2020-12-10 22:07:35 +00:00
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import copy
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import torch
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import torch.nn.functional as F
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from torch import nn
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from data.byol_attachment import reconstructed_shared_regions
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2022-03-16 18:04:00 +00:00
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from models.image_latents.byol.byol_model_wrapper import singleton, EMA, get_module_device, set_requires_grad, \
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2020-12-10 22:07:35 +00:00
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update_moving_average
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2020-12-25 05:50:14 +00:00
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from trainer.networks import create_model, register_model
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2020-12-10 22:07:35 +00:00
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from utils.util import checkpoint
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# loss function
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def structural_loss_fn(x, y):
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# Combine the structural dimensions into the batch dimension, then compute the "normal" BYOL loss.
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x = x.permute(0,2,3,1).flatten(0,2)
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y = y.permute(0,2,3,1).flatten(0,2)
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x = F.normalize(x, dim=-1, p=2)
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y = F.normalize(y, dim=-1, p=2)
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return 2 - 2 * (x * y).sum(dim=-1)
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class StructuralTail(nn.Module):
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def __init__(self, channels, projection_size, hidden_size=512):
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super().__init__()
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self.net = nn.Sequential(
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nn.Conv2d(channels, hidden_size, kernel_size=1),
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nn.BatchNorm2d(hidden_size),
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nn.ReLU(inplace=True),
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nn.Conv2d(hidden_size, projection_size, kernel_size=1),
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)
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def forward(self, x):
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return self.net(x)
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# a wrapper class for the base neural network
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# will manage the interception of the hidden layer output
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# and pipe it into the projecter and predictor nets
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class NetWrapper(nn.Module):
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def __init__(self, net, projection_size, projection_hidden_size, layer=-2):
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super().__init__()
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self.net = net
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self.layer = layer
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self.projector = None
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self.projection_size = projection_size
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self.projection_hidden_size = projection_hidden_size
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self.hidden = None
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self.hook_registered = False
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def _find_layer(self):
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if type(self.layer) == str:
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modules = dict([*self.net.named_modules()])
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return modules.get(self.layer, None)
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elif type(self.layer) == int:
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children = [*self.net.children()]
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return children[self.layer]
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return None
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def _hook(self, _, __, output):
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self.hidden = output
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def _register_hook(self):
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layer = self._find_layer()
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assert layer is not None, f'hidden layer ({self.layer}) not found'
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handle = layer.register_forward_hook(self._hook)
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self.hook_registered = True
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@singleton('projector')
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def _get_projector(self, hidden):
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projector = StructuralTail(hidden.shape[1], self.projection_size, self.projection_hidden_size)
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return projector.to(hidden)
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def get_representation(self, x):
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if self.layer == -1:
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return self.net(x)
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if not self.hook_registered:
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self._register_hook()
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unused = self.net(x)
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hidden = self.hidden
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self.hidden = None
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assert hidden is not None, f'hidden layer {self.layer} never emitted an output'
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return hidden
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def forward(self, x):
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representation = self.get_representation(x)
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projector = self._get_projector(representation)
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projection = checkpoint(projector, representation)
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return projection
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class StructuralBYOL(nn.Module):
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def __init__(
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self,
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net,
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image_size,
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hidden_layer=-2,
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projection_size=256,
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projection_hidden_size=512,
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moving_average_decay=0.99,
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use_momentum=True,
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pretrained_state_dict=None,
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freeze_until=0
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):
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super().__init__()
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if pretrained_state_dict:
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net.load_state_dict(torch.load(pretrained_state_dict), strict=True)
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self.freeze_until = freeze_until
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2020-12-15 06:59:11 +00:00
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self.frozen = False
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2020-12-10 22:07:35 +00:00
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if self.freeze_until > 0:
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for p in net.parameters():
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p.DO_NOT_TRAIN = True
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self.frozen = True
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self.online_encoder = NetWrapper(net, projection_size, projection_hidden_size, layer=hidden_layer)
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self.use_momentum = use_momentum
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self.target_encoder = None
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self.target_ema_updater = EMA(moving_average_decay)
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self.online_predictor = StructuralTail(projection_size, projection_size, projection_hidden_size)
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# get device of network and make wrapper same device
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device = get_module_device(net)
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self.to(device)
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# send a mock image tensor to instantiate singleton parameters
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self.forward(torch.randn(2, 3, image_size, image_size, device=device),
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torch.randn(2, 3, image_size, image_size, device=device), None)
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@singleton('target_encoder')
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def _get_target_encoder(self):
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target_encoder = copy.deepcopy(self.online_encoder)
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set_requires_grad(target_encoder, False)
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return target_encoder
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def reset_moving_average(self):
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del self.target_encoder
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self.target_encoder = None
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def update_for_step(self, step, __):
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assert self.use_momentum, 'you do not need to update the moving average, since you have turned off momentum for the target encoder'
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assert self.target_encoder is not None, 'target encoder has not been created yet'
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update_moving_average(self.target_ema_updater, self.target_encoder, self.online_encoder)
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if self.frozen and self.freeze_until < step:
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print("Unfreezing model weights. Let the latent training commence..")
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for p in self.online_encoder.net.parameters():
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del p.DO_NOT_TRAIN
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self.frozen = False
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def forward(self, image_one, image_two, similar_region_params):
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online_proj_one = self.online_encoder(image_one)
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online_proj_two = self.online_encoder(image_two)
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online_pred_one = self.online_predictor(online_proj_one)
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online_pred_two = self.online_predictor(online_proj_two)
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with torch.no_grad():
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target_encoder = self._get_target_encoder() if self.use_momentum else self.online_encoder
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target_proj_one = target_encoder(image_one).detach()
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target_proj_two = target_encoder(image_two).detach()
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# In the structural BYOL, only the regions of the source image that are shared between the two augments are
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# compared. These regions can be extracted from the latents using `reconstruct_shared_regions`.
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if similar_region_params is not None:
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online_pred_one, target_proj_two = reconstructed_shared_regions(online_pred_one, target_proj_two, similar_region_params)
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loss_one = structural_loss_fn(online_pred_one, target_proj_two.detach())
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if similar_region_params is not None:
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online_pred_two, target_proj_one = reconstructed_shared_regions(online_pred_two, target_proj_one, similar_region_params)
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loss_two = structural_loss_fn(online_pred_two, target_proj_one.detach())
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loss = loss_one + loss_two
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return loss.mean()
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2020-12-11 19:01:09 +00:00
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def get_projection(self, image):
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enc = self.online_encoder(image)
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proj = self.online_predictor(enc)
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2020-12-25 05:50:14 +00:00
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return enc, proj
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@register_model
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def register_structural_byol(opt_net, opt):
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subnet = create_model(opt, opt_net['subnet'])
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return StructuralBYOL(subnet, opt_net['image_size'], opt_net['hidden_layer'],
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pretrained_state_dict=opt_get(opt_net, ["pretrained_path"]),
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freeze_until=opt_get(opt_net, ['freeze_until'], 0))
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