard_writer"].add_scalar(
"lr", lr, config["global_step"])
config["tensorboard_writer"].add_scalar(
"example/sec", example_per_second, config["global_step"])
for i, name in enumerate(losses_name):
value = _loss if i == 0 else losses[i]
config["tensorboard_writer"].add_scalar(
name, value, config["global_step"])
lr_scheduler.step()
# net.train(False)
checkpoint_path = _save_checkpoint(net.state_dict(), config)
# net.train(True)
logging.info("Bye~")
return checkpoint_path
def test(config):
is_training = False
# Load and initialize network
net = ModelMain(config, is_training=is_training)
net.train(is_training)
# Set data parallel
net = nn.DataParallel(net)
net = net.cuda()
# Restore pretrain model
if config["pretrain_snapshot"]:
logging.info("load checkpoint from {}".format(
config["pretrain_snapshot"]))
state_dict = torch.load(config["pretrain_snapshot"])
net.load_state_dict(state_dict)
else:
raise Exception("missing pretrain_snapshot!!!")
# YOLO loss with 3 scales
yolo_losses = []
for i in range(3):
yolo_losses.append(
YOLOLoss(config["yolo"]["anchors"][i], config["yolo"]["classes"],
(config["img_w"], config["img_h"])))
# prepare images path
images_name = os.listdir(config["images_path"])
images_path = [
os.path.join(config["images_path"], name) for name in images_name
]
if len(images_path) == 0:
raise Exception("no image found in {}".format(config["images_path"]))
# Start inference
batch_size = config["batch_size"]
for step in range(0, len(images_path), batch_size):
# preprocess
images = []
images_origin = []
for path in images_path[step * batch_size:(step + 1) * batch_size]:
logging.info("processing: {}".format(path))
image = cv2.imread(path, cv2.IMREAD_COLOR)
if image is None:
logging.error("read path error: {}. skip it.".format(path))
continue
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
images_origin.append(image) # keep for save result
image = cv2.resize(image, (config["img_w"], config["img_h"]),
interpolation=cv2.INTER_LINEAR)
image = image.astype(np.float32)
image /= 255.0
image = np.transpose(image, (2, 0, 1))
image = image.astype(np.float32)
images.append(image)
images = np.asarray(images)
images = torch.from_numpy(images).cuda()
# inference
with torch.no_grad():
outputs = net(images)
output_list = []
for i in range(3):
output_list.append(yolo_losses[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(
output,
config["yolo"]["classes"],
conf_thres=config["confidence_threshold"])
# write result images. Draw bounding boxes and labels of detections
classes = open(config["classes_names_path"],
"r").read().split("\n")[:-1]
if not os.path.isdir("./output/"):
os.makedirs("./output/")
for idx, detections in enumerate(batch_detections):
plt.figure()
fig, ax = plt.subplots(1)
ax.imshow(images_origin[idx])
if detections is not None:
unique_labels = detections[:, -1].cpu().unique()
n_cls_preds = len(unique_labels)
bbox_colors = random.sample(colors, n_cls_preds)
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
color = bbox_colors[int(
np.where(unique_labels == int(cls_pred))[0])]
# Rescale coordinates to original dimensions
ori_h, ori_w = images_origin[idx].shape[:2]
pre_h, pre_w = config["img_h"], config["img_w"]
box_h = ((y2 - y1) / pre_h) * ori_h
box_w = ((x2 - x1) / pre_w) * ori_w
y1 = (y1 / pre_h) * ori_h
x1 = (x1 / pre_w) * ori_w
# Create a Rectangle patch
bbox = patches.Rectangle((x1, y1),
box_w,
box_h,
linewidth=2,
edgecolor=color,
facecolor='none')
# Add the bbox to the plot
ax.add_patch(bbox)
# Add label
plt.text(x1,
y1,
s=classes[int(cls_pred)],
color='white',
verticalalignment='top',
bbox={
'color': color,
'pad': 0
})
# Save generated image with detections
plt.axis('off')
plt.gca().xaxis.set_major_locator(NullLocator())
plt.gca().yaxis.set_major_locator(NullLocator())
plt.savefig('output/{}_{}.jpg'.format(step, idx),
bbox_inches='tight',
pad_inches=0.0)
plt.close()
logging.info("Save all results to ./output/")
def evaluate(config):
is_training = False
# Load and initialize network
net = ModelMain(config, is_training=is_training)
net.train(is_training)
# Set data parallel
net = nn.DataParallel(net)
net = net.cuda()
# Restore pretrain model
if config["pretrain_snapshot"]:
state_dict = torch.load(config["pretrain_snapshot"])
net.load_state_dict(state_dict)
else:
logging.warning("missing pretrain_snapshot!!!")
# YOLO loss with 3 scales
yolo_losses = []
for i in range(3):
yolo_losses.append(YOLOLoss(config["yolo"]["anchors"][i],
config["yolo"]["classes"], (config["img_w"], config["img_h"])))
# DataLoader
dataloader = torch.utils.data.DataLoader(dataset=COCODataset(config["test_path"], config["img_w"]),
batch_size=config["batch_size"],
shuffle=False, num_workers=8, pin_memory=False)
# Start the eval loop
#logging.info("Start eval.")
n_gt = 0
correct = 0
#logging.debug('%s' % str(dataloader))
gt_histro={}
pred_histro = {}
correct_histro = {}
for i in range(config["yolo"]["classes"]):
gt_histro[i] = 1
pred_histro[i] = 1
correct_histro[i] = 0
# images 是一个batch里的全部图片，labels是一个batch里面的全部标签
for step, (images, labels) in enumerate(dataloader):
labels = labels.cuda()
with torch.no_grad():
outputs = net(images)
output_list = []
for i in range(3):
output_list.append(yolo_losses[i](outputs[i]))
# 把三个尺度上的预测结果在第1维度(第0维度是batch里的照片，第1维度是一张照片里面的各个预测框，第2维度是各个预测数值)上拼接起来
output = torch.cat(output_list, dim=1)
#logging.info('%s' % str(output.shape))
# 进行NMS抑制
#output = non_max_suppression(prediction=output, num_classes=config["yolo"]["classes"], conf_thres=config["conf_thresh"], nms_thres=config["nms_thresh"])
output = class_nms(prediction=output, num_classes=config["yolo"]["classes"],conf_thres=config["conf_thresh"], nms_thres=config["nms_thresh"])
# calculate
for sample_i in range(labels.size(0)):
# 计算所有的预测数量
sample_pred = output[sample_i]
if sample_pred is not None:
#logging.debug(sample_pred.shape)
for i in range(sample_pred.shape[0]):
pred_histro[int(sample_pred[i,6])] += 1
# Get labels for sample where width is not zero (dummies)
target_sample = labels[sample_i, labels[sample_i, :, 3] != 0]
# Ground truth的 分类编号obj_cls、相对中心x、相对中心y、相对宽w、相对高h
n_gt=0
correct=0
for obj_cls, tx, ty, tw, th in target_sample:
# Get rescaled gt coordinates
# 转化为输入像素尺寸的 左上角像素tx1 ty1，右下角像素tx2 ty2
tx1, tx2 = config["img_w"] * (tx - tw / 2), config["img_w"] * (tx + tw / 2)
ty1, ty2 = config["img_h"] * (ty - th / 2), config["img_h"] * (ty + th / 2)
# 计算ground truth数量，用于统计信息
n_gt += 1
gt_histro[int(obj_cls)] += 1
# 转化为 shape(1,4)的tensor，用来计算IoU
box_gt = torch.cat([coord.unsqueeze(0) for coord in [tx1, ty1, tx2, ty2]]).view(1, -1)
# logging.info('%s' % str(box_gt.shape))
sample_pred = output[sample_i]
if sample_pred is not None:
# Iterate through predictions where the class predicted is same as gt
# 对于每一个ground truth，遍历预测结果
for x1, y1, x2, y2, conf, obj_conf, obj_pred in sample_pred[sample_pred[:, 6] == obj_cls]: # 如果当前预测分类 == 当前真实分类
#logging.info("%d" % obj_cls)
box_pred = torch.cat([coord.unsqueeze(0) for coord in [x1, y1, x2, y2]]).view(1, -1)
#pred_histro[int(obj_pred)] += 1
iou = bbox_iou(box_pred, box_gt)
#if iou >= config["iou_thres"] and obj_conf >= config["obj_thresh"]:
if iou >= config["iou_thresh"]:
correct += 1
correct_histro[int(obj_pred)] += 1
break
#logging.debug("----------------")
#logging.debug(correct_histro[4])
#logging.debug(pred_histro[4])
#logging.debug(gt_histro[4])
if n_gt:
types = config["types"]
reverse_types = {} # 建立一个反向的types
for key in types.keys():
reverse_types[types[key]] = key
#logging.info('Batch [%d/%d] mAP: %.5f' % (step, len(dataloader), float(correct / n_gt)))
logging.info('Precision:%s' % str([reverse_types[i] +':'+ str(int(100 * correct_histro[i] / pred_histro[i])) for i in range(config["yolo"]["classes"]) ]))
logging.info('Recall :%s' % str([reverse_types[i] +':'+ str(int(100 * correct_histro[i] / gt_histro[i])) for i in range(config["yolo"]["classes"])]))
def train(config):
config["global_step"] = config.get("start_step", 0)
is_training = False if config.get("export_onnx") else True
# Load and initialize network
net = ModelMain(config, is_training=is_training)
net.train(is_training)
# Optimizer and learning rate
optimizer = _get_optimizer(config, net)
lr_scheduler = optim.lr_scheduler.StepLR(
optimizer,
step_size=config["lr"]["decay_step"],
gamma=config["lr"]["decay_gamma"])
# Set data parallel
net = nn.DataParallel(net)
net = net.cuda()
# Restore pretrain model
if config["pretrain_snapshot"]:
logging.info("Load pretrained weights from {}".format(
config["pretrain_snapshot"]))
state_dict = torch.load(config["pretrain_snapshot"])
net.load_state_dict(state_dict)
# Only export onnx
# if config.get("export_onnx"):
# real_model = net.module
# real_model.eval()
# dummy_input = torch.randn(8, 3, config["img_h"], config["img_w"]).cuda()
# save_path = os.path.join(config["sub_working_dir"], "pytorch.onnx")
# logging.info("Exporting onnx to {}".format(save_path))
# torch.onnx.export(real_model, dummy_input, save_path, verbose=False)
# logging.info("Done. Exiting now.")
# sys.exit()
# Evaluate interface
# if config["evaluate_type"]:
# logging.info("Using {} to evaluate model.".format(config["evaluate_type"]))
# evaluate_func = importlib.import_module(config["evaluate_type"]).run_eval
# config["online_net"] = net
# YOLO loss with 3 scales
yolo_losses = []
for i in range(3):
yolo_losses.append(
YOLOLoss(config["yolo"]["anchors"][i], config["yolo"]["classes"],
(config["img_w"], config["img_h"])))
# DataLoader
dataloader = torch.utils.data.DataLoader(COCODataset(
config["train_path"], (config["img_w"], config["img_h"]),
is_training=True),
batch_size=config["batch_size"],
shuffle=True,
num_workers=32,
pin_memory=True)
# Start the training loop
logging.info("Start training.")
for epoch in range(config["epochs"]):
for step, samples in enumerate(dataloader):
images, labels = samples["image"], samples["label"]
start_time = time.time()
config["global_step"] += 1
# Forward and backward
optimizer.zero_grad()
outputs = net(images)
losses_name = ["total_loss", "x", "y", "w", "h", "conf", "cls"]
losses = []
for _ in range(len(losses_name)):
losses.append([])
for i in range(3):
_loss_item = yolo_losses[i](outputs[i], labels)
for j, l in enumerate(_loss_item):
losses[j].append(l)
losses = [sum(l) for l in losses]
loss = losses[0]
loss.backward()
optimizer.step()
if step > 0 and step % 10 == 0:
_loss = loss.item()
duration = float(time.time() - start_time)
example_per_second = config["batch_size"] / duration
lr = optimizer.param_groups[0]['lr']
logging.info(
"epoch [%.3d] iter = %d loss = %.2f example/sec = %.3f lr = %.5f "
% (epoch, step, _loss, example_per_second, lr))
config["tensorboard_writer"].add_scalar(
"lr", lr, config["global_step"])
config["tensorboard_writer"].add_scalar(
"example/sec", example_per_second, config["global_step"])
for i, name in enumerate(losses_name):
value = _loss if i == 0 else losses[i]
config["tensorboard_writer"].add_scalar(
name, value, config["global_step"])
# if step > 0 and step % 1000 == 0:
# net.train(False)
# _save_checkpoint(net.state_dict(), config)
# net.train(True)
_save_checkpoint(net.state_dict(), config)
lr_scheduler.step()
# net.train(False)
_save_checkpoint(net.state_dict(), config)
# net.train(True)
logging.info("Bye~")
def train(config):
device = config['device']
net = Yolo_v3(config)
# Restore pretrain model
if config["pretrain_snapshot"]:
net.LoadPretrainedModel(config['pretrain_snapshot'])
logging.info("Loaded checkpoint from {}".format(
config["pretrain_snapshot"]))
else:
logging.info('No pretrained model to use.')
# Optimizer and learning rate
optimizer = _get_optimizer(config, net)
lr_scheduler = optim.lr_scheduler.StepLR(
optimizer,
step_size=config["lr"]["decay_step"],
gamma=config["lr"]["decay_gamma"])
# Set data parallel
if device == 'cuda':
net = nn.DataParallel(net)
net = net.to(device)
params = [
param for param in net.parameters() if len(param.size()) == 4
and param.view(param.size(0), -1).size(1) > 20
]
# YOLO loss with 3 scales
yolo_losses = []
for i in range(3):
yolo_losses.append(
YOLOLoss(config["yolo"]["anchors"][i], config["yolo"]["classes"],
(config["img_w"], config["img_h"]), device))
with open(config['class_names'], 'r') as f:
class_names = f.read().split('\n')[:-1]
print(class_names)
dataset = DAC_SDC_2020_Dataset(dataset_path=config['train_path'],
class_names=class_names,
img_w=config['img_w'],
img_h=config['img_h'],
is_training=True)
dataloader = torch.utils.data.DataLoader(
dataset=dataset, batch_size=config['train_batch_size'], shuffle=True)
print('here')
# Start the training loop
logging.info("Start training.")
for epoch in range(config["epochs"]):
logging.info(
f'~~~~~~~~~~~~~~~~~~~~~~~~~~~~ epoch = {epoch} ~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
)
net.train()
for step, samples in enumerate(dataloader):
start_time = time.time()
images, labels = samples["image"], samples["label"]
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = net(images)
losses_name = ["total_loss", "x", "y", "w", "h", "conf", "cls"]
losses = []
for _ in range(len(losses_name)):
losses.append([])
for i in range(3):
_loss_item = yolo_losses[i](outputs[i], labels)
for j, l in enumerate(_loss_item):
losses[j].append(l)
losses = [sum(l) for l in losses]
loss = losses[0]
loss.backward()
optimizer.step()
if step > 0 and step % config['display_interval'] == 0:
_loss = loss.item()
duration = float(time.time() - start_time)
example_per_second = config["train_batch_size"] / duration
lr = optimizer.param_groups[0]['lr']
logging.info(
"epoch [%.3d] iter = %d loss = %.6f example/sec = %.3f lr = %.5f "
% (epoch, step, _loss, example_per_second, lr))
info = Evaluate(net, config)
logging.info('net iou: ' + str(info['mean_iou']) + ' acc: ' +
str(info['accuracy']))
info['state_dict'] = net.state_dict()
info['epoch'] = epoch
checkpoint_path = os.path.join(config["sub_working_dir"],
'epoch_' + str(epoch) + '_net.pth')
torch.save(info, checkpoint_path)
logging.info("Model checkpoint saved to %s" % checkpoint_path)
lr_scheduler.step()
logging.info("Bye~")
