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MNIST_generative_transformer.py
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MNIST_generative_transformer.py
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## Import transformers
from transformers import get_linear_schedule_with_warmup
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import BertModel, BertConfig
from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, GPT2Model
## import MNIST from torchvision package
from torchvision import datasets, transforms
## import torch
import os
from os.path import join
from tqdm import tqdm, trange
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import AdamW, Adam
from torch.utils.data import Dataset, DataLoader
from torchvision.utils import make_grid, save_image
import matplotlib.pyplot as plt
#%%
from torchvision.datasets import MNIST
dataset = MNIST(root='/home/binxu/Datasets', download=True, transform=transforms.ToTensor())
#%%
# image to patch sequence by convolution
def img2patch(img, patch_size=2):
# img: (batch_size, channel, height, width)
# patch_size: int
# return: (batch_size, channel, height//patch_size, width//patch_size, patch_size**2)
batch_size, channel, height, width = img.shape
img = img.reshape(batch_size, channel, height//patch_size, patch_size, width//patch_size, patch_size)
img = img.permute(0, 2, 4, 1, 3, 5)
img = img.reshape(batch_size, height//patch_size, width//patch_size, channel * patch_size**2)
return img
def patch2seq(patches):
batch_size, height, width, channel = patches.shape
return patches.reshape(batch_size, height*width, channel)
def seq2img(patch_seq, patch_size=2):
# patch_seq: (batch_size, channel, height//patch_size, width//patch_size, patch_size**2)
# patch_size: int
# return: (batch_size, channel, height, width)
batch_size, HW, hidden = patch_seq.shape
height = width = int(math.sqrt(HW))
channel = hidden // patch_size**2
patch_seq = patch_seq.reshape(batch_size, height, width, channel, patch_size, patch_size)
patch_seq = patch_seq.permute(0, 3, 1, 4, 2, 5)
imgtsr = patch_seq.reshape(batch_size, channel, height*patch_size, width*patch_size)
return imgtsr
#%%
# GPT2Config()
dataloaders = DataLoader(dataset, batch_size=64, shuffle=True)
imgs, labels = next(iter(dataloaders))
print(patch2seq(img2patch(imgs, patch_size=4)).shape)
assert torch.allclose(seq2img(patch2seq(img2patch(imgs,)),), imgs)
assert torch.allclose(seq2img(patch2seq(img2patch(imgs, patch_size=4)), patch_size=4), imgs)
#%%
#%% Conditional model
patch_size = 4
config = GPT2Config(n_embd=128, n_layer=24, n_head=16, n_positions=256,
vocab_size=100, bos_token_id=101, eos_token_id=102,
add_cross_attention=True, )
model = GPT2Model(config).cuda()
patch_emb = nn.Linear(patch_size * patch_size, config.n_embd).cuda()
patch_readout = nn.Linear(config.n_embd, patch_size * patch_size).cuda()
digit_emb = nn.Embedding(10, config.n_embd).cuda()
optimizer = AdamW([*model.parameters(),
*patch_emb.parameters(),
*patch_readout.parameters(),
*digit_emb.parameters()], lr=5e-4)
#%%
def generate_img(prompt_digit, prompt_patch, model, digit_emb, patch_emb, patch_readout,
patch_size=4, pixel_size=28):
max_seq_len = (pixel_size // patch_size) ** 2 - 1
prompt_digit_emb = digit_emb(torch.tensor([*prompt_digit]).cuda())[:, None, :]
patch_seq = [prompt_patch]
input_patch_emb = patch_emb(prompt_patch)
with torch.no_grad():
for i in range(max_seq_len):
output = model(inputs_embeds=input_patch_emb,
encoder_hidden_states=prompt_digit_emb)
output_hiddens = output.last_hidden_state
next_patch = patch_readout(output_hiddens[:, -1:, :])
input_patch_emb = torch.cat([input_patch_emb, patch_emb(next_patch)], dim=1)
patch_seq.append(next_patch)
patch_seq_tsr = torch.cat(patch_seq, dim=1)
gen_imgs = seq2img(patch_seq_tsr, patch_size=patch_size)
return gen_imgs
#%%
# saveroot = r"D:\DL_Projects\Vision\pixel_GPT"
saveroot = r"/home/binxu/DL_Projects/patchGPT"
runname = "conditional"
os.makedirs(join(saveroot, runname), exist_ok=True)
batch_size = 512
dataloaders = DataLoader(dataset, batch_size=batch_size, shuffle=True)
for epoch in trange(1,50):
pbar = tqdm(dataloaders)
model.train()
for ibatch, (imgs, labels) in enumerate(pbar):
digit_hiddens = digit_emb(labels.cuda())[:, None, :]
patch_seq = patch2seq(img2patch(imgs.cuda(), patch_size=patch_size))
input_embeds = patch_emb(patch_seq)
output = model(inputs_embeds=input_embeds, encoder_hidden_states=digit_hiddens)
output_hiddens = output.last_hidden_state
output_patches = patch_readout(output_hiddens)
loss = F.mse_loss(output_patches[:, :-1, :], patch_seq[:, 1:, :])
optimizer.zero_grad()
loss.backward()
optimizer.step()
pbar.set_description(f"loss: {loss.item():.4f}")
# print(loss.item())
prompt_digit = range(10)
prompt_patch = torch.zeros(10, 1, patch_size * patch_size).cuda()
model.eval()
gen_imgs = generate_img(prompt_digit, prompt_patch, model, digit_emb, patch_emb, patch_readout,
patch_size=patch_size, pixel_size=28)
save_image(make_grid(gen_imgs, nrow=5), join(saveroot, runname, f'{epoch}_genimages.png'))
model.save_pretrained(join(saveroot, runname, "model"))
patch_emb.cpu().save(join(saveroot, runname, "patch_emb.pth"))
patch_readout.cpu().save(join(saveroot, runname, "patch_readout.pth"))
#%%
#%%
prompt_digit = range(10)
prompt_patch = torch.zeros(10, 1, patch_size * patch_size).cuda()
gen_imgs = generate_img(prompt_digit, prompt_patch, model, digit_emb, patch_emb, patch_readout,
patch_size=patch_size, pixel_size=28)
plt.figure()
plt.imshow(make_grid(gen_imgs, nrow=5).permute(1, 2, 0).cpu())
plt.show()
#%%
def generate_img_uncond(prompt_patch, model, patch_emb, patch_readout,
patch_size=4, pixel_size=28):
max_seq_len = (pixel_size // patch_size) ** 2 - 1
patch_seq = [prompt_patch]
input_patch_emb = patch_emb(prompt_patch)
with torch.no_grad():
for i in range(max_seq_len):
output = model(inputs_embeds=input_patch_emb,)
output_hiddens = output.last_hidden_state
next_patch = patch_readout(output_hiddens[:, -1:, :])
input_patch_emb = torch.cat([input_patch_emb, patch_emb(next_patch)], dim=1)
patch_seq.append(next_patch)
patch_seq_tsr = torch.cat(patch_seq, dim=1)
gen_imgs = seq2img(patch_seq_tsr, patch_size=patch_size)
return gen_imgs
#%%
patch_size = 4
config = GPT2Config(n_embd=128, n_layer=24, n_head=16, n_positions=256,
vocab_size=100, bos_token_id=101, eos_token_id=102, )
model = GPT2Model(config).cuda()
patch_emb = nn.Linear(patch_size * patch_size, config.n_embd).cuda()
patch_readout = nn.Linear(config.n_embd, patch_size * patch_size).cuda()
optimizer = AdamW([*model.parameters(),
*patch_emb.parameters(),
*patch_readout.parameters()], lr=5e-4)
saveroot = r"/home/binxu/DL_Projects/patchGPT"
runname = "unconditional"
os.makedirs(join(saveroot, runname), exist_ok=True)
#%%
batch_size = 512
dataloaders = DataLoader(dataset, batch_size=batch_size, shuffle=True)
for epoch in range(50):
pbar = tqdm(dataloaders)
model.train()
for ibatch, (imgs, labels) in enumerate(pbar):
patch_seq = patch2seq(img2patch(imgs.cuda(), patch_size=patch_size))
input_embeds = patch_emb(patch_seq)
output = model(inputs_embeds=input_embeds)
output_hiddens = output.last_hidden_state
output_patches = patch_readout(output_hiddens)
loss = F.mse_loss(output_patches[:, :-1, :], patch_seq[:, 1:, :])
optimizer.zero_grad()
loss.backward()
optimizer.step()
pbar.set_description(f"loss: {loss.item():.4f}")
# print(loss.item())
prompt_digit = range(10)
prompt_patch = torch.zeros(10, 1, patch_size * patch_size).cuda()
model.eval()
gen_imgs = generate_img_uncond(prompt_patch, model, patch_emb, patch_readout,
patch_size=patch_size, pixel_size=28)
save_image(make_grid(gen_imgs, nrow=5), join(saveroot, runname, f'{epoch}_genimages.png'))
model.save_pretrained(join(saveroot, runname, "model"))
patch_emb.cpu().save(join(saveroot, runname, "patch_emb.pth"))
patch_readout.cpu().save(join(saveroot, runname, "patch_readout.pth"))
#%%
make_grid(seq2img(output_patches, patch_size=patch_size))
# plt.imshow(make_grid(imgs[:16, :, :, :]).permute(1, 2, 0))
plt.figure()
plt.imshow(make_grid(seq2img(output_patches.detach().cpu(), patch_size=patch_size)).permute(1, 2, 0))
plt.show()