forked from mrq/DL-Art-School
151 lines
4.7 KiB
Python
151 lines
4.7 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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import torchvision
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from models.steps.injectors import Injector
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from utils.util import checkpoint
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def create_injector(opt, env):
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type = opt['type']
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if type == 'igpt_resolve':
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return ResolveInjector(opt, env)
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return None
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class ResolveInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.gen = opt['generator']
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self.samples = opt['num_samples']
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self.temperature = opt['temperature']
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def forward(self, state):
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gen = self.env['generators'][self.opt['generator']].module
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img = state[self.input]
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b, c, h, w = img.shape
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qimg = gen.quantize(img)
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s, b = qimg.shape
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qimg = qimg[:s//2, :]
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output = qimg.repeat(1, self.samples)
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pad = torch.zeros(1, self.samples, dtype=torch.long).cuda() # to pad prev output
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with torch.no_grad():
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for _ in range(s//2):
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logits, _ = gen(torch.cat((output, pad), dim=0), already_quantized=True)
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logits = logits[-1, :, :] / self.temperature
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probs = F.softmax(logits, dim=-1)
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pred = torch.multinomial(probs, num_samples=1).transpose(1, 0)
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output = torch.cat((output, pred), dim=0)
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output = gen.unquantize(output.reshape(h, w, -1))
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return {self.output: output.permute(2,3,0,1).contiguous()}
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class Block(nn.Module):
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def __init__(self, embed_dim, num_heads):
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super(Block, self).__init__()
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self.ln_1 = nn.LayerNorm(embed_dim)
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self.ln_2 = nn.LayerNorm(embed_dim)
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self.attn = nn.MultiheadAttention(embed_dim, num_heads)
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self.mlp = nn.Sequential(
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nn.Linear(embed_dim, embed_dim * 4),
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nn.GELU(),
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nn.Linear(embed_dim * 4, embed_dim),
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)
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def forward(self, x):
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attn_mask = torch.full(
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(len(x), len(x)), -float("Inf"), device=x.device, dtype=x.dtype
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)
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attn_mask = torch.triu(attn_mask, diagonal=1)
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x = self.ln_1(x)
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a, _ = self.attn(x, x, x, attn_mask=attn_mask, need_weights=False)
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x = x + a
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m = self.mlp(self.ln_2(x))
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x = x + m
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return x
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class iGPT2(nn.Module):
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def __init__(
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self, embed_dim, num_heads, num_layers, num_positions, num_vocab, centroids_file
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):
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super().__init__()
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self.centroids = nn.Parameter(
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torch.from_numpy(np.load(centroids_file)), requires_grad=False
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)
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self.embed_dim = embed_dim
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# start of sequence token
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self.sos = torch.nn.Parameter(torch.zeros(embed_dim))
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nn.init.normal_(self.sos)
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self.token_embeddings = nn.Embedding(num_vocab, embed_dim)
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self.position_embeddings = nn.Embedding(num_positions, embed_dim)
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self.layers = nn.ModuleList()
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for _ in range(num_layers):
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self.layers.append(Block(embed_dim, num_heads))
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self.ln_f = nn.LayerNorm(embed_dim)
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self.head = nn.Linear(embed_dim, num_vocab, bias=False)
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self.clf_head = nn.Linear(embed_dim, 10) # Fixed num_classes, this is not a classifier.
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def squared_euclidean_distance(self, a, b):
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b = torch.transpose(b, 0, 1)
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a2 = torch.sum(torch.square(a), dim=1, keepdims=True)
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b2 = torch.sum(torch.square(b), dim=0, keepdims=True)
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ab = torch.matmul(a, b)
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d = a2 - 2 * ab + b2
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return d
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def quantize(self, x):
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b, c, h, w = x.shape
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# [B, C, H, W] => [B, H, W, C]
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x = x.permute(0, 2, 3, 1).contiguous()
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x = x.view(-1, c) # flatten to pixels
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d = self.squared_euclidean_distance(x, self.centroids)
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x = torch.argmin(d, 1)
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x = x.view(b, h, w)
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# Reshape output to [seq_len, batch].
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x = x.view(x.shape[0], -1) # flatten images into sequences
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x = x.transpose(0, 1).contiguous() # to shape [seq len, batch]
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return x
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def unquantize(self, x):
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return self.centroids[x]
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def forward(self, x, already_quantized=False):
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"""
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Expect input as shape [b, c, h, w]
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"""
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if not already_quantized:
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x = self.quantize(x)
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length, batch = x.shape
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h = self.token_embeddings(x)
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# prepend sos token
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sos = torch.ones(1, batch, self.embed_dim, device=x.device) * self.sos
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h = torch.cat([sos, h[:-1, :, :]], axis=0)
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# add positional embeddings
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positions = torch.arange(length, device=x.device).unsqueeze(-1)
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h = h + self.position_embeddings(positions).expand_as(h)
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# transformer
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for layer in self.layers:
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h = checkpoint(layer, h)
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h = self.ln_f(h)
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logits = self.head(h)
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return logits, x
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