当我构建FCN进行分割时,我希望图像保持输入数据的原始大小,所以我使用完全卷积层。当我选择固定的输入大小时,例如(224224,224),transpose conv可以正常工作。但是,当我将using(224224224)的代码更改为(h,w)时,我遇到了以下错误。我以前在谷歌上搜索过,但我没发现。有人能帮我吗?谢谢。在
错误信息:
InvalidArgumentError (see above for traceback): Conv2DSlowBackpropInput: Size
of out_backprop doesn't match computed: actual = 62, computed =
63spatial_dim: 2 input: 500 filter: 16 output: 62 stride: 8 dilation: 1
[[Node: deconv_layer/conv2d_transpose_2 =
Conv2DBackpropInput[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1],
padding="SAME", strides=[1, 1, 8, 8], use_cudnn_on_gpu=true,
_device="/job:localhost/replica:0/task:0/device:GPU:0"]
(deconv_layer/conv2d_transpose_2-0-VecPermuteNHWCToNCHW-
LayoutOptimizer/_1961, deconv_layer/deconv3/kernel/read,
deconv_layer/Add_1)]]
[[Node: losses/_2091 = _Recv[client_terminated=false,
recv_device="/job:localhost/replica:0/task:0/device:CPU:0",
send_device="/job:localhost/replica:0/task:0/device:GPU:0",
send_device_incarnation=1, tensor_name="edge_4480_losses",
tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]
()]]
代码:
^{pr2}$没有自定义函数的版本如下:
with tf.variable_scope("deconv_layer"):
deconv1_shape = block2.get_shape()
shape1 = [4, 4, deconv1_shape[3].value, 2048]
deconv1_kernel = tf.Variable(initial_value=tf.truncated_normal(shape1,
stddev=0.02),
trainable=True,
name="deconv1/kernel")
deconv1 = tf.nn.conv2d_transpose(value=block4,
filter=deconv1_kernel,
# output_shape=[BATCH_SIZE,
tf.shape(block2)[1], tf.shape(block2)[2], 512],
output_shape=tf.shape(block2),
strides=[1, 2, 2, 1],
padding='SAME',
data_format='NHWC'
)
print('deconv1', deconv1.shape)
fuse1 = tf.add(deconv1, block2) # fuse1 = pool4 + deconv2(pool5)
tf.identity(fuse1, name="fuse1")
deconv2_shape = block1.get_shape()
shape2 = [4, 4, deconv2_shape[3].value, deconv1_shape[3].value]
deconv2_kernel = tf.Variable(initial_value=tf.truncated_normal(shape2,
stddev=0.02),
trainable=True,
name="deconv2/kernel")
deconv2 = tf.nn.conv2d_transpose(value=fuse1,
filter=deconv2_kernel,
output_shape=tf.shape(block1),
strides=[1, 2, 2, 1],
padding='SAME',
data_format='NHWC'
)
print('deconv2', deconv2.shape)
fuse2 = tf.add(deconv2, block1)
tf.identity(fuse2, name="fuse2")
deconv3_shape = tf.stack([tf.shape(features)[0], tf.shape(features)[1],
tf.shape(features)[2], num_classes])
shape3 = [16, 16, num_classes, deconv2_shape[3].value]
deconv_final_kernel = tf.Variable(initial_value=tf.truncated_normal(shape3, stddev=0.02),
trainable=True,
name="deconv3/kernel")
seg_logits = tf.nn.conv2d_transpose(value=fuse2,
filter=deconv_final_kernel,
output_shape=deconv3_shape,
strides=[1, 8, 8, 1],
padding='SAME',
data_format='NHWC')
我在尝试在tensorflow中复制pytorch的transposeconv2d函数时遇到了类似的问题。在传递给conv2d_transpose()函数之前,我试图对输入进行填充,然后对分解后的输出再次进行填充。这就是为什么图形被正确初始化,但在计算梯度时出现错误的原因。我通过删除所有手动填充并在函数内部更改padding=“SAME”来解决这个错误。我想这是在函数内部处理的。如果我错了,请纠正我。我不知道这对实际产出有多大影响。在
这是因为你的步调>;1。计算不可能总是正确的。这篇文章解释了这一点。在
FCN中的conv网和Deconv网是由不同的结构组成的,它们之间可能不一致。在本例中,conv net使用带有
padding='VALID'
的conv,而deconv net则使用带有padding='SAME
的所有conv峎transpose。因此,形状不一样,导致上述问题。在相关问题 更多 >
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