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()
net = net.to(device)
# import torch.onnx
# # 按照输入格式，设计随机输入
# dummy_input =torch.randn(1, 3, 416, 416).to(device)
# # 导出模型
# torch.onnx.export(net.module,dummy_input, 'darknet.onnx',verbose=True)
# exit()
# 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()
images = torch.from_numpy(images).to(device)
# 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"],
nms_thres=0.45)
# 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 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()
net = net.to(device)
# 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=0,
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]
# print(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)
lr_scheduler.step()
# net.train(False)
_save_checkpoint(net.state_dict(), config)
# net.train(True)
logging.info("Bye~")
