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gpu的输出中存在很多-inf，但输入数据其实就都为0或者大于0.001
jt.Var([[[[-3.4028235e+38 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.4927946e-01 2.4003847e-01 3.7717146e-01 4.0253597e-01 4.0253597e-01 4.0253597e-01 4.0253597e-01 4.0253597e-01 4.0253597e-01 4.0253597e-01 4.0253597e-01 1.9055609e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 -3.4028235e+38] [-3.4028235e+38 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.4927946e-01 2.4003847e-01 3.7717146e-01 4.0253597e-01 4.1456258e-01 4.1456258e-01 4.1456258e-01 4.1456258e-01 4.1456258e-01 4.1456258e-01 4.1456258e-01 4.1456258e-01 1.2646900e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 -3.4028235e+38] [-3.4028235e+38 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.4927946e-01 2.4003847e-01 3.7717146e-01 4.0775770e-01 5.0950021e-01 5.0950021e-01 5.0950021e-01 5.0950021e-01 5.0950021e-01 5.0950021e-01 5.0950021e-01 5.0950021e-01 2.8187984e-01 5.5362388e-02 0.0000000e+00 0.0000000e+00 -3.4028235e+38] [-3.4028235e+38 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.4927946e-01 2.4003847e-01 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import jittor import numpy as np import copy import random seed = 42 random.seed(seed) np.random.seed(seed) jittor.seed(seed) def chebyshev_distance(A: np.ndarray, B: np.ndarray): if A is None or B is None: return 0.0 if A.shape != B.shape: return 9999999 else: return float(np.max(np.abs(A - B))) # data = np.load('./maxpool2d_input_2.npz')['inp'] data = [ [ [ [0., 0., 0., 0., 0., 0.15685005, 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.2040826, 0.3494063, 0.40253597, 0., 0., 0., 0., 0.], [0., 0., 0., 0.14927946, 0.24003847, 0.37717146, 0.07421438, 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0.17000304, 0.04403594, 0.04129967, 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0.02591759, 0., 0.09126398, 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0.05346677, 0., 0.12202908, 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0.08989008, 0., 0.1905561, 0., 0., 0., 0.], [0., 0., 0., 0., 0.01642773, 0.21769403, 0., 0.41456258, 0.126469, 0., 0., 0.], [0., 0., 0., 0., 0.13199194, 0.17417265, 0.4077577, 0.5095002, 0.28187984, 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0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0.01187035, 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.] ] ] ] jittor.flags.use_cuda = 0 input_c = jittor.array(data).float32() pool_cpu = jittor.nn.MaxPool2d(kernel_size=8, stride=1, ceil_mode=False, return_indices=False, padding=(1, 8)) output_c = pool_cpu(input_c) jittor.flags.use_cuda = 1 input_g = jittor.array(data).float32() pool_gpu = copy.deepcopy(pool_cpu) output_g = pool_gpu(input_g) print(output_c) print(output_g)
输出无异常值
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gpu的输出中存在很多-inf，但输入数据其实就都为0或者大于0.001
Minimal Reproduce
Expected behavior
输出无异常值
The text was updated successfully, but these errors were encountered: