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https://github.com/zebrajr/pytorch.git
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773d80747c
- Rename `test_pytorch_common` -> `pytorch_test_common`, `test_onnx_common` -> `onnx_test_common`, removing the test_ prefix to show that the files are not test cases - Remove import * in `test_pytorch_common` and adjust to import from `testing._internal.common_utils` (where functions are actually defined) instead - Import modules only in `test_pytorch_onnx_onnxruntime` (too many to handle in a single PR in other tests) (The skips are exceptions) Pull Request resolved: https://github.com/pytorch/pytorch/pull/81141 Approved by: https://github.com/BowenBao
55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
import sys
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import onnx
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import pytorch_test_common
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import caffe2.python.onnx.backend as c2
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import torch
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import torch.jit
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from torch.autograd import Variable
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torch.set_default_tensor_type("torch.FloatTensor")
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try:
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import torch
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except ImportError:
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print("Cannot import torch, hence caffe2-torch test will not run.")
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sys.exit(0)
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def run_embed_params(proto, model, input, state_dict=None, use_gpu=True):
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"""
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This is only a helper debug function so we can test embed_params=False
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case as well on pytorch front
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This should likely be removed from the release version of the code
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"""
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device = "CPU"
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if use_gpu:
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device = "CUDA"
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model_def = onnx.ModelProto.FromString(proto)
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onnx.checker.check_model(model_def)
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prepared = c2.prepare(model_def, device=device)
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if state_dict:
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parameters = []
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# Passed in state_dict may have a different order. Make
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# sure our order is consistent with the model's order.
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# TODO: Even better: keyword arguments!
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for k in model.state_dict():
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if k in state_dict:
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parameters.append(state_dict[k])
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else:
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parameters = list(model.state_dict().values())
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W = {}
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for k, v in zip(
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model_def.graph.input, pytorch_test_common.flatten((input, parameters))
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):
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if isinstance(v, Variable):
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W[k.name] = v.data.cpu().numpy()
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else:
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W[k.name] = v.cpu().numpy()
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caffe2_out = prepared.run(inputs=W)
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return caffe2_out
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