def main():
device = cfg.DEVICE
data = dataset.KITTIDataset(cfg.PATH, cfg, mode='eval')
val_dataloader = DataLoader(data, cfg.VAL_BATCH, shuffle=False)
# Initialize the model
mobilenetv2 = MobileNetV2()
model = Model(features=mobilenetv2.features, bins=cfg.BIN).to(device)
model_list = os.listdir(cfg.MODEL_DIR)
# model.load_state_dict(torch.load(cfg.MODEL_DIR + "/%s" % sorted(model_list)[-1]))
model.load_state_dict(
torch.load(cfg.MODEL_DIR + '/model_2020-05-13-10-06-12.pth'))
print(sorted(model_list)[-1])
model.eval()
for i, (batch, info) in enumerate(val_dataloader):
# batch, centerAngle, info = data.EvalBatch()
batch = torch.FloatTensor(batch).to(device)
[orient, conf] = model(batch)
orient = orient.cpu().data.numpy()
conf = conf.cpu().data.numpy()
alpha = angle_utils.recover_angle(conf.squeeze(0), orient.squeeze(0),
cfg.BIN)
rot_y = angle_utils.compute_orientaion(P2, info['bbox2d'], alpha)
angle_utils.save_result(info, alpha, rot_y, i, mode='eval')
import numpy as np
import paddle
from utils.model import Model
from utils.data import process
from utils.decoder import ctc_greedy_decoder
with open('dataset/vocabulary.txt', 'r', encoding='utf-8') as f:
vocabulary = f.readlines()
vocabulary = [v.replace('\n', '') for v in vocabulary]
save_model = 'models/'
model = Model(vocabulary, image_height=32)
model.set_state_dict(paddle.load(os.path.join(save_model, 'model.pdparams')))
model.eval()
def infer(path):
data = process(path, img_height=32)
data = data[np.newaxis, :]
data = paddle.to_tensor(data, dtype='float32')
# 执行识别
out = model(data)
out = paddle.transpose(out, perm=[1, 0, 2])
out = paddle.nn.functional.softmax(out)[0]
# 解码获取识别结果
out_string = ctc_greedy_decoder(out, vocabulary)
print('预测结果：%s' % out_string)
def main():
device = cfg.DEVICE
if cfg.DEBUG:
data = dataset.KITTIDataset(cfg.PATH,
cfg,
mode='predict',
save_debug_pic=True)
else:
data = dataset.KITTIDataset(cfg.PATH, cfg, mode='predict')
val_dataloader = DataLoader(data, cfg.VAL_BATCH, shuffle=False)
# Initialize the model
mobilenetv2 = MobileNetV2()
model = Model(features=mobilenetv2.features, bins=cfg.BIN).to(device)
model_list = os.listdir(cfg.MODEL_DIR)
# model.load_state_dict(torch.load(cfg.MODEL_DIR + "/%s" % sorted(model_list)[-1]))
model.load_state_dict(
torch.load(cfg.MODEL_DIR + '/2binmodel_2020-06-04-10-38-19.pth',
map_location=torch.device('cpu')))
print(sorted(model_list)[-1])
model.eval()
if cfg.DEBUG:
debug_count = 0
debug_save_dir = r'C:\Users\vincent.xu\Desktop\orientation\orientation_easimation\data\debug\compare'
error_pred_dir = r'C:\Users\vincent.xu\Desktop\orientation\orientation_easimation\data\debug\error'
for i, (batch, info, debug_pic, gt_conf) in enumerate(val_dataloader):
# batch, centerAngle, info = data.EvalBatch()
batch = torch.FloatTensor(batch).to(device)
[orient, conf] = model(batch)
orient = orient.cpu().data.numpy()
conf = conf.cpu().data.numpy()
alpha = angle_utils.recover_angle(conf.squeeze(0),
orient.squeeze(0), cfg.BIN)
rot_y = angle_utils.compute_orientaion(P2, info['bbox2d'], alpha)
angle_utils.save_result(info, alpha, rot_y, i, mode='predict')
debug_pic = Image.fromarray(debug_pic[0].numpy()).convert('RGB')
draw = ImageDraw.Draw(debug_pic)
# draw.text((5, 5), 'GT_bin:' + str(np.argmax(conf)), fill=(255, 0, 0))
draw.text((5, 15),
'pred_bin:' + str(np.argmax(conf)),
fill=(255, 0, 0))
debug_pic.save(
os.path.join(debug_save_dir,
str(debug_count).zfill(6) + '.jpg'))
if gt_conf != np.argmax(conf):
debug_pic.save(
os.path.join(error_pred_dir,
str(debug_count).zfill(6) + '.jpg'))
debug_count += 1
else:
for i, (batch, info) in enumerate(val_dataloader):
# batch, centerAngle, info = data.EvalBatch()
batch = torch.FloatTensor(batch).to(device)
[orient, conf] = model(batch)
orient = orient.cpu().data.numpy()
conf = conf.cpu().data.numpy()
alpha = angle_utils.recover_angle(conf.squeeze(0),
orient.squeeze(0), cfg.BIN)
rot_y = angle_utils.compute_orientaion(P2, info['bbox2d'], alpha)
angle_utils.save_result(info, alpha, rot_y, i, mode='predict')