ef slide2(y): l = y.shape[0] y = tf.reshape(y, [1, -1, 1]) input = keras.Input(shape=(l, 1)) output = Conv1D(filters=1, kernel_size=3, padding='causal', activation='linear', trainable=False, use_bias=False)(input) model = keras.Model(inputs=input, outputs=output) weights_list = model.get_weights() weights = np.ones(3) / 3 weights_list[0] = weights.reshape((3, 1, 1)) model.set_weights(weights_list) result = model.predict(y) result = tf.reshape(result, [-1, 1]) return result怎么使用 tf.stop_gradient 函数来将其视为常数

如果你想把 `Conv1D` 操作视为常数，可以使用 `tf.stop_gradient` 函数将其包装起来。修改代码如下：

def slide2(y):
    l = y.shape[0]
    y = tf.reshape(y, [1, -1, 1])
    input = keras.Input(shape=(l, 1))
    output = Conv1D(filters=1, kernel_size=3, padding='causal', activation='linear', trainable=False, use_bias=False)(input)
    output = tf.stop_gradient(output)  # 将 output 视为常数
    model = keras.Model(inputs=input, outputs=output)
    weights_list = model.get_weights()
    weights = np.ones(3) / 3
    weights_list[0] = weights.reshape((3, 1, 1))
    model.set_weights(weights_list)
    result = model.predict(y)
    result = tf.reshape(result, [-1, 1])
    return result

这样，`output` 就会被视为常数，不会参与梯度计算。

相关问题

程序执行提示AttributeError: 'point_cloud_generator' object has no attribute 'widthself'，优化程序class point_cloud_generator(): def __init__(self, rgb_file, depth_file, save_ply, camera_intrinsics=[784.0, 779.0, 649.0, 405.0]): self.rgb_file = rgb_file self.depth_file = depth_file self.save_ply = save_ply self.rgb = cv2.imread(rgb_file) self.depth = cv2.imread(self.depth_file, -1) print("your depth image shape is:", self.depth.shape) self.width = self.rgb.shape[1] self.height = self.rgb.shape[0] self.camera_intrinsics = camera_intrinsics self.depth_scale = 1000 def compute(self): t1 = time.time() depth = np.asarray(self.depth, dtype=np.uint16).T # depth[depth==65535]=0 self.Z = depth / self.depth_scale fx, fy, cx, cy = self.camera_intrinsics X = np.zeros((self.width, self.height)) Y = np.zeros((self.width, self.height)) for i in range(self.width): X[i, :] = np.full(X.shape[1], i) self.X = ((X - cx / 2) * self.Z) / fx for i in range(self.height): Y[:, i] = np.full(Y.shape[0], i) self.Y = ((Y - cy / 2) * self.Z) / fy data_ply = np.zeros((6, self.width * self.height)) data_ply[0] = self.X.T.reshape(-1)[:self.widthself.height] data_ply[1] = -self.Y.T.reshape(-1)[:self.widthself.height] data_ply[2] = -self.Z.T.reshape(-1)[:self.widthself.height] img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1)[:self.widthself.height] data_ply[4] = img[:, :, 1:2].reshape(-1)[:self.widthself.height] data_ply[5] = img[:, :, 2:3].reshape(-1)[:self.widthself.height] self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1)

在代码中有两处出现了 `self.widthself.height`，这应该是想要写成 `self.width * self.height`。你需要修改这两处，如下所示：

data_ply[0] = self.X.T.reshape(-1)[:self.width * self.height]
data_ply[1] = -self.Y.T.reshape(-1)[:self.width * self.height]
data_ply[2] = -self.Z.T.reshape(-1)[:self.width * self.height]
img = np.array(self.rgb, dtype=np.uint8)
data_ply[3] = img[:, :, 0:1].reshape(-1)[:self.width * self.height]
data_ply[4] = img[:, :, 1:2].reshape(-1)[:self.width * self.height]
data_ply[5] = img[:, :, 2:3].reshape(-1)[:self.width * self.height]

这样就可以避免出现 `AttributeError: 'point_cloud_generator' object has no attribute 'widthself'` 的错误了。

程序运行提示ValueError: could not broadcast input array from shape (921600,) into shape (307200,)，优化程序 def compute(self): t1 = time.time() depth = np.asarray(self.depth, dtype=np.uint16).T # depth[depth==65535]=0 self.Z = depth / self.depth_scale fx, fy, cx, cy = self.camera_intrinsics X = np.zeros((self.width, self.height)) Y = np.zeros((self.width, self.height)) for i in range(self.width): X[i, :] = np.full(X.shape[1], i) self.X = ((X - cx / 2) * self.Z) / fx for i in range(self.height): Y[:, i] = np.full(Y.shape[0], i) self.Y = ((Y - cy / 2) * self.Z) / fy data_ply = np.zeros((6, self.width * self.height)) data_ply[0] = self.X.T.reshape(-1) data_ply[1] = -self.Y.T.reshape(-1) data_ply[2] = -self.Z.T.reshape(-1) img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1) data_ply[4] = img[:, :, 1:2].reshape(-1) data_ply[5] = img[:, :, 2:3].reshape(-1) self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1)

根据报错信息，可以看出是在数据维度转换的过程中出现了问题。具体来说，`self.X.T.reshape(-1)`、`-self.Y.T.reshape(-1)` 和 `-self.Z.T.reshape(-1)` 这三行代码中，将二维数组转换成了一维数组，并且数组长度发生了变化。其中，`self.X`、`self.Y` 和 `self.Z` 都是形状为 `(self.width, self.height)` 的二维数组，转换成一维数组之后，长度应该是 `self.width * self.height`。但是由于错误提示中提到的维度不匹配问题，可以猜测有些数据的长度没有正确计算。

为了解决这个问题，可以在调用 `reshape` 函数之前，先打印出相应数组的形状，确保长度计算正确。同时，也可以使用 `flatten` 函数代替 `reshape(-1)`，这样可以保证不改变数组的长度和维度。修改后的代码如下：

data_ply[0] = self.X.T.flatten()
data_ply[1] = -self.Y.T.flatten()
data_ply[2] = -self.Z.T.flatten()