1 报错描述
1.1 系统环境
Hardware Environment(Ascend/GPU/CPU): Ascend Software Environment: -- MindSpore version (source or binary): 1.6.0 -- Python version (e.g., Python 3.7.5): 3.7.6 -- OS platform and distribution (e.g., Linux Ubuntu 16.04): Ubuntu 4.15.0-74-generic -- GCC/Compiler version (if compiled from source):
1.2 基本信息
1.2.1 脚本
训练脚本是通过构建Conv2D的单算子网络，来实现2d卷积操作的。脚本如下：
01 class Net(nn.Cell):
02 def __init__(self, in_channels, out_channels, kernel_size):
03 super(Net, self).__init__()
04 self.in_channels = in_channels
05 self.out_channels = out_channels
06 self.kernel_size = kernel_size
07 # self.pad_mode = "valid"
08 self.conv2d = nn.Conv2d(self.in_channels, self.out_channels, self.kernel_size, pad_mode="valid")
09
10 def construct(self, x):
11 return self.conv2d(x)
12
13 net = Net(in_channels=2, out_channels=10, kernel_size=64)
14 x = Tensor(np.random.randn(1, 2, 4, 64), mindspore.float32)
15 y = net(x).shape
16 print("otput shape: ", y)
1.2.2 报错
这里报错信息如下：
Traceback (most recent call last):
File "C:/Users/user1/PycharmProjects/q2_map/new/I4GYSL.py", line 20, in <module>
y = net(x).shape
File "C:\Users\user1\PycharmProjects\q2_map\lib\site-packages\mindspore\nn\cell.py", line 586, in __call__
out = self.compile_and_run(*args)
File "C:\Users\user1\PycharmProjects\q2_map\lib\site-packages\mindspore\nn\cell.py", line 964, in compile_and_run
self.compile(*inputs)
File "C:\Users\user1\PycharmProjects\q2_map\lib\site-packages\mindspore\nn\cell.py", line 937, in compile
_cell_graph_executor.compile(self, *inputs, phase=self.phase, auto_parallel_mode=self._auto_parallel_mode)
File "C:\Users\user1\PycharmProjects\q2_map\lib\site-packages\mindspore\common\api.py", line 1006, in compile
result = self._graph_executor.compile(obj, args_list, phase, self._use_vm_mode())
ValueError: build\mindspore\merge\mindspore\core\ops_merge.cc:6605 CheckShapeAnyAndPositive] Conv2D output_shape shape element [2] must be positive integer or SHP_ANY, but got -59
WARNING: Logging before InitGoogleLogging() is written to STDERR
[CRITICAL] CORE(2540,1,?):2022-6-28 21:12:34 [build\mindspore\merge\mindspore\core\ops_merge.cc:6605] CheckShapeAnyAndPositive] Conv2D output_shape shape element [2] must be positive integer or SHP_ANY, but got -59
2 原因分析
我们看报错信息，在ValueError中，写到Conv2D output_shape shape element [2] must be positive integer or SHP_ANY, but got -59，意思是Conv2d的输出形状的第三维也就是H-out应该是大于0的整数或者-1， 而实际得到的是-59. 结合官网API对Conv2 D的output描述，当pad_mode为“valid”时，选择第二组公式代入数据得到H-out为-59. 因此解决的方法有调整传入的参数大小或者将pad_mode设置为“same”
3 解决方法
基于上面已知的原因，很容易做出如下修改：
01 class Net(nn.Cell):
02 def __init__(self, in_channels, out_channels, kernel_size):
03 super(Net, self).__init__()
04 self.in_channels = in_channels
05 self.out_channels = out_channels
06 self.kernel_size = kernel_size
07 # self.pad_mode = "valid"
08 self.conv2d = nn.Conv2d(self.in_channels, self.out_channels, self.kernel_size, pad_mode="valid")
09
10 def construct(self, x):
11 return self.conv2d(x)
12
13 net = Net(in_channels=2, out_channels=10, kernel_size=64)
14 x = Tensor(np.random.randn(1, 2, 64, 64), mindspore.float32)
15 y = net(x).shape
16 print("otput shape: ", y)
此时执行成功，输出如下：
otput shape: (1, 10, 1, 1)
4 总结
定位报错问题的步骤：
1、找到报错的用户代码行： y = net(x).shape ;
2、 根据日志报错信息中的关键字，缩小分析问题的范围 Conv2D output_shape shape element [2] must be positive integer or SHP_ANY, but got -59 ;
3、需要重点关注变量定义、初始化的正确性。