qinxianyuzi opened a new issue #5218: TVMError: Check failed: ObjectTypeChecker: :Check(ptr): Expect relay.Expr but get IRModule URL: https://github.com/apache/incubator-tvm/issues/5218 When I use this code for evaluating, An TVMError has occurred. `with relay.build_config(opt_level=0): intrp = relay.build_module.create_executor('graph', sym, tvm.cpu(0), target) dtype = 'float32' func = intrp.evaluate(sym)` TVMError: Check failed: ObjectTypeChecker: :Check(ptr): Expect relay.Expr but get IRModule The onnx graph: ` graph(%input.1 : Float(1, 3, 224, 224), %conv1.weight : Float(32, 3, 3, 3), %bn1.weight : Float(32), %bn1.bias : Float(32), %bn1.running_mean : Float(32), %bn1.running_var : Float(32), %bn1.num_batches_tracked : Long(), %layer1.0.conv1.weight : Float(32, 32, 3, 3), %layer1.0.bn2.weight : Float(32), %layer1.0.bn2.bias : Float(32), %layer1.0.bn2.running_mean : Float(32), %layer1.0.bn2.running_var : Float(32), %layer1.0.bn2.num_batches_tracked : Long(), %layer1.0.conv2.weight : Float(32, 32, 3, 3), %layer1.0.bn3.weight : Float(32), %layer1.0.bn3.bias : Float(32), %layer1.0.bn3.running_mean : Float(32), %layer1.0.bn3.running_var : Float(32), %layer1.0.bn3.num_batches_tracked : Long(), %layer1.0.downsample.0.weight : Float(32, 32, 1, 1), %layer1.0.downsample.1.weight : Float(32), %layer1.0.downsample.1.bias : Float(32), %layer1.0.downsample.1.running_mean : Float(32), %layer1.0.downsample.1.running_var : Float(32), %layer1.0.downsample.1.num_batches_tracked : Long(), %layer1.1.conv1.weight : Float(32, 32, 3, 3), %layer1.1.bn2.weight : Float(32), %layer1.1.bn2.bias : Float(32), %layer1.1.bn2.running_mean : Float(32), %layer1.1.bn2.running_var : Float(32), %layer1.1.bn2.num_batches_tracked : Long(), %layer1.1.conv2.weight : Float(32, 32, 3, 3), %layer1.1.bn3.weight : Float(32), %layer1.1.bn3.bias : Float(32), %layer1.1.bn3.running_mean : Float(32), %layer1.1.bn3.running_var : Float(32), %layer1.1.bn3.num_batches_tracked : Long(), %layer2.0.conv1.weight : Float(64, 32, 3, 3), %layer2.0.bn2.weight : Float(64), %layer2.0.bn2.bias : Float(64), %layer2.0.bn2.running_mean : Float(64), %layer2.0.bn2.running_var : Float(64), %layer2.0.bn2.num_batches_tracked : Long(), %layer2.0.conv2.weight : Float(64, 64, 3, 3), %layer2.0.bn3.weight : Float(64), %layer2.0.bn3.bias : Float(64), %layer2.0.bn3.running_mean : Float(64), %layer2.0.bn3.running_var : Float(64), %layer2.0.bn3.num_batches_tracked : Long(), %layer2.0.downsample.0.weight : Float(64, 32, 1, 1), %layer2.0.downsample.1.weight : Float(64), %layer2.0.downsample.1.bias : Float(64), %layer2.0.downsample.1.running_mean : Float(64), %layer2.0.downsample.1.running_var : Float(64), %layer2.0.downsample.1.num_batches_tracked : Long(), %layer2.1.conv1.weight : Float(64, 64, 3, 3), %layer2.1.bn2.weight : Float(64), %layer2.1.bn2.bias : Float(64), %layer2.1.bn2.running_mean : Float(64), %layer2.1.bn2.running_var : Float(64), %layer2.1.bn2.num_batches_tracked : Long(), %layer2.1.conv2.weight : Float(64, 64, 3, 3), %layer2.1.bn3.weight : Float(64), %layer2.1.bn3.bias : Float(64), %layer2.1.bn3.running_mean : Float(64), %layer2.1.bn3.running_var : Float(64), %layer2.1.bn3.num_batches_tracked : Long(), %layer3.0.conv1.weight : Float(128, 64, 3, 3), %layer3.0.bn2.weight : Float(128), %layer3.0.bn2.bias : Float(128), %layer3.0.bn2.running_mean : Float(128), %layer3.0.bn2.running_var : Float(128), %layer3.0.bn2.num_batches_tracked : Long(), %layer3.0.conv2.weight : Float(128, 128, 3, 3), %layer3.0.bn3.weight : Float(128), %layer3.0.bn3.bias : Float(128), %layer3.0.bn3.running_mean : Float(128), %layer3.0.bn3.running_var : Float(128), %layer3.0.bn3.num_batches_tracked : Long(), %layer3.0.downsample.0.weight : Float(128, 64, 1, 1), %layer3.0.downsample.1.weight : Float(128), %layer3.0.downsample.1.bias : Float(128), %layer3.0.downsample.1.running_mean : Float(128), %layer3.0.downsample.1.running_var : Float(128), %layer3.0.downsample.1.num_batches_tracked : Long(), %layer3.1.conv1.weight : Float(128, 128, 3, 3), %layer3.1.bn2.weight : Float(128), %layer3.1.bn2.bias : Float(128), %layer3.1.bn2.running_mean : Float(128), %layer3.1.bn2.running_var : Float(128), %layer3.1.bn2.num_batches_tracked : Long(), %layer3.1.conv2.weight : Float(128, 128, 3, 3), %layer3.1.bn3.weight : Float(128), %layer3.1.bn3.bias : Float(128), %layer3.1.bn3.running_mean : Float(128), %layer3.1.bn3.running_var : Float(128), %layer3.1.bn3.num_batches_tracked : Long(), %layer4.0.conv1.weight : Float(384, 128, 3, 3), %layer4.0.bn2.weight : Float(384), %layer4.0.bn2.bias : Float(384), %layer4.0.bn2.running_mean : Float(384), %layer4.0.bn2.running_var : Float(384), %layer4.0.bn2.num_batches_tracked : Long(), %layer4.0.conv2.weight : Float(384, 384, 3, 3), %layer4.0.bn3.weight : Float(384), %layer4.0.bn3.bias : Float(384), %layer4.0.bn3.running_mean : Float(384), %layer4.0.bn3.running_var : Float(384), %layer4.0.bn3.num_batches_tracked : Long(), %layer4.0.downsample.0.weight : Float(384, 128, 1, 1), %layer4.0.downsample.1.weight : Float(384), %layer4.0.downsample.1.bias : Float(384), %layer4.0.downsample.1.running_mean : Float(384), %layer4.0.downsample.1.running_var : Float(384), %layer4.0.downsample.1.num_batches_tracked : Long(), %layer4.1.conv1.weight : Float(384, 384, 3, 3), %layer4.1.bn2.weight : Float(384), %layer4.1.bn2.bias : Float(384), %layer4.1.bn2.running_mean : Float(384), %layer4.1.bn2.running_var : Float(384), %layer4.1.bn2.num_batches_tracked : Long(), %layer4.1.conv2.weight : Float(384, 384, 3, 3), %layer4.1.bn3.weight : Float(384), %layer4.1.bn3.bias : Float(384), %layer4.1.bn3.running_mean : Float(384), %layer4.1.bn3.running_var : Float(384), %layer4.1.bn3.num_batches_tracked : Long(), %classifier.weight : Float(8, 384), %classifier.bias : Float(8)): %129 : Float(1, 32, 112, 112) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%input.1, %conv1.weight), scope: LResNet2/Conv2d[conv1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %130 : Float(1, 32, 112, 112) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%129, %bn1.weight, %bn1.bias, %bn1.running_mean, %bn1.running_var), scope: LResNet2/BatchNorm2d[bn1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %131 : Float(1, 32, 112, 112) = onnx::Relu(%130), scope: LResNet2/ReLU[ReLU1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:912:0 %132 : Float(1, 32, 112, 112) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%131, %layer1.0.conv1.weight), scope: LResNet2/Sequential[layer1]/BlockIR2[0]/Conv2d[conv1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %133 : Float(1, 32, 112, 112) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%132, %layer1.0.bn2.weight, %layer1.0.bn2.bias, %layer1.0.bn2.running_mean, %layer1.0.bn2.running_var), scope: LResNet2/Sequential[layer1]/BlockIR2[0]/BatchNorm2d[bn2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %134 : Float(1, 32, 112, 112) = onnx::Relu(%133), scope: LResNet2/Sequential[layer1]/BlockIR2[0]/ReLU[ReLU1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:912:0 %135 : Float(1, 32, 56, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%134, %layer1.0.conv2.weight), scope: LResNet2/Sequential[layer1]/BlockIR2[0]/Conv2d[conv2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %136 : Float(1, 32, 56, 56) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%135, %layer1.0.bn3.weight, %layer1.0.bn3.bias, %layer1.0.bn3.running_mean, %layer1.0.bn3.running_var), scope: LResNet2/Sequential[layer1]/BlockIR2[0]/BatchNorm2d[bn3] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %137 : Float(1, 32, 56, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2]](%131, %layer1.0.downsample.0.weight), scope: LResNet2/Sequential[layer1]/BlockIR2[0]/Sequential[downsample]/Conv2d[0] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %138 : Float(1, 32, 56, 56) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%137, %layer1.0.downsample.1.weight, %layer1.0.downsample.1.bias, %layer1.0.downsample.1.running_mean, %layer1.0.downsample.1.running_var), scope: LResNet2/Sequential[layer1]/BlockIR2[0]/Sequential[downsample]/BatchNorm2d[1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %139 : Float(1, 32, 56, 56) = onnx::Add(%136, %138), scope: LResNet2/Sequential[layer1]/BlockIR2[0] # /home/huangry/program/LResNet18E.py:44:0 %140 : Float(1, 32, 56, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%139, %layer1.1.conv1.weight), scope: LResNet2/Sequential[layer1]/BlockIR2[1]/Conv2d[conv1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %141 : Float(1, 32, 56, 56) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%140, %layer1.1.bn2.weight, %layer1.1.bn2.bias, %layer1.1.bn2.running_mean, %layer1.1.bn2.running_var), scope: LResNet2/Sequential[layer1]/BlockIR2[1]/BatchNorm2d[bn2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %142 : Float(1, 32, 56, 56) = onnx::Relu(%141), scope: LResNet2/Sequential[layer1]/BlockIR2[1]/ReLU[ReLU1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:912:0 %143 : Float(1, 32, 56, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%142, %layer1.1.conv2.weight), scope: LResNet2/Sequential[layer1]/BlockIR2[1]/Conv2d[conv2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %144 : Float(1, 32, 56, 56) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%143, %layer1.1.bn3.weight, %layer1.1.bn3.bias, %layer1.1.bn3.running_mean, %layer1.1.bn3.running_var), scope: LResNet2/Sequential[layer1]/BlockIR2[1]/BatchNorm2d[bn3] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %145 : Float(1, 32, 56, 56) = onnx::Add(%144, %139), scope: LResNet2/Sequential[layer1]/BlockIR2[1] # /home/huangry/program/LResNet18E.py:44:0 %146 : Float(1, 64, 56, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%145, %layer2.0.conv1.weight), scope: LResNet2/Sequential[layer2]/BlockIR2[0]/Conv2d[conv1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %147 : Float(1, 64, 56, 56) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%146, %layer2.0.bn2.weight, %layer2.0.bn2.bias, %layer2.0.bn2.running_mean, %layer2.0.bn2.running_var), scope: LResNet2/Sequential[layer2]/BlockIR2[0]/BatchNorm2d[bn2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %148 : Float(1, 64, 56, 56) = onnx::Relu(%147), scope: LResNet2/Sequential[layer2]/BlockIR2[0]/ReLU[ReLU1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:912:0 %149 : Float(1, 64, 28, 28) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%148, %layer2.0.conv2.weight), scope: LResNet2/Sequential[layer2]/BlockIR2[0]/Conv2d[conv2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %150 : Float(1, 64, 28, 28) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%149, %layer2.0.bn3.weight, %layer2.0.bn3.bias, %layer2.0.bn3.running_mean, %layer2.0.bn3.running_var), scope: LResNet2/Sequential[layer2]/BlockIR2[0]/BatchNorm2d[bn3] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %151 : Float(1, 64, 28, 28) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2]](%145, %layer2.0.downsample.0.weight), scope: LResNet2/Sequential[layer2]/BlockIR2[0]/Sequential[downsample]/Conv2d[0] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %152 : Float(1, 64, 28, 28) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%151, %layer2.0.downsample.1.weight, %layer2.0.downsample.1.bias, %layer2.0.downsample.1.running_mean, %layer2.0.downsample.1.running_var), scope: LResNet2/Sequential[layer2]/BlockIR2[0]/Sequential[downsample]/BatchNorm2d[1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %153 : Float(1, 64, 28, 28) = onnx::Add(%150, %152), scope: LResNet2/Sequential[layer2]/BlockIR2[0] # /home/huangry/program/LResNet18E.py:44:0 %154 : Float(1, 64, 28, 28) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%153, %layer2.1.conv1.weight), scope: LResNet2/Sequential[layer2]/BlockIR2[1]/Conv2d[conv1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %155 : Float(1, 64, 28, 28) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%154, %layer2.1.bn2.weight, %layer2.1.bn2.bias, %layer2.1.bn2.running_mean, %layer2.1.bn2.running_var), scope: LResNet2/Sequential[layer2]/BlockIR2[1]/BatchNorm2d[bn2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %156 : Float(1, 64, 28, 28) = onnx::Relu(%155), scope: LResNet2/Sequential[layer2]/BlockIR2[1]/ReLU[ReLU1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:912:0 %157 : Float(1, 64, 28, 28) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%156, %layer2.1.conv2.weight), scope: LResNet2/Sequential[layer2]/BlockIR2[1]/Conv2d[conv2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %158 : Float(1, 64, 28, 28) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%157, %layer2.1.bn3.weight, %layer2.1.bn3.bias, %layer2.1.bn3.running_mean, %layer2.1.bn3.running_var), scope: LResNet2/Sequential[layer2]/BlockIR2[1]/BatchNorm2d[bn3] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %159 : Float(1, 64, 28, 28) = onnx::Add(%158, %153), scope: LResNet2/Sequential[layer2]/BlockIR2[1] # /home/huangry/program/LResNet18E.py:44:0 %160 : Float(1, 128, 28, 28) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%159, %layer3.0.conv1.weight), scope: LResNet2/Sequential[layer3]/BlockIR2[0]/Conv2d[conv1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %161 : Float(1, 128, 28, 28) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%160, %layer3.0.bn2.weight, %layer3.0.bn2.bias, %layer3.0.bn2.running_mean, %layer3.0.bn2.running_var), scope: LResNet2/Sequential[layer3]/BlockIR2[0]/BatchNorm2d[bn2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %162 : Float(1, 128, 28, 28) = onnx::Relu(%161), scope: LResNet2/Sequential[layer3]/BlockIR2[0]/ReLU[ReLU1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:912:0 %163 : Float(1, 128, 14, 14) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%162, %layer3.0.conv2.weight), scope: LResNet2/Sequential[layer3]/BlockIR2[0]/Conv2d[conv2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %164 : Float(1, 128, 14, 14) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%163, %layer3.0.bn3.weight, %layer3.0.bn3.bias, %layer3.0.bn3.running_mean, %layer3.0.bn3.running_var), scope: LResNet2/Sequential[layer3]/BlockIR2[0]/BatchNorm2d[bn3] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %165 : Float(1, 128, 14, 14) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2]](%159, %layer3.0.downsample.0.weight), scope: LResNet2/Sequential[layer3]/BlockIR2[0]/Sequential[downsample]/Conv2d[0] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %166 : Float(1, 128, 14, 14) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%165, %layer3.0.downsample.1.weight, %layer3.0.downsample.1.bias, %layer3.0.downsample.1.running_mean, %layer3.0.downsample.1.running_var), scope: LResNet2/Sequential[layer3]/BlockIR2[0]/Sequential[downsample]/BatchNorm2d[1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %167 : Float(1, 128, 14, 14) = onnx::Add(%164, %166), scope: LResNet2/Sequential[layer3]/BlockIR2[0] # /home/huangry/program/LResNet18E.py:44:0 %168 : Float(1, 128, 14, 14) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%167, %layer3.1.conv1.weight), scope: LResNet2/Sequential[layer3]/BlockIR2[1]/Conv2d[conv1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %169 : Float(1, 128, 14, 14) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%168, %layer3.1.bn2.weight, %layer3.1.bn2.bias, %layer3.1.bn2.running_mean, %layer3.1.bn2.running_var), scope: LResNet2/Sequential[layer3]/BlockIR2[1]/BatchNorm2d[bn2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %170 : Float(1, 128, 14, 14) = onnx::Relu(%169), scope: LResNet2/Sequential[layer3]/BlockIR2[1]/ReLU[ReLU1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:912:0 %171 : Float(1, 128, 14, 14) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%170, %layer3.1.conv2.weight), scope: LResNet2/Sequential[layer3]/BlockIR2[1]/Conv2d[conv2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %172 : Float(1, 128, 14, 14) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%171, %layer3.1.bn3.weight, %layer3.1.bn3.bias, %layer3.1.bn3.running_mean, %layer3.1.bn3.running_var), scope: LResNet2/Sequential[layer3]/BlockIR2[1]/BatchNorm2d[bn3] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %173 : Float(1, 128, 14, 14) = onnx::Add(%172, %167), scope: LResNet2/Sequential[layer3]/BlockIR2[1] # /home/huangry/program/LResNet18E.py:44:0 %174 : Float(1, 384, 14, 14) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%173, %layer4.0.conv1.weight), scope: LResNet2/Sequential[layer4]/BlockIR2[0]/Conv2d[conv1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %175 : Float(1, 384, 14, 14) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%174, %layer4.0.bn2.weight, %layer4.0.bn2.bias, %layer4.0.bn2.running_mean, %layer4.0.bn2.running_var), scope: LResNet2/Sequential[layer4]/BlockIR2[0]/BatchNorm2d[bn2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %176 : Float(1, 384, 14, 14) = onnx::Relu(%175), scope: LResNet2/Sequential[layer4]/BlockIR2[0]/ReLU[ReLU1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:912:0 %177 : Float(1, 384, 7, 7) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%176, %layer4.0.conv2.weight), scope: LResNet2/Sequential[layer4]/BlockIR2[0]/Conv2d[conv2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %178 : Float(1, 384, 7, 7) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%177, %layer4.0.bn3.weight, %layer4.0.bn3.bias, %layer4.0.bn3.running_mean, %layer4.0.bn3.running_var), scope: LResNet2/Sequential[layer4]/BlockIR2[0]/BatchNorm2d[bn3] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %179 : Float(1, 384, 7, 7) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2]](%173, %layer4.0.downsample.0.weight), scope: LResNet2/Sequential[layer4]/BlockIR2[0]/Sequential[downsample]/Conv2d[0] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %180 : Float(1, 384, 7, 7) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%179, %layer4.0.downsample.1.weight, %layer4.0.downsample.1.bias, %layer4.0.downsample.1.running_mean, %layer4.0.downsample.1.running_var), scope: LResNet2/Sequential[layer4]/BlockIR2[0]/Sequential[downsample]/BatchNorm2d[1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %181 : Float(1, 384, 7, 7) = onnx::Add(%178, %180), scope: LResNet2/Sequential[layer4]/BlockIR2[0] # /home/huangry/program/LResNet18E.py:44:0 %182 : Float(1, 384, 7, 7) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%181, %layer4.1.conv1.weight), scope: LResNet2/Sequential[layer4]/BlockIR2[1]/Conv2d[conv1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %183 : Float(1, 384, 7, 7) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%182, %layer4.1.bn2.weight, %layer4.1.bn2.bias, %layer4.1.bn2.running_mean, %layer4.1.bn2.running_var), scope: LResNet2/Sequential[layer4]/BlockIR2[1]/BatchNorm2d[bn2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %184 : Float(1, 384, 7, 7) = onnx::Relu(%183), scope: LResNet2/Sequential[layer4]/BlockIR2[1]/ReLU[ReLU1] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:912:0 %185 : Float(1, 384, 7, 7) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%184, %layer4.1.conv2.weight), scope: LResNet2/Sequential[layer4]/BlockIR2[1]/Conv2d[conv2] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py:342:0 %186 : Float(1, 384, 7, 7) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%185, %layer4.1.bn3.weight, %layer4.1.bn3.bias, %layer4.1.bn3.running_mean, %layer4.1.bn3.running_var), scope: LResNet2/Sequential[layer4]/BlockIR2[1]/BatchNorm2d[bn3] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1670:0 %187 : Float(1, 384, 7, 7) = onnx::Add(%186, %181), scope: LResNet2/Dropout[drop] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:807:0 %188 : Tensor = onnx::Pad[mode="constant", pads=[0, 0, 0, 0, 0, 0, 0, 0], value=0](%187), scope: LResNet2/AvgPool2d[avgpool] %189 : Float(1, 384, 1, 1) = onnx::AveragePool[kernel_shape=[7, 7], pads=[0, 0, 0, 0], strides=[1, 1]](%188), scope: LResNet2/AvgPool2d[avgpool] # /home/huangry/anaconda3/lib/python3.7/site-packages/torch/nn/modules/pooling.py:554:0 %190 : Long() = onnx::Constant[value={0}](), scope: LResNet2 %191 : Tensor = onnx::Shape(%189), scope: LResNet2 %192 : Long() = onnx::Gather[axis=0](%191, %190), scope: LResNet2 # /home/huangry/program/LResNet18E.py:104:0 %193 : Long() = onnx::Constant[value={-1}](), scope: LResNet2 %194 : Tensor = onnx::Unsqueeze[axes=[0]](%192) %195 : Tensor = onnx::Unsqueeze[axes=[0]](%193) %196 : Tensor = onnx::Concat[axis=0](%194, %195) %197 : Float(1, 384) = onnx::Reshape(%189, %196), scope: LResNet2 # /home/huangry/program/LResNet18E.py:104:0 %198 : Float(1, 8) = onnx::Gemm[alpha=1, beta=1, transB=1](%197, %classifier.weight, %classifier.bias), scope: LResNet2/Linear[classifier] # /home/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1370:0 return (%198)`
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