327 lines
12 KiB
Python
327 lines
12 KiB
Python
import copy
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import os
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import random
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from functools import wraps
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import kornia.augmentation as augs
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import torch
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import torch.nn.functional as F
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import torchvision
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from PIL import Image
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from kornia import filters, apply_hflip
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from torch import nn
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from torchvision.transforms import ToTensor
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from data.byol_attachment import RandomApply
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from trainer.networks import register_model, create_model
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from utils.util import checkpoint, opt_get
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def default(val, def_val):
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return def_val if val is None else val
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def flatten(t):
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return t.reshape(t.shape[0], -1)
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def singleton(cache_key):
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def inner_fn(fn):
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@wraps(fn)
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def wrapper(self, *args, **kwargs):
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instance = getattr(self, cache_key)
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if instance is not None:
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return instance
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instance = fn(self, *args, **kwargs)
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setattr(self, cache_key, instance)
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return instance
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return wrapper
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return inner_fn
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def get_module_device(module):
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return next(module.parameters()).device
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def set_requires_grad(model, val):
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for p in model.parameters():
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p.requires_grad = val
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# loss fn
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def loss_fn(x, y):
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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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# exponential moving average
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class EMA():
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def __init__(self, beta):
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super().__init__()
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self.beta = beta
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def update_average(self, old, new):
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if old is None:
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return new
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return old * self.beta + (1 - self.beta) * new
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def update_moving_average(ema_updater, ma_model, current_model):
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for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):
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old_weight, up_weight = ma_params.data, current_params.data
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ma_params.data = ema_updater.update_average(old_weight, up_weight)
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# MLP class for projector and predictor
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class MLP(nn.Module):
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def __init__(self, dim, projection_size, hidden_size=4096):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(dim, hidden_size),
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nn.BatchNorm1d(hidden_size),
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nn.ReLU(inplace=True),
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nn.Linear(hidden_size, projection_size)
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)
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def forward(self, x):
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x = flatten(x)
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return self.net(x)
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# A wrapper class for training against networks that do not collapse into a small-dimensioned latent.
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class StructuralMLP(nn.Module):
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def __init__(self, dim, projection_size, hidden_size=4096):
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super().__init__()
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b, c, h, w = dim
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flattened_dim = c * h // 4 * w // 4
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self.net = nn.Sequential(
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nn.Conv2d(c, c, kernel_size=3, padding=1, stride=2),
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nn.BatchNorm2d(c),
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nn.ReLU(inplace=True),
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nn.Conv2d(c, c, kernel_size=3, padding=1, stride=2),
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nn.BatchNorm2d(c),
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nn.ReLU(inplace=True),
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nn.Flatten(),
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nn.Linear(flattened_dim, hidden_size),
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nn.BatchNorm1d(hidden_size),
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nn.ReLU(inplace=True),
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nn.Linear(hidden_size, projection_size)
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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, use_structural_mlp=False):
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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.structural_mlp = use_structural_mlp
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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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if self.structural_mlp:
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projector = StructuralMLP(hidden.shape, self.projection_size, self.projection_hidden_size)
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else:
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_, dim = hidden.flatten(1,-1).shape
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projector = MLP(dim, 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 BYOL(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=4096,
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moving_average_decay=0.99,
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use_momentum=True,
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structural_mlp=False,
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do_augmentation=False # In DLAS this was intended to be done at the dataset level. For massive batch sizes
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# this can overwhelm the CPU though, and it becomes desirable to do the augmentations
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# on the GPU again.
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):
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super().__init__()
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self.online_encoder = NetWrapper(net, projection_size, projection_hidden_size, layer=hidden_layer,
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use_structural_mlp=structural_mlp)
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self.do_aug = do_augmentation
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if self.do_aug:
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augmentations = [ \
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RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
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augs.RandomGrayscale(p=0.2),
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augs.RandomHorizontalFlip(),
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RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
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augs.RandomResizedCrop((self.cropped_img_size, self.cropped_img_size))]
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self.aug = nn.Sequential(*augmentations)
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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 = MLP(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))
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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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for p in target_encoder.parameters():
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p.DO_NOT_TRAIN = True
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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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def get_debug_values(self, step, __):
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# In the BYOL paper, this is made to increase over time. Not yet implemented, but still logging the value.
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return {'target_ema_beta': self.target_ema_updater.beta}
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def visual_dbg(self, step, path):
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if self.do_aug:
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torchvision.utils.save_image(self.im1.cpu().float(), os.path.join(path, "%i_image1.png" % (step,)))
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torchvision.utils.save_image(self.im2.cpu().float(), os.path.join(path, "%i_image2.png" % (step,)))
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def forward(self, image_one, image_two):
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if self.do_aug:
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image_one = self.aug(image_one)
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image_two = self.aug(image_two)
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# Keep copies on hand for visual_dbg.
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self.im1 = image_one.detach().copy()
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self.im2 = image_two.detach().copy()
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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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loss_one = loss_fn(online_pred_one, target_proj_two.detach())
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loss_two = 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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class PointwiseAugmentor(nn.Module):
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def __init__(self, img_size=224):
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super().__init__()
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self.jitter = RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8)
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self.gray = augs.RandomGrayscale(p=0.2)
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self.blur = RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1)
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self.rrc = augs.RandomResizedCrop((img_size, img_size))
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# Given a point in the *destination* image, returns the same point in the source image, given the kornia RRC params.
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def reverse_rrc(self, dest_point, params):
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dh, dw = params['dst'][:,2,1]-params['dst'][:,0,1], params['dst'][:,2,0] - params['dst'][:,0,0]
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sh, sw = params['src'][:,2,1]-params['src'][:,0,1], params['src'][:,2,0] - params['src'][:,0,0]
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scale_h, scale_w = sh.float() / dh.float(), sw.float() / dw.float()
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t, l = dest_point
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t = (t.float() * scale_h).int()
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l = (l.float() * scale_w).int()
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return t + params['src'][:,0,1], l + params['src'][:,0,0]
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def reverse_horizontal_flip(self, pt, input):
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t, l = pt
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center = input.shape[-1] // 2
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return t, 2 * center - l
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def forward(self, x, points):
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d = self.jitter(x)
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d = self.gray(d)
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will_flip = random.random() > .5
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if will_flip:
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d = apply_hflip(d)
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d = self.blur(d)
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params = self.rrc.generate_parameters(d.shape)
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d = self.rrc(d, params=params)
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rev = self.reverse_rrc(points, params)
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if will_flip:
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rev = self.reverse_horizontal_flip(rev, x)
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if __name__ == '__main__':
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p = PointwiseAugmentor(256)
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t = ToTensor()(Image.open('E:\\4k6k\\datasets\\ns_images\\imagesets\\000001_152761.jpg')).unsqueeze(0).repeat(8,1,1,1)
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points = (torch.randint(0,224,(t.shape[0],)),torch.randint(0,224,(t.shape[0],)))
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p(t, points)
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@register_model
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def register_byol(opt_net, opt):
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subnet = create_model(opt, opt_net['subnet'])
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return BYOL(subnet, opt_net['image_size'], opt_net['hidden_layer'],
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structural_mlp=opt_get(opt_net, ['use_structural_mlp'], False),
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do_augmentation=opt_get(opt_net, ['gpu_augmentation'], False)) |