def create_model(model_name, num_classes=1000, pretrained=False, **kwargs):
if 'test_time_pool' in kwargs:
test_time_pool = kwargs.pop('test_time_pool')
else:
test_time_pool = True
if 'extra' in kwargs:
extra = kwargs.pop('extra')
else:
extra = True
if model_name == 'dpn68':
model = dpn68(
num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
elif model_name == 'dpn68b':
model = dpn68b(
num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
elif model_name == 'dpn92':
model = dpn92(
num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool, extra=extra)
elif model_name == 'dpn98':
model = dpn98(
num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
elif model_name == 'dpn131':
model = dpn131(
num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
elif model_name == 'dpn107':
model = dpn107(
num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
elif model_name == 'resnet18':
model = resnet18(num_classes=num_classes, pretrained=pretrained, **kwargs)
elif model_name == 'resnet34':
model = resnet34(num_classes=num_classes, pretrained=pretrained, **kwargs)
elif model_name == 'resnet50':
model = resnet50(num_classes=num_classes, pretrained=pretrained, **kwargs)
elif model_name == 'resnet101':
model = resnet101(num_classes=num_classes, pretrained=pretrained, **kwargs)
elif model_name == 'resnet152':
model = resnet152(num_classes=num_classes, pretrained=pretrained, **kwargs)
elif model_name == 'densenet121':
model = densenet121(num_classes=num_classes, pretrained=pretrained, **kwargs)
elif model_name == 'densenet161':
model = densenet161(num_classes=num_classes, pretrained=pretrained, **kwargs)
elif model_name == 'densenet169':
model = densenet169(num_classes=num_classes, pretrained=pretrained, **kwargs)
elif model_name == 'densenet201':
model = densenet201(num_classes=num_classes, pretrained=pretrained, **kwargs)
elif model_name == 'inception_v3':
model = inception_v3(
num_classes=num_classes, pretrained=pretrained, transform_input=False, **kwargs)
else:
assert False, "Unknown model architecture (%s)" % model_name
return model
def test_densenet(self):
# Densenet-121 model
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(densenet121()), toC(x))
def __init__(self, pretrained=True, progress=True, **kwargs):
super(densenet121, self).__init__()
backbone = densenet.densenet121(pretrained, progress, **kwargs)
self.backbone = backbone.features
def create_model(model_name, num_classes=1000, pretrained=False, **kwargs):
if 'test_time_pool' in kwargs:
test_time_pool = kwargs.pop('test_time_pool')
else:
test_time_pool = True
if model_name == 'dpn68':
model = dpn68(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool,
**kwargs)
elif model_name == 'dpn92':
model = dpn92(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool,
**kwargs)
elif model_name == 'dpn98':
model = dpn98(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool,
**kwargs)
elif model_name == 'dpn131':
model = dpn131(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool,
**kwargs)
elif model_name == 'dpn107':
model = dpn107(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool,
**kwargs)
elif model_name == 'resnet18':
model = resnet18(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet34':
model = resnet34(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet50':
model = resnet50(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet101':
model = resnet101(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet152':
model = resnet152(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet121':
model = densenet121(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet161':
model = densenet161(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet169':
model = densenet169(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet201':
model = densenet201(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'inception_v3':
model = inception_v3(num_classes=num_classes,
pretrained=pretrained,
transform_input=False,
**kwargs)
else:
assert False, "Unknown model architecture (%s)" % model_name
return model
def loss_selector(loss_net):
#base
if loss_net == "vgg16":
from torchvision.models.vgg import vgg16
net = vgg16(pretrained=True)
loss_network = nn.Sequential(*list(net.features)[:31]).eval()
return loss_network
elif loss_net == "vgg16_bn":
from torchvision.models.vgg import vgg16_bn
net = vgg16_bn(pretrained=True)
loss_network = nn.Sequential(*list(net.features)[:44]).eval()
return loss_network
elif loss_net == "resnet50":
from torchvision.models.resnet import resnet50
net=resnet50(pretrained=True)
loss_network=nn.Sequential(*[child_module for child_module in net.children()][:-2]).eval()
return loss_network
elif loss_net == "resnet101":
from torchvision.models.resnet import resnet101
net=resnet101(pretrained=True)
loss_network=nn.Sequential(*[child_module for child_module in net.children()][:-2]).eval()
return loss_network
elif loss_net == "resnet152":
from torchvision.models.resnet import resnet152
net=resnet152(pretrained=True)
loss_network=nn.Sequential(*[child_module for child_module in net.children()][:-2]).eval()
return loss_network
elif loss_net == "squeezenet1_1":
from torchvision.models.squeezenet import squeezenet1_1
net=squeezenet1_1(pretrained=True)
classifier=[item for item in net.classifier.modules()][1:-1]
loss_network=nn.Sequential(*[net.features,*classifier]).eval()
return loss_network
elif loss_net == "densenet121":
from torchvision.models.densenet import densenet121
net=densenet121(pretrained=True)
loss_network=nn.Sequential(*[net.features,nn.ReLU()]).eval()
return loss_network
elif loss_net == "densenet169":
from torchvision.models.densenet import densenet169
net=densenet169(pretrained=True)
loss_network=nn.Sequential(*[net.features,nn.ReLU()]).eval()
return loss_network
elif loss_net == "densenet201":
from torchvision.models.densenet import densenet201
net=densenet201(pretrained=True)
loss_network=nn.Sequential(*[net.features,nn.ReLU()]).eval()
return loss_network
elif loss_net == "mobilenet_v2":
from torchvision.models.mobilenet import mobilenet_v2
net=mobilenet_v2(pretrained=True)
loss_network=nn.Sequential(*[net.features]).eval()
return loss_network
elif loss_net == "resnext50_32x4d":
from torchvision.models.resnet import resnext50_32x4d
net=resnext50_32x4d(pretrained=True)
loss_network=nn.Sequential(*[child_module for child_module in net.children()][:-2]).eval()
return loss_network
elif loss_net == "resnext101_32x8d":
from torchvision.models.resnet import resnext101_32x8d
net=resnext101_32x8d(pretrained=True)
loss_network=nn.Sequential(*[child_module for child_module in net.children()][:-2]).eval()
return loss_network
elif loss_net == "wide_resnet50_2":
from torchvision.models.resnet import wide_resnet50_2
net=wide_resnet50_2(pretrained=True)
loss_network=nn.Sequential(*[child_module for child_module in net.children()][:-2]).eval()
return loss_network
elif loss_net == "wide_resnet101_2":
from torchvision.models.resnet import wide_resnet101_2
net=wide_resnet101_2(pretrained=True)
loss_network=nn.Sequential(*[child_module for child_module in net.children()][:-2]).eval()
return loss_network
elif loss_net == "inception_v3":
def _create_model(self, scratch: bool, num_classes: int):
model = densenet121(pretrained=not scratch)
num_features = model.classifier.in_features
model.classifier = nn.Linear(num_features, num_classes)
return model
from torchvision.models import densenet
from torchvision.models.densenet import _DenseBlock, _Transition
#import code
from copy import deepcopy
from collections import OrderedDict
# Project imports
from util.util import print # print with a header for this project
# Leveraged Torchvision implementation of DenseNet at:
# https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py
_use_pretrained = True
name_to_model_fns = {
"densenet121": lambda: densenet.densenet121(pretrained=_use_pretrained),
"densenet169": lambda: densenet.densenet169(pretrained=_use_pretrained),
"densenet201": lambda: densenet.densenet201(
pretrained=_use_pretrained) # We're just going to use this one
,
"densenet161": lambda: densenet.densenet161(pretrained=_use_pretrained)
}
def get_densenet(name):
return name_to_model_fns[name]() # Call the function
"""
old_model.modules is more accurate of all indiv. layers
old_model.features decomposes structure more, but doesn't include final FC classifier
def __init__(self):
super().__init__()
self.model = densenet.densenet121(pretrained=False, num_classes=1)
del self.model.classifier
def test_densenet(self):
# Densenet-121 model
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
self.exportTest(toC(densenet121()), toC(x))
import numpy as np
import torch
from torch.autograd import Variable
from onnx2keras_custom import onnx_to_keras, check_torch_keras_error
import onnx
from torchvision.models.densenet import densenet121
if __name__ == '__main__':
model = densenet121()
model.eval()
input_np = np.random.uniform(0, 1, (1, 3, 224, 224))
input_var = Variable(torch.FloatTensor(input_np), requires_grad=False)
output = model(input_var)
torch.onnx.export(model, (input_var),
"_tmpnet.onnx",
verbose=True,
input_names=['test_in1'],
output_names=['test_out'])
onnx_model = onnx.load('_tmpnet.onnx')
k_model = onnx_to_keras(onnx_model, ['test_in1', 'test_in2'],
change_ordering=True)
error = check_torch_keras_error(model,
k_model,
input_np,
change_ordering=True)
print('Max error: {0}'.format(error))
def get_denset_backbone_layers(pretrained: bool):
dnet = densenet121(pretrained=pretrained)
original_model = list(dnet.children())[0]
return nn.ModuleList(original_model.children()), 1024
def __init__(self, *args, **kwargs):
super().__init__()
self.densenet = densenet.densenet121(*args, **kwargs)
self._is_modified = False
def densnet_bc121(nb_classes=1000, pretrained=False):
return densenet121(pretrained=pretrained, num_classes=nb_classes)
def __init__(self):
super(NIHDense, self).__init__()
self.densenet121 = Densenet.densenet121(pretrained=True)
def test_densenet(self):
state_dict = model_zoo.load_url(model_urls['densenet121'], progress=False)
self.run_model_test(densenet121(), train=False, batch_size=BATCH_SIZE,
state_dict=state_dict, atol=1e-7)
def test_densenet(self):
state_dict = model_zoo.load_url(model_urls['densenet121'], progress=False)
self.run_model_test(densenet121(), train=False, batch_size=BATCH_SIZE,
state_dict=state_dict, atol=1e-7)
def __init__(self):
super(DenseNet121, self).__init__()
densenet = densenet121(pretrained=True)
self.features = densenet.features
# test_loader = DataLoader(dataset=test_data, batch_size=BATCH_SIZE)
# ============================ step 2/5 模型 ============================
device = torch.device(DEVICE) # cpu, cuda:0
# net = LeNet(classes=2)
# net.initialize_weights()
if BACKBONE == 'resnet18':
net = resnet18(num_classes=num_classes).to(device)
elif BACKBONE == 'resnet34':
net = resnet34(num_classes=num_classes).to(device)
elif BACKBONE == 'resnet50':
net = resnet50(num_classes=num_classes).to(device)
elif BACKBONE == 'densenet121':
net = densenet121(num_classes=num_classes).to(device)
elif BACKBONE == 'mobilenet_v2':
net = mobilenet_v2(num_classes=num_classes, width_mult=1.0, inverted_residual_setting=None, round_nearest=8).to(device)
elif BACKBONE == 'shufflenet_v2_x1_5':
net = shufflenet_v2_x1_5(num_classes=num_classes).to(device)
elif BACKBONE == 'squeezenet1_0':
net = squeezenet1_0(num_classes=num_classes).to(device)
elif BACKBONE == 'squeezenet1_1':
net = squeezenet1_1(num_classes=num_classes).to(device)
elif BACKBONE == 'mnasnet0_5':
net = mnasnet0_5(num_classes=num_classes).to(device)
elif BACKBONE == 'mnasnet1_0':
net = mnasnet1_0(num_classes=num_classes).to(device)
elif BACKBONE == 'mobilenet_v1':
net = MobileNetV1(num_classes=num_classes).to(device)
else:
def create_model(model_name, num_classes=1000, pretrained=False, **kwargs):
if 'test_time_pool' in kwargs:
test_time_pool = kwargs.pop('test_time_pool')
else:
test_time_pool = True
if 'extra' in kwargs:
extra = kwargs.pop('extra')
else:
extra = True
if model_name == 'dpn68':
model = dpn68(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'dpn68b':
model = dpn68b(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'dpn92':
model = dpn92(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool,
extra=extra)
elif model_name == 'dpn92_re':
model = dpn92_re(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool,
extra=extra)
elif model_name == 'dpn98':
model = dpn98(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'dpn131':
model = dpn131(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'dpn107':
model = dpn107(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'resnet18':
model = resnet18(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet18_sigmoid':
model = resnet18_sigmoid(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet34':
model = resnet34(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet50':
model = resnet50(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet50_bestfitting':
model = resnet50_bestfitting(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet50_sigmoid':
model = resnet50_sigmoid(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet101':
model = resnet101(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet152':
model = resnet152(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet152_sigmoid':
model = resnet152_sigmoid(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet121':
model = densenet121(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet161':
model = densenet161(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet169':
model = densenet169(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet201':
model = densenet201(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'inception_v3':
model = inception_v3(num_classes=num_classes,
pretrained=pretrained,
transform_input=False,
**kwargs)
elif model_name == 'vgg16_bn':
model = vgg16_bn(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'ConvNet_sigmoid':
model = ConvNet()
else:
assert False, "Unknown model architecture (%s)" % model_name
return model
