def static_graph_case(self):
main = fluid.Program()
start = fluid.Program()
with fluid.unique_name.guard():
with fluid.program_guard(main, start):
self.channel_last = self.data_format == "NDHWC"
if self.channel_last:
x = x = fluid.data(
"input", (-1, -1, -1, -1, self.in_channels),
dtype=self.dtype)
else:
x = fluid.data(
"input", (-1, self.in_channels, -1, -1, -1),
dtype=self.dtype)
weight = fluid.data(
"weight", self.weight_shape, dtype=self.dtype)
if not self.no_bias:
bias = fluid.data("bias", self.bias_shape, dtype=self.dtype)
y = F.conv3d(
x,
weight,
None if self.no_bias else bias,
padding=self.padding,
stride=self.stride,
dilation=self.dilation,
groups=self.groups,
data_format=self.data_format)
if self.act == 'sigmoid':
y = F.sigmoid(y)
def functional(self, place):
main = fluid.Program()
start = fluid.Program()
with fluid.unique_name.guard():
with fluid.program_guard(main, start):
input_shape = (-1, -1, -1, -1, self.num_channels) \
if self.channel_last else (-1, self.num_channels, -1, -1, -1)
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.conv3d(
x_var,
w_var,
None if self.no_bias else b_var,
padding=self.padding,
stride=self.stride,
dilation=self.dilation,
groups=self.groups,
act=self.act,
use_cudnn=self.use_cudnn,
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.conv3d(x,
w,
b,
padding=self.padding,
stride=self.stride,
dilation=self.dilation,
groups=self.groups,
data_format=self.data_format)
def dygraph_case(self):
with dg.guard(self.place):
x = dg.to_variable(self.input)
weight = dg.to_variable(self.weight)
bias = None if self.no_bias else dg.to_variable(self.bias)
y = F.conv3d(x,
weight,
bias,
padding=self.padding,
stride=self.stride,
dilation=self.dilation,
act=self.act,
groups=self.groups,
data_format=self.data_format,
use_cudnn=self.use_cudnn)
out = y.numpy()
return out
def static_graph_case_2(self):
main = fluid.Program()
start = fluid.Program()
with fluid.unique_name.guard():
with fluid.program_guard(main, start):
if self.channel_last:
x = x = fluid.data("input",
(-1, -1, -1, -1, self.in_channels),
dtype=self.dtype)
else:
x = fluid.data("input", (-1, self.in_channels, -1, -1, -1),
dtype=self.dtype)
weight = fluid.data("weight",
self.weight.shape,
dtype=self.dtype)
if not self.no_bias:
bias = fluid.data("bias",
self.bias.shape,
dtype=self.dtype)
y = F.conv3d(x,
weight,
None if self.no_bias else bias,
padding=self.padding,
stride=self.stride,
dilation=self.dilation,
groups=self.groups,
data_format=self.data_format)
if self.act == 'sigmoid':
y = F.sigmoid(y)
exe = fluid.Executor(self.place)
exe.run(start)
feed_dict = {"input": self.input, "weight": self.weight}
if not self.no_bias:
feed_dict["bias"] = self.bias
out, = exe.run(main, feed=feed_dict, fetch_list=[y])
return out
print('================= 逐元素乘、1x1x1的3D卷积 ==================')
N = 2
in_C = 3
out_C = 16
D = 32 # 增加一个维：深度
H = 64
W = 64
C = out_C
# w = paddle.randn((C, in_C, kH, kW))
x = paddle.randn((N, in_C, D, H, W))
w = paddle.randn((C, in_C, 1, 1, 1))
y = F.conv3d(x, w) # [N, C, D, H, W]
x_in = L.reshape(x, (N, 1, in_C, D, H, W))
w_r = L.reshape(w, (1, C, in_C, 1, 1, 1))
y2 = x_in * w_r # [N, C, in_C, D, H, W]
y2 = L.reduce_sum(y2, dim=[
2,
]) # [N, C, D, H, W]
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