| @@ -55,10 +55,7 @@ pnnx.Output output 1 0 out | |||||
| int step = captured_params.at("steps").i; | int step = captured_params.at("steps").i; | ||||
| if (axis == 0 && start == -1 && end == INT_MIN + 1 && step == -1) | if (axis == 0 && start == -1 && end == INT_MIN + 1 && step == -1) | ||||
| { | |||||
| fprintf(stderr, "aaa %d %d %d\n", start, end, step); | |||||
| return true; | return true; | ||||
| } | |||||
| } | } | ||||
| else // if (captured_params.at("axes").type == 5) | else // if (captured_params.at("axes").type == 5) | ||||
| { | { | ||||
| @@ -70,14 +67,10 @@ pnnx.Output output 1 0 out | |||||
| for (size_t i = 0; i < axes.size(); i++) | for (size_t i = 0; i < axes.size(); i++) | ||||
| { | { | ||||
| if (starts[i] != -1 || ends[i] != INT_MIN + 1 || steps[i] != -1) | if (starts[i] != -1 || ends[i] != INT_MIN + 1 || steps[i] != -1) | ||||
| { | |||||
| fprintf(stderr, "%d %d %d\n", starts[i], ends[i], steps[i]); | |||||
| return false; | return false; | ||||
| } | |||||
| } | } | ||||
| } | } | ||||
| fprintf(stderr, "bbb\n"); | |||||
| return true; | return true; | ||||
| } | } | ||||
| @@ -4,6 +4,7 @@ | |||||
| import torch | import torch | ||||
| import torch.nn as nn | import torch.nn as nn | ||||
| import torch.nn.functional as F | import torch.nn.functional as F | ||||
| from packaging import version | |||||
| class Model(nn.Module): | class Model(nn.Module): | ||||
| def __init__(self): | def __init__(self): | ||||
| @@ -44,6 +45,9 @@ class Model(nn.Module): | |||||
| return x0, y0, y1, y2, z0, z1, z2, z3, z4, z5, z6, w0, w1, w2, w3, w4, w5, w6, w7, w8, w9, w10, w11, w12, w13, w14 | return x0, y0, y1, y2, z0, z1, z2, z3, z4, z5, z6, w0, w1, w2, w3, w4, w5, w6, w7, w8, w9, w10, w11, w12, w13, w14 | ||||
| def test(): | def test(): | ||||
| if version.parse(torch.__version__) < version.parse('1.12'): | |||||
| return True | |||||
| net = Model() | net = Model() | ||||
| net.eval() | net.eval() | ||||