mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
e845b0ab51
Summary: This PR: - Adds eliminate_unused_items pass that removes unused inputs and initializers. - Fixes run_embed_params function so it doesn't export unnecessary parameters. - Removes test_modifying_params in test_verify since it's no longer needed. Pull Request resolved: https://github.com/pytorch/pytorch/pull/42743 Reviewed By: hl475 Differential Revision: D23058954 Pulled By: houseroad fbshipit-source-id: cd1e81463285a0bf4e60766c8c87fc9a350d9c7e
59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
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 in state_dict:
|
|
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
|