If I run this as a python script without using jupyter notebook. I get these errors instead :
_testbed\tvm_test1.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], 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], 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], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
%15 = nn.conv2d(%9, %stage1_unit1_sc_weight, 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], 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], 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], 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], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */;
%34 = nn.conv2d(%28, %stage2_unit1_sc_weight, strides=[2, 2], 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], 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], 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], 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], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] */;
%53 = nn.conv2d(%47, %stage3_unit1_sc_weight, strides=[2, 2], 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], 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], 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], 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], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] */;
%72 = nn.conv2d(%66, %stage4_unit1_sc_weight, strides=[2, 2], 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], 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], 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] */
}
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('dense', (1, 512, 'float32'), (1000, 512, 'float32'), 0, 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 512, 7, 7, 'float32'), (512, 512, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 256, 14, 14, 'float32'), (512, 256, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 256, 14, 14, 'float32'), (256, 256, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 128, 28, 28, 'float32'), (256, 128, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 128, 28, 28, 'float32'), (128, 128, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 64, 56, 56, 'float32'), (128, 64, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 64, 56, 56, 'float32'), (64, 64, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 3, 224, 224, 'float32'), (64, 3, 7, 7, 'float32'), (2, 2), (3, 3), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 64, 56, 56, 'float32'), (64, 64, 1, 1, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 64, 56, 56, 'float32'), (128, 64, 1, 1, 'float32'), (2, 2), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 128, 28, 28, 'float32'), (256, 128, 1, 1, 'float32'), (2, 2), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 256, 14, 14, 'float32'), (512, 256, 1, 1, 'float32'), (2, 2), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
[0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001]
['deploy_graph.json', 'deploy_lib.tar', 'deploy_param.params']
Traceback (most recent call last):
File "c:\Users\User\.vscode\extensions\ms-python.python-2020.1.58038\pythonFiles\ptvsd_launcher.py", line 43, in <module>
main(ptvsdArgs)
File "c:\Users\User\.vscode\extensions\ms-python.python-2020.1.58038\pythonFiles\lib\python\old_ptvsd\ptvsd\__main__.py", line 432, in main
run()
File "c:\Users\User\.vscode\extensions\ms-python.python-2020.1.58038\pythonFiles\lib\python\old_ptvsd\ptvsd\__main__.py", line 316, in run_file
runpy.run_path(target, run_name='__main__')
File "C:\Users\User\Anaconda3\lib\runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "C:\Users\User\Anaconda3\lib\runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "C:\Users\User\Anaconda3\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "d:\Codes\tvm_testbed\tvm_test1.py", line 157, in <module>
loaded_lib = tvm.module.load(path_lib)
File "C:\Users\User\Anaconda3\lib\site-packages\tvm-0.7.dev0-py3.7-win-amd64.egg\tvm\module.py", line 305, in load
_cc.create_shared(path + ".so", files)
File "C:\Users\User\Anaconda3\lib\site-packages\tvm-0.7.dev0-py3.7-win-amd64.egg\tvm\contrib\cc.py", line 50, in create_shared
_windows_shared(output, objects, options)
File "C:\Users\User\Anaconda3\lib\site-packages\tvm-0.7.dev0-py3.7-win-amd64.egg\tvm\contrib\cc.py", line 203, in _windows_shared
raise RuntimeError(msg)
RuntimeError: Compilation error:
Microsoft (R) C/C++ Optimizing Compiler Version 19.24.28316 for x64
Copyright (C) Microsoft Corporation. All rights reserved.
cl : Command line warning D9024 : unrecognized source file type 'C:\Users\User\AppData\Local\Temp\tmpbtjtlrw2\deploy_lib\devc.o', object file assumed
cl : Command line warning D9027 : source file 'C:\Users\User\AppData\Local\Temp\tmpbtjtlrw2\deploy_lib\devc.o' ignored
cl : Command line warning D9024 : unrecognized source file type 'C:\Users\User\AppData\Local\Temp\tmpbtjtlrw2\deploy_lib\lib0.o', object file assumed
cl : Command line warning D9027 : source file 'C:\Users\User\AppData\Local\Temp\tmpbtjtlrw2\deploy_lib\lib0.o' ignored
dllmain.cc
C:\Users\User\AppData\Local\Temp\tmpcp7qbnru\dllmain.cc(1): warning C4067: unexpected tokens following preprocessor directive - expected a newline
C:\Users\User\AppData\Local\Temp\tmpcp7qbnru\dllmain.cc(1): fatal error C1034: windows.h: no include path set
After removing the vc bin directory from PATH which I added earlier, and re-running the script in python I get the same error as jupyter notebook :
Traceback (most recent call last):
File "C:\Users\User\Anaconda3\lib\site-packages\tvm-0.7.dev0-py3.7-win-amd64.egg\tvm\contrib\cc.py", line 195, in _windows_shared
cl_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
File "C:\Users\User\Anaconda3\lib\subprocess.py", line 775, in __init__
restore_signals, start_new_session)
File "C:\Users\User\Anaconda3\lib\subprocess.py", line 1178, in _execute_child
startupinfo)
FileNotFoundError: [WinError 2] The system cannot find the file specified
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "c:\Users\User\.vscode\extensions\ms-python.python-2020.1.58038\pythonFiles\ptvsd_launcher.py", line 43, in <module>
main(ptvsdArgs)
File "c:\Users\User\.vscode\extensions\ms-python.python-2020.1.58038\pythonFiles\lib\python\old_ptvsd\ptvsd\__main__.py", line 432, in main
run()
File "c:\Users\User\.vscode\extensions\ms-python.python-2020.1.58038\pythonFiles\lib\python\old_ptvsd\ptvsd\__main__.py", line 316, in run_file
runpy.run_path(target, run_name='__main__')
File "C:\Users\User\Anaconda3\lib\runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "C:\Users\User\Anaconda3\lib\runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "C:\Users\User\Anaconda3\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "d:\Codes\tvm_testbed\tvm_test1.py", line 157, in <module>
loaded_lib = tvm.module.load(path_lib)
File "C:\Users\User\Anaconda3\lib\site-packages\tvm-0.7.dev0-py3.7-win-amd64.egg\tvm\module.py", line 305, in load
_cc.create_shared(path + ".so", files)
File "C:\Users\User\Anaconda3\lib\site-packages\tvm-0.7.dev0-py3.7-win-amd64.egg\tvm\contrib\cc.py", line 50, in create_shared
_windows_shared(output, objects, options)
File "C:\Users\User\Anaconda3\lib\site-packages\tvm-0.7.dev0-py3.7-win-amd64.egg\tvm\contrib\cc.py", line 198, in _windows_shared
raise RuntimeError("Can not find cl.exe,"
RuntimeError: Can not find cl.exe,please run this in Vistual Studio Command Prompt.