debug_embed_params.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import sys

import torch
import torch.jit
from torch.autograd import Variable

import onnx
import caffe2.python.onnx.backend as c2
from test_pytorch_common import flatten


torch.set_default_tensor_type('torch.FloatTensor')
try:
    import torch
except ImportError:
    print('Cannot import torch, hence caffe2-torch test will not run.')
    sys.exit(0)


def run_embed_params(proto, model, input, state_dict=None, use_gpu=True):
    """
    This is only a helper debug function so we can test embed_params=False
    case as well on pytorch front
    This should likely be removed from the release version of the code
    """
    device = 'CPU'
    if use_gpu:
        device = 'CUDA'
    model_def = onnx.ModelProto.FromString(proto)
    onnx.checker.check_model(model_def)
    prepared = c2.prepare(model_def, device=device)

    if state_dict:
        parameters = []
        # Passed in state_dict may have a different order.  Make
        # sure our order is consistent with the model's order.
        # TODO: Even better: keyword arguments!
        for k in model.state_dict():
            if k not in state_dict:
                # Once PyTorch Module adds unnecessary parameter, the old pre-trained model does not have it.
                # Just simply pass the new one.
                # TODO: Please don't export unnecessary parameter.
                parameters.append(model.state_dict()[k])
            else:
                parameters.append(state_dict[k])
    else:
        parameters = list(model.state_dict().values())

    W = {}
    for k, v in zip(model_def.graph.input, flatten((input, parameters))):
        if isinstance(v, Variable):
            W[k.name] = v.data.cpu().numpy()
        else:
            W[k.name] = v.cpu().numpy()

    caffe2_out = prepared.run(inputs=W)

    return caffe2_out