fbn.NumPyModel(model, bounds=(0, 1), data_format="foo")
fmodel = fbn.NumPyModel(model, bounds=(0, 1))
with pytest.raises(ValueError, match="data_format"):
x, _ = fbn.samples(fmodel, dataset="imagenet", batchsize=16)
fmodel = fbn.NumPyModel(model, bounds=(0, 1), data_format="channels_first")
with pytest.warns(UserWarning, match="returning NumPy arrays"):
x, _ = fbn.samples(fmodel, dataset="imagenet", batchsize=16)
x = ep.astensor(x)
y = fmodel(x).argmax(axis=-1)
return fmodel, x, y
@pytest.fixture(scope="session", params=list(models.keys()))
def fmodel_and_data_ext(request: Any) -> ModelDescriptionAndData:
global models
model_description = models[request.param]
model_and_data = model_description.model_fn(request)
return ModelDescriptionAndData(model_and_data, *model_description[1:])
@pytest.fixture(scope="session", params=models_for_attacks)
def fmodel_and_data_ext_for_attacks(request: Any) -> ModelDescriptionAndData:
global models
model_description = models[request.param]
model_and_data = model_description.model_fn(request)
return ModelDescriptionAndData(model_and_data, *model_description[1:])
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
]
trans = transforms.Compose(trans)
test_dataset, test_char_idx = create_dataset(args.root, test_data, trans)
# Load model
device = "cpu" if args.gpu < 0 else "cuda:{}".format(args.gpu)
saved_models = torch.load(args.model_fn, map_location="cpu")
trunk, model = create_models(*saved_models["args"])
models = {"trunk": trunk, "embedder": model}
for key in models.keys():
models[key].load_state_dict(saved_models[key])
models[key].to(device)
colormap = plt.get_cmap("hsv")
plots = []
for enroll, color in zip(args.n_enroll, np.linspace(0,0.8,len(args.n_enroll))):
print("Enroll", enroll)
enrolls, targets = split_enroll_target(test_char_idx, n_enroll=enroll, min_nimg=args.min_freq)
print(f"Found {len(enrolls)} characters.")
tops, prrcs = evaluate(models, test_dataset, enrolls, targets, mode=args.mode)
glpr, glrc, lopr, lorc = prrcs
p_glo, = plt.plot(glrc, glpr, color=colormap(color), ls="-")
p_loc, = plt.plot(lorc, lopr, color=colormap(color), ls=":")
plots.append((p_glo, p_loc))
'densenet169': 224,
'densenet201': 224,
'mobilenet_v2': 224,
'vgg11': 224,
'vgg11_bn': 224,
'vgg13': 224,
'vgg13_bn': 224,
'vgg16': 224,
'vgg16_bn': 224,
'vgg19': 224,
'vgg19_bn': 224,
'shufflenet_v2_x0_5': 224,
'shufflenet_v2_x1_0': 224,
'shufflenet_v2_x1_5': 224,
'shufflenet_v2_x2_0': 224,
'segmentation.deeplabv3_resnet101': 513,
#'segmentation.deeplabv3_resnet50':513
}
parser.add_argument('model', choices=sorted(models.keys()))
args = parser.parse_args()
modelFunc = getattr(torchvision.models, args.model)
model = modelFunc(pretrained=True)
if hasattr(model, 'transform_input'):
model.transform_input = False
input_size = models[args.model]
x = torch.randn(1, 3, input_size, input_size)
torch.onnx.export(model, x, args.model + '.onnx')
sys.exit(0)
models = {
'resnet18_clnet': resnet18_clnet,
'resnet34_clnet': resnet34_clnet,
'resnet50_clnet': resnet50_clnet,
'mobilenetv2_clnet': mobilenetv2_clnet,
'sphereface4': sphereface4,
'sphereface10': sphereface10,
'sphereface20': sphereface20,
'sphereface36': sphereface36,
'sphereface64': sphereface64,
'sphereface_mb2': mobilenet_sphereface,
'resnet18_ccs_net': resnet18_ccs_net,
'resnet34_ccs_net': resnet34_ccs_net,
'mobilenetv2_ccs_net': mobilenetv2_ccs_net
}
model_names = list(models.keys())
parser = argparse.ArgumentParser(
description='PyTorch Facial Recognition Net Training')
parser.add_argument('data', metavar='DIR', help='path to dataset')
parser.add_argument('--arch',
'-a',
metavar='ARCH',
default='resnet34_clnet',
choices=model_names,
help='model architecture: ' + ' | '.join(model_names) +
' (default: resnet18)')
parser.add_argument('-d',
'--dim',
default=128,
type=int,
#https://pytorch.org/docs/stable/torchvision/models.html
import os
import torch
import torchvision.models as models
models = {}
models['resnet18'] = {'dummy_input':'torch.randn(1, 3, 224, 224)'}
for model_name in models.keys():
dummy_input = models[model_name]['dummy_input']
py_file = '''
# Copyright 2018 The DNNC Authors. All Rights Reserved.
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.