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From GitBox <...@apache.org>
Subject [GitHub] [incubator-tvm] CallmeZhangChenchen opened a new issue #5273: Run relay_quick_start.py Wrong
Date Wed, 08 Apr 2020 03:45:12 GMT
CallmeZhangChenchen opened a new issue #5273: Run relay_quick_start.py Wrong
URL: https://github.com/apache/incubator-tvm/issues/5273
 
 
   zzjhtest@zzjhtest:~/ZCC/testtvm$ python3 relay_quick_start.py 
   v0.0.4
   def @main(%data: Tensor[(1, 3, 224, 224), float32], %bn_data_gamma: Tensor[(3), float32],
%bn_data_beta: Tensor[(3), float32], %bn_data_moving_mean: Tensor[(3), float32], %bn_data_moving_var:
Tensor[(3), float32], %conv0_weight: Tensor[(64, 3, 7, 7), float32], %bn0_gamma: Tensor[(64),
float32], %bn0_beta: Tensor[(64), float32], %bn0_moving_mean: Tensor[(64), float32], %bn0_moving_var:
Tensor[(64), float32], %stage1_unit1_bn1_gamma: Tensor[(64), float32], %stage1_unit1_bn1_beta:
Tensor[(64), float32], %stage1_unit1_bn1_moving_mean: Tensor[(64), float32], %stage1_unit1_bn1_moving_var:
Tensor[(64), float32], %stage1_unit1_conv1_weight: Tensor[(64, 64, 3, 3), float32], %stage1_unit1_bn2_gamma:
Tensor[(64), float32], %stage1_unit1_bn2_beta: Tensor[(64), float32], %stage1_unit1_bn2_moving_mean:
Tensor[(64), float32], %stage1_unit1_bn2_moving_var: Tensor[(64), float32], %stage1_unit1_conv2_weight:
Tensor[(64, 64, 3, 3), float32], %stage1_unit1_sc_weight: Tensor[(64, 64, 1, 1), float32],
%stage1_unit2_bn1_gamma: Tensor[(64), float32], %stage1_unit2_bn1_beta: Tensor[(64), float32],
%stage1_unit2_bn1_moving_mean: Tensor[(64), float32], %stage1_unit2_bn1_moving_var: Tensor[(64),
float32], %stage1_unit2_conv1_weight: Tensor[(64, 64, 3, 3), float32], %stage1_unit2_bn2_gamma:
Tensor[(64), float32], %stage1_unit2_bn2_beta: Tensor[(64), float32], %stage1_unit2_bn2_moving_mean:
Tensor[(64), float32], %stage1_unit2_bn2_moving_var: Tensor[(64), float32], %stage1_unit2_conv2_weight:
Tensor[(64, 64, 3, 3), float32], %stage2_unit1_bn1_gamma: Tensor[(64), float32], %stage2_unit1_bn1_beta:
Tensor[(64), float32], %stage2_unit1_bn1_moving_mean: Tensor[(64), float32], %stage2_unit1_bn1_moving_var:
Tensor[(64), float32], %stage2_unit1_conv1_weight: Tensor[(128, 64, 3, 3), float32], %stage2_unit1_bn2_gamma:
Tensor[(128), float32], %stage2_unit1_bn2_beta: Tensor[(128), float32], %stage2_unit1_bn2_moving_mean:
Tensor[(128), float32], %stage2_unit1_bn2_moving_var: Tensor[(128), float32], %stage2_unit1_conv2_weight:
Tensor[(128, 128, 3, 3), float32], %stage2_unit1_sc_weight: Tensor[(128, 64, 1, 1), float32],
%stage2_unit2_bn1_gamma: Tensor[(128), float32], %stage2_unit2_bn1_beta: Tensor[(128), float32],
%stage2_unit2_bn1_moving_mean: Tensor[(128), float32], %stage2_unit2_bn1_moving_var: Tensor[(128),
float32], %stage2_unit2_conv1_weight: Tensor[(128, 128, 3, 3), float32], %stage2_unit2_bn2_gamma:
Tensor[(128), float32], %stage2_unit2_bn2_beta: Tensor[(128), float32], %stage2_unit2_bn2_moving_mean:
Tensor[(128), float32], %stage2_unit2_bn2_moving_var: Tensor[(128), float32], %stage2_unit2_conv2_weight:
Tensor[(128, 128, 3, 3), float32], %stage3_unit1_bn1_gamma: Tensor[(128), float32], %stage3_unit1_bn1_beta:
Tensor[(128), float32], %stage3_unit1_bn1_moving_mean: Tensor[(128), float32], %stage3_unit1_bn1_moving_var:
Tensor[(128), float32], %stage3_unit1_conv1_weight: Tensor[(256, 128, 3, 3), float32], %stage3_unit1_bn2_gamma:
Tensor[(256), float32], %stage3_unit1_bn2_beta: Tensor[(256), float32], %stage3_unit1_bn2_moving_mean:
Tensor[(256), float32], %stage3_unit1_bn2_moving_var: Tensor[(256), float32], %stage3_unit1_conv2_weight:
Tensor[(256, 256, 3, 3), float32], %stage3_unit1_sc_weight: Tensor[(256, 128, 1, 1), float32],
%stage3_unit2_bn1_gamma: Tensor[(256), float32], %stage3_unit2_bn1_beta: Tensor[(256), float32],
%stage3_unit2_bn1_moving_mean: Tensor[(256), float32], %stage3_unit2_bn1_moving_var: Tensor[(256),
float32], %stage3_unit2_conv1_weight: Tensor[(256, 256, 3, 3), float32], %stage3_unit2_bn2_gamma:
Tensor[(256), float32], %stage3_unit2_bn2_beta: Tensor[(256), float32], %stage3_unit2_bn2_moving_mean:
Tensor[(256), float32], %stage3_unit2_bn2_moving_var: Tensor[(256), float32], %stage3_unit2_conv2_weight:
Tensor[(256, 256, 3, 3), float32], %stage4_unit1_bn1_gamma: Tensor[(256), float32], %stage4_unit1_bn1_beta:
Tensor[(256), float32], %stage4_unit1_bn1_moving_mean: Tensor[(256), float32], %stage4_unit1_bn1_moving_var:
Tensor[(256), float32], %stage4_unit1_conv1_weight: Tensor[(512, 256, 3, 3), float32], %stage4_unit1_bn2_gamma:
Tensor[(512), float32], %stage4_unit1_bn2_beta: Tensor[(512), float32], %stage4_unit1_bn2_moving_mean:
Tensor[(512), float32], %stage4_unit1_bn2_moving_var: Tensor[(512), float32], %stage4_unit1_conv2_weight:
Tensor[(512, 512, 3, 3), float32], %stage4_unit1_sc_weight: Tensor[(512, 256, 1, 1), float32],
%stage4_unit2_bn1_gamma: Tensor[(512), float32], %stage4_unit2_bn1_beta: Tensor[(512), float32],
%stage4_unit2_bn1_moving_mean: Tensor[(512), float32], %stage4_unit2_bn1_moving_var: Tensor[(512),
float32], %stage4_unit2_conv1_weight: Tensor[(512, 512, 3, 3), float32], %stage4_unit2_bn2_gamma:
Tensor[(512), float32], %stage4_unit2_bn2_beta: Tensor[(512), float32], %stage4_unit2_bn2_moving_mean:
Tensor[(512), float32], %stage4_unit2_bn2_moving_var: Tensor[(512), float32], %stage4_unit2_conv2_weight:
Tensor[(512, 512, 3, 3), float32], %bn1_gamma: Tensor[(512), float32], %bn1_beta: Tensor[(512),
float32], %bn1_moving_mean: Tensor[(512), float32], %bn1_moving_var: Tensor[(512), float32],
%fc1_weight: Tensor[(1000, 512), float32], %fc1_bias: Tensor[(1000), float32]) -> Tensor[(1,
1000), float32] {
     %0 = nn.batch_norm(%data, %bn_data_gamma, %bn_data_beta, %bn_data_moving_mean, %bn_data_moving_var,
epsilon=2e-05f, scale=False) /* ty=(Tensor[(1, 3, 224, 224), float32], Tensor[(3), float32],
Tensor[(3), float32]) */;
     %1 = %0.0;
     %2 = nn.conv2d(%1, %conv0_weight, strides=[2, 2], padding=[3, 3, 3, 3], channels=64,
kernel_size=[7, 7]) /* ty=Tensor[(1, 64, 112, 112), float32] */;
     %3 = nn.batch_norm(%2, %bn0_gamma, %bn0_beta, %bn0_moving_mean, %bn0_moving_var, epsilon=2e-05f)
/* ty=(Tensor[(1, 64, 112, 112), float32], Tensor[(64), float32], Tensor[(64), float32]) */;
     %4 = %3.0;
     %5 = nn.relu(%4) /* ty=Tensor[(1, 64, 112, 112), float32] */;
     %6 = nn.max_pool2d(%5, pool_size=[3, 3], strides=[2, 2], padding=[1, 1]) /* ty=Tensor[(1,
64, 56, 56), float32] */;
     %7 = nn.batch_norm(%6, %stage1_unit1_bn1_gamma, %stage1_unit1_bn1_beta, %stage1_unit1_bn1_moving_mean,
%stage1_unit1_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64),
float32], Tensor[(64), float32]) */;
     %8 = %7.0;
     %9 = nn.relu(%8) /* ty=Tensor[(1, 64, 56, 56), float32] */;
     %10 = nn.conv2d(%9, %stage1_unit1_conv1_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3,
3]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
     %11 = nn.batch_norm(%10, %stage1_unit1_bn2_gamma, %stage1_unit1_bn2_beta, %stage1_unit1_bn2_moving_mean,
%stage1_unit1_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64),
float32], Tensor[(64), float32]) */;
     %12 = %11.0;
     %13 = nn.relu(%12) /* ty=Tensor[(1, 64, 56, 56), float32] */;
     %14 = nn.conv2d(%13, %stage1_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3,
3]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
     %15 = nn.conv2d(%9, %stage1_unit1_sc_weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1,
1]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
     %16 = add(%14, %15) /* ty=Tensor[(1, 64, 56, 56), float32] */;
     %17 = nn.batch_norm(%16, %stage1_unit2_bn1_gamma, %stage1_unit2_bn1_beta, %stage1_unit2_bn1_moving_mean,
%stage1_unit2_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64),
float32], Tensor[(64), float32]) */;
     %18 = %17.0;
     %19 = nn.relu(%18) /* ty=Tensor[(1, 64, 56, 56), float32] */;
     %20 = nn.conv2d(%19, %stage1_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3,
3]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
     %21 = nn.batch_norm(%20, %stage1_unit2_bn2_gamma, %stage1_unit2_bn2_beta, %stage1_unit2_bn2_moving_mean,
%stage1_unit2_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64),
float32], Tensor[(64), float32]) */;
     %22 = %21.0;
     %23 = nn.relu(%22) /* ty=Tensor[(1, 64, 56, 56), float32] */;
     %24 = nn.conv2d(%23, %stage1_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3,
3]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
     %25 = add(%24, %16) /* ty=Tensor[(1, 64, 56, 56), float32] */;
     %26 = nn.batch_norm(%25, %stage2_unit1_bn1_gamma, %stage2_unit1_bn1_beta, %stage2_unit1_bn1_moving_mean,
%stage2_unit1_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64),
float32], Tensor[(64), float32]) */;
     %27 = %26.0;
     %28 = nn.relu(%27) /* ty=Tensor[(1, 64, 56, 56), float32] */;
     %29 = nn.conv2d(%28, %stage2_unit1_conv1_weight, strides=[2, 2], padding=[1, 1, 1, 1],
channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */;
     %30 = nn.batch_norm(%29, %stage2_unit1_bn2_gamma, %stage2_unit1_bn2_beta, %stage2_unit1_bn2_moving_mean,
%stage2_unit1_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 128, 28, 28), float32], Tensor[(128),
float32], Tensor[(128), float32]) */;
     %31 = %30.0;
     %32 = nn.relu(%31) /* ty=Tensor[(1, 128, 28, 28), float32] */;
     %33 = nn.conv2d(%32, %stage2_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=128,
kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */;
     %34 = nn.conv2d(%28, %stage2_unit1_sc_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=128,
kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 28, 28), float32] */;
     %35 = add(%33, %34) /* ty=Tensor[(1, 128, 28, 28), float32] */;
     %36 = nn.batch_norm(%35, %stage2_unit2_bn1_gamma, %stage2_unit2_bn1_beta, %stage2_unit2_bn1_moving_mean,
%stage2_unit2_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 128, 28, 28), float32], Tensor[(128),
float32], Tensor[(128), float32]) */;
     %37 = %36.0;
     %38 = nn.relu(%37) /* ty=Tensor[(1, 128, 28, 28), float32] */;
     %39 = nn.conv2d(%38, %stage2_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=128,
kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */;
     %40 = nn.batch_norm(%39, %stage2_unit2_bn2_gamma, %stage2_unit2_bn2_beta, %stage2_unit2_bn2_moving_mean,
%stage2_unit2_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 128, 28, 28), float32], Tensor[(128),
float32], Tensor[(128), float32]) */;
     %41 = %40.0;
     %42 = nn.relu(%41) /* ty=Tensor[(1, 128, 28, 28), float32] */;
     %43 = nn.conv2d(%42, %stage2_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=128,
kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */;
     %44 = add(%43, %35) /* ty=Tensor[(1, 128, 28, 28), float32] */;
     %45 = nn.batch_norm(%44, %stage3_unit1_bn1_gamma, %stage3_unit1_bn1_beta, %stage3_unit1_bn1_moving_mean,
%stage3_unit1_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 128, 28, 28), float32], Tensor[(128),
float32], Tensor[(128), float32]) */;
     %46 = %45.0;
     %47 = nn.relu(%46) /* ty=Tensor[(1, 128, 28, 28), float32] */;
     %48 = nn.conv2d(%47, %stage3_unit1_conv1_weight, strides=[2, 2], padding=[1, 1, 1, 1],
channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] */;
     %49 = nn.batch_norm(%48, %stage3_unit1_bn2_gamma, %stage3_unit1_bn2_beta, %stage3_unit1_bn2_moving_mean,
%stage3_unit1_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 256, 14, 14), float32], Tensor[(256),
float32], Tensor[(256), float32]) */;
     %50 = %49.0;
     %51 = nn.relu(%50) /* ty=Tensor[(1, 256, 14, 14), float32] */;
     %52 = nn.conv2d(%51, %stage3_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=256,
kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] */;
     %53 = nn.conv2d(%47, %stage3_unit1_sc_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=256,
kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 14, 14), float32] */;
     %54 = add(%52, %53) /* ty=Tensor[(1, 256, 14, 14), float32] */;
     %55 = nn.batch_norm(%54, %stage3_unit2_bn1_gamma, %stage3_unit2_bn1_beta, %stage3_unit2_bn1_moving_mean,
%stage3_unit2_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 256, 14, 14), float32], Tensor[(256),
float32], Tensor[(256), float32]) */;
     %56 = %55.0;
     %57 = nn.relu(%56) /* ty=Tensor[(1, 256, 14, 14), float32] */;
     %58 = nn.conv2d(%57, %stage3_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=256,
kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] */;
     %59 = nn.batch_norm(%58, %stage3_unit2_bn2_gamma, %stage3_unit2_bn2_beta, %stage3_unit2_bn2_moving_mean,
%stage3_unit2_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 256, 14, 14), float32], Tensor[(256),
float32], Tensor[(256), float32]) */;
     %60 = %59.0;
     %61 = nn.relu(%60) /* ty=Tensor[(1, 256, 14, 14), float32] */;
     %62 = nn.conv2d(%61, %stage3_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=256,
kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] */;
     %63 = add(%62, %54) /* ty=Tensor[(1, 256, 14, 14), float32] */;
     %64 = nn.batch_norm(%63, %stage4_unit1_bn1_gamma, %stage4_unit1_bn1_beta, %stage4_unit1_bn1_moving_mean,
%stage4_unit1_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 256, 14, 14), float32], Tensor[(256),
float32], Tensor[(256), float32]) */;
     %65 = %64.0;
     %66 = nn.relu(%65) /* ty=Tensor[(1, 256, 14, 14), float32] */;
     %67 = nn.conv2d(%66, %stage4_unit1_conv1_weight, strides=[2, 2], padding=[1, 1, 1, 1],
channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] */;
     %68 = nn.batch_norm(%67, %stage4_unit1_bn2_gamma, %stage4_unit1_bn2_beta, %stage4_unit1_bn2_moving_mean,
%stage4_unit1_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 512, 7, 7), float32], Tensor[(512),
float32], Tensor[(512), float32]) */;
     %69 = %68.0;
     %70 = nn.relu(%69) /* ty=Tensor[(1, 512, 7, 7), float32] */;
     %71 = nn.conv2d(%70, %stage4_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=512,
kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] */;
     %72 = nn.conv2d(%66, %stage4_unit1_sc_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=512,
kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 7, 7), float32] */;
     %73 = add(%71, %72) /* ty=Tensor[(1, 512, 7, 7), float32] */;
     %74 = nn.batch_norm(%73, %stage4_unit2_bn1_gamma, %stage4_unit2_bn1_beta, %stage4_unit2_bn1_moving_mean,
%stage4_unit2_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 512, 7, 7), float32], Tensor[(512),
float32], Tensor[(512), float32]) */;
     %75 = %74.0;
     %76 = nn.relu(%75) /* ty=Tensor[(1, 512, 7, 7), float32] */;
     %77 = nn.conv2d(%76, %stage4_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=512,
kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] */;
     %78 = nn.batch_norm(%77, %stage4_unit2_bn2_gamma, %stage4_unit2_bn2_beta, %stage4_unit2_bn2_moving_mean,
%stage4_unit2_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 512, 7, 7), float32], Tensor[(512),
float32], Tensor[(512), float32]) */;
     %79 = %78.0;
     %80 = nn.relu(%79) /* ty=Tensor[(1, 512, 7, 7), float32] */;
     %81 = nn.conv2d(%80, %stage4_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=512,
kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] */;
     %82 = add(%81, %73) /* ty=Tensor[(1, 512, 7, 7), float32] */;
     %83 = nn.batch_norm(%82, %bn1_gamma, %bn1_beta, %bn1_moving_mean, %bn1_moving_var, epsilon=2e-05f)
/* ty=(Tensor[(1, 512, 7, 7), float32], Tensor[(512), float32], Tensor[(512), float32]) */;
     %84 = %83.0;
     %85 = nn.relu(%84) /* ty=Tensor[(1, 512, 7, 7), float32] */;
     %86 = nn.global_avg_pool2d(%85) /* ty=Tensor[(1, 512, 1, 1), float32] */;
     %87 = nn.batch_flatten(%86) /* ty=Tensor[(1, 512), float32] */;
     %88 = nn.dense(%87, %fc1_weight, units=1000) /* ty=Tensor[(1, 1000), float32] */;
     %89 = nn.bias_add(%88, %fc1_bias, axis=-1) /* ty=Tensor[(1, 1000), float32] */;
     nn.softmax(%89) /* ty=Tensor[(1, 1000), float32] */
   }
   
   download failed due to URLError(ConnectionRefusedError(111, 'Connection refused'),), retrying,
2 attempts left
   download failed due to URLError(ConnectionRefusedError(111, 'Connection refused'),), retrying,
1 attempt left
   WARNING:root:Failed to download tophub package for cuda: <urlopen error [Errno 111]
Connection refused>
   Traceback (most recent call last):
   
     File "relay_quick_start.py", line 100, in <module>
       graph, lib, params = relay.build(mod, target, params=params)
   
     File "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/relay/build_module.py",
line 251, in build
       graph_json, mod, params = bld_mod.build(mod, target, target_host, params)
   
     File "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/relay/build_module.py",
line 120, in build
       self._build(mod, target, target_host)
   
     File "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/_ffi/_ctypes/packed_func.py",
line 213, in __call__
       raise get_last_ffi_error()
   
   tvm._ffi.base.TVMError: Traceback (most recent call last):
     [bt] (8) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xb09669)
[0x7f2d6a7c0669]
     [bt] (7) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xb095cc)
[0x7f2d6a7c05cc]
     [bt] (6) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ExprFunctor<tvm::runtime::ObjectRef
(tvm::RelayExpr const&)>::VisitExpr(tvm::RelayExpr const&)+0x91) [0x7f2d6a7ca881]
     [bt] (5) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ExprFunctor<tvm::runtime::ObjectRef
(tvm::RelayExpr const&)>::InitVTable()::{lambda(tvm::runtime::ObjectRef const&,
tvm::relay::ExprFunctor<tvm::runtime::ObjectRef (tvm::RelayExpr const&)>*)#6}::_FUN(tvm::runtime::ObjectRef
const&, tvm::relay::ExprFunctor<tvm::runtime::ObjectRef (tvm::RelayExpr const&)>*)+0x27)
[0x7f2d6a7c0da7]
     [bt] (4) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::Interpreter::VisitExpr_(tvm::relay::CallNode
const*)+0x554) [0x7f2d6a7cda74]
     [bt] (3) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::Interpreter::Invoke(tvm::relay::InterpreterClosure
const&, tvm::Array<tvm::runtime::ObjectRef, void> const&, tvm::relay::Var const&)+0xd38)
[0x7f2d6a7c9ce8]
     [bt] (2) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::Interpreter::InvokePrimitiveOp(tvm::relay::Function
const&, tvm::Array<tvm::runtime::ObjectRef, void> const&)+0x541) [0x7f2d6a7c7751]
     [bt] (1) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::CompileEngineImpl::JIT(tvm::relay::CCacheKey
const&)+0x14e) [0x7f2d6a79b4ce]
     [bt] (0) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xbd75db)
[0x7f2d6a88e5db]
     File "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/_ffi/_ctypes/packed_func.py",
line 78, in cfun
       rv = local_pyfunc(*pyargs)
     File "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/relay/backend/_backend.py",
line 83, in build
       return tvm.driver.build(mod, target=target, target_host=target_host)
     File "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/driver/build_module.py",
line 410, in build
       rt_mod_host = codegen.build_module(mod_host_all, target_host)
     File "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/target/codegen.py",
line 40, in build_module
       return _ffi_api.Build(mod, target)
     File "/home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/_ffi/_ctypes/packed_func.py",
line 213, in __call__
       raise get_last_ffi_error()
     [bt] (2) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(TVMFuncCall+0x65)
[0x7f2d6a893175]
     [bt] (1) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(std::_Function_handler<void
(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::runtime::TypedPackedFunc<tvm::runtime::Module
(tvm::IRModule, tvm::Target const&)>::AssignTypedLambda<tvm::runtime::Module (*)(tvm::IRModule,
tvm::Target const&)>(tvm::runtime::Module (*)(tvm::IRModule, tvm::Target const&))::{lambda(tvm::runtime::TVMArgs
const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&,
tvm::runtime::TVMRetValue*&&)+0x84) [0x7f2d6a402d94]
     [bt] (0) /home/zzjhtest/.local/lib/python3.6/site-packages/tvm-0.7.dev1-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::codegen::Build(tvm::IRModule,
tvm::Target const&)+0x91f) [0x7f2d6a3fdddf]
     File "/home/zzjhtest/ZCC/tvm/src/target/codegen.cc", line 53
   TVMError: Check failed: bf != nullptr: target.build.llvm is not enabled
   

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