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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from timm import create_model, list_models
from types import SimpleNamespace
from transformers import GPT2LMHeadModel, GPT2TokenizerFast, get_linear_schedule_with_warmup
import albumentations as A
from albumentations.pytorch import ToTensorV2
from PIL import Image
from pathlib import Path
from sklearn.model_selection import train_test_split
from torch.cuda.amp import GradScaler, autocast
from tqdm.auto import tqdm
import gc
import json
class GPT2Attention(nn.Module):
def __init__(self,config):
super().__init__()
self.embed_dim = config.embed_dim
self.n_heads = config.num_heads
assert self.embed_dim % self.n_heads == 0, 'embedding dimension by be divisible by number of heads'
self.head_size = self.embed_dim // self.n_heads
self.seq_len = config.seq_len
self.c_attn = nn.Linear(self.embed_dim, self.head_size * self.n_heads * 3,bias=True)
self.scale = self.head_size ** -0.5
self.register_buffer('mask',torch.tril(torch.ones(1,1,self.seq_len,self.seq_len)))
self.c_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
self.attn_dropout = nn.Dropout(config.attention_dropout)
self.resid_dropout = nn.Dropout(config.residual_dropout)
def forward(self, x):
b,t,c = x.shape
# q,k,v shape individually: batch_size x seq_len x embed_dim
# we know that qk_t = q x k_t, where q=bxtxhead_dim, k_t=bxhead_timxt
q,k,v = self.c_attn(x).chunk(3,dim=-1)
q = q.view(b,t,self.n_heads,self.head_size).permute(0,2,1,3) # batch x n_heads x seq_len x head_dim
k = k.view(b,t,self.n_heads,self.head_size).permute(0,2,1,3)
v = v.view(b,t,self.n_heads,self.head_size).permute(0,2,1,3)
qk_t = (q@k.transpose(-2,-1)) * self.scale
qk_t = qk_t.masked_fill(self.mask[:,:,:t,:t]==0,float('-inf'))
qk_t = F.softmax(qk_t,dim=-1)
weights = self.attn_dropout(qk_t)
attention = weights @ v # batch x n_heads x t x head_size
attention = attention.permute(0,2,1,3).contiguous().view(b,t,c) # batch x t x embed_dim
out = self.c_proj(attention)
out = self.resid_dropout(out)
return out
class GPT2CrossAttention(nn.Module):
def __init__(self,config):
super().__init__()
self.embed_dim = config.embed_dim
self.n_heads = config.num_heads
assert self.embed_dim % self.n_heads == 0, 'embedding dimension by be divisible by number of heads'
self.head_size = self.embed_dim // self.n_heads
self.seq_len = config.seq_len
self.q = nn.Linear(self.embed_dim,self.embed_dim)
self.k = nn.Linear(self.embed_dim,self.embed_dim)
self.v = nn.Linear(self.embed_dim,self.embed_dim)
self.scale = self.head_size ** -0.5
self.c_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True)
self.attn_dropout = nn.Dropout(config.attention_dropout)
self.resid_dropout = nn.Dropout(config.residual_dropout)
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
def forward(self, q,k,v):
b,t,c = q.shape
q = self.q(q)
k = self.k(k)
v = self.v(v)
q = q.view(b,q.size(1),self.n_heads,self.head_size).permute(0,2,1,3) # batch x n_heads x seq_len x head_dim
k = k.view(b,k.size(1),self.n_heads,self.head_size).permute(0,2,1,3)
v = v.view(b,v.size(1),self.n_heads,self.head_size).permute(0,2,1,3)
qk_t = (q@k.transpose(-2,-1)) * self.scale
qk_t = F.softmax(qk_t,dim=-1)
weights = self.attn_dropout(qk_t)
attention = weights @ v # batch x n_heads x t x head_size
attention = attention.permute(0,2,1,3).contiguous().view(b,t,c) # batch x t x embed_dim
out = self.c_proj(attention)
out = self.resid_dropout(out)
return out
class GPT2MLP(nn.Module):
def __init__(self,config):
super().__init__()
self.embed_dim = config.embed_dim
self.mlp_ratio = config.mlp_ratio
self.mlp_dropout = config.mlp_dropout
self.c_fc = nn.Linear(self.embed_dim,self.embed_dim*self.mlp_ratio)
self.c_proj = nn.Linear(self.embed_dim*self.mlp_ratio,self.embed_dim)
self.act = nn.GELU()
self.dropout = nn.Dropout(self.mlp_dropout)
def forward(self,x):
x = self.c_fc(x)
x = self.act(x)
x = self.c_proj(x)
x = self.dropout(x)
return x
class GPT2Block(nn.Module):
def __init__(self,config):
super().__init__()
self.embed_dim = config.embed_dim
self.ln_1 = nn.LayerNorm(self.embed_dim)
self.attn = GPT2Attention(config)
self.ln_2 = nn.LayerNorm(self.embed_dim)
self.mlp = GPT2MLP(config)
self.ln_3 = nn.LayerNorm(self.embed_dim)
self.cross_attn = GPT2CrossAttention(config)
def forward(self,x,enc_out):
x = x+self.attn(self.ln_1(x))
x = x+self.cross_attn(self.ln_2(x),enc_out,enc_out)
x = x+self.mlp(self.ln_3(x))
return x
class VisionGPT2Model(nn.Module):
def __init__(self,config):
super().__init__()
self.config = config
print(torch.cuda.is_available())
vit = create_model('vit_base_patch16_224',pretrained=True,num_classes=0)
self.patch_embed = vit.patch_embed
num_patches = self.patch_embed.num_patches
self.cls_token = vit.cls_token
embed_len = num_patches + vit.num_prefix_tokens
self.pos_embed = vit.pos_embed
self.pos_drop = nn.Dropout(p=0.)
self.blocks = nn.ModuleList([vit.blocks[i] for i in range(config.depth)])
self.transformer = nn.ModuleDict(dict(
wte = nn.Embedding(config.vocab_size,config.embed_dim),
wpe = nn.Embedding(config.seq_len,config.embed_dim),
drop = nn.Dropout(config.emb_dropout),
h = nn.ModuleList([GPT2Block(config) for _ in range(config.depth)]),
ln_f = nn.LayerNorm(config.embed_dim)
))
self.lm_head = nn.Linear(config.embed_dim,config.vocab_size,bias=False)
self.transformer.wte.weight = self.lm_head.weight
def _pos_embed(self,x):
pos_embed = self.pos_embed
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
x = x + pos_embed
return self.pos_drop(x)
def pretrained_layers_trainable(self,trainable=False):
layers = [
self.cls_token, self.patch_embed, self.pos_embed, self.blocks,
self.transformer.wte, self.transformer.wpe,
self.transformer.ln_f, self.lm_head
]
gpt_layers = [[
self.transformer.h[i].ln_1,self.transformer.h[i].ln_2,
self.transformer.h[i].attn,self.transformer.h[i].mlp
] for i in range(self.config.depth)]
for l in gpt_layers:
layers.extend(l)
for layer in layers:
if not isinstance(layer,nn.Parameter):
for p in layer.parameters():
p.requires_grad = trainable
else:
layer.requires_grad = trainable
total_frozen_params = sum([p.numel() for p in self.parameters() if not p.requires_grad])
print(f'{total_frozen_params=}')
def unfreeze_gpt_layers(self,):
gpt_layers = [[
self.transformer.h[i].ln_1,self.transformer.h[i].ln_2,
self.transformer.h[i].attn,self.transformer.h[i].mlp
] for i in range(self.config.depth)]
flatten = []
for l in gpt_layers:
flatten.extend(l)
for layer in flatten:
if not isinstance(layer,nn.Parameter):
for p in layer.parameters():
p.requires_grad = True
else:
layer.requires_grad = True
@classmethod
def from_pretrained(self,config):
model = VisionGPT2Model(config)
sd = model.state_dict()
keys = sd.keys()
ignore_matches = ['blocks.','cross_attn.','ln_3','cls_token','pos_embed','patch_embed.','.attn.mask']
vit_keys = [key for key in keys if any(match in key for match in ignore_matches)]
gpt_keys = [key for key in keys if key not in vit_keys]
gpt2_small = GPT2LMHeadModel.from_pretrained('gpt2')
sd_hf = gpt2_small.state_dict()
hf_keys = sd_hf.keys()
hf_keys = [k for k in hf_keys if not k.endswith('.attn.masked_bias')]
hf_keys = [k for k in hf_keys if not k.endswith('.attn.bias')]
transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
for k in hf_keys:
if any(match in k for match in ignore_matches):
continue
if any(k.endswith(w) for w in transposed):
assert sd_hf[k].shape[::-1] == sd[k].shape
with torch.no_grad():
sd[k].copy_(sd_hf[k].t())
else:
assert sd_hf[k].shape == sd[k].shape
with torch.no_grad():
sd[k].copy_(sd_hf[k])
model.load_state_dict(sd)
return model
def forward(self,image,input_ids,labels=None):
image = self.patch_embed(image)
image = self._pos_embed(image)
token_embeddings = self.transformer.wte(input_ids) # batch x seq_len
pos_embs = torch.arange(0, input_ids.size(1), device=self.config.device)
positional_embeddings = self.transformer.wpe(pos_embs)
input_ids = self.transformer.drop(token_embeddings+positional_embeddings)
for i in range(self.config.depth):
image = self.blocks[i](image)
input_ids = self.transformer.h[i](input_ids, image)
input_ids = self.transformer.ln_f(input_ids)
if labels is not None:
lm_logits = self.lm_head(input_ids)
loss = F.cross_entropy(lm_logits.view(-1, lm_logits.shape[-1]), labels.view(-1))
return loss
lm_logits = self.lm_head(input_ids[:,[-1],:])
return lm_logits
def generate(self,image,sequence,tokenizer,max_tokens=50,temperature=1.0,deterministic=False):
for _ in range(max_tokens):
out = self(image,sequence)
out = out[:,-1,:] / temperature
probs = F.softmax(out,dim=-1)
if deterministic:
next_token = torch.argmax(probs,dim=-1,keepdim=True)
else:
next_token = torch.multinomial(probs,num_samples=1)
sequence = torch.cat([sequence,next_token],dim=1)
if next_token.item() == tokenizer.eos_token_id:
break
return sequence.cpu().flatten()
model_config = SimpleNamespace(
vocab_size = 50_257,
embed_dim = 768,
num_heads = 12,
seq_len = 1024,
depth = 12,
attention_dropout = 0.1,
residual_dropout = 0.1,
mlp_ratio = 4,
mlp_dropout = 0.1,
emb_dropout = 0.1,
device='cpu'
)
model = VisionGPT2Model.from_pretrained(model_config)
model.load_state_dict(torch.load("captioner.pt", map_location='cpu')) # Use 'cuda' if you have a GPU
model.eval() # Set the model to evaluation mode
def generate_caption(image,max_tokens=50,temperature=0.9,deterministic=True):
tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
tokenizer.pad_token = tokenizer.eos_token
gen_tfms = A.Compose([
A.Resize(224,224),
A.Normalize(mean=[0.5,0.5,0.5],std=[0.5,0.5,0.5],always_apply=True),
ToTensorV2()
])
image = Image.open(image)
image = np.array(image)
image = gen_tfms(image=image)['image']
image = image.unsqueeze(0)
sequence = torch.ones(1,1).long() * tokenizer.bos_token_id
caption = model.generate(
image,
sequence,
tokenizer,
max_tokens=max_tokens,
temperature=temperature,
deterministic=deterministic,
)
caption = tokenizer.decode(caption.numpy(),skip_special_tokens=True)
print(caption)
return caption
image = "/Users/jkottu/Desktop/image-captioning-chest-xrays/sample_images/CXR191_IM-0591-1001.png"
generate_caption(image) |