353 lines
15 KiB
Python
353 lines
15 KiB
Python
import os, math
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import torch
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import torch.nn.functional as F
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import pytorch_lightning as pl
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from main import instantiate_from_config
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from taming.modules.util import SOSProvider
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def disabled_train(self, mode=True):
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"""Overwrite model.train with this function to make sure train/eval mode
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does not change anymore."""
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return self
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class Net2NetTransformer(pl.LightningModule):
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def __init__(self,
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transformer_config,
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first_stage_config,
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cond_stage_config,
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permuter_config=None,
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ckpt_path=None,
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ignore_keys=[],
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first_stage_key="image",
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cond_stage_key="depth",
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downsample_cond_size=-1,
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pkeep=1.0,
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sos_token=0,
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unconditional=False,
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):
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super().__init__()
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self.be_unconditional = unconditional
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self.sos_token = sos_token
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self.first_stage_key = first_stage_key
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self.cond_stage_key = cond_stage_key
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self.init_first_stage_from_ckpt(first_stage_config)
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self.init_cond_stage_from_ckpt(cond_stage_config)
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if permuter_config is None:
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permuter_config = {"target": "taming.modules.transformer.permuter.Identity"}
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self.permuter = instantiate_from_config(config=permuter_config)
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self.transformer = instantiate_from_config(config=transformer_config)
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if ckpt_path is not None:
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self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
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self.downsample_cond_size = downsample_cond_size
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self.pkeep = pkeep
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def init_from_ckpt(self, path, ignore_keys=list()):
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sd = torch.load(path, map_location="cpu")["state_dict"]
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for k in sd.keys():
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for ik in ignore_keys:
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if k.startswith(ik):
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self.print("Deleting key {} from state_dict.".format(k))
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del sd[k]
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self.load_state_dict(sd, strict=False)
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print(f"Restored from {path}")
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def init_first_stage_from_ckpt(self, config):
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model = instantiate_from_config(config)
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model = model.eval()
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model.train = disabled_train
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self.first_stage_model = model
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def init_cond_stage_from_ckpt(self, config):
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if config == "__is_first_stage__":
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print("Using first stage also as cond stage.")
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self.cond_stage_model = self.first_stage_model
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elif config == "__is_unconditional__" or self.be_unconditional:
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print(f"Using no cond stage. Assuming the training is intended to be unconditional. "
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f"Prepending {self.sos_token} as a sos token.")
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self.be_unconditional = True
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self.cond_stage_key = self.first_stage_key
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self.cond_stage_model = SOSProvider(self.sos_token)
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else:
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model = instantiate_from_config(config)
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model = model.eval()
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model.train = disabled_train
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self.cond_stage_model = model
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def forward(self, x, c):
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# one step to produce the logits
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_, z_indices = self.encode_to_z(x)
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_, c_indices = self.encode_to_c(c)
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if self.training and self.pkeep < 1.0:
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mask = torch.bernoulli(self.pkeep*torch.ones(z_indices.shape,
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device=z_indices.device))
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mask = mask.round().to(dtype=torch.int64)
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r_indices = torch.randint_like(z_indices, self.transformer.config.vocab_size)
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a_indices = mask*z_indices+(1-mask)*r_indices
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else:
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a_indices = z_indices
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cz_indices = torch.cat((c_indices, a_indices), dim=1)
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# target includes all sequence elements (no need to handle first one
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# differently because we are conditioning)
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target = z_indices
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# make the prediction
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logits, _ = self.transformer(cz_indices[:, :-1])
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# cut off conditioning outputs - output i corresponds to p(z_i | z_{<i}, c)
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logits = logits[:, c_indices.shape[1]-1:]
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return logits, target
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def top_k_logits(self, logits, k):
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v, ix = torch.topk(logits, k)
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out = logits.clone()
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out[out < v[..., [-1]]] = -float('Inf')
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return out
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@torch.no_grad()
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def sample(self, x, c, steps, temperature=1.0, sample=False, top_k=None,
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callback=lambda k: None):
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x = torch.cat((c,x),dim=1)
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block_size = self.transformer.get_block_size()
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assert not self.transformer.training
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if self.pkeep <= 0.0:
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# one pass suffices since input is pure noise anyway
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assert len(x.shape)==2
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noise_shape = (x.shape[0], steps-1)
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#noise = torch.randint(self.transformer.config.vocab_size, noise_shape).to(x)
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noise = c.clone()[:,x.shape[1]-c.shape[1]:-1]
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x = torch.cat((x,noise),dim=1)
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logits, _ = self.transformer(x)
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# take all logits for now and scale by temp
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logits = logits / temperature
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# optionally crop probabilities to only the top k options
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if top_k is not None:
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logits = self.top_k_logits(logits, top_k)
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# apply softmax to convert to probabilities
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probs = F.softmax(logits, dim=-1)
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# sample from the distribution or take the most likely
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if sample:
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shape = probs.shape
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probs = probs.reshape(shape[0]*shape[1],shape[2])
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ix = torch.multinomial(probs, num_samples=1)
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probs = probs.reshape(shape[0],shape[1],shape[2])
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ix = ix.reshape(shape[0],shape[1])
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else:
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_, ix = torch.topk(probs, k=1, dim=-1)
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# cut off conditioning
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x = ix[:, c.shape[1]-1:]
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else:
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for k in range(steps):
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callback(k)
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assert x.size(1) <= block_size # make sure model can see conditioning
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x_cond = x if x.size(1) <= block_size else x[:, -block_size:] # crop context if needed
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logits, _ = self.transformer(x_cond)
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# pluck the logits at the final step and scale by temperature
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logits = logits[:, -1, :] / temperature
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# optionally crop probabilities to only the top k options
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if top_k is not None:
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logits = self.top_k_logits(logits, top_k)
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# apply softmax to convert to probabilities
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probs = F.softmax(logits, dim=-1)
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# sample from the distribution or take the most likely
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if sample:
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ix = torch.multinomial(probs, num_samples=1)
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else:
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_, ix = torch.topk(probs, k=1, dim=-1)
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# append to the sequence and continue
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x = torch.cat((x, ix), dim=1)
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# cut off conditioning
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x = x[:, c.shape[1]:]
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return x
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@torch.no_grad()
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def encode_to_z(self, x):
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quant_z, _, info = self.first_stage_model.encode(x)
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indices = info[2].view(quant_z.shape[0], -1)
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indices = self.permuter(indices)
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return quant_z, indices
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@torch.no_grad()
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def encode_to_c(self, c):
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if self.downsample_cond_size > -1:
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c = F.interpolate(c, size=(self.downsample_cond_size, self.downsample_cond_size))
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quant_c, _, [_,_,indices] = self.cond_stage_model.encode(c)
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if len(indices.shape) > 2:
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indices = indices.view(c.shape[0], -1)
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return quant_c, indices
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@torch.no_grad()
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def decode_to_img(self, index, zshape):
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index = self.permuter(index, reverse=True)
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bhwc = (zshape[0],zshape[2],zshape[3],zshape[1])
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quant_z = self.first_stage_model.quantize.get_codebook_entry(
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index.reshape(-1), shape=bhwc)
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x = self.first_stage_model.decode(quant_z)
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return x
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@torch.no_grad()
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def log_images(self, batch, temperature=None, top_k=None, callback=None, lr_interface=False, **kwargs):
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log = dict()
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N = 4
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if lr_interface:
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x, c = self.get_xc(batch, N, diffuse=False, upsample_factor=8)
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else:
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x, c = self.get_xc(batch, N)
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x = x.to(device=self.device)
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c = c.to(device=self.device)
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quant_z, z_indices = self.encode_to_z(x)
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quant_c, c_indices = self.encode_to_c(c)
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# create a "half"" sample
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z_start_indices = z_indices[:,:z_indices.shape[1]//2]
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index_sample = self.sample(z_start_indices, c_indices,
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steps=z_indices.shape[1]-z_start_indices.shape[1],
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temperature=temperature if temperature is not None else 1.0,
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sample=True,
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top_k=top_k if top_k is not None else 100,
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callback=callback if callback is not None else lambda k: None)
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x_sample = self.decode_to_img(index_sample, quant_z.shape)
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# sample
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z_start_indices = z_indices[:, :0]
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index_sample = self.sample(z_start_indices, c_indices,
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steps=z_indices.shape[1],
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temperature=temperature if temperature is not None else 1.0,
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sample=True,
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top_k=top_k if top_k is not None else 100,
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callback=callback if callback is not None else lambda k: None)
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x_sample_nopix = self.decode_to_img(index_sample, quant_z.shape)
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# det sample
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z_start_indices = z_indices[:, :0]
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index_sample = self.sample(z_start_indices, c_indices,
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steps=z_indices.shape[1],
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sample=False,
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callback=callback if callback is not None else lambda k: None)
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x_sample_det = self.decode_to_img(index_sample, quant_z.shape)
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# reconstruction
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x_rec = self.decode_to_img(z_indices, quant_z.shape)
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log["inputs"] = x
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log["reconstructions"] = x_rec
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if self.cond_stage_key in ["objects_bbox", "objects_center_points"]:
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figure_size = (x_rec.shape[2], x_rec.shape[3])
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dataset = kwargs["pl_module"].trainer.datamodule.datasets["validation"]
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label_for_category_no = dataset.get_textual_label_for_category_no
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plotter = dataset.conditional_builders[self.cond_stage_key].plot
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log["conditioning"] = torch.zeros_like(log["reconstructions"])
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for i in range(quant_c.shape[0]):
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log["conditioning"][i] = plotter(quant_c[i], label_for_category_no, figure_size)
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log["conditioning_rec"] = log["conditioning"]
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elif self.cond_stage_key != "image":
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cond_rec = self.cond_stage_model.decode(quant_c)
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if self.cond_stage_key == "segmentation":
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# get image from segmentation mask
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num_classes = cond_rec.shape[1]
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c = torch.argmax(c, dim=1, keepdim=True)
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c = F.one_hot(c, num_classes=num_classes)
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c = c.squeeze(1).permute(0, 3, 1, 2).float()
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c = self.cond_stage_model.to_rgb(c)
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cond_rec = torch.argmax(cond_rec, dim=1, keepdim=True)
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cond_rec = F.one_hot(cond_rec, num_classes=num_classes)
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cond_rec = cond_rec.squeeze(1).permute(0, 3, 1, 2).float()
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cond_rec = self.cond_stage_model.to_rgb(cond_rec)
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log["conditioning_rec"] = cond_rec
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log["conditioning"] = c
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log["samples_half"] = x_sample
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log["samples_nopix"] = x_sample_nopix
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log["samples_det"] = x_sample_det
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return log
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def get_input(self, key, batch):
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x = batch[key]
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if len(x.shape) == 3:
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x = x[..., None]
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if len(x.shape) == 4:
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x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format)
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if x.dtype == torch.double:
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x = x.float()
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return x
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def get_xc(self, batch, N=None):
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x = self.get_input(self.first_stage_key, batch)
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c = self.get_input(self.cond_stage_key, batch)
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if N is not None:
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x = x[:N]
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c = c[:N]
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return x, c
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def shared_step(self, batch, batch_idx):
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x, c = self.get_xc(batch)
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logits, target = self(x, c)
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loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), target.reshape(-1))
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return loss
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def training_step(self, batch, batch_idx):
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loss = self.shared_step(batch, batch_idx)
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self.log("train/loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
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return loss
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def validation_step(self, batch, batch_idx):
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loss = self.shared_step(batch, batch_idx)
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self.log("val/loss", loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
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return loss
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def configure_optimizers(self):
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"""
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Following minGPT:
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This long function is unfortunately doing something very simple and is being very defensive:
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We are separating out all parameters of the model into two buckets: those that will experience
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weight decay for regularization and those that won't (biases, and layernorm/embedding weights).
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We are then returning the PyTorch optimizer object.
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"""
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# separate out all parameters to those that will and won't experience regularizing weight decay
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decay = set()
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no_decay = set()
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whitelist_weight_modules = (torch.nn.Linear, )
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blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)
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for mn, m in self.transformer.named_modules():
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for pn, p in m.named_parameters():
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fpn = '%s.%s' % (mn, pn) if mn else pn # full param name
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if pn.endswith('bias'):
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# all biases will not be decayed
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no_decay.add(fpn)
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elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
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# weights of whitelist modules will be weight decayed
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decay.add(fpn)
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elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
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# weights of blacklist modules will NOT be weight decayed
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no_decay.add(fpn)
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# special case the position embedding parameter in the root GPT module as not decayed
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no_decay.add('pos_emb')
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# validate that we considered every parameter
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param_dict = {pn: p for pn, p in self.transformer.named_parameters()}
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inter_params = decay & no_decay
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union_params = decay | no_decay
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assert len(inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (str(inter_params), )
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assert len(param_dict.keys() - union_params) == 0, "parameters %s were not separated into either decay/no_decay set!" \
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% (str(param_dict.keys() - union_params), )
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# create the pytorch optimizer object
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optim_groups = [
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{"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": 0.01},
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{"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0},
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]
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optimizer = torch.optim.AdamW(optim_groups, lr=self.learning_rate, betas=(0.9, 0.95))
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return optimizer
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