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Merge pull request #490 from fxyfxy777/fix_interpolate_copy
fix_interpolate
2 parents 75198ab + df03343 commit 6d8b781

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‎tester/api_config/torch_error_skip.txt‎

Lines changed: 41 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -3008,4 +3008,44 @@ paddle.rsqrt(x=Tensor([477218589, 3, 3],"float16"), )
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paddle.rsqrt(x=Tensor([715827883, 3, 2],"float16"), )
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paddle.linalg.matrix_rank(Tensor([3, 47721859, 5, 6],"float32"), None, False, )
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paddle.linalg.matrix_rank(x=Tensor([2, 67108865, 4, 4],"float64"), tol=None, hermitian=True, )
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paddle.linalg.matrix_rank(x=Tensor([4, 1073741824],"float32"), tol=None, hermitian=False, )
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paddle.linalg.matrix_rank(x=Tensor([4, 1073741824],"float32"), tol=None, hermitian=False, )
3012+
paddle.nn.functional.interpolate(Tensor([107374183, 10, 4],"float32"), size=None, scale_factor=list[0.7999999999999999,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
3013+
paddle.nn.functional.interpolate(Tensor([107374183, 10, 4],"float32"), size=None, scale_factor=list[0.7999999999999999,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
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paddle.nn.functional.interpolate(Tensor([107374183, 10, 4],"float32"), size=None, scale_factor=list[0.7999999999999999,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
3015+
paddle.nn.functional.interpolate(Tensor([107374183, 10, 4],"float32"), size=None, scale_factor=list[0.7999999999999999,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
3016+
paddle.nn.functional.interpolate(Tensor([2, 10, 107374183, 2],"float16"), size=None, scale_factor=list[0.6,0.6,], mode="bicubic", align_corners=False, align_mode=0, data_format="NHWC", name=None, )
3017+
paddle.nn.functional.interpolate(Tensor([2, 10, 107374183, 2],"float16"), size=None, scale_factor=list[0.6,0.6,], mode="bicubic", align_corners=True, align_mode=1, data_format="NHWC", name=None, )
3018+
paddle.nn.functional.interpolate(Tensor([2, 10, 107374183, 2],"float16"), size=None, scale_factor=list[0.6,0.6,], mode="bilinear", align_corners=False, align_mode=0, data_format="NHWC", name=None, )
3019+
paddle.nn.functional.interpolate(Tensor([2, 10, 107374183, 2],"float16"), size=None, scale_factor=list[0.6,0.6,], mode="bilinear", align_corners=True, align_mode=1, data_format="NHWC", name=None, )
3020+
paddle.nn.functional.interpolate(Tensor([2, 107374183, 10, 2],"float16"), size=None, scale_factor=list[0.6,0.6,], mode="bicubic", align_corners=False, align_mode=0, data_format="NHWC", name=None, )
3021+
paddle.nn.functional.interpolate(Tensor([2, 107374183, 10, 2],"float16"), size=None, scale_factor=list[0.6,0.6,], mode="bicubic", align_corners=True, align_mode=1, data_format="NHWC", name=None, )
3022+
paddle.nn.functional.interpolate(Tensor([2, 107374183, 10, 2],"float16"), size=None, scale_factor=list[0.6,0.6,], mode="bilinear", align_corners=False, align_mode=0, data_format="NHWC", name=None, )
3023+
paddle.nn.functional.interpolate(Tensor([2, 107374183, 10, 2],"float16"), size=None, scale_factor=list[0.6,0.6,], mode="bilinear", align_corners=True, align_mode=1, data_format="NHWC", name=None, )
3024+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[0.6,0.6,], mode="bicubic", align_corners=False, align_mode=0, data_format="NCHW", name=None, )
3025+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[0.6,0.6,], mode="bicubic", align_corners=True, align_mode=1, data_format="NCHW", name=None, )
3026+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[0.6,0.6,], mode="bilinear", align_corners=False, align_mode=0, data_format="NCHW", name=None, )
3027+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[0.6,0.6,], mode="bilinear", align_corners=True, align_mode=1, data_format="NCHW", name=None, )
3028+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[0.6,1.7999999999999998,], mode="bicubic", align_corners=False, align_mode=0, data_format="NCHW", name=None, )
3029+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[0.6,1.7999999999999998,], mode="bicubic", align_corners=True, align_mode=1, data_format="NCHW", name=None, )
3030+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[1.7999999999999998,0.6,], mode="bicubic", align_corners=False, align_mode=0, data_format="NCHW", name=None, )
3031+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[1.7999999999999998,0.6,], mode="bicubic", align_corners=True, align_mode=1, data_format="NCHW", name=None, )
3032+
paddle.nn.functional.interpolate(Tensor([2, 2, 57042535, 10],"float32"), size=None, scale_factor=list[0.6,0.6,], mode="bicubic", align_corners=False, align_mode=0, data_format="NCHW", name=None, )
3033+
paddle.nn.functional.interpolate(Tensor([2, 2, 57042535, 10],"float32"), size=None, scale_factor=list[0.6,0.6,], mode="bicubic", align_corners=True, align_mode=1, data_format="NCHW", name=None, )
3034+
paddle.nn.functional.interpolate(Tensor([2, 2, 57042535, 10],"float32"), size=None, scale_factor=list[0.6,0.6,], mode="bilinear", align_corners=False, align_mode=0, data_format="NCHW", name=None, )
3035+
paddle.nn.functional.interpolate(Tensor([2, 2, 57042535, 10],"float32"), size=None, scale_factor=list[0.6,0.6,], mode="bilinear", align_corners=True, align_mode=1, data_format="NCHW", name=None, )
3036+
paddle.nn.functional.interpolate(Tensor([2, 2, 57042535, 10],"float32"), size=None, scale_factor=list[0.6,1.7999999999999998,], mode="bicubic", align_corners=False, align_mode=0, data_format="NCHW", name=None, )
3037+
paddle.nn.functional.interpolate(Tensor([2, 2, 57042535, 10],"float32"), size=None, scale_factor=list[0.6,1.7999999999999998,], mode="bicubic", align_corners=True, align_mode=1, data_format="NCHW", name=None, )
3038+
paddle.nn.functional.interpolate(Tensor([2, 2, 57042535, 10],"float32"), size=None, scale_factor=list[1.7999999999999998,0.6,], mode="bicubic", align_corners=False, align_mode=0, data_format="NCHW", name=None, )
3039+
paddle.nn.functional.interpolate(Tensor([2, 2, 57042535, 10],"float32"), size=None, scale_factor=list[1.7999999999999998,0.6,], mode="bicubic", align_corners=True, align_mode=1, data_format="NCHW", name=None, )
3040+
paddle.nn.functional.interpolate(Tensor([2, 536870913, 4],"float16"), size=None, scale_factor=list[1.0000000000000002,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
3041+
paddle.nn.functional.interpolate(Tensor([2, 536870913, 4],"float16"), size=None, scale_factor=list[1.0000000000000002,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
3042+
paddle.nn.functional.interpolate(Tensor([2, 536870913, 4],"float16"), size=None, scale_factor=list[1.0000000000000002,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
3043+
paddle.nn.functional.interpolate(Tensor([2, 536870913, 4],"float16"), size=None, scale_factor=list[1.0000000000000002,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
3044+
paddle.nn.functional.interpolate(Tensor([2, 10, 107374183, 2],"float16"), size=None, scale_factor=list[0.6,0.6,], mode="nearest", align_corners=False, align_mode=0, data_format="NHWC", name=None, )
3045+
paddle.nn.functional.interpolate(Tensor([2, 10, 107374183, 2],"float16"), size=None, scale_factor=list[0.6,0.6,], mode="nearest", align_corners=False, align_mode=1, data_format="NHWC", name=None, )
3046+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[0.6,0.6,], mode="nearest", align_corners=False, align_mode=0, data_format="NCHW", name=None, )
3047+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[0.6,0.6,], mode="nearest", align_corners=False, align_mode=1, data_format="NCHW", name=None, )
3048+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[0.6,1.7999999999999998,], mode="nearest", align_corners=False, align_mode=0, data_format="NCHW", name=None, )
3049+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[0.6,1.7999999999999998,], mode="nearest", align_corners=False, align_mode=1, data_format="NCHW", name=None, )
3050+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[1.7999999999999998,0.6,], mode="nearest", align_corners=False, align_mode=0, data_format="NCHW", name=None, )
3051+
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 57042535],"float32"), size=None, scale_factor=list[1.7999999999999998,0.6,], mode="nearest", align_corners=False, align_mode=1, data_format="NCHW", name=None, )

‎tester/base_config.yaml‎

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -50,6 +50,7 @@ special_accuracy_atol_rtol:
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paddle.nn.functional.softmax: [1, 0.01]
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paddle.matmul: [2.0, 0.05]
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paddle.nn.functional.bilinear: [2.0, 0.08]
53+
paddle.nn.functional.interpolate: [1.5, 0.01]
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# All configs that report dtype diff when not in not_check_dtype list should be
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# copied to tester/api_config/5_accuracy/accuracy_gpu_error_dtype_diff.txt

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