Delete models/diffloss.py

models/diffloss.py

@@ -1,258 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.utils.checkpoint import checkpoint
-import math
-from timm.layers.mlp import SwiGLU
-from models.diffusion import create_diffusion
-
-
-class DiffLoss(nn.Module):
-    """Diffusion Loss"""
-    def __init__(self, target_channels, z_channels, depth, width, num_sampling_steps, grad_checkpointing=False, learn_sigma=False):
-        super(DiffLoss, self).__init__()
-        self.in_channels = target_channels
-        self.net = SimpleMLPAdaLN(
-            in_channels=target_channels,
-            model_channels=width,
-            out_channels=target_channels * 2 if learn_sigma else target_channels,
-            z_channels=z_channels,
-            num_res_blocks=depth,
-            grad_checkpointing=grad_checkpointing
-        )
-
-        self.train_diffusion = create_diffusion(timestep_respacing="", noise_schedule="cosine")
-        self.gen_diffusion = create_diffusion(timestep_respacing=num_sampling_steps, noise_schedule="cosine")
-
-    def forward(self, target, z, mask=None):
-        t = torch.randint(0, self.train_diffusion.num_timesteps, (target.shape[0],), device=target.device)  
-        model_kwargs = dict(c=z)    
-        loss_dict = self.train_diffusion.training_losses(self.net, target, t, model_kwargs)
-        loss = loss_dict["loss"]        
-        pred_xstart = loss_dict["pred_xstart"]      
-        if mask is not None:
-            loss = (loss * mask).sum() / mask.sum()
-        return loss.mean(), pred_xstart
-
-    def sample(self, z, temperature=1.0, cfg=1.0):
-        
-        if not cfg == 1.0:
-            noise = torch.randn(z.shape[0] // 2, self.in_channels).to(z.device)
-            noise = torch.cat([noise, noise], dim=0)                        
-            model_kwargs = dict(c=z, cfg_scale=cfg)
-            sample_fn = self.net.forward_with_cfg
-        else:
-            noise = torch.randn(z.shape[0], self.in_channels).to(z.device)
-            model_kwargs = dict(c=z)
-            sample_fn = self.net.forward
-
-        sampled_token_latent = self.gen_diffusion.p_sample_loop(
-            sample_fn, noise.shape, noise, clip_denoised=False, model_kwargs=model_kwargs, progress=False,
-            temperature=temperature
-        )
-
-        return sampled_token_latent
-
-
-def modulate(x, shift, scale):
-    return x * (1 + scale) + shift
-
-
-class TimestepEmbedder(nn.Module):
-    """
-    Embeds scalar timesteps into vector representations.
-    """
-    def __init__(self, hidden_size, frequency_embedding_size=256):
-        super().__init__()
-        self.mlp = nn.Sequential(
-            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
-            nn.SiLU(),
-            nn.Linear(hidden_size, hidden_size, bias=True),
-        )
-        self.frequency_embedding_size = frequency_embedding_size
-
-    @staticmethod
-    def timestep_embedding(t, dim, max_period=10000):
-        """
-        Create sinusoidal timestep embeddings.
-        :param t: a 1-D Tensor of N indices, one per batch element.
-                          These may be fractional.
-        :param dim: the dimension of the output.
-        :param max_period: controls the minimum frequency of the embeddings.
-        :return: an (N, D) Tensor of positional embeddings.
-        """
-        
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-        ).to(device=t.device)
-        args = t[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-        return embedding
-
-    def forward(self, t):
-        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
-        t_emb = self.mlp(t_freq)
-        return t_emb
-
-
-class ResBlock(nn.Module):
-    """
-    A residual block that can optionally change the number of channels.
-    :param channels: the number of input channels.
-    """
-
-    def __init__(
-        self,
-        channels
-    ):
-        super().__init__()
-        self.channels = channels
-
-        self.in_ln = nn.LayerNorm(channels, eps=1e-6)
-        
-        self.mlp = nn.Sequential(
-                nn.Linear(channels, channels, bias=True),
-                nn.SiLU(),
-                nn.Linear(channels, channels, bias=True),
-            )
-
-        
-        self.adaLN_modulation = nn.Sequential(
-                nn.SiLU(),
-                nn.Linear(channels, 3 * channels, bias=True)
-            )
-
-    def forward(self, x, y):
-        shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(y).chunk(3, dim=-1)
-        h = modulate(self.in_ln(x), shift_mlp, scale_mlp)
-        h = self.mlp(h)
-        return x + gate_mlp * h
-
-
-class FinalLayer(nn.Module):
-    """
-    The final layer adopted from DiT.
-    """
-    def __init__(self, model_channels, out_channels):
-        super().__init__()
-        self.norm_final = nn.LayerNorm(model_channels, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(model_channels, out_channels, bias=True)
-        
-        self.adaLN_modulation = nn.Sequential(
-                nn.SiLU(),
-                nn.Linear(model_channels, 2 * model_channels, bias=True)
-            )
-
-    def forward(self, x, c):
-        shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
-        x = modulate(self.norm_final(x), shift, scale)
-        x = self.linear(x)
-        return x
-
-
-class SimpleMLPAdaLN(nn.Module):
-    """
-    The MLP for Diffusion Loss.
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param z_channels: channels in the condition.
-    :param num_res_blocks: number of residual blocks per downsample.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        model_channels,
-        out_channels,
-        z_channels,
-        num_res_blocks,
-        grad_checkpointing=False
-    ):
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.grad_checkpointing = grad_checkpointing
-
-        self.time_embed = TimestepEmbedder(model_channels)          
-        self.cond_embed = nn.Linear(z_channels, model_channels)     
-
-        self.input_proj = nn.Linear(in_channels, model_channels)    
-
-        res_blocks = []
-        for i in range(num_res_blocks):  
-            res_blocks.append(ResBlock(
-                model_channels
-            ))
-
-        self.res_blocks = nn.ModuleList(res_blocks)
-        self.final_layer = FinalLayer(model_channels, out_channels)   
-
-        self.initialize_weights()
-
-    def initialize_weights(self):
-        def _basic_init(module):
-            if isinstance(module, nn.Linear):
-                torch.nn.init.xavier_uniform_(module.weight)
-                if module.bias is not None:
-                    nn.init.constant_(module.bias, 0)
-        self.apply(_basic_init)
-
-        # Initialize timestep embedding MLP
-        nn.init.normal_(self.time_embed.mlp[0].weight, std=0.02)
-        nn.init.normal_(self.time_embed.mlp[2].weight, std=0.02)
-
-        # Zero-out adaLN modulation layers
-
-        for block in self.res_blocks:
-            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
-            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
-
-        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
-        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
-        nn.init.constant_(self.final_layer.linear.weight, 0)
-        nn.init.constant_(self.final_layer.linear.bias, 0)
-
-    def forward(self, x, t, c):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C] Tensor of inputs.
-        :param t: a 1-D batch of timesteps.
-        :param c: conditioning from AR transformer.
-        :return: an [N x C] Tensor of outputs.
-        """
-
-
-        
-        x = x.float()  
-
-        x = self.input_proj(x)  
-        t = self.time_embed(t)  
-        c = self.cond_embed(c)  
-        
-
-        y = t + c
-
-        if self.grad_checkpointing and not torch.jit.is_scripting():
-            for block in self.res_blocks:
-                x = checkpoint(block, x, y)
-        else:
-            for block in self.res_blocks:
-                x = block(x, y)   
-
-        return self.final_layer(x, y)
-
-    def forward_with_cfg(self, x, t, c, cfg_scale):
-        half = x[: len(x) // 2]                     
-        combined = torch.cat([half, half], dim=0)   
-        model_out = self.forward(combined, t, c)    
-        eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
-        cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)   
-        half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)    
-        eps = torch.cat([half_eps, half_eps], dim=0)                    
-        return torch.cat([eps, rest], dim=1)