def functional(self, place):
main = fluid.Program()
start = fluid.Program()
with fluid.unique_name.guard():
with fluid.program_guard(main, start):
input_shape = (-1, self.num_channels,
-1) if not self.channel_last else (
-1, -1, self.num_channels)
x_var = fluid.data("input", input_shape, dtype=self.dtype)
w_var = fluid.data("weight",
self.weight_shape,
dtype=self.dtype)
b_var = fluid.data("bias", (self.num_filters, ),
dtype=self.dtype)
y_var = F.conv1d(x_var,
w_var,
b_var if not self.no_bias else None,
padding=self.padding,
stride=self.stride,
dilation=self.dilation,
groups=self.groups,
data_format=self.data_format)
feed_dict = {"input": self.input, "weight": self.weight}
if self.bias is not None:
feed_dict["bias"] = self.bias
exe = fluid.Executor(place)
exe.run(start)
y_np, = exe.run(main, feed=feed_dict, fetch_list=[y_var])
return y_np
def dygraph_case(self):
with dg.guard():
x = dg.to_variable(self.input, dtype=paddle.float32)
w = dg.to_variable(self.filter, dtype=paddle.float32)
b = None if self.bias is None else dg.to_variable(
self.bias, dtype=paddle.float32)
y = F.conv1d(x,
w,
b,
padding=self.padding,
stride=self.stride,
dilation=self.dilation,
groups=self.groups,
data_format=self.data_format)
1x1x1的3D卷积也有相似的结论。只不过口诀由
13、03、02变成了（即"1"的个数+1）
14、04、03
'''
print('================= 1D的in逐元素乘、1x1的1D卷积 ==================')
N = 256
C = 128
H = 80
x = paddle.randn((N, C, H))
w = paddle.randn((N, C, 1)) # in归一化时会乘以标准差的倒数，这里的w相当于标准差的倒数。
y = x * w # [N, C, H] 也类似于yolov3中的置信位*分类概率。
x_in = L.reshape(x, (1, N * C, H)) # in即把N和C结合成新的通道维
w_r = L.reshape(w, (N * C, 1, 1))
y2 = F.conv1d(x_in, w_r, None, groups=N * C) # [1, N*C, H]
y2 = L.reshape(y2, (N, C, H))
y = y.numpy()
y2 = y2.numpy()
d = np.sum((y - y2)**2)
print(d)
'''
总结：
1x1x1的3D卷积也有相似的结论。只不过口诀由
13、03、02变成了（即"1"的个数+1）
14、04、03
'''