def __init__(self, model, n_classes):
# Last layer (the classifier), fully convolutional
from torchvision.models.segmentation.fcn import FCNHead
# Modify classifier layer to predict requested number of classes
classifier = FCNHead(model.classifier[0].in_channels, n_classes)
# Modify aux classifier layer to predict requested number of classes
aux_classifier = FCNHead(model.aux_classifier[0].in_channels,
n_classes)
# Call Module constructor to initialize network module
super(torchvision.models.segmentation.fcn.FCN,
self).__init__(model.backbone, classifier, aux_classifier)
def __init__(self, num_convs=3, fine_tune=False):
super(MyModel, self).__init__()
assert 8 >= num_convs > 1, "Cannot have less than 1 or greater than 8 convolutional+pooling layers."
self.num_convs = num_convs
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
# Simple Segmentation Model
self.backbone = models.resnet50(
pretrained=True, replace_stride_with_dilation=[False, True, True])
self.backbone = IntermediateLayerGetter(
self.backbone, return_layers={'layer4': 'out'})
if not fine_tune:
for p in self.backbone.parameters():
p.requires_grad = False
in_channels = 1028
classes = 200 # actually classes
# Graph convolution layers and graph pooling layers
self.classifier = FCNHead(in_channels, classes)
self.convolutions = dict()
for conv in range(num_convs):
self.convolutions['conv' + str(conv + 1)] = GCNConv(2048, 2048).to(
self.device)
self.convolutions['pool' + str(conv + 1)] = TopKPooling(
2048, ratio=.6).to(self.device)
#Final Output
self.lin1 = torch.nn.Linear(2048, 1024).to(self.device)
self.lin2 = torch.nn.Linear(1024, 1024).to(self.device)
self.lin3 = torch.nn.Linear(1024, classes).to(self.device)
self.act1 = torch.nn.ReLU().to(self.device)
self.act2 = torch.nn.ReLU().to(self.device)
def _segm_resnet(name, backbone_name, num_classes, aux, pretrained_backbone=True, **kwargs):
backbone = resnet.__dict__[backbone_name](
pretrained=pretrained_backbone,
replace_stride_with_dilation=[False, True, True],
resnet_local_path=kwargs["resnet_local_path"] if "resnet_local_path" in kwargs else None
)
return_layers = {'layer4': 'out'}
if aux:
return_layers['layer3'] = 'aux'
backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
aux_classifier = None
if aux:
inplanes = 1024
aux_classifier = FCNHead(inplanes, num_classes)
model_map = {
'deeplabv3': (DeepLabHead, DeepLabV3),
'fcn': (FCNHead, FCN),
}
inplanes = 2048
classifier = model_map[name][0](inplanes, num_classes)
base_model = model_map[name][1]
model = base_model(backbone, classifier, aux_classifier)
return model
def _segm_resnet(name,
backbone_name,
num_classes,
aux,
pretrained_backbone=True):
backbone = resnet.__dict__[backbone_name](
pretrained=pretrained_backbone,
replace_stride_with_dilation=[False, True, True])
backbone.conv1 = nn.Conv2d(3,
64,
kernel_size=(7, 7),
stride=(2, 2),
padding=(3, 3),
bias=False)
return_layers = {'layer4': 'out'}
if aux:
return_layers['layer3'] = 'aux'
backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
aux_classifier = None
if aux:
inplanes = 1024
aux_classifier = FCNHead(inplanes, num_classes)
model_map = {
'fcn': (FCNHead, FCN),
}
inplanes = 2048
classifier = model_map[name][0](inplanes, num_classes)
base_model = model_map[name][1]
model = base_model(backbone, classifier, aux_classifier)
return model
def _segm_resnet(name, backbone_name, num_classes, aux, **kwargs):
# FIXME: 1000 and _
if isinstance(backbone_name, dict):
backbone = model_utils.which_architecture(backbone_name['arch'],
backbone_name['customs'])
elif os.path.isfile(backbone_name):
backbone, _ = model_utils.which_network_classification(
backbone_name, 1000, **kwargs)
else:
backbone = model_utils.which_architecture(backbone_name, **kwargs)
return_layers = {'layer4': 'out'}
if aux:
return_layers['layer3'] = 'aux'
backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
aux_classifier = None
if aux:
layer3 = list(backbone.layer3)[-1]
inplanes = list(layer3.children())[-2].num_features
aux_classifier = FCNHead(inplanes, num_classes)
model_map = {
'deeplabv3': (DeepLabHead, DeepLabV3),
'fcn': (FCNHead, FCN),
}
layer4 = list(backbone.layer4)[-1]
inplanes = list(layer4.children())[-2].num_features
classifier = model_map[name][0](inplanes, num_classes)
base_model = model_map[name][1]
model = ColourTransferModel(
base_model(backbone, classifier, aux_classifier), **kwargs)
return model
def __init__(self, num_classes, backbone_fn, chip_size=224):
super().__init__()
if getattr(backbone_fn, '_is_multispectral', False):
self.backbone = create_body(backbone_fn,
pretrained=True,
cut=_get_backbone_meta(
backbone_fn.__name__)['cut'])
else:
self.backbone = create_body(backbone_fn, pretrained=True)
backbone_name = backbone_fn.__name__
## Support for different backbones
if "densenet" in backbone_name or "vgg" in backbone_name:
hookable_modules = list(self.backbone.children())[0]
else:
hookable_modules = list(self.backbone.children())
if "vgg" in backbone_name:
modify_dilation_index = -5
else:
modify_dilation_index = -2
if backbone_name == 'resnet18' or backbone_name == 'resnet34':
module_to_check = 'conv'
else:
module_to_check = 'conv2'
## Hook at the index where we need to get the auxillary logits out
self.hook = hook_output(hookable_modules[modify_dilation_index])
custom_idx = 0
for i, module in enumerate(hookable_modules[modify_dilation_index:]):
dilation = 2 * (i + 1)
padding = 2 * (i + 1)
for n, m in module.named_modules():
if module_to_check in n:
m.dilation, m.padding, m.stride = (dilation, dilation), (
padding, padding), (1, 1)
elif 'downsample.0' in n:
m.stride = (1, 1)
if "vgg" in backbone_fn.__name__:
if isinstance(module, nn.Conv2d):
dilation = 2 * (custom_idx + 1)
padding = 2 * (custom_idx + 1)
module.dilation, module.padding, module.stride = (
dilation, dilation), (padding, padding), (1, 1)
custom_idx += 1
## returns the size of various activations
feature_sizes = model_sizes(self.backbone, size=(chip_size, chip_size))
## Geting the number of channel persent in stored activation inside of the hook
num_channels_aux_classifier = self.hook.stored.shape[1]
## Get number of channels in the last layer
num_channels_classifier = feature_sizes[-1][1]
self.classifier = DeepLabHead(num_channels_classifier, num_classes)
self.aux_classifier = FCNHead(num_channels_aux_classifier, num_classes)
def _create_deeplab(num_class, backbone, pretrained=True, **kwargs):
'''
Create default torchvision pretrained model with resnet101.
'''
model = models.segmentation.deeplabv3_resnet101(pretrained=True, progress=True, **kwargs)
model = _DeepLabOverride(model.backbone, model.classifier, model.aux_classifier)
model.classifier = DeepLabHead(2048, num_class)
model.aux_classifier = FCNHead(1024, num_class)
return model
def __init__(self, num_classes=23):
super(SegmentationNN, self).__init__()
#######################################################################
# YOUR CODE #
#######################################################################
# self.backbone = models.vgg16_bn(pretrained=True)
# self.backbone.classifier = nn.Sequential(
# nn.Conv2d(512, 4096, kernel_size=1),
# nn.BatchNorm2d(4096),
# nn.ReLU(True),
# nn.Conv2d(4096, 4096, kernel_size=1),
# nn.BatchNorm2d(4096),
# nn.ReLU(True),
# nn.Conv2d(4096, num_classes, kernel_size=1)
# )
# self.avgpool = nn.AvgPool2d(kernel_size=2)
# self.upsample1 = nn.Sequential(
# nn.ConvTranspose2d(num_classes, num_classes, kernel_size=2,
# stride=2, padding=0, output_padding=1),
# nn.BatchNorm2d(num_classes),
# nn.ReLU()
# )
# self.upsample2 = nn.Sequential(
# nn.ConvTranspose2d(num_classes, num_classes, kernel_size=2,
# stride=2, padding=0),
# nn.BatchNorm2d(num_classes),
# nn.ReLU()
# )
# self.upsample3 = nn.Sequential(
# nn.ConvTranspose2d(num_classes, num_classes, kernel_size=4,
# stride=8, padding=1, dilation=3),
# # nn.BatchNorm2d(num_classes),
# # nn.ReLU(True)
# )
# self.conv1 = nn.Sequential(
# nn.Conv2d(256, num_classes, 1),
# nn.BatchNorm2d(num_classes),
# nn.ReLU()
# )
# self.conv2 = nn.Sequential(
# nn.Conv2d(512, num_classes, 1),
# nn.BatchNorm2d(num_classes),
# nn.ReLU()
# )
self.fcn = models.segmentation.fcn_resnet101(pretrained=True)
self.fcn.classifier = FCNHead(2048, num_classes)
self.fcn.aux_classifier = None
for name, param in self.fcn.named_parameters():
if "classifier" in name:
param.requires_grad = True
else:
param.requires_grad = False
def __init__(self, in_chan=3, out_chan=2, pretrained=False):
super(FCN50, self).__init__()
self.model = torchvision.models.segmentation.fcn_resnet50(
pretrained=False, pretrained_backbone=pretrained)
self.model.classifier = FCNHead(2048, out_chan)
if in_chan != 3:
self.model.backbone.conv1 = nn.Conv2d(in_chan,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
def _segm_resnet(name, backbone_name, num_classes, aux, **kwargs):
# FIXME: 1000 and _
if isinstance(backbone_name, dict):
if 'pretrained' in kwargs:
del kwargs['pretrained']
backbone = model_utils.which_architecture(
backbone_name['arch'], backbone_name['customs']
)
elif os.path.isfile(backbone_name):
if 'pretrained' in kwargs:
del kwargs['pretrained']
backbone, _ = model_utils.which_network_classification(
backbone_name, 1000, **kwargs
)
else:
backbone = model_utils.which_architecture(backbone_name, **kwargs)
return_layers = {'layer4': 'out'}
if aux:
return_layers['layer3'] = 'aux'
backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
aux_classifier = None
if aux:
layer3 = list(backbone.layer3)[-1]
# Depending on Bottleneck or basic choose the num_features
if isinstance(layer3, (cresnet.Bottleneck, presnet.Bottleneck)):
inplanes = list(layer3.children())[-2].num_features
else:
inplanes = list(layer3.children())[-1].num_features
aux_classifier = FCNHead(inplanes, num_classes)
model_map = {
'deeplabv3': (DeepLabHead, DeepLabV3),
'fcn': (FCNHead, FCN),
}
layer4 = list(backbone.layer4)[-1]
if isinstance(layer4, (cresnet.Bottleneck, presnet.Bottleneck)):
inplanes = list(layer4.children())[-2].num_features
else:
inplanes = list(layer4.children())[-1].num_features
classifier = model_map[name][0](inplanes, num_classes)
base_model = model_map[name][1]
model = base_model(backbone, classifier, aux_classifier)
return model
def pre_fcn_resnet101(in_channel, out_channel):
model = fcn_resnet101(pretrained=False, progress=False)
url = "https://download.pytorch.org/models/fcn_resnet101_coco-7ecb50ca.pth" # COCO
model_dict = model.state_dict()
pretrained_dict = model_zoo.load_url(url, progress=False)
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model.backbone.conv1 = nn.Conv2d(in_channel,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
model.classifier = FCNHead(2048, out_channel)
return model
def deeplabv3(pretrained=False,
resnet="res103",
head_in_ch=2048,
num_classes=21):
resnet = {"res101": resnet101, "res103": resnet103}[resnet]
net = SmallDeepLab(backbone=IntermediateLayerGetter(resnet103(
pretrained=True, replace_stride_with_dilation=[False, True, True]),
return_layers={
'layer2': 'res2',
'layer4': 'out'
}),
classifier=DeepLabHead(head_in_ch, num_classes),
aux_classifier=FCNHead(head_in_ch // 2, num_classes))
if pretrained:
state_dict = load_state_dict_from_url(
'https://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pth',
progress=True)
net.load_state_dict(state_dict)
return net
def createFCNResnet101(outputchannels=1, feature_extract=True):
"""FCNResnet101 class with custom head
Args:
outputchannels (int, optional): The number of output channels
in your dataset masks. Defaults to 1.
feature_extract (bool, optional): If False the whole model is
trained otherwise only the classifier (head) is trained
Returns:
model: Returns the FCN model with the ResNet101 backbone.
"""
model = models.segmentation.fcn_resnet101(pretrained=True, progress=True)
print(model)
if feature_extract:
for param in model.parameters():
param.requires_grad = False
model.classifier = FCNHead(2048, outputchannels)
# Set the model in training mode
model.train()
return model
def build_fcn_resnet(name,
backbone_fct,
num_classes,
aux,
layers,
pretrained_backbone=False):
"""Constructs a custom ResNet backbone and modifies for fully convolutional, semantic segmentation
Args:
name (str): either deeplabv3 or fcn
backbone_fct (function): the model function for the non-fcn-only backbone
num_classes (int): number of classes
aux (bool): use of auxiliary loss
layers (list): configuration of layer-blocks (Bottlenecks)
pretrained_backbone (bool): If True, returns a model pre-trained on COCO train2017 which
contains the same classes as Pascal VOC
"""
backbone = backbone_fct(layers=layers,
pretrained=pretrained_backbone,
replace_stride_with_dilation=[False, True, True])
return_layers = {'layer4': 'out'}
if aux:
return_layers['layer3'] = 'aux'
backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
aux_classifier = None
if aux:
inplanes = 1024
aux_classifier = FCNHead(inplanes, num_classes)
model_map = {
'deeplabv3': (DeepLabHead, DeepLabV3),
'fcn': (FCNHead, FCN),
}
inplanes = 2048
classifier = model_map[name][0](inplanes, num_classes)
base_model = model_map[name][1]
model = base_model(backbone, classifier, aux_classifier)
return model
def _segm_resnet(name,
backbone_name,
num_classes,
aux,
dilation,
pretrained_backbone=True):
backbone = resnet.__dict__[backbone_name](
pretrained=pretrained_backbone, replace_stride_with_dilation=dilation)
return_layers = {'layer4': 'out'}
if aux:
return_layers['layer3'] = 'aux'
backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
aux_classifier = None
if aux:
inplanes = 1024
aux_classifier = FCNHead(inplanes, num_classes)
model_map = {
'deeplabv3': (CustomDeepLabHead, DeepLabV3),
'fcn': (FCNHead, FCN),
}
inplanes = 2048
# print('dilation: {}'.format(dilation))
if name == 'fcn':
classifier = model_map[name][0](inplanes, num_classes)
else:
if (not dilation[0]) and dilation[1] and dilation[2]:
classifier = model_map[name][0](inplanes, num_classes,
[12, 24, 36])
elif (not dilation[0]) and (not dilation[1]) and dilation[2]:
classifier = model_map[name][0](inplanes, num_classes, [6, 12, 18])
else:
print('invalid dilation value')
return None
base_model = model_map[name][1]
model = base_model(backbone, classifier, aux_classifier)
return model
def _segm_resnet(name, backbone_name, num_classes, aux, pretrained_backbone=True, replace_stride_with_dilation=[False, True, True], rates=[12, 24, 36], return_layers = {'layer4': 'out'}):
backbone = resnet.__dict__[backbone_name](
pretrained=pretrained_backbone,
replace_stride_with_dilation=replace_stride_with_dilation)
backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
aux_classifier = None
if aux:
inplanes = 1024
aux_classifier = FCNHead(inplanes, num_classes)
model_map = {
'deeplabv3': (DeepLabHead, DeepLabV3),
'fcn': (FCNHead, FCN),
}
inplanes = 2048
classifier = model_map[name][0](inplanes, num_classes, rates)
base_model = model_map[name][1]
model = base_model(backbone, classifier, aux_classifier)
return model
def __init__(self, num_classes: int = 2, ignore_index: Optional[int] = None, lr: float = 0.001,
weight_decay: float = 0.001, aux_loss_factor: float = 0.3):
super().__init__()
self.num_classes = num_classes
self.ignore_index = ignore_index
self.lr = lr
self.weight_decay = weight_decay
self.aux_loss_factor = aux_loss_factor
# Create model from pre-trained DeepLabv3
self.model = deeplabv3_resnet101(pretrained=True, progress=True)
self.model.aux_classifier = FCNHead(1024, self.num_classes)
self.model.classifier = DeepLabHead(2048, self.num_classes)
# Setup trainable layers
self.model.requires_grad_(True)
self.model.backbone.requires_grad_(False)
# Loss function and metrics
self.focal_tversky_loss = FocalTverskyMetric(
self.num_classes,
alpha=0.7,
beta=0.3,
gamma=4.0 / 3.0,
ignore_index=self.ignore_index,
)
self.accuracy_metric = Accuracy(ignore_index=self.ignore_index)
self.iou_metric = JaccardIndex(
num_classes=self.num_classes,
reduction="none",
ignore_index=self.ignore_index,
)
self.precision_metric = Precision(num_classes=self.num_classes, ignore_index=self.ignore_index,
average='weighted', mdmc_average='global')
self.recall_metric = Recall(num_classes=self.num_classes, ignore_index=self.ignore_index,
average='weighted', mdmc_average='global')
def loadModel(model_arch="",
classes=None,
pre_trained_path=None,
expType=None,
trainable_backbone_flag=False,
lower_features=False):
print("Load model architecture ... ")
if (model_arch == "deeplabv3_resnet101_orig"):
print("deeplab_resnet architecture selected ...")
model = models.segmentation.deeplabv3_resnet101(pretrained=True,
progress=True)
for params in model.parameters():
params.requires_grad = trainable_backbone_flag
model.classifier[-1] = torch.nn.Conv2d(256,
len(classes),
kernel_size=(1, 1))
model.aux_classifier[-1] = torch.nn.Conv2d(256,
len(classes),
kernel_size=(1, 1))
features = model.backbone
if (pre_trained_path != None):
print("load pre-trained-weights ... ")
model_dict_state = torch.load(
pre_trained_path) # + "/best_model.pth")
model.load_state_dict(model_dict_state['net'])
return model, features
elif (model_arch == "fcn_resnet101_orig"):
print("deeplab_resnet architecture selected ...")
model = models.segmentation.fcn_resnet101(pretrained=True,
progress=True)
for params in model.parameters():
params.requires_grad = trainable_backbone_flag
model.classifier[-1] = torch.nn.Conv2d(512,
len(classes),
kernel_size=(1, 1))
model.aux_classifier[-1] = torch.nn.Conv2d(256,
len(classes),
kernel_size=(1, 1))
features = model.backbone
if (pre_trained_path != None):
print("load pre-trained-weights ... ")
model_dict_state = torch.load(
pre_trained_path) # + "/best_model.pth")
model.load_state_dict(model_dict_state['net'])
return model, features
elif (model_arch == "deeplabv3_resnet101"):
print("deeplabv3_resnet101 architecture selected ...")
backbone_net = CNN(model_arch="resnet101",
n_classes=len(classes),
include_top=False,
pretrained=trainable_backbone_flag,
lower_features=lower_features)
if (lower_features == True):
classifier = nn.Sequential(DeepLabHead(256, len(classes)),
# nn.Softmax()
)
else:
classifier = nn.Sequential(DeepLabHead(2048, len(classes)),
# nn.Softmax()
)
features = backbone_net
model = models.segmentation.DeepLabV3(backbone=backbone_net,
classifier=classifier,
aux_classifier=None)
if (pre_trained_path != None):
print("load pre-trained-weights ... ")
model_dict_state = torch.load(
pre_trained_path) # + "/best_model.pth")
model.load_state_dict(model_dict_state['net'])
return model, features
elif (model_arch == "deeplabv3_vgg16"):
print("deeplabv3_vgg architecture selected ...")
# backbone_net = CNN(model_arch="resnet101", n_classes=len(classes), include_top=False)
backbone_net = CNN(model_arch="vgg16",
n_classes=len(classes),
include_top=False,
pretrained=trainable_backbone_flag,
lower_features=lower_features)
if (lower_features == True):
classifier = nn.Sequential(DeepLabHead(64, len(classes)),
# nn.Softmax()
)
else:
classifier = nn.Sequential(DeepLabHead(512, len(classes)),
# nn.Softmax()
)
features = backbone_net
model = models.segmentation.DeepLabV3(backbone=backbone_net,
classifier=classifier,
aux_classifier=None)
#print(model)
#exit()
if (pre_trained_path != None):
print("load pre-trained-weights ... ")
model_dict_state = torch.load(
pre_trained_path) # + "/best_model.pth")
model.load_state_dict(model_dict_state['net'])
# Find total parameters and trainable parameters
total_params = sum(p.numel() for p in model.parameters())
print("total_params:" + str(total_params))
total_trainable_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("total_trainable_params: " + str(total_trainable_params))
#exit()
return model, features
elif (model_arch == "deeplabv3_mobilenet"):
print("deeplabv3_mobilenet architecture selected ...")
backbone_net = CNN(model_arch="mobilenet",
n_classes=len(classes),
include_top=False,
pretrained=trainable_backbone_flag,
lower_features=lower_features)
if (lower_features == True):
classifier = nn.Sequential(DeepLabHead(32, len(classes)),
# nn.Softmax()
)
else:
classifier = nn.Sequential(DeepLabHead(1280, len(classes)),
# nn.Softmax()
)
features = backbone_net
model = models.segmentation.DeepLabV3(backbone=backbone_net,
classifier=classifier,
aux_classifier=None)
if (pre_trained_path != None):
print("load pre-trained-weights ... ")
model_dict_state = torch.load(pre_trained_path)
model.load_state_dict(model_dict_state['net'])
return model, features
elif (model_arch == "deeplabv3_squeezenet"):
print("deeplabv3_mobilenet architecture selected ...")
backbone_net = CNN(model_arch="squeezenet",
n_classes=len(classes),
include_top=False,
pretrained=trainable_backbone_flag,
lower_features=lower_features)
if (lower_features == True):
classifier = nn.Sequential(DeepLabHead(128, len(classes)),
# nn.Softmax()
)
else:
classifier = nn.Sequential(DeepLabHead(512, len(classes)),
# nn.Softmax()
)
features = backbone_net
model = models.segmentation.DeepLabV3(backbone=backbone_net,
classifier=classifier,
aux_classifier=None)
if (pre_trained_path != None):
print("load pre-trained-weights ... ")
model_dict_state = torch.load(
pre_trained_path) # + "/best_model.pth")
model.load_state_dict(model_dict_state['net'])
return model, features
elif (model_arch == "fcn_vgg16"):
print("fcn_vgg16 architecture selected ...")
backbone_net = CNN(model_arch="vgg16",
n_classes=len(classes),
include_top=False,
pretrained=trainable_backbone_flag,
lower_features=lower_features)
if (lower_features == True):
classifier = nn.Sequential(FCNHead(64, len(classes)),
# nn.Softmax()
)
else:
classifier = nn.Sequential(FCNHead(512, len(classes)),
# nn.Softmax()
)
features = backbone_net
model = models.segmentation.FCN(backbone=backbone_net,
classifier=classifier,
aux_classifier=None)
# print(model)
if (pre_trained_path != None):
print("load pre-trained-weights ... ")
model_dict_state = torch.load(
pre_trained_path) # + "/best_model.pth")
model.load_state_dict(model_dict_state['net'])
return model, features
elif (model_arch == "fcn_resnet101"):
print("fcn_resnet101 architecture selected ...")
backbone_net = CNN(model_arch="resnet101",
n_classes=len(classes),
include_top=False,
pretrained=trainable_backbone_flag,
lower_features=lower_features)
if (lower_features == True):
classifier = nn.Sequential(FCNHead(256, len(classes)),
# nn.Softmax()
)
else:
classifier = nn.Sequential(FCNHead(2048, len(classes)),
# nn.Softmax()
)
features = backbone_net
model = models.segmentation.FCN(backbone=backbone_net,
classifier=classifier,
aux_classifier=None)
if (pre_trained_path != None):
print("load pre-trained-weights ... ")
model_dict_state = torch.load(
pre_trained_path) # + "/best_model.pth")
model.load_state_dict(model_dict_state['net'])
# Find total parameters and trainable parameters
total_params = sum(p.numel() for p in model.parameters())
print("total_params:" + str(total_params))
total_trainable_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("total_trainable_params: " + str(total_trainable_params))
#exit()
return model, features
elif (model_arch == "fcn_squeezenet"):
print("deeplabv3_squeezenet architecture selected ...")
backbone_net = CNN(model_arch="squeezenet",
n_classes=len(classes),
include_top=False,
pretrained=trainable_backbone_flag,
lower_features=lower_features)
if (lower_features == True):
classifier = nn.Sequential(FCNHead(128, len(classes)),
# nn.Softmax()
)
else:
classifier = nn.Sequential(FCNHead(512, len(classes)),
# nn.Softmax()
)
features = backbone_net
model = models.segmentation.FCN(backbone=backbone_net,
classifier=classifier,
aux_classifier=None)
if (pre_trained_path != None):
print("load pre-trained-weights ... ")
model_dict_state = torch.load(
pre_trained_path) # + "/best_model.pth")
model.load_state_dict(model_dict_state['net'])
# Find total parameters and trainable parameters
total_params = sum(p.numel() for p in model.parameters())
print("total_params:" + str(total_params))
total_trainable_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("total_trainable_params: " + str(total_trainable_params))
# exit()
return model, features
elif (model_arch == "fcn_mobilenet"):
print("deeplabv3_mobilenet architecture selected ...")
backbone_net = CNN(model_arch="mobilenet",
n_classes=len(classes),
include_top=False,
pretrained=trainable_backbone_flag,
lower_features=lower_features)
if (lower_features == True):
classifier = nn.Sequential(FCNHead(32, len(classes)),
# nn.Softmax()
)
else:
classifier = nn.Sequential(FCNHead(1280, len(classes)),
# nn.Softmax()
)
features = backbone_net
model = models.segmentation.FCN(backbone=backbone_net,
classifier=classifier,
aux_classifier=None)
if (pre_trained_path != None):
print("load pre-trained-weights ... ")
model_dict_state = torch.load(
pre_trained_path) # + "/best_model.pth")
model.load_state_dict(model_dict_state['net'])
# Find total parameters and trainable parameters
total_params = sum(p.numel() for p in model.parameters())
print("total_params:" + str(total_params))
total_trainable_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("total_trainable_params: " + str(total_trainable_params))
# exit()
return model, features
else:
print("ERROR: select valid model architecture!")
exit()
