Add switch norm, up dropout rate, detach selector
This commit is contained in:
James Betker
parent
97d895aebe
commit
96bc80313c
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@ -7,6 +7,7 @@ from lambda_networks import LambdaLayer
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from torch.nn import init, Conv2d, MSELoss
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import torch.nn.functional as F
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from tqdm import tqdm
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import torch.distributed as dist
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class SwitchedConvHardRoutingFunction(torch.autograd.Function):
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@ -37,11 +38,90 @@ class SwitchedConvHardRoutingFunction(torch.autograd.Function):
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return grad, grad_sel, grad_w, grad_b, None
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"""
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SwitchNorm is meant to be applied against the Softmax output of an switching function across a large set of
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switch computations. It is meant to promote an equal distribution of switch weights by decreasing the magnitude
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of switch weights that are over-used and increasing the magnitude of under-used weights.
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The return value has the exact same format as a normal Softmax output and can be used directly into the input of an
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switch equation.
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Since the whole point of convolutional switch is to enable training extra-wide networks to operate on a large number
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of image categories, it makes almost no sense to perform this type of norm against a single mini-batch of images: some
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of the switches will not be used in such a small context - and that's good! This is solved by accumulating. Every
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forward pass computes a norm across the current minibatch. That norm is added into a rotating buffer of size
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<accumulator_size>. The actual normalization occurs across the entire rotating buffer.
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You should set accumulator size according to two factors:
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- Your batch size. Smaller batch size should mean greater accumulator size.
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- Your image diversity. More diverse images have less need for the accumulator.
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- How wide your switch/switching group size is. More groups mean you're going to want more accumulation.
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Note: This norm makes the (potentially flawed) assumption that each forward() pass has unique data. For maximum
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effectiveness, avoid doing this - or make alterations to work around it.
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Note: This norm does nothing for the first <accumulator_size> iterations.
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"""
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class SwitchNorm(nn.Module):
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def __init__(self, group_size, accumulator_size=128):
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super().__init__()
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self.accumulator_desired_size = accumulator_size
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self.group_size = group_size
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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(accumulator_size, group_size))
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def add_norm_to_buffer(self, x):
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flat = x.sum(dim=[0, 2, 3])
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norm = flat / torch.mean(flat)
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self.accumulator[self.accumulator_index] = norm.detach().clone()
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self.accumulator_index += 1
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if self.accumulator_index >= self.accumulator_desired_size:
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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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# Input into forward is a switching tensor of shape (batch,groups,width,height)
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def forward(self, x: torch.Tensor, update_attention_norm=True):
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assert len(x.shape) == 4
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# Push the accumulator to the right device on the first iteration.
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if self.accumulator.device != x.device:
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self.accumulator = self.accumulator.to(x.device)
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# In eval, don't change the norm buffer.
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if self.training and update_attention_norm:
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self.add_norm_to_buffer(x)
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# Reduce across all distributed entities, if needed
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if dist.is_available() and dist.is_initialized():
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dist.all_reduce(self.accumulator, op=dist.ReduceOp.SUM)
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self.accumulator /= dist.get_world_size()
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# Compute the norm factor.
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if self.accumulator_filled > 0:
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norm = torch.mean(self.accumulator, dim=0)
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else:
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norm = torch.ones(self.group_size, device=self.accumulator.device)
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x = x / norm.view(1,-1,1,1)
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# Need to re-normalize x so that the groups dimension sum to 1, just like when it was fed in.
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return x / x.sum(dim=1, keepdim=True)
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class SwitchedConvHardRouting(nn.Module):
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def __init__(self, in_c, out_c, kernel_sz, breadth, stride=1, bias=True, dropout_rate=0.0,
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include_coupler: bool = False, # A 'coupler' is a latent converter which can make any bxcxhxw tensor a compatible switchedconv selector by performing a linear 1x1 conv, softmax and interpolate.
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coupler_mode: str = 'standard',
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coupler_dim_in: int = 0,):
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def __init__(self,
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in_c,
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out_c,
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kernel_sz,
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breadth,
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stride=1,
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bias=True,
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dropout_rate=0.0,
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include_coupler: bool = False, # A 'coupler' is a latent converter which can make any bxcxhxw tensor a compatible switchedconv selector by performing a linear 1x1 conv, softmax and interpolate.
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coupler_mode: str = 'standard',
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coupler_dim_in: int = 0,
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switch_norm: bool = True):
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super().__init__()
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self.in_channels = in_c
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self.out_channels = out_c
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@ -50,12 +130,22 @@ class SwitchedConvHardRouting(nn.Module):
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self.has_bias = bias
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self.breadth = breadth
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self.dropout_rate = dropout_rate
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if switch_norm:
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self.switch_norm = SwitchNorm(breadth, accumulator_size=512)
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else:
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self.switch_norm = None
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if include_coupler:
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if coupler_mode == 'standard':
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self.coupler = Conv2d(coupler_dim_in, breadth, kernel_size=1)
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elif coupler_mode == 'lambda':
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self.coupler = LambdaLayer(dim=coupler_dim_in, dim_out=breadth, r=23, dim_k=16, heads=2, dim_u=1)
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self.coupler = nn.Sequential(nn.Conv2d(coupler_dim_in, coupler_dim_in, 1),
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nn.BatchNorm2d(coupler_dim_in),
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nn.ReLU(),
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LambdaLayer(dim=coupler_dim_in, dim_out=breadth, r=23, dim_k=16, heads=2, dim_u=1),
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nn.BatchNorm2d(breadth),
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nn.ReLU(),
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Conv2d(breadth, breadth, 1))
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else:
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self.coupler = None
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@ -85,11 +175,14 @@ class SwitchedConvHardRouting(nn.Module):
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# If a coupler was specified, run that to convert selector into a softmax distribution.
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if self.coupler:
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if selector is None: # A coupler can convert from any input to a selector, so 'None' is allowed.
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selector = input
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selector = input.detach()
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selector = F.softmax(self.coupler(selector), dim=1)
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self.last_select = selector.detach().clone()
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assert selector is not None
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# Perform normalization on the selector if applicable.
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if self.switch_norm:
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selector = self.switch_norm(selector)
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# Apply dropout at the batch level per kernel.
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if self.training and self.dropout_rate > 0:
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b, c, h, w = selector.shape
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@ -99,6 +192,10 @@ class SwitchedConvHardRouting(nn.Module):
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drop = drop.logical_or(fix_blank)
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selector = drop * selector
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# Debugging variables
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self.last_select = selector.detach().clone()
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self.latest_masks = (selector.max(dim=1, keepdim=True)[0].repeat(1,self.breadth,1,1) == selector).float().argmax(dim=1)
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return SwitchedConvHardRoutingFunction.apply(input, selector, self.weight, self.bias, self.stride)
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@ -107,6 +204,8 @@ class SwitchedConvHardRouting(nn.Module):
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def convert_conv_net_state_dict_to_switched_conv(module, switch_breadth, ignore_list=[]):
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state_dict = module.state_dict()
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for name, m in module.named_modules():
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if not isinstance(m, nn.Conv2d):
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continue
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ignored = False
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for smod in ignore_list:
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if smod in name:
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@ -114,8 +213,7 @@ def convert_conv_net_state_dict_to_switched_conv(module, switch_breadth, ignore_
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continue
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if ignored:
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continue
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if isinstance(m, nn.Conv2d):
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state_dict[f'{name}.weight'] = state_dict[f'{name}.weight'].unsqueeze(2).repeat(1,1,switch_breadth,1,1)
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state_dict[f'{name}.weight'] = state_dict[f'{name}.weight'].unsqueeze(2).repeat(1,1,switch_breadth,1,1)
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return state_dict
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@ -0,0 +1,293 @@
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import os
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import torch
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import torchvision
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from torch import nn
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from torch.nn import functional as F
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import torch.distributed as distributed
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from models.switched_conv_hard_routing import SwitchedConvHardRouting, \
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convert_conv_net_state_dict_to_switched_conv
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from trainer.networks import register_model
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from utils.util import checkpoint, opt_get
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# Upsamples and blurs (similar to StyleGAN). Replaces ConvTranspose2D from the original paper.
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class UpsampleConv(nn.Module):
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def __init__(self, in_filters, out_filters, breadth, kernel_size, padding):
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super().__init__()
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self.conv = SwitchedConvHardRouting(in_filters, out_filters, kernel_size, breadth, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_filters, dropout_rate=0.4)
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def forward(self, x):
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up = torch.nn.functional.interpolate(x, scale_factor=2)
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return self.conv(up)
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class Quantize(nn.Module):
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def __init__(self, dim, n_embed, decay=0.99, eps=1e-5):
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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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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):
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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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_, embed_ind = (-dist).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.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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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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class ResBlock(nn.Module):
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def __init__(self, in_channel, channel, breadth):
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super().__init__()
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self.conv = nn.Sequential(
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channel, channel, 3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(channel, in_channel, 1),
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)
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def forward(self, input):
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out = self.conv(input)
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out += input
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return out
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class Encoder(nn.Module):
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def __init__(self, in_channel, channel, n_res_block, n_res_channel, stride, breadth):
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super().__init__()
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if stride == 4:
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blocks = [
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nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2),
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nn.ReLU(inplace=True),
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SwitchedConvHardRouting(channel // 2, channel, 5, breadth, stride=2, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel // 2, dropout_rate=0.4),
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nn.ReLU(inplace=True),
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SwitchedConvHardRouting(channel, channel, 3, breadth, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel, dropout_rate=0.4),
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]
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elif stride == 2:
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blocks = [
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nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2),
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nn.ReLU(inplace=True),
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SwitchedConvHardRouting(channel // 2, channel, 3, breadth, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel // 2, dropout_rate=0.4),
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]
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for i in range(n_res_block):
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blocks.append(ResBlock(channel, n_res_channel, breadth))
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blocks.append(nn.ReLU(inplace=True))
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self.blocks = nn.Sequential(*blocks)
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def forward(self, input):
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return self.blocks(input)
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class Decoder(nn.Module):
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def __init__(
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self, in_channel, out_channel, channel, n_res_block, n_res_channel, stride, breadth
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):
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super().__init__()
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blocks = [SwitchedConvHardRouting(in_channel, channel, 3, breadth, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_channel, dropout_rate=0.4)]
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for i in range(n_res_block):
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blocks.append(ResBlock(channel, n_res_channel, breadth))
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blocks.append(nn.ReLU(inplace=True))
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if stride == 4:
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blocks.extend(
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[
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UpsampleConv(channel, channel // 2, breadth, 5, padding=2),
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nn.ReLU(inplace=True),
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UpsampleConv(
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channel // 2, out_channel, breadth, 5, padding=2
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),
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]
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)
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elif stride == 2:
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blocks.append(
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UpsampleConv(channel, out_channel, breadth, 5, padding=2)
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)
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self.blocks = nn.Sequential(*blocks)
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def forward(self, input):
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return self.blocks(input)
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class VQVAE(nn.Module):
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def __init__(
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self,
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in_channel=3,
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channel=128,
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n_res_block=2,
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n_res_channel=32,
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codebook_dim=64,
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codebook_size=512,
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decay=0.99,
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breadth=8,
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):
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super().__init__()
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self.breadth = breadth
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self.enc_b = Encoder(in_channel, channel, n_res_block, n_res_channel, stride=4, breadth=breadth)
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self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2, breadth=breadth)
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self.quantize_conv_t = nn.Conv2d(channel, codebook_dim, 1)
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self.quantize_t = Quantize(codebook_dim, codebook_size)
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self.dec_t = Decoder(
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codebook_dim, codebook_dim, channel, n_res_block, n_res_channel, stride=2, breadth=breadth
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)
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self.quantize_conv_b = nn.Conv2d(codebook_dim + channel, codebook_dim, 1)
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self.quantize_b = Quantize(codebook_dim, codebook_size*2)
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self.upsample_t = UpsampleConv(
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codebook_dim, codebook_dim, breadth, 5, padding=2
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)
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self.dec = Decoder(
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codebook_dim + codebook_dim,
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in_channel,
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channel,
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n_res_block,
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n_res_channel,
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stride=4,
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breadth=breadth
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)
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def forward(self, input):
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quant_t, quant_b, diff, _, _ = self.encode(input)
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dec = self.decode(quant_t, quant_b)
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return dec, diff
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def save_attention_to_image_rgb(self, output_file, attention_out, attention_size, cmap_discrete_name='viridis'):
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from matplotlib import cm
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magnitude, indices = torch.topk(attention_out, 3, dim=1)
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indices = indices.cpu()
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colormap = cm.get_cmap(cmap_discrete_name, attention_size)
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img = torch.tensor(colormap(indices[:, 0, :, :].detach().numpy())) # TODO: use other k's
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img = img.permute((0, 3, 1, 2))
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torchvision.utils.save_image(img, output_file)
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def visual_dbg(self, step, path):
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convs = [self.dec.blocks[-1].conv, self.dec_t.blocks[-1].conv, self.enc_b.blocks[-4], self.enc_t.blocks[-4]]
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for i, c in enumerate(convs):
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self.save_attention_to_image_rgb(os.path.join(path, "%i_selector_%i.png" % (step, i+1)), c.last_select, self.breadth)
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def get_debug_values(self, step, __):
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switched_convs = [('enc_b_blk2', self.enc_b.blocks[2]),
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('enc_b_blk4', self.enc_b.blocks[4]),
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('enc_t_blk2', self.enc_t.blocks[2]),
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('dec_t_blk0', self.dec_t.blocks[0]),
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('dec_t_blk-1', self.dec_t.blocks[-1].conv),
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('dec_blk0', self.dec.blocks[0]),
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('dec_blk-1', self.dec.blocks[-1].conv),
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('dec_blk-3', self.dec.blocks[-3].conv)]
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logs = {}
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for name, swc in switched_convs:
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logs[f'{name}_histogram_switch_usage'] = swc.latest_masks
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return logs
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def encode(self, input):
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enc_b = checkpoint(self.enc_b, input)
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enc_t = checkpoint(self.enc_t, enc_b)
|
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|
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quant_t = self.quantize_conv_t(enc_t).permute(0, 2, 3, 1)
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quant_t, diff_t, id_t = self.quantize_t(quant_t)
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quant_t = quant_t.permute(0, 3, 1, 2)
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diff_t = diff_t.unsqueeze(0)
|
||||
|
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dec_t = checkpoint(self.dec_t, quant_t)
|
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enc_b = torch.cat([dec_t, enc_b], 1)
|
||||
|
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quant_b = checkpoint(self.quantize_conv_b, enc_b).permute(0, 2, 3, 1)
|
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quant_b, diff_b, id_b = self.quantize_b(quant_b)
|
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quant_b = quant_b.permute(0, 3, 1, 2)
|
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diff_b = diff_b.unsqueeze(0)
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|
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return quant_t, quant_b, diff_t + diff_b, id_t, id_b
|
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|
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def decode(self, quant_t, quant_b):
|
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upsample_t = self.upsample_t(quant_t)
|
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quant = torch.cat([upsample_t, quant_b], 1)
|
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dec = checkpoint(self.dec, quant)
|
||||
|
||||
return dec
|
||||
|
||||
def decode_code(self, code_t, code_b):
|
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quant_t = self.quantize_t.embed_code(code_t)
|
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quant_t = quant_t.permute(0, 3, 1, 2)
|
||||
quant_b = self.quantize_b.embed_code(code_b)
|
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quant_b = quant_b.permute(0, 3, 1, 2)
|
||||
|
||||
dec = self.decode(quant_t, quant_b)
|
||||
|
||||
return dec
|
||||
|
||||
|
||||
def convert_weights(weights_file):
|
||||
sd = torch.load(weights_file)
|
||||
import models.vqvae.vqvae_no_conv_transpose as stdvq
|
||||
std_model = stdvq.VQVAE()
|
||||
std_model.load_state_dict(sd)
|
||||
nsd = convert_conv_net_state_dict_to_switched_conv(std_model, 8, ['quantize_conv_t', 'quantize_conv_b',
|
||||
'enc_b.blocks.0', 'enc_t.blocks.0',
|
||||
'conv.1', 'conv.3'])
|
||||
torch.save(nsd, "converted.pth")
|
||||
|
||||
|
||||
@register_model
|
||||
def register_vqvae_norm_hard_switched_conv_lambda(opt_net, opt):
|
||||
kw = opt_get(opt_net, ['kwargs'], {})
|
||||
return VQVAE(**kw)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
v = VQVAE(breadth=8).cuda()
|
||||
print(v(torch.randn(1,3,128,128).cuda())[0].shape)
|
||||
#convert_weights("../../../experiments/50000_generator.pth")
|
||||
|
|
@ -7,8 +7,7 @@ from torch.nn import functional as F
|
|||
|
||||
import torch.distributed as distributed
|
||||
|
||||
from models.switched_conv_hard_routing import SwitchedConvHardRouting, \
|
||||
convert_conv_net_state_dict_to_switched_conv
|
||||
from models.switched_conv import SwitchedConv, convert_conv_net_state_dict_to_switched_conv
|
||||
from trainer.networks import register_model
|
||||
from utils.util import checkpoint, opt_get
|
||||
|
||||
|
|
@ -17,7 +16,7 @@ from utils.util import checkpoint, opt_get
|
|||
class UpsampleConv(nn.Module):
|
||||
def __init__(self, in_filters, out_filters, breadth, kernel_size, padding):
|
||||
super().__init__()
|
||||
self.conv = SwitchedConvHardRouting(in_filters, out_filters, kernel_size, breadth, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_filters, dropout_rate=0.2)
|
||||
self.conv = SwitchedConv(in_filters, out_filters, kernel_size, breadth, padding=padding, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_filters)
|
||||
|
||||
def forward(self, x):
|
||||
up = torch.nn.functional.interpolate(x, scale_factor=2)
|
||||
|
|
@ -84,9 +83,9 @@ class ResBlock(nn.Module):
|
|||
|
||||
self.conv = nn.Sequential(
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(in_channel, channel, 3, padding=1),
|
||||
SwitchedConv(in_channel, channel, 3, breadth, padding=1, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_channel),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(channel, in_channel, 1),
|
||||
SwitchedConv(channel, in_channel, 1, breadth, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel),
|
||||
)
|
||||
|
||||
def forward(self, input):
|
||||
|
|
@ -102,18 +101,18 @@ class Encoder(nn.Module):
|
|||
|
||||
if stride == 4:
|
||||
blocks = [
|
||||
SwitchedConvHardRouting(in_channel, channel // 2, 5, breadth, stride=2, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_channel, dropout_rate=0.2),
|
||||
SwitchedConv(in_channel, channel // 2, 5, breadth, stride=2, padding=2, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_channel),
|
||||
nn.ReLU(inplace=True),
|
||||
SwitchedConvHardRouting(channel // 2, channel, 5, breadth, stride=2, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel // 2, dropout_rate=0.2),
|
||||
SwitchedConv(channel // 2, channel, 5, breadth, stride=2, padding=2, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel // 2),
|
||||
nn.ReLU(inplace=True),
|
||||
SwitchedConvHardRouting(channel, channel, 3, breadth, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel, dropout_rate=0.2),
|
||||
SwitchedConv(channel, channel, 3, breadth, padding=1, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel),
|
||||
]
|
||||
|
||||
elif stride == 2:
|
||||
blocks = [
|
||||
SwitchedConvHardRouting(in_channel, channel // 2, 5, breadth, stride=2, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_channel, dropout_rate=0.2),
|
||||
SwitchedConv(in_channel, channel // 2, 5, breadth, stride=2, padding=2, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_channel),
|
||||
nn.ReLU(inplace=True),
|
||||
SwitchedConvHardRouting(channel // 2, channel, 3, breadth, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel // 2, dropout_rate=0.2),
|
||||
SwitchedConv(channel // 2, channel, 3, breadth, padding=1, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel // 2),
|
||||
]
|
||||
|
||||
for i in range(n_res_block):
|
||||
|
|
@ -133,7 +132,7 @@ class Decoder(nn.Module):
|
|||
):
|
||||
super().__init__()
|
||||
|
||||
blocks = [SwitchedConvHardRouting(in_channel, channel, 3, breadth, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_channel, dropout_rate=0.2)]
|
||||
blocks = [SwitchedConv(in_channel, channel, 3, breadth, padding=1, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_channel)]
|
||||
|
||||
for i in range(n_res_block):
|
||||
blocks.append(ResBlock(channel, n_res_channel, breadth))
|
||||
|
|
@ -172,7 +171,7 @@ class VQVAE(nn.Module):
|
|||
codebook_dim=64,
|
||||
codebook_size=512,
|
||||
decay=0.99,
|
||||
breadth=8,
|
||||
breadth=4,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
|
|
@ -261,8 +260,7 @@ def convert_weights(weights_file):
|
|||
import models.vqvae.vqvae_no_conv_transpose as stdvq
|
||||
std_model = stdvq.VQVAE()
|
||||
std_model.load_state_dict(sd)
|
||||
nsd = convert_conv_net_state_dict_to_switched_conv(std_model, 1, ['quantize_conv_t', 'quantize_conv_b',
|
||||
'conv.1', 'conv.3'])
|
||||
nsd = convert_conv_net_state_dict_to_switched_conv(std_model, 4, ['quantize_conv_t', 'quantize_conv_b'])
|
||||
torch.save(nsd, "converted.pth")
|
||||
|
||||
|
||||
|
|
@ -273,6 +271,6 @@ def register_vqvae_norm_switched_conv_lambda(opt_net, opt):
|
|||
|
||||
|
||||
if __name__ == '__main__':
|
||||
v = VQVAE(breadth=8).cuda()
|
||||
print(v(torch.randn(1,3,128,128).cuda())[0].shape)
|
||||
#convert_weights("../../../experiments/50000_generator.pth")
|
||||
#v = VQVAE()
|
||||
#print(v(torch.randn(1,3,128,128))[0].shape)
|
||||
convert_weights("../../../experiments/4000_generator.pth")
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user