上传Clip模型

.gitattributes

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+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.ts filter=lfs diff=lfs merge=lfs -text

dataset.py

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+import os
+import glob
+import cv2
+import numpy as np
+import torch
+import random
+from torch.utils.data import Dataset
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+
+class YOLODataset(Dataset):
+    def __init__(self, img_dir, label_dir, img_size=640, is_train=True):
+        self.img_dir = img_dir
+        self.label_dir = label_dir
+        self.img_size = img_size
+        self.is_train = is_train
+        self.use_mosaic = is_train # 新增标志位，默认训练时开启
+        
+        # 支持多种图片格式
+        self.img_files = sorted(
+            glob.glob(os.path.join(img_dir, "*.jpg")) + 
+            glob.glob(os.path.join(img_dir, "*.png")) +
+            glob.glob(os.path.join(img_dir, "*.jpeg"))
+        )
+        
+        # --- 1. 对齐 Ultralytics 的 Albumentations 配置 ---
+        # default.yaml: hsv_h: 0.015, hsv_s: 0.7, hsv_v: 0.4
+        # OpenCV Hue range is [0, 179], Sat/Val is [0, 255]
+        h_limit = int(0.015 * 179) # ~2
+        s_limit = int(0.7 * 255)   # ~178
+        v_limit = int(0.4 * 255)   # ~102
+        
+        # default.yaml: translate: 0.1, scale: 0.5 (0.5~1.5), degrees: 0.0
+        
+        if is_train:
+            self.transform = A.Compose([
+                # 几何增强 (Mosaic 之后再做一次微调，或者处理非 Mosaic 的情况)
+                # 注意：Mosaic 输出已经是大图，这里主要负责最后的随机扰动
+                A.Affine(
+                    scale=(0.5, 1.5),          # scale: 0.5
+                    translate_percent=(0.1, 0.1), # translate: 0.1
+                    rotate=(-0, 0),            # degrees: 0.0 (COCO default)
+                    shear=(-0, 0),             # shear: 0.0
+                    p=0.5
+                ),
+                
+                # 色彩增强 (严格对齐 default.yaml)
+                A.HueSaturationValue(
+                    hue_shift_limit=h_limit, 
+                    sat_shift_limit=s_limit, 
+                    val_shift_limit=v_limit, 
+                    p=0.5
+                ),
+                
+                A.Blur(p=0.01),
+                A.MedianBlur(p=0.01),
+                A.ToGray(p=0.01),
+                A.CLAHE(p=0.01),
+                
+                # 翻转
+                A.HorizontalFlip(p=0.5), # fliplr: 0.5
+                
+                # 最终处理
+                A.Resize(img_size, img_size), # 确保最后尺寸一致
+                A.Normalize(mean=(0, 0, 0), std=(1, 1, 1), max_pixel_value=255.0),
+                ToTensorV2()
+            ], bbox_params=A.BboxParams(format='yolo', min_visibility=0.0, label_fields=['class_labels']))
+        else:
+            # 验证集：Letterbox (保持比例填充)
+            self.transform = A.Compose([
+                A.LongestMaxSize(max_size=img_size),
+                A.PadIfNeeded(min_height=img_size, min_width=img_size, border_mode=cv2.BORDER_CONSTANT),
+                A.Normalize(mean=(0, 0, 0), std=(1, 1, 1), max_pixel_value=255.0),
+                ToTensorV2()
+            ], bbox_params=A.BboxParams(format='yolo', min_visibility=0.1, label_fields=['class_labels']))
+
+    def close_mosaic(self):
+        """关闭 Mosaic 增强"""
+        self.use_mosaic = False
+        print("Mosaic augmentation disabled.")
+    
+    def __len__(self):
+        return len(self.img_files)
+
+    def load_image(self, index):
+        """加载单张图片并调整长边到 img_size"""
+        img_path = self.img_files[index]
+        img = cv2.imread(img_path)
+        if img is None:
+            raise FileNotFoundError(f"Image not found: {img_path}")
+        
+        h, w = img.shape[:2]
+        r = self.img_size / max(h, w)
+        if r != 1:
+            img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR)
+        
+        return img, (h, w), img.shape[:2] # img, original_hw, resized_hw
+
+    def load_label(self, index, img_shape):
+        """加载标签并归一化"""
+        img_path = self.img_files[index]
+        label_path = self._get_label_path(img_path)
+        h, w = img_shape
+        
+        labels = []
+        if os.path.exists(label_path):
+            with open(label_path, 'r') as f:
+                for line in f:
+                    parts = line.strip().split()
+                    if len(parts) >= 5:
+                        cls = int(parts[0])
+                        bx, by, bw, bh = map(float, parts[1:5])
+                        labels.append([cls, bx, by, bw, bh])
+        
+        return np.array(labels, dtype=np.float32) if labels else np.zeros((0, 5), dtype=np.float32)
+
+    def load_mosaic(self, index):
+        """
+        实现 YOLO 的 Mosaic 增强 (4张图拼成一张)
+        """
+        labels4 = []
+        s = self.img_size
+        # 修复: 列表推导式需要遍历两次以生成 yc 和 xc
+        yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in [-s // 2, -s // 2]]
+        
+        # 随机选3个额外索引
+        indices = [index] + [random.randint(0, len(self.img_files) - 1) for _ in range(3)]
+        random.shuffle(indices)
+        
+        # 初始化大图 (2x size)
+        img4 = np.full((s * 2, s * 2, 3), 114, dtype=np.uint8)
+        
+        for i, idx in enumerate(indices):
+            # 加载图片
+            img, _, (h, w) = self.load_image(idx)
+            
+            # 放置位置: top-left, top-right, bottom-left, bottom-right
+            if i == 0:  # top left
+                x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc
+                x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h
+            elif i == 1:  # top right
+                x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
+                x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
+            elif i == 2:  # bottom left
+                x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(yc + h, s * 2)
+                x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
+            elif i == 3:  # bottom right
+                x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(yc + h, s * 2)
+                x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
+
+            # 贴图
+            img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # type: ignore
+            padw = x1a - x1b # type: ignore
+            padh = y1a - y1b # type: ignore
+
+            # 处理标签
+            labels = self.load_label(idx, (h, w))
+            if labels.size > 0:
+                # Normalized xywh -> Pixel xywh
+                labels[:, 1] = labels[:, 1] * w
+                labels[:, 2] = labels[:, 2] * h
+                labels[:, 3] = labels[:, 3] * w
+                labels[:, 4] = labels[:, 4] * h
+                
+                # xywh -> xyxy (Pixel)
+                xyxy = np.copy(labels)
+                xyxy[:, 1] = labels[:, 1] - labels[:, 3] / 2 + padw
+                xyxy[:, 2] = labels[:, 2] - labels[:, 4] / 2 + padh
+                xyxy[:, 3] = labels[:, 1] + labels[:, 3] / 2 + padw
+                xyxy[:, 4] = labels[:, 2] + labels[:, 4] / 2 + padh
+                
+                labels4.append(xyxy)
+
+        # Concat labels
+        if len(labels4):
+            labels4 = np.concatenate(labels4, 0)
+            # Clip to mosaic image border
+            np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:])
+            
+            # 转换回 Normalized xywh (相对于 2*s 的大图)
+            # Albumentations 需要 normalized xywh
+            new_labels = np.copy(labels4)
+            w_mosaic, h_mosaic = s * 2, s * 2
+            
+            # xyxy -> xywh
+            new_labels[:, 1] = (labels4[:, 1] + labels4[:, 3]) / 2 / w_mosaic
+            new_labels[:, 2] = (labels4[:, 2] + labels4[:, 4]) / 2 / h_mosaic
+            new_labels[:, 3] = (labels4[:, 3] - labels4[:, 1]) / w_mosaic
+            new_labels[:, 4] = (labels4[:, 4] - labels4[:, 2]) / h_mosaic
+            
+            return img4, new_labels
+        else:
+            return img4, np.zeros((0, 5))
+
+    def __getitem__(self, index):
+        try:
+            if self.is_train:
+                # 修改判断逻辑：同时检查 is_train 和 use_mosaic
+                if self.use_mosaic and random.random() < 1.0: 
+                    img, labels = self.load_mosaic(index)
+                else:
+                    img, _, _ = self.load_image(index)
+                    h, w = img.shape[:2]
+                    labels = self.load_label(index, (h, w))
+                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # type: ignore
+            else:
+                img = cv2.imread(self.img_files[index])
+                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # type: ignore
+                h, w = img.shape[:2]
+                labels = self.load_label(index, (h, w))
+
+            # --- 修复开始: 清洗边界框 ---
+            # labels 格式: [cls, x, y, w, h] (Normalized)
+            
+            valid_bboxes = []
+            valid_labels = []
+            
+            h_img, w_img = img.shape[:2]
+            
+            for i in range(len(labels)):
+                cls = labels[i, 0]
+                x, y, w, h = labels[i, 1:]
+                
+                # 1. 限制在 [0, 1] 范围内 (处理 Mosaic 裁剪产生的越界)
+                x1 = np.clip(x - w / 2, 0, 1)
+                y1 = np.clip(y - h / 2, 0, 1)
+                x2 = np.clip(x + w / 2, 0, 1)
+                y2 = np.clip(y + h / 2, 0, 1)
+                
+                # 2. 重新计算宽高
+                w_new = x2 - x1
+                h_new = y2 - y1
+                
+                # 3. 重新计算中心点
+                x_new = x1 + w_new / 2
+                y_new = y1 + h_new / 2
+                
+                # 4. 过滤掉极小的框 (例如小于 2 个像素)
+                if w_new * w_img > 2 and h_new * h_img > 2:
+                    valid_bboxes.append([x_new, y_new, w_new, h_new])
+                    valid_labels.append(cls)
+            
+            if len(valid_bboxes) == 0:
+                # 如果这张图的所有框都被过滤掉了，尝试下一张
+                return self.__getitem__((index + 1) % len(self))
+                
+            bboxes = valid_bboxes
+            class_labels = valid_labels
+            # --- 修复结束 ---
+            
+            # 应用增强
+            transformed = self.transform(image=img, bboxes=bboxes, class_labels=class_labels)
+            image = transformed['image']
+            bboxes = transformed['bboxes']
+            class_labels = transformed['class_labels']
+            
+            # 构建 Target Tensor
+            n = len(bboxes)
+            targets = torch.zeros((n, 6))
+            if n > 0:
+                targets[:, 1] = torch.tensor(class_labels)
+                targets[:, 2:] = torch.tensor(bboxes)
+            
+            return image, targets, self.img_files[index]
+            
+        except Exception as e:
+            # 打印更详细的错误信息以便调试，但不要中断训练
+            # print(f"Error loading data {index}: {e}")
+            return self.__getitem__((index + 1) % len(self))
+
+    def _get_label_path(self, img_path):
+        filename = os.path.basename(img_path).rsplit('.', 1)[0] + ".txt"
+        return os.path.join(self.label_dir, filename)
+
+    @staticmethod
+    def collate_fn(batch):
+        imgs, targets, paths = zip(*batch)
+        imgs = torch.stack(imgs, 0)
+        new_targets = []
+        for i, t in enumerate(targets):
+            if t.shape[0] > 0:
+                t[:, 0] = i 
+                new_targets.append(t)
+        if new_targets:
+            targets = torch.cat(new_targets, 0)
+        else:
+            targets = torch.zeros((0, 6))
+        return imgs, targets, paths

inference.py

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+import torch
+import cv2
+import numpy as np
+import torchvision
+from yolo11_standalone import YOLO11
+
+# COCO 80类 类别名称
+CLASSES = [
+    "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
+    "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
+    "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
+    "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
+    "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
+    "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
+    "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
+    "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
+    "hair drier", "toothbrush"
+]
+
+# 生成随机颜色用于绘图
+COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
+
+def letterbox(im, new_shape=(640, 640), color=(114, 114, 114)):
+    """
+    将图像缩放并填充到指定大小 (保持纵横比)
+    """
+    shape = im.shape[:2]  # current shape [height, width]
+    if isinstance(new_shape, int):
+        new_shape = (new_shape, new_shape)
+
+    # 计算缩放比例
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+
+    # 计算padding
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
+    dw, dh = dw / 2, dh / 2  # divide padding into 2 sides
+
+    if shape[::-1] != new_unpad:  # resize
+        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
+    
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    
+    # 添加边框
+    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
+    return im, r, (dw, dh)
+
+def xywh2xyxy(x):
+    """Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2]"""
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[..., 0] = x[..., 0] - x[..., 2] / 2  # top left x
+    y[..., 1] = x[..., 1] - x[..., 3] / 2  # top left y
+    y[..., 2] = x[..., 0] + x[..., 2] / 2  # bottom right x
+    y[..., 3] = x[..., 1] + x[..., 3] / 2  # bottom right y
+    return y
+
+def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, max_det=300):
+    """
+    非极大值抑制 (NMS)
+    prediction: [Batch, 84, Anchors]
+    """
+    # 1. 转置: [Batch, 84, Anchors] -> [Batch, Anchors, 84]
+    prediction = prediction.transpose(1, 2)
+    
+    bs = prediction.shape[0]  # batch size
+    nc = prediction.shape[2] - 4  # number of classes
+    
+    # 修复: 使用 max(-1) 在最后一个维度(类别)上寻找最大置信度
+    # 之前的 max(1) 错误地在 Anchors 维度上操作了
+    xc = prediction[..., 4:].max(-1)[0] > conf_thres  # candidates
+
+    output = [torch.zeros((0, 6), device=prediction.device)] * bs
+    
+    for xi, x in enumerate(prediction):  # image index, image inference
+        x = x[xc[xi]]  # confidence filtering
+
+        if not x.shape[0]:
+            continue
+
+        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
+        box = xywh2xyxy(x[:, :4])
+
+        # Confidence and Class
+        conf, j = x[:, 4:].max(1, keepdim=True)
+        x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
+
+        # Check shape
+        n = x.shape[0]
+        if not n:
+            continue
+        elif n > max_det:
+            x = x[x[:, 4].argsort(descending=True)[:max_det]]
+
+        # Batched NMS
+        c = x[:, 5:6] * 7680  # classes
+        boxes, scores = x[:, :4] + c, x[:, 4]
+        i = torchvision.ops.nms(boxes, scores, iou_thres)
+        output[xi] = x[i]
+
+    return output
+
+def main():
+    # 1. 初始化模型
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+    
+    model = YOLO11(nc=80, scale='s')
+    # 加载你之前转换好的纯净权重
+    model.load_weights("yolo11s.pth") 
+    model.to(device)
+    model.eval()
+    # model.train()
+
+    # 2. 读取图片
+    img_path = "1.jpg" # 请替换为你本地的图片路径
+    
+    img0 = cv2.imread(img_path)
+    assert img0 is not None, f"Image Not Found {img_path}"
+
+    # 3. 预处理
+    # Letterbox resize
+    img, ratio, (dw, dh) = letterbox(img0, new_shape=(640, 640))
+    
+    # BGR to RGB, HWC to CHW
+    img = img[:, :, ::-1].transpose(2, 0, 1)  
+    img = np.ascontiguousarray(img)
+    
+    img_tensor = torch.from_numpy(img).to(device)
+    img_tensor = img_tensor.float()
+    img_tensor /= 255.0  # 0 - 255 to 0.0 - 1.0
+    if img_tensor.ndim == 3:
+        img_tensor = img_tensor.unsqueeze(0)
+
+    # 4. 推理
+    print("开始推理...")
+    with torch.no_grad():
+        pred = model(img_tensor)
+
+    # 5. 后处理 (NMS)
+    pred = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.45)
+
+    # 6. 绘制结果
+    det = pred[0] # 仅处理第一张图片
+    
+    if len(det):
+        # 将坐标映射回原图尺寸
+        # det[:, :4] 是 x1, y1, x2, y2
+        det[:, [0, 2]] -= dw  # x padding
+        det[:, [1, 3]] -= dh  # y padding
+        det[:, :4] /= ratio
+        
+        # 裁剪坐标防止越界
+        det[:, 0].clamp_(0, img0.shape[1])
+        det[:, 1].clamp_(0, img0.shape[0])
+        det[:, 2].clamp_(0, img0.shape[1])
+        det[:, 3].clamp_(0, img0.shape[0])
+
+        print(f"检测到 {len(det)} 个目标")
+
+        for *xyxy, conf, cls in det:
+            c = int(cls)
+            label = f'{CLASSES[c]} {conf:.2f}'
+            p1, p2 = (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3]))
+            
+            # 画框
+            color = COLORS[c]
+            cv2.rectangle(img0, p1, p2, color, 2, lineType=cv2.LINE_AA)
+            
+            # 画标签背景
+            t_size = cv2.getTextSize(label, 0, fontScale=0.5, thickness=1)[0]
+            p2_label = p1[0] + t_size[0], p1[1] - t_size[1] - 3
+            cv2.rectangle(img0, p1, p2_label, color, -1, cv2.LINE_AA)
+            
+            # 画文字
+            cv2.putText(img0, label, (p1[0], p1[1] - 2), 0, 0.5, [255, 255, 255], thickness=1, lineType=cv2.LINE_AA)
+            
+            print(f" - {label} at {p1}-{p2}")
+
+    # 7. 显示/保存结果
+    cv2.imwrite("result.jpg", img0)
+    print("结果已保存至 result.jpg")
+
+def import_os_exists(path):
+    import os
+    return os.path.exists(path)
+
+if __name__ == "__main__":
+    main()

inference_yoloe.py

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+import torch
+import cv2
+import numpy as np
+import torchvision
+from pathlib import Path
+
+# 导入你的模块
+from yolo11_standalone import YOLO11E
+from mobile_clip_standalone import MobileCLIP
+
+from ultralytics import YOLOE
+
+
+# --- 配置 ---
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+YOLO_WEIGHTS = "yoloe-11l-seg.pth"  # 替换为你的 YOLO 权重路径
+CLIP_WEIGHTS = "mobileclip_blt.ts"  # 替换为你的 MobileCLIP 权重路径
+CLIP_SIZE = "blt"                   # 对应 MobileCLIP 的 size
+IMAGE_PATH = "1.jpg"                # 待检测图片
+
+# 自定义检测类别 (Open Vocabulary)
+CUSTOM_CLASSES = ["girl", "red balloon"] 
+
+# 绘图颜色
+COLORS = np.random.uniform(0, 255, size=(len(CUSTOM_CLASSES), 3))
+
+# --- 辅助函数 (Letterbox, NMS 等) ---
+def letterbox(im, new_shape=(640, 640), color=(114, 114, 114)):
+    shape = im.shape[:2]
+    if isinstance(new_shape, int): new_shape = (new_shape, new_shape)
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]
+    dw, dh = dw / 2, dh / 2
+    if shape[::-1] != new_unpad:
+        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
+    return im, r, (dw, dh)
+
+def xywh2xyxy(x):
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[..., 0] = x[..., 0] - x[..., 2] / 2
+    y[..., 1] = x[..., 1] - x[..., 3] / 2
+    y[..., 2] = x[..., 0] + x[..., 2] / 2
+    y[..., 3] = x[..., 1] + x[..., 3] / 2
+    return y
+
+def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.7, max_det=300):
+    prediction = prediction.transpose(1, 2)
+    bs = prediction.shape[0]
+    xc = prediction[..., 4:].max(-1)[0] > conf_thres
+    output = [torch.zeros((0, 6), device=prediction.device)] * bs
+    for xi, x in enumerate(prediction):
+        x = x[xc[xi]]
+        if not x.shape[0]: continue
+        box = xywh2xyxy(x[:, :4])
+        conf, j = x[:, 4:].max(1, keepdim=True)
+        x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
+        n = x.shape[0]
+        if not n: continue
+        elif n > max_det: x = x[x[:, 4].argsort(descending=True)[:max_det]]
+        c = x[:, 5:6] * 7680
+        boxes, scores = x[:, :4] + c, x[:, 4]
+        i = torchvision.ops.nms(boxes, scores, iou_thres)
+        output[xi] = x[i]
+    return output
+
+def main():
+    print(f"Using device: {DEVICE}")
+
+    # 1. 加载 MobileCLIP 文本编码器
+    print(f"Loading MobileCLIP from {CLIP_WEIGHTS}...")
+    if not Path(CLIP_WEIGHTS).exists():
+        raise FileNotFoundError(f"MobileCLIP weights not found: {CLIP_WEIGHTS}")
+    
+    clip_model = MobileCLIP(checkpoint=CLIP_WEIGHTS, size=CLIP_SIZE, device=DEVICE)
+
+    # 2. 生成文本 Embeddings
+    print(f"Encoding classes: {CUSTOM_CLASSES}")
+    prompts = [f"{c}" for c in CUSTOM_CLASSES] 
+    
+    tokens = clip_model.tokenize(prompts)
+    text_embeddings = clip_model.encode_text(tokens) # Shape: (N, 512)
+    
+    # 调整维度为 (1, N, 512) 以匹配 YOLO11E 输入
+    text_embeddings = text_embeddings.unsqueeze(0) 
+
+    # 3. 加载 YOLO11E 检测模型
+    print(f"Loading YOLO11E from {YOLO_WEIGHTS}...")
+    if not Path(YOLO_WEIGHTS).exists():
+        raise FileNotFoundError(f"YOLO weights not found: {YOLO_WEIGHTS}")
+
+    # 注意：scale='l' 必须与你的权重文件匹配 (s, m, l, x)
+    yolo_model = YOLO11E(nc=80, scale='l') 
+    yolo_model.load_weights(YOLO_WEIGHTS)
+    yolo_model.to(DEVICE)  # 使用半精度to(DEVICE)
+    yolo_model.eval()
+
+    head = yolo_model.model[-1]
+    
+    with torch.no_grad():
+        text_pe = head.get_tpe(text_embeddings) # type: ignore
+
+    yolo_model.set_classes(CUSTOM_CLASSES, text_pe)
+
+    # 5. 图像预处理
+    img0 = cv2.imread(IMAGE_PATH)
+    assert img0 is not None, f"Image Not Found {IMAGE_PATH}"
+    img, ratio, (dw, dh) = letterbox(img0, new_shape=(640, 640))
+    img = img[:, :, ::-1].transpose(2, 0, 1)
+    img = np.ascontiguousarray(img)
+    img_tensor = torch.from_numpy(img).to(DEVICE)
+    img_tensor = img_tensor.float()
+    img_tensor /= 255.0
+    if img_tensor.ndim == 3:
+        img_tensor = img_tensor.unsqueeze(0)
+
+    # 6. 推理
+    print("Running inference...")
+    with torch.no_grad():
+        pred = yolo_model(img_tensor)
+        if isinstance(pred, tuple):
+            pred = pred[0]
+        nc = len(CUSTOM_CLASSES)
+        pred = pred[:, :4+nc, :]
+
+    # 7. 后处理 (NMS)
+    pred = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.7)
+
+    print(pred)
+
+    # 8. 可视化
+    det = pred[0]
+    if len(det):
+        det[:, [0, 2]] -= dw
+        det[:, [1, 3]] -= dh
+        det[:, :4] /= ratio
+        det[:, 0].clamp_(0, img0.shape[1])
+        det[:, 1].clamp_(0, img0.shape[0])
+        det[:, 2].clamp_(0, img0.shape[1])
+        det[:, 3].clamp_(0, img0.shape[0])
+
+        print(f"Detected {len(det)} objects:")
+        for *xyxy, conf, cls in det:
+            c = int(cls)
+            class_name = CUSTOM_CLASSES[c] if c < len(CUSTOM_CLASSES) else str(c)
+            label = f'{class_name} {conf:.2f}'
+            
+            p1, p2 = (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3]))
+            color = COLORS[c]
+            
+            cv2.rectangle(img0, p1, p2, color, 2, cv2.LINE_AA)
+            t_size = cv2.getTextSize(label, 0, 0.5, 1)[0]
+            p2_label = p1[0] + t_size[0], p1[1] - t_size[1] - 3
+            cv2.rectangle(img0, p1, p2_label, color, -1, cv2.LINE_AA)
+            cv2.putText(img0, label, (p1[0], p1[1] - 2), 0, 0.5, [255, 255, 255], 1, cv2.LINE_AA)
+            print(f" - {label}")
+    else:
+        print("No objects detected.")
+
+    output_path = "result_full.jpg"
+    cv2.imwrite(output_path, img0)
+    print(f"Result saved to {output_path}")
+
+if __name__ == "__main__":
+    main()

loss.py

@@ -0,0 +1,448 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+import numpy as np
+
+# ==============================================================================
+# 1. 基础工具函数 (Utils)
+# ==============================================================================
+
+def xywh2xyxy(x):
+    """Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right."""
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[..., 0] = x[..., 0] - x[..., 2] / 2  # top left x
+    y[..., 1] = x[..., 1] - x[..., 3] / 2  # top left y
+    y[..., 2] = x[..., 0] + x[..., 2] / 2  # bottom right x
+    y[..., 3] = x[..., 1] + x[..., 3] / 2  # bottom right y
+    return y
+
+def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
+    """
+    Calculate Intersection over Union (IoU) of box1(1, 4) to box2(n, 4).
+    Args:
+        box1 (torch.Tensor): predicted, shape (N, 4)
+        box2 (torch.Tensor): target, shape (N, 4)
+        xywh (bool): If True, input boxes are (x, y, w, h). If False, (x1, y1, x2, y2).
+        CIoU (bool): If True, calculate Complete IoU.
+    """
+    # Get the coordinates of bounding boxes
+    if xywh:  # transform from xywh to xyxy
+        (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
+        w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
+        b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
+        b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
+    else:  # x1, y1, x2, y2
+        b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
+        b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
+        w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
+        w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
+
+    # Intersection area
+    inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
+            (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
+
+    # Union Area
+    union = w1 * h1 + w2 * h2 - inter + eps
+
+    # IoU
+    iou = inter / union
+    if CIoU or DIoU or GIoU:
+        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)  # convex (smallest enclosing box) width
+        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height
+        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
+            c2 = cw ** 2 + ch ** 2 + eps  # convex diagonal squared
+            rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center dist ** 2
+            if CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
+                v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
+                with torch.no_grad():
+                    alpha = v / (v - iou + (1 + eps))
+                return iou - (rho2 / c2 + v * alpha)  # CIoU
+            return iou - rho2 / c2  # DIoU
+        c_area = cw * ch + eps  # convex area
+        return iou - (c_area - union) / c_area  # GIoU
+    return iou
+
+def make_anchors(feats, strides, grid_cell_offset=0.5):
+    """Generate anchors from features."""
+    anchor_points, stride_tensor = [], []
+    assert feats is not None
+    dtype, device = feats[0].dtype, feats[0].device
+    for i, stride in enumerate(strides):
+        h, w = feats[i].shape[2:]
+        sx = torch.arange(end=w, device=device, dtype=dtype) + grid_cell_offset
+        sy = torch.arange(end=h, device=device, dtype=dtype) + grid_cell_offset
+        sy, sx = torch.meshgrid(sy, sx, indexing="ij")
+        anchor_points.append(torch.stack((sx, sy), -1).view(-1, 2))
+        stride_tensor.append(torch.full((h * w, 1), stride, dtype=dtype, device=device))
+    return torch.cat(anchor_points), torch.cat(stride_tensor)
+
+def dist2bbox(distance, anchor_points, xywh=True, dim=-1):
+    """Transform distance(ltrb) to box(xywh or xyxy)."""
+    lt, rb = distance.chunk(2, dim)
+    x1y1 = anchor_points - lt
+    x2y2 = anchor_points + rb
+    if xywh:
+        c_xy = (x1y1 + x2y2) / 2
+        wh = x2y2 - x1y1
+        return torch.cat([c_xy, wh], dim)
+    return torch.cat((x1y1, x2y2), dim)
+
+def bbox2dist(anchor_points, bbox, reg_max):
+    """Transform bbox(xyxy) to dist(ltrb)."""
+    x1y1, x2y2 = bbox.chunk(2, -1)
+    return torch.cat((anchor_points - x1y1, x2y2 - anchor_points), -1).clamp_(0, reg_max - 0.01)
+
+
+class TaskAlignedAssigner(nn.Module):
+    def __init__(self, topk=13, num_classes=80, alpha=1.0, beta=6.0, eps=1e-9):
+        super().__init__()
+        self.topk = topk
+        self.num_classes = num_classes
+        self.alpha = alpha
+        self.beta = beta
+        self.eps = eps
+
+    @torch.no_grad()
+    def forward(self, pd_scores, pd_bboxes, anc_points, gt_labels, gt_bboxes, mask_gt):
+        self.bs = pd_scores.shape[0]
+        self.n_max_boxes = gt_bboxes.shape[1]
+
+        if self.n_max_boxes == 0:
+            return (
+                torch.full_like(pd_scores[..., 0], self.num_classes),
+                torch.zeros_like(pd_bboxes),
+                torch.zeros_like(pd_scores),
+                torch.zeros_like(pd_scores[..., 0]),
+                torch.zeros_like(pd_scores[..., 0]),
+            )
+
+        mask_pos, align_metric, overlaps = self.get_pos_mask(
+            pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points, mask_gt
+        )
+
+        target_gt_idx, fg_mask, mask_pos = self.select_highest_overlaps(mask_pos, overlaps, self.n_max_boxes)
+
+        target_labels, target_bboxes, target_scores = self.get_targets(gt_labels, gt_bboxes, target_gt_idx, fg_mask)
+
+        # Normalize
+        align_metric *= mask_pos
+        pos_align_metrics = align_metric.amax(dim=-1, keepdim=True)
+        pos_overlaps = (overlaps * mask_pos).amax(dim=-1, keepdim=True)
+        norm_align_metric = (align_metric * pos_overlaps / (pos_align_metrics + self.eps)).amax(-2).unsqueeze(-1)
+        target_scores = target_scores * norm_align_metric
+
+        return target_labels, target_bboxes, target_scores, fg_mask.bool(), target_gt_idx
+
+    def get_pos_mask(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points, mask_gt):
+        mask_in_gts = self.select_candidates_in_gts(anc_points, gt_bboxes)
+        align_metric, overlaps = self.get_box_metrics(pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_in_gts * mask_gt)
+        mask_topk = self.select_topk_candidates(align_metric, topk_mask=mask_gt.expand(-1, -1, self.topk).bool())
+        mask_pos = mask_topk * mask_in_gts * mask_gt
+        return mask_pos, align_metric, overlaps
+
+    def get_box_metrics(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_gt):
+        na = pd_bboxes.shape[-2]
+        mask_gt = mask_gt.bool()
+        overlaps = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_bboxes.dtype, device=pd_bboxes.device)
+        bbox_scores = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_scores.dtype, device=pd_scores.device)
+
+        ind = torch.zeros([2, self.bs, self.n_max_boxes], dtype=torch.long)
+        ind[0] = torch.arange(end=self.bs).view(-1, 1).expand(-1, self.n_max_boxes)
+        ind[1] = gt_labels.squeeze(-1)
+        
+        # Clamp labels to handle case where no GT exists or background index issues
+        ind[1] = ind[1].clamp(max=self.num_classes - 1)
+
+        bbox_scores[mask_gt] = pd_scores[ind[0], :, ind[1]][mask_gt]
+
+        pd_boxes = pd_bboxes.unsqueeze(1).expand(-1, self.n_max_boxes, -1, -1)[mask_gt]
+        gt_boxes = gt_bboxes.unsqueeze(2).expand(-1, -1, na, -1)[mask_gt]
+        overlaps[mask_gt] = bbox_iou(gt_boxes, pd_boxes, xywh=False, CIoU=True).squeeze(-1).clamp_(0)
+
+        align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta)
+        return align_metric, overlaps
+
+    def select_topk_candidates(self, metrics, topk_mask=None):
+        topk_metrics, topk_idxs = torch.topk(metrics, self.topk, dim=-1, largest=True)
+        if topk_mask is None:
+            topk_mask = (topk_metrics.max(-1, keepdim=True)[0] > self.eps).expand_as(topk_idxs)
+        topk_idxs.masked_fill_(~topk_mask, 0)
+
+        count_tensor = torch.zeros(metrics.shape, dtype=torch.int8, device=topk_idxs.device)
+        ones = torch.ones_like(topk_idxs[:, :, :1], dtype=torch.int8, device=topk_idxs.device)
+        for k in range(self.topk):
+            count_tensor.scatter_add_(-1, topk_idxs[:, :, k : k + 1], ones)
+        count_tensor.masked_fill_(count_tensor > 1, 0)
+        return count_tensor.to(metrics.dtype)
+
+    def get_targets(self, gt_labels, gt_bboxes, target_gt_idx, fg_mask):
+        batch_ind = torch.arange(end=self.bs, dtype=torch.int64, device=gt_labels.device)[..., None]
+        target_gt_idx = target_gt_idx + batch_ind * self.n_max_boxes
+        target_labels = gt_labels.long().flatten()[target_gt_idx]
+
+        target_bboxes = gt_bboxes.view(-1, gt_bboxes.shape[-1])[target_gt_idx]
+        target_labels.clamp_(0)
+
+        target_scores = torch.zeros(
+            (target_labels.shape[0], target_labels.shape[1], self.num_classes),
+            dtype=torch.int64,
+            device=target_labels.device,
+        )
+        target_scores.scatter_(2, target_labels.unsqueeze(-1), 1)
+
+        fg_scores_mask = fg_mask[:, :, None].repeat(1, 1, self.num_classes)
+        target_scores = torch.where(fg_scores_mask > 0, target_scores, 0)
+
+        return target_labels, target_bboxes, target_scores
+
+    @staticmethod
+    def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
+        n_anchors = xy_centers.shape[0]
+        bs, n_boxes, _ = gt_bboxes.shape
+        lt, rb = gt_bboxes.view(-1, 1, 4).chunk(2, 2)
+        bbox_deltas = torch.cat((xy_centers[None] - lt, rb - xy_centers[None]), dim=2).view(bs, n_boxes, n_anchors, -1)
+        return bbox_deltas.amin(3).gt_(eps)
+
+    @staticmethod
+    def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
+        fg_mask = mask_pos.sum(-2)
+        if fg_mask.max() > 1:
+            mask_multi_gts = (fg_mask.unsqueeze(1) > 1).expand(-1, n_max_boxes, -1)
+            max_overlaps_idx = overlaps.argmax(1)
+
+            is_max_overlaps = torch.zeros(mask_pos.shape, dtype=mask_pos.dtype, device=mask_pos.device)
+            is_max_overlaps.scatter_(1, max_overlaps_idx.unsqueeze(1), 1)
+
+            mask_pos = torch.where(mask_multi_gts, is_max_overlaps, mask_pos).float()
+            fg_mask = mask_pos.sum(-2)
+        target_gt_idx = mask_pos.argmax(-2)
+        return target_gt_idx, fg_mask, mask_pos
+
+# ==============================================================================
+# 3. Loss 模块 (Loss Modules - from loss.py)
+# ==============================================================================
+
+class DFLoss(nn.Module):
+    """Distribution Focal Loss (DFL)."""
+    def __init__(self, reg_max: int = 16) -> None:
+        super().__init__()
+        self.reg_max = reg_max
+
+    def __call__(self, pred_dist: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+        target = target.clamp_(0, self.reg_max - 1 - 0.01)
+        tl = target.long()  # target left
+        tr = tl + 1  # target right
+        wl = tr - target  # weight left
+        wr = 1 - wl  # weight right
+        return (
+            F.cross_entropy(pred_dist, tl.view(-1), reduction="none").view(tl.shape) * wl
+            + F.cross_entropy(pred_dist, tr.view(-1), reduction="none").view(tl.shape) * wr
+        ).mean(-1, keepdim=True)
+
+class BboxLoss(nn.Module):
+    """Criterion for computing training losses for bounding boxes (IoU + DFL)."""
+    def __init__(self, reg_max: int = 16):
+        super().__init__()
+        self.dfl_loss = DFLoss(reg_max) if reg_max > 1 else None
+
+    def forward(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask):
+        weight = target_scores.sum(-1)[fg_mask].unsqueeze(-1)
+        iou = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask], xywh=False, CIoU=True)
+        loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum
+
+        if self.dfl_loss:
+            target_ltrb = bbox2dist(anchor_points, target_bboxes, self.dfl_loss.reg_max - 1)
+            loss_dfl = self.dfl_loss(pred_dist[fg_mask].view(-1, self.dfl_loss.reg_max), target_ltrb[fg_mask]) * weight
+            loss_dfl = loss_dfl.sum() / target_scores_sum
+        else:
+            loss_dfl = torch.tensor(0.0).to(pred_dist.device)
+
+        return loss_iou, loss_dfl
+
+class YOLOv8DetectionLoss:
+    """
+    独立版的 YOLOv8/v11 Detection Loss。
+    Refactored to assume specific inputs and remove reliance on full Model object.
+    """
+    def __init__(self, nc=80, reg_max=16, stride=None, hyp=None):
+        """
+        Args:
+            nc (int): Number of classes.
+            reg_max (int): DFL channels (default 16).
+            stride (list/torch.Tensor): Model strides (e.g., [8, 16, 32]).
+            hyp (dict): Hyperparameters (box, cls, dfl gains).
+        """
+        if stride is None:
+            stride = [8, 16, 32] # Default strides
+        if hyp is None:
+            # Default YOLOv8 hyperparameters
+            hyp = {'box': 7.5, 'cls': 0.5, 'dfl': 1.5}
+            
+        self.nc = nc
+        self.reg_max = reg_max
+        self.stride = stride
+        self.hyp = hyp
+        self.no = nc + reg_max * 4 # Output channels per anchor
+        self.use_dfl = reg_max > 1
+        
+        # Components
+        self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
+        self.bbox_loss = BboxLoss(reg_max)
+        self.bce = nn.BCEWithLogitsLoss(reduction="none")
+        self.proj = torch.arange(reg_max, dtype=torch.float) # Will move to device in method
+
+    def preprocess(self, targets, batch_size, scale_tensor):
+        """Preprocesses targets: converts [idx, cls, xywh] to internal format."""
+        if targets.shape[0] == 0:
+            out = torch.zeros(batch_size, 0, 5, device=targets.device)
+        else:
+            i = targets[:, 0]  # image index
+            _, counts = i.unique(return_counts=True)
+            counts = counts.to(dtype=torch.int32)
+            out = torch.zeros(batch_size, counts.max(), 5, device=targets.device)
+            for j in range(batch_size):
+                matches = i == j
+                n = matches.sum()
+                if n:
+                    out[j, :n] = targets[matches, 1:] # cls, x, y, w, h
+            out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
+        return out
+
+    def bbox_decode(self, anchor_points, pred_dist):
+        """Decode predicted object bounding box coordinates from anchor points and distribution."""
+        if self.use_dfl:
+            b, a, c = pred_dist.shape  # batch, anchors, channels
+            # Project distributions to scalars using self.proj (0, 1, 2, ..., reg_max-1)
+            pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.to(pred_dist.device).type(pred_dist.dtype))
+        return dist2bbox(pred_dist, anchor_points, xywh=False)
+
+    def __call__(self, preds, batch):
+        """
+        Args:
+            preds: List of prediction tensors [B, C, H, W] for each stride level.
+                   C should be reg_max * 4 + nc.
+            batch: Dict containing:
+                   'batch_idx': Tensor [N], image index for each target
+                   'cls': Tensor [N], class index for each target
+                   'bboxes': Tensor [N, 4], normalized xywh format
+        Returns:
+            total_loss: scalar tensor (sum of box, cls, dfl), multiplied by batch_size as per original logic if needed,
+                        but here we return mean or sum based on reduction. 
+                        WARNING: Ultralytics returns (loss * bs) usually.
+            loss_items: Tensor used for logging [box, cls, dfl]
+        """
+        # Ensure proj is on correct device
+        self.device = preds[0].device
+        loss = torch.zeros(3, device=self.device)  # box, cls, dfl
+        
+        feats = preds
+        
+        # Concat features from different strides: (B, no, Total_Anchors)
+        pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
+            (self.reg_max * 4, self.nc), 1
+        )
+
+        pred_scores = pred_scores.permute(0, 2, 1).contiguous()   # (B, Anchors, nc)
+        pred_distri = pred_distri.permute(0, 2, 1).contiguous()   # (B, Anchors, reg_max * 4)
+
+        dtype = pred_scores.dtype
+        batch_size = pred_scores.shape[0]
+        
+        # Calculate image size from features and first stride (assuming square output implies inputs)
+        # Ultralytics uses provided imgsz, but we can infer roughly or pass it. 
+        # Here assuming feature map H*stride[0] = img H
+        imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0]
+        
+        # Generate anchors
+        anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
+
+        # Process Targets
+        if "batch_idx" not in batch: 
+            # Fallback if simplified batch input passed (targets assumed [N, 6] -> idx, cls, x, y, w, h)
+            # This handles cases where user passes raw target tensor instead of dict
+            raise ValueError("Batch dict must contain 'batch_idx', 'cls', and 'bboxes'")
+
+        targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
+        # Check if Normalized (xywh)
+        targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
+        gt_labels, gt_bboxes = targets.split((1, 4), 2)  # cls, xyxy
+        mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0.0)
+
+        # Decode Boxes
+        pred_bboxes = self.bbox_decode(anchor_points, pred_distri)  # xyxy, (b, h*w, 4)
+
+        # Task Aligned Assignment
+        _, target_bboxes, target_scores, fg_mask, _ = self.assigner(
+            pred_scores.detach().sigmoid(),
+            (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
+            anchor_points * stride_tensor,
+            gt_labels,
+            gt_bboxes,
+            mask_gt,
+        )
+
+        target_scores_sum = max(target_scores.sum(), 1)
+
+        # CLS Loss
+        loss[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum 
+
+        # BBox Loss (IoU + DFL)
+        if fg_mask.sum():
+            target_bboxes /= stride_tensor
+            loss[0], loss[2] = self.bbox_loss(
+                pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask
+            )
+
+        # Apply Gains
+        loss[0] *= self.hyp['box']
+        loss[1] *= self.hyp['cls']
+        loss[2] *= self.hyp['dfl']
+
+        return loss.sum() * batch_size, loss.detach() # scaling by batch_size to match Ultralytics behavior
+
+# ==============================================================================
+# Usage Example
+# ==============================================================================
+if __name__ == "__main__":
+    # 配置
+    num_classes = 80
+    reg_max = 16
+    strides = [8, 16, 32]
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    # 初始化 Loss
+    criterion = YOLOv8DetectionLoss(nc=num_classes, reg_max=reg_max, stride=strides)
+
+    # 模拟输入 (Batch Size = 2)
+    bs = 2
+    # 模拟模型输出: 3 个层级的特征图
+    # [BS, Channels, H, W], Channels = reg_max * 4 + num_classes
+    channels = reg_max * 4 + num_classes
+    preds = [
+        torch.randn(bs, channels, 80, 80, device=device, requires_grad=True), # stride 8
+        torch.randn(bs, channels, 40, 40, device=device, requires_grad=True), # stride 16
+        torch.randn(bs, channels, 20, 20, device=device, requires_grad=True), # stride 32
+    ]
+
+    # 模拟 Ground Truth
+    # batch: idx, cls, x, y, w, h (normalized 0-1)
+    target_batch = {
+        "batch_idx": torch.tensor([0, 0, 1], device=device),  # Image indices
+        "cls": torch.tensor([1, 5, 10], device=device),       # Class indices
+        "bboxes": torch.tensor([                              # Normalized xywh
+            [0.5, 0.5, 0.2, 0.3],
+            [0.1, 0.1, 0.1, 0.1],
+            [0.8, 0.8, 0.2, 0.2]
+        ], device=device)
+    }
+
+    # 计算 Loss
+    total_loss, loss_items = criterion(preds, target_batch)
+
+    print(f"Total Loss: {total_loss.item()}")
+    print(f"Loss Items (Box, Cls, DFL): {loss_items}")
+
+    # 反向传播测试
+    total_loss.backward()
+    print("Backward pass successful.")

metrics.py

@@ -0,0 +1,148 @@
+import torch
+import numpy as np
+import torchvision
+
+def xywh2xyxy(x):
+    """Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right"""
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[..., 0] = x[..., 0] - x[..., 2] / 2  # top left x
+    y[..., 1] = x[..., 1] - x[..., 3] / 2  # top left y
+    y[..., 2] = x[..., 0] + x[..., 2] / 2  # bottom right x
+    y[..., 3] = x[..., 1] + x[..., 3] / 2  # bottom right y
+    return y
+
+def box_iou(box1, box2):
+    """
+    Return intersection-over-union (Jaccard index) of boxes.
+    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+    box1: [N, 4]
+    box2: [M, 4]
+    Returns: [N, M]
+    """
+    def box_area(box):
+        return (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])
+
+    area1 = box_area(box1)
+    area2 = box_area(box2)
+
+    lt = torch.max(box1[:, None, :2], box2[:, :2])  # [N,M,2]
+    rb = torch.min(box1[:, None, 2:], box2[:, 2:])  # [N,M,2]
+
+    wh = (rb - lt).clamp(min=0)  # [N,M,2]
+    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
+
+    union = area1[:, None] + area2 - inter
+    return inter / (union + 1e-6)
+
+def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, max_det=300):
+    """
+    Non-Maximum Suppression (NMS) on inference results
+    prediction: [Batch, 84, 8400] (for YOLOv8/11)
+    """
+    # [Batch, 84, Anchors] -> [Batch, Anchors, 84]
+    prediction = prediction.transpose(1, 2)
+    
+    bs = prediction.shape[0]
+    nc = prediction.shape[2] - 4
+    xc = prediction[..., 4:].max(-1)[0] > conf_thres
+    
+    output = [torch.zeros((0, 6), device=prediction.device)] * bs
+    
+    for xi, x in enumerate(prediction):
+        x = x[xc[xi]]
+        if not x.shape[0]:
+            continue
+
+        # Box decoding
+        box = xywh2xyxy(x[:, :4])
+        
+        # Confidence and Class
+        conf, j = x[:, 4:].max(1, keepdim=True)
+        x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
+        
+        n = x.shape[0]
+        if not n:
+            continue
+        elif n > max_det:
+            x = x[x[:, 4].argsort(descending=True)[:max_det]]
+        
+        # Batched NMS
+        c = x[:, 5:6] * 7680
+        boxes, scores = x[:, :4] + c, x[:, 4]
+        i = torchvision.ops.nms(boxes, scores, iou_thres)
+        output[xi] = x[i]
+        
+    return output
+
+def compute_ap(recall, precision):
+    """ Compute the average precision, given the recall and precision curves """
+    # Append sentinel values to beginning and end
+    mrec = np.concatenate(([0.0], recall, [1.0]))
+    mpre = np.concatenate(([1.0], precision, [0.0]))
+
+    # Compute the precision envelope
+    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
+
+    # Integrate area under curve
+    method = 'interp'  # methods: 'continuous', 'interp'
+    if method == 'interp':
+        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
+        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
+    else:  # 'continuous'
+        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
+        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
+
+    return ap, mpre, mrec
+
+def smooth(y, f=0.05):
+    """Box filter of fraction f"""
+    nf = round(len(y) * f * 2) // 2 + 1  # number of filter elements (must be odd)
+    p = np.ones(nf // 2)  # ones padding
+    yp = np.concatenate((p * y[0], y, p * y[-1]), 0)  # y padded
+    return np.convolve(yp, np.ones(nf) / nf, mode='valid')  # y-smoothed
+
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16):
+    """ Compute the average precision, given the recall and precision curves. """
+    # Sort by objectness
+    i = np.argsort(-conf)
+    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
+
+    # Find unique classes
+    unique_classes, nt = np.unique(target_cls, return_counts=True)
+    nc = unique_classes.shape[0]  # number of classes, number of detections
+
+    # Create Precision-Recall curve and compute AP for each class
+    px, py = np.linspace(0, 1, 1000), []  # for plotting
+    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
+    for ci, c in enumerate(unique_classes):
+        i = pred_cls == c
+        n_l = (target_cls == c).sum()  # number of labels
+        n_p = i.sum()  # number of predictions
+
+        if n_p == 0 or n_l == 0:
+            continue
+
+        # Accumulate FPs and TPs
+        fpc = (1 - tp[i]).cumsum(0)
+        tpc = tp[i].cumsum(0)
+
+        # Recall
+        recall = tpc / (n_l + eps)  # recall curve
+        r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
+
+        # Precision
+        precision = tpc / (tpc + fpc)  # precision curve
+        p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
+
+        # AP from recall-precision curve
+        for j in range(tp.shape[1]):
+            ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
+
+    # Compute F1 (harmonic mean of precision and recall)
+    f1 = 2 * p * r / (p + r + eps)
+    i = smooth(f1.mean(0), 0.1).argmax()  # max F1 index
+    p, r, f1 = p[:, i], r[:, i], f1[:, i]
+    tp = (r * nt).round().astype(int)
+    fp = (tp / (p + eps) - tp).astype(int)
+
+    return tp, fp, p, r, f1, ap, unique_classes.astype(int)

mobile_clip_standalone.py

@@ -0,0 +1,117 @@
+import torch
+import torch.nn as nn
+from pathlib import Path
+from typing import List, Union
+import mobileclip
+
+class TextModel(nn.Module):
+    """TextModel 基类，定义接口"""
+    def __init__(self):
+        super().__init__()
+
+    def tokenize(self, texts):
+        raise NotImplementedError
+
+    def encode_text(self, texts, dtype):
+        raise NotImplementedError
+
+class MobileCLIP(TextModel):
+    """
+    MobileCLIP 文本编码器。
+    """
+    config_size_map = {"s0": "s0", "s1": "s1", "s2": "s2", "b": "b", "blt": "b"}
+
+    def __init__(self, checkpoint: str, size: str = "s0", device: Union[str, torch.device] = "cpu") -> None:
+        """
+        初始化 MobileCLIP 文本编码器。
+
+        Args:
+            checkpoint (str): 模型权重文件路径 (.pt 或 .ts).
+            size (str): 模型大小标识符 ('s0', 's1', 's2', 'b', 'blt').
+            device (torch.device): 加载模型的设备.
+        """
+        super().__init__()
+        
+        if isinstance(device, str):
+            device = torch.device(device)
+
+        if not Path(checkpoint).exists():
+            raise FileNotFoundError(f"找不到权重文件: {checkpoint}")
+
+        if size not in self.config_size_map:
+            raise ValueError(f"不支持的大小: {size}. 可选: {list(self.config_size_map.keys())}")
+
+        config = self.config_size_map[size]
+        
+        # 1. 加载 Tokenizer
+        self.tokenizer = mobileclip.get_tokenizer(f"mobileclip_{config}")
+
+        # 2. 加载模型
+        if str(checkpoint).endswith('.ts'):
+            # TorchScript 格式 (例如 mobileclip_blt.ts)
+            print(f"Loading TorchScript model from {checkpoint}...")
+            self.model = torch.jit.load(checkpoint, map_location=device)
+            self.is_torchscript = True
+        else:
+            # PyTorch 格式 (.pt)
+            print(f"Loading PyTorch model from {checkpoint}...")
+            self.model = mobileclip.create_model_and_transforms(
+                f"mobileclip_{config}", 
+                pretrained=checkpoint, 
+                device=device
+            )[0]
+            self.is_torchscript = False
+        
+        self.to(device)
+        self.device = device
+        self.eval()
+
+    def tokenize(self, texts: List[str]) -> torch.Tensor:
+        """
+        将文本转换为 token。
+        """
+        return self.tokenizer(texts).to(self.device)
+
+    def encode_text(self, texts: torch.Tensor, dtype: torch.dtype = torch.float32) -> torch.Tensor:
+        """
+        编码 tokenized 文本并进行归一化。
+        """
+        with torch.no_grad():
+            if self.is_torchscript:
+                return self.model(texts).to(dtype)
+
+            text_features = self.model.encode_text(texts).to(dtype) # type: ignore
+            text_features /= text_features.norm(p=2, dim=-1, keepdim=True)
+            return text_features
+
+# --- 使用示例 ---
+if __name__ == "__main__":
+    # 1. 设置设备
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+
+    # 指定本地模型路径
+    checkpoint_path = "mobileclip_blt.ts"
+
+    try:
+        if Path(checkpoint_path).exists():
+            # 2. 初始化模型 (指定本地路径和对应大小)
+            # 注意：blt 对应 size="blt"
+            model = MobileCLIP(checkpoint=checkpoint_path, size="blt", device=device)
+
+            # 3. 准备文本
+            input_texts = ["a photo of a cat", "a photo of a dog"]
+
+            # 4. Tokenize
+            tokens = model.tokenize(input_texts)
+            print(f"Tokens shape: {tokens.shape}")
+
+            # 5. Encode
+            features = model.encode_text(tokens)
+            print(f"Features shape: {features.shape}")
+            print("运行成功！")
+        else:
+            print(f"权重文件不存在: {checkpoint_path}")
+
+    except Exception as e:
+        print(f"发生错误: {e}")

mobileclip/__init__.py

@@ -0,0 +1,98 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import json
+import os
+from typing import Any, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from torchvision.transforms import (
+    CenterCrop,
+    Compose,
+    InterpolationMode,
+    Resize,
+    ToTensor,
+)
+
+from mobileclip.clip import CLIP
+from mobileclip.modules.common.mobileone import reparameterize_model
+from mobileclip.modules.text.tokenizer import (
+    ClipTokenizer,
+)
+
+
+def create_model_and_transforms(
+    model_name: str,
+    pretrained: str | None = None,
+    reparameterize: bool | None = True,
+    device: str | torch.device = "cpu",
+) -> tuple[nn.Module, Any, Any]:
+    """Method to instantiate model and pre-processing transforms necessary for inference.
+
+    Args:
+        model_name: Model name. Choose from ['mobileclip_s0', 'mobileclip_s1', 'mobileclip_s2', 'mobileclip_b']
+        pretrained: Location of pretrained checkpoint.
+        reparameterize: When set to True, re-parameterizable branches get folded for faster inference.
+        device: Device identifier for model placement.
+
+    Returns:
+        Tuple of instantiated model, and preprocessing transforms for inference.
+    """
+    # Config files
+    root_dir = os.path.dirname(os.path.abspath(__file__))
+    configs_dir = os.path.join(root_dir, "configs")
+    model_cfg_file = os.path.join(configs_dir, model_name + ".json")
+
+    # Get config from yaml file
+    if not os.path.exists(model_cfg_file):
+        raise ValueError(f"Unsupported model name: {model_name}")
+    model_cfg = json.load(open(model_cfg_file))
+
+    # Build preprocessing transforms for inference
+    resolution = model_cfg["image_cfg"]["image_size"]
+    resize_size = resolution
+    centercrop_size = resolution
+    aug_list = [
+        Resize(
+            resize_size,
+            interpolation=InterpolationMode.BILINEAR,
+        ),
+        CenterCrop(centercrop_size),
+        ToTensor(),
+    ]
+    preprocess = Compose(aug_list)
+
+    # Build model
+    model = CLIP(cfg=model_cfg)
+    model.to(device)
+    model.eval()
+
+    # Load checkpoint
+    if pretrained is not None:
+        chkpt = torch.load(pretrained)
+        model.load_state_dict(chkpt)
+
+    # Reparameterize model for inference (if specified)
+    if reparameterize:
+        model = reparameterize_model(model)
+
+    return model, None, preprocess
+
+
+def get_tokenizer(model_name: str) -> nn.Module:
+    # Config files
+    root_dir = os.path.dirname(os.path.abspath(__file__))
+    configs_dir = os.path.join(root_dir, "configs")
+    model_cfg_file = os.path.join(configs_dir, model_name + ".json")
+
+    # Get config from yaml file
+    model_cfg = json.load(open(model_cfg_file))
+
+    # Build tokenizer
+    text_tokenizer = ClipTokenizer(model_cfg)
+    return text_tokenizer

mobileclip/clip.py

@@ -0,0 +1,69 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+#
+"""Model schema in open_clip format for inference only."""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from mobileclip.text_encoder import (
+    TextTransformer,
+)
+
+from .image_encoder import MCi
+
+
+class CLIP(nn.Module):
+    """Base class for multi-modal image-text data."""
+
+    def __init__(self, cfg: dict, output_dict: bool = False, *args, **kwargs) -> None:
+        super().__init__()
+        self.output_dict = output_dict
+        self.projection_dim = cfg["embed_dim"]
+        if self.projection_dim is None:
+            raise ValueError("Please specify `embed_dim` in model config.")
+
+        self.image_encoder = MCi(
+            model_name=cfg["image_cfg"]["model_name"],
+            projection_dim=self.projection_dim,
+        )
+        self.text_encoder = TextTransformer(cfg=cfg["text_cfg"], projection_dim=self.projection_dim)
+        self.logit_scale = nn.Parameter(torch.ones([]) * math.log(1.0 / 0.07))
+
+    def _exponentiate_and_clip_logits(self, max_scale: float = 100.0):
+        scale = self.logit_scale.exp()
+        scale = torch.clamp(scale, 0, max_scale)
+        return scale
+
+    def encode_image(self, image: torch.Tensor, normalize: bool = False):
+        image_encoder_out = self.image_encoder(image)
+        if isinstance(image_encoder_out, dict):
+            features = image_encoder_out["logits"]
+        else:
+            features = image_encoder_out
+        return F.normalize(features, dim=-1) if normalize else features
+
+    def encode_text(self, text: torch.Tensor, normalize: bool = False):
+        text_features = self.text_encoder(text_tokens=text, key_padding_mask=None)
+        return F.normalize(text_features, dim=-1) if normalize else text_features
+
+    def forward(self, image: torch.Tensor | None = None, text: torch.Tensor | None = None, *args, **kwargs) -> Any:
+        image_embeddings = self.encode_image(image, normalize=True) if image is not None else None
+        text_embeddings = self.encode_text(text, normalize=True) if text is not None else None
+
+        if self.output_dict:
+            return {
+                "image_features": image_embeddings,
+                "text_features": text_embeddings,
+                "logit_scale": self._exponentiate_and_clip_logits(),
+            }
+        return image_embeddings, text_embeddings, self._exponentiate_and_clip_logits()

mobileclip/configs/mobileclip_b.json

@@ -0,0 +1,18 @@
+{
+  "embed_dim": 512,
+  "image_cfg": {
+    "image_size": 224,
+    "model_name": "vit_b16"
+  },
+  "text_cfg": {
+    "context_length": 77,
+    "vocab_size": 49408,
+    "dim": 512,
+    "ffn_multiplier_per_layer": 4.0,
+    "n_heads_per_layer": 8,
+    "n_transformer_layers": 12,
+    "norm_layer": "layer_norm_fp32",
+    "causal_masking": true,
+    "model_name": "base"
+  }
+}

mobileclip/configs/mobileclip_s0.json

@@ -0,0 +1,18 @@
+{
+  "embed_dim": 512,
+  "image_cfg": {
+    "image_size": 256,
+    "model_name": "mci0"
+  },
+  "text_cfg": {
+    "context_length": 77,
+    "vocab_size": 49408,
+    "dim": 512,
+    "ffn_multiplier_per_layer": 4.0,
+    "n_heads_per_layer": 8,
+    "n_transformer_layers": 4,
+    "norm_layer": "layer_norm_fp32",
+    "causal_masking": false,
+    "model_name": "mct"
+  }
+}

mobileclip/configs/mobileclip_s1.json

@@ -0,0 +1,18 @@
+{
+  "embed_dim": 512,
+  "image_cfg": {
+    "image_size": 256,
+    "model_name": "mci1"
+  },
+  "text_cfg": {
+    "context_length": 77,
+    "vocab_size": 49408,
+    "dim": 512,
+    "ffn_multiplier_per_layer": 4.0,
+    "n_heads_per_layer": 8,
+    "n_transformer_layers": 12,
+    "norm_layer": "layer_norm_fp32",
+    "causal_masking": false,
+    "model_name": "base"
+  }
+}

mobileclip/configs/mobileclip_s2.json

@@ -0,0 +1,18 @@
+{
+  "embed_dim": 512,
+  "image_cfg": {
+    "image_size": 256,
+    "model_name": "mci2"
+  },
+  "text_cfg": {
+    "context_length": 77,
+    "vocab_size": 49408,
+    "dim": 512,
+    "ffn_multiplier_per_layer": 4.0,
+    "n_heads_per_layer": 8,
+    "n_transformer_layers": 12,
+    "norm_layer": "layer_norm_fp32",
+    "causal_masking": false,
+    "model_name": "base"
+  }
+}

mobileclip/image_encoder.py

@@ -0,0 +1,63 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+#
+from typing import Any
+
+import torch.nn as nn
+from timm.models import create_model
+
+from mobileclip import models  # noqa added to register models
+from mobileclip.modules.image.image_projection import GlobalPool2D
+
+
+class MCi(nn.Module):
+    """This class implements `MCi Models <https://arxiv.org/pdf/2311.17049.pdf>`_."""
+
+    def __init__(self, model_name: str, *args, **kwargs) -> None:
+        super().__init__()
+        self.projection_dim = None
+        if "projection_dim" in kwargs:
+            self.projection_dim = kwargs.get("projection_dim")
+
+        # Create model
+        self.model = create_model(model_name, projection_dim=self.projection_dim)
+
+        # Build out projection head.
+        if self.projection_dim is not None:
+            if hasattr(self.model, "head"):
+                self.model.head = MCi._update_image_classifier(
+                    image_classifier=self.model.head, projection_dim=self.projection_dim
+                )
+
+    def forward(self, x: Any, *args, **kwargs) -> Any:
+        """A forward function of the model."""
+        x = self.model(x)
+        return x
+
+    @staticmethod
+    def _get_in_feature_dimension(image_classifier: nn.Module) -> int:
+        """Return the input feature dimension to the image classification head."""
+        in_features = None
+        if isinstance(image_classifier, nn.Sequential):
+            # Classifier that uses nn.Sequential usually has global pooling and
+            # multiple linear layers. Find the first linear layer and get its
+            # in_features
+            for layer in image_classifier:
+                if isinstance(layer, nn.Linear):
+                    in_features = layer.in_features
+                    break
+        elif isinstance(image_classifier, nn.Linear):
+            in_features = image_classifier.in_features
+
+        if in_features is None:
+            raise NotImplementedError(f"Cannot get input feature dimension of {image_classifier}.")
+        return in_features
+
+    @staticmethod
+    def _update_image_classifier(image_classifier: nn.Module, projection_dim: int, *args, **kwargs) -> nn.Module:
+        in_features = MCi._get_in_feature_dimension(image_classifier)
+        new_img_classifier = GlobalPool2D(in_dim=in_features, out_dim=projection_dim)
+        return new_img_classifier

mobileclip/logger.py

@@ -0,0 +1,120 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import os
+import sys
+import time
+import traceback
+
+text_colors = {
+    "logs": "\033[34m",  # 033 is the escape code and 34 is the color code
+    "info": "\033[32m",
+    "warning": "\033[33m",
+    "debug": "\033[93m",
+    "error": "\033[31m",
+    "bold": "\033[1m",
+    "end_color": "\033[0m",
+    "light_red": "\033[36m",
+}
+
+
+def get_curr_time_stamp() -> str:
+    return time.strftime("%Y-%m-%d %H:%M:%S")
+
+
+def error(message: str) -> None:
+    time_stamp = get_curr_time_stamp()
+    error_str = text_colors["error"] + text_colors["bold"] + "ERROR  " + text_colors["end_color"]
+
+    # exiting with code -1 does not tell any information about the error (e.g., NaN encountered in the loss).
+    # For more descriptive error messages, we replace exit(-1) with sys.exit(ERROR_MESSAGE).
+    # This allows us to handle specific exceptions in the tests.
+
+    # print("{} - {} - {}".format(time_stamp, error_str, message), flush=True)
+    # print("{} - {} - {}".format(time_stamp, error_str, "Exiting!!!"), flush=True)
+    # exit(-1)
+
+    if sys.exc_info()[0] is None:
+        traceback.print_stack()
+    else:
+        traceback.print_exc()
+    sys.exit(f"{time_stamp} - {error_str} - {message}. Exiting!!!")
+
+
+def color_text(in_text: str) -> str:
+    return text_colors["light_red"] + in_text + text_colors["end_color"]
+
+
+def log(message: str, end="\n") -> None:
+    time_stamp = get_curr_time_stamp()
+    log_str = text_colors["logs"] + text_colors["bold"] + "LOGS   " + text_colors["end_color"]
+    print(f"{time_stamp} - {log_str} - {message}", end=end)
+
+
+def warning(message: str | Warning) -> None:
+    if isinstance(message, Warning):
+        message = f"{type(message).__name__}({','.join(map(repr, message.args))}"
+
+    time_stamp = get_curr_time_stamp()
+    warn_str = text_colors["warning"] + text_colors["bold"] + "WARNING" + text_colors["end_color"]
+    print(f"{time_stamp} - {warn_str} - {message}")
+
+
+def ignore_exception_with_warning(message: str) -> None:
+    """After catching a tolerable exception E1 (e.g. when Model.forward() fails during profiling with try-catch, it'll
+    be helpful to log the exception for future investigation. But printing the error stack trace, as is, could be
+    confusing when an uncaught (non-tolerable) exception "E2" raises down the road. Then, the log will contain two
+    stack traces for E1, E2. When looking for errors in logs, users should look for E2, but they may find E1.
+
+    This function appends "(WARNING)" at the end of all lines of the E1 traceback, so that the user can distinguish E1
+    from uncaught exception E2.
+
+    Args:
+        message: Extra explanation and context for debugging. (Note: the exception obj will be automatically fetched
+            from python. No need to pass it as an argument or as message)
+    """
+    warning(f"{message}:\n{traceback.format_exc()}".replace("\n", "\n(WARNING)"))
+
+
+def info(message: str, print_line: bool | None = False) -> None:
+    time_stamp = get_curr_time_stamp()
+    info_str = text_colors["info"] + text_colors["bold"] + "INFO   " + text_colors["end_color"]
+    print(f"{time_stamp} - {info_str} - {message}")
+    if print_line:
+        double_dash_line(dashes=150)
+
+
+def debug(message: str) -> None:
+    time_stamp = get_curr_time_stamp()
+    log_str = text_colors["debug"] + text_colors["bold"] + "DEBUG   " + text_colors["end_color"]
+    print(f"{time_stamp} - {log_str} - {message}")
+
+
+def double_dash_line(dashes: int | None = 75) -> None:
+    print(text_colors["error"] + "=" * dashes + text_colors["end_color"])
+
+
+def singe_dash_line(dashes: int | None = 67) -> None:
+    print("-" * dashes)
+
+
+def print_header(header: str) -> None:
+    double_dash_line()
+    print(text_colors["info"] + text_colors["bold"] + "=" * 50 + str(header) + text_colors["end_color"])
+    double_dash_line()
+
+
+def print_header_minor(header: str) -> None:
+    print(text_colors["warning"] + text_colors["bold"] + "=" * 25 + str(header) + text_colors["end_color"])
+
+
+def disable_printing():
+    sys.stdout = open(os.devnull, "w")
+
+
+def enable_printing():
+    sys.stdout = sys.__stdout__

mobileclip/models/__init__.py

@@ -0,0 +1,12 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All rights reserved.
+#
+from .mci import (
+    mci0,
+    mci1,
+    mci2,
+)
+from .vit import vit_b16

mobileclip/models/mci.py

@@ -0,0 +1,888 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import copy
+from functools import partial
+
+import torch
+import torch.nn as nn
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from timm.models import register_model
+from timm.models.layers import DropPath, trunc_normal_
+
+from mobileclip.modules.common.mobileone import MobileOneBlock
+from mobileclip.modules.image.replknet import ReparamLargeKernelConv
+
+
+def _cfg(url="", **kwargs):
+    return {
+        "url": url,
+        "num_classes": 1000,
+        "input_size": (3, 256, 256),
+        "pool_size": None,
+        "crop_pct": 0.95,
+        "interpolation": "bicubic",
+        "mean": IMAGENET_DEFAULT_MEAN,
+        "std": IMAGENET_DEFAULT_STD,
+        "classifier": "head",
+        **kwargs,
+    }
+
+
+default_cfgs = {
+    "fastvit_t": _cfg(crop_pct=0.9),
+    "fastvit_s": _cfg(crop_pct=0.9),
+    "fastvit_m": _cfg(crop_pct=0.95),
+}
+
+
+def convolutional_stem(in_channels: int, out_channels: int, inference_mode: bool = False) -> nn.Sequential:
+    """Build convolutional stem with MobileOne blocks.
+
+    Args:
+        in_channels: Number of input channels.
+        out_channels: Number of output channels.
+        inference_mode: Flag to instantiate model in inference mode. Default: ``False``
+
+    Returns:
+        nn.Sequential object with stem elements.
+    """
+    return nn.Sequential(
+        MobileOneBlock(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            groups=1,
+            inference_mode=inference_mode,
+            use_se=False,
+            num_conv_branches=1,
+        ),
+        MobileOneBlock(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            groups=out_channels,
+            inference_mode=inference_mode,
+            use_se=False,
+            num_conv_branches=1,
+        ),
+        MobileOneBlock(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            groups=1,
+            inference_mode=inference_mode,
+            use_se=False,
+            num_conv_branches=1,
+        ),
+    )
+
+
+class MHSA(nn.Module):
+    """Multi-headed Self Attention module.
+
+    Source modified from:
+    https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        head_dim: int = 32,
+        qkv_bias: bool = False,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        """Build MHSA module that can handle 3D or 4D input tensors.
+
+        Args:
+            dim: Number of embedding dimensions.
+            head_dim: Number of hidden dimensions per head. Default: ``32``
+            qkv_bias: Use bias or not. Default: ``False``
+            attn_drop: Dropout rate for attention tensor.
+            proj_drop: Dropout rate for projection tensor.
+        """
+        super().__init__()
+        assert dim % head_dim == 0, "dim should be divisible by head_dim"
+        self.head_dim = head_dim
+        self.num_heads = dim // head_dim
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shape = x.shape
+        B, C, H, W = shape
+        N = H * W
+        if len(shape) == 4:
+            x = torch.flatten(x, start_dim=2).transpose(-2, -1)  # (B, N, C)
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
+
+        # trick here to make q@k.t more stable
+        attn = (q * self.scale) @ k.transpose(-2, -1)
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        if len(shape) == 4:
+            x = x.transpose(-2, -1).reshape(B, C, H, W)
+
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """Convolutional patch embedding layer."""
+
+    def __init__(
+        self,
+        patch_size: int,
+        stride: int,
+        in_channels: int,
+        embed_dim: int,
+        inference_mode: bool = False,
+        use_se: bool = False,
+    ) -> None:
+        """Build patch embedding layer.
+
+        Args:
+            patch_size: Patch size for embedding computation.
+            stride: Stride for convolutional embedding layer.
+            in_channels: Number of channels of input tensor.
+            embed_dim: Number of embedding dimensions.
+            inference_mode: Flag to instantiate model in inference mode. Default: ``False``
+            use_se: If ``True`` SE block will be used.
+        """
+        super().__init__()
+        block = list()
+        block.append(
+            ReparamLargeKernelConv(
+                in_channels=in_channels,
+                out_channels=embed_dim,
+                kernel_size=patch_size,
+                stride=stride,
+                groups=in_channels,
+                small_kernel=3,
+                inference_mode=inference_mode,
+                use_se=use_se,
+            )
+        )
+        block.append(
+            MobileOneBlock(
+                in_channels=embed_dim,
+                out_channels=embed_dim,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                groups=1,
+                inference_mode=inference_mode,
+                use_se=False,
+                num_conv_branches=1,
+            )
+        )
+        self.proj = nn.Sequential(*block)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        return x
+
+
+class RepMixer(nn.Module):
+    """Reparameterizable token mixer.
+
+    For more details, please refer to our paper: `FastViT: A Fast Hybrid Vision Transformer using Structural
+    Reparameterization <https://arxiv.org/pdf/2303.14189.pdf>`_
+    """
+
+    def __init__(
+        self,
+        dim,
+        kernel_size=3,
+        use_layer_scale=True,
+        layer_scale_init_value=1e-5,
+        inference_mode: bool = False,
+    ):
+        """Build RepMixer Module.
+
+        Args:
+            dim: Input feature map dimension. :math:`C_{in}` from an expected input of size :math:`(B, C_{in}, H, W)`.
+            kernel_size: Kernel size for spatial mixing. Default: 3
+            use_layer_scale: If True, learnable layer scale is used. Default: ``True``
+            layer_scale_init_value: Initial value for layer scale. Default: 1e-5
+            inference_mode: If True, instantiates model in inference mode. Default: ``False``
+        """
+        super().__init__()
+        self.dim = dim
+        self.kernel_size = kernel_size
+        self.inference_mode = inference_mode
+
+        if inference_mode:
+            self.reparam_conv = nn.Conv2d(
+                in_channels=self.dim,
+                out_channels=self.dim,
+                kernel_size=self.kernel_size,
+                stride=1,
+                padding=self.kernel_size // 2,
+                groups=self.dim,
+                bias=True,
+            )
+        else:
+            self.norm = MobileOneBlock(
+                dim,
+                dim,
+                kernel_size,
+                padding=kernel_size // 2,
+                groups=dim,
+                use_act=False,
+                use_scale_branch=False,
+                num_conv_branches=0,
+            )
+            self.mixer = MobileOneBlock(
+                dim,
+                dim,
+                kernel_size,
+                padding=kernel_size // 2,
+                groups=dim,
+                use_act=False,
+            )
+            self.use_layer_scale = use_layer_scale
+            if use_layer_scale:
+                self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if hasattr(self, "reparam_conv"):
+            x = self.reparam_conv(x)
+            return x
+        else:
+            if self.use_layer_scale:
+                x = x + self.layer_scale * (self.mixer(x) - self.norm(x))
+            else:
+                x = x + self.mixer(x) - self.norm(x)
+            return x
+
+    def reparameterize(self) -> None:
+        """Reparameterize mixer and norm into a single convolutional layer for efficient inference."""
+        if self.inference_mode:
+            return
+
+        self.mixer.reparameterize()
+        self.norm.reparameterize()
+
+        if self.use_layer_scale:
+            w = self.mixer.id_tensor + self.layer_scale.unsqueeze(-1) * (
+                self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
+            )
+            b = torch.squeeze(self.layer_scale) * (self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias)
+        else:
+            w = self.mixer.id_tensor + self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
+            b = self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias
+
+        self.reparam_conv = nn.Conv2d(
+            in_channels=self.dim,
+            out_channels=self.dim,
+            kernel_size=self.kernel_size,
+            stride=1,
+            padding=self.kernel_size // 2,
+            groups=self.dim,
+            bias=True,
+        )
+        self.reparam_conv.weight.data = w
+        self.reparam_conv.bias.data = b
+
+        for para in self.parameters():
+            para.detach_()
+        self.__delattr__("mixer")
+        self.__delattr__("norm")
+        if self.use_layer_scale:
+            self.__delattr__("layer_scale")
+
+
+class ConvFFN(nn.Module):
+    """Convolutional FFN Module."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        hidden_channels: int | None = None,
+        out_channels: int | None = None,
+        act_layer: nn.Module = nn.GELU,
+        drop: float = 0.0,
+    ) -> None:
+        """Build convolutional FFN module.
+
+        Args:
+            in_channels: Number of input channels.
+            hidden_channels: Number of channels after expansion. Default: None
+            out_channels: Number of output channels. Default: None
+            act_layer: Activation layer. Default: ``GELU``
+            drop: Dropout rate. Default: ``0.0``.
+        """
+        super().__init__()
+        out_channels = out_channels or in_channels
+        hidden_channels = hidden_channels or in_channels
+        self.conv = nn.Sequential()
+        self.conv.add_module(
+            "conv",
+            nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=7,
+                padding=3,
+                groups=in_channels,
+                bias=False,
+            ),
+        )
+        self.conv.add_module("bn", nn.BatchNorm2d(num_features=out_channels))
+        self.fc1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=1)
+        self.act = act_layer()
+        self.fc2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=1)
+        self.drop = nn.Dropout(drop)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m: nn.Module) -> None:
+        if isinstance(m, nn.Conv2d):
+            trunc_normal_(m.weight, std=0.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv(x)
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class RepCPE(nn.Module):
+    """Implementation of conditional positional encoding.
+
+    For more details refer to paper: `Conditional Positional Encodings for Vision Transformers
+    <https://arxiv.org/pdf/2102.10882.pdf>`_
+
+    In our implementation, we can reparameterize this module to eliminate a skip connection.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        embed_dim: int = 768,
+        spatial_shape: int | tuple[int, int] = (7, 7),
+        inference_mode=False,
+    ) -> None:
+        """Build reparameterizable conditional positional encoding.
+
+        Args:
+            in_channels: Number of input channels.
+            embed_dim: Number of embedding dimensions. Default: 768
+            spatial_shape: Spatial shape of kernel for positional encoding. Default: (7, 7)
+            inference_mode: Flag to instantiate block in inference mode. Default: ``False``
+        """
+        super().__init__()
+        if isinstance(spatial_shape, int):
+            spatial_shape = tuple([spatial_shape] * 2)
+        assert isinstance(spatial_shape, tuple), (
+            f'"spatial_shape" must by a sequence or int, get {type(spatial_shape)} instead.'
+        )
+        assert len(spatial_shape) == 2, f'Length of "spatial_shape" should be 2, got {len(spatial_shape)} instead.'
+
+        self.spatial_shape = spatial_shape
+        self.embed_dim = embed_dim
+        self.in_channels = in_channels
+        self.groups = embed_dim
+
+        if inference_mode:
+            self.reparam_conv = nn.Conv2d(
+                in_channels=self.in_channels,
+                out_channels=self.embed_dim,
+                kernel_size=self.spatial_shape,
+                stride=1,
+                padding=int(self.spatial_shape[0] // 2),
+                groups=self.embed_dim,
+                bias=True,
+            )
+        else:
+            self.pe = nn.Conv2d(
+                in_channels,
+                embed_dim,
+                spatial_shape,
+                1,
+                int(spatial_shape[0] // 2),
+                bias=True,
+                groups=embed_dim,
+            )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if hasattr(self, "reparam_conv"):
+            x = self.reparam_conv(x)
+            return x
+        else:
+            x = self.pe(x) + x
+            return x
+
+    def reparameterize(self) -> None:
+        # Build equivalent Id tensor
+        input_dim = self.in_channels // self.groups
+        kernel_value = torch.zeros(
+            (
+                self.in_channels,
+                input_dim,
+                self.spatial_shape[0],
+                self.spatial_shape[1],
+            ),
+            dtype=self.pe.weight.dtype,
+            device=self.pe.weight.device,
+        )
+        for i in range(self.in_channels):
+            kernel_value[
+                i,
+                i % input_dim,
+                self.spatial_shape[0] // 2,
+                self.spatial_shape[1] // 2,
+            ] = 1
+        id_tensor = kernel_value
+
+        # Reparameterize Id tensor and conv
+        w_final = id_tensor + self.pe.weight
+        b_final = self.pe.bias
+
+        # Introduce reparam conv
+        self.reparam_conv = nn.Conv2d(
+            in_channels=self.in_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.spatial_shape,
+            stride=1,
+            padding=int(self.spatial_shape[0] // 2),
+            groups=self.embed_dim,
+            bias=True,
+        )
+        self.reparam_conv.weight.data = w_final
+        self.reparam_conv.bias.data = b_final
+
+        for para in self.parameters():
+            para.detach_()
+        self.__delattr__("pe")
+
+
+class RepMixerBlock(nn.Module):
+    """Implementation of Metaformer block with RepMixer as token mixer.
+
+    For more details on Metaformer structure, please refer to: `MetaFormer Is Actually What You Need for Vision
+    <https://arxiv.org/pdf/2111.11418.pdf>`_
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        kernel_size: int = 3,
+        mlp_ratio: float = 4.0,
+        act_layer: nn.Module = nn.GELU,
+        drop: float = 0.0,
+        drop_path: float = 0.0,
+        use_layer_scale: bool = True,
+        layer_scale_init_value: float = 1e-5,
+        inference_mode: bool = False,
+    ):
+        """Build RepMixer Block.
+
+        Args:
+            dim: Number of embedding dimensions.
+            kernel_size: Kernel size for repmixer. Default: 3
+            mlp_ratio: MLP expansion ratio. Default: 4.0
+            act_layer: Activation layer. Default: ``nn.GELU``
+            drop: Dropout rate. Default: 0.0
+            drop_path: Drop path rate. Default: 0.0
+            use_layer_scale: Flag to turn on layer scale. Default: ``True``
+            layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
+            inference_mode: Flag to instantiate block in inference mode. Default: ``False``
+        """
+        super().__init__()
+
+        self.token_mixer = RepMixer(
+            dim,
+            kernel_size=kernel_size,
+            use_layer_scale=use_layer_scale,
+            layer_scale_init_value=layer_scale_init_value,
+            inference_mode=inference_mode,
+        )
+
+        assert mlp_ratio > 0, f"MLP ratio should be greater than 0, found: {mlp_ratio}"
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.convffn = ConvFFN(
+            in_channels=dim,
+            hidden_channels=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+        )
+
+        # Drop Path
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        # Layer Scale
+        self.use_layer_scale = use_layer_scale
+        if use_layer_scale:
+            self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)
+
+    def forward(self, x):
+        if self.use_layer_scale:
+            x = self.token_mixer(x)
+            x = x + self.drop_path(self.layer_scale * self.convffn(x))
+        else:
+            x = self.token_mixer(x)
+            x = x + self.drop_path(self.convffn(x))
+        return x
+
+
+class AttentionBlock(nn.Module):
+    """Implementation of metaformer block with MHSA as token mixer.
+
+    For more details on Metaformer structure, please refer to: `MetaFormer Is Actually What You Need for Vision
+    <https://arxiv.org/pdf/2111.11418.pdf>`_
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        mlp_ratio: float = 4.0,
+        act_layer: nn.Module = nn.GELU,
+        norm_layer: nn.Module = nn.BatchNorm2d,
+        drop: float = 0.0,
+        drop_path: float = 0.0,
+        use_layer_scale: bool = True,
+        layer_scale_init_value: float = 1e-5,
+    ):
+        """Build Attention Block.
+
+        Args:
+            dim: Number of embedding dimensions.
+            mlp_ratio: MLP expansion ratio. Default: 4.0
+            act_layer: Activation layer. Default: ``nn.GELU``
+            norm_layer: Normalization layer. Default: ``nn.BatchNorm2d``
+            drop: Dropout rate. Default: 0.0
+            drop_path: Drop path rate. Default: 0.0
+            use_layer_scale: Flag to turn on layer scale. Default: ``True``
+            layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
+        """
+        super().__init__()
+
+        self.norm = norm_layer(dim)
+        self.token_mixer = MHSA(dim=dim)
+
+        assert mlp_ratio > 0, f"MLP ratio should be greater than 0, found: {mlp_ratio}"
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.convffn = ConvFFN(
+            in_channels=dim,
+            hidden_channels=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+        )
+
+        # Drop path
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        # Layer Scale
+        self.use_layer_scale = use_layer_scale
+        if use_layer_scale:
+            self.layer_scale_1 = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)
+            self.layer_scale_2 = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)
+
+    def forward(self, x):
+        if self.use_layer_scale:
+            x = x + self.drop_path(self.layer_scale_1 * self.token_mixer(self.norm(x)))
+            x = x + self.drop_path(self.layer_scale_2 * self.convffn(x))
+        else:
+            x = x + self.drop_path(self.token_mixer(self.norm(x)))
+            x = x + self.drop_path(self.convffn(x))
+        return x
+
+
+def basic_blocks(
+    dim: int,
+    block_index: int,
+    num_blocks: list[int],
+    token_mixer_type: str,
+    kernel_size: int = 3,
+    mlp_ratio: float = 4.0,
+    act_layer: nn.Module = nn.GELU,
+    norm_layer: nn.Module = nn.BatchNorm2d,
+    drop_rate: float = 0.0,
+    drop_path_rate: float = 0.0,
+    use_layer_scale: bool = True,
+    layer_scale_init_value: float = 1e-5,
+    inference_mode=False,
+) -> nn.Sequential:
+    """Build FastViT blocks within a stage.
+
+    Args:
+        dim: Number of embedding dimensions.
+        block_index: block index.
+        num_blocks: List containing number of blocks per stage.
+        token_mixer_type: Token mixer type.
+        kernel_size: Kernel size for repmixer.
+        mlp_ratio: MLP expansion ratio.
+        act_layer: Activation layer.
+        norm_layer: Normalization layer.
+        drop_rate: Dropout rate.
+        drop_path_rate: Drop path rate.
+        use_layer_scale: Flag to turn on layer scale regularization.
+        layer_scale_init_value: Layer scale value at initialization.
+        inference_mode: Flag to instantiate block in inference mode.
+
+    Returns:
+        nn.Sequential object of all the blocks within the stage.
+    """
+    blocks = []
+    for block_idx in range(num_blocks[block_index]):
+        block_dpr = drop_path_rate * (block_idx + sum(num_blocks[:block_index])) / (sum(num_blocks) - 1)
+        if token_mixer_type == "repmixer":
+            blocks.append(
+                RepMixerBlock(
+                    dim,
+                    kernel_size=kernel_size,
+                    mlp_ratio=mlp_ratio,
+                    act_layer=act_layer,
+                    drop=drop_rate,
+                    drop_path=block_dpr,
+                    use_layer_scale=use_layer_scale,
+                    layer_scale_init_value=layer_scale_init_value,
+                    inference_mode=inference_mode,
+                )
+            )
+        elif token_mixer_type == "attention":
+            blocks.append(
+                AttentionBlock(
+                    dim,
+                    mlp_ratio=mlp_ratio,
+                    act_layer=act_layer,
+                    norm_layer=norm_layer,
+                    drop=drop_rate,
+                    drop_path=block_dpr,
+                    use_layer_scale=use_layer_scale,
+                    layer_scale_init_value=layer_scale_init_value,
+                )
+            )
+        else:
+            raise ValueError(f"Token mixer type: {token_mixer_type} not supported")
+    blocks = nn.Sequential(*blocks)
+
+    return blocks
+
+
+class FastViT(nn.Module):
+    """This class implements `FastViT architecture <https://arxiv.org/pdf/2303.14189.pdf>`_."""
+
+    def __init__(
+        self,
+        layers,
+        token_mixers: tuple[str, ...],
+        embed_dims=None,
+        mlp_ratios=None,
+        downsamples=None,
+        se_downsamples=None,
+        repmixer_kernel_size=3,
+        norm_layer: nn.Module = nn.BatchNorm2d,
+        act_layer: nn.Module = nn.GELU,
+        num_classes=1000,
+        pos_embs=None,
+        down_patch_size=7,
+        down_stride=2,
+        drop_rate=0.0,
+        drop_path_rate=0.0,
+        use_layer_scale=True,
+        layer_scale_init_value=1e-5,
+        init_cfg=None,
+        pretrained=None,
+        cls_ratio=2.0,
+        inference_mode=False,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+
+        self.num_classes = num_classes
+        if pos_embs is None:
+            pos_embs = [None] * len(layers)
+
+        if se_downsamples is None:
+            se_downsamples = [False] * len(layers)
+
+        # Convolutional stem
+        self.patch_embed = convolutional_stem(3, embed_dims[0], inference_mode)
+
+        # Build the main stages of the network architecture
+        network = []
+        for i in range(len(layers)):
+            # Add position embeddings if requested
+            if pos_embs[i] is not None:
+                network.append(pos_embs[i](embed_dims[i], embed_dims[i], inference_mode=inference_mode))
+            stage = basic_blocks(
+                embed_dims[i],
+                i,
+                layers,
+                token_mixer_type=token_mixers[i],
+                kernel_size=repmixer_kernel_size,
+                mlp_ratio=mlp_ratios[i],
+                act_layer=act_layer,
+                norm_layer=norm_layer,
+                drop_rate=drop_rate,
+                drop_path_rate=drop_path_rate,
+                use_layer_scale=use_layer_scale,
+                layer_scale_init_value=layer_scale_init_value,
+                inference_mode=inference_mode,
+            )
+            network.append(stage)
+            if i >= len(layers) - 1:
+                break
+
+            # Patch merging/downsampling between stages.
+            if downsamples[i] or embed_dims[i] != embed_dims[i + 1]:
+                network.append(
+                    PatchEmbed(
+                        patch_size=down_patch_size,
+                        stride=down_stride,
+                        in_channels=embed_dims[i],
+                        embed_dim=embed_dims[i + 1],
+                        inference_mode=inference_mode,
+                        use_se=se_downsamples[i + 1],
+                    )
+                )
+        self.network = nn.ModuleList(network)
+
+        # Classifier head
+        self.conv_exp = MobileOneBlock(
+            in_channels=embed_dims[-1],
+            out_channels=int(embed_dims[-1] * cls_ratio),
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=embed_dims[-1],
+            inference_mode=inference_mode,
+            use_se=True,
+            num_conv_branches=1,
+        )
+        self.head = nn.Linear(int(embed_dims[-1] * cls_ratio), num_classes) if num_classes > 0 else nn.Identity()
+        self.apply(self.cls_init_weights)
+        self.init_cfg = copy.deepcopy(init_cfg)
+
+    def cls_init_weights(self, m: nn.Module) -> None:
+        """Init.
+
+        for classification.
+        """
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+    def forward_embeddings(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        return x
+
+    def forward_tokens(self, x: torch.Tensor) -> torch.Tensor:
+        for idx, block in enumerate(self.network):
+            x = block(x)
+        # output only the features of last layer for image classification
+        return x
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # input embedding
+        x = self.forward_embeddings(x)
+        # through backbone
+        x = self.forward_tokens(x)
+        # for image classification
+        x = self.conv_exp(x)
+        cls_out = self.head(x)
+        return cls_out
+
+
+@register_model
+def mci0(pretrained=False, **kwargs):
+    """Instantiate MCi0 model variant."""
+    layers = [2, 6, 10, 2]
+    embed_dims = [64, 128, 256, 512]
+    mlp_ratios = [3, 3, 3, 3]
+    downsamples = [True, True, True, True]
+    se_downsamples = [False, False, True, True]
+    pos_embs = [None, None, None, partial(RepCPE, spatial_shape=(7, 7))]
+    token_mixers = ("repmixer", "repmixer", "repmixer", "attention")
+    model = FastViT(
+        layers,
+        token_mixers=token_mixers,
+        embed_dims=embed_dims,
+        pos_embs=pos_embs,
+        mlp_ratios=mlp_ratios,
+        downsamples=downsamples,
+        se_downsamples=se_downsamples,
+        **kwargs,
+    )
+    model.default_cfg = default_cfgs["fastvit_s"]
+    if pretrained:
+        raise ValueError("Functionality not implemented.")
+    return model
+
+
+@register_model
+def mci1(pretrained=False, **kwargs):
+    """Instantiate MCi1 model variant."""
+    layers = [4, 12, 20, 4]
+    embed_dims = [64, 128, 256, 512]
+    mlp_ratios = [3, 3, 3, 3]
+    downsamples = [True, True, True, True]
+    se_downsamples = [False, False, True, True]
+    pos_embs = [None, None, None, partial(RepCPE, spatial_shape=(7, 7))]
+    token_mixers = ("repmixer", "repmixer", "repmixer", "attention")
+    model = FastViT(
+        layers,
+        token_mixers=token_mixers,
+        embed_dims=embed_dims,
+        pos_embs=pos_embs,
+        mlp_ratios=mlp_ratios,
+        downsamples=downsamples,
+        se_downsamples=se_downsamples,
+        **kwargs,
+    )
+    model.default_cfg = default_cfgs["fastvit_s"]
+    if pretrained:
+        raise ValueError("Functionality not implemented.")
+    return model
+
+
+@register_model
+def mci2(pretrained=False, **kwargs):
+    """Instantiate MCi2 model variant."""
+    layers = [4, 12, 24, 4]
+    embed_dims = [80, 160, 320, 640]
+    mlp_ratios = [3, 3, 3, 3]
+    downsamples = [True, True, True, True]
+    se_downsamples = [False, False, True, True]
+    pos_embs = [None, None, None, partial(RepCPE, spatial_shape=(7, 7))]
+    token_mixers = ("repmixer", "repmixer", "repmixer", "attention")
+    model = FastViT(
+        layers,
+        token_mixers=token_mixers,
+        embed_dims=embed_dims,
+        pos_embs=pos_embs,
+        mlp_ratios=mlp_ratios,
+        downsamples=downsamples,
+        se_downsamples=se_downsamples,
+        **kwargs,
+    )
+    model.default_cfg = default_cfgs["fastvit_m"]
+    if pretrained:
+        raise ValueError("Functionality not implemented.")
+    return model

mobileclip/models/vit.py

@@ -0,0 +1,389 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+#
+"""
+Implementation of the following modules is borrowed from ml-cvnets repo:
+https://github.com/apple/ml-cvnets/blob/main/cvnets/models/classification/vit.py.
+
+Please see ACKNOWLEDGMENTS for license details.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import torch
+from timm.models import register_model
+from torch import Tensor, nn
+
+from mobileclip import logger
+from mobileclip.modules.common.transformer import (
+    PositionalEmbedding,
+    TransformerEncoder,
+    get_normalization_layer,
+)
+from mobileclip.modules.image.image_projection import SimpleImageProjectionHead
+
+
+class ConvNormAct(nn.Module):
+    """Applies an N-dimensional convolution over an input.
+
+    Args:
+        cfg: Model configuration.
+        in_channels: :math:`C_{out}` from an expected output of size :math:`(bs, C_{in}, X_{1}, ..., X_{N})`.
+        out_channels: :math:`C_{out}` from an expected output of size :math:`(bs, C_{out}, Y_{1}, ..., Y_{N})`.
+        kernel_size: Kernel size for convolution. An integer, or tuple of length ``N``.
+        stride: Stride for convolution. An integer, or tuple of length ``N``. Default: 1.
+        dilation: Dilation rate for convolution. An integer, or tuple of length ``N``. Default: ``1``.
+        padding: Padding for convolution. An integer, or tuple of length ``N``. If not specified, padding is
+            automatically computed based on kernel size and dilation range. Default : ``None`` (equivalent to ``[
+            int((kernel_size[i] - 1) / 2) * dilation[i] for i in range(N)]``).
+        groups: Number of groups in convolution. Default: ``1``.
+        bias: Use bias. Default: ``False``.
+        padding_mode: Padding mode ('zeros', 'reflect', 'replicate' or 'circular'). Default: ``zeros``.
+        use_norm: Use normalization layer after convolution. Default: ``True``.
+        use_act: Use activation layer after convolution (or convolution and normalization). Default: ``True``.
+        norm_layer: If not None, the provided normalization layer object will be used. Otherwise, a normalization object
+            will be created based on config ``model.normalization.*`` opts.
+        act_layer: If not None, the provided activation function will be used. Otherwise, an activation function will be
+            created based on config ``model.activation.*`` opts.
+
+    Notes:
+        - Input: :math:`(bs, C_{in}, X_{1}, ..., X_{N})`.
+        - Output: :math:`(bs, C_{out}, Y_{1}, ..., Y_{N})`.
+        - For depth-wise convolution, `groups=C_{in}=C_{out}`.
+    """
+
+    def __init__(
+        self,
+        cfg: dict,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int | tuple[int, ...],
+        stride: int | tuple[int, ...] = 1,
+        dilation: int | tuple[int, ...] = 1,
+        padding: int | tuple[int, ...] | None = None,
+        groups: int = 1,
+        bias: bool = False,
+        padding_mode: str = "zeros",
+        use_norm: bool = True,
+        use_act: bool = True,
+        norm_layer: nn.Module | None = None,
+        act_layer: nn.Module | None = None,
+        *args,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.ndim = 2
+
+        if norm_layer is None and use_norm:
+            norm_type = cfg.get("normalization", "batch_norm")
+            if norm_type == "batch_norm":
+                norm_layer = nn.BatchNorm2d(
+                    num_features=out_channels,
+                    momentum=cfg.get("momentum", 0.1),
+                )
+            else:
+                norm_layer = get_normalization_layer(num_features=out_channels, norm_type=norm_type)
+        elif norm_layer is not None and use_norm:
+            logger.error(f"When use_norm is False, norm_layer should be None, but norm_layer={norm_layer} is provided.")
+
+        if act_layer is None and use_act:
+            act_layer = nn.GELU()  # Default to GELU
+        elif act_layer is not None and use_act:
+            logger.error(f"When use_act is False, act_layer should be None, but act_layer={act_layer} is provided.")
+
+        if use_norm and any(param[0] == "bias" for param in norm_layer.named_parameters()) and bias:
+            assert not bias, "Do not use bias when using normalization layers with bias."
+
+        if isinstance(kernel_size, int):
+            kernel_size = (kernel_size,) * self.ndim
+
+        if isinstance(stride, int):
+            stride = (stride,) * self.ndim
+
+        if isinstance(dilation, int):
+            dilation = (dilation,) * self.ndim
+
+        assert isinstance(kernel_size, tuple)
+        assert isinstance(stride, tuple)
+        assert isinstance(dilation, tuple)
+
+        if padding is None:
+            padding = (int((kernel_size[i] - 1) / 2) * dilation[i] for i in range(self.ndim))
+
+        if in_channels % groups != 0:
+            logger.error(f"Input channels are not divisible by groups. {in_channels}%{groups} != 0 ")
+        if out_channels % groups != 0:
+            logger.error(f"Output channels are not divisible by groups. {out_channels}%{groups} != 0 ")
+
+        block = nn.Sequential()
+
+        conv_layer = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,  # type: ignore
+            stride=stride,  # type: ignore
+            padding=padding,
+            dilation=dilation,  # type: ignore
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+        )
+
+        block.add_module(name="conv", module=conv_layer)
+
+        self.norm_name = None
+        if use_norm:
+            block.add_module(name="norm", module=norm_layer)
+            self.norm_name = norm_layer.__class__.__name__
+
+        self.act_name = None
+        if use_act:
+            block.add_module(name="act", module=act_layer)
+            self.act_name = act_layer.__class__.__name__
+
+        self.block = block
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.stride = stride
+        self.groups = groups
+        self.kernel_size = conv_layer.kernel_size
+        self.bias = bias
+        self.dilation = dilation
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.block(x)
+
+
+class VisionTransformer(nn.Module):
+    """This class defines the `Vision Transformer architecture <https://arxiv.org/abs/2010.11929>`_. Our model
+    implementation is inspired from `Early Convolutions Help Transformers See
+    Better <https://arxiv.org/abs/2106.14881>`_.
+
+    .. note::
+        Our implementation is different from the original implementation in two ways:
+        1. Kernel size is odd.
+        2. Our positional encoding implementation allows us to use ViT with any multiple input scales
+        3. We do not use StochasticDepth
+        4. We do not add positional encoding to class token (if enabled), as suggested in `DeiT-3 paper <https://arxiv.org/abs/2204.07118>`_
+    """
+
+    def __init__(self, cfg, *args, **kwargs) -> None:
+        super().__init__()
+        image_channels = 3
+        num_classes = cfg.get("n_classes", 1000)
+
+        self.projection_dim = None
+        if "projection_dim" in kwargs:
+            self.projection_dim = kwargs.get("projection_dim")
+
+        kernel_sizes_conv_stem = [4, 2, 2]
+        strides_conv_stem = [4, 2, 2]
+
+        # Typically, in the ImageNet dataset, we use 224x224 as a resolution.
+        # For out ViT implementation, patch size is 16 (16 = 4 * 2 * 2)
+        # Therefore, total number of embeddings along width and height are (224 / 16)^2
+        num_embeddings = (224 // 16) ** 2
+
+        embed_dim = cfg["embed_dim"]
+        ffn_dim = cfg["embed_dim"] * 4
+        pos_emb_drop_p = cfg.get("pos_emb_drop_p", 0.0)
+        n_transformer_layers = cfg["n_transformer_layers"]
+        num_heads = cfg["n_attn_heads"]
+        attn_dropout = cfg.get("attn_dropout", 0.0)
+        dropout = cfg.get("dropout", 0.0)
+        ffn_dropout = cfg.get("ffn_dropout", 0.0)
+        norm_layer = cfg.get("norm_layer", "layer_norm")
+
+        conv_stem_proj_dim = max(32, embed_dim // 4)
+        patch_emb = [
+            ConvNormAct(
+                cfg=cfg,
+                in_channels=image_channels,
+                out_channels=conv_stem_proj_dim,
+                kernel_size=kernel_sizes_conv_stem[0],
+                stride=strides_conv_stem[0],
+                bias=False,
+                use_norm=True,
+                use_act=True,
+            ),
+            ConvNormAct(
+                cfg=cfg,
+                in_channels=conv_stem_proj_dim,
+                out_channels=conv_stem_proj_dim,
+                kernel_size=kernel_sizes_conv_stem[1],
+                stride=strides_conv_stem[1],
+                bias=False,
+                use_norm=True,
+                use_act=True,
+            ),
+            ConvNormAct(
+                cfg=cfg,
+                in_channels=conv_stem_proj_dim,
+                out_channels=embed_dim,
+                kernel_size=kernel_sizes_conv_stem[2],
+                stride=strides_conv_stem[2],
+                bias=True,
+                use_norm=False,
+                use_act=False,
+            ),
+        ]
+
+        self.patch_emb = nn.Sequential(*patch_emb)
+
+        use_cls_token = not cfg.get("no_cls_token", False)
+        stochastic_dropout = cfg.get("stochastic_dropout", 0.0)
+        per_layer_stochastic_drop_rate = [round(x, 3) for x in np.linspace(0, stochastic_dropout, n_transformer_layers)]
+        transformer_blocks = [
+            TransformerEncoder(
+                embed_dim=embed_dim,
+                ffn_latent_dim=ffn_dim,
+                num_heads=num_heads,
+                attn_dropout=attn_dropout,
+                dropout=dropout,
+                ffn_dropout=ffn_dropout,
+                transformer_norm_layer=norm_layer,
+                stochastic_dropout=per_layer_stochastic_drop_rate[layer_idx],
+            )
+            for layer_idx in range(n_transformer_layers)
+        ]
+
+        self.post_transformer_norm = get_normalization_layer(num_features=embed_dim, norm_type=norm_layer)
+
+        self.transformer = nn.Sequential(*transformer_blocks)
+
+        if self.projection_dim is None:
+            self.classifier = nn.Linear(embed_dim, num_classes)
+        else:
+            self.classifier = SimpleImageProjectionHead(embed_dim, self.projection_dim)
+
+        if use_cls_token:
+            self.cls_token = nn.Parameter(torch.zeros(size=(1, 1, embed_dim)))
+            torch.nn.init.trunc_normal_(self.cls_token, std=0.02)
+        else:
+            self.cls_token = None
+
+        self.pos_embed = PositionalEmbedding(
+            num_embeddings=num_embeddings,
+            embedding_dim=embed_dim,
+            padding_idx=None,
+            interpolation_mode="bilinear",
+        )
+        self.emb_dropout = nn.Dropout(p=pos_emb_drop_p)
+
+    def extract_patch_embeddings(self, x: Tensor) -> tuple[Tensor, tuple[int, int]]:
+        # input is of shape [Batch, in_channels, height, width]. in_channels is mostly 3 (for RGB images)
+        batch_size = x.shape[0]
+
+        # [Batch, in_channels, height, width] --> [Batch, emb_dim, num_patches_height, num_patches_width]
+        patch_emb = self.patch_emb(x)
+        n_h, n_w = patch_emb.shape[-2:]
+
+        # [Batch, emb_dim, num_patches_height, num_patches_width] --> [Batch, emb_dim, num_patches]
+        patch_emb = patch_emb.flatten(2)
+        # [Batch, emb_dim, num_patches] --> [Batch, num_patches, emb_dim]
+        patch_emb = patch_emb.transpose(1, 2).contiguous()
+
+        n_patches = patch_emb.shape[1]
+        # we resize the positional encodings dynamically.
+        pos_emb = self.pos_embed(n_patches).to(patch_emb.dtype)
+
+        # add positional encodings
+        patch_emb = pos_emb + patch_emb
+
+        # add classification token
+        if self.cls_token is not None:
+            # [1, 1, emb_dim] --> [Batch, 1, emb_dim]
+            cls_tokens = self.cls_token.expand(batch_size, -1, -1)
+            # Concat([Batch, 1, emb_dim], [Batch, num_patches, emb_dim]) --> [Batch, num_patches + 1, emb_dim]
+            patch_emb = torch.cat((cls_tokens, patch_emb), dim=1)
+
+        # dropout
+        patch_emb = self.emb_dropout(patch_emb)
+        return patch_emb, (n_h, n_w)
+
+    def _features_from_transformer(self, x: Tensor, *args, **kwargs) -> tuple[Tensor, tuple[int, int]]:
+        # this function extract patch embeddings and then apply transformer module to learn
+        # inter-patch representations
+
+        # [B, N, C] --> [N, B, embed_dim], where B is batch size, N is number of tokens,
+        # and embed_dim is feature dim
+        x, (n_h, n_w) = self.extract_patch_embeddings(x)
+
+        for layer in self.transformer:
+            x = layer(x)
+        x = self.post_transformer_norm(x)
+
+        return x, (n_h, n_w)
+
+    def extract_features(self, x: Tensor, *args, **kwargs) -> tuple[Tensor, Tensor | None]:
+        # The extract_features function for ViT returns two outputs: (1) embedding corresponding to CLS token
+        # and (2) image embeddings of the shape [B, C, h//o, w//o], where the value of o is typically 16.
+        return_image_embeddings = kwargs.get("return_image_embeddings", False)
+
+        # [B, C, H, W] --> [B, N + 1, embed_dim] or [B, N, embed_dim]
+        # here, B is batch size, C is input channels
+        # H and W are input height and width
+        # N is the number of pixels (or tokens) after processing input with conv stem and reshaping
+        # We add +1 for cls token (if applicable)
+        # embed_dim --> embedding dimension
+        x, (n_h, n_w) = self._features_from_transformer(x, *args, **kwargs)
+
+        if self.cls_token is not None:
+            # [B, N + 1, embed_dim] --> [B, embed_dim], [B, N, embed_dim]
+            cls_embedding, image_embedding = torch.split(x, split_size_or_sections=[1, x.shape[1] - 1], dim=1)
+            cls_embedding = cls_embedding.squeeze(1)
+        else:
+            # [B, N, embed_dim] -> [B, embed_dim]
+            cls_embedding = torch.mean(x, dim=1)
+            # [B, N, embed_dim]
+            image_embedding = x
+
+        if return_image_embeddings:
+            # reshape image embedding to 4-D tensor
+            # [B, N, C] --> [B, C, N]
+            image_embedding = image_embedding.transpose(1, 2).contiguous()
+            image_embedding = image_embedding.reshape(image_embedding.shape[0], -1, n_h, n_w)
+
+            return cls_embedding, image_embedding
+        else:
+            return cls_embedding, None
+
+    def forward_classifier(self, x: Tensor, *args, **kwargs) -> tuple[Tensor, Tensor]:
+        cls_embedding, image_embedding = self.extract_features(x, *args, **kwargs)
+        # classify based on CLS token
+        cls_embedding = self.classifier(cls_embedding)
+        return cls_embedding, image_embedding
+
+    def forward(self, x: Tensor, *args, **kwargs) -> Tensor | dict[str, Tensor]:
+        # In ViT model, we can return either classifier embeddings (logits) or image embeddings or both.
+        # To return the image embeddings, we need to set keyword argument (return_image_embeddings) as True.
+        if kwargs.get("return_image_embeddings", False):
+            out_dict = dict()
+            prediction, image_embedding = self.forward_classifier(x, *args, **kwargs)
+            out_dict.update({"logits": prediction})
+            if image_embedding is not None:
+                out_dict.update({"image_embeddings": image_embedding})
+            return out_dict
+        else:
+            prediction, _ = self.forward_classifier(x, *args, **kwargs)
+            return prediction
+
+
+@register_model
+def vit_b16(pretrained=False, **kwargs):
+    # Vision transformer config
+    cfg = {
+        "norm_layer": "layer_norm_fp32",
+        "act_layer": "gelu",
+        "embed_dim": 768,
+        "n_transformer_layers": 12,
+        "n_attn_heads": 12,
+    }
+    model = VisionTransformer(cfg=cfg, **kwargs)
+    if pretrained:
+        raise ValueError("Functionality not implemented.")
+    return model

mobileclip/modules/__init__.py

@@ -0,0 +1,6 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All rights reserved.
+#

mobileclip/modules/common/__init__.py

@@ -0,0 +1,6 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All rights reserved.
+#

mobileclip/modules/common/mobileone.py

@@ -0,0 +1,330 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import copy
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+__all__ = ["MobileOneBlock", "reparameterize_model"]
+
+
+class SEBlock(nn.Module):
+    """Squeeze and Excite module.
+
+    Pytorch implementation of `Squeeze-and-Excitation Networks` - https://arxiv.org/pdf/1709.01507.pdf
+    """
+
+    def __init__(self, in_channels: int, rd_ratio: float = 0.0625) -> None:
+        """Construct a Squeeze and Excite Module.
+
+        Args:
+            in_channels: Number of input channels.
+            rd_ratio: Input channel reduction ratio.
+        """
+        super().__init__()
+        self.reduce = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=int(in_channels * rd_ratio),
+            kernel_size=1,
+            stride=1,
+            bias=True,
+        )
+        self.expand = nn.Conv2d(
+            in_channels=int(in_channels * rd_ratio),
+            out_channels=in_channels,
+            kernel_size=1,
+            stride=1,
+            bias=True,
+        )
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        """Apply forward pass."""
+        _b, c, h, w = inputs.size()
+        x = F.avg_pool2d(inputs, kernel_size=[h, w])
+        x = self.reduce(x)
+        x = F.relu(x)
+        x = self.expand(x)
+        x = torch.sigmoid(x)
+        x = x.view(-1, c, 1, 1)
+        return inputs * x
+
+
+class MobileOneBlock(nn.Module):
+    """MobileOne building block.
+
+    This block has a multi-branched architecture at train-time and plain-CNN style architecture at inference time For
+    more details, please refer to our paper: `An Improved One millisecond Mobile Backbone` -
+    https://arxiv.org/pdf/2206.04040.pdf
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        padding: int = 0,
+        dilation: int = 1,
+        groups: int = 1,
+        inference_mode: bool = False,
+        use_se: bool = False,
+        use_act: bool = True,
+        use_scale_branch: bool = True,
+        num_conv_branches: int = 1,
+        activation: nn.Module = nn.GELU(),
+    ) -> None:
+        """Construct a MobileOneBlock module.
+
+        Args:
+            in_channels: Number of channels in the input.
+            out_channels: Number of channels produced by the block.
+            kernel_size: Size of the convolution kernel.
+            stride: Stride size.
+            padding: Zero-padding size.
+            dilation: Kernel dilation factor.
+            groups: Group number.
+            inference_mode: If True, instantiates model in inference mode.
+            use_se: Whether to use SE-ReLU activations.
+            use_act: Whether to use activation. Default: ``True``
+            use_scale_branch: Whether to use scale branch. Default: ``True``
+            num_conv_branches: Number of linear conv branches.
+        """
+        super().__init__()
+        self.inference_mode = inference_mode
+        self.groups = groups
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+        self.kernel_size = kernel_size
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_conv_branches = num_conv_branches
+
+        # Check if SE-ReLU is requested
+        if use_se:
+            self.se = SEBlock(out_channels)
+        else:
+            self.se = nn.Identity()
+
+        if use_act:
+            self.activation = activation
+        else:
+            self.activation = nn.Identity()
+
+        if inference_mode:
+            self.reparam_conv = nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                groups=groups,
+                bias=True,
+            )
+        else:
+            # Re-parameterizable skip connection
+            self.rbr_skip = (
+                nn.BatchNorm2d(num_features=in_channels) if out_channels == in_channels and stride == 1 else None
+            )
+
+            # Re-parameterizable conv branches
+            if num_conv_branches > 0:
+                rbr_conv = list()
+                for _ in range(self.num_conv_branches):
+                    rbr_conv.append(self._conv_bn(kernel_size=kernel_size, padding=padding))
+                self.rbr_conv = nn.ModuleList(rbr_conv)
+            else:
+                self.rbr_conv = None
+
+            # Re-parameterizable scale branch
+            self.rbr_scale = None
+            if not isinstance(kernel_size, int):
+                kernel_size = kernel_size[0]
+            if (kernel_size > 1) and use_scale_branch:
+                self.rbr_scale = self._conv_bn(kernel_size=1, padding=0)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply forward pass."""
+        # Inference mode forward pass.
+        if self.inference_mode:
+            return self.activation(self.se(self.reparam_conv(x)))
+
+        # Multi-branched train-time forward pass.
+        # Skip branch output
+        identity_out = 0
+        if self.rbr_skip is not None:
+            identity_out = self.rbr_skip(x)
+
+        # Scale branch output
+        scale_out = 0
+        if self.rbr_scale is not None:
+            scale_out = self.rbr_scale(x)
+
+        # Other branches
+        out = scale_out + identity_out
+        if self.rbr_conv is not None:
+            for ix in range(self.num_conv_branches):
+                out += self.rbr_conv[ix](x)
+
+        return self.activation(self.se(out))
+
+    def reparameterize(self):
+        """Following works like `RepVGG: Making VGG-style ConvNets Great Again` - https://arxiv.org/pdf/2101.03697.pdf.
+        We re-parameterize multi-branched architecture used at training time to obtain a plain CNN-like
+        structure for inference.
+        """
+        if self.inference_mode:
+            return
+        kernel, bias = self._get_kernel_bias()
+        self.reparam_conv = nn.Conv2d(
+            in_channels=self.in_channels,
+            out_channels=self.out_channels,
+            kernel_size=self.kernel_size,
+            stride=self.stride,
+            padding=self.padding,
+            dilation=self.dilation,
+            groups=self.groups,
+            bias=True,
+        )
+        self.reparam_conv.weight.data = kernel
+        self.reparam_conv.bias.data = bias
+
+        # Delete un-used branches
+        for para in self.parameters():
+            para.detach_()
+        self.__delattr__("rbr_conv")
+        self.__delattr__("rbr_scale")
+        if hasattr(self, "rbr_skip"):
+            self.__delattr__("rbr_skip")
+
+        self.inference_mode = True
+
+    def _get_kernel_bias(self) -> tuple[torch.Tensor, torch.Tensor]:
+        """Method to obtain re-parameterized kernel and bias. Reference:
+        https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L83.
+
+        Returns:
+            Tuple of (kernel, bias) after fusing branches.
+        """
+        # get weights and bias of scale branch
+        kernel_scale = 0
+        bias_scale = 0
+        if self.rbr_scale is not None:
+            kernel_scale, bias_scale = self._fuse_bn_tensor(self.rbr_scale)
+            # Pad scale branch kernel to match conv branch kernel size.
+            pad = self.kernel_size // 2
+            kernel_scale = torch.nn.functional.pad(kernel_scale, [pad, pad, pad, pad])
+
+        # get weights and bias of skip branch
+        kernel_identity = 0
+        bias_identity = 0
+        if self.rbr_skip is not None:
+            kernel_identity, bias_identity = self._fuse_bn_tensor(self.rbr_skip)
+
+        # get weights and bias of conv branches
+        kernel_conv = 0
+        bias_conv = 0
+        if self.rbr_conv is not None:
+            for ix in range(self.num_conv_branches):
+                _kernel, _bias = self._fuse_bn_tensor(self.rbr_conv[ix])
+                kernel_conv += _kernel
+                bias_conv += _bias
+
+        kernel_final = kernel_conv + kernel_scale + kernel_identity
+        bias_final = bias_conv + bias_scale + bias_identity
+        return kernel_final, bias_final
+
+    def _fuse_bn_tensor(self, branch: nn.Sequential | nn.BatchNorm2d) -> tuple[torch.Tensor, torch.Tensor]:
+        """Method to fuse batchnorm layer with preceeding conv layer. Reference:
+        https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95.
+
+        Args:
+            branch: Sequence of ops to be fused.
+
+        Returns:
+            Tuple of (kernel, bias) after fusing batchnorm.
+        """
+        if isinstance(branch, nn.Sequential):
+            kernel = branch.conv.weight
+            running_mean = branch.bn.running_mean
+            running_var = branch.bn.running_var
+            gamma = branch.bn.weight
+            beta = branch.bn.bias
+            eps = branch.bn.eps
+        else:
+            assert isinstance(branch, nn.BatchNorm2d)
+            if not hasattr(self, "id_tensor"):
+                input_dim = self.in_channels // self.groups
+
+                kernel_size = self.kernel_size
+                if isinstance(self.kernel_size, int):
+                    kernel_size = (self.kernel_size, self.kernel_size)
+
+                kernel_value = torch.zeros(
+                    (self.in_channels, input_dim, kernel_size[0], kernel_size[1]),
+                    dtype=branch.weight.dtype,
+                    device=branch.weight.device,
+                )
+                for i in range(self.in_channels):
+                    kernel_value[i, i % input_dim, kernel_size[0] // 2, kernel_size[1] // 2] = 1
+                self.id_tensor = kernel_value
+            kernel = self.id_tensor
+            running_mean = branch.running_mean
+            running_var = branch.running_var
+            gamma = branch.weight
+            beta = branch.bias
+            eps = branch.eps
+        std = (running_var + eps).sqrt()
+        t = (gamma / std).reshape(-1, 1, 1, 1)
+        return kernel * t, beta - running_mean * gamma / std
+
+    def _conv_bn(self, kernel_size: int, padding: int) -> nn.Sequential:
+        """Helper method to construct conv-batchnorm layers.
+
+        Args:
+            kernel_size: Size of the convolution kernel.
+            padding: Zero-padding size.
+
+        Returns:
+            Conv-BN module.
+        """
+        mod_list = nn.Sequential()
+        mod_list.add_module(
+            "conv",
+            nn.Conv2d(
+                in_channels=self.in_channels,
+                out_channels=self.out_channels,
+                kernel_size=kernel_size,
+                stride=self.stride,
+                padding=padding,
+                groups=self.groups,
+                bias=False,
+            ),
+        )
+        mod_list.add_module("bn", nn.BatchNorm2d(num_features=self.out_channels))
+        return mod_list
+
+
+def reparameterize_model(model: torch.nn.Module) -> nn.Module:
+    """Method returns a model where a multi-branched structure used in training is re-parameterized into a single branch
+    for inference.
+
+    Args:
+        model: MobileOne model in train mode.
+
+    Returns:
+        MobileOne model in inference mode.
+    """
+    # Avoid editing original graph
+    model = copy.deepcopy(model)
+    for module in model.modules():
+        if hasattr(module, "reparameterize"):
+            module.reparameterize()
+    return model

mobileclip/modules/common/transformer.py

@@ -0,0 +1,410 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+#
+"""
+Implementation of the following modules is borrowed from ml-cvnets repo:
+https://github.com/apple/ml-cvnets/blob/main/cvnets/layers/multi_head_attention.py
+https://github.com/apple/ml-cvnets/blob/main/cvnets/text_encoders/transformer.py.
+
+Please see ACKNOWLEDGMENTS for license details.
+"""
+
+from __future__ import annotations
+
+import torch
+from torch import Size, Tensor, nn
+from torch.nn import functional as F
+from torchvision.ops import StochasticDepth
+
+from mobileclip import logger
+
+
+class LayerNormFP32(nn.LayerNorm):
+    """Applies `Layer Normalization <https://arxiv.org/abs/1607.06450>`_ over a input tensor with FP32 precision."""
+
+    def __init__(
+        self,
+        normalized_shape: int | list[int] | Size,
+        eps: float | None = 1e-5,
+        elementwise_affine: bool | None = True,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(
+            normalized_shape=normalized_shape,
+            eps=eps,
+            elementwise_affine=elementwise_affine,
+            *args,
+            **kwargs,
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        # Convert input from dtype X to FP32 and perform normalization operation.
+        # This may help with underflow/overflow issues that we typically see with normalization layers
+        inp_dtype = x.dtype
+        return super().forward(x.to(torch.float32)).to(inp_dtype)
+
+
+def get_normalization_layer(norm_type, num_features):
+    if norm_type == "layer_norm":
+        return nn.LayerNorm(num_features)
+    elif norm_type == "layer_norm_fp32":
+        return LayerNormFP32(num_features)
+    else:
+        raise NotImplementedError(f"Option: {norm_type} not supported.")
+
+
+class PositionalEmbedding(nn.Module):
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        padding_idx: int | None = None,
+        is_learnable: bool | None = False,
+        interpolation_mode: str | None = "bilinear",
+        *args,
+        **kwargs,
+    ):
+        super().__init__()
+        # Add other pos embedding here and logic to choose between them
+        module = LearnablePositionalEmbedding
+
+        self.pos_embed = module(
+            num_embeddings=num_embeddings,
+            embedding_dim=embedding_dim,
+            padding_idx=padding_idx,
+            interpolation_mode=interpolation_mode,
+            *args,
+            **kwargs,
+        )
+
+    def forward(self, seq_len: int, *args, **kwargs) -> Tensor:
+        return self.pos_embed(seq_len, *args, **kwargs)
+
+    def __repr__(self):
+        return self.pos_embed.__repr__()
+
+
+class LearnablePositionalEmbedding(nn.Module):
+    """Learnable Positional embedding."""
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        padding_idx: int | None = None,
+        interpolation_mode: str | None = "bilinear",
+        *args,
+        **kwargs,
+    ):
+        super().__init__()
+        self.pos_embed = nn.Parameter(torch.empty(1, 1, num_embeddings, embedding_dim))
+        self.embedding_dim = embedding_dim
+        self.num_embeddings = num_embeddings
+        self.padding_idx = padding_idx
+        self.interpolation_mode = interpolation_mode
+
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        nn.init.trunc_normal_(self.pos_embed, mean=0, std=self.embedding_dim**-0.5)
+        if self.padding_idx is not None:
+            with torch.no_grad():
+                self.pos_embed[:, :, self.padding_idx, ...] = 0.0
+
+    def forward(self, seq_len: int, *args, **kwargs) -> Tensor:
+        # scale pos embedding
+        pos_embed = self.pos_embed
+        if self.padding_idx is not None:
+            with torch.no_grad():
+                pos_embed[:, :, self.padding_idx, ...] = 0.0
+
+        if seq_len != self.num_embeddings:
+            pos_embed = F.interpolate(
+                pos_embed,
+                size=(seq_len, self.embedding_dim),
+                mode=self.interpolation_mode,
+            )
+
+        # Input is of the form [Batch, Seq_len, Embedding_dim]
+        return pos_embed.reshape(1, seq_len, self.embedding_dim)
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(num_embeddings={self.num_embeddings}, embedding_dim={self.embedding_dim}, padding_idx={self.padding_idx})"
+
+
+class MultiHeadAttention(nn.Module):
+    """This layer applies a multi-head self- or cross-attention as described in `Attention is all you need
+    <https://arxiv.org/abs/1706.03762>`_ paper.
+
+    Args:
+        embed_dim (int): :math:`C_{in}` from an expected input of size :math:`(N, S, C_{in})`
+        num_heads (int): Number of heads in multi-head attention
+        attn_dropout (Optional[float]): Attention dropout. Default: 0.0
+        bias (Optional[bool]): Use bias or not. Default: ``True``
+
+    Notes:
+        - Input:
+           - Query tensor (x_q) :math:`(N, S, C_{in})` where :math:`N` is batch size, :math:`S` is number of source tokens,
+        and: math:`C_{in}` is input embedding dim
+           - Optional Key-Value tensor (x_kv) :math:`(N, T, C_{in})` where :math:`T` is number of target tokens
+        - Output: same shape as the input
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        attn_dropout: float | None = 0.0,
+        bias: bool | None = True,
+        output_dim: int | None = None,
+        *args,
+        **kwargs,
+    ) -> None:
+        if output_dim is None:
+            output_dim = embed_dim
+        super().__init__()
+        if embed_dim % num_heads != 0:
+            logger.error(
+                f"Embedding dim must be divisible by number of heads in {self.__class__.__name__}. Got: embed_dim={embed_dim} and num_heads={num_heads}"
+            )
+
+        self.qkv_proj = nn.Linear(in_features=embed_dim, out_features=3 * embed_dim, bias=bias)
+
+        self.attn_dropout = nn.Dropout(p=attn_dropout)
+        self.out_proj = nn.Linear(in_features=embed_dim, out_features=output_dim, bias=bias)
+
+        self.head_dim = embed_dim // num_heads
+        self.scaling = self.head_dim**-0.5
+        self.softmax = nn.Softmax(dim=-1)
+        self.num_heads = num_heads
+        self.embed_dim = embed_dim
+        self.use_separate_proj_weight = embed_dim != output_dim
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(head_dim={self.head_dim}, num_heads={self.num_heads}, attn_dropout={self.attn_dropout.p})"
+
+    def _forward_impl(
+        self,
+        x_q: Tensor,
+        x_kv: Tensor | None = None,
+        key_padding_mask: Tensor | None = None,
+        attn_mask: Tensor | None = None,
+    ) -> Tensor:
+        # [N, S, C]
+        b_sz, S_len, _in_channels = x_q.shape
+
+        if x_kv is None:
+            # self-attention
+            # [N, S, C] --> [N, S, 3C] --> [N, S, 3, h, c] where C = hc
+            qkv = self.qkv_proj(x_q).reshape(b_sz, S_len, 3, self.num_heads, -1)
+            # [N, S, 3, h, c] --> [N, h, 3, S, C]
+            qkv = qkv.transpose(1, 3).contiguous()
+
+            # [N, h, 3, S, C] --> [N, h, S, C] x 3
+            query, key, value = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
+        else:
+            T_len = x_kv.shape[1]
+
+            # cross-attention
+            # [N, S, C]
+            query = F.linear(
+                x_q,
+                weight=self.qkv_proj.weight[: self.embed_dim, ...],
+                bias=self.qkv_proj.bias[: self.embed_dim] if self.qkv_proj.bias is not None else None,
+            )
+            # [N, S, C] --> [N, S, h, c] --> [N, h, S, c]
+            query = query.reshape(b_sz, S_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+            # [N, T, C] --> [N, T, 2C]
+            kv = F.linear(
+                x_kv,
+                weight=self.qkv_proj.weight[self.embed_dim :, ...],
+                bias=self.qkv_proj.bias[self.embed_dim :] if self.qkv_proj.bias is not None else None,
+            )
+            # [N, T, 2C] --> [N, T, 2, h, c]
+            kv = kv.reshape(b_sz, T_len, 2, self.num_heads, self.head_dim)
+            # [N, T, 2, h, c] --> [N, h, 2, T, c]
+            kv = kv.transpose(1, 3).contiguous()
+            key, value = kv[:, :, 0], kv[:, :, 1]
+
+        query = query * self.scaling
+
+        # [N h, T, c] --> [N, h, c, T]
+        key = key.transpose(-1, -2)
+
+        # QK^T
+        # [N, h, S, c] x [N, h, c, T] --> [N, h, S, T]
+        attn = torch.matmul(query, key)
+
+        batch_size, _num_heads, num_src_tokens, num_tgt_tokens = attn.shape
+        if attn_mask is not None:
+            # attn_mask shape should be the same as attn
+            assert list(attn_mask.shape) == [
+                batch_size,
+                num_src_tokens,
+                num_tgt_tokens,
+            ], (
+                f"Shape of attention mask should be [{batch_size}, {num_src_tokens}, {num_tgt_tokens}]. Got: {attn_mask.shape}"
+            )
+            # [N, S, T] --> [N, 1, S, T]
+            attn_mask = attn_mask.unsqueeze(1)
+            attn = attn + attn_mask
+
+        if key_padding_mask is not None:
+            # Do not attend to padding positions
+            # key padding mask size is [N, T]
+            assert key_padding_mask.dim() == 2 and list(key_padding_mask.shape) == [
+                batch_size,
+                num_tgt_tokens,
+            ], (
+                f"Key_padding_mask should be 2-dimension with shape [{batch_size}, {num_tgt_tokens}]. Got: {key_padding_mask.shape}"
+            )
+            attn = attn.masked_fill(
+                key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),  # [N, T] --> [N, 1, 1, T]
+                float("-inf"),
+            )
+
+        attn_dtype = attn.dtype
+        attn_as_float = self.softmax(attn.float())
+        attn = attn_as_float.to(attn_dtype)
+        attn = self.attn_dropout(attn)
+
+        # weighted sum
+        # [N, h, S, T] x [N, h, T, c] --> [N, h, S, c]
+        out = torch.matmul(attn, value)
+
+        # [N, h, S, c] --> [N, S, h, c] --> [N, S, C]
+        out = out.transpose(1, 2).reshape(b_sz, S_len, -1)
+        out = self.out_proj(out)
+
+        return out
+
+    def forward(
+        self,
+        x_q: Tensor,
+        x_kv: Tensor | None = None,
+        key_padding_mask: Tensor | None = None,
+        attn_mask: Tensor | None = None,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        # [Batch , Sequence, Hidden_dim]
+        return self._forward_impl(
+            x_q=x_q,
+            x_kv=x_kv,
+            key_padding_mask=key_padding_mask,
+            attn_mask=attn_mask,
+        )
+
+
+class TransformerEncoder(nn.Module):
+    """This class defines the pre-norm `Transformer encoder <https://arxiv.org/abs/1706.03762>`_.
+
+    Args:
+        embed_dim: :math:`C_{in}` from an expected input of size :math:`(N, P, C_{in})`.
+        ffn_latent_dim: Inner dimension of the FFN.
+        num_heads: Number of heads in multi-head attention. Default: 8.
+        attn_dropout: Dropout rate for attention in multi-head attention. Default: 0.0
+        dropout: Dropout rate. Default: 0.0.
+        ffn_dropout: Dropout between FFN layers. Default: 0.0.
+        transformer_norm_layer: Normalization layer. Default: layer_norm.
+        stochastic_dropout: Stochastic dropout setting. Default: 0.0.
+
+    Notes:
+        - Input: :math:`(N, P, C_{in})` where :math:`N` is batch size, :math:`P` is number of patches,
+        and: math:`C_{in}` is input embedding dim
+        - Output: same shape as the input
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        ffn_latent_dim: int,
+        num_heads: int | None = 8,
+        attn_dropout: float | None = 0.0,
+        dropout: float | None = 0.0,
+        ffn_dropout: float | None = 0.0,
+        transformer_norm_layer: str | None = "layer_norm",
+        stochastic_dropout: float | None = 0.0,
+        *args,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+
+        # Build attention layer
+        attn_unit = MultiHeadAttention(
+            embed_dim,
+            num_heads,
+            attn_dropout=attn_dropout,
+            bias=True,
+        )
+
+        self.pre_norm_mha = nn.Sequential(
+            get_normalization_layer(norm_type=transformer_norm_layer, num_features=embed_dim),
+            attn_unit,
+            nn.Dropout(p=dropout),
+        )
+
+        act_name = nn.GELU()
+        self.pre_norm_ffn = nn.Sequential(
+            get_normalization_layer(norm_type=transformer_norm_layer, num_features=embed_dim),
+            nn.Linear(in_features=embed_dim, out_features=ffn_latent_dim, bias=True),
+            act_name,
+            nn.Dropout(p=ffn_dropout),
+            nn.Linear(in_features=ffn_latent_dim, out_features=embed_dim, bias=True),
+            nn.Dropout(p=dropout),
+        )
+
+        self.drop_path = nn.Identity()
+        if stochastic_dropout > 0.0:
+            if dropout > 0.0:
+                logger.error(
+                    "Stochastic dropout and dropout are mutually exclusive. "
+                    "Use either of them, but not both."
+                    f"Got: {stochastic_dropout} and {dropout}"
+                )
+            self.drop_path = StochasticDepth(p=stochastic_dropout, mode="row")
+
+        self.embed_dim = embed_dim
+        self.ffn_dim = ffn_latent_dim
+        self.ffn_dropout = ffn_dropout
+        self.stochastic_dropout = stochastic_dropout
+        self.std_dropout = dropout
+        self.attn_fn_name = attn_unit.__class__.__name__
+        self.act_fn_name = act_name.__class__.__name__
+        self.norm_type = transformer_norm_layer
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(embed_dim={self.embed_dim}, ffn_dim={self.ffn_dim}, dropout={self.std_dropout}, ffn_dropout={self.ffn_dropout}, stochastic_dropout={self.stochastic_dropout}, attn_fn={self.attn_fn_name}, act_fn={self.act_fn_name}, norm_fn={self.norm_type})"
+
+    def forward(
+        self,
+        x: Tensor,
+        x_prev: Tensor | None = None,
+        key_padding_mask: Tensor | None = None,
+        attn_mask: Tensor | None = None,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        # Multi-head attention
+        res = x
+        x = self.pre_norm_mha[0](x)  # norm
+        x = self.pre_norm_mha[1](
+            x_q=x,
+            x_kv=x_prev,
+            key_padding_mask=key_padding_mask,
+            attn_mask=attn_mask,
+            *args,
+            **kwargs,
+        )  # mha
+
+        x = self.drop_path(self.pre_norm_mha[2](x))  # applying stochastic depth
+        x = x + res
+
+        # Feed forward network
+        x = x + self.drop_path(self.pre_norm_ffn(x))
+        return x

mobileclip/modules/image/__init__.py

@@ -0,0 +1,6 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All rights reserved.
+#

mobileclip/modules/image/image_projection.py

@@ -0,0 +1,97 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+from mobileclip import logger
+
+
+class GlobalPool(nn.Module):
+    """This layers applies global pooling over a 4D or 5D input tensor.
+
+    Args:
+        pool_type (Optional[str]): Pooling type. It can be mean, rms, or abs. Default: `mean`
+        keep_dim (Optional[bool]): Do not squeeze the dimensions of a tensor. Default: `False`
+
+    Notes:
+        - Input: :math:`(N, C, H, W)` or :math:`(N, C, D, H, W)`
+        - Output: :math:`(N, C, 1, 1)` or :math:`(N, C, 1, 1, 1)` if keep_dim else :math:`(N, C)`
+    """
+
+    pool_types = ["mean", "rms", "abs"]
+
+    def __init__(self, pool_type: str | None = "mean", keep_dim: bool | None = False, *args, **kwargs) -> None:
+        super().__init__()
+        if pool_type not in self.pool_types:
+            logger.error(f"Supported pool types are: {self.pool_types}. Got {pool_type}")
+        self.pool_type = pool_type
+        self.keep_dim = keep_dim
+
+    def _global_pool(self, x: Tensor, dims: list):
+        if self.pool_type == "rms":  # root mean square
+            x = x**2
+            x = torch.mean(x, dim=dims, keepdim=self.keep_dim)
+            x = x**-0.5
+        elif self.pool_type == "abs":  # absolute
+            x = torch.mean(torch.abs(x), dim=dims, keepdim=self.keep_dim)
+        else:
+            # default is mean
+            # same as AdaptiveAvgPool
+            x = torch.mean(x, dim=dims, keepdim=self.keep_dim)
+        return x
+
+    def forward(self, x: Tensor) -> Tensor:
+        if x.dim() == 4:
+            dims = [-2, -1]
+        elif x.dim() == 5:
+            dims = [-3, -2, -1]
+        else:
+            raise NotImplementedError("Currently 2D and 3D global pooling supported")
+        return self._global_pool(x, dims=dims)
+
+
+class GlobalPool2D(nn.Module):
+    """This class implements global pooling with linear projection."""
+
+    def __init__(self, in_dim: int, out_dim: int, *args, **kwargs) -> None:
+        super().__init__()
+        scale = in_dim**-0.5
+        self.pool = GlobalPool(pool_type="mean", keep_dim=False)
+        self.proj = nn.Parameter(scale * torch.randn(size=(in_dim, out_dim)))
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+    def forward(self, x: Tensor, *args, **kwargs) -> Tensor:
+        # x is of shape [batch, in_dim]
+        assert x.dim() == 4, f"Input should be 4-dimensional (Batch x in_dim x in_height x in_width). Got: {x.shape}"
+
+        # [batch, in_dim, in_height, in_width] --> [batch, in_dim]
+        x = self.pool(x)
+        # [batch, in_dim]  x [in_dim, out_dim] --> [batch, out_dim]
+        x = x @ self.proj
+        return x
+
+
+class SimpleImageProjectionHead(nn.Module):
+    """This class implements linear projection head."""
+
+    def __init__(self, in_dim: int, out_dim: int) -> None:
+        super().__init__()
+        scale = in_dim**-0.5
+        self.proj = nn.Parameter(scale * torch.randn(size=(in_dim, out_dim)))
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+    def forward(self, x: Tensor, *args, **kwargs) -> Tensor:
+        # x is of shape [batch, in_dim]
+        assert x.dim() == 2, f"Input should be 2-dimensional (Batch x in_dim). Got: {x.shape}"
+
+        # [batch, in_dim] x [in_dim, out_dim] --> [batch, out_dim]
+        x = x @ self.proj
+        return x

mobileclip/modules/image/replknet.py

@@ -0,0 +1,177 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For acknowledgment see accompanying ACKNOWLEDGMENTS file.
+# Copyright (C) 2024 Apple Inc. All rights reserved.
+#
+
+import torch
+import torch.nn as nn
+from timm.models.layers import SqueezeExcite
+
+__all__ = ["ReparamLargeKernelConv"]
+
+
+class ReparamLargeKernelConv(nn.Module):
+    """Building Block of RepLKNet.
+
+    This class defines overparameterized large kernel conv block introduced in `RepLKNet
+    <https://arxiv.org/abs/2203.06717>`_
+
+    Reference: https://github.com/DingXiaoH/RepLKNet-pytorch
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int,
+        groups: int,
+        small_kernel: int,
+        inference_mode: bool = False,
+        use_se: bool = False,
+        activation: nn.Module = nn.GELU(),
+    ) -> None:
+        """Construct a ReparamLargeKernelConv module.
+
+        Args:
+            in_channels: Number of input channels.
+            out_channels: Number of output channels.
+            kernel_size: Kernel size of the large kernel conv branch.
+            stride: Stride size. Default: 1
+            groups: Group number. Default: 1
+            small_kernel: Kernel size of small kernel conv branch.
+            inference_mode: If True, instantiates model in inference mode. Default: ``False``
+            activation: Activation module. Default: ``nn.GELU``
+        """
+        super().__init__()
+
+        self.stride = stride
+        self.groups = groups
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.activation = activation
+
+        self.kernel_size = kernel_size
+        self.small_kernel = small_kernel
+        self.padding = kernel_size // 2
+
+        # Check if SE is requested
+        if use_se:
+            self.se = SqueezeExcite(out_channels, rd_ratio=0.25)
+        else:
+            self.se = nn.Identity()
+
+        if inference_mode:
+            self.lkb_reparam = nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=self.padding,
+                dilation=1,
+                groups=groups,
+                bias=True,
+            )
+        else:
+            self.lkb_origin = self._conv_bn(kernel_size=kernel_size, padding=self.padding)
+            if small_kernel is not None:
+                assert small_kernel <= kernel_size, (
+                    "The kernel size for re-param cannot be larger than the large kernel!"
+                )
+                self.small_conv = self._conv_bn(kernel_size=small_kernel, padding=small_kernel // 2)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply forward pass."""
+        if hasattr(self, "lkb_reparam"):
+            out = self.lkb_reparam(x)
+        else:
+            out = self.lkb_origin(x)
+            if hasattr(self, "small_conv"):
+                out += self.small_conv(x)
+
+        return self.activation(self.se(out))
+
+    def get_kernel_bias(self) -> tuple[torch.Tensor, torch.Tensor]:
+        """Method to obtain re-parameterized kernel and bias. Reference: https://github.com/DingXiaoH/RepLKNet-pytorch.
+
+        Returns:
+            Tuple of (kernel, bias) after fusing branches.
+        """
+        eq_k, eq_b = self._fuse_bn(self.lkb_origin.conv, self.lkb_origin.bn)
+        if hasattr(self, "small_conv"):
+            small_k, small_b = self._fuse_bn(self.small_conv.conv, self.small_conv.bn)
+            eq_b += small_b
+            eq_k += nn.functional.pad(small_k, [(self.kernel_size - self.small_kernel) // 2] * 4)
+        return eq_k, eq_b
+
+    def reparameterize(self) -> None:
+        """Following works like `RepVGG: Making VGG-style ConvNets Great Again` - https://arxiv.org/pdf/2101.03697.pdf.
+        We re-parameterize multi-branched architecture used at training time to obtain a plain CNN-like
+        structure for inference.
+        """
+        eq_k, eq_b = self.get_kernel_bias()
+        self.lkb_reparam = nn.Conv2d(
+            in_channels=self.in_channels,
+            out_channels=self.out_channels,
+            kernel_size=self.kernel_size,
+            stride=self.stride,
+            padding=self.padding,
+            dilation=self.lkb_origin.conv.dilation,
+            groups=self.groups,
+            bias=True,
+        )
+
+        self.lkb_reparam.weight.data = eq_k
+        self.lkb_reparam.bias.data = eq_b
+        self.__delattr__("lkb_origin")
+        if hasattr(self, "small_conv"):
+            self.__delattr__("small_conv")
+
+    @staticmethod
+    def _fuse_bn(conv: torch.Tensor, bn: nn.BatchNorm2d) -> tuple[torch.Tensor, torch.Tensor]:
+        """Method to fuse batchnorm layer with conv layer.
+
+        Args:
+            conv: Convolutional kernel weights.
+            bn: Batchnorm 2d layer.
+
+        Returns:
+            Tuple of (kernel, bias) after fusing batchnorm.
+        """
+        kernel = conv.weight
+        running_mean = bn.running_mean
+        running_var = bn.running_var
+        gamma = bn.weight
+        beta = bn.bias
+        eps = bn.eps
+        std = (running_var + eps).sqrt()
+        t = (gamma / std).reshape(-1, 1, 1, 1)
+        return kernel * t, beta - running_mean * gamma / std
+
+    def _conv_bn(self, kernel_size: int, padding: int = 0) -> nn.Sequential:
+        """Helper method to construct conv-batchnorm layers.
+
+        Args:
+            kernel_size: Size of the convolution kernel.
+            padding: Zero-padding size.
+
+        Returns:
+            A nn.Sequential Conv-BN module.
+        """
+        mod_list = nn.Sequential()
+        mod_list.add_module(
+            "conv",
+            nn.Conv2d(
+                in_channels=self.in_channels,
+                out_channels=self.out_channels,
+                kernel_size=kernel_size,
+                stride=self.stride,
+                padding=padding,
+                groups=self.groups,
+                bias=False,
+            ),
+        )
+        mod_list.add_module("bn", nn.BatchNorm2d(num_features=self.out_channels))
+        return mod_list

mobileclip/modules/text/__init__.py

@@ -0,0 +1,6 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All rights reserved.
+#

mobileclip/modules/text/repmixer.py

@@ -0,0 +1,265 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+from timm.models.layers import DropPath, trunc_normal_
+
+from mobileclip.modules.common.mobileone import MobileOneBlock
+
+
+class ConvFFN(nn.Module):
+    """Convolutional FFN Module."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        context_size: int,
+        hidden_channels: int | None = None,
+        out_channels: int | None = None,
+        act_layer: nn.Module = nn.GELU,
+        drop: float = 0.0,
+    ) -> None:
+        """Build convolutional FFN module.
+
+        Args:
+            in_channels: Number of input channels.
+            context_size: Context size for 1D signals.
+            hidden_channels: Number of channels after expansion. Default: None
+            out_channels: Number of output channels. Default: None
+            act_layer: Activation layer. Default: ``GELU``
+            drop: Dropout rate. Default: ``0.0``.
+        """
+        super().__init__()
+        out_channels = out_channels or in_channels
+        hidden_channels = hidden_channels or in_channels
+        self.conv = nn.Sequential()
+        self.conv.add_module(
+            "conv",
+            nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=(1, int(context_size)),
+                padding=(0, int(context_size // 2)),
+                groups=in_channels,
+                bias=False,
+            ),
+        )
+        self.conv.add_module("bn", nn.BatchNorm2d(num_features=out_channels))
+        self.fc1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=1)
+        self.act = act_layer()
+        self.fc2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=1)
+        self.drop = nn.Dropout(drop)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m: nn.Module) -> None:
+        if isinstance(m, nn.Conv2d):
+            trunc_normal_(m.weight, std=0.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv(x)
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class RepMixer(nn.Module):
+    """Reparameterizable token mixer.
+
+    For more details, please refer to our paper: `FastViT: A Fast Hybrid Vision Transformer using Structural
+    Reparameterization <https://arxiv.org/pdf/2303.14189.pdf>`_
+    """
+
+    def __init__(
+        self,
+        dim,
+        kernel_size=3,
+        use_layer_scale=True,
+        layer_scale_init_value=1e-5,
+        inference_mode: bool = False,
+    ):
+        """Build RepMixer Module.
+
+        Args:
+            dim: Input feature map dimension. :math:`C_{in}` from an expected input of size :math:`(B, C_{in}, H, W)`.
+            kernel_size: Kernel size for spatial mixing. Default: 3
+            use_layer_scale: If True, learnable layer scale is used. Default: ``True``
+            layer_scale_init_value: Initial value for layer scale. Default: 1e-5
+            inference_mode: If True, instantiates model in inference mode. Default: ``False``
+        """
+        super().__init__()
+        self.dim = dim
+        self.kernel_size = kernel_size
+        self.inference_mode = inference_mode
+
+        if inference_mode:
+            self.reparam_conv = nn.Conv2d(
+                in_channels=self.dim,
+                out_channels=self.dim,
+                kernel_size=(1, self.kernel_size),
+                stride=1,
+                padding=(0, self.kernel_size // 2),
+                groups=self.dim,
+                bias=True,
+            )
+        else:
+            self.norm = MobileOneBlock(
+                dim,
+                dim,
+                (1, kernel_size),
+                padding=(0, kernel_size // 2),
+                groups=dim,
+                use_act=False,
+                use_scale_branch=False,
+                num_conv_branches=0,
+            )
+            self.mixer = MobileOneBlock(
+                dim,
+                dim,
+                (1, kernel_size),
+                padding=(0, kernel_size // 2),
+                groups=dim,
+                use_act=False,
+            )
+            self.use_layer_scale = use_layer_scale
+            if use_layer_scale:
+                self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if hasattr(self, "reparam_conv"):
+            x = self.reparam_conv(x)
+            return x
+        else:
+            if self.use_layer_scale:
+                x = x + self.layer_scale * (self.mixer(x) - self.norm(x))
+            else:
+                x = x + self.mixer(x) - self.norm(x)
+            return x
+
+    def reparameterize(self) -> None:
+        """Reparameterize mixer and norm into a single convolutional layer for efficient inference."""
+        if self.inference_mode:
+            return
+
+        self.mixer.reparameterize()
+        self.norm.reparameterize()
+
+        if self.use_layer_scale:
+            w = self.mixer.id_tensor + self.layer_scale.unsqueeze(-1) * (
+                self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
+            )
+            b = torch.squeeze(self.layer_scale) * (self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias)
+        else:
+            w = self.mixer.id_tensor + self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
+            b = self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias
+
+        self.reparam_conv = nn.Conv2d(
+            in_channels=self.dim,
+            out_channels=self.dim,
+            kernel_size=(1, self.kernel_size),
+            stride=1,
+            padding=(0, self.kernel_size // 2),
+            groups=self.dim,
+            bias=True,
+        )
+        self.reparam_conv.weight.data = w
+        self.reparam_conv.bias.data = b
+
+        for para in self.parameters():
+            para.detach_()
+        self.__delattr__("mixer")
+        self.__delattr__("norm")
+        if self.use_layer_scale:
+            self.__delattr__("layer_scale")
+
+
+class RepMixerBlock(nn.Module):
+    """Implementation of Metaformer block with RepMixer as token mixer.
+
+    For more details on Metaformer structure, please refer to: `MetaFormer Is Actually What You Need for Vision
+    <https://arxiv.org/pdf/2111.11418.pdf>`_
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        kernel_size: int = 11,
+        mlp_ratio: float = 4.0,
+        act_layer: nn.Module = nn.GELU,
+        drop: float = 0.0,
+        drop_path: float = 0.0,
+        use_layer_scale: bool = True,
+        layer_scale_init_value: float = 1e-5,
+        inference_mode: bool = False,
+        *args,
+        **kwargs,
+    ):
+        """Build RepMixer Block.
+
+        Args:
+            dim: Number of embedding dimensions.
+            kernel_size: Kernel size for repmixer. Default: 3
+            mlp_ratio: MLP expansion ratio. Default: 4.0
+            act_layer: Activation layer. Default: ``nn.GELU``
+            drop: Dropout rate. Default: 0.0
+            drop_path: Drop path rate. Default: 0.0
+            use_layer_scale: Flag to turn on layer scale. Default: ``True``
+            layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
+            inference_mode: Flag to instantiate block in inference mode. Default: ``False``
+        """
+        super().__init__()
+
+        self.token_mixer = RepMixer(
+            dim,
+            kernel_size=kernel_size,
+            use_layer_scale=use_layer_scale,
+            layer_scale_init_value=layer_scale_init_value,
+            inference_mode=inference_mode,
+        )
+
+        assert mlp_ratio > 0, f"MLP ratio should be greater than 0, found: {mlp_ratio}"
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.convffn = ConvFFN(
+            in_channels=dim,
+            context_size=kernel_size,
+            hidden_channels=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+        )
+
+        # Drop Path
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        # Layer Scale
+        self.use_layer_scale = use_layer_scale
+        if use_layer_scale:
+            self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)
+
+    def forward(self, x, *args, **kwargs):
+        if x.dim() == 3:
+            # B, C, D --- where C is the context length
+            # Convert to B, D, C --- to match RepMixer impl.
+            x = x.permute(0, 2, 1)
+            x = torch.unsqueeze(x, dim=2)
+        else:
+            raise ValueError(f"Expected tensor of dim=3, obtained tensor of dim={x.dim()}")
+
+        if self.use_layer_scale:
+            x = self.token_mixer(x)
+            x = x + self.drop_path(self.layer_scale * self.convffn(x))
+        else:
+            x = self.token_mixer(x)
+            x = x + self.drop_path(self.convffn(x))
+
+        # Convert tensors back
+        x = x.squeeze(dim=2).permute(0, 2, 1)
+        return x

mobileclip/modules/text/tokenizer.py

@@ -0,0 +1,39 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+#
+
+import open_clip
+from torch import Tensor, nn
+
+
+class ClipTokenizer(nn.Module):
+    def __init__(self, cfg, *args, **kwargs):
+        super().__init__()
+        self.context_length = cfg["text_cfg"]["context_length"]
+        model_name = getattr(cfg["text_cfg"], "open_clip_tokenizer", "ViT-B-16")
+        self.tokenizer = open_clip.get_tokenizer(model_name)
+
+    def get_vocab_size(self) -> int:
+        return len(self.tokenizer.encoder)
+
+    def get_encodings(self) -> dict[str, int]:
+        return self.tokenizer.encoder
+
+    def get_eot_token(self) -> int:
+        # Tokenizing an empty string returns a list [sot_id, eot_id]
+        return self.tokenizer("")[1]
+
+    def get_sot_token(self) -> int:
+        # Tokenizing an empty string returns a list [sot_id, eot_id]
+        return self.tokenizer("")[0]
+
+    def forward(self, input_sentence: str, *args, **kwargs) -> Tensor:
+        # tokenizer returns indices as a string
+        tokenized_sentence = self.tokenizer(input_sentence, self.context_length)
+        assert tokenized_sentence.shape[-1] == self.context_length, (
+            "Tokenized tensor should be exactly `context_length` long."
+        )
+        return tokenized_sentence

mobileclip/text_encoder.py

@@ -0,0 +1,218 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import math
+from collections.abc import Sequence
+
+import torch
+from torch import Tensor, nn
+
+from mobileclip import logger
+from mobileclip.modules.common.transformer import (
+    PositionalEmbedding,
+    TransformerEncoder,
+    get_normalization_layer,
+)
+from mobileclip.modules.text.repmixer import RepMixerBlock
+
+
+class TextTransformer(nn.Module):
+    def __init__(self, cfg: dict, projection_dim: int, *args, **kwargs) -> None:
+        super().__init__()
+
+        model_dim = cfg["dim"]
+        no_scale_embedding = cfg.get("no_scale_embedding", False)
+        no_pos_embedding = cfg.get("no_pos_embedding", False)
+        embed_dropout = cfg.get("embed_dropout", 0.0)
+        norm_layer = cfg["norm_layer"]
+        variant = cfg["model_name"]
+        self.vocab_size = cfg["vocab_size"]
+        self.projection_dim = projection_dim
+
+        # Token embedding layer
+        self.embedding_layer = nn.Embedding(embedding_dim=model_dim, num_embeddings=self.vocab_size)
+        self.embed_scale = 1.0 if no_scale_embedding else model_dim**-0.5
+
+        # Context length
+        context_length = cfg["context_length"]
+        assert context_length is not None, "Context length can't be None. Please set value accordingly."
+
+        self.positional_embedding = (
+            None if no_pos_embedding else PositionalEmbedding(num_embeddings=context_length, embedding_dim=model_dim)
+        )
+
+        self.embedding_dropout = nn.Dropout(p=embed_dropout)
+
+        # Transformer layer
+        n_transformer_layers = cfg["n_transformer_layers"]
+
+        # FFN multipliers for transformer layer
+        ffn_multipliers = cfg["ffn_multiplier_per_layer"]
+        if isinstance(ffn_multipliers, (float, int)):
+            ffn_multipliers = [ffn_multipliers] * n_transformer_layers
+
+        if not isinstance(ffn_multipliers, Sequence):
+            logger.error(
+                f"{self.__class__.__name__} expects FFN multipliers as a list, whose length is the same as"
+                f" number of transformer layers. Got: {type(ffn_multipliers)}"
+            )
+        elif isinstance(ffn_multipliers, Sequence) and len(ffn_multipliers) != n_transformer_layers:
+            logger.error(
+                f"We need FFN multiplier for each transformer layer. Got {len(ffn_multipliers)} ffn"
+                f" multipliers while number of transformer layers = {n_transformer_layers}"
+            )
+        ffn_dims = [int(math.ceil(model_dim * ffn_mult / 16.0) * 16.0) for ffn_mult in ffn_multipliers]
+
+        # Heads for transformer layers
+        mha_heads = cfg["n_heads_per_layer"]
+        if isinstance(mha_heads, int):
+            mha_heads = [mha_heads] * n_transformer_layers
+
+        if not isinstance(mha_heads, Sequence):
+            logger.error(
+                f"{self.__class__.__name__} expects MHA heads as a list, whose length is the same as number of "
+                f"transformer layers. Got: {type(mha_heads)}"
+            )
+        elif isinstance(mha_heads, Sequence) and len(mha_heads) != n_transformer_layers:
+            logger.error(
+                f"{self.__class__.__name__} needs MHA heads for each transformer layer. Got {len(mha_heads)} mha heads while"
+                f" number of transformer layers = {n_transformer_layers}"
+            )
+
+        if variant == "base":
+            self.transformer = nn.ModuleList(
+                [
+                    TransformerEncoder(
+                        embed_dim=model_dim,
+                        num_heads=mha_heads[layer_idx],
+                        ffn_latent_dim=ffn_dims[layer_idx],
+                        transformer_norm_layer=norm_layer,
+                    )
+                    for layer_idx in range(n_transformer_layers)
+                ]
+            )
+        elif variant == "mct":
+            self.transformer = nn.ModuleList([RepMixerBlock(dim=model_dim)])
+            self.transformer.extend(
+                [
+                    TransformerEncoder(
+                        embed_dim=model_dim,
+                        num_heads=mha_heads[layer_idx],
+                        ffn_latent_dim=ffn_dims[layer_idx],
+                        transformer_norm_layer=norm_layer,
+                    )
+                    for layer_idx in range(n_transformer_layers)
+                ]
+            )
+            self.transformer.extend([RepMixerBlock(dim=model_dim)])
+        else:
+            raise ValueError(f"Unrecognized text encoder variant {variant}")
+
+        self.final_layer_norm = get_normalization_layer(num_features=model_dim, norm_type=norm_layer)
+
+        self.projection_layer = nn.Parameter(torch.empty(model_dim, self.projection_dim))
+        self.model_dim = model_dim
+        self.causal_masking = cfg["causal_masking"]
+
+    def forward_embedding(self, text_tokens: Tensor) -> Tensor:
+        """Return text embedding for all tokens.
+
+        Args:
+            text_tokens: a tensor of token indices. Shape: [batch_size, context_length]
+
+        Returns:
+            A tensor of [batch_size, context_length, hidden_dim].
+        """
+        # [batch_size, context_length] --> [batch_size, context_length, hidden_dim]
+        token_emb = self.embedding_layer(text_tokens)
+        seq_len = token_emb.shape[1]
+        if self.positional_embedding is not None:
+            token_emb = token_emb + self.positional_embedding(seq_len).to(token_emb.dtype)
+        token_emb = self.embedding_dropout(token_emb)
+        return token_emb
+
+    @staticmethod
+    @torch.jit.script  # use scripting to avoid device constant
+    def build_attention_mask(text_tokens: torch.Tensor) -> Tensor:
+        """Build causal attention mask [batch_size, context_length, context_length]."""
+        # Build mask with full attention between the tokens
+        # pytorch uses additive attention mask; fill with -inf
+        batch_size, context_length = text_tokens.shape
+        mask = torch.empty(context_length, context_length, device=text_tokens.device, dtype=torch.float32)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)  # zero out the lower diagonal
+        mask = mask.unsqueeze(0)  # add dummy batch dimension
+        mask = mask.expand(batch_size, -1, -1)
+        return mask
+
+    def encode_text(
+        self,
+        text_tokens: Tensor,
+        key_padding_mask: Tensor | None = None,
+        return_all_tokens: bool = False,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        """Return text token embeddings.
+
+        Args:
+            text_tokens: a tensor of token indices. Shape: [batch_size, context_length]
+            key_padding_mask: a tensor of boolean values as the padding mask of shape [batch_size, context_length]
+            return_all_tokens: a boolean flag to return all tokens, defaults to False to return only EOT token
+                embedding.
+
+        Returns:
+            A tensor of [batch_size, context_length, hidden_dim] if return_all_tokens is
+            True, otherwise a tensor of [batch_size, hidden_dim].
+        """
+        # Discrete tokens to continuous embeddings
+        # [batch_size, context_length] --> [batch_size, context_length, hidden_dim]
+        token_emb = self.forward_embedding(text_tokens)
+
+        # [1, context_length, context_length]
+        attn_mask = None
+        if self.causal_masking:
+            attn_mask = self.build_attention_mask(text_tokens=text_tokens)
+            key_padding_mask = None
+
+        for layer in self.transformer:
+            token_emb = layer(
+                token_emb,
+                key_padding_mask=key_padding_mask,
+                attn_mask=attn_mask,
+            )
+
+        # Apply layer norm
+        token_emb = self.final_layer_norm(token_emb)
+
+        if return_all_tokens:
+            return token_emb
+
+        # Take features from the eot embedding (eot_token is the highest number in each sequence)
+        token_emb = token_emb[torch.arange(text_tokens.shape[0]), text_tokens.argmax(dim=-1)]
+
+        token_emb = token_emb @ self.projection_layer
+        return token_emb
+
+    def forward(
+        self,
+        text_tokens: Tensor,
+        key_padding_mask: Tensor | None = None,
+        return_all_tokens: bool = False,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        # Image-text pair data with single caption
+        # [B, CL] --> [B, d]
+        text_tokens = self.encode_text(
+            text_tokens=text_tokens,
+            key_padding_mask=key_padding_mask,
+            return_all_tokens=return_all_tokens,
+            *args,
+            **kwargs,
+        )
+        return text_tokens

train.py

@@ -0,0 +1,64 @@
+import torch
+from torch.utils.data import DataLoader
+
+# --- 引入你的模块 ---
+from dataset import YOLODataset
+from yolo11_standalone import YOLO11
+from loss import YOLOv8DetectionLoss
+
+# --- 引入刚刚写的 Trainer ---
+from trainer import YOLO11Trainer
+
+def run_training():
+    # --- 1.全局配置 ---
+    img_dir_train = "E:\\Datasets\\coco\\images\\train2017" 
+    label_dir_train = "E:\\Datasets\\coco\\labels\\train2017"
+    img_dir_val = "E:\\Datasets\\coco\\images\\val2017"
+    label_dir_val = "E:\\Datasets\\coco\\labels\\val2017"
+    
+    epochs = 50
+    batch_size = 36
+    img_size = 640
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    # --- 2. 准备数据 ---
+    print("Loading Data...")
+    train_dataset = YOLODataset(img_dir_train, label_dir_train, img_size=img_size, is_train=True)
+    val_dataset = YOLODataset(img_dir_val, label_dir_val, img_size=img_size, is_train=False)
+
+    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, 
+                              collate_fn=YOLODataset.collate_fn, num_workers=8, pin_memory=True)
+    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, 
+                            collate_fn=YOLODataset.collate_fn, num_workers=8, pin_memory=True)
+
+    # --- 3. 初始化模型 ---
+    print("Initializing Model...")
+    model = YOLO11(nc=80, scale='s')
+    # model.load_weights("yolo11s.pth") 
+    # model.to(device)
+
+    strides = [8, 16, 32] 
+    hyp = {
+        'box': 7.5, 
+        'cls': 0.5, 
+        'dfl': 1.5
+    }
+    loss_fn = YOLOv8DetectionLoss(nc=80, reg_max=16, stride=strides, hyp=hyp)
+
+    # --- 5. 初始化 Trainer 并开始训练 ---
+    trainer = YOLO11Trainer(
+        model=model,
+        train_loader=train_loader,
+        val_loader=val_loader,
+        loss_fn=loss_fn,
+        epochs=epochs,
+        lr0=0.01,
+        device=device,
+        save_dir='./my_yolo_result'
+    )
+
+    trainer.train()
+
+if __name__ == "__main__":
+    # Windows下多进程dataloader需要这个保护
+    run_training()

trainer.py

@@ -0,0 +1,275 @@
+import math
+import copy
+import time
+import logging
+from pathlib import Path
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from tqdm import tqdm
+from metrics import ap_per_class, box_iou, non_max_suppression, xywh2xyxy
+
+# 配置日志
+logging.basicConfig(format="%(message)s", level=logging.INFO)
+LOGGER = logging.getLogger("YOLO_Trainer")
+
+# ==============================================================================
+# Helper Class: Model EMA (Exponential Moving Average)
+# ==============================================================================
+class ModelEMA:
+    """ Updated Exponential Moving Average (EMA) from Ultralytics """
+    def __init__(self, model, decay=0.9999, tau=2000, updates=0):
+        self.ema = copy.deepcopy(model).eval()  # FP32 EMA
+        self.updates = updates
+        # decay exponential ramp (to help early epochs)
+        self.decay = lambda x: decay * (1 - math.exp(-x / tau))
+        for p in self.ema.parameters():
+            p.requires_grad_(False)
+
+    def update(self, model):
+        self.updates += 1
+        d = self.decay(self.updates)
+        
+        msd = model.state_dict()
+        for k, v in self.ema.state_dict().items():
+            if k in msd:
+                tmp = msd[k].to(v.device)
+                if v.dtype.is_floating_point:
+                    v *= d
+                    v += (1 - d) * tmp
+
+# ==============================================================================
+# Main Trainer Class
+# ==============================================================================
+class YOLO11Trainer:
+    def __init__(self, 
+                 model, 
+                 train_loader, 
+                 val_loader, 
+                 loss_fn, 
+                 epochs=100, 
+                 lr0=0.01,
+                 lrf=0.01,
+                 device='cuda',
+                 save_dir='./runs/train',
+                 warmup_epochs=3.0):
+        
+        self.device = torch.device(device if torch.cuda.is_available() else 'cpu')
+        self.model = model.to(self.device)
+        self.train_loader = train_loader
+        self.val_loader = val_loader
+        self.loss_fn = loss_fn
+        self.epochs = epochs
+        self.save_dir = Path(save_dir)
+        self.save_dir.mkdir(parents=True, exist_ok=True)
+        self.warmup_epochs = warmup_epochs
+        self.start_epoch = 0
+
+        # --- Optimizer Building ---
+        g = [], [], []  # optimizer parameter groups
+        bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k)
+        
+        for v in self.model.modules():
+            for p_name, p in v.named_parameters(recurse=False):
+                if p_name == 'bias':
+                    g[2].append(p)  # biases
+                elif isinstance(v, bn):
+                    g[1].append(p)  # bn weights (no decay)
+                else:
+                    g[0].append(p)  # weights (decay)
+        
+        self.optimizer = optim.SGD(g[2], lr=lr0, momentum=0.937, nesterov=True)
+        self.optimizer.add_param_group({'params': g[0], 'weight_decay': 0.0005})
+        self.optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0})
+        
+        LOGGER.info(f"Optimizer: weights={len(g[0])}, bn={len(g[1])}, biases={len(g[2])}")
+
+        # --- Scheduler ---
+        self.lf = lambda x: ((1 - math.cos(x * math.pi / self.epochs)) / 2) * (lrf - 1) + 1
+        self.scheduler = optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=self.lf)
+
+        # --- AMP & EMA ---
+        self.scaler = torch.amp.GradScaler('cuda', enabled=True)
+        self.ema = ModelEMA(self.model)
+
+    def train_one_epoch(self, epoch):
+        self.model.train()
+        pbar = tqdm(enumerate(self.train_loader), total=len(self.train_loader), 
+                    desc=f'Epoch {epoch+1}/{self.epochs}', leave=True)
+        
+        mloss = torch.zeros(3, device=self.device)
+        self.optimizer.zero_grad()
+        nb = len(self.train_loader)
+        
+        for i, batch in pbar:
+            ni = i + nb * epoch
+            
+            imgs, targets, paths = batch
+            imgs = imgs.to(self.device, non_blocking=True)
+            targets = targets.to(self.device)
+
+            # --- Warmup ---
+            if ni <= nb * self.warmup_epochs:
+                xp = [0, nb * self.warmup_epochs]
+                for j, x in enumerate(self.optimizer.param_groups):
+                    lr_target = x['initial_lr'] * self.lf(epoch)
+                    x['lr'] = np.interp(ni, xp, [0.1 if j == 0 else 0.0, lr_target])
+                    if 'momentum' in x:
+                        x['momentum'] = np.interp(ni, xp, [0.8, 0.937])
+
+            # --- Forward ---
+            with torch.amp.autocast('cuda', enabled=True):
+                preds = self.model(imgs)
+                
+                target_batch = {
+                    "batch_idx": targets[:, 0],
+                    "cls": targets[:, 1],
+                    "bboxes": targets[:, 2:],
+                }
+                
+                loss, loss_items = self.loss_fn(preds, target_batch)
+
+            # --- Backward ---
+            self.scaler.scale(loss).backward()
+            self.scaler.unscale_(self.optimizer)
+            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=10.0)
+            self.scaler.step(self.optimizer)
+            self.scaler.update()
+            self.optimizer.zero_grad()
+            
+            # --- EMA ---
+            self.ema.update(self.model)
+
+            # --- Logging ---
+            loss_items = loss_items.detach()
+            mloss = (mloss * i + loss_items) / (i + 1)
+            mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'
+            
+            pbar.set_postfix({
+                'mem': mem,
+                'box': f"{mloss[0]:.4f}",
+                'cls': f"{mloss[1]:.4f}",
+                'dfl': f"{mloss[2]:.4f}",
+                'lr': f"{self.optimizer.param_groups[0]['lr']:.5f}"
+            })
+
+    def validate(self):
+        model = self.ema.ema
+        device = self.device
+        
+        # --- Metrics Config ---
+        conf_thres = 0.001  # Low threshold for mAP calculation
+        iou_thres = 0.7     # NMS IoU threshold
+        iouv = torch.linspace(0.5, 0.95, 10, device=device)  # IoU vector for mAP@0.5:0.95
+        
+        loss_sum = torch.zeros(3, device=device)
+        stats = [] # [(correct, conf, pred_cls, target_cls)]
+        
+        LOGGER.info("\nValidating...")
+
+        model.eval()
+        
+        pbar = tqdm(self.val_loader, desc="Calc Metrics")
+        with torch.no_grad():
+            for batch in pbar:
+                imgs, targets, _ = batch
+                imgs = imgs.to(device, non_blocking=True)
+                targets = targets.to(device)
+                _, _, height, width = imgs.shape
+
+                # Inference
+                preds = model(imgs)
+                
+                # NMS
+                preds = non_max_suppression(preds, conf_thres=conf_thres, iou_thres=iou_thres)
+                
+                # Metrics Processing
+                for si, pred in enumerate(preds):
+                    labels = targets[targets[:, 0] == si]
+                    nl = len(labels)
+                    tcls = labels[:, 1].tolist() if nl else []
+                    
+                    if len(pred) == 0:
+                        if nl:
+                            stats.append((torch.zeros(0, iouv.numel(), dtype=torch.bool), 
+                                          torch.Tensor(), torch.Tensor(), torch.tensor(tcls)))
+                        continue
+
+                    # Predictions
+                    predn = pred.clone()
+                    
+                    # Ground Truth
+                    if nl:
+                        tbox = xywh2xyxy(labels[:, 2:6])
+                        tbox[:, [0, 2]] *= width
+                        tbox[:, [1, 3]] *= height
+                        labelsn = torch.cat((labels[:, 1:2], tbox), 1) # [cls, x1, y1, x2, y2]
+                        
+                        # Match predictions to GT
+                        correct = self._process_batch(predn, labelsn, iouv)
+                    else:
+                        correct = torch.zeros(pred.shape[0], iouv.numel(), dtype=torch.bool)
+                        
+                    stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), torch.tensor(tcls)))
+        
+        mp, mr, map50, map5095 = 0.0, 0.0, 0.0, 0.0
+        if len(stats) and stats[0][0].any():
+            stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)]  # to numpy
+            tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats)
+            ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95
+            mp, mr, map50, map5095 = p.mean(), r.mean(), ap50.mean(), ap.mean()
+
+        LOGGER.info(f"Val Results: Prec={mp:.3f}, Recall={mr:.3f} mAP50={map50:.3f} mAP50-95={map5095:.3f}")
+
+        return map50
+
+    def _process_batch(self, detections, labels, iouv):
+        """
+        Return correct prediction matrix
+        detections: [N, 6] (x1, y1, x2, y2, conf, cls)
+        labels: [M, 5] (cls, x1, y1, x2, y2)
+        """
+        correct = torch.zeros(detections.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device)
+        iou = box_iou(labels[:, 1:], detections[:, :4])
+        
+        x = torch.where((iou >= iouv[0]) & (labels[:, 0:1] == detections[:, 5]))
+        
+        if x[0].shape[0]:
+            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
+            if x[0].shape[0] > 1:
+                matches = matches[matches[:, 2].argsort()[::-1]]
+                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+                matches = matches[matches[:, 2].argsort()[::-1]]
+                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+            
+            matches = torch.from_numpy(matches).to(iouv.device)
+            correct[matches[:, 1].long()] = matches[:, 2:3] >= iouv
+            
+        return correct
+
+    def train(self):
+        LOGGER.info(f"Starting training on {self.device} for {self.epochs} epochs...")
+        start_time = time.time()
+        best_fitness = 0.0 
+        
+        for epoch in range(self.start_epoch, self.epochs):
+            self.train_one_epoch(epoch)
+            self.scheduler.step()
+            map50 = self.validate()
+            
+            ckpt = {
+                'epoch': epoch,
+                'model': self.model.state_dict(),
+                'ema': self.ema.ema.state_dict(),
+                'optimizer': self.optimizer.state_dict(),
+            }
+            
+            torch.save(ckpt, self.save_dir / 'last.pt')
+            
+            if map50 > best_fitness:
+                best_fitness = map50
+                torch.save(ckpt, self.save_dir / 'best.pt')
+                LOGGER.info(f"--> Saved best model with Recall/mAP: {best_fitness:.4f}")
+
+        LOGGER.info(f"\nTraining completed in {(time.time() - start_time) / 3600:.3f} hours.")

yolo11_standalone.py

@@ -0,0 +1,923 @@
+import math
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+def make_divisible(x, divisor):
+    if isinstance(x, torch.Tensor):
+        return x
+    return math.ceil(x / divisor) * divisor
+
+def autopad(k, p=None, d=1):
+    if d > 1:
+        k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]
+    if p is None:
+        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]
+    return p
+
+def make_anchors(feats, strides, grid_cell_offset=0.5):
+    anchor_points, stride_tensor = [], []
+    assert feats is not None
+    dtype, device = feats[0].dtype, feats[0].device
+    for i, stride in enumerate(strides):
+        _, _, h, w = feats[i].shape
+        sx = torch.arange(end=w, device=device, dtype=dtype) + grid_cell_offset
+        sy = torch.arange(end=h, device=device, dtype=dtype) + grid_cell_offset
+        sy, sx = torch.meshgrid(sy, sx, indexing="ij")
+        anchor_points.append(torch.stack((sx, sy), -1).view(-1, 2))
+        stride_tensor.append(torch.full((h * w, 1), stride, dtype=dtype, device=device))
+    return torch.cat(anchor_points), torch.cat(stride_tensor)
+
+def dist2bbox(distance, anchor_points, xywh=True, dim=-1):
+    lt, rb = distance.chunk(2, dim)
+    x1y1 = anchor_points - lt
+    x2y2 = anchor_points + rb
+    if xywh:
+        c_xy = (x1y1 + x2y2) / 2
+        wh = x2y2 - x1y1
+        return torch.cat((c_xy, wh), dim)
+    return torch.cat((x1y1, x2y2), dim)
+
+class Concat(nn.Module):
+    def __init__(self, dimension=1):
+        super().__init__()
+        self.d = dimension
+
+    def forward(self, x: List[torch.Tensor]):
+        return torch.cat(x, self.d)
+
+
+class Conv(nn.Module):
+    default_act = nn.SiLU(inplace=True)
+
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
+        super().__init__()
+        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False) # type: ignore
+        self.bn = nn.BatchNorm2d(c2, eps=0.001, momentum=0.03)
+        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
+
+    def forward(self, x):
+        return self.act(self.bn(self.conv(x)))
+
+    def forward_fuse(self, x):
+        return self.act(self.conv(x))
+    
+class DWConv(Conv):
+    def __init__(self, c1, c2, k=1, s=1, d=1, act=True):
+        super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
+
+class DFL(nn.Module):
+    def __init__(self, c1: int = 16):
+        super().__init__()
+        self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False)
+        x = torch.arange(c1, dtype=torch.float)
+        self.conv.weight.data[:] = nn.Parameter(x.view(1, c1, 1, 1))
+        self.c1 = c1
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        b, _, a = x.shape
+        return self.conv(x.view(b, 4, self.c1, a).transpose(2, 1).softmax(1)).view(b, 4, a)
+    
+class Bottleneck(nn.Module):
+    def __init__(
+        self, c1: int, c2: int, shortcut: bool = True, g: int = 1, k: Tuple[int, int] = (3, 3), e: float = 0.5
+    ):
+        super().__init__()
+        c_ = int(c2 * e)
+        self.cv1 = Conv(c1, c_, k[0], 1)
+        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+    
+class C2f(nn.Module):
+    def __init__(self, c1: int, c2: int, n: int = 1, shortcut: bool = False, g: int = 1, e: float = 0.5):
+        super().__init__()
+        self.c = int(c2 * e)
+        self.cv1 = Conv(c1, 2 * self.c, 1, 1)
+        self.cv2 = Conv((2 + n) * self.c, c2, 1)
+        self.m = nn.ModuleList(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n)) # type: ignore
+
+    def forward(self, x):
+        chunk_result = self.cv1(x).chunk(2, 1)
+        y = [chunk_result[0], chunk_result[1]]
+        
+        for m_module in self.m:
+            y.append(m_module(y[-1]))
+        return self.cv2(torch.cat(y, 1))
+
+    def forward_split(self, x: torch.Tensor) -> torch.Tensor:
+        y = self.cv1(x).split((self.c, self.c), 1)
+        y = [y[0], y[1]]
+        y.extend(m(y[-1]) for m in self.m)
+        return self.cv2(torch.cat(y, 1))
+    
+class C3(nn.Module):
+    def __init__(self, c1: int, c2: int, n: int = 1, shortcut: bool = True, g: int = 1, e: float = 0.5):
+        super().__init__()
+        c_ = int(c2 * e)
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.cv3 = Conv(2 * c_, c2, 1)
+        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, k=((1, 1), (3, 3)), e=1.0) for _ in range(n))) # type: ignore
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
+
+class C3k(C3):
+    def __init__(self, c1: int, c2: int, n: int = 1, shortcut: bool = True, g: int = 1, e: float = 0.5, k: int = 3):
+        super().__init__(c1, c2, n, shortcut, g, e)
+        c_ = int(c2 * e)
+        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, k=(k, k), e=1.0) for _ in range(n)))
+    
+class C3k2(C2f):
+    def __init__(
+        self, c1: int, c2: int, n: int = 1, c3k: bool = False, e: float = 0.5, g: int = 1, shortcut: bool = True
+    ):
+        super().__init__(c1, c2, n, shortcut, g, e)
+        self.m = nn.ModuleList(
+            C3k(self.c, self.c, 2, shortcut, g) if c3k else Bottleneck(self.c, self.c, shortcut, g) for _ in range(n)
+        )
+
+class SPPF(nn.Module):
+    def __init__(self, c1: int, c2: int, k: int = 5):
+        super().__init__()
+        c_ = c1 // 2
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_ * 4, c2, 1, 1)
+        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        y = [self.cv1(x)]
+        y.extend(self.m(y[-1]) for _ in range(3))
+        return self.cv2(torch.cat(y, 1))
+    
+class Attention(nn.Module):
+    def __init__(self, dim: int, num_heads: int = 8, attn_ratio: float = 0.5):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.key_dim = int(self.head_dim * attn_ratio)
+        self.scale = self.key_dim**-0.5
+        nh_kd = self.key_dim * num_heads
+        h = dim + nh_kd * 2
+        self.qkv = Conv(dim, h, 1, act=False)
+        self.proj = Conv(dim, dim, 1, act=False)
+        self.pe = Conv(dim, dim, 3, 1, g=dim, act=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, C, H, W = x.shape
+        N = H * W
+        qkv = self.qkv(x)
+        q, k, v = qkv.view(B, self.num_heads, self.key_dim * 2 + self.head_dim, N).split(
+            [self.key_dim, self.key_dim, self.head_dim], dim=2
+        )
+
+        attn = (q.transpose(-2, -1) @ k) * self.scale
+        attn = attn.softmax(dim=-1)
+        x = (v @ attn.transpose(-2, -1)).view(B, C, H, W) + self.pe(v.reshape(B, C, H, W))
+        x = self.proj(x)
+        return x
+    
+class PSABlock(nn.Module):
+    def __init__(self, c: int, attn_ratio: float = 0.5, num_heads: int = 4, shortcut: bool = True) -> None:
+        super().__init__()
+        self.attn = Attention(c, attn_ratio=attn_ratio, num_heads=num_heads)
+        self.ffn = nn.Sequential(Conv(c, c * 2, 1), Conv(c * 2, c, 1, act=False))
+        self.add = shortcut
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.attn(x) if self.add else self.attn(x)
+        x = x + self.ffn(x) if self.add else self.ffn(x)
+        return x
+    
+class C2PSA(nn.Module):
+    def __init__(self, c1: int, c2: int, n: int = 1, e: float = 0.5):
+        super().__init__()
+        assert c1 == c2
+        self.c = int(c1 * e)
+        self.cv1 = Conv(c1, 2 * self.c, 1, 1)
+        self.cv2 = Conv(2 * self.c, c1, 1)
+
+        self.m = nn.Sequential(*(PSABlock(self.c, attn_ratio=0.5, num_heads=self.c // 64) for _ in range(n)))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        a, b = self.cv1(x).split((self.c, self.c), dim=1)
+        b = self.m(b)
+        return self.cv2(torch.cat((a, b), 1))
+    
+class Proto(nn.Module):
+    def __init__(self, c1: int, c_: int = 256, c2: int = 32):
+        super().__init__()
+        self.cv1 = Conv(c1, c_, k=3)
+        self.upsample = nn.ConvTranspose2d(c_, c_, 2, 2, 0, bias=True)
+        self.cv2 = Conv(c_, c_, k=3)
+        self.cv3 = Conv(c_, c2)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.cv3(self.cv2(self.upsample(self.cv1(x))))
+
+class BNContrastiveHead(nn.Module):
+    def __init__(self, embed_dims: int):
+        super().__init__()
+        self.norm = nn.BatchNorm2d(embed_dims)
+        self.bias = nn.Parameter(torch.tensor([-10.0]))
+        self.logit_scale = nn.Parameter(-1.0 * torch.ones([]))
+
+    def fuse(self):
+        del self.norm
+        del self.bias
+        del self.logit_scale
+        self.forward = self.forward_fuse
+
+    def forward_fuse(self, x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
+        return x
+
+    def forward(self, x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
+        x = self.norm(x)
+        w = F.normalize(w, dim=-1, p=2)
+
+        x = torch.einsum("bchw,bkc->bkhw", x, w)
+        return x * self.logit_scale.exp() + self.bias
+    
+class SwiGLUFFN(nn.Module):
+    def __init__(self, gc: int, ec: int, e: int = 4) -> None:
+        super().__init__()
+        self.w12 = nn.Linear(gc, e * ec)
+        self.w3 = nn.Linear(e * ec // 2, ec)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x12 = self.w12(x)
+        x1, x2 = x12.chunk(2, dim=-1)
+        hidden = F.silu(x1) * x2
+        return self.w3(hidden)
+    
+class Residual(nn.Module):
+    def __init__(self, m: nn.Module) -> None:
+        super().__init__()
+        self.m = m
+        nn.init.zeros_(self.m.w3.bias)
+        # For models with l scale, please change the initialization to
+        # nn.init.constant_(self.m.w3.weight, 1e-6)
+        nn.init.zeros_(self.m.w3.weight)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x + self.m(x)
+    
+class SAVPE(nn.Module):
+    def __init__(self, ch: List[int], c3: int, embed: int):
+        super().__init__()
+        self.cv1 = nn.ModuleList(
+            nn.Sequential(
+                Conv(x, c3, 3), Conv(c3, c3, 3), nn.Upsample(scale_factor=i * 2) if i in {1, 2} else nn.Identity()
+            )
+            for i, x in enumerate(ch)
+        )
+
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c3, 1), nn.Upsample(scale_factor=i * 2) if i in {1, 2} else nn.Identity())
+            for i, x in enumerate(ch)
+        )
+
+        self.c = 16
+        self.cv3 = nn.Conv2d(3 * c3, embed, 1)
+        self.cv4 = nn.Conv2d(3 * c3, self.c, 3, padding=1)
+        self.cv5 = nn.Conv2d(1, self.c, 3, padding=1)
+        self.cv6 = nn.Sequential(Conv(2 * self.c, self.c, 3), nn.Conv2d(self.c, self.c, 3, padding=1))
+
+    def forward(self, x: List[torch.Tensor], vp: torch.Tensor) -> torch.Tensor:
+        y = [self.cv2[i](xi) for i, xi in enumerate(x)]
+        y = self.cv4(torch.cat(y, dim=1))
+
+        x = [self.cv1[i](xi) for i, xi in enumerate(x)]
+        x = self.cv3(torch.cat(x, dim=1))
+
+        B, C, H, W = x.shape # type: ignore
+
+        Q = vp.shape[1]
+
+        x = x.view(B, C, -1) # type: ignore
+
+        y = y.reshape(B, 1, self.c, H, W).expand(-1, Q, -1, -1, -1).reshape(B * Q, self.c, H, W)
+        vp = vp.reshape(B, Q, 1, H, W).reshape(B * Q, 1, H, W)
+
+        y = self.cv6(torch.cat((y, self.cv5(vp)), dim=1))
+
+        y = y.reshape(B, Q, self.c, -1)
+        vp = vp.reshape(B, Q, 1, -1)
+
+        score = y * vp + torch.logical_not(vp) * torch.finfo(y.dtype).min
+        score = F.softmax(score, dim=-1).to(y.dtype)
+        aggregated = score.transpose(-2, -3) @ x.reshape(B, self.c, C // self.c, -1).transpose(-1, -2)
+
+        return F.normalize(aggregated.transpose(-2, -3).reshape(B, Q, -1), dim=-1, p=2)
+    
+class Detect(nn.Module):
+    dynamic = False
+    export = False
+    shape = None
+    anchors = torch.empty(0)
+    strides = torch.empty(0)
+
+    def __init__(self, nc=80, ch=()):
+        super().__init__()
+        self.nc = nc
+        self.nl = len(ch)
+        self.reg_max = 16
+        self.no = nc + self.reg_max * 4
+        self.stride = torch.tensor([8., 16., 32.])
+        
+        c2, c3 = max((16, ch[0] // 4, self.reg_max * 4)), max(ch[0], min(self.nc, 100))
+        
+        self.cv2 = nn.ModuleList(
+            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch
+        )
+        self.cv3 = nn.ModuleList(
+            nn.Sequential(
+                nn.Sequential(DWConv(x, x, 3), Conv(x, c3, 1)),
+                nn.Sequential(DWConv(c3, c3, 3), Conv(c3, c3, 1)),
+                nn.Conv2d(c3, self.nc, 1),
+            )
+            for x in ch
+        )
+        self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
+
+    def forward(self, x):
+        for i in range(self.nl):
+            x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
+        
+        if self.training:
+            return x
+            
+        # Inference path
+        shape = x[0].shape
+        x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
+        
+        if self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
+            self.shape = shape
+
+        box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        
+        return torch.cat((dbox, cls.sigmoid()), 1)
+
+    def bias_init(self):
+        m = self
+        for a, b, s in zip(m.cv2, m.cv3, m.stride):
+            a[-1].bias.data[:] = 1.0 # type: ignore
+            b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # type: ignore
+
+    def decode_bboxes(self, bboxes: torch.Tensor, anchors: torch.Tensor, xywh: bool = True) -> torch.Tensor:
+        return dist2bbox(bboxes, anchors, xywh=xywh and not (self.end2end or self.xyxy), dim=1)
+
+    @staticmethod
+    def postprocess(preds: torch.Tensor, max_det: int, nc: int = 80) -> torch.Tensor:
+        batch_size, anchors, _ = preds.shape
+        boxes, scores = preds.split([4, nc], dim=-1)
+        index = scores.amax(dim=-1).topk(min(max_det, anchors))[1].unsqueeze(-1)
+        boxes = boxes.gather(dim=1, index=index.repeat(1, 1, 4))
+        scores = scores.gather(dim=1, index=index.repeat(1, 1, nc))
+        scores, index = scores.flatten(1).topk(min(max_det, anchors))
+        i = torch.arange(batch_size)[..., None]
+        return torch.cat([boxes[i, index // nc], scores[..., None], (index % nc)[..., None].float()], dim=-1)
+    
+class YOLOEDetect(Detect):
+    def __init__(self, nc: int = 80, embed: int = 512, ch: Tuple = ()):
+        super().__init__(nc, ch)
+        c3 = max(ch[0], min(self.nc, 100))
+        assert c3 <= embed
+        self.cv3 = (
+            nn.ModuleList(
+                nn.Sequential(
+                    nn.Sequential(DWConv(x, x, 3), Conv(x, c3, 1)),
+                    nn.Sequential(DWConv(c3, c3, 3), Conv(c3, c3, 1)),
+                    nn.Conv2d(c3, embed, 1),
+                )
+                for x in ch
+            )
+        )
+
+        self.cv4 = nn.ModuleList(BNContrastiveHead(embed) for _ in ch)
+
+        self.reprta = Residual(SwiGLUFFN(embed, embed))
+        self.savpe = SAVPE(ch, c3, embed) # type: ignore
+        self.embed = embed
+
+    def get_tpe(self, tpe: Optional[torch.Tensor]) -> Optional[torch.Tensor]:
+        return None if tpe is None else F.normalize(self.reprta(tpe), dim=-1, p=2)
+
+    def get_vpe(self, x: List[torch.Tensor], vpe: torch.Tensor) -> torch.Tensor:
+        if vpe.shape[1] == 0:  # no visual prompt embeddings
+            return torch.zeros(x[0].shape[0], 0, self.embed, device=x[0].device)
+        if vpe.ndim == 4:  # (B, N, H, W)
+            vpe = self.savpe(x, vpe)
+        assert vpe.ndim == 3  # (B, N, D)
+        return vpe
+
+    def forward( # type: ignore
+        self, x: List[torch.Tensor], cls_pe: torch.Tensor
+    ) -> Union[torch.Tensor, Tuple]:
+        for i in range(self.nl):
+            x[i] = torch.cat((self.cv2[i](x[i]), self.cv4[i](self.cv3[i](x[i]), cls_pe)), 1)
+        if self.training:
+            return x # type: ignore
+        self.no = self.nc + self.reg_max * 4  # self.nc could be changed when inference with different texts
+        # Inference path
+        shape = x[0].shape
+        x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
+        
+        if self.dynamic or self.shape != shape:
+            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
+            self.shape = shape
+
+        box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)
+        dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
+        
+        return torch.cat((dbox, cls.sigmoid()), 1)
+
+    def bias_init(self):
+        m = self
+        for a, b, c, s in zip(m.cv2, m.cv3, m.cv4, m.stride):
+            a[-1].bias.data[:] = 1.0  # box
+            b[-1].bias.data[:] = 0.0
+            c.bias.data[:] = math.log(5 / m.nc / (640 / s) ** 2)
+
+class YOLOESegment(YOLOEDetect):
+    def __init__(
+        self, nc: int = 80, nm: int = 32, npr: int = 256, embed: int = 512, ch: Tuple = ()
+    ):
+        super().__init__(nc, embed, ch)
+        self.nm = nm
+        self.npr = npr
+        self.proto = Proto(ch[0], self.npr, self.nm)
+
+        c5 = max(ch[0] // 4, self.nm)
+        self.cv5 = nn.ModuleList(nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nm, 1)) for x in ch)
+
+    def forward(self, x: List[torch.Tensor], text: torch.Tensor) -> Union[Tuple, torch.Tensor]:
+        p = self.proto(x[0])  # mask protos
+        bs = p.shape[0]  # batch size
+
+        mc = torch.cat([self.cv5[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)  # mask coefficients
+        x = YOLOEDetect.forward(self, x, text)
+
+        if self.training:
+            return x, mc, p
+
+        return (torch.cat([x, mc], 1), p)
+    
+
+    
+class YOLO11(nn.Module):
+    def __init__(self, nc=80, scale='n'):
+        super().__init__()
+        self.nc = nc
+        
+        # Scales: [depth, width, max_channels]
+        # 对应 yolo11.yaml 中的 scales 参数
+        scales = {
+            'n': [0.50, 0.25, 1024],
+            's': [0.50, 0.50, 1024],
+            'm': [0.50, 1.00, 512],
+            'l': [1.00, 1.00, 512],
+            'x': [1.00, 1.50, 512],
+        }
+        
+        if scale not in scales:
+            raise ValueError(f"Invalid scale '{scale}'. Available scales: {list(scales.keys())}")
+            
+        depth, width, max_channels = scales[scale]
+
+        if scale in ['n', 's']:
+            c3k_override = False
+        else:
+            c3k_override = True
+        
+        # 辅助函数：计算通道数 (Width Scaling)
+        def gw(channels):
+            return make_divisible(min(channels, max_channels) * width, 8)
+        
+        # 辅助函数：计算层重复次数 (Depth Scaling)
+        def gd(n):
+            return max(round(n * depth), 1) if n > 1 else n
+
+        self.model = nn.ModuleList()
+        
+        # --- Backbone ---
+        # 0: Conv [64, 3, 2]
+        self.model.append(Conv(3, gw(64), 3, 2))
+        
+        # 1: Conv [128, 3, 2]
+        self.model.append(Conv(gw(64), gw(128), 3, 2))
+        
+        # 2: C3k2 [256, False, 0.25] -> n=2
+        self.model.append(C3k2(gw(128), gw(256), n=gd(2), c3k=False or c3k_override, e=0.25))
+        
+        # 3: Conv [256, 3, 2]
+        self.model.append(Conv(gw(256), gw(256), 3, 2))
+        
+        # 4: C3k2 [512, False, 0.25] -> n=2
+        self.model.append(C3k2(gw(256), gw(512), n=gd(2), c3k=False or c3k_override, e=0.25))
+        
+        # 5: Conv [512, 3, 2]
+        self.model.append(Conv(gw(512), gw(512), 3, 2))
+        
+        # 6: C3k2 [512, True] -> n=2
+        self.model.append(C3k2(gw(512), gw(512), n=gd(2), c3k=True))
+        
+        # 7: Conv [1024, 3, 2]
+        self.model.append(Conv(gw(512), gw(1024), 3, 2))
+        
+        # 8: C3k2 [1024, True] -> n=2
+        self.model.append(C3k2(gw(1024), gw(1024), n=gd(2), c3k=True))
+        
+        # 9: SPPF [1024, 5]
+        self.model.append(SPPF(gw(1024), gw(1024), 5))
+        
+        # 10: C2PSA [1024] -> n=2 (YAML args=[1024], repeats=2)
+        self.model.append(C2PSA(gw(1024), gw(1024), n=gd(2)))
+        
+        # --- Head ---
+        
+        # 11: Upsample
+        self.model.append(nn.Upsample(scale_factor=2, mode='nearest'))
+        
+        # 12: Concat [-1, 6] (P4)
+        self.model.append(Concat(dimension=1))
+        
+        # 13: C3k2 [512, False] -> n=2. Input: P5_up(gw(1024)) + P4(gw(512))
+        self.model.append(C3k2(gw(1024) + gw(512), gw(512), n=gd(2), c3k=False or c3k_override))
+        
+        # 14: Upsample
+        self.model.append(nn.Upsample(scale_factor=2, mode='nearest'))
+        
+        # 15: Concat [-1, 4] (P3)
+        self.model.append(Concat(dimension=1))
+        
+        # 16: C3k2 [256, False] -> n=2. Input: P4_up(gw(512)) + P3(gw(512))
+        # 注意：Layer 4 输出是 gw(512)，Layer 13 输出是 gw(512)
+        self.model.append(C3k2(gw(512) + gw(512), gw(256), n=gd(2), c3k=False or c3k_override))
+        
+        # 17: Conv [256, 3, 2]
+        self.model.append(Conv(gw(256), gw(256), 3, 2))
+        
+        # 18: Concat [-1, 13] (Head P4)
+        self.model.append(Concat(dimension=1))
+        
+        # 19: C3k2 [512, False] -> n=2. Input: P3_down(gw(256)) + Head_P4(gw(512))
+        self.model.append(C3k2(gw(256) + gw(512), gw(512), n=gd(2), c3k=False or c3k_override))
+        
+        # 20: Conv [512, 3, 2]
+        self.model.append(Conv(gw(512), gw(512), 3, 2))
+        
+        # 21: Concat [-1, 10] (P5)
+        self.model.append(Concat(dimension=1))
+        
+        # 22: C3k2 [1024, True] -> n=2. Input: P4_down(gw(512)) + P5(gw(1024))
+        self.model.append(C3k2(gw(512) + gw(1024), gw(1024), n=gd(2), c3k=True))
+        
+        # 23: Detect [nc]
+        self.model.append(Detect(nc, ch=[gw(256), gw(512), gw(1024)]))
+
+        # --- 初始化权重 ---
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        """初始化模型权重，特别是 Detect 头的 Bias"""
+        for m in self.modules():
+            if isinstance(m, (nn.Conv2d, nn.Linear)):
+                # 使用 Kaiming 初始化或其他合适的初始化
+                pass 
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+        detect_layer = self.model[-1]
+        if isinstance(detect_layer, Detect):
+            detect_layer.bias_init()
+
+    def forward(self, x):
+        # Backbone
+        x = self.model[0](x)
+        x = self.model[1](x)
+        x = self.model[2](x)
+        x = self.model[3](x)
+        p3 = self.model[4](x)           # 保存 P3 (layer 4)
+        x = self.model[5](p3)
+        p4 = self.model[6](x)           # 保存 P4 (layer 6)
+        x = self.model[7](p4)
+        x = self.model[8](x)
+        x = self.model[9](x)
+        p5 = self.model[10](x)          # 保存 P5 (layer 10)
+        
+        # Head
+        x = self.model[11](p5)          # Upsample
+        x = self.model[12]([x, p4])     # Concat P4
+        h1 = self.model[13](x)          # Head P4 (layer 13)
+        
+        x = self.model[14](h1)          # Upsample
+        x = self.model[15]([x, p3])     # Concat P3
+        h2 = self.model[16](x)          # Output P3 (layer 16)
+        
+        x = self.model[17](h2)          # Conv
+        x = self.model[18]([x, h1])     # Concat Head P4
+        h3 = self.model[19](x)          # Output P4 (layer 19)
+        
+        x = self.model[20](h3)          # Conv
+        x = self.model[21]([x, p5])     # Concat P5
+        h4 = self.model[22](x)          # Output P5 (layer 22)
+        
+        return self.model[23]([h2, h3, h4]) # Detect
+
+    def load_weights(self, pth_file):
+        state_dict = torch.load(pth_file, map_location='cpu', weights_only=False)
+        # 移除可能存在的 'model.' 前缀 (如果权重来自 ultralytics 官方)
+        # 官方权重通常是 model.model.0.conv... 这种格式，或者直接是 model.0.conv...
+        # 这里做一个简单的兼容性处理
+        new_state_dict = {}
+        for k, v in state_dict.items():
+            # 处理 ultralytics 权重字典中的 'model' 键
+            if k == 'model':
+                # 如果是完整的 checkpoint，权重在 'model' 键下
+                # 且通常是 model.state_dict()
+                if hasattr(v, 'state_dict'):
+                    v = v.state_dict()
+                elif isinstance(v, dict):
+                    pass # v 就是 state_dict
+                else:
+                    # 可能是 model 对象本身
+                    try:
+                        v = v.float().state_dict()
+                    except:
+                        continue
+                
+                for sub_k, sub_v in v.items():
+                    new_state_dict[sub_k] = sub_v
+                break
+            else:
+                new_state_dict[k] = v
+        
+        if not new_state_dict:
+            new_state_dict = state_dict
+
+        # 尝试加载
+        try:
+            self.load_state_dict(new_state_dict, strict=True)
+            print(f"Successfully loaded weights from {pth_file}")
+        except Exception as e:
+            print(f"Error loading weights: {e}")
+            print("Trying to load with strict=False...")
+            self.load_state_dict(new_state_dict, strict=False)
+
+class YOLO11E(nn.Module):
+    def __init__(self, nc=80, scale='n'):
+        super().__init__()
+        self.nc = nc
+        self.pe = None 
+        
+        # Scales: [depth, width, max_channels]
+        # 对应 yolo11.yaml 中的 scales 参数
+        scales = {
+            'n': [0.50, 0.25, 1024],
+            's': [0.50, 0.50, 1024],
+            'm': [0.50, 1.00, 512],
+            'l': [1.00, 1.00, 512],
+            'x': [1.00, 1.50, 512],
+        }
+        
+        if scale not in scales:
+            raise ValueError(f"Invalid scale '{scale}'. Available scales: {list(scales.keys())}")
+            
+        depth, width, max_channels = scales[scale]
+
+        if scale in ['n', 's']:
+            c3k_override = False
+        else:
+            c3k_override = True
+        
+        # 辅助函数：计算通道数 (Width Scaling)
+        def gw(channels):
+            return make_divisible(min(channels, max_channels) * width, 8)
+        
+        # 辅助函数：计算层重复次数 (Depth Scaling)
+        def gd(n):
+            return max(round(n * depth), 1) if n > 1 else n
+
+        self.model = nn.ModuleList()
+        
+        # --- Backbone ---
+        # 0: Conv [64, 3, 2]
+        self.model.append(Conv(3, gw(64), 3, 2))
+        
+        # 1: Conv [128, 3, 2]
+        self.model.append(Conv(gw(64), gw(128), 3, 2))
+        
+        # 2: C3k2 [256, False, 0.25] -> n=2
+        self.model.append(C3k2(gw(128), gw(256), n=gd(2), c3k=False or c3k_override, e=0.25))
+        
+        # 3: Conv [256, 3, 2]
+        self.model.append(Conv(gw(256), gw(256), 3, 2))
+        
+        # 4: C3k2 [512, False, 0.25] -> n=2
+        self.model.append(C3k2(gw(256), gw(512), n=gd(2), c3k=False or c3k_override, e=0.25))
+        
+        # 5: Conv [512, 3, 2]
+        self.model.append(Conv(gw(512), gw(512), 3, 2))
+        
+        # 6: C3k2 [512, True] -> n=2
+        self.model.append(C3k2(gw(512), gw(512), n=gd(2), c3k=True))
+        
+        # 7: Conv [1024, 3, 2]
+        self.model.append(Conv(gw(512), gw(1024), 3, 2))
+        
+        # 8: C3k2 [1024, True] -> n=2
+        self.model.append(C3k2(gw(1024), gw(1024), n=gd(2), c3k=True))
+        
+        # 9: SPPF [1024, 5]
+        self.model.append(SPPF(gw(1024), gw(1024), 5))
+        
+        # 10: C2PSA [1024] -> n=2 (YAML args=[1024], repeats=2)
+        self.model.append(C2PSA(gw(1024), gw(1024), n=gd(2)))
+        
+        # --- Head ---
+        
+        # 11: Upsample
+        self.model.append(nn.Upsample(scale_factor=2, mode='nearest'))
+        
+        # 12: Concat [-1, 6] (P4)
+        self.model.append(Concat(dimension=1))
+        
+        # 13: C3k2 [512, False] -> n=2. Input: P5_up(gw(1024)) + P4(gw(512))
+        self.model.append(C3k2(gw(1024) + gw(512), gw(512), n=gd(2), c3k=False or c3k_override))
+        
+        # 14: Upsample
+        self.model.append(nn.Upsample(scale_factor=2, mode='nearest'))
+        
+        # 15: Concat [-1, 4] (P3)
+        self.model.append(Concat(dimension=1))
+        
+        # 16: C3k2 [256, False] -> n=2. Input: P4_up(gw(512)) + P3(gw(512))
+        # 注意：Layer 4 输出是 gw(512)，Layer 13 输出是 gw(512)
+        self.model.append(C3k2(gw(512) + gw(512), gw(256), n=gd(2), c3k=False or c3k_override))
+        
+        # 17: Conv [256, 3, 2]
+        self.model.append(Conv(gw(256), gw(256), 3, 2))
+        
+        # 18: Concat [-1, 13] (Head P4)
+        self.model.append(Concat(dimension=1))
+        
+        # 19: C3k2 [512, False] -> n=2. Input: P3_down(gw(256)) + Head_P4(gw(512))
+        self.model.append(C3k2(gw(256) + gw(512), gw(512), n=gd(2), c3k=False or c3k_override))
+        
+        # 20: Conv [512, 3, 2]
+        self.model.append(Conv(gw(512), gw(512), 3, 2))
+        
+        # 21: Concat [-1, 10] (P5)
+        self.model.append(Concat(dimension=1))
+        
+        # 22: C3k2 [1024, True] -> n=2. Input: P4_down(gw(512)) + P5(gw(1024))
+        self.model.append(C3k2(gw(512) + gw(1024), gw(1024), n=gd(2), c3k=True))
+        
+        # 23: Detect [nc]
+        self.model.append(YOLOESegment(nc, ch=[gw(256), gw(512), gw(1024)]))
+
+        # --- 初始化权重 ---
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        """初始化模型权重，特别是 Detect 头的 Bias"""
+        for m in self.modules():
+            if isinstance(m, (nn.Conv2d, nn.Linear)):
+                # 使用 Kaiming 初始化或其他合适的初始化
+                pass 
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+        detect_layer = self.model[-1]
+        if isinstance(detect_layer, Detect):
+            detect_layer.bias_init()
+
+    def set_classes(self, names: List[str], embeddings: torch.Tensor):
+        assert embeddings.ndim == 3, "Embeddings must be (1, N, D)"
+        self.pe = embeddings
+        self.model[-1].nc = len(names) # type: ignore
+        self.nc = len(names)
+
+    def forward(self, x, tpe=None, vpe=None):
+        # Backbone
+        x = self.model[0](x)
+        x = self.model[1](x)
+        x = self.model[2](x)
+        x = self.model[3](x)
+        p3 = self.model[4](x)           # 保存 P3 (layer 4)
+        x = self.model[5](p3)
+        p4 = self.model[6](x)           # 保存 P4 (layer 6)
+        x = self.model[7](p4)
+        x = self.model[8](x)
+        x = self.model[9](x)
+        p5 = self.model[10](x)          # 保存 P5 (layer 10)
+        
+        # Head
+        x = self.model[11](p5)          # Upsample
+        x = self.model[12]([x, p4])     # Concat P4
+        h1 = self.model[13](x)          # Head P4 (layer 13)
+        
+        x = self.model[14](h1)          # Upsample
+        x = self.model[15]([x, p3])     # Concat P3
+        h2 = self.model[16](x)          # Output P3 (layer 16)
+        
+        x = self.model[17](h2)          # Conv
+        x = self.model[18]([x, h1])     # Concat Head P4
+        h3 = self.model[19](x)          # Output P4 (layer 19)
+        
+        x = self.model[20](h3)          # Conv
+        x = self.model[21]([x, p5])     # Concat P5
+        h4 = self.model[22](x)          # Output P5 (layer 22)
+        
+        head = self.model[23]
+        feats = [h2, h3, h4]
+        
+        processed_tpe = head.get_tpe(tpe) # type: ignore
+        
+        processed_vpe = head.get_vpe(feats, vpe) if vpe is not None else None # type: ignore
+        
+        all_pe = []
+        if processed_tpe is not None:
+            all_pe.append(processed_tpe)
+        if processed_vpe is not None:
+            all_pe.append(processed_vpe)
+            
+        if not all_pe:
+            if self.pe is not None:
+                all_pe.append(self.pe.to(device=x.device, dtype=x.dtype))
+            else:
+                all_pe.append(torch.zeros(1, self.nc, head.embed, device=x.device, dtype=x.dtype))
+        
+        cls_pe = torch.cat(all_pe, dim=1)
+        
+        b = x.shape[0]
+        if cls_pe.shape[0] != b:
+            cls_pe = cls_pe.expand(b, -1, -1)
+            
+        return head(feats, cls_pe)
+
+    def load_weights(self, pth_file):
+        state_dict = torch.load(pth_file, map_location='cpu', weights_only=False)
+        # 移除可能存在的 'model.' 前缀 (如果权重来自 ultralytics 官方)
+        # 官方权重通常是 model.model.0.conv... 这种格式，或者直接是 model.0.conv...
+        # 这里做一个简单的兼容性处理
+        new_state_dict = {}
+        for k, v in state_dict.items():
+            # 处理 ultralytics 权重字典中的 'model' 键
+            if k == 'model':
+                # 如果是完整的 checkpoint，权重在 'model' 键下
+                # 且通常是 model.state_dict()
+                if hasattr(v, 'state_dict'):
+                    v = v.state_dict()
+                elif isinstance(v, dict):
+                    pass # v 就是 state_dict
+                else:
+                    # 可能是 model 对象本身
+                    try:
+                        v = v.float().state_dict()
+                    except:
+                        continue
+                
+                for sub_k, sub_v in v.items():
+                    new_state_dict[sub_k] = sub_v
+                break
+            else:
+                new_state_dict[k] = v
+        
+        if not new_state_dict:
+            new_state_dict = state_dict
+
+        # 尝试加载
+        try:
+            self.load_state_dict(new_state_dict, strict=True)
+            print(f"Successfully loaded weights from {pth_file}")
+        except Exception as e:
+            print(f"Error loading weights: {e}")
+            print("Trying to load with strict=False...")
+            self.load_state_dict(new_state_dict, strict=False)
+
+if __name__ == "__main__":
+    model = YOLO11E(nc=80, scale='l')
+    model.load_weights("yoloe-11l-seg.pth")
+
+    # 模拟 set_classes
+    # 假设我们有2个类，embedding维度是512
+    fake_embeddings = torch.randn(1, 2, 512) 
+    model.set_classes(["class1", "class2"], fake_embeddings)
+    
+    # 推理
+    dummy_input = torch.randn(1, 3, 640, 640)
+    model.eval()
+    output = model(dummy_input)
+    print("Output shape:", output[0].shape) # 应该是 (1, 4+mask_coeffs+num_classes, anchors)