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all_models.py
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all_models.py
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import torch
import torch.nn as nn
import torchvision
from nets.unet_model import UNet
from nets.uInterp_model import uInterp, uInterp_topdown, uInterp_multistream
# FOR CERTIFICATE ISSUES
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
# from torchvision.models.segmentation.segmentation import model_urls as segmentation_urls
# segmentation_urls['fcn_resnet101_coco'] = segmentation_urls['fcn_resnet101_coco'].replace('https://', 'http://')
# segmentation_urls['deeplabv3_resnet101_coco'] = segmentation_urls['deeplabv3_resnet101_coco'].replace('https://', 'http://')
class ResNet18(nn.Module):
def __init__(self, num_classes=2):
super(ResNet18, self).__init__()
self.base_net = torchvision.models.resnet18(pretrained=True)
self.num_classes = num_classes
self.base_net.fc = nn.Linear(512, num_classes)
def forward(self, x):
x = self.base_net(x)
return x
class ResNet50(nn.Module):
def __init__(self, num_classes=2):
super(ResNet50, self).__init__()
self.base_net = torchvision.models.resnet50(pretrained=True)
self.num_classes = num_classes
self.base_net.fc = nn.Linear(2048, num_classes)
def forward(self, x):
x = self.base_net(x)
return x
class UNetMirc(nn.Module):
def __init__(self, num_parts=5):
super(UNetMirc, self).__init__()
self.num_parts = num_parts
self.base_net = UNet(n_channels=3, n_classes=self.num_parts)
def forward(self, x):
x = self.base_net(x)
return x
class FCNMirc(nn.Module):
def __init__(self, num_parts=5):
super(FCNMirc, self).__init__()
self.num_parts = num_parts
self.base_net = torchvision.models.segmentation.fcn_resnet101(pretrained=True)
#self.base_net = torchvision.models.segmentation.fcn_resnet50()
self.base_net.classifier[4] = nn.Conv2d(
in_channels=512,
out_channels=self.num_parts,
kernel_size=1,
stride=1
)
def forward(self, x):
x = self.base_net(x)
return x['out']
class DeepLabMirc(nn.Module):
def __init__(self, num_parts=5):
super(DeepLabMirc, self).__init__()
self.num_parts = num_parts
self.base_net = torchvision.models.segmentation.deeplabv3_resnet101(pretrained=True)
#self.base_net = torchvision.models.segmentation.deeplabv3_resnet50()
self.base_net.classifier[4] = nn.Conv2d(
in_channels=256,
out_channels=self.num_parts,
kernel_size=1,
stride=1
)
def forward(self, x):
x = self.base_net(x)
return x['out']
class RecUNetMirc(nn.Module):
def __init__(self, loss_ratio=1, num_parts=5, topdown_class=False, multistream_class=False):
super(RecUNetMirc, self).__init__()
self.loss_ratio = loss_ratio
self.num_parts = num_parts
self.num_classes = 2
self.topdown_class = topdown_class
self.multistream_class = multistream_class
if self.topdown_class:
self.interp_net = uInterp_topdown(n_channels=3, n_classes=self.num_parts)
elif self.multistream_class:
self.interp_net = uInterp_multistream(n_channels=3, n_classes=self.num_parts)
else:
self.interp_net = uInterp(n_channels=3, n_classes=self.num_parts)
self.class_net = nn.Sequential(
nn.Dropout(),
nn.Linear(576, 128), # 3136
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(128, 128),
nn.ReLU(inplace=True),
nn.Linear(128, self.num_classes),
)
def forward(self, x):
interp_out, features = self.interp_net(x)
class_out = self.class_net(features)
return class_out, interp_out
def load_model(model_name, loss_ratio=[1, 1], num_internal_parts=5):
print('loading network model: %s..' % model_name)
if model_name == 'ResNet18':
model = ResNet18()
model.num_parts = 0
model.output_type = 'img2class'
elif model_name == 'ResNet50':
model = ResNet50()
model.num_parts = 0
model.output_type = 'img2class'
elif model_name == 'UNetMirc':
model = UNetMirc(num_parts=num_internal_parts + 1) # TODO Dice: change to num_parts=num_internal_parts
model.output_type = 'img2intep'
elif model_name == 'DeepLabMirc':
model = DeepLabMirc(num_parts=num_internal_parts + 1)
model.output_type = 'img2intep'
elif model_name == 'FCNMirc':
model = FCNMirc(num_parts=num_internal_parts + 1)
model.output_type = 'img2intep'
elif model_name == 'RecUNetMirc':
model = RecUNetMirc(loss_ratio=loss_ratio, num_parts=num_internal_parts+1)
model.output_type = 'img2dual'
elif model_name == 'RecUNetMircTD':
model = RecUNetMirc(loss_ratio=loss_ratio, num_parts=num_internal_parts+1, topdown_class=True)
model.output_type = 'img2dual'
elif model_name == 'RecUNetMircMulti':
model = RecUNetMirc(loss_ratio=loss_ratio, num_parts=num_internal_parts+1, topdown_class=False, multistream_class=True)
model.output_type = 'img2dual'
else:
print('ERROR: model name does not exist..')
return
return model
if __name__ == '__main__':
img = torch.rand(10, 3, 108, 108)
m = load_model('ResNet50')
#m = load_model('RecUNetMircTD')
#m = load_model('RecUNetMircMulti')
#m = load_model('UNetMirc')
#m = load_model('DeepLabMirc')
#m = load_model('FCNMirc')
img.requires_grad_()
m.train()
o = m(img)
# interp_gt = torch.randint(low=0, high=6, size=(1, 108, 108)).long() # torch.rand(1, 1)
# cri = load_loss('Dice', num_parts=5+1)
# loss = cri(m(img), interp_gt)
# optimizer = torch.optim.Adam(m.parameters(), lr=1e-4)
# loss.backward()
# optimizer.step()
# print('hi')
pass
# t = torch.rand(1, 1)
# a = MetaResCoordiNet50()
# print(a(img, t))
# print(a.train_meta(img, t, coords, l2loss)[0])
# optimizer = torch.optim.Adam(a.resnet.parameters(), lr=1e-4)
# optimizer.step()
# print(a(img, t))