coop.py

import os.path as osp

import os
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.cuda.amp import GradScaler, autocast
import torchvision

from dassl.engine import TRAINER_REGISTRY, TrainerX
from dassl.metrics import compute_accuracy
from dassl.utils import load_pretrained_weights, load_checkpoint
from dassl.optim import build_optimizer, build_lr_scheduler

# breakpoint()
from models import clip
from models.clip.simple_tokenizer import SimpleTokenizer as _Tokenizer

_tokenizer = _Tokenizer()


from .backdoor import NoiseTrigger, PatchTrigger


def load_clip_to_cpu(cfg):
    if cfg.MODEL_NAME == 'clip':
        print("\n\n\nUsing CLIP\n\n\n")
        model_path = os.path.join(cfg.MODEL_ROOT, "clip", 'openai_clip_vit_b32.pt')
        # model_path = "/data-nvme/asif.hanif/pre-trained-models/vlps/clip/openai_clip_vit_b32.pt"

    elif cfg.MODEL_NAME == 'plip':
        print("\n\n\nUsing PLIP\n\n\n")
        model_path = os.path.join(cfg.MODEL_ROOT, "plip", 'plip_vit_b32.pt')
        # model_path = "/data-nvme/asif.hanif/pre-trained-models/vlps/plip/plip_vit_b32.pt"
    
    elif cfg.MODEL_NAME == 'quiltnet':
        print("\n\n\nUsing QuiltNet\n\n\n")
        model_path = os.path.join(cfg.MODEL_ROOT, "quiltnet", 'quiltnet_b32.pt')
        # model_path = "/data-nvme/asif.hanif/pre-trained-models/vlps/quiltnet_b32/quiltnet_b32.pt"
    else:
        raise ValueError(f"Model '{cfg.MODEL_NAME}' not found. Please choose from 'clip', 'plip', 'quiltnet'")

    try:
        # loading JIT archive
        model = torch.jit.load(model_path, map_location="cpu").eval()
        state_dict = None

    except RuntimeError:
        state_dict = torch.load(model_path, map_location="cpu")


    model = clip.build_model(state_dict or model.state_dict())
                            
    return model


class TextEncoder(nn.Module):
    def __init__(self, clip_model):
        super().__init__()
        self.transformer = clip_model.transformer
        self.positional_embedding = clip_model.positional_embedding
        self.ln_final = clip_model.ln_final
        self.text_projection = clip_model.text_projection
        self.dtype = clip_model.dtype

    def forward(self, prompts, tokenized_prompts):
        x = prompts + self.positional_embedding.type(self.dtype)
        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.transformer(x)
        x = x.permute(1, 0, 2)  # LND -> NLD
        x = self.ln_final(x).type(self.dtype)

        # x.shape = [batch_size, n_ctx, transformer.width]
        # take features from the eot embedding (eot_token is the highest number in each sequence)
        x = x[torch.arange(x.shape[0]), tokenized_prompts.argmax(dim=-1)] @ self.text_projection

        return x


    
class PromptLearner(nn.Module):
    def __init__(self, cfg, classnames, clip_model):
        super().__init__()

        # breakpoint()

        n_cls = len(classnames)
        n_ctx = cfg.TRAINER.COOP.N_CTX
        ctx_init = cfg.TRAINER.COOP.CTX_INIT
        dtype = clip_model.dtype
        ctx_dim = clip_model.ln_final.weight.shape[0]
        clip_imsize = clip_model.visual.input_resolution
        cfg_imsize = cfg.INPUT.SIZE[0]
        assert cfg_imsize == clip_imsize, f"cfg_imsize ({cfg_imsize}) must equal to clip_imsize ({clip_imsize})"
        
        if ctx_init:
            raise NotImplementedError("This part is not yet implemented.")
        
            # # use given words to initialize context vectors
            # ctx_init = ctx_init.replace("_", " ")
            # n_ctx = len(ctx_init.split(" "))

            # prompt = clip.tokenize(ctx_init)

            # with torch.no_grad():
            #     embedding = clip_model.token_embedding(prompt).type(dtype)

            # ctx_vectors = embedding[0, 1 : 1 + n_ctx, :]
            # prompt_prefix = ctx_init

        else:
            # random initialization
            if cfg.TRAINER.COOP.CSC:
                print("Initializing class-specific contexts")
                ctx_vectors = torch.empty(n_cls, n_ctx, ctx_dim, dtype=dtype)
            else:
                print("Initializing Generic Context")
                ctx_vectors = torch.empty(n_ctx, ctx_dim, dtype=dtype)


            nn.init.normal_(ctx_vectors, std=0.02)
            prompt_prefix = " ".join(["X"] * n_ctx)


        print(f'Initial Context: "{prompt_prefix}"')
        print(f"Number of context words (tokens): {n_ctx}")

        # print("\n\nUsing Random Context Initialization\n\n")
        # self.ctx = nn.Parameter(ctx_vectors)  # to be optimized
       
        print("\n\nUsing Pre-trained Context Initialization\n\n")
        self.ctx = nn.Parameter(torch.load(os.path.join(os.getcwd(), 'models', 'ctx_vectors', f'ctx_{cfg.MODEL_NAME}_{cfg.DATASET_NAME}_s{cfg.SEED}.pt')))
        # Note: This context is pre-trained using the clean images of the few-shot train dataset (i.e. with POISON_PERCENTAGE=0)

        
        classnames = [name.replace("_", " ") for name in classnames]
        name_lens = [len(_tokenizer.encode(name)) for name in classnames]
        prompts = [prompt_prefix + " " + name + "." for name in classnames]

        tokenized_prompts = torch.cat([clip.tokenize(p) for p in prompts])
        with torch.no_grad():
            embedding = clip_model.token_embedding(tokenized_prompts).type(dtype)
            
        # These token vectors will be saved when in save_model(),
        # but they should be ignored in load_model() as we want to use
        # those computed using the current class names
        self.register_buffer("token_prefix", embedding[:, :1, :])  # SOS
        self.register_buffer("token_suffix", embedding[:, 1 + n_ctx :, :])  # CLS, EOS
        self.n_cls = n_cls
        self.n_ctx = n_ctx
        self.tokenized_prompts = tokenized_prompts  # torch.Tensor
        self.name_lens = name_lens
        self.class_token_position = cfg.TRAINER.COOP.CLASS_TOKEN_POSITION


    def forward(self):
        # breakpoint()
        ctx = self.ctx

        if ctx.dim() == 2:
            ctx = ctx.unsqueeze(0).expand(self.n_cls, -1, -1)

        prefix = self.token_prefix
        suffix = self.token_suffix

        if self.class_token_position == "end":
            prompts = torch.cat(
                [
                    prefix,  # (n_cls, 1, dim)
                    ctx,     # (n_cls, n_ctx, dim)
                    suffix,  # (n_cls, *, dim)
                ],
                dim=1,
            )

        else:
            raise ValueError

        return prompts


class CustomCLIP(nn.Module):
    def __init__(self, cfg, classnames, clip_model, device):
        super().__init__()
        
        self.prompt_learner = PromptLearner(cfg, classnames, clip_model)
        self.tokenized_prompts = self.prompt_learner.tokenized_prompts
        self.image_encoder = clip_model.visual
        self.text_encoder = TextEncoder(clip_model)
        self.logit_scale = clip_model.logit_scale
        self.dtype = clip_model.dtype
        self.device = device

        cfg.defrost()
        cfg.DTYPE = str(self.dtype).split(".")[1]
        cfg.DEVICE = str(self.device)
        cfg.freeze()        


        self.noise_trigger = NoiseTrigger(cfg)
        self.patch_trigger = PatchTrigger(cfg)
        self.normalize = torchvision.transforms.Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD)
     

    def forward(self, image, backdoor_tags=None):
        image = self.patch_trigger(image.type(self.dtype), backdoor_tags) # add patch trigger to backdoor images
        image = self.noise_trigger(image.type(self.dtype), backdoor_tags) # add noise trigger to backdoor images
        image_features = self.image_encoder(self.normalize(image))

        prompts = self.prompt_learner()
        tokenized_prompts = self.tokenized_prompts
        text_features = self.text_encoder(prompts, tokenized_prompts)

        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
        text_features = text_features / text_features.norm(dim=-1, keepdim=True)

        logit_scale = self.logit_scale.exp()
        logits = logit_scale * image_features @ text_features.t()

        return logits



@TRAINER_REGISTRY.register()
class CoOp(TrainerX):
    """Context Optimization (CoOp).

    Learning to Prompt for Vision-Language Models
    https://arxiv.org/abs/2109.01134
    """

    def check_cfg(self, cfg):
        assert cfg.TRAINER.COOP.PREC in ["fp16", "fp32", "amp"]

    def build_model(self):
        cfg = self.cfg
        classnames = self.dm.dataset.classnames
        print(f"Loading CLIP (backbone: {cfg.MODEL.BACKBONE.NAME})")
        clip_model = load_clip_to_cpu(cfg)
        
        if cfg.TRAINER.COOP.PREC == "fp32" or cfg.TRAINER.COOP.PREC == "amp":
            # CLIP's default precision is fp16
            clip_model.float()

        print("Building custom CLIP")
        self.model = CustomCLIP(cfg, classnames, clip_model, self.device)

        print("\n\nTurning off gradients in both the image and the text encoder")
        for name, param in self.model.named_parameters():
            if not (("prompt_learner" in name) or ("noise_trigger" in name)):
                param.requires_grad_(False)
                # print(f"Not Learnable: {name}")
            else: 
                print(f"Learnable: {name}")
        print("\n\n")   

        if cfg.MODEL.INIT_WEIGHTS:
            load_pretrained_weights(self.model.prompt_learner, cfg.MODEL.INIT_WEIGHTS)

        self.model.to(self.device)
        # NOTE: only give prompt_learner to the optimizer
        self.optim = build_optimizer(self.model.prompt_learner, cfg.OPTIM)
        self.sched = build_lr_scheduler(self.optim, cfg.OPTIM)

        self.register_model("baple", nn.Sequential(self.model.prompt_learner, self.model.noise_trigger) , self.optim, self.sched)

        self.scaler = GradScaler() if cfg.TRAINER.COOP.PREC == "amp" else None

        # Note that multi-gpu training could be slow because CLIP's size is
        # big, which slows down the copy operation in DataParallel
        device_count = torch.cuda.device_count()
        if device_count > 1:
            print(f"Multiple GPUs detected (n_gpus={device_count}), use all of them!")
            self.model = nn.DataParallel(self.model)


    def forward_backward(self, batch):
        
        self.model.noise_trigger.noise.requires_grad = True
        image, label, backdoor_tag = self.parse_batch_train(batch)  # image: [B, C, H, W]

        prec = self.cfg.TRAINER.COOP.PREC

        if prec == "amp":
            raise NotImplementedError("AMP is not yet supported.")
        else:
            output = self.model(image, backdoor_tag)
            
            lambda_clean = 1.0
            lambda_adv = 1.0

            clean_exists = any(~backdoor_tag)
            backdoor_exists = any(backdoor_tag)
            
            
            if clean_exists :
                loss_clean = F.cross_entropy(output[~backdoor_tag], label[~backdoor_tag])
                
            if backdoor_exists : 
                loss_adv = F.cross_entropy(output[backdoor_tag], label[backdoor_tag]) 

            
            if clean_exists and backdoor_exists:
                loss = lambda_clean*loss_clean + lambda_adv*loss_adv 
            elif clean_exists and not backdoor_exists:
                loss = 1.0*loss_clean
            elif not clean_exists and backdoor_exists:
                loss = 1.0*loss_adv
            else:
                raise ValueError("No clean or backdoor images found. Check the backdoor tag assignments in Dataset class.")
            

            self.model_backward_and_update(loss)


            # update trigger noise
            if backdoor_exists:
                trigger_noise_grad  = self.model.noise_trigger.noise.grad.data
                self.model.noise_trigger.noise.data -= trigger_noise_grad.sign()*0.01
                eps=self.cfg.BACKDOOR.NOISE_EPS/255.0
                self.model.noise_trigger.noise.data.clamp_(-eps,eps)
                self.model.noise_trigger.noise.detach_()



        loss_summary = {
            "loss": loss.item(),
            "acc": compute_accuracy(output, label)[0].item(),
        }

        if (self.batch_idx + 1) == self.num_batches:
            self.update_lr()

        return loss_summary

    def parse_batch_train(self, batch):
        input = batch["img"]
        label = batch["label"]
        input = input.to(self.device)
        label = label.to(self.device)
        backdoor_tag = batch["backdoor_tag"]
        return input, label, backdoor_tag==1


    def load_model(self, directory, epoch=None):
        if not directory:
            print("Note that load_model() is skipped as no pretrained model is given")
            return

        names = self.get_model_names()
        
        # By default, the best model is loaded
        model_file = "model-best.pth.tar"

        if epoch is not None:
            model_file = "model.pth.tar-" + str(epoch)
        
        for name in names:
            model_path = osp.join(directory, name, model_file)

            if not osp.exists(model_path):
                raise FileNotFoundError('Model not found at "{}"'.format(model_path))

            checkpoint = load_checkpoint(model_path)
            state_dict = checkpoint["state_dict"]
            epoch = checkpoint["epoch"]

            # Ignore fixed token vectors
            if "token_prefix" in state_dict:
                del state_dict["token_prefix"]

            if "token_suffix" in state_dict:
                del state_dict["token_suffix"]

            print("Loading weights to {} " 'from "{}" (epoch = {})'.format(name, model_path, epoch))
            # set strict=False
            self._models[name].load_state_dict(state_dict, strict=False)