391 lines
14 KiB
Python
391 lines
14 KiB
Python
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import functools
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from math import sqrt
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import torch
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import torch.distributed as distributed
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange
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def default(val, d):
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return val if val is not None else d
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def eval_decorator(fn):
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def inner(model, *args, **kwargs):
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was_training = model.training
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model.eval()
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out = fn(model, *args, **kwargs)
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model.train(was_training)
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return out
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return inner
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# Quantizer implemented by the rosinality vqvae repo.
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# Credit: https://github.com/rosinality/vq-vae-2-pytorch
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class Quantize(nn.Module):
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def __init__(self, dim, n_embed, decay=0.99, eps=1e-5, balancing_heuristic=False, new_return_order=False):
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super().__init__()
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self.dim = dim
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self.n_embed = n_embed
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self.decay = decay
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self.eps = eps
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self.balancing_heuristic = balancing_heuristic
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self.codes = None
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self.max_codes = 64000
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self.codes_full = False
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self.new_return_order = new_return_order
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embed = torch.randn(dim, n_embed)
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self.register_buffer("embed", embed)
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self.register_buffer("cluster_size", torch.zeros(n_embed))
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self.register_buffer("embed_avg", embed.clone())
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def forward(self, input, return_soft_codes=False):
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if self.balancing_heuristic and self.codes_full:
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h = torch.histc(self.codes, bins=self.n_embed, min=0, max=self.n_embed) / len(self.codes)
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mask = torch.logical_or(h > .9, h < .01).unsqueeze(1)
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ep = self.embed.permute(1,0)
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ea = self.embed_avg.permute(1,0)
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rand_embed = torch.randn_like(ep) * mask
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self.embed = (ep * ~mask + rand_embed).permute(1,0)
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self.embed_avg = (ea * ~mask + rand_embed).permute(1,0)
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self.cluster_size = self.cluster_size * ~mask.squeeze()
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if torch.any(mask):
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print(f"Reset {torch.sum(mask)} embedding codes.")
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self.codes = None
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self.codes_full = False
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flatten = input.reshape(-1, self.dim)
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dist = (
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flatten.pow(2).sum(1, keepdim=True)
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- 2 * flatten @ self.embed
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+ self.embed.pow(2).sum(0, keepdim=True)
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)
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soft_codes = -dist
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_, embed_ind = soft_codes.max(1)
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embed_onehot = F.one_hot(embed_ind, self.n_embed).type(flatten.dtype)
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embed_ind = embed_ind.view(*input.shape[:-1])
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quantize = self.embed_code(embed_ind)
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if self.balancing_heuristic:
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if self.codes is None:
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self.codes = embed_ind.flatten()
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else:
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self.codes = torch.cat([self.codes, embed_ind.flatten()])
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if len(self.codes) > self.max_codes:
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self.codes = self.codes[-self.max_codes:]
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self.codes_full = True
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if self.training:
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embed_onehot_sum = embed_onehot.sum(0)
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embed_sum = flatten.transpose(0, 1) @ embed_onehot
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if distributed.is_initialized() and distributed.get_world_size() > 1:
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distributed.all_reduce(embed_onehot_sum)
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distributed.all_reduce(embed_sum)
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self.cluster_size.data.mul_(self.decay).add_(
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embed_onehot_sum, alpha=1 - self.decay
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)
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self.embed_avg.data.mul_(self.decay).add_(embed_sum, alpha=1 - self.decay)
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n = self.cluster_size.sum()
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cluster_size = (
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(self.cluster_size + self.eps) / (n + self.n_embed * self.eps) * n
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)
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embed_normalized = self.embed_avg / cluster_size.unsqueeze(0)
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self.embed.data.copy_(embed_normalized)
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diff = (quantize.detach() - input).pow(2).mean()
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quantize = input + (quantize - input).detach()
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if return_soft_codes:
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return quantize, diff, embed_ind, soft_codes.view(input.shape[:-1] + (-1,))
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elif self.new_return_order:
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return quantize, embed_ind, diff
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else:
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return quantize, diff, embed_ind
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def embed_code(self, embed_id):
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return F.embedding(embed_id, self.embed.transpose(0, 1))
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# Fits a soft-discretized input to a normal-PDF across the specified dimension.
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# In other words, attempts to force the discretization function to have a mean equal utilization across all discrete
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# values with the specified expected variance.
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class DiscretizationLoss(nn.Module):
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def __init__(self, discrete_bins, dim, expected_variance, store_past=0):
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super().__init__()
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self.discrete_bins = discrete_bins
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self.dim = dim
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self.dist = torch.distributions.Normal(0, scale=expected_variance)
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if store_past > 0:
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self.record_past = True
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self.register_buffer("accumulator_index", torch.zeros(1, dtype=torch.long, device='cpu'))
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self.register_buffer("accumulator_filled", torch.zeros(1, dtype=torch.long, device='cpu'))
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self.register_buffer("accumulator", torch.zeros(store_past, discrete_bins))
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else:
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self.record_past = False
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def forward(self, x):
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other_dims = set(range(len(x.shape)))-set([self.dim])
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averaged = x.sum(dim=tuple(other_dims)) / x.sum()
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averaged = averaged - averaged.mean()
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if self.record_past:
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acc_count = self.accumulator.shape[0]
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avg = averaged.detach().clone()
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if self.accumulator_filled > 0:
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averaged = torch.mean(self.accumulator, dim=0) * (acc_count-1) / acc_count + \
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averaged / acc_count
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# Also push averaged into the accumulator.
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self.accumulator[self.accumulator_index] = avg
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self.accumulator_index += 1
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if self.accumulator_index >= acc_count:
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self.accumulator_index *= 0
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if self.accumulator_filled <= 0:
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self.accumulator_filled += 1
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return torch.sum(-self.dist.log_prob(averaged))
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class ResBlock(nn.Module):
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def __init__(self, chan, conv, activation):
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super().__init__()
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self.net = nn.Sequential(
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conv(chan, chan, 3, padding = 1),
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activation(),
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conv(chan, chan, 3, padding = 1),
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activation(),
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conv(chan, chan, 1)
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)
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def forward(self, x):
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return self.net(x) + x
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class UpsampledConv(nn.Module):
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def __init__(self, conv, *args, **kwargs):
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super().__init__()
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assert 'stride' in kwargs.keys()
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self.stride = kwargs['stride']
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del kwargs['stride']
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self.conv = conv(*args, **kwargs)
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def forward(self, x):
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up = nn.functional.interpolate(x, scale_factor=self.stride, mode='nearest')
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return self.conv(up)
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# DiscreteVAE partially derived from lucidrains DALLE implementation
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# Credit: https://github.com/lucidrains/DALLE-pytorch
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class DiscreteVAE(nn.Module):
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def __init__(
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self,
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positional_dims=2,
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num_tokens = 512,
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codebook_dim = 512,
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num_layers = 3,
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num_resnet_blocks = 0,
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hidden_dim = 64,
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channels = 3,
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stride = 2,
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kernel_size = 4,
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use_transposed_convs = True,
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encoder_norm = False,
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activation = 'relu',
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smooth_l1_loss = False,
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straight_through = False,
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normalization = None, # ((0.5,) * 3, (0.5,) * 3),
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record_codes = False,
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discretization_loss_averaging_steps = 100,
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lr_quantizer_args = {},
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):
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super().__init__()
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has_resblocks = num_resnet_blocks > 0
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self.num_tokens = num_tokens
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self.num_layers = num_layers
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self.straight_through = straight_through
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self.positional_dims = positional_dims
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self.discrete_loss = DiscretizationLoss(num_tokens, 2, 1 / (num_tokens*2), discretization_loss_averaging_steps)
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assert positional_dims > 0 and positional_dims < 3 # This VAE only supports 1d and 2d inputs for now.
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if positional_dims == 2:
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conv = nn.Conv2d
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conv_transpose = nn.ConvTranspose2d
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else:
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conv = nn.Conv1d
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conv_transpose = nn.ConvTranspose1d
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if not use_transposed_convs:
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conv_transpose = functools.partial(UpsampledConv, conv)
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if activation == 'relu':
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act = nn.ReLU
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elif activation == 'silu':
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act = nn.SiLU
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else:
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assert NotImplementedError()
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enc_layers = []
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dec_layers = []
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if num_layers > 0:
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enc_chans = [hidden_dim * 2 ** i for i in range(num_layers)]
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dec_chans = list(reversed(enc_chans))
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enc_chans = [channels, *enc_chans]
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dec_init_chan = codebook_dim if not has_resblocks else dec_chans[0]
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dec_chans = [dec_init_chan, *dec_chans]
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enc_chans_io, dec_chans_io = map(lambda t: list(zip(t[:-1], t[1:])), (enc_chans, dec_chans))
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pad = (kernel_size - 1) // 2
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for (enc_in, enc_out), (dec_in, dec_out) in zip(enc_chans_io, dec_chans_io):
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enc_layers.append(nn.Sequential(conv(enc_in, enc_out, kernel_size, stride = stride, padding = pad), act()))
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if encoder_norm:
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enc_layers.append(nn.GroupNorm(8, enc_out))
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dec_layers.append(nn.Sequential(conv_transpose(dec_in, dec_out, kernel_size, stride = stride, padding = pad), act()))
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dec_out_chans = dec_chans[-1]
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innermost_dim = dec_chans[0]
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else:
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enc_layers.append(nn.Sequential(conv(channels, hidden_dim, 1), act()))
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dec_out_chans = hidden_dim
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innermost_dim = hidden_dim
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for _ in range(num_resnet_blocks):
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dec_layers.insert(0, ResBlock(innermost_dim, conv, act))
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enc_layers.append(ResBlock(innermost_dim, conv, act))
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if num_resnet_blocks > 0:
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dec_layers.insert(0, conv(codebook_dim, innermost_dim, 1))
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enc_layers.append(conv(innermost_dim, codebook_dim, 1))
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dec_layers.append(conv(dec_out_chans, channels, 1))
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self.encoder = nn.Sequential(*enc_layers)
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self.decoder = nn.Sequential(*dec_layers)
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self.loss_fn = F.smooth_l1_loss if smooth_l1_loss else F.mse_loss
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self.codebook = Quantize(codebook_dim, num_tokens, new_return_order=True)
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# take care of normalization within class
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self.normalization = normalization
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self.record_codes = record_codes
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if record_codes:
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self.codes = torch.zeros((1228800,), dtype=torch.long)
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self.code_ind = 0
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self.total_codes = 0
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self.internal_step = 0
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def norm(self, images):
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if not self.normalization is not None:
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return images
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means, stds = map(lambda t: torch.as_tensor(t).to(images), self.normalization)
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arrange = 'c -> () c () ()' if self.positional_dims == 2 else 'c -> () c ()'
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means, stds = map(lambda t: rearrange(t, arrange), (means, stds))
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images = images.clone()
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images.sub_(means).div_(stds)
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return images
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def get_debug_values(self, step, __):
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if self.record_codes and self.total_codes > 0:
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# Report annealing schedule
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return {'histogram_codes': self.codes[:self.total_codes]}
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else:
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return {}
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@torch.no_grad()
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@eval_decorator
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def get_codebook_indices(self, images):
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img = self.norm(images)
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logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
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sampled, codes, _ = self.codebook(logits)
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self.log_codes(codes)
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return codes
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def decode(
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self,
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img_seq
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):
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self.log_codes(img_seq)
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if hasattr(self.codebook, 'embed_code'):
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image_embeds = self.codebook.embed_code(img_seq)
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else:
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image_embeds = F.embedding(img_seq, self.codebook.codebook)
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b, n, d = image_embeds.shape
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kwargs = {}
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if self.positional_dims == 1:
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arrange = 'b n d -> b d n'
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else:
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h = w = int(sqrt(n))
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arrange = 'b (h w) d -> b d h w'
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kwargs = {'h': h, 'w': w}
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image_embeds = rearrange(image_embeds, arrange, **kwargs)
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images = [image_embeds]
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for layer in self.decoder:
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images.append(layer(images[-1]))
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return images[-1], images[-2]
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def infer(self, img):
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img = self.norm(img)
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logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
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sampled, codes, commitment_loss = self.codebook(logits)
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return self.decode(codes)
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# Note: This module is not meant to be run in forward() except while training. It has special logic which performs
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# evaluation using quantized values when it detects that it is being run in eval() mode, which will be substantially
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# more lossy (but useful for determining network performance).
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||
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|
def forward(
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||
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|
self,
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||
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|
img
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||
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|
):
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||
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|
img = self.norm(img)
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||
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|
logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
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||
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|
sampled, codes, commitment_loss = self.codebook(logits)
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||
|
|
sampled = sampled.permute((0,3,1,2) if len(img.shape) == 4 else (0,2,1))
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||
|
|
|
||
|
|
if self.training:
|
||
|
|
out = sampled
|
||
|
|
for d in self.decoder:
|
||
|
|
out = d(out)
|
||
|
|
self.log_codes(codes)
|
||
|
|
else:
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||
|
|
# This is non-differentiable, but gives a better idea of how the network is actually performing.
|
||
|
|
out, _ = self.decode(codes)
|
||
|
|
|
||
|
|
# reconstruction loss
|
||
|
|
recon_loss = self.loss_fn(img, out, reduction='none')
|
||
|
|
|
||
|
|
return recon_loss, commitment_loss, out
|
||
|
|
|
||
|
|
def log_codes(self, codes):
|
||
|
|
# This is so we can debug the distribution of codes being learned.
|
||
|
|
if self.record_codes and self.internal_step % 10 == 0:
|
||
|
|
codes = codes.flatten()
|
||
|
|
l = codes.shape[0]
|
||
|
|
i = self.code_ind if (self.codes.shape[0] - self.code_ind) > l else self.codes.shape[0] - l
|
||
|
|
self.codes[i:i+l] = codes.cpu()
|
||
|
|
self.code_ind = self.code_ind + l
|
||
|
|
if self.code_ind >= self.codes.shape[0]:
|
||
|
|
self.code_ind = 0
|
||
|
|
self.total_codes += 1
|
||
|
|
self.internal_step += 1
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
v = DiscreteVAE(channels=80, normalization=None, positional_dims=1, num_tokens=8192, codebook_dim=2048,
|
||
|
|
hidden_dim=512, num_resnet_blocks=3, kernel_size=3, num_layers=1, use_transposed_convs=False)
|
||
|
|
r,l,o=v(torch.randn(1,80,256))
|
||
|
|
v.decode(torch.randint(0,8192,(1,256)))
|
||
|
|
print(o.shape, l.shape)
|