cos_img = yc[1][0][0].data.numpy()
sin_img = yc[2][0][0].data.numpy()
width_img = yc[3][0][0].data.numpy()
plt.subplot(231)
plt.title('depth_input')
plt.imshow(depth_img)
plt.subplot(232)
plt.title('rgb_input')
plt.imshow(rgb_img)
plt.subplot(233)
plt.title('pos_input')
plt.imshow(pos_img)
plt.subplot(234)
plt.title('cos_input')
plt.imshow(cos_img)
plt.subplot(235)
plt.title('sin_input')
plt.imshow(sin_img)
plt.subplot(236)
plt.title('width_input')
plt.imshow(width_img)
plt.show()
#实例化一个网络
net = GGCNN(4)
#将输入传递到网络并计算输出
pos, cos, sin, width = net.forward(xc)
#待解决：目前的输入还未经过裁剪处理，结合当前的网络参数还不能获得理想的输出，所以暂时还不能进行训练
img.crop((left, top), (left + 300, top + 300))
a = img.img
a_points = grasps_pre[0].as_gr.points.astype(np.uint8) #预测出的抓取
b_points = grasps_true.points
color1 = (255, 255, 0)
color2 = (255, 0, 0)
for i in range(3):
img = cv2.line(a, tuple(a_points[i]), tuple(a_points[i + 1]),
color1 if i % 2 == 0 else color2, 1)
img = cv2.line(a, tuple(a_points[3]), tuple(a_points[0]), color2, 1)
color1 = (0, 0, 0)
color2 = (0, 255, 0)
for b_point in b_points:
for i in range(3):
img = cv2.line(a, tuple(b_point[i]), tuple(b_point[i + 1]),
color1 if i % 2 == 0 else color2, 1)
img = cv2.line(a, tuple(b_point[3]), tuple(b_point[0]), color2, 1)
#cv2.imshow('img',a)
cv2.imwrite('img.png', a)
#cv2.waitKey(1000)
if __name__ == '__main__':
net = GGCNN(4)
run(net)
torch.save(net, 'trained_models/model_v')
from ggcnn import GGCNN
batch_size = 16
#准备数据集
cornell_data = Cornell('../cornell', output_size=300)
dataset = torch.utils.data.DataLoader(cornell_data, batch_size=batch_size)
#从数据集中读取一个样本
for x, y, _ in dataset:
xc = x
yc = y
break
#实例化一个网络
net = GGCNN(4)
#定义一个优化器
optimizer = optim.Adam(net.parameters())
#设置GPU设备
device = torch.device("cuda:0")
net = net.to(device)
x = xc.to(device)
y = [yy.to(device) for yy in yc]
#动态显示每次优化过后的预测结果
plt.ion()
plt.show()
result = 0
for grasp_pre in grasps_pre:
if max_iou(grasp_pre, grasps_true) > 0.25:
result = 1
break
if result:
val_result['correct'] += 1
else:
val_result['failed'] += 1
acc = val_result['correct'] / (val_result['correct'] +
val_result['failed'])
val_result['acc'] = acc
print(time.ctime())
print('Correct: {}/{}, val_loss: {:0.4f}, acc: {:0.4f}'.format(
val_result['correct'], val_result['correct'] + val_result['failed'],
val_result['loss'].numpy()[0], acc))
return val_result
if __name__ == '__main__':
use_gpu = True
paddle.set_device('gpu:0') if use_gpu else paddle.set_device('cpu')
net = GGCNN(include_depth + include_rgb * 3)
paddle.summary(net, (1, 4, 300, 300))
for epoch_num in range(epoch_nums):
train_result = train(net, epoch_num, train_batches)
print('validating...')
val_result = eval_net(net, val_batches)
fluid.dygraph.save_dygraph(net.state_dict(), "save_dir/params")