def main():
use_gpu = torch.cuda.is_available()
# Create model
# models.resnet18(num_classes=365)
# model = ColorNet()
#args = get_args()
#model = MODEL(args)
# state_dict = torch.load("./checkpoint/checkpoint6/model_epoch133_step1.pth")
# new_state_dict = OrderedDict()
# for k, v in state_dict.items():
# k = k.replace('module.', '')
# new_state_dict[k] = v
# model = torch.nn.DataParallel(model)
# model.load_state_dict(new_state_dict)
parser = argparse.ArgumentParser()
# Input paths
parser.add_argument('--model',
type=str,
default='',
help='path to model to evaluate')
parser.add_argument('--cnn_model',
type=str,
default='resnet101',
help='resnet101, resnet152')
parser.add_argument('--infos_path',
type=str,
default='',
help='path to infos to evaluate')
parser.add_argument('--only_lang_eval',
type=int,
default=0,
help='lang eval on saved results')
parser.add_argument(
'--force',
type=int,
default=0,
help='force to evaluate no matter if there are results available')
opts.add_eval_options(parser)
opts.add_diversity_opts(parser)
opt = parser.parse_args()
opt.caption_model = 'newfc'
opt.infos_path = '/home/zzgyf/github_yifan/ImageCaptioning.pytorch/models/infos_fc_nsc-best.pkl'
with open(opt.infos_path, 'rb') as f:
infos = utils.pickle_load(f)
replace = [
'input_fc_dir', 'input_att_dir', 'input_box_dir', 'input_label_h5',
'input_json', 'batch_size', 'id'
]
ignore = ['start_from']
for k in vars(infos['opt']).keys():
if k in replace:
setattr(opt, k, getattr(opt, k) or getattr(infos['opt'], k, ''))
elif k not in ignore:
if not k in vars(opt):
vars(opt).update({k: vars(infos['opt'])[k]
}) # copy over options from model
vocab = infos['vocab'] # ix -> word mapping
opt.vocab = vocab
model = models.setup(opt)
checkpoint = torch.load(
"/home/zzgyf/github_yifan/ImageCaptioning.pytorch/models/model-best.pth"
)
model.load_state_dict(checkpoint)
# print(model)
#input_shape = (1, 256, 256)
cocotest_bu_fc_size = (10, 2048)
cocotest_bu_att_size = (10, 0, 0)
labels_size = (10, 5, 18)
masks_size = (10, 5, 18)
model_onnx_path = "./image_captioning.onnx"
model.train(False)
# Export the model to an ONNX file
# dummy_input = Variable(torch.randn(1, *input_shape))
# dummy_input = Variable(torch.randn(10, 2048), torch.randn(10, 0, 0), torch.randint(5200, (10, 5, 18)), torch.randint(1, (10, 5, 18)))
dummy_cocotest_bu_fc = Variable(torch.randn(10, 2048))
dummy_cocotest_bu_att = Variable(torch.randn(10, 0, 0))
dummy_labels = Variable(torch.randint(5200, (10, 5, 18)))
dummy_masks = Variable(torch.randint(1, (10, 5, 18)))
#output = torch_onnx.export(model, dummy_input, model_onnx_path, verbose=False)
output = torch_onnx.export(model,
(dummy_cocotest_bu_fc, dummy_cocotest_bu_att,
dummy_labels, dummy_masks),
model_onnx_path,
verbose=False)
print("Export of torch_model.onnx complete!")
help='resnet101, resnet152')
parser.add_argument('--infos_path',
type=str,
default='',
help='path to infos to evaluate')
parser.add_argument('--only_lang_eval',
type=int,
default=0,
help='lang eval on saved results')
parser.add_argument(
'--force',
type=int,
default=0,
help='force to evaluate no matter if there are results available')
opts.add_eval_options(parser)
opts.add_diversity_opts(parser)
opt = parser.parse_args()
# Load infos
with open(opt.infos_path, 'rb') as f:
infos = utils.pickle_load(f)
# override and collect parameters
replace = [
'input_fc_dir', 'input_att_dir', 'input_box_dir', 'input_label_h5',
'input_json', 'batch_size', 'id'
]
ignore = ['start_from']
for k in vars(infos['opt']).keys():
if k in replace:
def check():
parser = argparse.ArgumentParser()
# Input paths
parser.add_argument('--model',
type=str,
default='',
help='path to model to evaluate')
parser.add_argument('--cnn_model',
type=str,
default='resnet101',
help='resnet101, resnet152')
parser.add_argument('--infos_path',
type=str,
default='',
help='path to infos to evaluate')
parser.add_argument('--only_lang_eval',
type=int,
default=0,
help='lang eval on saved results')
parser.add_argument(
'--force',
type=int,
default=0,
help='force to evaluate no matter if there are results available')
opts.add_eval_options(parser)
opts.add_diversity_opts(parser)
opt = parser.parse_args()
opt.caption_model = 'newfc'
opt.infos_path = '/home/zzgyf/github_yifan/ImageCaptioning.pytorch/models/infos_fc_nsc-best.pkl'
with open(opt.infos_path, 'rb') as f:
infos = utils.pickle_load(f)
replace = [
'input_fc_dir', 'input_att_dir', 'input_box_dir', 'input_label_h5',
'input_json', 'batch_size', 'id'
]
ignore = ['start_from']
for k in vars(infos['opt']).keys():
if k in replace:
setattr(opt, k, getattr(opt, k) or getattr(infos['opt'], k, ''))
elif k not in ignore:
if not k in vars(opt):
vars(opt).update({k: vars(infos['opt'])[k]
}) # copy over options from model
vocab = infos['vocab'] # ix -> word mapping
opt.vocab = vocab
model = models.setup(opt)
checkpoint = torch.load(
"/home/zzgyf/github_yifan/ImageCaptioning.pytorch/models/model-best.pth"
)
model.load_state_dict(checkpoint)
# torch.nn.utils.remove_weight_norm(model.head[0])
# for i in range(2):
# for j in [0,2,3]:
# torch.nn.utils.remove_weight_norm(model.body[i].body[j])
# torch.nn.utils.remove_weight_norm(model.tail[0])
# torch.nn.utils.remove_weight_norm(model.skip[0])
model.eval()
ort_session = onnxruntime.InferenceSession("image_captioning.onnx")
dummy_cocotest_bu_fc = Variable(torch.randn(10, 2048))
dummy_cocotest_bu_att = Variable(torch.randn(10, 0, 0))
dummy_labels = Variable(torch.randint(5200, (10, 5, 18)))
dummy_masks = Variable(torch.randint(1, (10, 5, 18)))
x = (dummy_cocotest_bu_fc, dummy_cocotest_bu_att, dummy_labels,
dummy_masks)
#x = torch.randn(1, 3, 392, 392, requires_grad=False)
#torch_out = model(x)
# # Load the ONNX model
# model = onnx.load("wdsr_b.onnx")
# # Check that the IR is well formed
# onnx.checker.check_model(model)
# # Print a human readable representation of the graph
# onnx.helper.printable_graph(model.graph)
# compute ONNX Runtime output prediction
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(x)}
ort_outs = ort_session.run(None, ort_inputs)
# compare ONNX Runtime and PyTorch results
np.testing.assert_allclose(to_numpy(torch_out),
ort_outs[0],
rtol=1e-03,
atol=1e-05)
