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from torch import nn
from torch.nn import BatchNorm2d, PReLU, Sequential, Module
from torchvision.models import resnet34
from models.hyperstyle.hypernetworks.refinement_blocks import HyperRefinementBlock, RefinementBlock, RefinementBlockSeparable
from models.hyperstyle.hypernetworks.shared_weights_hypernet import SharedWeightsHypernet
class SharedWeightsHyperNetResNet(Module):
def __init__(self, opts):
super(SharedWeightsHyperNetResNet, self).__init__()
self.conv1 = nn.Conv2d(opts.input_nc, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = BatchNorm2d(64)
self.relu = PReLU(64)
resnet_basenet = resnet34(pretrained=True)
blocks = [
resnet_basenet.layer1,
resnet_basenet.layer2,
resnet_basenet.layer3,
resnet_basenet.layer4
]
modules = []
for block in blocks:
for bottleneck in block:
modules.append(bottleneck)
self.body = Sequential(*modules)
if len(opts.layers_to_tune) == 0:
self.layers_to_tune = list(range(opts.n_hypernet_outputs))
else:
self.layers_to_tune = [int(l) for l in opts.layers_to_tune.split(',')]
self.shared_layers = [0, 2, 3, 5, 6, 8, 9, 11, 12]
self.shared_weight_hypernet = SharedWeightsHypernet(in_size=512, out_size=512, mode=None, device=opts.device)
self.refinement_blocks = nn.ModuleList()
self.n_outputs = opts.n_hypernet_outputs
for layer_idx in range(self.n_outputs):
if layer_idx in self.layers_to_tune:
if layer_idx in self.shared_layers:
refinement_block = HyperRefinementBlock(self.shared_weight_hypernet, n_channels=512, inner_c=128)
else:
refinement_block = RefinementBlock(layer_idx, opts, n_channels=512, inner_c=256)
else:
refinement_block = None
self.refinement_blocks.append(refinement_block)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.body(x)
weight_deltas = []
for j in range(self.n_outputs):
if self.refinement_blocks[j] is not None:
delta = self.refinement_blocks[j](x)
else:
delta = None
weight_deltas.append(delta)
return weight_deltas
class SharedWeightsHyperNetResNetSeparable(Module):
def __init__(self, opts):
super(SharedWeightsHyperNetResNetSeparable, self).__init__()
self.conv1 = nn.Conv2d(opts.input_nc, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = BatchNorm2d(64)
self.relu = PReLU(64)
resnet_basenet = resnet34(pretrained=True)
blocks = [
resnet_basenet.layer1,
resnet_basenet.layer2,
resnet_basenet.layer3,
resnet_basenet.layer4
]
modules = []
for block in blocks:
for bottleneck in block:
modules.append(bottleneck)
self.body = Sequential(*modules)
if len(opts.layers_to_tune) == 0:
self.layers_to_tune = list(range(opts.n_hypernet_outputs))
else:
self.layers_to_tune = [int(l) for l in opts.layers_to_tune.split(',')]
self.shared_layers = [0, 2, 3, 5, 6, 8, 9, 11, 12]
self.shared_weight_hypernet = SharedWeightsHypernet(in_size=512, out_size=512, mode=None, device=opts.device)
self.refinement_blocks = nn.ModuleList()
self.n_outputs = opts.n_hypernet_outputs
for layer_idx in range(self.n_outputs):
if layer_idx in self.layers_to_tune:
if layer_idx in self.shared_layers:
refinement_block = HyperRefinementBlock(self.shared_weight_hypernet, n_channels=512, inner_c=128)
else:
refinement_block = RefinementBlockSeparable(layer_idx, opts, n_channels=512, inner_c=256)
else:
refinement_block = None
self.refinement_blocks.append(refinement_block)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.body(x)
weight_deltas = []
for j in range(self.n_outputs):
if self.refinement_blocks[j] is not None:
delta = self.refinement_blocks[j](x)
else:
delta = None
weight_deltas.append(delta)
return weight_deltas
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