Feature Request - Dealing with int64 in the exported ONNX model

Hi,

Background

We are trying to run models from silero-models via onnx.js. We have had various issues with ONNX export, but most of them finally were resolved using built-in messages and just testing using onnxruntime (we had to heavily simplify some model parts).

Model Export

The models were ported as follows:

Original model in PyTorch =>
Fusing convolutions (w/o quantization) =>
Simplified model in TorchScript =>
ONNX (=> TensorFlow via onnx-tensorflow)

Model export script in PyTorch (step 4 above)

torch.onnx.export(onnx_model,                # model being run
                  inputs,                    # model input (or a tuple for multiple inputs)
                  "en_v1_test.onnx",         # where to save the model (can be a file or file-like object)
                  export_params=True,        # store the trained parameter weights inside the model file
                  opset_version=12,          # the ONNX version to export the model to
                  do_constant_folding=True,  # whether to execute constant folding for optimization
                  input_names = ['input'],   # the model's input names
                  output_names = ['output'], # the model's output names
                  dynamic_axes={'input' : {0: 'batch',
                                           1: 'samples'},    
                                'output' : {0: 'batch',
                                            1: 'frames'}},
                  verbose=True
                 )

We have tested that the converted models work fine with:

onnxruntime
TensorFlow (on CPU and GPU)

Problem

But when we try to run the model in onnx-js, we face an issue similar to https://github.com/microsoft/onnxjs/issues/168 .

Upon closer inspection via netron app we see that:

slicing operators (static and dynamic alike) are stored as int64 values
some constants like 0, 1, -1 are stored as int64 values

If we then inspect PyTorch export log, we can see that it has Long() = 228 times, so I believe it is not some idiosyncrasy of our models (except for the first normalization and STFT we mostly use off-the-shelf components) but a feature of ONNX export in general.

Model conversion log (very verbose!)

```text graph(%input : Float(1:73728, 73728:1), %stft.forward_basis : Float(322:320, 1:0, 320:1), %audio_normalize.filter_ : Float(1:161, 1:161, 161:1), %encoder.0.layers.0.0.weight : Float(512:1127, 161:7, 7:1), %encoder.0.layers.0.0.bias : Float(512:1), %encoder.0.layers.4.0.weight : Float(512:512, 512:1, 1:1), %encoder.0.layers.4.0.bias : Float(512:1), %encoder.1.layers.0.0.weight : Float(512:448, 64:7, 7:1), %encoder.1.layers.0.0.bias : Float(512:1), %encoder.1.layers.4._se_reduce.weight : Float(102:512, 512:1, 1:1), %encoder.1.layers.4._se_reduce.bias : Float(102:1), %encoder.1.layers.4._se_expand.weight : Float(512:102, 102:1, 1:1), %encoder.1.layers.4._se_expand.bias : Float(512:1), %encoder.1.layers.5.0.weight : Float(512:512, 512:1, 1:1), %encoder.1.layers.5.0.bias : Float(512:1), %encoder.1.layers.9.0.weight : Float(512:448, 64:7, 7:1), %encoder.1.layers.9.0.bias : Float(512:1), %encoder.1.layers.13._se_reduce.weight : Float(102:512, 512:1, 1:1), %encoder.1.layers.13._se_reduce.bias : Float(102:1), %encoder.1.layers.13._se_expand.weight : Float(512:102, 102:1, 1:1), %encoder.1.layers.13._se_expand.bias : Float(512:1), %encoder.1.layers.14.0.weight : Float(512:512, 512:1, 1:1), %encoder.1.layers.14.0.bias : Float(512:1), %encoder.2.layers.0.0.weight : Float(512:448, 64:7, 7:1), %encoder.2.layers.0.0.bias : Float(512:1), %encoder.2.layers.4._se_reduce.weight : Float(102:512, 512:1, 1:1), %encoder.2.layers.4._se_reduce.bias : Float(102:1), %encoder.2.layers.4._se_expand.weight : Float(512:102, 102:1, 1:1), %encoder.2.layers.4._se_expand.bias : Float(512:1), %encoder.2.layers.5.0.weight : Float(512:512, 512:1, 1:1), %encoder.2.layers.5.0.bias : Float(512:1), %encoder.2.layers.9.0.weight : Float(512:448, 64:7, 7:1), %encoder.2.layers.9.0.bias : Float(512:1), %encoder.2.layers.13._se_reduce.weight : Float(102:512, 512:1, 1:1), %encoder.2.layers.13._se_reduce.bias : Float(102:1), %encoder.2.layers.13._se_expand.weight : Float(512:102, 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%decoder.layers.4.self_attn.out_proj.bias : Float(512:1), %decoder.layers.4.linear1.bias : Float(512:1), %decoder.layers.4.linear2.bias : Float(512:1), %decoder.layers.4.norm1.weight : Float(512:1), %decoder.layers.4.norm1.bias : Float(512:1), %decoder.layers.4.norm2.weight : Float(512:1), %decoder.layers.4.norm2.bias : Float(512:1), %decoder.layers.5.self_attn.in_proj_bias : Float(1536:1), %decoder.layers.5.self_attn.out_proj.bias : Float(512:1), %decoder.layers.5.linear1.bias : Float(512:1), %decoder.layers.5.linear2.bias : Float(512:1), %decoder.layers.5.norm1.weight : Float(512:1), %decoder.layers.5.norm1.bias : Float(512:1), %decoder.layers.5.norm2.weight : Float(512:1), %decoder.layers.5.norm2.bias : Float(512:1), %decoder.layers.6.self_attn.in_proj_bias : Float(1536:1), %decoder.layers.6.self_attn.out_proj.bias : Float(512:1), %decoder.layers.6.linear1.bias : Float(512:1), %decoder.layers.6.linear2.bias : Float(512:1), %decoder.layers.6.norm1.weight : Float(512:1), %decoder.layers.6.norm1.bias : Float(512:1), %decoder.layers.6.norm2.weight : Float(512:1), %decoder.layers.6.norm2.bias : Float(512:1), %decoder.layers.7.self_attn.in_proj_bias : Float(1536:1), %decoder.layers.7.self_attn.out_proj.bias : Float(512:1), %decoder.layers.7.linear1.bias : Float(512:1), %decoder.layers.7.linear2.bias : Float(512:1), %decoder.layers.7.norm1.weight : Float(512:1), %decoder.layers.7.norm1.bias : Float(512:1), %decoder.layers.7.norm2.weight : Float(512:1), %decoder.layers.7.norm2.bias : Float(512:1), %decoder.layers.8.self_attn.in_proj_bias : Float(1536:1), %decoder.layers.8.self_attn.out_proj.bias : Float(512:1), %decoder.layers.8.linear1.bias : Float(512:1), %decoder.layers.8.linear2.bias : Float(512:1), %decoder.layers.8.norm1.weight : Float(512:1), %decoder.layers.8.norm1.bias : Float(512:1), %decoder.layers.8.norm2.weight : Float(512:1), %decoder.layers.8.norm2.bias : Float(512:1), %decoder.layers.9.self_attn.in_proj_bias : Float(1536:1), %decoder.layers.9.self_attn.out_proj.bias : Float(512:1), %decoder.layers.9.linear1.bias : Float(512:1), %decoder.layers.9.linear2.bias : Float(512:1), %decoder.layers.9.norm1.weight : Float(512:1), %decoder.layers.9.norm1.bias : Float(512:1), %decoder.layers.9.norm2.weight : Float(512:1), %decoder.layers.9.norm2.bias : Float(512:1), %decoder.layers.10.self_attn.in_proj_bias : Float(1536:1), %decoder.layers.10.self_attn.out_proj.bias : Float(512:1), %decoder.layers.10.linear1.bias : Float(512:1), %decoder.layers.10.linear2.bias : Float(512:1), %decoder.layers.10.norm1.weight : Float(512:1), %decoder.layers.10.norm1.bias : Float(512:1), %decoder.layers.10.norm2.weight : Float(512:1), %decoder.layers.10.norm2.bias : Float(512:1), %decoder.layers.11.self_attn.in_proj_bias : Float(1536:1), %decoder.layers.11.self_attn.out_proj.bias : Float(512:1), %decoder.layers.11.linear1.bias : Float(512:1), %decoder.layers.11.linear2.bias : Float(512:1), %decoder.layers.11.norm1.weight : Float(512:1), %decoder.layers.11.norm1.bias : Float(512:1), %decoder.layers.11.norm2.weight : Float(512:1), %decoder.layers.11.norm2.bias : Float(512:1), %decoder.layers.12.self_attn.in_proj_bias : Float(1536:1), %decoder.layers.12.self_attn.out_proj.bias : Float(512:1), %decoder.layers.12.linear1.bias : Float(512:1), %decoder.layers.12.linear2.bias : Float(512:1), %decoder.layers.12.norm1.weight : Float(512:1), %decoder.layers.12.norm1.bias : Float(512:1), %decoder.layers.12.norm2.weight : Float(512:1), %decoder.layers.12.norm2.bias : Float(512:1), %decoder.layers.13.self_attn.in_proj_bias : Float(1536:1), %decoder.layers.13.self_attn.out_proj.bias : Float(512:1), %decoder.layers.13.linear1.bias : Float(512:1), %decoder.layers.13.linear2.bias : Float(512:1), %decoder.layers.13.norm1.weight : Float(512:1), %decoder.layers.13.norm1.bias : Float(512:1), %decoder.layers.13.norm2.weight : Float(512:1), %decoder.layers.13.norm2.bias : Float(512:1), %decoder.layers.14.self_attn.in_proj_bias : Float(1536:1), %decoder.layers.14.self_attn.out_proj.bias : Float(512:1), %decoder.layers.14.linear1.bias : Float(512:1), %decoder.layers.14.linear2.bias : Float(512:1), %decoder.layers.14.norm1.weight : Float(512:1), %decoder.layers.14.norm1.bias : Float(512:1), %decoder.layers.14.norm2.weight : Float(512:1), %decoder.layers.14.norm2.bias : Float(512:1), %decoder.layers.15.self_attn.in_proj_bias : Float(1536:1), %decoder.layers.15.self_attn.out_proj.bias : Float(512:1), %decoder.layers.15.linear1.bias : Float(512:1), %decoder.layers.15.linear2.bias : Float(512:1), %decoder.layers.15.norm1.weight : Float(512:1), %decoder.layers.15.norm1.bias : Float(512:1), %decoder.layers.15.norm2.weight : Float(512:1), %decoder.layers.15.norm2.bias : Float(512:1), %fc.weight : Float(999:512, 512:1, 1:1), %fc.bias : Float(999:1), %2460 : Long(1:1), %2461 : Float(), %2462 : Float(), %2463 : Float(512:1, 1536:512), %2464 : Long(1:1), %2465 : Long(1:1), %2466 : Float(512:1, 512:512), %2467 : Float(), %2468 : Float(512:1, 512:512), %2469 : Float(512:1, 512:512), %2470 : Float(), %2471 : Float(512:1, 1536:512), %2472 : Long(1:1), %2473 : Long(1:1), %2474 : Float(512:1, 512:512), %2475 : Float(), %2476 : Float(512:1, 512:512), %2477 : Float(512:1, 512:512), %2478 : Float(), %2479 : Float(512:1, 1536:512), %2480 : Long(1:1), %2481 : Long(1:1), %2482 : Float(512:1, 512:512), %2483 : Float(), %2484 : Float(512:1, 512:512), %2485 : Float(512:1, 512:512), %2486 : Float(), %2487 : Float(512:1, 1536:512), %2488 : Long(1:1), %2489 : Long(1:1), %2490 : Float(512:1, 512:512), %2491 : Float(), %2492 : Float(512:1, 512:512), %2493 : Float(512:1, 512:512), %2494 : Float(), %2495 : Float(512:1, 1536:512), %2496 : Long(1:1), %2497 : Long(1:1), %2498 : Float(512:1, 512:512), %2499 : Float(), %2500 : Float(512:1, 512:512), %2501 : Float(512:1, 512:512), %2502 : Float(), %2503 : Float(512:1, 1536:512), %2504 : Long(1:1), %2505 : Long(1:1), %2506 : Float(512:1, 512:512), %2507 : Float(), %2508 : Float(512:1, 512:512), %2509 : Float(512:1, 512:512), %2510 : Float(), %2511 : Float(512:1, 1536:512), %2512 : Long(1:1), %2513 : Long(1:1), %2514 : Float(512:1, 512:512), %2515 : Float(), %2516 : Float(512:1, 512:512), %2517 : Float(512:1, 512:512), %2518 : Float(), %2519 : Float(512:1, 1536:512), %2520 : Long(1:1), %2521 : Long(1:1), %2522 : Float(512:1, 512:512), %2523 : Float(), %2524 : Float(512:1, 512:512), %2525 : Float(512:1, 512:512), %2526 : Float(), %2527 : Float(512:1, 1536:512), %2528 : Long(1:1), %2529 : Long(1:1), %2530 : Float(512:1, 512:512), %2531 : Float(), %2532 : Float(512:1, 512:512), %2533 : Float(512:1, 512:512), %2534 : Float(), %2535 : Float(512:1, 1536:512), %2536 : Long(1:1), %2537 : Long(1:1), %2538 : Float(512:1, 512:512), %2539 : Float(), %2540 : Float(512:1, 512:512), %2541 : Float(512:1, 512:512), %2542 : Float(), %2543 : Float(512:1, 1536:512), %2544 : Long(1:1), %2545 : Long(1:1), %2546 : Float(512:1, 512:512), %2547 : Float(), %2548 : Float(512:1, 512:512), %2549 : Float(512:1, 512:512), %2550 : Float(), %2551 : Float(512:1, 1536:512), %2552 : Long(1:1), %2553 : Long(1:1), %2554 : Float(512:1, 512:512), %2555 : Float(), %2556 : Float(512:1, 512:512), %2557 : Float(512:1, 512:512), %2558 : Float(), %2559 : Float(512:1, 1536:512), %2560 : Long(1:1), %2561 : Long(1:1), %2562 : Float(512:1, 512:512), %2563 : Float(), %2564 : Float(512:1, 512:512), %2565 : Float(512:1, 512:512), %2566 : Float(), %2567 : Float(512:1, 1536:512), %2568 : Long(1:1), %2569 : Long(1:1), %2570 : Float(512:1, 512:512), %2571 : Float(), %2572 : Float(512:1, 512:512), %2573 : Float(512:1, 512:512), %2574 : Float(), %2575 : Float(512:1, 1536:512), %2576 : Long(1:1), %2577 : Long(1:1), %2578 : Float(512:1, 512:512), %2579 : Float(), %2580 : Float(512:1, 512:512), %2581 : Float(512:1, 512:512), %2582 : Float(), %2583 : Float(512:1, 1536:512), %2584 : Long(1:1), %2585 : Long(1:1), %2586 : Float(512:1, 512:512), %2587 : Float(), %2588 : Float(512:1, 512:512), %2589 : Float(512:1, 512:512), %2590 : Float()): %504 : Tensor = onnx::Shape(%input) %505 : Tensor = onnx::Constant[value={0}]() %506 : Long() = onnx::Gather[axis=0](%504, %505) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/pytorch_stft.py:49:0 %507 : Tensor = onnx::Shape(%input) %508 : Tensor = onnx::Constant[value={1}]() %509 : Long() = onnx::Gather[axis=0](%507, %508) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/pytorch_stft.py:50:0 %511 : Tensor = onnx::Unsqueeze[axes=[0]](%506) %513 : Tensor = onnx::Unsqueeze[axes=[0]](%509) %514 : Tensor = onnx::Concat[axis=0](%511, %2460, %513) %515 : Float(1:73728, 1:73728, 73728:1) = onnx::Reshape(%input, %514) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/pytorch_stft.py:55:0 %516 : Float(1:73728, 1:73728, 1:73728, 73728:1) = onnx::Unsqueeze[axes=[1]](%515) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/pytorch_stft.py:57:0 %517 : Float(1:74048, 1:74048, 1:74048, 74048:1) = onnx::Pad[mode="reflect", pads=[0, 0, 0, 160, 0, 0, 0, 160]](%516) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3569:0 %518 : Float(1:74048, 1:74048, 74048:1) = onnx::Squeeze[axes=[1]](%517) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/pytorch_stft.py:60:0 %519 : Float(1:148442, 322:461, 461:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[320], pads=[0, 0], strides=[160]](%518, %stft.forward_basis) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/pytorch_stft.py:70:0 %520 : Tensor = onnx::Constant[value={1}]() %521 : Tensor = onnx::Constant[value={0}]() %522 : Tensor = onnx::Constant[value={161}]() %523 : Tensor = onnx::Constant[value={1}]() %524 : Float(1:148442, 161:461, 461:1) = onnx::Slice(%519, %521, %522, %520, %523) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/pytorch_stft.py:69:0 %525 : Tensor = onnx::Constant[value={1}]() %526 : Tensor = onnx::Constant[value={161}]() %527 : Tensor = onnx::Constant[value={9223372036854775807}]() %528 : Tensor = onnx::Constant[value={1}]() %529 : Float(1:148442, 161:461, 461:1) = onnx::Slice(%519, %526, %527, %525, %528) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/pytorch_stft.py:70:0 %532 : Float(1:148442, 161:461, 461:1) = onnx::Pow(%524, %2461) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/pytorch_stft.py:72:0 %535 : Float(1:148442, 161:461, 461:1) = onnx::Pow(%529, %2462) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/pytorch_stft.py:72:0 %536 : Float(1:74221, 161:461, 461:1) = onnx::Add(%532, %535) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/pytorch_stft.py:72:0 %537 : Float(1:74221, 161:461, 461:1) = onnx::Sqrt(%536) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/pytorch_stft.py:72:0 %538 : Float() = onnx::Constant[value={1.04858e+06}]() %539 : Float(1:74221, 161:461, 461:1) = onnx::Mul(%537, %538) %540 : Float() = onnx::Constant[value={1}]() %541 : FloatTensor = onnx::Add(%540, %539) %542 : Float(1:74221, 161:461, 461:1) = onnx::Log(%541) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model.py:444:0 %543 : Float(1:461, 1:461, 461:1) = onnx::ReduceMean[axes=[1], keepdims=1](%542) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model.py:447:0 %544 : Float(1:621, 1:621, 621:1) = onnx::Pad[mode="reflect", pads=[0, 0, 80, 0, 0, 80]](%543) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3558:0 %545 : Float(1:461, 1:461, 461:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[161], pads=[0, 0], strides=[1]](%544, %audio_normalize.filter_) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model.py:449:0 %546 : Float(1:1, 1:1, 1:1) = onnx::ReduceMean[axes=[-1], keepdims=1](%545) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model.py:450:0 %547 : Float(1:1, 1:1, 1:1) = onnx::Neg(%546) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model.py:451:0 %548 : Float(1:74221, 161:461, 461:1) = onnx::Add(%542, %547) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model.py:451:0 %549 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[7], pads=[3, 3], strides=[2]](%548, %encoder.0.layers.0.0.weight, %encoder.0.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %550 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%549) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %551 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%550, %encoder.0.layers.4.0.weight, %encoder.0.layers.4.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %552 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%551) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %553 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%552, %encoder.1.layers.0.0.weight, %encoder.1.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %554 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%553) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %555 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%554) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %556 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%555) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %557 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%556, %encoder.1.layers.4._se_reduce.weight, %encoder.1.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %558 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%557) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %559 : Float(1:102, 102:1, 1:1) = onnx::Mul(%557, %558) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %560 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%559, %encoder.1.layers.4._se_expand.weight, %encoder.1.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %561 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%560) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %562 : Float(1:118272, 512:231, 231:1) = onnx::Mul(%561, %554) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %563 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%562, %encoder.1.layers.5.0.weight, %encoder.1.layers.5.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %564 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%563) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %565 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%564, %encoder.1.layers.9.0.weight, %encoder.1.layers.9.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %566 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%565) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %567 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%566) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %568 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%567) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %569 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%568, %encoder.1.layers.13._se_reduce.weight, %encoder.1.layers.13._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %570 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%569) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %571 : Float(1:102, 102:1, 1:1) = onnx::Mul(%569, %570) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %572 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%571, %encoder.1.layers.13._se_expand.weight, %encoder.1.layers.13._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %573 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%572) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %574 : Float(1:118272, 512:231, 231:1) = onnx::Mul(%573, %566) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %575 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%574, %encoder.1.layers.14.0.weight, %encoder.1.layers.14.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %576 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%575) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %577 : Float(1:118272, 512:231, 231:1) = onnx::Add(%576, %552) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %578 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%577, %encoder.2.layers.0.0.weight, %encoder.2.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %579 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%578) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %580 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%579) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %581 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%580) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %582 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%581, %encoder.2.layers.4._se_reduce.weight, %encoder.2.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %583 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%582) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %584 : Float(1:102, 102:1, 1:1) = onnx::Mul(%582, %583) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %585 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%584, %encoder.2.layers.4._se_expand.weight, %encoder.2.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %586 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%585) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %587 : Float(1:118272, 512:231, 231:1) = onnx::Mul(%586, %579) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %588 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%587, %encoder.2.layers.5.0.weight, %encoder.2.layers.5.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %589 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%588) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %590 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%589, %encoder.2.layers.9.0.weight, %encoder.2.layers.9.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %591 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%590) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %592 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%591) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %593 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%592) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %594 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%593, %encoder.2.layers.13._se_reduce.weight, %encoder.2.layers.13._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %595 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%594) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %596 : Float(1:102, 102:1, 1:1) = onnx::Mul(%594, %595) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %597 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%596, %encoder.2.layers.13._se_expand.weight, %encoder.2.layers.13._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %598 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%597) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %599 : Float(1:118272, 512:231, 231:1) = onnx::Mul(%598, %591) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %600 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%599, %encoder.2.layers.14.0.weight, %encoder.2.layers.14.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %601 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%600) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %602 : Float(1:118272, 512:231, 231:1) = onnx::Add(%601, %577) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %603 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%602, %encoder.3.layers.0.0.weight, %encoder.3.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %604 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%603) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %605 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%604) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %606 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%605) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %607 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%606, %encoder.3.layers.4._se_reduce.weight, %encoder.3.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %608 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%607) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %609 : Float(1:102, 102:1, 1:1) = onnx::Mul(%607, %608) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %610 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%609, %encoder.3.layers.4._se_expand.weight, %encoder.3.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %611 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%610) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %612 : Float(1:118272, 512:231, 231:1) = onnx::Mul(%611, %604) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %613 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%612, %encoder.3.layers.5.0.weight, %encoder.3.layers.5.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %614 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%613) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %615 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%614, %encoder.3.layers.9.0.weight, %encoder.3.layers.9.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %616 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%615) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %617 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%616) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %618 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%617) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %619 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%618, %encoder.3.layers.13._se_reduce.weight, %encoder.3.layers.13._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %620 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%619) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %621 : Float(1:102, 102:1, 1:1) = onnx::Mul(%619, %620) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %622 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%621, %encoder.3.layers.13._se_expand.weight, %encoder.3.layers.13._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %623 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%622) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %624 : Float(1:118272, 512:231, 231:1) = onnx::Mul(%623, %616) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %625 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%624, %encoder.3.layers.14.0.weight, %encoder.3.layers.14.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %626 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%625) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %627 : Float(1:118272, 512:231, 231:1) = onnx::Add(%626, %602) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %628 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%627, %encoder.4.layers.0.0.weight, %encoder.4.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %629 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%628) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %630 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%629) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %631 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%630) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %632 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%631, %encoder.4.layers.4._se_reduce.weight, %encoder.4.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %633 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%632) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %634 : Float(1:102, 102:1, 1:1) = onnx::Mul(%632, %633) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %635 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%634, %encoder.4.layers.4._se_expand.weight, %encoder.4.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %636 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%635) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %637 : Float(1:118272, 512:231, 231:1) = onnx::Mul(%636, %629) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %638 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%637, %encoder.4.layers.5.0.weight, %encoder.4.layers.5.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %639 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%638) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %640 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%639, %encoder.4.layers.9.0.weight, %encoder.4.layers.9.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %641 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%640) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %642 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%641) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %643 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%642) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %644 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%643, %encoder.4.layers.13._se_reduce.weight, %encoder.4.layers.13._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %645 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%644) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %646 : Float(1:102, 102:1, 1:1) = onnx::Mul(%644, %645) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %647 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%646, %encoder.4.layers.13._se_expand.weight, %encoder.4.layers.13._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %648 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%647) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %649 : Float(1:118272, 512:231, 231:1) = onnx::Mul(%648, %641) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %650 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%649, %encoder.4.layers.14.0.weight, %encoder.4.layers.14.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %651 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%650) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %652 : Float(1:118272, 512:231, 231:1) = onnx::Add(%651, %627) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %653 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%652, %encoder.5.layers.0.0.weight, %encoder.5.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %654 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%653) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %655 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%654) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %656 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%655) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %657 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%656, %encoder.5.layers.4._se_reduce.weight, %encoder.5.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %658 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%657) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %659 : Float(1:102, 102:1, 1:1) = onnx::Mul(%657, %658) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %660 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%659, %encoder.5.layers.4._se_expand.weight, %encoder.5.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %661 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%660) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %662 : Float(1:118272, 512:231, 231:1) = onnx::Mul(%661, %654) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %663 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%662, %encoder.5.layers.5.0.weight, %encoder.5.layers.5.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %664 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%663) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %665 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%664, %encoder.5.layers.9.0.weight, %encoder.5.layers.9.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %666 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%665) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %667 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%666) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %668 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%667) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %669 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%668, %encoder.5.layers.13._se_reduce.weight, %encoder.5.layers.13._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %670 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%669) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %671 : Float(1:102, 102:1, 1:1) = onnx::Mul(%669, %670) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %672 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%671, %encoder.5.layers.13._se_expand.weight, %encoder.5.layers.13._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %673 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%672) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %674 : Float(1:118272, 512:231, 231:1) = onnx::Mul(%673, %666) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %675 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%674, %encoder.5.layers.14.0.weight, %encoder.5.layers.14.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %676 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%675) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %677 : Float(1:118272, 512:231, 231:1) = onnx::Add(%676, %652) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %678 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%677, %encoder.6.layers.0.0.weight, %encoder.6.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %679 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%678) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %680 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%679) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %681 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%680) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %682 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%681, %encoder.6.layers.4._se_reduce.weight, %encoder.6.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %683 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%682) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %684 : Float(1:102, 102:1, 1:1) = onnx::Mul(%682, %683) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %685 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%684, %encoder.6.layers.4._se_expand.weight, %encoder.6.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %686 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%685) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %687 : Float(1:118272, 512:231, 231:1) = onnx::Mul(%686, %679) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %688 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%687, %encoder.6.layers.5.0.weight, %encoder.6.layers.5.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %689 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%688) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %690 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%689, %encoder.6.layers.9.0.weight, %encoder.6.layers.9.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %691 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%690) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %692 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%691) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %693 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%692) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %694 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%693, %encoder.6.layers.13._se_reduce.weight, %encoder.6.layers.13._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %695 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%694) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %696 : Float(1:102, 102:1, 1:1) = onnx::Mul(%694, %695) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %697 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%696, %encoder.6.layers.13._se_expand.weight, %encoder.6.layers.13._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %698 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%697) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %699 : Float(1:118272, 512:231, 231:1) = onnx::Mul(%698, %691) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %700 : Float(1:118272, 512:231, 231:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%699, %encoder.6.layers.14.0.weight, %encoder.6.layers.14.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %701 : Float(1:118272, 512:231, 231:1) = onnx::Relu(%700) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %702 : Float(1:118272, 512:231, 231:1) = onnx::Add(%701, %677) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %703 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[2]](%702, %encoder.7.layers.0.0.weight, %encoder.7.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %704 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%703) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %705 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%704, %encoder.7.layers.4.0.weight, %encoder.7.layers.4.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %706 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%705) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %707 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%706, %encoder.8.layers.0.0.weight, %encoder.8.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %708 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%707) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %709 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%708) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %710 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%709) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %711 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%710, %encoder.8.layers.4._se_reduce.weight, %encoder.8.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %712 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%711) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %713 : Float(1:102, 102:1, 1:1) = onnx::Mul(%711, %712) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %714 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%713, %encoder.8.layers.4._se_expand.weight, %encoder.8.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %715 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%714) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %716 : Float(1:59392, 512:116, 116:1) = onnx::Mul(%715, %708) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %717 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%716, %encoder.8.layers.5.0.weight, %encoder.8.layers.5.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %718 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%717) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %719 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%718, %encoder.8.layers.9.0.weight, %encoder.8.layers.9.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %720 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%719) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %721 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%720) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %722 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%721) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %723 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%722, %encoder.8.layers.13._se_reduce.weight, %encoder.8.layers.13._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %724 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%723) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %725 : Float(1:102, 102:1, 1:1) = onnx::Mul(%723, %724) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %726 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%725, %encoder.8.layers.13._se_expand.weight, %encoder.8.layers.13._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %727 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%726) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %728 : Float(1:59392, 512:116, 116:1) = onnx::Mul(%727, %720) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %729 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%728, %encoder.8.layers.14.0.weight, %encoder.8.layers.14.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %730 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%729) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %731 : Float(1:59392, 512:116, 116:1) = onnx::Add(%730, %706) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %732 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%731, %encoder.9.layers.0.0.weight, %encoder.9.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %733 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%732) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %734 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%733) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %735 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%734) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %736 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%735, %encoder.9.layers.4._se_reduce.weight, %encoder.9.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %737 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%736) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %738 : Float(1:102, 102:1, 1:1) = onnx::Mul(%736, %737) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %739 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%738, %encoder.9.layers.4._se_expand.weight, %encoder.9.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %740 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%739) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %741 : Float(1:59392, 512:116, 116:1) = onnx::Mul(%740, %733) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %742 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%741, %encoder.9.layers.5.0.weight, %encoder.9.layers.5.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %743 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%742) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %744 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%743, %encoder.9.layers.9.0.weight, %encoder.9.layers.9.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %745 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%744) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %746 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%745) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %747 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%746) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %748 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%747, %encoder.9.layers.13._se_reduce.weight, %encoder.9.layers.13._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %749 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%748) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %750 : Float(1:102, 102:1, 1:1) = onnx::Mul(%748, %749) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %751 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%750, %encoder.9.layers.13._se_expand.weight, %encoder.9.layers.13._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %752 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%751) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %753 : Float(1:59392, 512:116, 116:1) = onnx::Mul(%752, %745) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %754 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%753, %encoder.9.layers.14.0.weight, %encoder.9.layers.14.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %755 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%754) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %756 : Float(1:59392, 512:116, 116:1) = onnx::Add(%755, %731) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %757 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%756, %encoder.10.layers.0.0.weight, %encoder.10.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %758 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%757) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %759 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%758) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %760 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%759) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %761 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%760, %encoder.10.layers.4._se_reduce.weight, %encoder.10.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %762 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%761) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %763 : Float(1:102, 102:1, 1:1) = onnx::Mul(%761, %762) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %764 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%763, %encoder.10.layers.4._se_expand.weight, %encoder.10.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %765 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%764) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %766 : Float(1:59392, 512:116, 116:1) = onnx::Mul(%765, %758) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %767 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%766, %encoder.10.layers.5.0.weight, %encoder.10.layers.5.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %768 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%767) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %769 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%768, %encoder.10.layers.9.0.weight, %encoder.10.layers.9.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %770 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%769) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %771 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%770) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %772 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%771) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %773 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%772, %encoder.10.layers.13._se_reduce.weight, %encoder.10.layers.13._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %774 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%773) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %775 : Float(1:102, 102:1, 1:1) = onnx::Mul(%773, %774) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %776 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%775, %encoder.10.layers.13._se_expand.weight, %encoder.10.layers.13._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %777 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%776) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %778 : Float(1:59392, 512:116, 116:1) = onnx::Mul(%777, %770) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %779 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%778, %encoder.10.layers.14.0.weight, %encoder.10.layers.14.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %780 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%779) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %781 : Float(1:59392, 512:116, 116:1) = onnx::Add(%780, %756) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %782 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%781, %encoder.11.layers.0.0.weight, %encoder.11.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %783 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%782) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %784 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%783) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %785 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%784) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %786 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%785, %encoder.11.layers.4._se_reduce.weight, %encoder.11.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %787 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%786) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %788 : Float(1:102, 102:1, 1:1) = onnx::Mul(%786, %787) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %789 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%788, %encoder.11.layers.4._se_expand.weight, %encoder.11.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %790 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%789) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %791 : Float(1:59392, 512:116, 116:1) = onnx::Mul(%790, %783) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %792 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%791, %encoder.11.layers.5.0.weight, %encoder.11.layers.5.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %793 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%792) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %794 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%793, %encoder.11.layers.9.0.weight, %encoder.11.layers.9.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %795 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%794) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %796 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%795) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %797 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%796) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %798 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%797, %encoder.11.layers.13._se_reduce.weight, %encoder.11.layers.13._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %799 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%798) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %800 : Float(1:102, 102:1, 1:1) = onnx::Mul(%798, %799) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %801 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%800, %encoder.11.layers.13._se_expand.weight, %encoder.11.layers.13._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %802 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%801) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %803 : Float(1:59392, 512:116, 116:1) = onnx::Mul(%802, %795) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %804 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%803, %encoder.11.layers.14.0.weight, %encoder.11.layers.14.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %805 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%804) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %806 : Float(1:59392, 512:116, 116:1) = onnx::Add(%805, %781) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %807 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%806, %encoder.12.layers.0.0.weight, %encoder.12.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %808 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%807) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %809 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%808) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %810 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%809) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %811 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%810, %encoder.12.layers.4._se_reduce.weight, %encoder.12.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %812 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%811) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %813 : Float(1:102, 102:1, 1:1) = onnx::Mul(%811, %812) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %814 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%813, %encoder.12.layers.4._se_expand.weight, %encoder.12.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %815 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%814) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %816 : Float(1:59392, 512:116, 116:1) = onnx::Mul(%815, %808) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %817 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%816, %encoder.12.layers.5.0.weight, %encoder.12.layers.5.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %818 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%817) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %819 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%818, %encoder.12.layers.9.0.weight, %encoder.12.layers.9.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %820 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%819) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %821 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%820) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %822 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%821) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %823 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%822, %encoder.12.layers.13._se_reduce.weight, %encoder.12.layers.13._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %824 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%823) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %825 : Float(1:102, 102:1, 1:1) = onnx::Mul(%823, %824) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %826 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%825, %encoder.12.layers.13._se_expand.weight, %encoder.12.layers.13._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %827 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%826) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %828 : Float(1:59392, 512:116, 116:1) = onnx::Mul(%827, %820) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %829 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%828, %encoder.12.layers.14.0.weight, %encoder.12.layers.14.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %830 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%829) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %831 : Float(1:59392, 512:116, 116:1) = onnx::Add(%830, %806) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %832 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%831, %encoder.13.layers.0.0.weight, %encoder.13.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %833 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%832) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %834 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%833) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %835 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%834) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %836 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%835, %encoder.13.layers.4._se_reduce.weight, %encoder.13.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %837 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%836) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %838 : Float(1:102, 102:1, 1:1) = onnx::Mul(%836, %837) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %839 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%838, %encoder.13.layers.4._se_expand.weight, %encoder.13.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %840 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%839) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %841 : Float(1:59392, 512:116, 116:1) = onnx::Mul(%840, %833) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %842 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%841, %encoder.13.layers.5.0.weight, %encoder.13.layers.5.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %843 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%842) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %844 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%843, %encoder.13.layers.9.0.weight, %encoder.13.layers.9.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %845 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%844) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %846 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%845) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %847 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%846) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %848 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%847, %encoder.13.layers.13._se_reduce.weight, %encoder.13.layers.13._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %849 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%848) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %850 : Float(1:102, 102:1, 1:1) = onnx::Mul(%848, %849) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %851 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%850, %encoder.13.layers.13._se_expand.weight, %encoder.13.layers.13._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %852 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%851) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %853 : Float(1:59392, 512:116, 116:1) = onnx::Mul(%852, %845) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %854 : Float(1:59392, 512:116, 116:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%853, %encoder.13.layers.14.0.weight, %encoder.13.layers.14.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %855 : Float(1:59392, 512:116, 116:1) = onnx::Relu(%854) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %856 : Float(1:59392, 512:116, 116:1) = onnx::Add(%855, %831) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %857 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[2]](%856, %encoder.14.layers.0.0.weight, %encoder.14.layers.0.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %858 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%857) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %859 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%858, %encoder.14.layers.4.0.weight, %encoder.14.layers.4.0.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %860 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%859) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %861 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%860, %encoder.15.layers.0.weight) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %862 : Float(1:29696, 512:58, 58:1) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%861, %encoder.15.layers.1.weight, %encoder.15.layers.1.bias, %encoder.15.layers.1.running_mean, %encoder.15.layers.1.running_var) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2016:0 %863 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%862) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %864 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%863) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %865 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%864) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %866 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%865, %encoder.15.layers.4._se_reduce.weight, %encoder.15.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %867 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%866) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %868 : Float(1:102, 102:1, 1:1) = onnx::Mul(%866, %867) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %869 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%868, %encoder.15.layers.4._se_expand.weight, %encoder.15.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %870 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%869) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %871 : Float(1:29696, 512:58, 58:1) = onnx::Mul(%870, %863) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %872 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%871, %encoder.15.layers.5.weight, %encoder.15.layers.5.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %873 : Float(1:29696, 512:58, 58:1) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%872, %encoder.15.layers.6.weight, %encoder.15.layers.6.bias, %encoder.15.layers.6.running_mean, %encoder.15.layers.6.running_var) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2016:0 %874 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%873) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %875 : Float(1:29696, 512:58, 58:1) = onnx::Add(%874, %860) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %876 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%875, %encoder.16.layers.0.weight) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %877 : Float(1:29696, 512:58, 58:1) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%876, %encoder.16.layers.1.weight, %encoder.16.layers.1.bias, %encoder.16.layers.1.running_mean, %encoder.16.layers.1.running_var) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2016:0 %878 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%877) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %879 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%878) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %880 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%879) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %881 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%880, %encoder.16.layers.4._se_reduce.weight, %encoder.16.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %882 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%881) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %883 : Float(1:102, 102:1, 1:1) = onnx::Mul(%881, %882) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %884 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%883, %encoder.16.layers.4._se_expand.weight, %encoder.16.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %885 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%884) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %886 : Float(1:29696, 512:58, 58:1) = onnx::Mul(%885, %878) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %887 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%886, %encoder.16.layers.5.weight, %encoder.16.layers.5.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %888 : Float(1:29696, 512:58, 58:1) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%887, %encoder.16.layers.6.weight, %encoder.16.layers.6.bias, %encoder.16.layers.6.running_mean, %encoder.16.layers.6.running_var) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2016:0 %889 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%888) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %890 : Float(1:29696, 512:58, 58:1) = onnx::Add(%889, %875) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %891 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%890, %encoder.17.layers.0.weight) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %892 : Float(1:29696, 512:58, 58:1) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%891, %encoder.17.layers.1.weight, %encoder.17.layers.1.bias, %encoder.17.layers.1.running_mean, %encoder.17.layers.1.running_var) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2016:0 %893 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%892) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %894 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%893) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %895 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%894) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %896 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%895, %encoder.17.layers.4._se_reduce.weight, %encoder.17.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %897 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%896) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %898 : Float(1:102, 102:1, 1:1) = onnx::Mul(%896, %897) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %899 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%898, %encoder.17.layers.4._se_expand.weight, %encoder.17.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %900 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%899) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %901 : Float(1:29696, 512:58, 58:1) = onnx::Mul(%900, %893) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %902 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%901, %encoder.17.layers.5.weight, %encoder.17.layers.5.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %903 : Float(1:29696, 512:58, 58:1) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%902, %encoder.17.layers.6.weight, %encoder.17.layers.6.bias, %encoder.17.layers.6.running_mean, %encoder.17.layers.6.running_var) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2016:0 %904 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%903) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %905 : Float(1:29696, 512:58, 58:1) = onnx::Add(%904, %890) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %906 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%905, %encoder.18.layers.0.weight) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %907 : Float(1:29696, 512:58, 58:1) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%906, %encoder.18.layers.1.weight, %encoder.18.layers.1.bias, %encoder.18.layers.1.running_mean, %encoder.18.layers.1.running_var) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2016:0 %908 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%907) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %909 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%908) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %910 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%909) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %911 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%910, %encoder.18.layers.4._se_reduce.weight, %encoder.18.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %912 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%911) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %913 : Float(1:102, 102:1, 1:1) = onnx::Mul(%911, %912) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %914 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%913, %encoder.18.layers.4._se_expand.weight, %encoder.18.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %915 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%914) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %916 : Float(1:29696, 512:58, 58:1) = onnx::Mul(%915, %908) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %917 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%916, %encoder.18.layers.5.weight, %encoder.18.layers.5.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %918 : Float(1:29696, 512:58, 58:1) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%917, %encoder.18.layers.6.weight, %encoder.18.layers.6.bias, %encoder.18.layers.6.running_mean, %encoder.18.layers.6.running_var) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2016:0 %919 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%918) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %920 : Float(1:29696, 512:58, 58:1) = onnx::Add(%919, %905) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %921 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%920, %encoder.19.layers.0.weight) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %922 : Float(1:29696, 512:58, 58:1) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%921, %encoder.19.layers.1.weight, %encoder.19.layers.1.bias, %encoder.19.layers.1.running_mean, %encoder.19.layers.1.running_var) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2016:0 %923 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%922) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %924 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%923) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %925 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%924) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %926 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%925, %encoder.19.layers.4._se_reduce.weight, %encoder.19.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %927 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%926) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %928 : Float(1:102, 102:1, 1:1) = onnx::Mul(%926, %927) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %929 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%928, %encoder.19.layers.4._se_expand.weight, %encoder.19.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %930 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%929) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %931 : Float(1:29696, 512:58, 58:1) = onnx::Mul(%930, %923) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %932 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%931, %encoder.19.layers.5.weight, %encoder.19.layers.5.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %933 : Float(1:29696, 512:58, 58:1) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%932, %encoder.19.layers.6.weight, %encoder.19.layers.6.bias, %encoder.19.layers.6.running_mean, %encoder.19.layers.6.running_var) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2016:0 %934 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%933) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %935 : Float(1:29696, 512:58, 58:1) = onnx::Add(%934, %920) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %936 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=8, kernel_shape=[7], pads=[3, 3], strides=[1]](%935, %encoder.20.layers.0.weight) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %937 : Float(1:29696, 512:58, 58:1) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%936, %encoder.20.layers.1.weight, %encoder.20.layers.1.bias, %encoder.20.layers.1.running_mean, %encoder.20.layers.1.running_var) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2016:0 %938 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%937) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %939 : Float(1:512, 512:1) = onnx::ReduceMean[axes=[2], keepdims=0](%938) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %940 : Float(1:512, 512:1, 1:1) = onnx::Unsqueeze[axes=[2]](%939) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:69:0 %941 : Float(1:102, 102:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%940, %encoder.20.layers.4._se_reduce.weight, %encoder.20.layers.4._se_reduce.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %942 : Float(1:102, 102:1, 1:1) = onnx::Sigmoid(%941) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %943 : Float(1:102, 102:1, 1:1) = onnx::Mul(%941, %942) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model_blocks.py:28:0 %944 : Float(1:512, 512:1, 1:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%943, %encoder.20.layers.4._se_expand.weight, %encoder.20.layers.4._se_expand.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %945 : Float(1:512, 512:1, 1:1) = onnx::Sigmoid(%944) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py:299:0 %946 : Float(1:29696, 512:58, 58:1) = onnx::Mul(%945, %938) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:59:0 %947 : Float(1:29696, 512:58, 58:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%946, %encoder.20.layers.5.weight, %encoder.20.layers.5.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %948 : Float(1:29696, 512:58, 58:1) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%947, %encoder.20.layers.6.weight, %encoder.20.layers.6.bias, %encoder.20.layers.6.running_mean, %encoder.20.layers.6.running_var) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2016:0 %949 : Float(1:29696, 512:58, 58:1) = onnx::Relu(%948) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %950 : Float(1:29696, 512:58, 58:1) = onnx::Add(%949, %935) # /opt/conda/lib/python3.7/site-packages/torch/nn/quantized/modules/functional_modules.py:45:0 %951 : Float(58:512, 1:512, 512:1) = onnx::Transpose[perm=[2, 0, 1]](%950) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model.py:668:0 %952 : Tensor = onnx::Shape(%951) %953 : Tensor = onnx::Constant[value={0}]() %954 : Long() = onnx::Gather[axis=0](%952, %953) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %955 : Tensor = onnx::Shape(%951) %956 : Tensor = onnx::Constant[value={1}]() %957 : Long() = onnx::Gather[axis=0](%955, %956) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %958 : Tensor = onnx::Shape(%951) %959 : Tensor = onnx::Constant[value={2}]() %960 : Long() = onnx::Gather[axis=0](%958, %959) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %961 : Long() = onnx::Constant[value={2}]() %962 : LongTensor = onnx::Div(%960, %961) %963 : Tensor = onnx::Cast[to=7](%962) %964 : Long() = onnx::Cast[to=7](%963) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %966 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%951, %2463) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %967 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%966, %decoder.layers.0.self_attn.in_proj_bias) %968 : Float(58:1536, 1:1536, 512:1), %969 : Float(58:512, 1:512, 512:1), %970 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%967) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %971 : Float() = onnx::Constant[value={0.0625}]() %972 : Float(58:512, 1:512, 512:1) = onnx::Mul(%968, %971) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %973 : Long() = onnx::Constant[value={2}]() %974 : Long() = onnx::Mul(%957, %973) %975 : Tensor = onnx::Unsqueeze[axes=[0]](%954) %976 : Tensor = onnx::Unsqueeze[axes=[0]](%974) %977 : Tensor = onnx::Unsqueeze[axes=[0]](%964) %978 : Tensor = onnx::Concat[axis=0](%975, %976, %977) %979 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%972, %978) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %980 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%979) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %981 : Long() = onnx::Constant[value={2}]() %982 : Long() = onnx::Mul(%957, %981) %985 : Tensor = onnx::Unsqueeze[axes=[0]](%982) %986 : Tensor = onnx::Unsqueeze[axes=[0]](%964) %987 : Tensor = onnx::Concat[axis=0](%2464, %985, %986) %988 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%969, %987) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %989 : Long() = onnx::Constant[value={2}]() %990 : Long() = onnx::Mul(%957, %989) %993 : Tensor = onnx::Unsqueeze[axes=[0]](%990) %994 : Tensor = onnx::Unsqueeze[axes=[0]](%964) %995 : Tensor = onnx::Concat[axis=0](%2465, %993, %994) %996 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%970, %995) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %997 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%996) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %998 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%988) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %999 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%980, %998) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1000 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%999) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1001 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%1000, %997) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %1002 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1001) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1003 : Tensor = onnx::Unsqueeze[axes=[0]](%954) %1004 : Tensor = onnx::Unsqueeze[axes=[0]](%957) %1005 : Tensor = onnx::Unsqueeze[axes=[0]](%960) %1006 : Tensor = onnx::Concat[axis=0](%1003, %1004, %1005) %1007 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%1002, %1006) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1009 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1007, %2466) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1010 : Float(58:512, 1:512, 512:1) = onnx::Add(%1009, %decoder.layers.0.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1011 : Float(58:512, 1:512, 512:1) = onnx::Add(%951, %1010) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %1013 : Tensor = onnx::ReduceMean[axes=[-1]](%1011) %1014 : FloatTensor = onnx::Sub(%1011, %1013) %1015 : Tensor = onnx::Cast[to=1](%1014) %1017 : Tensor = onnx::Pow(%1015, %2467) %1018 : Tensor = onnx::ReduceMean[axes=[-1]](%1017) %1019 : Float() = onnx::Constant[value={1e-05}]() %1020 : FloatTensor = onnx::Add(%1018, %1019) %1021 : Tensor = onnx::Sqrt(%1020) %1022 : FloatTensor = onnx::Div(%1014, %1021) %1023 : FloatTensor = onnx::Mul(%1022, %decoder.layers.0.norm1.weight) %1024 : Float(58:512, 1:512, 512:1) = onnx::Add(%1023, %decoder.layers.0.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1026 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1024, %2468) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1027 : Float(58:512, 1:512, 512:1) = onnx::Add(%1026, %decoder.layers.0.linear1.bias) %1028 : Float(58:512, 1:512, 512:1) = onnx::Relu(%1027) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1030 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1028, %2469) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1031 : Float(58:512, 1:512, 512:1) = onnx::Add(%1030, %decoder.layers.0.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1032 : Float(58:512, 1:512, 512:1) = onnx::Add(%1024, %1031) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %1034 : Tensor = onnx::ReduceMean[axes=[-1]](%1032) %1035 : FloatTensor = onnx::Sub(%1032, %1034) %1036 : Tensor = onnx::Cast[to=1](%1035) %1038 : Tensor = onnx::Pow(%1036, %2470) %1039 : Tensor = onnx::ReduceMean[axes=[-1]](%1038) %1040 : Float() = onnx::Constant[value={1e-05}]() %1041 : FloatTensor = onnx::Add(%1039, %1040) %1042 : Tensor = onnx::Sqrt(%1041) %1043 : FloatTensor = onnx::Div(%1035, %1042) %1044 : FloatTensor = onnx::Mul(%1043, %decoder.layers.0.norm2.weight) %1045 : Float(58:512, 1:512, 512:1) = onnx::Add(%1044, %decoder.layers.0.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1046 : Tensor = onnx::Shape(%1045) %1047 : Tensor = onnx::Constant[value={0}]() %1048 : Long() = onnx::Gather[axis=0](%1046, %1047) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1049 : Tensor = onnx::Shape(%1045) %1050 : Tensor = onnx::Constant[value={1}]() %1051 : Long() = onnx::Gather[axis=0](%1049, %1050) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1052 : Tensor = onnx::Shape(%1045) %1053 : Tensor = onnx::Constant[value={2}]() %1054 : Long() = onnx::Gather[axis=0](%1052, %1053) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1055 : Long() = onnx::Constant[value={2}]() %1056 : LongTensor = onnx::Div(%1054, %1055) %1057 : Tensor = onnx::Cast[to=7](%1056) %1058 : Long() = onnx::Cast[to=7](%1057) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %1060 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%1045, %2471) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1061 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%1060, %decoder.layers.1.self_attn.in_proj_bias) %1062 : Float(58:1536, 1:1536, 512:1), %1063 : Float(58:512, 1:512, 512:1), %1064 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%1061) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1065 : Float() = onnx::Constant[value={0.0625}]() %1066 : Float(58:512, 1:512, 512:1) = onnx::Mul(%1062, %1065) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1067 : Long() = onnx::Constant[value={2}]() %1068 : Long() = onnx::Mul(%1051, %1067) %1069 : Tensor = onnx::Unsqueeze[axes=[0]](%1048) %1070 : Tensor = onnx::Unsqueeze[axes=[0]](%1068) %1071 : Tensor = onnx::Unsqueeze[axes=[0]](%1058) %1072 : Tensor = onnx::Concat[axis=0](%1069, %1070, %1071) %1073 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1066, %1072) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1074 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1073) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1075 : Long() = onnx::Constant[value={2}]() %1076 : Long() = onnx::Mul(%1051, %1075) %1079 : Tensor = onnx::Unsqueeze[axes=[0]](%1076) %1080 : Tensor = onnx::Unsqueeze[axes=[0]](%1058) %1081 : Tensor = onnx::Concat[axis=0](%2472, %1079, %1080) %1082 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1063, %1081) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %1083 : Long() = onnx::Constant[value={2}]() %1084 : Long() = onnx::Mul(%1051, %1083) %1087 : Tensor = onnx::Unsqueeze[axes=[0]](%1084) %1088 : Tensor = onnx::Unsqueeze[axes=[0]](%1058) %1089 : Tensor = onnx::Concat[axis=0](%2473, %1087, %1088) %1090 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1064, %1089) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1091 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1090) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1092 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%1082) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1093 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%1074, %1092) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1094 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%1093) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1095 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%1094, %1091) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %1096 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1095) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1097 : Tensor = onnx::Unsqueeze[axes=[0]](%1048) %1098 : Tensor = onnx::Unsqueeze[axes=[0]](%1051) %1099 : Tensor = onnx::Unsqueeze[axes=[0]](%1054) %1100 : Tensor = onnx::Concat[axis=0](%1097, %1098, %1099) %1101 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%1096, %1100) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1103 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1101, %2474) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1104 : Float(58:512, 1:512, 512:1) = onnx::Add(%1103, %decoder.layers.1.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1105 : Float(58:512, 1:512, 512:1) = onnx::Add(%1045, %1104) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %1107 : Tensor = onnx::ReduceMean[axes=[-1]](%1105) %1108 : FloatTensor = onnx::Sub(%1105, %1107) %1109 : Tensor = onnx::Cast[to=1](%1108) %1111 : Tensor = onnx::Pow(%1109, %2475) %1112 : Tensor = onnx::ReduceMean[axes=[-1]](%1111) %1113 : Float() = onnx::Constant[value={1e-05}]() %1114 : FloatTensor = onnx::Add(%1112, %1113) %1115 : Tensor = onnx::Sqrt(%1114) %1116 : FloatTensor = onnx::Div(%1108, %1115) %1117 : FloatTensor = onnx::Mul(%1116, %decoder.layers.1.norm1.weight) %1118 : Float(58:512, 1:512, 512:1) = onnx::Add(%1117, %decoder.layers.1.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1120 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1118, %2476) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1121 : Float(58:512, 1:512, 512:1) = onnx::Add(%1120, %decoder.layers.1.linear1.bias) %1122 : Float(58:512, 1:512, 512:1) = onnx::Relu(%1121) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1124 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1122, %2477) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1125 : Float(58:512, 1:512, 512:1) = onnx::Add(%1124, %decoder.layers.1.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1126 : Float(58:512, 1:512, 512:1) = onnx::Add(%1118, %1125) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %1128 : Tensor = onnx::ReduceMean[axes=[-1]](%1126) %1129 : FloatTensor = onnx::Sub(%1126, %1128) %1130 : Tensor = onnx::Cast[to=1](%1129) %1132 : Tensor = onnx::Pow(%1130, %2478) %1133 : Tensor = onnx::ReduceMean[axes=[-1]](%1132) %1134 : Float() = onnx::Constant[value={1e-05}]() %1135 : FloatTensor = onnx::Add(%1133, %1134) %1136 : Tensor = onnx::Sqrt(%1135) %1137 : FloatTensor = onnx::Div(%1129, %1136) %1138 : FloatTensor = onnx::Mul(%1137, %decoder.layers.1.norm2.weight) %1139 : Float(58:512, 1:512, 512:1) = onnx::Add(%1138, %decoder.layers.1.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1140 : Tensor = onnx::Shape(%1139) %1141 : Tensor = onnx::Constant[value={0}]() %1142 : Long() = onnx::Gather[axis=0](%1140, %1141) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1143 : Tensor = onnx::Shape(%1139) %1144 : Tensor = onnx::Constant[value={1}]() %1145 : Long() = onnx::Gather[axis=0](%1143, %1144) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1146 : Tensor = onnx::Shape(%1139) %1147 : Tensor = onnx::Constant[value={2}]() %1148 : Long() = onnx::Gather[axis=0](%1146, %1147) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1149 : Long() = onnx::Constant[value={2}]() %1150 : LongTensor = onnx::Div(%1148, %1149) %1151 : Tensor = onnx::Cast[to=7](%1150) %1152 : Long() = onnx::Cast[to=7](%1151) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %1154 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%1139, %2479) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1155 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%1154, %decoder.layers.2.self_attn.in_proj_bias) %1156 : Float(58:1536, 1:1536, 512:1), %1157 : Float(58:512, 1:512, 512:1), %1158 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%1155) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1159 : Float() = onnx::Constant[value={0.0625}]() %1160 : Float(58:512, 1:512, 512:1) = onnx::Mul(%1156, %1159) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1161 : Long() = onnx::Constant[value={2}]() %1162 : Long() = onnx::Mul(%1145, %1161) %1163 : Tensor = onnx::Unsqueeze[axes=[0]](%1142) %1164 : Tensor = onnx::Unsqueeze[axes=[0]](%1162) %1165 : Tensor = onnx::Unsqueeze[axes=[0]](%1152) %1166 : Tensor = onnx::Concat[axis=0](%1163, %1164, %1165) %1167 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1160, %1166) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1168 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1167) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1169 : Long() = onnx::Constant[value={2}]() %1170 : Long() = onnx::Mul(%1145, %1169) %1173 : Tensor = onnx::Unsqueeze[axes=[0]](%1170) %1174 : Tensor = onnx::Unsqueeze[axes=[0]](%1152) %1175 : Tensor = onnx::Concat[axis=0](%2480, %1173, %1174) %1176 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1157, %1175) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %1177 : Long() = onnx::Constant[value={2}]() %1178 : Long() = onnx::Mul(%1145, %1177) %1181 : Tensor = onnx::Unsqueeze[axes=[0]](%1178) %1182 : Tensor = onnx::Unsqueeze[axes=[0]](%1152) %1183 : Tensor = onnx::Concat[axis=0](%2481, %1181, %1182) %1184 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1158, %1183) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1185 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1184) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1186 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%1176) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1187 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%1168, %1186) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1188 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%1187) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1189 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%1188, %1185) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %1190 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1189) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1191 : Tensor = onnx::Unsqueeze[axes=[0]](%1142) %1192 : Tensor = onnx::Unsqueeze[axes=[0]](%1145) %1193 : Tensor = onnx::Unsqueeze[axes=[0]](%1148) %1194 : Tensor = onnx::Concat[axis=0](%1191, %1192, %1193) %1195 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%1190, %1194) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1197 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1195, %2482) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1198 : Float(58:512, 1:512, 512:1) = onnx::Add(%1197, %decoder.layers.2.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1199 : Float(58:512, 1:512, 512:1) = onnx::Add(%1139, %1198) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %1201 : Tensor = onnx::ReduceMean[axes=[-1]](%1199) %1202 : FloatTensor = onnx::Sub(%1199, %1201) %1203 : Tensor = onnx::Cast[to=1](%1202) %1205 : Tensor = onnx::Pow(%1203, %2483) %1206 : Tensor = onnx::ReduceMean[axes=[-1]](%1205) %1207 : Float() = onnx::Constant[value={1e-05}]() %1208 : FloatTensor = onnx::Add(%1206, %1207) %1209 : Tensor = onnx::Sqrt(%1208) %1210 : FloatTensor = onnx::Div(%1202, %1209) %1211 : FloatTensor = onnx::Mul(%1210, %decoder.layers.2.norm1.weight) %1212 : Float(58:512, 1:512, 512:1) = onnx::Add(%1211, %decoder.layers.2.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1214 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1212, %2484) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1215 : Float(58:512, 1:512, 512:1) = onnx::Add(%1214, %decoder.layers.2.linear1.bias) %1216 : Float(58:512, 1:512, 512:1) = onnx::Relu(%1215) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1218 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1216, %2485) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1219 : Float(58:512, 1:512, 512:1) = onnx::Add(%1218, %decoder.layers.2.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1220 : Float(58:512, 1:512, 512:1) = onnx::Add(%1212, %1219) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %1222 : Tensor = onnx::ReduceMean[axes=[-1]](%1220) %1223 : FloatTensor = onnx::Sub(%1220, %1222) %1224 : Tensor = onnx::Cast[to=1](%1223) %1226 : Tensor = onnx::Pow(%1224, %2486) %1227 : Tensor = onnx::ReduceMean[axes=[-1]](%1226) %1228 : Float() = onnx::Constant[value={1e-05}]() %1229 : FloatTensor = onnx::Add(%1227, %1228) %1230 : Tensor = onnx::Sqrt(%1229) %1231 : FloatTensor = onnx::Div(%1223, %1230) %1232 : FloatTensor = onnx::Mul(%1231, %decoder.layers.2.norm2.weight) %1233 : Float(58:512, 1:512, 512:1) = onnx::Add(%1232, %decoder.layers.2.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1234 : Tensor = onnx::Shape(%1233) %1235 : Tensor = onnx::Constant[value={0}]() %1236 : Long() = onnx::Gather[axis=0](%1234, %1235) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1237 : Tensor = onnx::Shape(%1233) %1238 : Tensor = onnx::Constant[value={1}]() %1239 : Long() = onnx::Gather[axis=0](%1237, %1238) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1240 : Tensor = onnx::Shape(%1233) %1241 : Tensor = onnx::Constant[value={2}]() %1242 : Long() = onnx::Gather[axis=0](%1240, %1241) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1243 : Long() = onnx::Constant[value={2}]() %1244 : LongTensor = onnx::Div(%1242, %1243) %1245 : Tensor = onnx::Cast[to=7](%1244) %1246 : Long() = onnx::Cast[to=7](%1245) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %1248 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%1233, %2487) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1249 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%1248, %decoder.layers.3.self_attn.in_proj_bias) %1250 : Float(58:1536, 1:1536, 512:1), %1251 : Float(58:512, 1:512, 512:1), %1252 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%1249) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1253 : Float() = onnx::Constant[value={0.0625}]() %1254 : Float(58:512, 1:512, 512:1) = onnx::Mul(%1250, %1253) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1255 : Long() = onnx::Constant[value={2}]() %1256 : Long() = onnx::Mul(%1239, %1255) %1257 : Tensor = onnx::Unsqueeze[axes=[0]](%1236) %1258 : Tensor = onnx::Unsqueeze[axes=[0]](%1256) %1259 : Tensor = onnx::Unsqueeze[axes=[0]](%1246) %1260 : Tensor = onnx::Concat[axis=0](%1257, %1258, %1259) %1261 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1254, %1260) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1262 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1261) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1263 : Long() = onnx::Constant[value={2}]() %1264 : Long() = onnx::Mul(%1239, %1263) %1267 : Tensor = onnx::Unsqueeze[axes=[0]](%1264) %1268 : Tensor = onnx::Unsqueeze[axes=[0]](%1246) %1269 : Tensor = onnx::Concat[axis=0](%2488, %1267, %1268) %1270 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1251, %1269) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %1271 : Long() = onnx::Constant[value={2}]() %1272 : Long() = onnx::Mul(%1239, %1271) %1275 : Tensor = onnx::Unsqueeze[axes=[0]](%1272) %1276 : Tensor = onnx::Unsqueeze[axes=[0]](%1246) %1277 : Tensor = onnx::Concat[axis=0](%2489, %1275, %1276) %1278 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1252, %1277) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1279 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1278) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1280 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%1270) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1281 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%1262, %1280) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1282 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%1281) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1283 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%1282, %1279) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %1284 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1283) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1285 : Tensor = onnx::Unsqueeze[axes=[0]](%1236) %1286 : Tensor = onnx::Unsqueeze[axes=[0]](%1239) %1287 : Tensor = onnx::Unsqueeze[axes=[0]](%1242) %1288 : Tensor = onnx::Concat[axis=0](%1285, %1286, %1287) %1289 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%1284, %1288) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1291 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1289, %2490) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1292 : Float(58:512, 1:512, 512:1) = onnx::Add(%1291, %decoder.layers.3.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1293 : Float(58:512, 1:512, 512:1) = onnx::Add(%1233, %1292) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %1295 : Tensor = onnx::ReduceMean[axes=[-1]](%1293) %1296 : FloatTensor = onnx::Sub(%1293, %1295) %1297 : Tensor = onnx::Cast[to=1](%1296) %1299 : Tensor = onnx::Pow(%1297, %2491) %1300 : Tensor = onnx::ReduceMean[axes=[-1]](%1299) %1301 : Float() = onnx::Constant[value={1e-05}]() %1302 : FloatTensor = onnx::Add(%1300, %1301) %1303 : Tensor = onnx::Sqrt(%1302) %1304 : FloatTensor = onnx::Div(%1296, %1303) %1305 : FloatTensor = onnx::Mul(%1304, %decoder.layers.3.norm1.weight) %1306 : Float(58:512, 1:512, 512:1) = onnx::Add(%1305, %decoder.layers.3.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1308 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1306, %2492) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1309 : Float(58:512, 1:512, 512:1) = onnx::Add(%1308, %decoder.layers.3.linear1.bias) %1310 : Float(58:512, 1:512, 512:1) = onnx::Relu(%1309) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1312 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1310, %2493) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1313 : Float(58:512, 1:512, 512:1) = onnx::Add(%1312, %decoder.layers.3.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1314 : Float(58:512, 1:512, 512:1) = onnx::Add(%1306, %1313) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %1316 : Tensor = onnx::ReduceMean[axes=[-1]](%1314) %1317 : FloatTensor = onnx::Sub(%1314, %1316) %1318 : Tensor = onnx::Cast[to=1](%1317) %1320 : Tensor = onnx::Pow(%1318, %2494) %1321 : Tensor = onnx::ReduceMean[axes=[-1]](%1320) %1322 : Float() = onnx::Constant[value={1e-05}]() %1323 : FloatTensor = onnx::Add(%1321, %1322) %1324 : Tensor = onnx::Sqrt(%1323) %1325 : FloatTensor = onnx::Div(%1317, %1324) %1326 : FloatTensor = onnx::Mul(%1325, %decoder.layers.3.norm2.weight) %1327 : Float(58:512, 1:512, 512:1) = onnx::Add(%1326, %decoder.layers.3.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1328 : Tensor = onnx::Shape(%1327) %1329 : Tensor = onnx::Constant[value={0}]() %1330 : Long() = onnx::Gather[axis=0](%1328, %1329) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1331 : Tensor = onnx::Shape(%1327) %1332 : Tensor = onnx::Constant[value={1}]() %1333 : Long() = onnx::Gather[axis=0](%1331, %1332) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1334 : Tensor = onnx::Shape(%1327) %1335 : Tensor = onnx::Constant[value={2}]() %1336 : Long() = onnx::Gather[axis=0](%1334, %1335) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1337 : Long() = onnx::Constant[value={2}]() %1338 : LongTensor = onnx::Div(%1336, %1337) %1339 : Tensor = onnx::Cast[to=7](%1338) %1340 : Long() = onnx::Cast[to=7](%1339) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %1342 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%1327, %2495) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1343 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%1342, %decoder.layers.4.self_attn.in_proj_bias) %1344 : Float(58:1536, 1:1536, 512:1), %1345 : Float(58:512, 1:512, 512:1), %1346 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%1343) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1347 : Float() = onnx::Constant[value={0.0625}]() %1348 : Float(58:512, 1:512, 512:1) = onnx::Mul(%1344, %1347) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1349 : Long() = onnx::Constant[value={2}]() %1350 : Long() = onnx::Mul(%1333, %1349) %1351 : Tensor = onnx::Unsqueeze[axes=[0]](%1330) %1352 : Tensor = onnx::Unsqueeze[axes=[0]](%1350) %1353 : Tensor = onnx::Unsqueeze[axes=[0]](%1340) %1354 : Tensor = onnx::Concat[axis=0](%1351, %1352, %1353) %1355 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1348, %1354) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1356 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1355) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1357 : Long() = onnx::Constant[value={2}]() %1358 : Long() = onnx::Mul(%1333, %1357) %1361 : Tensor = onnx::Unsqueeze[axes=[0]](%1358) %1362 : Tensor = onnx::Unsqueeze[axes=[0]](%1340) %1363 : Tensor = onnx::Concat[axis=0](%2496, %1361, %1362) %1364 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1345, %1363) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %1365 : Long() = onnx::Constant[value={2}]() %1366 : Long() = onnx::Mul(%1333, %1365) %1369 : Tensor = onnx::Unsqueeze[axes=[0]](%1366) %1370 : Tensor = onnx::Unsqueeze[axes=[0]](%1340) %1371 : Tensor = onnx::Concat[axis=0](%2497, %1369, %1370) %1372 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1346, %1371) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1373 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1372) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1374 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%1364) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1375 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%1356, %1374) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1376 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%1375) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1377 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%1376, %1373) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %1378 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1377) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1379 : Tensor = onnx::Unsqueeze[axes=[0]](%1330) %1380 : Tensor = onnx::Unsqueeze[axes=[0]](%1333) %1381 : Tensor = onnx::Unsqueeze[axes=[0]](%1336) %1382 : Tensor = onnx::Concat[axis=0](%1379, %1380, %1381) %1383 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%1378, %1382) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1385 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1383, %2498) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1386 : Float(58:512, 1:512, 512:1) = onnx::Add(%1385, %decoder.layers.4.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1387 : Float(58:512, 1:512, 512:1) = onnx::Add(%1327, %1386) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %1389 : Tensor = onnx::ReduceMean[axes=[-1]](%1387) %1390 : FloatTensor = onnx::Sub(%1387, %1389) %1391 : Tensor = onnx::Cast[to=1](%1390) %1393 : Tensor = onnx::Pow(%1391, %2499) %1394 : Tensor = onnx::ReduceMean[axes=[-1]](%1393) %1395 : Float() = onnx::Constant[value={1e-05}]() %1396 : FloatTensor = onnx::Add(%1394, %1395) %1397 : Tensor = onnx::Sqrt(%1396) %1398 : FloatTensor = onnx::Div(%1390, %1397) %1399 : FloatTensor = onnx::Mul(%1398, %decoder.layers.4.norm1.weight) %1400 : Float(58:512, 1:512, 512:1) = onnx::Add(%1399, %decoder.layers.4.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1402 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1400, %2500) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1403 : Float(58:512, 1:512, 512:1) = onnx::Add(%1402, %decoder.layers.4.linear1.bias) %1404 : Float(58:512, 1:512, 512:1) = onnx::Relu(%1403) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1406 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1404, %2501) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1407 : Float(58:512, 1:512, 512:1) = onnx::Add(%1406, %decoder.layers.4.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1408 : Float(58:512, 1:512, 512:1) = onnx::Add(%1400, %1407) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %1410 : Tensor = onnx::ReduceMean[axes=[-1]](%1408) %1411 : FloatTensor = onnx::Sub(%1408, %1410) %1412 : Tensor = onnx::Cast[to=1](%1411) %1414 : Tensor = onnx::Pow(%1412, %2502) %1415 : Tensor = onnx::ReduceMean[axes=[-1]](%1414) %1416 : Float() = onnx::Constant[value={1e-05}]() %1417 : FloatTensor = onnx::Add(%1415, %1416) %1418 : Tensor = onnx::Sqrt(%1417) %1419 : FloatTensor = onnx::Div(%1411, %1418) %1420 : FloatTensor = onnx::Mul(%1419, %decoder.layers.4.norm2.weight) %1421 : Float(58:512, 1:512, 512:1) = onnx::Add(%1420, %decoder.layers.4.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1422 : Tensor = onnx::Shape(%1421) %1423 : Tensor = onnx::Constant[value={0}]() %1424 : Long() = onnx::Gather[axis=0](%1422, %1423) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1425 : Tensor = onnx::Shape(%1421) %1426 : Tensor = onnx::Constant[value={1}]() %1427 : Long() = onnx::Gather[axis=0](%1425, %1426) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1428 : Tensor = onnx::Shape(%1421) %1429 : Tensor = onnx::Constant[value={2}]() %1430 : Long() = onnx::Gather[axis=0](%1428, %1429) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1431 : Long() = onnx::Constant[value={2}]() %1432 : LongTensor = onnx::Div(%1430, %1431) %1433 : Tensor = onnx::Cast[to=7](%1432) %1434 : Long() = onnx::Cast[to=7](%1433) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %1436 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%1421, %2503) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1437 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%1436, %decoder.layers.5.self_attn.in_proj_bias) %1438 : Float(58:1536, 1:1536, 512:1), %1439 : Float(58:512, 1:512, 512:1), %1440 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%1437) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1441 : Float() = onnx::Constant[value={0.0625}]() %1442 : Float(58:512, 1:512, 512:1) = onnx::Mul(%1438, %1441) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1443 : Long() = onnx::Constant[value={2}]() %1444 : Long() = onnx::Mul(%1427, %1443) %1445 : Tensor = onnx::Unsqueeze[axes=[0]](%1424) %1446 : Tensor = onnx::Unsqueeze[axes=[0]](%1444) %1447 : Tensor = onnx::Unsqueeze[axes=[0]](%1434) %1448 : Tensor = onnx::Concat[axis=0](%1445, %1446, %1447) %1449 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1442, %1448) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1450 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1449) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1451 : Long() = onnx::Constant[value={2}]() %1452 : Long() = onnx::Mul(%1427, %1451) %1455 : Tensor = onnx::Unsqueeze[axes=[0]](%1452) %1456 : Tensor = onnx::Unsqueeze[axes=[0]](%1434) %1457 : Tensor = onnx::Concat[axis=0](%2504, %1455, %1456) %1458 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1439, %1457) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %1459 : Long() = onnx::Constant[value={2}]() %1460 : Long() = onnx::Mul(%1427, %1459) %1463 : Tensor = onnx::Unsqueeze[axes=[0]](%1460) %1464 : Tensor = onnx::Unsqueeze[axes=[0]](%1434) %1465 : Tensor = onnx::Concat[axis=0](%2505, %1463, %1464) %1466 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1440, %1465) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1467 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1466) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1468 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%1458) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1469 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%1450, %1468) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1470 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%1469) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1471 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%1470, %1467) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %1472 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1471) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1473 : Tensor = onnx::Unsqueeze[axes=[0]](%1424) %1474 : Tensor = onnx::Unsqueeze[axes=[0]](%1427) %1475 : Tensor = onnx::Unsqueeze[axes=[0]](%1430) %1476 : Tensor = onnx::Concat[axis=0](%1473, %1474, %1475) %1477 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%1472, %1476) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1479 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1477, %2506) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1480 : Float(58:512, 1:512, 512:1) = onnx::Add(%1479, %decoder.layers.5.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1481 : Float(58:512, 1:512, 512:1) = onnx::Add(%1421, %1480) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %1483 : Tensor = onnx::ReduceMean[axes=[-1]](%1481) %1484 : FloatTensor = onnx::Sub(%1481, %1483) %1485 : Tensor = onnx::Cast[to=1](%1484) %1487 : Tensor = onnx::Pow(%1485, %2507) %1488 : Tensor = onnx::ReduceMean[axes=[-1]](%1487) %1489 : Float() = onnx::Constant[value={1e-05}]() %1490 : FloatTensor = onnx::Add(%1488, %1489) %1491 : Tensor = onnx::Sqrt(%1490) %1492 : FloatTensor = onnx::Div(%1484, %1491) %1493 : FloatTensor = onnx::Mul(%1492, %decoder.layers.5.norm1.weight) %1494 : Float(58:512, 1:512, 512:1) = onnx::Add(%1493, %decoder.layers.5.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1496 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1494, %2508) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1497 : Float(58:512, 1:512, 512:1) = onnx::Add(%1496, %decoder.layers.5.linear1.bias) %1498 : Float(58:512, 1:512, 512:1) = onnx::Relu(%1497) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1500 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1498, %2509) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1501 : Float(58:512, 1:512, 512:1) = onnx::Add(%1500, %decoder.layers.5.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1502 : Float(58:512, 1:512, 512:1) = onnx::Add(%1494, %1501) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %1504 : Tensor = onnx::ReduceMean[axes=[-1]](%1502) %1505 : FloatTensor = onnx::Sub(%1502, %1504) %1506 : Tensor = onnx::Cast[to=1](%1505) %1508 : Tensor = onnx::Pow(%1506, %2510) %1509 : Tensor = onnx::ReduceMean[axes=[-1]](%1508) %1510 : Float() = onnx::Constant[value={1e-05}]() %1511 : FloatTensor = onnx::Add(%1509, %1510) %1512 : Tensor = onnx::Sqrt(%1511) %1513 : FloatTensor = onnx::Div(%1505, %1512) %1514 : FloatTensor = onnx::Mul(%1513, %decoder.layers.5.norm2.weight) %1515 : Float(58:512, 1:512, 512:1) = onnx::Add(%1514, %decoder.layers.5.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1516 : Tensor = onnx::Shape(%1515) %1517 : Tensor = onnx::Constant[value={0}]() %1518 : Long() = onnx::Gather[axis=0](%1516, %1517) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1519 : Tensor = onnx::Shape(%1515) %1520 : Tensor = onnx::Constant[value={1}]() %1521 : Long() = onnx::Gather[axis=0](%1519, %1520) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1522 : Tensor = onnx::Shape(%1515) %1523 : Tensor = onnx::Constant[value={2}]() %1524 : Long() = onnx::Gather[axis=0](%1522, %1523) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1525 : Long() = onnx::Constant[value={2}]() %1526 : LongTensor = onnx::Div(%1524, %1525) %1527 : Tensor = onnx::Cast[to=7](%1526) %1528 : Long() = onnx::Cast[to=7](%1527) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %1530 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%1515, %2511) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1531 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%1530, %decoder.layers.6.self_attn.in_proj_bias) %1532 : Float(58:1536, 1:1536, 512:1), %1533 : Float(58:512, 1:512, 512:1), %1534 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%1531) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1535 : Float() = onnx::Constant[value={0.0625}]() %1536 : Float(58:512, 1:512, 512:1) = onnx::Mul(%1532, %1535) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1537 : Long() = onnx::Constant[value={2}]() %1538 : Long() = onnx::Mul(%1521, %1537) %1539 : Tensor = onnx::Unsqueeze[axes=[0]](%1518) %1540 : Tensor = onnx::Unsqueeze[axes=[0]](%1538) %1541 : Tensor = onnx::Unsqueeze[axes=[0]](%1528) %1542 : Tensor = onnx::Concat[axis=0](%1539, %1540, %1541) %1543 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1536, %1542) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1544 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1543) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1545 : Long() = onnx::Constant[value={2}]() %1546 : Long() = onnx::Mul(%1521, %1545) %1549 : Tensor = onnx::Unsqueeze[axes=[0]](%1546) %1550 : Tensor = onnx::Unsqueeze[axes=[0]](%1528) %1551 : Tensor = onnx::Concat[axis=0](%2512, %1549, %1550) %1552 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1533, %1551) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %1553 : Long() = onnx::Constant[value={2}]() %1554 : Long() = onnx::Mul(%1521, %1553) %1557 : Tensor = onnx::Unsqueeze[axes=[0]](%1554) %1558 : Tensor = onnx::Unsqueeze[axes=[0]](%1528) %1559 : Tensor = onnx::Concat[axis=0](%2513, %1557, %1558) %1560 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1534, %1559) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1561 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1560) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1562 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%1552) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1563 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%1544, %1562) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1564 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%1563) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1565 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%1564, %1561) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %1566 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1565) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1567 : Tensor = onnx::Unsqueeze[axes=[0]](%1518) %1568 : Tensor = onnx::Unsqueeze[axes=[0]](%1521) %1569 : Tensor = onnx::Unsqueeze[axes=[0]](%1524) %1570 : Tensor = onnx::Concat[axis=0](%1567, %1568, %1569) %1571 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%1566, %1570) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1573 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1571, %2514) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1574 : Float(58:512, 1:512, 512:1) = onnx::Add(%1573, %decoder.layers.6.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1575 : Float(58:512, 1:512, 512:1) = onnx::Add(%1515, %1574) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %1577 : Tensor = onnx::ReduceMean[axes=[-1]](%1575) %1578 : FloatTensor = onnx::Sub(%1575, %1577) %1579 : Tensor = onnx::Cast[to=1](%1578) %1581 : Tensor = onnx::Pow(%1579, %2515) %1582 : Tensor = onnx::ReduceMean[axes=[-1]](%1581) %1583 : Float() = onnx::Constant[value={1e-05}]() %1584 : FloatTensor = onnx::Add(%1582, %1583) %1585 : Tensor = onnx::Sqrt(%1584) %1586 : FloatTensor = onnx::Div(%1578, %1585) %1587 : FloatTensor = onnx::Mul(%1586, %decoder.layers.6.norm1.weight) %1588 : Float(58:512, 1:512, 512:1) = onnx::Add(%1587, %decoder.layers.6.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1590 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1588, %2516) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1591 : Float(58:512, 1:512, 512:1) = onnx::Add(%1590, %decoder.layers.6.linear1.bias) %1592 : Float(58:512, 1:512, 512:1) = onnx::Relu(%1591) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1594 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1592, %2517) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1595 : Float(58:512, 1:512, 512:1) = onnx::Add(%1594, %decoder.layers.6.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1596 : Float(58:512, 1:512, 512:1) = onnx::Add(%1588, %1595) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %1598 : Tensor = onnx::ReduceMean[axes=[-1]](%1596) %1599 : FloatTensor = onnx::Sub(%1596, %1598) %1600 : Tensor = onnx::Cast[to=1](%1599) %1602 : Tensor = onnx::Pow(%1600, %2518) %1603 : Tensor = onnx::ReduceMean[axes=[-1]](%1602) %1604 : Float() = onnx::Constant[value={1e-05}]() %1605 : FloatTensor = onnx::Add(%1603, %1604) %1606 : Tensor = onnx::Sqrt(%1605) %1607 : FloatTensor = onnx::Div(%1599, %1606) %1608 : FloatTensor = onnx::Mul(%1607, %decoder.layers.6.norm2.weight) %1609 : Float(58:512, 1:512, 512:1) = onnx::Add(%1608, %decoder.layers.6.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1610 : Tensor = onnx::Shape(%1609) %1611 : Tensor = onnx::Constant[value={0}]() %1612 : Long() = onnx::Gather[axis=0](%1610, %1611) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1613 : Tensor = onnx::Shape(%1609) %1614 : Tensor = onnx::Constant[value={1}]() %1615 : Long() = onnx::Gather[axis=0](%1613, %1614) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1616 : Tensor = onnx::Shape(%1609) %1617 : Tensor = onnx::Constant[value={2}]() %1618 : Long() = onnx::Gather[axis=0](%1616, %1617) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1619 : Long() = onnx::Constant[value={2}]() %1620 : LongTensor = onnx::Div(%1618, %1619) %1621 : Tensor = onnx::Cast[to=7](%1620) %1622 : Long() = onnx::Cast[to=7](%1621) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %1624 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%1609, %2519) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1625 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%1624, %decoder.layers.7.self_attn.in_proj_bias) %1626 : Float(58:1536, 1:1536, 512:1), %1627 : Float(58:512, 1:512, 512:1), %1628 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%1625) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1629 : Float() = onnx::Constant[value={0.0625}]() %1630 : Float(58:512, 1:512, 512:1) = onnx::Mul(%1626, %1629) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1631 : Long() = onnx::Constant[value={2}]() %1632 : Long() = onnx::Mul(%1615, %1631) %1633 : Tensor = onnx::Unsqueeze[axes=[0]](%1612) %1634 : Tensor = onnx::Unsqueeze[axes=[0]](%1632) %1635 : Tensor = onnx::Unsqueeze[axes=[0]](%1622) %1636 : Tensor = onnx::Concat[axis=0](%1633, %1634, %1635) %1637 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1630, %1636) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1638 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1637) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1639 : Long() = onnx::Constant[value={2}]() %1640 : Long() = onnx::Mul(%1615, %1639) %1643 : Tensor = onnx::Unsqueeze[axes=[0]](%1640) %1644 : Tensor = onnx::Unsqueeze[axes=[0]](%1622) %1645 : Tensor = onnx::Concat[axis=0](%2520, %1643, %1644) %1646 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1627, %1645) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %1647 : Long() = onnx::Constant[value={2}]() %1648 : Long() = onnx::Mul(%1615, %1647) %1651 : Tensor = onnx::Unsqueeze[axes=[0]](%1648) %1652 : Tensor = onnx::Unsqueeze[axes=[0]](%1622) %1653 : Tensor = onnx::Concat[axis=0](%2521, %1651, %1652) %1654 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1628, %1653) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1655 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1654) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1656 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%1646) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1657 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%1638, %1656) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1658 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%1657) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1659 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%1658, %1655) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %1660 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1659) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1661 : Tensor = onnx::Unsqueeze[axes=[0]](%1612) %1662 : Tensor = onnx::Unsqueeze[axes=[0]](%1615) %1663 : Tensor = onnx::Unsqueeze[axes=[0]](%1618) %1664 : Tensor = onnx::Concat[axis=0](%1661, %1662, %1663) %1665 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%1660, %1664) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1667 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1665, %2522) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1668 : Float(58:512, 1:512, 512:1) = onnx::Add(%1667, %decoder.layers.7.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1669 : Float(58:512, 1:512, 512:1) = onnx::Add(%1609, %1668) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %1671 : Tensor = onnx::ReduceMean[axes=[-1]](%1669) %1672 : FloatTensor = onnx::Sub(%1669, %1671) %1673 : Tensor = onnx::Cast[to=1](%1672) %1675 : Tensor = onnx::Pow(%1673, %2523) %1676 : Tensor = onnx::ReduceMean[axes=[-1]](%1675) %1677 : Float() = onnx::Constant[value={1e-05}]() %1678 : FloatTensor = onnx::Add(%1676, %1677) %1679 : Tensor = onnx::Sqrt(%1678) %1680 : FloatTensor = onnx::Div(%1672, %1679) %1681 : FloatTensor = onnx::Mul(%1680, %decoder.layers.7.norm1.weight) %1682 : Float(58:512, 1:512, 512:1) = onnx::Add(%1681, %decoder.layers.7.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1684 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1682, %2524) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1685 : Float(58:512, 1:512, 512:1) = onnx::Add(%1684, %decoder.layers.7.linear1.bias) %1686 : Float(58:512, 1:512, 512:1) = onnx::Relu(%1685) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1688 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1686, %2525) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1689 : Float(58:512, 1:512, 512:1) = onnx::Add(%1688, %decoder.layers.7.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1690 : Float(58:512, 1:512, 512:1) = onnx::Add(%1682, %1689) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %1692 : Tensor = onnx::ReduceMean[axes=[-1]](%1690) %1693 : FloatTensor = onnx::Sub(%1690, %1692) %1694 : Tensor = onnx::Cast[to=1](%1693) %1696 : Tensor = onnx::Pow(%1694, %2526) %1697 : Tensor = onnx::ReduceMean[axes=[-1]](%1696) %1698 : Float() = onnx::Constant[value={1e-05}]() %1699 : FloatTensor = onnx::Add(%1697, %1698) %1700 : Tensor = onnx::Sqrt(%1699) %1701 : FloatTensor = onnx::Div(%1693, %1700) %1702 : FloatTensor = onnx::Mul(%1701, %decoder.layers.7.norm2.weight) %1703 : Float(58:512, 1:512, 512:1) = onnx::Add(%1702, %decoder.layers.7.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1704 : Tensor = onnx::Shape(%1703) %1705 : Tensor = onnx::Constant[value={0}]() %1706 : Long() = onnx::Gather[axis=0](%1704, %1705) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1707 : Tensor = onnx::Shape(%1703) %1708 : Tensor = onnx::Constant[value={1}]() %1709 : Long() = onnx::Gather[axis=0](%1707, %1708) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1710 : Tensor = onnx::Shape(%1703) %1711 : Tensor = onnx::Constant[value={2}]() %1712 : Long() = onnx::Gather[axis=0](%1710, %1711) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1713 : Long() = onnx::Constant[value={2}]() %1714 : LongTensor = onnx::Div(%1712, %1713) %1715 : Tensor = onnx::Cast[to=7](%1714) %1716 : Long() = onnx::Cast[to=7](%1715) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %1718 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%1703, %2527) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1719 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%1718, %decoder.layers.8.self_attn.in_proj_bias) %1720 : Float(58:1536, 1:1536, 512:1), %1721 : Float(58:512, 1:512, 512:1), %1722 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%1719) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1723 : Float() = onnx::Constant[value={0.0625}]() %1724 : Float(58:512, 1:512, 512:1) = onnx::Mul(%1720, %1723) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1725 : Long() = onnx::Constant[value={2}]() %1726 : Long() = onnx::Mul(%1709, %1725) %1727 : Tensor = onnx::Unsqueeze[axes=[0]](%1706) %1728 : Tensor = onnx::Unsqueeze[axes=[0]](%1726) %1729 : Tensor = onnx::Unsqueeze[axes=[0]](%1716) %1730 : Tensor = onnx::Concat[axis=0](%1727, %1728, %1729) %1731 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1724, %1730) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1732 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1731) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1733 : Long() = onnx::Constant[value={2}]() %1734 : Long() = onnx::Mul(%1709, %1733) %1737 : Tensor = onnx::Unsqueeze[axes=[0]](%1734) %1738 : Tensor = onnx::Unsqueeze[axes=[0]](%1716) %1739 : Tensor = onnx::Concat[axis=0](%2528, %1737, %1738) %1740 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1721, %1739) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %1741 : Long() = onnx::Constant[value={2}]() %1742 : Long() = onnx::Mul(%1709, %1741) %1745 : Tensor = onnx::Unsqueeze[axes=[0]](%1742) %1746 : Tensor = onnx::Unsqueeze[axes=[0]](%1716) %1747 : Tensor = onnx::Concat[axis=0](%2529, %1745, %1746) %1748 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1722, %1747) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1749 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1748) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1750 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%1740) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1751 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%1732, %1750) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1752 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%1751) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1753 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%1752, %1749) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %1754 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1753) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1755 : Tensor = onnx::Unsqueeze[axes=[0]](%1706) %1756 : Tensor = onnx::Unsqueeze[axes=[0]](%1709) %1757 : Tensor = onnx::Unsqueeze[axes=[0]](%1712) %1758 : Tensor = onnx::Concat[axis=0](%1755, %1756, %1757) %1759 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%1754, %1758) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1761 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1759, %2530) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1762 : Float(58:512, 1:512, 512:1) = onnx::Add(%1761, %decoder.layers.8.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1763 : Float(58:512, 1:512, 512:1) = onnx::Add(%1703, %1762) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %1765 : Tensor = onnx::ReduceMean[axes=[-1]](%1763) %1766 : FloatTensor = onnx::Sub(%1763, %1765) %1767 : Tensor = onnx::Cast[to=1](%1766) %1769 : Tensor = onnx::Pow(%1767, %2531) %1770 : Tensor = onnx::ReduceMean[axes=[-1]](%1769) %1771 : Float() = onnx::Constant[value={1e-05}]() %1772 : FloatTensor = onnx::Add(%1770, %1771) %1773 : Tensor = onnx::Sqrt(%1772) %1774 : FloatTensor = onnx::Div(%1766, %1773) %1775 : FloatTensor = onnx::Mul(%1774, %decoder.layers.8.norm1.weight) %1776 : Float(58:512, 1:512, 512:1) = onnx::Add(%1775, %decoder.layers.8.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1778 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1776, %2532) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1779 : Float(58:512, 1:512, 512:1) = onnx::Add(%1778, %decoder.layers.8.linear1.bias) %1780 : Float(58:512, 1:512, 512:1) = onnx::Relu(%1779) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1782 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1780, %2533) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1783 : Float(58:512, 1:512, 512:1) = onnx::Add(%1782, %decoder.layers.8.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1784 : Float(58:512, 1:512, 512:1) = onnx::Add(%1776, %1783) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %1786 : Tensor = onnx::ReduceMean[axes=[-1]](%1784) %1787 : FloatTensor = onnx::Sub(%1784, %1786) %1788 : Tensor = onnx::Cast[to=1](%1787) %1790 : Tensor = onnx::Pow(%1788, %2534) %1791 : Tensor = onnx::ReduceMean[axes=[-1]](%1790) %1792 : Float() = onnx::Constant[value={1e-05}]() %1793 : FloatTensor = onnx::Add(%1791, %1792) %1794 : Tensor = onnx::Sqrt(%1793) %1795 : FloatTensor = onnx::Div(%1787, %1794) %1796 : FloatTensor = onnx::Mul(%1795, %decoder.layers.8.norm2.weight) %1797 : Float(58:512, 1:512, 512:1) = onnx::Add(%1796, %decoder.layers.8.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1798 : Tensor = onnx::Shape(%1797) %1799 : Tensor = onnx::Constant[value={0}]() %1800 : Long() = onnx::Gather[axis=0](%1798, %1799) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1801 : Tensor = onnx::Shape(%1797) %1802 : Tensor = onnx::Constant[value={1}]() %1803 : Long() = onnx::Gather[axis=0](%1801, %1802) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1804 : Tensor = onnx::Shape(%1797) %1805 : Tensor = onnx::Constant[value={2}]() %1806 : Long() = onnx::Gather[axis=0](%1804, %1805) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1807 : Long() = onnx::Constant[value={2}]() %1808 : LongTensor = onnx::Div(%1806, %1807) %1809 : Tensor = onnx::Cast[to=7](%1808) %1810 : Long() = onnx::Cast[to=7](%1809) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %1812 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%1797, %2535) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1813 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%1812, %decoder.layers.9.self_attn.in_proj_bias) %1814 : Float(58:1536, 1:1536, 512:1), %1815 : Float(58:512, 1:512, 512:1), %1816 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%1813) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1817 : Float() = onnx::Constant[value={0.0625}]() %1818 : Float(58:512, 1:512, 512:1) = onnx::Mul(%1814, %1817) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1819 : Long() = onnx::Constant[value={2}]() %1820 : Long() = onnx::Mul(%1803, %1819) %1821 : Tensor = onnx::Unsqueeze[axes=[0]](%1800) %1822 : Tensor = onnx::Unsqueeze[axes=[0]](%1820) %1823 : Tensor = onnx::Unsqueeze[axes=[0]](%1810) %1824 : Tensor = onnx::Concat[axis=0](%1821, %1822, %1823) %1825 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1818, %1824) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1826 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1825) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1827 : Long() = onnx::Constant[value={2}]() %1828 : Long() = onnx::Mul(%1803, %1827) %1831 : Tensor = onnx::Unsqueeze[axes=[0]](%1828) %1832 : Tensor = onnx::Unsqueeze[axes=[0]](%1810) %1833 : Tensor = onnx::Concat[axis=0](%2536, %1831, %1832) %1834 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1815, %1833) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %1835 : Long() = onnx::Constant[value={2}]() %1836 : Long() = onnx::Mul(%1803, %1835) %1839 : Tensor = onnx::Unsqueeze[axes=[0]](%1836) %1840 : Tensor = onnx::Unsqueeze[axes=[0]](%1810) %1841 : Tensor = onnx::Concat[axis=0](%2537, %1839, %1840) %1842 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1816, %1841) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1843 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1842) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1844 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%1834) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1845 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%1826, %1844) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1846 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%1845) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1847 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%1846, %1843) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %1848 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1847) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1849 : Tensor = onnx::Unsqueeze[axes=[0]](%1800) %1850 : Tensor = onnx::Unsqueeze[axes=[0]](%1803) %1851 : Tensor = onnx::Unsqueeze[axes=[0]](%1806) %1852 : Tensor = onnx::Concat[axis=0](%1849, %1850, %1851) %1853 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%1848, %1852) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1855 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1853, %2538) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1856 : Float(58:512, 1:512, 512:1) = onnx::Add(%1855, %decoder.layers.9.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1857 : Float(58:512, 1:512, 512:1) = onnx::Add(%1797, %1856) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %1859 : Tensor = onnx::ReduceMean[axes=[-1]](%1857) %1860 : FloatTensor = onnx::Sub(%1857, %1859) %1861 : Tensor = onnx::Cast[to=1](%1860) %1863 : Tensor = onnx::Pow(%1861, %2539) %1864 : Tensor = onnx::ReduceMean[axes=[-1]](%1863) %1865 : Float() = onnx::Constant[value={1e-05}]() %1866 : FloatTensor = onnx::Add(%1864, %1865) %1867 : Tensor = onnx::Sqrt(%1866) %1868 : FloatTensor = onnx::Div(%1860, %1867) %1869 : FloatTensor = onnx::Mul(%1868, %decoder.layers.9.norm1.weight) %1870 : Float(58:512, 1:512, 512:1) = onnx::Add(%1869, %decoder.layers.9.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1872 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1870, %2540) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1873 : Float(58:512, 1:512, 512:1) = onnx::Add(%1872, %decoder.layers.9.linear1.bias) %1874 : Float(58:512, 1:512, 512:1) = onnx::Relu(%1873) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1876 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1874, %2541) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1877 : Float(58:512, 1:512, 512:1) = onnx::Add(%1876, %decoder.layers.9.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1878 : Float(58:512, 1:512, 512:1) = onnx::Add(%1870, %1877) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %1880 : Tensor = onnx::ReduceMean[axes=[-1]](%1878) %1881 : FloatTensor = onnx::Sub(%1878, %1880) %1882 : Tensor = onnx::Cast[to=1](%1881) %1884 : Tensor = onnx::Pow(%1882, %2542) %1885 : Tensor = onnx::ReduceMean[axes=[-1]](%1884) %1886 : Float() = onnx::Constant[value={1e-05}]() %1887 : FloatTensor = onnx::Add(%1885, %1886) %1888 : Tensor = onnx::Sqrt(%1887) %1889 : FloatTensor = onnx::Div(%1881, %1888) %1890 : FloatTensor = onnx::Mul(%1889, %decoder.layers.9.norm2.weight) %1891 : Float(58:512, 1:512, 512:1) = onnx::Add(%1890, %decoder.layers.9.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1892 : Tensor = onnx::Shape(%1891) %1893 : Tensor = onnx::Constant[value={0}]() %1894 : Long() = onnx::Gather[axis=0](%1892, %1893) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1895 : Tensor = onnx::Shape(%1891) %1896 : Tensor = onnx::Constant[value={1}]() %1897 : Long() = onnx::Gather[axis=0](%1895, %1896) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1898 : Tensor = onnx::Shape(%1891) %1899 : Tensor = onnx::Constant[value={2}]() %1900 : Long() = onnx::Gather[axis=0](%1898, %1899) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1901 : Long() = onnx::Constant[value={2}]() %1902 : LongTensor = onnx::Div(%1900, %1901) %1903 : Tensor = onnx::Cast[to=7](%1902) %1904 : Long() = onnx::Cast[to=7](%1903) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %1906 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%1891, %2543) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1907 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%1906, %decoder.layers.10.self_attn.in_proj_bias) %1908 : Float(58:1536, 1:1536, 512:1), %1909 : Float(58:512, 1:512, 512:1), %1910 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%1907) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1911 : Float() = onnx::Constant[value={0.0625}]() %1912 : Float(58:512, 1:512, 512:1) = onnx::Mul(%1908, %1911) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1913 : Long() = onnx::Constant[value={2}]() %1914 : Long() = onnx::Mul(%1897, %1913) %1915 : Tensor = onnx::Unsqueeze[axes=[0]](%1894) %1916 : Tensor = onnx::Unsqueeze[axes=[0]](%1914) %1917 : Tensor = onnx::Unsqueeze[axes=[0]](%1904) %1918 : Tensor = onnx::Concat[axis=0](%1915, %1916, %1917) %1919 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1912, %1918) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1920 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1919) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %1921 : Long() = onnx::Constant[value={2}]() %1922 : Long() = onnx::Mul(%1897, %1921) %1925 : Tensor = onnx::Unsqueeze[axes=[0]](%1922) %1926 : Tensor = onnx::Unsqueeze[axes=[0]](%1904) %1927 : Tensor = onnx::Concat[axis=0](%2544, %1925, %1926) %1928 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1909, %1927) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %1929 : Long() = onnx::Constant[value={2}]() %1930 : Long() = onnx::Mul(%1897, %1929) %1933 : Tensor = onnx::Unsqueeze[axes=[0]](%1930) %1934 : Tensor = onnx::Unsqueeze[axes=[0]](%1904) %1935 : Tensor = onnx::Concat[axis=0](%2545, %1933, %1934) %1936 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%1910, %1935) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1937 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1936) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %1938 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%1928) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1939 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%1920, %1938) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %1940 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%1939) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1941 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%1940, %1937) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %1942 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%1941) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1943 : Tensor = onnx::Unsqueeze[axes=[0]](%1894) %1944 : Tensor = onnx::Unsqueeze[axes=[0]](%1897) %1945 : Tensor = onnx::Unsqueeze[axes=[0]](%1900) %1946 : Tensor = onnx::Concat[axis=0](%1943, %1944, %1945) %1947 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%1942, %1946) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %1949 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1947, %2546) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1950 : Float(58:512, 1:512, 512:1) = onnx::Add(%1949, %decoder.layers.10.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1951 : Float(58:512, 1:512, 512:1) = onnx::Add(%1891, %1950) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %1953 : Tensor = onnx::ReduceMean[axes=[-1]](%1951) %1954 : FloatTensor = onnx::Sub(%1951, %1953) %1955 : Tensor = onnx::Cast[to=1](%1954) %1957 : Tensor = onnx::Pow(%1955, %2547) %1958 : Tensor = onnx::ReduceMean[axes=[-1]](%1957) %1959 : Float() = onnx::Constant[value={1e-05}]() %1960 : FloatTensor = onnx::Add(%1958, %1959) %1961 : Tensor = onnx::Sqrt(%1960) %1962 : FloatTensor = onnx::Div(%1954, %1961) %1963 : FloatTensor = onnx::Mul(%1962, %decoder.layers.10.norm1.weight) %1964 : Float(58:512, 1:512, 512:1) = onnx::Add(%1963, %decoder.layers.10.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1966 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1964, %2548) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1967 : Float(58:512, 1:512, 512:1) = onnx::Add(%1966, %decoder.layers.10.linear1.bias) %1968 : Float(58:512, 1:512, 512:1) = onnx::Relu(%1967) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1970 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%1968, %2549) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %1971 : Float(58:512, 1:512, 512:1) = onnx::Add(%1970, %decoder.layers.10.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %1972 : Float(58:512, 1:512, 512:1) = onnx::Add(%1964, %1971) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %1974 : Tensor = onnx::ReduceMean[axes=[-1]](%1972) %1975 : FloatTensor = onnx::Sub(%1972, %1974) %1976 : Tensor = onnx::Cast[to=1](%1975) %1978 : Tensor = onnx::Pow(%1976, %2550) %1979 : Tensor = onnx::ReduceMean[axes=[-1]](%1978) %1980 : Float() = onnx::Constant[value={1e-05}]() %1981 : FloatTensor = onnx::Add(%1979, %1980) %1982 : Tensor = onnx::Sqrt(%1981) %1983 : FloatTensor = onnx::Div(%1975, %1982) %1984 : FloatTensor = onnx::Mul(%1983, %decoder.layers.10.norm2.weight) %1985 : Float(58:512, 1:512, 512:1) = onnx::Add(%1984, %decoder.layers.10.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %1986 : Tensor = onnx::Shape(%1985) %1987 : Tensor = onnx::Constant[value={0}]() %1988 : Long() = onnx::Gather[axis=0](%1986, %1987) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1989 : Tensor = onnx::Shape(%1985) %1990 : Tensor = onnx::Constant[value={1}]() %1991 : Long() = onnx::Gather[axis=0](%1989, %1990) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1992 : Tensor = onnx::Shape(%1985) %1993 : Tensor = onnx::Constant[value={2}]() %1994 : Long() = onnx::Gather[axis=0](%1992, %1993) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %1995 : Long() = onnx::Constant[value={2}]() %1996 : LongTensor = onnx::Div(%1994, %1995) %1997 : Tensor = onnx::Cast[to=7](%1996) %1998 : Long() = onnx::Cast[to=7](%1997) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %2000 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%1985, %2551) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2001 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%2000, %decoder.layers.11.self_attn.in_proj_bias) %2002 : Float(58:1536, 1:1536, 512:1), %2003 : Float(58:512, 1:512, 512:1), %2004 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%2001) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2005 : Float() = onnx::Constant[value={0.0625}]() %2006 : Float(58:512, 1:512, 512:1) = onnx::Mul(%2002, %2005) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2007 : Long() = onnx::Constant[value={2}]() %2008 : Long() = onnx::Mul(%1991, %2007) %2009 : Tensor = onnx::Unsqueeze[axes=[0]](%1988) %2010 : Tensor = onnx::Unsqueeze[axes=[0]](%2008) %2011 : Tensor = onnx::Unsqueeze[axes=[0]](%1998) %2012 : Tensor = onnx::Concat[axis=0](%2009, %2010, %2011) %2013 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2006, %2012) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2014 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2013) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2015 : Long() = onnx::Constant[value={2}]() %2016 : Long() = onnx::Mul(%1991, %2015) %2019 : Tensor = onnx::Unsqueeze[axes=[0]](%2016) %2020 : Tensor = onnx::Unsqueeze[axes=[0]](%1998) %2021 : Tensor = onnx::Concat[axis=0](%2552, %2019, %2020) %2022 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2003, %2021) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %2023 : Long() = onnx::Constant[value={2}]() %2024 : Long() = onnx::Mul(%1991, %2023) %2027 : Tensor = onnx::Unsqueeze[axes=[0]](%2024) %2028 : Tensor = onnx::Unsqueeze[axes=[0]](%1998) %2029 : Tensor = onnx::Concat[axis=0](%2553, %2027, %2028) %2030 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2004, %2029) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2031 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2030) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2032 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%2022) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %2033 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%2014, %2032) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %2034 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%2033) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2035 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%2034, %2031) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %2036 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2035) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %2037 : Tensor = onnx::Unsqueeze[axes=[0]](%1988) %2038 : Tensor = onnx::Unsqueeze[axes=[0]](%1991) %2039 : Tensor = onnx::Unsqueeze[axes=[0]](%1994) %2040 : Tensor = onnx::Concat[axis=0](%2037, %2038, %2039) %2041 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%2036, %2040) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %2043 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2041, %2554) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2044 : Float(58:512, 1:512, 512:1) = onnx::Add(%2043, %decoder.layers.11.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2045 : Float(58:512, 1:512, 512:1) = onnx::Add(%1985, %2044) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %2047 : Tensor = onnx::ReduceMean[axes=[-1]](%2045) %2048 : FloatTensor = onnx::Sub(%2045, %2047) %2049 : Tensor = onnx::Cast[to=1](%2048) %2051 : Tensor = onnx::Pow(%2049, %2555) %2052 : Tensor = onnx::ReduceMean[axes=[-1]](%2051) %2053 : Float() = onnx::Constant[value={1e-05}]() %2054 : FloatTensor = onnx::Add(%2052, %2053) %2055 : Tensor = onnx::Sqrt(%2054) %2056 : FloatTensor = onnx::Div(%2048, %2055) %2057 : FloatTensor = onnx::Mul(%2056, %decoder.layers.11.norm1.weight) %2058 : Float(58:512, 1:512, 512:1) = onnx::Add(%2057, %decoder.layers.11.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %2060 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2058, %2556) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2061 : Float(58:512, 1:512, 512:1) = onnx::Add(%2060, %decoder.layers.11.linear1.bias) %2062 : Float(58:512, 1:512, 512:1) = onnx::Relu(%2061) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2064 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2062, %2557) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2065 : Float(58:512, 1:512, 512:1) = onnx::Add(%2064, %decoder.layers.11.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2066 : Float(58:512, 1:512, 512:1) = onnx::Add(%2058, %2065) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %2068 : Tensor = onnx::ReduceMean[axes=[-1]](%2066) %2069 : FloatTensor = onnx::Sub(%2066, %2068) %2070 : Tensor = onnx::Cast[to=1](%2069) %2072 : Tensor = onnx::Pow(%2070, %2558) %2073 : Tensor = onnx::ReduceMean[axes=[-1]](%2072) %2074 : Float() = onnx::Constant[value={1e-05}]() %2075 : FloatTensor = onnx::Add(%2073, %2074) %2076 : Tensor = onnx::Sqrt(%2075) %2077 : FloatTensor = onnx::Div(%2069, %2076) %2078 : FloatTensor = onnx::Mul(%2077, %decoder.layers.11.norm2.weight) %2079 : Float(58:512, 1:512, 512:1) = onnx::Add(%2078, %decoder.layers.11.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %2080 : Tensor = onnx::Shape(%2079) %2081 : Tensor = onnx::Constant[value={0}]() %2082 : Long() = onnx::Gather[axis=0](%2080, %2081) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %2083 : Tensor = onnx::Shape(%2079) %2084 : Tensor = onnx::Constant[value={1}]() %2085 : Long() = onnx::Gather[axis=0](%2083, %2084) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %2086 : Tensor = onnx::Shape(%2079) %2087 : Tensor = onnx::Constant[value={2}]() %2088 : Long() = onnx::Gather[axis=0](%2086, %2087) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %2089 : Long() = onnx::Constant[value={2}]() %2090 : LongTensor = onnx::Div(%2088, %2089) %2091 : Tensor = onnx::Cast[to=7](%2090) %2092 : Long() = onnx::Cast[to=7](%2091) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %2094 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%2079, %2559) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2095 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%2094, %decoder.layers.12.self_attn.in_proj_bias) %2096 : Float(58:1536, 1:1536, 512:1), %2097 : Float(58:512, 1:512, 512:1), %2098 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%2095) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2099 : Float() = onnx::Constant[value={0.0625}]() %2100 : Float(58:512, 1:512, 512:1) = onnx::Mul(%2096, %2099) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2101 : Long() = onnx::Constant[value={2}]() %2102 : Long() = onnx::Mul(%2085, %2101) %2103 : Tensor = onnx::Unsqueeze[axes=[0]](%2082) %2104 : Tensor = onnx::Unsqueeze[axes=[0]](%2102) %2105 : Tensor = onnx::Unsqueeze[axes=[0]](%2092) %2106 : Tensor = onnx::Concat[axis=0](%2103, %2104, %2105) %2107 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2100, %2106) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2108 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2107) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2109 : Long() = onnx::Constant[value={2}]() %2110 : Long() = onnx::Mul(%2085, %2109) %2113 : Tensor = onnx::Unsqueeze[axes=[0]](%2110) %2114 : Tensor = onnx::Unsqueeze[axes=[0]](%2092) %2115 : Tensor = onnx::Concat[axis=0](%2560, %2113, %2114) %2116 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2097, %2115) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %2117 : Long() = onnx::Constant[value={2}]() %2118 : Long() = onnx::Mul(%2085, %2117) %2121 : Tensor = onnx::Unsqueeze[axes=[0]](%2118) %2122 : Tensor = onnx::Unsqueeze[axes=[0]](%2092) %2123 : Tensor = onnx::Concat[axis=0](%2561, %2121, %2122) %2124 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2098, %2123) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2125 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2124) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2126 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%2116) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %2127 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%2108, %2126) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %2128 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%2127) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2129 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%2128, %2125) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %2130 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2129) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %2131 : Tensor = onnx::Unsqueeze[axes=[0]](%2082) %2132 : Tensor = onnx::Unsqueeze[axes=[0]](%2085) %2133 : Tensor = onnx::Unsqueeze[axes=[0]](%2088) %2134 : Tensor = onnx::Concat[axis=0](%2131, %2132, %2133) %2135 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%2130, %2134) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %2137 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2135, %2562) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2138 : Float(58:512, 1:512, 512:1) = onnx::Add(%2137, %decoder.layers.12.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2139 : Float(58:512, 1:512, 512:1) = onnx::Add(%2079, %2138) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %2141 : Tensor = onnx::ReduceMean[axes=[-1]](%2139) %2142 : FloatTensor = onnx::Sub(%2139, %2141) %2143 : Tensor = onnx::Cast[to=1](%2142) %2145 : Tensor = onnx::Pow(%2143, %2563) %2146 : Tensor = onnx::ReduceMean[axes=[-1]](%2145) %2147 : Float() = onnx::Constant[value={1e-05}]() %2148 : FloatTensor = onnx::Add(%2146, %2147) %2149 : Tensor = onnx::Sqrt(%2148) %2150 : FloatTensor = onnx::Div(%2142, %2149) %2151 : FloatTensor = onnx::Mul(%2150, %decoder.layers.12.norm1.weight) %2152 : Float(58:512, 1:512, 512:1) = onnx::Add(%2151, %decoder.layers.12.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %2154 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2152, %2564) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2155 : Float(58:512, 1:512, 512:1) = onnx::Add(%2154, %decoder.layers.12.linear1.bias) %2156 : Float(58:512, 1:512, 512:1) = onnx::Relu(%2155) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2158 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2156, %2565) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2159 : Float(58:512, 1:512, 512:1) = onnx::Add(%2158, %decoder.layers.12.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2160 : Float(58:512, 1:512, 512:1) = onnx::Add(%2152, %2159) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %2162 : Tensor = onnx::ReduceMean[axes=[-1]](%2160) %2163 : FloatTensor = onnx::Sub(%2160, %2162) %2164 : Tensor = onnx::Cast[to=1](%2163) %2166 : Tensor = onnx::Pow(%2164, %2566) %2167 : Tensor = onnx::ReduceMean[axes=[-1]](%2166) %2168 : Float() = onnx::Constant[value={1e-05}]() %2169 : FloatTensor = onnx::Add(%2167, %2168) %2170 : Tensor = onnx::Sqrt(%2169) %2171 : FloatTensor = onnx::Div(%2163, %2170) %2172 : FloatTensor = onnx::Mul(%2171, %decoder.layers.12.norm2.weight) %2173 : Float(58:512, 1:512, 512:1) = onnx::Add(%2172, %decoder.layers.12.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %2174 : Tensor = onnx::Shape(%2173) %2175 : Tensor = onnx::Constant[value={0}]() %2176 : Long() = onnx::Gather[axis=0](%2174, %2175) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %2177 : Tensor = onnx::Shape(%2173) %2178 : Tensor = onnx::Constant[value={1}]() %2179 : Long() = onnx::Gather[axis=0](%2177, %2178) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %2180 : Tensor = onnx::Shape(%2173) %2181 : Tensor = onnx::Constant[value={2}]() %2182 : Long() = onnx::Gather[axis=0](%2180, %2181) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %2183 : Long() = onnx::Constant[value={2}]() %2184 : LongTensor = onnx::Div(%2182, %2183) %2185 : Tensor = onnx::Cast[to=7](%2184) %2186 : Long() = onnx::Cast[to=7](%2185) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %2188 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%2173, %2567) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2189 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%2188, %decoder.layers.13.self_attn.in_proj_bias) %2190 : Float(58:1536, 1:1536, 512:1), %2191 : Float(58:512, 1:512, 512:1), %2192 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%2189) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2193 : Float() = onnx::Constant[value={0.0625}]() %2194 : Float(58:512, 1:512, 512:1) = onnx::Mul(%2190, %2193) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2195 : Long() = onnx::Constant[value={2}]() %2196 : Long() = onnx::Mul(%2179, %2195) %2197 : Tensor = onnx::Unsqueeze[axes=[0]](%2176) %2198 : Tensor = onnx::Unsqueeze[axes=[0]](%2196) %2199 : Tensor = onnx::Unsqueeze[axes=[0]](%2186) %2200 : Tensor = onnx::Concat[axis=0](%2197, %2198, %2199) %2201 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2194, %2200) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2202 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2201) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2203 : Long() = onnx::Constant[value={2}]() %2204 : Long() = onnx::Mul(%2179, %2203) %2207 : Tensor = onnx::Unsqueeze[axes=[0]](%2204) %2208 : Tensor = onnx::Unsqueeze[axes=[0]](%2186) %2209 : Tensor = onnx::Concat[axis=0](%2568, %2207, %2208) %2210 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2191, %2209) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %2211 : Long() = onnx::Constant[value={2}]() %2212 : Long() = onnx::Mul(%2179, %2211) %2215 : Tensor = onnx::Unsqueeze[axes=[0]](%2212) %2216 : Tensor = onnx::Unsqueeze[axes=[0]](%2186) %2217 : Tensor = onnx::Concat[axis=0](%2569, %2215, %2216) %2218 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2192, %2217) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2219 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2218) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2220 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%2210) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %2221 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%2202, %2220) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %2222 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%2221) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2223 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%2222, %2219) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %2224 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2223) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %2225 : Tensor = onnx::Unsqueeze[axes=[0]](%2176) %2226 : Tensor = onnx::Unsqueeze[axes=[0]](%2179) %2227 : Tensor = onnx::Unsqueeze[axes=[0]](%2182) %2228 : Tensor = onnx::Concat[axis=0](%2225, %2226, %2227) %2229 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%2224, %2228) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %2231 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2229, %2570) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2232 : Float(58:512, 1:512, 512:1) = onnx::Add(%2231, %decoder.layers.13.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2233 : Float(58:512, 1:512, 512:1) = onnx::Add(%2173, %2232) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %2235 : Tensor = onnx::ReduceMean[axes=[-1]](%2233) %2236 : FloatTensor = onnx::Sub(%2233, %2235) %2237 : Tensor = onnx::Cast[to=1](%2236) %2239 : Tensor = onnx::Pow(%2237, %2571) %2240 : Tensor = onnx::ReduceMean[axes=[-1]](%2239) %2241 : Float() = onnx::Constant[value={1e-05}]() %2242 : FloatTensor = onnx::Add(%2240, %2241) %2243 : Tensor = onnx::Sqrt(%2242) %2244 : FloatTensor = onnx::Div(%2236, %2243) %2245 : FloatTensor = onnx::Mul(%2244, %decoder.layers.13.norm1.weight) %2246 : Float(58:512, 1:512, 512:1) = onnx::Add(%2245, %decoder.layers.13.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %2248 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2246, %2572) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2249 : Float(58:512, 1:512, 512:1) = onnx::Add(%2248, %decoder.layers.13.linear1.bias) %2250 : Float(58:512, 1:512, 512:1) = onnx::Relu(%2249) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2252 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2250, %2573) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2253 : Float(58:512, 1:512, 512:1) = onnx::Add(%2252, %decoder.layers.13.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2254 : Float(58:512, 1:512, 512:1) = onnx::Add(%2246, %2253) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %2256 : Tensor = onnx::ReduceMean[axes=[-1]](%2254) %2257 : FloatTensor = onnx::Sub(%2254, %2256) %2258 : Tensor = onnx::Cast[to=1](%2257) %2260 : Tensor = onnx::Pow(%2258, %2574) %2261 : Tensor = onnx::ReduceMean[axes=[-1]](%2260) %2262 : Float() = onnx::Constant[value={1e-05}]() %2263 : FloatTensor = onnx::Add(%2261, %2262) %2264 : Tensor = onnx::Sqrt(%2263) %2265 : FloatTensor = onnx::Div(%2257, %2264) %2266 : FloatTensor = onnx::Mul(%2265, %decoder.layers.13.norm2.weight) %2267 : Float(58:512, 1:512, 512:1) = onnx::Add(%2266, %decoder.layers.13.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %2268 : Tensor = onnx::Shape(%2267) %2269 : Tensor = onnx::Constant[value={0}]() %2270 : Long() = onnx::Gather[axis=0](%2268, %2269) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %2271 : Tensor = onnx::Shape(%2267) %2272 : Tensor = onnx::Constant[value={1}]() %2273 : Long() = onnx::Gather[axis=0](%2271, %2272) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %2274 : Tensor = onnx::Shape(%2267) %2275 : Tensor = onnx::Constant[value={2}]() %2276 : Long() = onnx::Gather[axis=0](%2274, %2275) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %2277 : Long() = onnx::Constant[value={2}]() %2278 : LongTensor = onnx::Div(%2276, %2277) %2279 : Tensor = onnx::Cast[to=7](%2278) %2280 : Long() = onnx::Cast[to=7](%2279) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %2282 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%2267, %2575) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2283 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%2282, %decoder.layers.14.self_attn.in_proj_bias) %2284 : Float(58:1536, 1:1536, 512:1), %2285 : Float(58:512, 1:512, 512:1), %2286 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%2283) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2287 : Float() = onnx::Constant[value={0.0625}]() %2288 : Float(58:512, 1:512, 512:1) = onnx::Mul(%2284, %2287) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2289 : Long() = onnx::Constant[value={2}]() %2290 : Long() = onnx::Mul(%2273, %2289) %2291 : Tensor = onnx::Unsqueeze[axes=[0]](%2270) %2292 : Tensor = onnx::Unsqueeze[axes=[0]](%2290) %2293 : Tensor = onnx::Unsqueeze[axes=[0]](%2280) %2294 : Tensor = onnx::Concat[axis=0](%2291, %2292, %2293) %2295 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2288, %2294) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2296 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2295) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2297 : Long() = onnx::Constant[value={2}]() %2298 : Long() = onnx::Mul(%2273, %2297) %2301 : Tensor = onnx::Unsqueeze[axes=[0]](%2298) %2302 : Tensor = onnx::Unsqueeze[axes=[0]](%2280) %2303 : Tensor = onnx::Concat[axis=0](%2576, %2301, %2302) %2304 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2285, %2303) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %2305 : Long() = onnx::Constant[value={2}]() %2306 : Long() = onnx::Mul(%2273, %2305) %2309 : Tensor = onnx::Unsqueeze[axes=[0]](%2306) %2310 : Tensor = onnx::Unsqueeze[axes=[0]](%2280) %2311 : Tensor = onnx::Concat[axis=0](%2577, %2309, %2310) %2312 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2286, %2311) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2313 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2312) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2314 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%2304) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %2315 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%2296, %2314) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %2316 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%2315) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2317 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%2316, %2313) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %2318 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2317) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %2319 : Tensor = onnx::Unsqueeze[axes=[0]](%2270) %2320 : Tensor = onnx::Unsqueeze[axes=[0]](%2273) %2321 : Tensor = onnx::Unsqueeze[axes=[0]](%2276) %2322 : Tensor = onnx::Concat[axis=0](%2319, %2320, %2321) %2323 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%2318, %2322) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %2325 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2323, %2578) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2326 : Float(58:512, 1:512, 512:1) = onnx::Add(%2325, %decoder.layers.14.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2327 : Float(58:512, 1:512, 512:1) = onnx::Add(%2267, %2326) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %2329 : Tensor = onnx::ReduceMean[axes=[-1]](%2327) %2330 : FloatTensor = onnx::Sub(%2327, %2329) %2331 : Tensor = onnx::Cast[to=1](%2330) %2333 : Tensor = onnx::Pow(%2331, %2579) %2334 : Tensor = onnx::ReduceMean[axes=[-1]](%2333) %2335 : Float() = onnx::Constant[value={1e-05}]() %2336 : FloatTensor = onnx::Add(%2334, %2335) %2337 : Tensor = onnx::Sqrt(%2336) %2338 : FloatTensor = onnx::Div(%2330, %2337) %2339 : FloatTensor = onnx::Mul(%2338, %decoder.layers.14.norm1.weight) %2340 : Float(58:512, 1:512, 512:1) = onnx::Add(%2339, %decoder.layers.14.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %2342 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2340, %2580) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2343 : Float(58:512, 1:512, 512:1) = onnx::Add(%2342, %decoder.layers.14.linear1.bias) %2344 : Float(58:512, 1:512, 512:1) = onnx::Relu(%2343) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2346 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2344, %2581) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2347 : Float(58:512, 1:512, 512:1) = onnx::Add(%2346, %decoder.layers.14.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2348 : Float(58:512, 1:512, 512:1) = onnx::Add(%2340, %2347) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %2350 : Tensor = onnx::ReduceMean[axes=[-1]](%2348) %2351 : FloatTensor = onnx::Sub(%2348, %2350) %2352 : Tensor = onnx::Cast[to=1](%2351) %2354 : Tensor = onnx::Pow(%2352, %2582) %2355 : Tensor = onnx::ReduceMean[axes=[-1]](%2354) %2356 : Float() = onnx::Constant[value={1e-05}]() %2357 : FloatTensor = onnx::Add(%2355, %2356) %2358 : Tensor = onnx::Sqrt(%2357) %2359 : FloatTensor = onnx::Div(%2351, %2358) %2360 : FloatTensor = onnx::Mul(%2359, %decoder.layers.14.norm2.weight) %2361 : Float(58:512, 1:512, 512:1) = onnx::Add(%2360, %decoder.layers.14.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %2362 : Tensor = onnx::Shape(%2361) %2363 : Tensor = onnx::Constant[value={0}]() %2364 : Long() = onnx::Gather[axis=0](%2362, %2363) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %2365 : Tensor = onnx::Shape(%2361) %2366 : Tensor = onnx::Constant[value={1}]() %2367 : Long() = onnx::Gather[axis=0](%2365, %2366) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %2368 : Tensor = onnx::Shape(%2361) %2369 : Tensor = onnx::Constant[value={2}]() %2370 : Long() = onnx::Gather[axis=0](%2368, %2369) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3948:0 %2371 : Long() = onnx::Constant[value={2}]() %2372 : LongTensor = onnx::Div(%2370, %2371) %2373 : Tensor = onnx::Cast[to=7](%2372) %2374 : Long() = onnx::Cast[to=7](%2373) # /opt/conda/lib/python3.7/site-packages/torch/tensor.py:424:0 %2376 : Float(58:1536, 1:1536, 1536:1) = onnx::MatMul(%2361, %2583) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2377 : Float(58:1536, 1:1536, 1536:1) = onnx::Add(%2376, %decoder.layers.15.self_attn.in_proj_bias) %2378 : Float(58:1536, 1:1536, 512:1), %2379 : Float(58:512, 1:512, 512:1), %2380 : Float(58:512, 1:512, 512:1) = onnx::Split[axis=-1, split=[512, 512, 512]](%2377) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2381 : Float() = onnx::Constant[value={0.0625}]() %2382 : Float(58:512, 1:512, 512:1) = onnx::Mul(%2378, %2381) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2383 : Long() = onnx::Constant[value={2}]() %2384 : Long() = onnx::Mul(%2367, %2383) %2385 : Tensor = onnx::Unsqueeze[axes=[0]](%2364) %2386 : Tensor = onnx::Unsqueeze[axes=[0]](%2384) %2387 : Tensor = onnx::Unsqueeze[axes=[0]](%2374) %2388 : Tensor = onnx::Concat[axis=0](%2385, %2386, %2387) %2389 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2382, %2388) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2390 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2389) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4077:0 %2391 : Long() = onnx::Constant[value={2}]() %2392 : Long() = onnx::Mul(%2367, %2391) %2395 : Tensor = onnx::Unsqueeze[axes=[0]](%2392) %2396 : Tensor = onnx::Unsqueeze[axes=[0]](%2374) %2397 : Tensor = onnx::Concat[axis=0](%2584, %2395, %2396) %2398 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2379, %2397) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4079:0 %2399 : Long() = onnx::Constant[value={2}]() %2400 : Long() = onnx::Mul(%2367, %2399) %2403 : Tensor = onnx::Unsqueeze[axes=[0]](%2400) %2404 : Tensor = onnx::Unsqueeze[axes=[0]](%2374) %2405 : Tensor = onnx::Concat[axis=0](%2585, %2403, %2404) %2406 : Float(58:512, 2:256, 256:1) = onnx::Reshape(%2380, %2405) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2407 : Float(2:256, 58:512, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2406) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4081:0 %2408 : Float(2:256, 256:1, 58:512) = onnx::Transpose[perm=[1, 2, 0]](%2398) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %2409 : Float(2:3364, 58:58, 58:1) = onnx::MatMul(%2390, %2408) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4108:0 %2410 : Float(2:3364, 58:58, 58:1) = onnx::Softmax[axis=2](%2409) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2411 : Float(2:14848, 58:256, 256:1) = onnx::MatMul(%2410, %2407) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4130:0 %2412 : Float(58:512, 2:256, 256:1) = onnx::Transpose[perm=[1, 0, 2]](%2411) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %2413 : Tensor = onnx::Unsqueeze[axes=[0]](%2364) %2414 : Tensor = onnx::Unsqueeze[axes=[0]](%2367) %2415 : Tensor = onnx::Unsqueeze[axes=[0]](%2370) %2416 : Tensor = onnx::Concat[axis=0](%2413, %2414, %2415) %2417 : Float(58:512, 1:512, 512:1) = onnx::Reshape(%2412, %2416) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:4132:0 %2419 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2417, %2586) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2420 : Float(58:512, 1:512, 512:1) = onnx::Add(%2419, %decoder.layers.15.self_attn.out_proj.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2421 : Float(58:512, 1:512, 512:1) = onnx::Add(%2361, %2420) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:295:0 %2423 : Tensor = onnx::ReduceMean[axes=[-1]](%2421) %2424 : FloatTensor = onnx::Sub(%2421, %2423) %2425 : Tensor = onnx::Cast[to=1](%2424) %2427 : Tensor = onnx::Pow(%2425, %2587) %2428 : Tensor = onnx::ReduceMean[axes=[-1]](%2427) %2429 : Float() = onnx::Constant[value={1e-05}]() %2430 : FloatTensor = onnx::Add(%2428, %2429) %2431 : Tensor = onnx::Sqrt(%2430) %2432 : FloatTensor = onnx::Div(%2424, %2431) %2433 : FloatTensor = onnx::Mul(%2432, %decoder.layers.15.norm1.weight) %2434 : Float(58:512, 1:512, 512:1) = onnx::Add(%2433, %decoder.layers.15.norm1.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %2436 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2434, %2588) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2437 : Float(58:512, 1:512, 512:1) = onnx::Add(%2436, %decoder.layers.15.linear1.bias) %2438 : Float(58:512, 1:512, 512:1) = onnx::Relu(%2437) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2440 : Float(58:512, 1:512, 512:1) = onnx::MatMul(%2438, %2589) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1676:0 %2441 : Float(58:512, 1:512, 512:1) = onnx::Add(%2440, %decoder.layers.15.linear2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:973:0 %2442 : Float(58:512, 1:512, 512:1) = onnx::Add(%2434, %2441) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/transformer.py:298:0 %2444 : Tensor = onnx::ReduceMean[axes=[-1]](%2442) %2445 : FloatTensor = onnx::Sub(%2442, %2444) %2446 : Tensor = onnx::Cast[to=1](%2445) %2448 : Tensor = onnx::Pow(%2446, %2590) %2449 : Tensor = onnx::ReduceMean[axes=[-1]](%2448) %2450 : Float() = onnx::Constant[value={1e-05}]() %2451 : FloatTensor = onnx::Add(%2449, %2450) %2452 : Tensor = onnx::Sqrt(%2451) %2453 : FloatTensor = onnx::Div(%2445, %2452) %2454 : FloatTensor = onnx::Mul(%2453, %decoder.layers.15.norm2.weight) %2455 : Float(58:512, 1:512, 512:1) = onnx::Add(%2454, %decoder.layers.15.norm2.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2049:0 %2456 : Float(1:29696, 512:58, 58:1) = onnx::Transpose[perm=[1, 2, 0]](%2455) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model.py:668:0 %2457 : Float(1:57942, 999:58, 58:1) = onnx::Conv[dilations=[1], group=1, kernel_shape=[1], pads=[0, 0], strides=[1]](%2456, %fc.weight, %fc.bias) # /opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py:257:0 %2458 : Float(1:57942, 58:999, 999:1) = onnx::Transpose[perm=[0, 2, 1]](%2457) # /home/keras/notebook/nvme/aveysov/open_stt_pretrained_deploy/stt_pretrained/models/model.py:671:0 %output : Float(1:57942, 58:999, 999:1) = onnx::Softmax[axis=2](%2458) # /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1498:0 return (%output) ```

Question

Looks like int64 storage of some constants is an artefact of ONNX and PyTorch export. I have seen that in other ONNX-related projects this issue was widely addressed. Can this be done here as well? Or does anyone have a recipe to export models differently?

Alternative Solutions

Transform to TensorFlow, then to tf.js. Looks like an inferior option, just because you have to transform 3 times instead of one. I will also raise a similar issue on PyTorch forums.

microsoft / onnxjs