Merge pull request #488 from HeyDavid633/fix_class_center_sample

[Big Tensor] Fix big tensor problem for paddle.nn.functional.class_center_sample

Author: cangtianhuang

tester/api_config/8_big_tensor/big_tensor_1_8.txt

@@ -30330,6 +30330,13 @@ paddle.nn.functional.channel_shuffle(Tensor([29826162, 4, 4, 9],"float16"), 3, "
 paddle.nn.functional.channel_shuffle(Tensor([29826162, 4, 4, 9],"float16"), 3, "NHWC", None, )
 paddle.nn.functional.channel_shuffle(Tensor([29826162, 9, 4, 4],"float16"), 3, "NCHW", )
 paddle.nn.functional.channel_shuffle(Tensor([29826162, 9, 4, 4],"float16"), 3, "NCHW", None, )
+paddle.nn.functional.class_center_sample(Tensor([356493278],"int32"), 10, 8, )
+paddle.nn.functional.class_center_sample(Tensor([356493280],"int32"), 10, 8, )
+paddle.nn.functional.class_center_sample(Tensor([2294967295],"int32"), 10, 8, )
+paddle.nn.functional.class_center_sample(Tensor([4294967295],"int32"), 10, 8, )
+paddle.nn.functional.class_center_sample(Tensor([4294967295],"int32"), 20, 6, )
+paddle.nn.functional.class_center_sample(Tensor([4294967295],"int32"), 20, 8, )
+paddle.nn.functional.class_center_sample(Tensor([4294967295],"int32"), num_classes=10, num_samples=6, group=None, )
 paddle.nn.functional.conv1d(Tensor([1, 1024, 2228225],"float32"), Tensor([1024, 1024, 3],"float32"), bias=Tensor([1024],"float32"), padding=1, stride=list[2,], dilation=list[1,], groups=1, data_format="NCL", )
 paddle.nn.functional.conv1d(Tensor([1, 128, 17825793],"float32"), Tensor([128, 128, 3],"float32"), bias=Tensor([128],"float32"), padding=1, stride=list[1,], dilation=list[1,], groups=1, data_format="NCL", )
 paddle.nn.functional.conv1d(Tensor([1, 128, 17825793],"float32"), Tensor([128, 128, 3],"float32"), bias=Tensor([128],"float32"), padding=3, stride=list[1,], dilation=list[3,], groups=1, data_format="NCL", )

tester/api_config/torch_error_skip.txt

@@ -1328,6 +1328,11 @@ paddle.nn.functional.avg_pool3d(x=Tensor([8, 2739138, 4, 7, 7],"float32"), kerne
 paddle.nn.functional.avg_pool3d(x=Tensor([8, 342393, 32, 7, 7],"float32"), kernel_size=list[32,7,7,], stride=1, data_format="NCDHW", )
 paddle.nn.functional.avg_pool3d(Tensor([2, 3, 8, 8, 8],"float64"), kernel_size=list[3,3,3,], stride=list[3,2,1,], padding=list[1,2,1,], ceil_mode=False, exclusive=True, divisor_override=None, data_format="NCDHW", name=None, )
 paddle.nn.functional.avg_pool3d(x=Tensor([2, 3, 8, 8, 8],"float64"), kernel_size=list[3,3,3,], stride=list[3,2,1,], padding=list[1,2,1,], exclusive=True, )
+paddle.nn.functional.class_center_sample(Tensor([2294967295],"int32"), 10, 8, )
+paddle.nn.functional.class_center_sample(Tensor([4294967295],"int32"), 10, 8, )
+paddle.nn.functional.class_center_sample(Tensor([4294967295],"int32"), 20, 6, )
+paddle.nn.functional.class_center_sample(Tensor([4294967295],"int32"), 20, 8, )
+paddle.nn.functional.class_center_sample(Tensor([4294967295],"int32"), num_classes=10, num_samples=6, group=None, )
 paddle.nn.functional.lp_pool1d(Tensor([2, 3, 380283564],"float32"), 7.0, 2, None, 1, True, "NCL", None, )
 paddle.nn.functional.lp_pool1d(Tensor([2, 3, 380283564],"float32"), norm_type=7, kernel_size=2, stride=2, padding=list[1,], ceil_mode=True, )
 paddle.nn.functional.lp_pool1d(Tensor([2, 3, 715827883],"float16"), 5.0, 5, 3, 0, False, "NCL", None, )

tester/base_config.yaml

@@ -27,6 +27,9 @@ paddle_error_dismiss:
   paddle.linalg.solve: "(PreconditionNotMet) Input tensor 'A' or 'B' has too many elements (> INT_MAX)."
   paddle.nn.functional.group_norm: "(Unimplemented) GroupNorm kernel launch failed"
   paddle.einsum: "(Unimplemented) cublas GEMM does not support N >"
+  paddle.nn.functional.class_center_sample:
+    - "(InvalidArgument) The total number of elements for 'label' should be less than"
+    - "(InvalidArgument) Illegal memory allocation, total allocated space must be greater than 0"
 
 # some accuracy error can be considered tolerable
 special_accuracy_atol_rtol: