import torch from torch import nn from torch.utils.data import Dataset, DataLoader from torchvision.models import resnet50 from torchvision import transforms from PIL import Image import matplotlib.pyplot as plt from transformers import BertTokenizer, BertModel import os import json import numpy as np from collections import defaultdict import random from tqdm.notebook import tqdm from torchvision import models from torch.nn.utils.rnn import pad_sequence import matplotlib.patches as patches import math import time import os from PIL import Image import requests import nltk import os import cv2 import colorsys from numpy import asarray import math from transformers import GPT2LMHeadModel, GPT2Config from transformers import BertTokenizer from scipy.optimize import linear_sum_assignment class CocoDataset(Dataset): def __init__(self, root_dir, annotation_file, instance_file, max_objects=40, transform=None): self.root_dir = root_dir self.transform = transform self.max_objects = max_objects self.img_cache = dict() # Cache for images # Load instance file only once with open(instance_file, 'r') as file: data = json.load(file) instances = data['annotations'] categories = data['categories'] with open(annotation_file, 'r') as file: annotations = json.load(file)['annotations'] self.image_captions = defaultdict(list) for annotation in annotations: img_id = annotation['image_id'] self.image_captions[img_id].append(annotation['caption']) self.image_instances = defaultdict(list) self.category_id_to_name = {category['id']: category['name'] for category in categories} for instance in instances: img_id = instance['image_id'] bbox = instance['bbox'] category_id = instance['category_id'] self.image_instances[img_id].append((bbox, category_id)) self.img_ids = list(self.image_captions.keys()) def __len__(self): return len(self.img_ids) def __getitem__(self, index): img_id = self.img_ids[index] img_path = os.path.join(self.root_dir, f'{str(img_id).zfill(12)}.jpg') # Use cached image if available if img_id in self.img_cache: img = self.img_cache[img_id] else: img = Image.open(img_path).convert("RGB") self.img_cache[img_id] = img captions = self.image_captions[img_id] caption = random.choice(captions) annotations = self.image_instances[img_id] bboxes = [] labels = [] for obbox, label_id in annotations: bbox = torch.tensor(obbox) # Convert to PyTorch tensor immediately bbox[0] = bbox[0] / img.width + (bbox[2] / img.width)/2 bbox[1] = bbox[1] / img.height + (bbox[3] / img.height)/2 bbox[2] = bbox[2] / img.width bbox[3] = bbox[3] / img.height label = self.category_id_to_name[label_id] bboxes.append(bbox) labels.append(label) bboxes.append(torch.tensor([0.5, 0.5, 1, 1])) labels.append(caption) total_boxes = len(bboxes) if total_boxes < 40: for _ in range(40-total_boxes): bboxes.append(torch.tensor([0, 0, 0 ,0])) labels.append("na") else: bboxes = bboxes[:40] labels = labels[:40] if self.transform: img = self.transform(img) return img, bboxes, labels # Définir les transformations transform = transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) def custom_collate(batch): images, boxes_list, labels_list = zip(*batch) # Convert list of PIL images to a single PyTorch tensor stacked_images = torch.stack(images) # Convert list of list of boxes to a list of PyTorch tensors stacked_boxes = [torch.stack([box.clone().detach() for box in boxes]) for boxes in boxes_list] # Since labels are strings, we can keep them as a list of lists # labels_list is already in the desired format return stacked_images, stacked_boxes, labels_list def train_fn(data_loader, model, criterion, optimizer, device, scheduler, epoch): model.train() criterion.train() summary_loss = AverageMeter() tk0 = tqdm(data_loader, total=len(data_loader)-1) for step, (images, bboxes, captions) in enumerate(tk0): try: flattened_captions = [caption for sublist in captions for caption in sublist] captions = tokenizer(flattened_captions, padding=True, return_tensors="pt", truncation=True) captions = captions["input_ids"] input_ids = captions.reshape(batch_size, num_queries, -1).to(device) min_length = 2 except RuntimeError as e: print("Reshape failed:", e) continue ''' min_length = 2 if input_ids.size(-1) < min_length: padding_needed = min_length - input_ids.size(-1) input_ids = F.pad(input_ids, (0, padding_needed), 'constant', PAD_TOKEN) targets = build_targets(bboxes, input_ids[:, :, 1:]) targets = [{k: v.to(device) for k, v in t.items()} for t in targets] images = list(image.to(device) for image in images) output = model(images,input_ids[:, :,:-1]) ''' min_length = 2 if input_ids.size(-1) < min_length: padding_needed = min_length - input_ids.size(-1) input_ids = F.pad(input_ids, (0, padding_needed), 'constant', PAD_TOKEN) # input_ids = captions["input_ids"] # input_ids = input_ids.reshape(batch_size, num_queries, -1).to(device) targets = build_targets(bboxes, input_ids[:, :, 1:]) #targets = build_targets(bboxes, captions[:,:,1:]) images = list(image.to(device) for image in images) targets = [{k: v.to(device) for k, v in t.items()} for t in targets] output = model(images,input_ids[:,:,:-1]) loss_dict = criterion(output, targets) weight_dict = criterion.weight_dict losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict) optimizer.zero_grad() losses.backward() optimizer.step() if scheduler is not None: scheduler.step() # Detach and delete tensors loss_dict = {k: v.detach() for k, v in loss_dict.items()} del images, bboxes, captions, output, targets, loss_dict torch.cuda.empty_cache() # Clear cache summary_loss.update(losses.item(),BATCH_SIZE) tk0.set_postfix(loss=summary_loss.avg) return summary_loss class HungarianMatcher(nn.Module): """This class computes an assignment between the targets and the predictions of the network For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, while the others are un-matched (and thus treated as non-objects). """ def __init__(self, cost_class: float = 1, cost_bbox: float = 1, cost_giou: float = 1): """Creates the matcher Params: cost_class: This is the relative weight of the classification error in the matching cost cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost """ super().__init__() self.cost_class = cost_class self.cost_bbox = cost_bbox self.cost_giou = cost_giou assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, "all costs cant be 0" @torch.no_grad() def forward(self, outputs, targets): bs, num_queries = outputs["pred_logits"].shape[:2] # We flatten to compute the cost matrices in a batch # out_prob = outputs["pred_logits"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes] out_prob = outputs["pred_logits"].flatten(0,2 ).softmax(-1) # [batch_size * num_queries * seq_length, vocab_size ] out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4] # Also concat the target labels and boxes tgt_ids = torch.cat([v["labels"] for v in targets]) tgt_bbox = torch.cat([v["boxes"] for v in targets]) # Compute the classification cost. Contrary to the loss, we don't use the NLL, # but approximate it in 1 - proba[target class]. # The 1 is a constant that doesn't change the matching, it can be ommitted. cost_class = -out_prob[:, tgt_ids] # Compute the L1 cost between boxes cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1) # Compute the giou cost betwen boxes cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox)) # Final cost matrix C = self.cost_bbox * cost_bbox + self.cost_class * cost_class.mean() + self.cost_giou * cost_giou #C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou C = C.view(bs, num_queries, -1).cpu() sizes = [len(v["boxes"]) for v in targets] indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))] return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] def build_matcher(args): return HungarianMatcher(cost_class=args.set_cost_class, cost_bbox=args.set_cost_bbox, cost_giou=args.set_cost_giou) class SetCriterion(nn.Module): """ This class computes the loss for DETR. The process happens in two steps: 1) we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of matched ground-truth / prediction (supervise class and box) """ def __init__(self, vocab_size, matcher, weight_dict, eos_coef, losses,pad_token): """ Create the criterion. Parameters: vocab_size : es number of object categories, omitting the special no-object category matcher: module able to compute a matching between targets and proposals weight_dict: dict containing as key the names of the losses and as values their relative weight. eos_coef: relative classification weight applied to the no-object category losses: list of all the losses to be applied. See get_loss for list of available losses. """ super().__init__() self.vocab_size = vocab_size self.matcher = matcher self.weight_dict = weight_dict self.eos_coef = eos_coef self.losses = losses self.pad_token=pad_token empty_weight = torch.ones(self.vocab_size) # empty_weight[-1] = self.eos_coef self.register_buffer('empty_weight', empty_weight) self.criterion = nn.CrossEntropyLoss(ignore_index=pad_token) def loss_labels(self, outputs, targets, indices, num_boxes, log=False): """Classification loss (NLL) for sequences targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes, seq_length] """ assert 'pred_logits' in outputs src_logits = outputs['pred_logits'] batch_size, num_boxes , sequence_length, _ = src_logits.size() # Get the indices for the permutation batch_idx, src_idx = self._get_src_permutation_idx(indices) target_classes = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) # Ensure the target classes are valid assert (target_classes >= 0).all() and (target_classes < self.vocab_size).all(), "Invalid token index in target!" # loss_ce = criterion(outputs.reshape(-1, vocab_size), captions.view(-1)) loss_ce = self.criterion(src_logits.reshape(batch_size * num_boxes * sequence_length, -1), target_classes.reshape(-1)) # loss_ce = torchmetrics.functional.smooth_cross_entropy(src_logits[batch_idx], target_classes, ignore_index=PAD_TOKEN) losses = {'loss_ce': loss_ce} return losses ''' criterion = nn.CrossEntropyLoss(ignore_index=self.PAD_TOKEN) loss_ce = criterion(src_logits, target_classes_for_loss) losses = {'loss_ce': loss_ce} ''' @torch.no_grad() def loss_cardinality(self, outputs, targets, indices, num_boxes): """ Compute the cardinality error, ie the absolute error in the number of predicted non-empty boxes This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients """ pred_logits = outputs['pred_logits'] device = pred_logits.device tgt_lengths = torch.as_tensor([len(v["labels"]) for v in targets], device=device) # Count the number of predictions that are NOT "no-object" (which is the last class) card_pred = (pred_logits.argmax(-1) != pred_logits.shape[-1] - 1).sum(1) card_pred = card_pred.sum(dim=1) card_err = F.l1_loss(card_pred.float(), tgt_lengths.float()) losses = {'cardinality_error': card_err} return losses def loss_boxes(self, outputs, targets, indices, num_boxes): """Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4] The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size. """ assert 'pred_boxes' in outputs idx = self._get_src_permutation_idx(indices) src_boxes = outputs['pred_boxes'][idx] target_boxes = torch.cat([t['boxes'][i] for t, (_, i) in zip(targets, indices)], dim=0) loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction='none') losses = {} losses['loss_bbox'] = loss_bbox.sum() / num_boxes loss_giou = 1 - torch.diag(generalized_box_iou( box_cxcywh_to_xyxy(src_boxes), box_cxcywh_to_xyxy(target_boxes))) losses['loss_giou'] = loss_giou.sum() / num_boxes return losses def loss_masks(self, outputs, targets, indices, num_boxes): """Compute the losses related to the masks: the focal loss and the dice loss. targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w] """ assert "pred_masks" in outputs src_idx = self._get_src_permutation_idx(indices) tgt_idx = self._get_tgt_permutation_idx(indices) src_masks = outputs["pred_masks"] src_masks = src_masks[src_idx] masks = [t["masks"] for t in targets] # TODO use valid to mask invalid areas due to padding in loss target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() target_masks = target_masks.to(src_masks) target_masks = target_masks[tgt_idx] # upsample predictions to the target size src_masks = interpolate(src_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False) src_masks = src_masks[:, 0].flatten(1) target_masks = target_masks.flatten(1) target_masks = target_masks.view(src_masks.shape) losses = { "loss_mask": sigmoid_focal_loss(src_masks, target_masks, num_boxes), "loss_dice": dice_loss(src_masks, target_masks, num_boxes), } return losses def _get_src_permutation_idx(self, indices): # permute predictions following indices batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) src_idx = torch.cat([src for (src, _) in indices]) return batch_idx, src_idx def _get_tgt_permutation_idx(self, indices): # permute targets following indices batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) tgt_idx = torch.cat([tgt for (_, tgt) in indices]) return batch_idx, tgt_idx def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs): loss_map = { 'labels': self.loss_labels, 'cardinality': self.loss_cardinality, 'boxes': self.loss_boxes, 'masks': self.loss_masks } assert loss in loss_map, f'do you really want to compute {loss} loss?' return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs) def forward(self, outputs, targets): """ This performs the loss computation. Parameters: outputs: dict of tensors, see the output specification of the model for the format targets: list of dicts, such that len(targets) == batch_size. The expected keys in each dict depends on the losses applied, see each loss' doc """ outputs_without_aux = {k: v for k, v in outputs.items() if k != 'aux_outputs'} # Retrieve the matching between the outputs of the last layer and the targets indices = self.matcher(outputs_without_aux, targets) # print("indice len", len(indices), "len (indices[0]) ", len (indices[0])) # print( " shape indices 0 0 ", indices [0][0].shape , " shape indices 0 1 ", indices [0][1].shape) # Compute the average number of target boxes accross all nodes, for normalization purposes num_boxes = sum(len(t["labels"]) for t in targets) num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device) if is_dist_avail_and_initialized(): torch.distributed.all_reduce(num_boxes) num_boxes = torch.clamp(num_boxes / get_world_size(), min=1).item() # print("num_boxes",num_boxes) # Compute all the requested losses losses = {} for loss in self.losses: losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes)) ''' # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. if 'aux_outputs' in outputs: for i, aux_outputs in enumerate(outputs['aux_outputs']): indices = self.matcher(aux_outputs, targets) for loss in self.losses: if loss == 'masks': # Intermediate masks losses are too costly to compute, we ignore them. continue kwargs = {} if loss == 'labels': # Logging is enabled only for the last layer kwargs = {'log': False} l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_boxes, **kwargs) l_dict = {k + f'_{i}': v for k, v in l_dict.items()} losses.update(l_dict) ''' return losses def eval_fn(data_loader, model,criterion, device): model.eval() criterion.eval() summary_loss = AverageMeter() with torch.no_grad(): #tk0 = tqdm(data_loader, total=len(data_loader)) #for step, (images, bboxes, captions) in enumerate(tk0): #pbar = tqdm(range(len(data_loader)))** tk0 = tqdm(data_loader, total=len(data_loader)-1) for step, (images, bboxes, captions) in enumerate(tk0): try: flattened_captions = [caption for sublist in captions for caption in sublist] captions = tokenizer(flattened_captions, padding=True, return_tensors="pt", truncation=True) captions = captions["input_ids"] input_ids = captions.reshape(batch_size, num_queries, -1).to(device) min_length = 2 except RuntimeError as e: print("Reshape failed:", e) continue if input_ids.size(-1) < min_length: padding_needed = min_length - input_ids.size(-1) input_ids = F.pad(input_ids, (0, padding_needed), 'constant', PAD_TOKEN) # input_ids = captions["input_ids"] # input_ids = input_ids.reshape(batch_size, num_queries, -1).to(device) targets = build_targets(bboxes, input_ids[:, :, 1:]) #targets = build_targets(bboxes, captions[:,:,1:]) images = list(image.to(device) for image in images) targets = [{k: v.to(device) for k, v in t.items()} for t in targets] output = model(images,input_ids[:,:,:-1]) loss_dict = criterion(output, targets) weight_dict = criterion.weight_dict losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict) summary_loss.update(losses.item(),BATCH_SIZE) # # Detach and delete tensors loss_dict = {k: v.detach() for k, v in loss_dict.items()} del images, bboxes, captions, output, targets, loss_dict torch.cuda.empty_cache() # Clear cache tk0.set_postfix(loss=summary_loss.avg) #data_loader.on_epoch_end() return summary_loss def build_targets(bboxes, captions): targets = [] for i, (bbox, caption) in enumerate(zip(bboxes, captions)): target = { "boxes": bbox, "labels": caption, } targets.append(target) return targets if __name__ == "__main__": # Créer les datasets train_dataset = CocoDataset(root_dir="../data/coco91/train2017", annotation_file="../data/coco91/annotations/captions_train2017.json", instance_file="../data/coco91/annotations/instances_train2017.json", transform=transform) val_dataset = CocoDataset(root_dir="../data/coco91/val2017", annotation_file="../data/coco91/annotations/captions_val2017.json", instance_file="../data/coco91/annotations/instances_val2017.json", transform=transform) batch_size=4 train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=custom_collate) val_loader = DataLoader(val_dataset, batch_size=batch_size, collate_fn=custom_collate) # Initialiser le tokenizer BERT tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') # Obtenir le token de padding et son ID #PAD_TOKEN = tokenizer.pad_token PAD_TOKEN = tokenizer.pad_token_id # Obtenir le token de début de séquence et son ID # Pour BERT, le token de début de séquence est souvent le même que le token [CLS] #start_of_sequence_token = tokenizer.cls_token PAD_SOS = tokenizer.cls_token_id # Obtenir la taille du vocabulaire vocab_size = tokenizer.vocab_size print(f"Pad token: {PAD_TOKEN}") print(f"Start of Sequence token: {PAD_SOS}, ID: {PAD_SOS}") print(f"Vocab size: {vocab_size}") matcher = HungarianMatcher() weight_dict = weight_dict = {'loss_ce': 1, 'loss_bbox': 1 , 'loss_giou': 1} losses = ['labels', 'boxes', 'cardinality'] criterion = nn.CrossEntropyLoss(ignore_index=PAD_TOKEN) model = LLMEyaCapModel(num_queries=NUM_QUERIES,vocab_size=vocab_size) model = model.to(device) criterion = SetCriterion(vocab_size, matcher=matcher, weight_dict=weight_dict, eos_coef = NULL_CLASS_COEF, losses=losses) criterion = criterion.to(device) optimizer = torch.optim.AdamW(model.parameters(), lr=LR) best_loss = 10**5 LR = 2e-6 #LR = 2e-4 optimizer = torch.optim.AdamW(model.parameters(), lr=LR) #, weight_decay=0.0001) EPOCHS=1 num_queries=NUM_QUERIES batch_size=4 for epoch in range(EPOCHS): time_start = time.time() train_loss = train_fn(train_loader, model,criterion, optimizer,device,scheduler=None,epoch=epoch) valid_loss = eval_fn(val_loader, model,criterion, device) elapsed = time.time() - time_start chk_name = f'LLMEyeCap_01_e{epoch}.bin' torch.save(model.state_dict(), chk_name) print(f"[Epoch {epoch+1:2d} / {EPOCHS:2d}] Train loss: {train_loss.avg:.3f}. Val loss: {valid_loss.avg:.3f} --> {chk_name} [{elapsed/60:.0f} mins]") if valid_loss.avg < best_loss: best_loss = valid_loss.avg print(f'Best model found in epoch {epoch+1}........Saving Model') torch.save(model.state_dict(), 'LLMEyeCap_01_model.bin')