diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..14c4ab17230fe832ceea81c96f8f03170c89082d
--- /dev/null
+++ b/app.py
@@ -0,0 +1,218 @@
+import gradio as gr
+import torch
+from matplotlib import pyplot as plt
+import numpy as np
+from groundingdino.util.inference import load_model, load_image, predict
+from segment_anything import SamPredictor, sam_model_registry
+from torchvision.ops import box_convert
+
+model_type = "vit_b"
+sam_checkpoint = "weights/sam_vit_b.pth"
+config = "groundingdino/config/GroundingDINO_SwinT_OGC.py"
+dino_checkpoint = "weights/groundingdino_swint_ogc.pth"
+sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
+predictor = SamPredictor(sam)
+device = "cpu"
+model = load_model(config, dino_checkpoint, device)
+box_threshold = 0.35
+text_threshold = 0.25
+
+def show_mask(mask, ax, random_color=False):
+ if random_color:
+ color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
+ else:
+ color = np.array([30/255, 144/255, 255/255, 0.6])
+ h, w = mask.shape[-2:]
+ mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
+ ax.imshow(mask_image)
+
+def show_box(box, ax, label = None):
+ x0, y0 = box[0], box[1]
+ w, h = box[2] - box[0], box[3] - box[1]
+ ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='red', facecolor=(0,0,0,0), lw=2))
+ if label is not None:
+ ax.text(x0, y0, label, fontsize=12, color='white', backgroundcolor='red', ha='left', va='top')
+
+def extract_object_with_transparent_background(image, masks):
+ mask_expanded = np.expand_dims(masks[0], axis=-1)
+ mask_expanded = np.repeat(mask_expanded, 3, axis=-1)
+ segment = image * mask_expanded
+ rgba_segment = np.zeros((segment.shape[0], segment.shape[1], 4), dtype=np.uint8)
+ rgba_segment[:, :, :3] = segment
+ rgba_segment[:, :, 3] = masks[0] * 255
+ return rgba_segment
+
+def extract_remaining_image(image, masks):
+ inverse_mask = np.logical_not(masks[0])
+ inverse_mask_expanded = np.expand_dims(inverse_mask, axis=-1)
+ inverse_mask_expanded = np.repeat(inverse_mask_expanded, 3, axis=-1)
+ remaining_image = image * inverse_mask_expanded
+ return remaining_image
+
+def overlay_masks_boxes_on_image(image, masks, boxes, labels, show_masks, show_boxes):
+ fig, ax = plt.subplots()
+ ax.imshow(image)
+ if show_masks:
+ for mask in masks:
+ show_mask(mask, ax, random_color=False)
+
+ if show_boxes:
+ for input_box, label in zip(boxes, labels):
+ show_box(input_box, ax, label)
+
+ ax.axis('off')
+ plt.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0, hspace=0)
+ plt.margins(0, 0)
+
+ fig.canvas.draw()
+ output_image = np.array(fig.canvas.buffer_rgba())
+
+ plt.close(fig)
+ return output_image
+
+
+def detect_objects(image, prompt, show_masks, show_boxes, crop_options):
+ image_source, image = load_image(image)
+ predictor.set_image(image_source)
+
+ boxes, logits, phrases = predict(
+ model=model,
+ image=image,
+ caption=prompt,
+ box_threshold=box_threshold,
+ text_threshold=text_threshold,
+ device=device
+ )
+
+ h, w, _ = image_source.shape
+ boxes = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy") * torch.Tensor([w, h, w, h])
+ boxes = np.round(boxes.numpy()).astype(int)
+
+ labels = [f"{phrase} {logit:.2f}" for phrase, logit in zip(phrases, logits)]
+
+ masks_list = []
+
+ for input_box, label in zip(boxes, labels):
+ x1, y1, x2, y2 = input_box
+ width = x2 - x1
+ height = y2 - y1
+ avg_size = (width + height) / 2
+ d = avg_size * 0.1
+
+ center_point = np.array([(x1 + x2) / 2, (y1 + y2) / 2])
+ points = []
+ points.append([center_point[0], center_point[1] - d])
+ points.append([center_point[0], center_point[1] + d])
+ points.append([center_point[0] - d, center_point[1]])
+ points.append([center_point[0] + d, center_point[1]])
+ input_point = np.array(points)
+ input_label = np.array([1] * len(input_point))
+
+ masks, scores, logits = predictor.predict(
+ point_coords=input_point,
+ point_labels=input_label,
+ multimask_output=True,
+ )
+ mask_input = logits[np.argmax(scores), :, :]
+
+ masks, _, _ = predictor.predict(
+ point_coords=input_point,
+ point_labels=input_label,
+ mask_input=mask_input[None, :, :],
+ multimask_output=False
+ )
+ masks_list.append(masks)
+
+ if crop_options == "Crop":
+ composite_image = np.zeros_like(image_source)
+ for masks in masks_list:
+ rgba_segment = extract_object_with_transparent_background(image_source, masks)
+ composite_image = np.maximum(composite_image, rgba_segment[:, :, :3])
+ output_image = overlay_masks_boxes_on_image(composite_image, masks_list, boxes, labels, show_masks, show_boxes)
+ elif crop_options == "Inverse Crop":
+ remaining_image = image_source.copy()
+ for masks in masks_list:
+ remaining_image = extract_remaining_image(remaining_image, masks)
+ output_image = overlay_masks_boxes_on_image(remaining_image, masks_list, boxes, labels, show_masks, show_boxes)
+ else:
+ output_image = overlay_masks_boxes_on_image(image_source, masks_list, boxes, labels, show_masks, show_boxes)
+
+ output_image_path = 'output_image.jpeg'
+ plt.imsave(output_image_path, output_image)
+
+ return output_image_path
+
+block = gr.Blocks(css=".gradio-container {background-color: #f8f8f8; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif}")
+
+with block:
+ gr.HTML("""
+ <style>
+ body {
+ background-color: #f5f5f5;
+ font-family: 'Roboto', sans-serif;
+ padding: 30px;
+ }
+ </style>
+ """)
+
+ gr.HTML("<h1 style='text-align: center;'>Segment Any Image</h1>")
+ gr.HTML("<h3 style='text-align: center;'>Zero-Shot Object Detection, Segmentation and Cropping</h3>")
+ with gr.Row():
+ with gr.Column(width="auto"):
+ input_image = gr.Image(type='filepath', label="Upload Image")
+ with gr.Column(width="auto"):
+ output_image = gr.Image(type='filepath', label="Result")
+ with gr.Row():
+ with gr.Column(width="auto"):
+ object_search = gr.Textbox(
+ label="Object to Detect",
+ placeholder="Enter any text, comma separated if multiple objects needed",
+ show_label=True,
+ lines=1,
+ )
+ with gr.Column(width="auto"):
+ show_masks = gr.Checkbox(label="Show Masks", default=True)
+ show_boxes = gr.Checkbox(label="Show Boxes", default=True)
+ with gr.Column(width="auto"):
+ crop_options = gr.Radio(choices=["None", "Crop", "Inverse Crop"], label="Crop Options", default="None")
+ with gr.Row():
+ submit = gr.Button(value="Send", variant="secondary").style(full_width=True)
+
+ gr.Examples(
+ examples=[
+ ["images/tiger.jpeg", "animal from cat family", True, True],
+ ["images/car.jpeg", "a blue sports car", True, False],
+ ["images/bags.jpeg", "black bag next to the red bag", False, True],
+ ["images/deer.jpeg", "deer jumping and running across the road", True, True],
+ ["images/penn.jpeg", "sign board", True, False],
+ ],
+ inputs=[input_image, object_search, show_masks, show_boxes],
+ )
+ gr.HTML("""
+ <div style="text-align:center">
+ <p>Developed by <a href='https://www.linkedin.com/in/dekay/'>Github and Huggingface: Volkopat</a></p>
+ <p>Powered by <a href='https://segment-anything.com'>Segment Anything</a> and <a href='https://arxiv.org/abs/2303.05499'>Grounding DINO</a></p>
+ <p>Just upload an image and enter the objects to detect, segment, crop, etc. That's all folks!</p>
+ <p>What's Zero-Shot? It means you can detect objects without any training samples!</p>
+ <p>This project is for demonstration purposes. Credits for State of the Art models go to Meta AI and IDEA Research.</p>
+ </div>
+ <style>
+ p {
+ margin-bottom: 10px;
+ font-size: 16px;
+ }
+ a {
+ color: #3867d6;
+ text-decoration: none;
+ }
+ a:hover {
+ text-decoration: underline;
+ }
+ </style>
+ """)
+
+ submit.click(fn=detect_objects,
+ inputs=[input_image, object_search, show_masks, show_boxes, crop_options],
+ outputs=[output_image])
+
+block.launch(width=800)
\ No newline at end of file
diff --git a/groundingdino/.DS_Store b/groundingdino/.DS_Store
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diff --git a/groundingdino/__init__.py b/groundingdino/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/groundingdino/__pycache__/__init__.cpython-310.pyc b/groundingdino/__pycache__/__init__.cpython-310.pyc
new file mode 100644
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diff --git a/groundingdino/config/GroundingDINO_SwinB_cfg.py b/groundingdino/config/GroundingDINO_SwinB_cfg.py
new file mode 100644
index 0000000000000000000000000000000000000000..f490c4bbd598a35de43d36ceafcbd769e7ff21bf
--- /dev/null
+++ b/groundingdino/config/GroundingDINO_SwinB_cfg.py
@@ -0,0 +1,43 @@
+batch_size = 1
+modelname = "groundingdino"
+backbone = "swin_B_384_22k"
+position_embedding = "sine"
+pe_temperatureH = 20
+pe_temperatureW = 20
+return_interm_indices = [1, 2, 3]
+backbone_freeze_keywords = None
+enc_layers = 6
+dec_layers = 6
+pre_norm = False
+dim_feedforward = 2048
+hidden_dim = 256
+dropout = 0.0
+nheads = 8
+num_queries = 900
+query_dim = 4
+num_patterns = 0
+num_feature_levels = 4
+enc_n_points = 4
+dec_n_points = 4
+two_stage_type = "standard"
+two_stage_bbox_embed_share = False
+two_stage_class_embed_share = False
+transformer_activation = "relu"
+dec_pred_bbox_embed_share = True
+dn_box_noise_scale = 1.0
+dn_label_noise_ratio = 0.5
+dn_label_coef = 1.0
+dn_bbox_coef = 1.0
+embed_init_tgt = True
+dn_labelbook_size = 2000
+max_text_len = 256
+text_encoder_type = "bert-base-uncased"
+use_text_enhancer = True
+use_fusion_layer = True
+use_checkpoint = True
+use_transformer_ckpt = True
+use_text_cross_attention = True
+text_dropout = 0.0
+fusion_dropout = 0.0
+fusion_droppath = 0.1
+sub_sentence_present = True
diff --git a/groundingdino/config/GroundingDINO_SwinT_OGC.py b/groundingdino/config/GroundingDINO_SwinT_OGC.py
new file mode 100644
index 0000000000000000000000000000000000000000..9158d5f6260ec74bded95377d382387430d7cd70
--- /dev/null
+++ b/groundingdino/config/GroundingDINO_SwinT_OGC.py
@@ -0,0 +1,43 @@
+batch_size = 1
+modelname = "groundingdino"
+backbone = "swin_T_224_1k"
+position_embedding = "sine"
+pe_temperatureH = 20
+pe_temperatureW = 20
+return_interm_indices = [1, 2, 3]
+backbone_freeze_keywords = None
+enc_layers = 6
+dec_layers = 6
+pre_norm = False
+dim_feedforward = 2048
+hidden_dim = 256
+dropout = 0.0
+nheads = 8
+num_queries = 900
+query_dim = 4
+num_patterns = 0
+num_feature_levels = 4
+enc_n_points = 4
+dec_n_points = 4
+two_stage_type = "standard"
+two_stage_bbox_embed_share = False
+two_stage_class_embed_share = False
+transformer_activation = "relu"
+dec_pred_bbox_embed_share = True
+dn_box_noise_scale = 1.0
+dn_label_noise_ratio = 0.5
+dn_label_coef = 1.0
+dn_bbox_coef = 1.0
+embed_init_tgt = True
+dn_labelbook_size = 2000
+max_text_len = 256
+text_encoder_type = "bert-base-uncased"
+use_text_enhancer = True
+use_fusion_layer = True
+use_checkpoint = True
+use_transformer_ckpt = True
+use_text_cross_attention = True
+text_dropout = 0.0
+fusion_dropout = 0.0
+fusion_droppath = 0.1
+sub_sentence_present = True
diff --git a/groundingdino/config/__init__.py b/groundingdino/config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/groundingdino/datasets/__init__.py b/groundingdino/datasets/__init__.py
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diff --git a/groundingdino/datasets/__pycache__/transforms.cpython-310.pyc b/groundingdino/datasets/__pycache__/transforms.cpython-310.pyc
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diff --git a/groundingdino/datasets/transforms.py b/groundingdino/datasets/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..91cf9269e4b31008a3ddca34a19b038a9b399991
--- /dev/null
+++ b/groundingdino/datasets/transforms.py
@@ -0,0 +1,311 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Transforms and data augmentation for both image + bbox.
+"""
+import os
+import random
+
+import PIL
+import torch
+import torchvision.transforms as T
+import torchvision.transforms.functional as F
+
+from groundingdino.util.box_ops import box_xyxy_to_cxcywh
+from groundingdino.util.misc import interpolate
+
+
+def crop(image, target, region):
+ cropped_image = F.crop(image, *region)
+
+ target = target.copy()
+ i, j, h, w = region
+
+ # should we do something wrt the original size?
+ target["size"] = torch.tensor([h, w])
+
+ fields = ["labels", "area", "iscrowd", "positive_map"]
+
+ if "boxes" in target:
+ boxes = target["boxes"]
+ max_size = torch.as_tensor([w, h], dtype=torch.float32)
+ cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
+ cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
+ cropped_boxes = cropped_boxes.clamp(min=0)
+ area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
+ target["boxes"] = cropped_boxes.reshape(-1, 4)
+ target["area"] = area
+ fields.append("boxes")
+
+ if "masks" in target:
+ # FIXME should we update the area here if there are no boxes?
+ target["masks"] = target["masks"][:, i : i + h, j : j + w]
+ fields.append("masks")
+
+ # remove elements for which the boxes or masks that have zero area
+ if "boxes" in target or "masks" in target:
+ # favor boxes selection when defining which elements to keep
+ # this is compatible with previous implementation
+ if "boxes" in target:
+ cropped_boxes = target["boxes"].reshape(-1, 2, 2)
+ keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
+ else:
+ keep = target["masks"].flatten(1).any(1)
+
+ for field in fields:
+ if field in target:
+ target[field] = target[field][keep]
+
+ if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO":
+ # for debug and visualization only.
+ if "strings_positive" in target:
+ target["strings_positive"] = [
+ _i for _i, _j in zip(target["strings_positive"], keep) if _j
+ ]
+
+ return cropped_image, target
+
+
+def hflip(image, target):
+ flipped_image = F.hflip(image)
+
+ w, h = image.size
+
+ target = target.copy()
+ if "boxes" in target:
+ boxes = target["boxes"]
+ boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(
+ [w, 0, w, 0]
+ )
+ target["boxes"] = boxes
+
+ if "masks" in target:
+ target["masks"] = target["masks"].flip(-1)
+
+ return flipped_image, target
+
+
+def resize(image, target, size, max_size=None):
+ # size can be min_size (scalar) or (w, h) tuple
+
+ def get_size_with_aspect_ratio(image_size, size, max_size=None):
+ w, h = image_size
+ if max_size is not None:
+ min_original_size = float(min((w, h)))
+ max_original_size = float(max((w, h)))
+ if max_original_size / min_original_size * size > max_size:
+ size = int(round(max_size * min_original_size / max_original_size))
+
+ if (w <= h and w == size) or (h <= w and h == size):
+ return (h, w)
+
+ if w < h:
+ ow = size
+ oh = int(size * h / w)
+ else:
+ oh = size
+ ow = int(size * w / h)
+
+ return (oh, ow)
+
+ def get_size(image_size, size, max_size=None):
+ if isinstance(size, (list, tuple)):
+ return size[::-1]
+ else:
+ return get_size_with_aspect_ratio(image_size, size, max_size)
+
+ size = get_size(image.size, size, max_size)
+ rescaled_image = F.resize(image, size)
+
+ if target is None:
+ return rescaled_image, None
+
+ ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
+ ratio_width, ratio_height = ratios
+
+ target = target.copy()
+ if "boxes" in target:
+ boxes = target["boxes"]
+ scaled_boxes = boxes * torch.as_tensor(
+ [ratio_width, ratio_height, ratio_width, ratio_height]
+ )
+ target["boxes"] = scaled_boxes
+
+ if "area" in target:
+ area = target["area"]
+ scaled_area = area * (ratio_width * ratio_height)
+ target["area"] = scaled_area
+
+ h, w = size
+ target["size"] = torch.tensor([h, w])
+
+ if "masks" in target:
+ target["masks"] = (
+ interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5
+ )
+
+ return rescaled_image, target
+
+
+def pad(image, target, padding):
+ # assumes that we only pad on the bottom right corners
+ padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
+ if target is None:
+ return padded_image, None
+ target = target.copy()
+ # should we do something wrt the original size?
+ target["size"] = torch.tensor(padded_image.size[::-1])
+ if "masks" in target:
+ target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1]))
+ return padded_image, target
+
+
+class ResizeDebug(object):
+ def __init__(self, size):
+ self.size = size
+
+ def __call__(self, img, target):
+ return resize(img, target, self.size)
+
+
+class RandomCrop(object):
+ def __init__(self, size):
+ self.size = size
+
+ def __call__(self, img, target):
+ region = T.RandomCrop.get_params(img, self.size)
+ return crop(img, target, region)
+
+
+class RandomSizeCrop(object):
+ def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):
+ # respect_boxes: True to keep all boxes
+ # False to tolerence box filter
+ self.min_size = min_size
+ self.max_size = max_size
+ self.respect_boxes = respect_boxes
+
+ def __call__(self, img: PIL.Image.Image, target: dict):
+ init_boxes = len(target["boxes"])
+ max_patience = 10
+ for i in range(max_patience):
+ w = random.randint(self.min_size, min(img.width, self.max_size))
+ h = random.randint(self.min_size, min(img.height, self.max_size))
+ region = T.RandomCrop.get_params(img, [h, w])
+ result_img, result_target = crop(img, target, region)
+ if (
+ not self.respect_boxes
+ or len(result_target["boxes"]) == init_boxes
+ or i == max_patience - 1
+ ):
+ return result_img, result_target
+ return result_img, result_target
+
+
+class CenterCrop(object):
+ def __init__(self, size):
+ self.size = size
+
+ def __call__(self, img, target):
+ image_width, image_height = img.size
+ crop_height, crop_width = self.size
+ crop_top = int(round((image_height - crop_height) / 2.0))
+ crop_left = int(round((image_width - crop_width) / 2.0))
+ return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
+
+
+class RandomHorizontalFlip(object):
+ def __init__(self, p=0.5):
+ self.p = p
+
+ def __call__(self, img, target):
+ if random.random() < self.p:
+ return hflip(img, target)
+ return img, target
+
+
+class RandomResize(object):
+ def __init__(self, sizes, max_size=None):
+ assert isinstance(sizes, (list, tuple))
+ self.sizes = sizes
+ self.max_size = max_size
+
+ def __call__(self, img, target=None):
+ size = random.choice(self.sizes)
+ return resize(img, target, size, self.max_size)
+
+
+class RandomPad(object):
+ def __init__(self, max_pad):
+ self.max_pad = max_pad
+
+ def __call__(self, img, target):
+ pad_x = random.randint(0, self.max_pad)
+ pad_y = random.randint(0, self.max_pad)
+ return pad(img, target, (pad_x, pad_y))
+
+
+class RandomSelect(object):
+ """
+ Randomly selects between transforms1 and transforms2,
+ with probability p for transforms1 and (1 - p) for transforms2
+ """
+
+ def __init__(self, transforms1, transforms2, p=0.5):
+ self.transforms1 = transforms1
+ self.transforms2 = transforms2
+ self.p = p
+
+ def __call__(self, img, target):
+ if random.random() < self.p:
+ return self.transforms1(img, target)
+ return self.transforms2(img, target)
+
+
+class ToTensor(object):
+ def __call__(self, img, target):
+ return F.to_tensor(img), target
+
+
+class RandomErasing(object):
+ def __init__(self, *args, **kwargs):
+ self.eraser = T.RandomErasing(*args, **kwargs)
+
+ def __call__(self, img, target):
+ return self.eraser(img), target
+
+
+class Normalize(object):
+ def __init__(self, mean, std):
+ self.mean = mean
+ self.std = std
+
+ def __call__(self, image, target=None):
+ image = F.normalize(image, mean=self.mean, std=self.std)
+ if target is None:
+ return image, None
+ target = target.copy()
+ h, w = image.shape[-2:]
+ if "boxes" in target:
+ boxes = target["boxes"]
+ boxes = box_xyxy_to_cxcywh(boxes)
+ boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
+ target["boxes"] = boxes
+ return image, target
+
+
+class Compose(object):
+ def __init__(self, transforms):
+ self.transforms = transforms
+
+ def __call__(self, image, target):
+ for t in self.transforms:
+ image, target = t(image, target)
+ return image, target
+
+ def __repr__(self):
+ format_string = self.__class__.__name__ + "("
+ for t in self.transforms:
+ format_string += "\n"
+ format_string += " {0}".format(t)
+ format_string += "\n)"
+ return format_string
diff --git a/groundingdino/models/.DS_Store b/groundingdino/models/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..968329171d9da93df04a6a88f1d87ecb7336f5f2
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diff --git a/groundingdino/models/GroundingDINO/__init__.py b/groundingdino/models/GroundingDINO/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2af819d61d589cfec2e0ca46612a7456f42b831a
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/__init__.py
@@ -0,0 +1,15 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+from .groundingdino import build_groundingdino
diff --git a/groundingdino/models/GroundingDINO/__pycache__/__init__.cpython-310.pyc b/groundingdino/models/GroundingDINO/__pycache__/__init__.cpython-310.pyc
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diff --git a/groundingdino/models/GroundingDINO/__pycache__/utils.cpython-310.pyc b/groundingdino/models/GroundingDINO/__pycache__/utils.cpython-310.pyc
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diff --git a/groundingdino/models/GroundingDINO/backbone/__init__.py b/groundingdino/models/GroundingDINO/backbone/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..76e4b272b479a26c63d120c818c140870cd8c287
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/backbone/__init__.py
@@ -0,0 +1 @@
+from .backbone import build_backbone
diff --git a/groundingdino/models/GroundingDINO/backbone/__pycache__/__init__.cpython-310.pyc b/groundingdino/models/GroundingDINO/backbone/__pycache__/__init__.cpython-310.pyc
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diff --git a/groundingdino/models/GroundingDINO/backbone/__pycache__/swin_transformer.cpython-310.pyc b/groundingdino/models/GroundingDINO/backbone/__pycache__/swin_transformer.cpython-310.pyc
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diff --git a/groundingdino/models/GroundingDINO/backbone/backbone.py b/groundingdino/models/GroundingDINO/backbone/backbone.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8340c723fad8e07e2fc62daaa3912487498814b
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/backbone/backbone.py
@@ -0,0 +1,221 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+Backbone modules.
+"""
+
+from typing import Dict, List
+
+import torch
+import torch.nn.functional as F
+import torchvision
+from torch import nn
+from torchvision.models._utils import IntermediateLayerGetter
+
+from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process
+
+from .position_encoding import build_position_encoding
+from .swin_transformer import build_swin_transformer
+
+
+class FrozenBatchNorm2d(torch.nn.Module):
+ """
+ BatchNorm2d where the batch statistics and the affine parameters are fixed.
+
+ Copy-paste from torchvision.misc.ops with added eps before rqsrt,
+ without which any other models than torchvision.models.resnet[18,34,50,101]
+ produce nans.
+ """
+
+ def __init__(self, n):
+ super(FrozenBatchNorm2d, self).__init__()
+ self.register_buffer("weight", torch.ones(n))
+ self.register_buffer("bias", torch.zeros(n))
+ self.register_buffer("running_mean", torch.zeros(n))
+ self.register_buffer("running_var", torch.ones(n))
+
+ def _load_from_state_dict(
+ self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+ ):
+ num_batches_tracked_key = prefix + "num_batches_tracked"
+ if num_batches_tracked_key in state_dict:
+ del state_dict[num_batches_tracked_key]
+
+ super(FrozenBatchNorm2d, self)._load_from_state_dict(
+ state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+ )
+
+ def forward(self, x):
+ # move reshapes to the beginning
+ # to make it fuser-friendly
+ w = self.weight.reshape(1, -1, 1, 1)
+ b = self.bias.reshape(1, -1, 1, 1)
+ rv = self.running_var.reshape(1, -1, 1, 1)
+ rm = self.running_mean.reshape(1, -1, 1, 1)
+ eps = 1e-5
+ scale = w * (rv + eps).rsqrt()
+ bias = b - rm * scale
+ return x * scale + bias
+
+
+class BackboneBase(nn.Module):
+ def __init__(
+ self,
+ backbone: nn.Module,
+ train_backbone: bool,
+ num_channels: int,
+ return_interm_indices: list,
+ ):
+ super().__init__()
+ for name, parameter in backbone.named_parameters():
+ if (
+ not train_backbone
+ or "layer2" not in name
+ and "layer3" not in name
+ and "layer4" not in name
+ ):
+ parameter.requires_grad_(False)
+
+ return_layers = {}
+ for idx, layer_index in enumerate(return_interm_indices):
+ return_layers.update(
+ {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)}
+ )
+
+ # if len:
+ # if use_stage1_feature:
+ # return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
+ # else:
+ # return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
+ # else:
+ # return_layers = {'layer4': "0"}
+ self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
+ self.num_channels = num_channels
+
+ def forward(self, tensor_list: NestedTensor):
+ xs = self.body(tensor_list.tensors)
+ out: Dict[str, NestedTensor] = {}
+ for name, x in xs.items():
+ m = tensor_list.mask
+ assert m is not None
+ mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
+ out[name] = NestedTensor(x, mask)
+ # import ipdb; ipdb.set_trace()
+ return out
+
+
+class Backbone(BackboneBase):
+ """ResNet backbone with frozen BatchNorm."""
+
+ def __init__(
+ self,
+ name: str,
+ train_backbone: bool,
+ dilation: bool,
+ return_interm_indices: list,
+ batch_norm=FrozenBatchNorm2d,
+ ):
+ if name in ["resnet18", "resnet34", "resnet50", "resnet101"]:
+ backbone = getattr(torchvision.models, name)(
+ replace_stride_with_dilation=[False, False, dilation],
+ pretrained=is_main_process(),
+ norm_layer=batch_norm,
+ )
+ else:
+ raise NotImplementedError("Why you can get here with name {}".format(name))
+ # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
+ assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available."
+ assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
+ num_channels_all = [256, 512, 1024, 2048]
+ num_channels = num_channels_all[4 - len(return_interm_indices) :]
+ super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
+
+
+class Joiner(nn.Sequential):
+ def __init__(self, backbone, position_embedding):
+ super().__init__(backbone, position_embedding)
+
+ def forward(self, tensor_list: NestedTensor):
+ xs = self[0](tensor_list)
+ out: List[NestedTensor] = []
+ pos = []
+ for name, x in xs.items():
+ out.append(x)
+ # position encoding
+ pos.append(self[1](x).to(x.tensors.dtype))
+
+ return out, pos
+
+
+def build_backbone(args):
+ """
+ Useful args:
+ - backbone: backbone name
+ - lr_backbone:
+ - dilation
+ - return_interm_indices: available: [0,1,2,3], [1,2,3], [3]
+ - backbone_freeze_keywords:
+ - use_checkpoint: for swin only for now
+
+ """
+ position_embedding = build_position_encoding(args)
+ train_backbone = True
+ if not train_backbone:
+ raise ValueError("Please set lr_backbone > 0")
+ return_interm_indices = args.return_interm_indices
+ assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
+ args.backbone_freeze_keywords
+ use_checkpoint = getattr(args, "use_checkpoint", False)
+
+ if args.backbone in ["resnet50", "resnet101"]:
+ backbone = Backbone(
+ args.backbone,
+ train_backbone,
+ args.dilation,
+ return_interm_indices,
+ batch_norm=FrozenBatchNorm2d,
+ )
+ bb_num_channels = backbone.num_channels
+ elif args.backbone in [
+ "swin_T_224_1k",
+ "swin_B_224_22k",
+ "swin_B_384_22k",
+ "swin_L_224_22k",
+ "swin_L_384_22k",
+ ]:
+ pretrain_img_size = int(args.backbone.split("_")[-2])
+ backbone = build_swin_transformer(
+ args.backbone,
+ pretrain_img_size=pretrain_img_size,
+ out_indices=tuple(return_interm_indices),
+ dilation=False,
+ use_checkpoint=use_checkpoint,
+ )
+
+ bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
+ else:
+ raise NotImplementedError("Unknown backbone {}".format(args.backbone))
+
+ assert len(bb_num_channels) == len(
+ return_interm_indices
+ ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}"
+
+ model = Joiner(backbone, position_embedding)
+ model.num_channels = bb_num_channels
+ assert isinstance(
+ bb_num_channels, List
+ ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels))
+ # import ipdb; ipdb.set_trace()
+ return model
diff --git a/groundingdino/models/GroundingDINO/backbone/position_encoding.py b/groundingdino/models/GroundingDINO/backbone/position_encoding.py
new file mode 100644
index 0000000000000000000000000000000000000000..eac7e896bbe85a670824bfe8ef487d0535d5bd99
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/backbone/position_encoding.py
@@ -0,0 +1,186 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+Various positional encodings for the transformer.
+"""
+import math
+
+import torch
+from torch import nn
+
+from groundingdino.util.misc import NestedTensor
+
+
+class PositionEmbeddingSine(nn.Module):
+ """
+ This is a more standard version of the position embedding, very similar to the one
+ used by the Attention is all you need paper, generalized to work on images.
+ """
+
+ def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+ super().__init__()
+ self.num_pos_feats = num_pos_feats
+ self.temperature = temperature
+ self.normalize = normalize
+ if scale is not None and normalize is False:
+ raise ValueError("normalize should be True if scale is passed")
+ if scale is None:
+ scale = 2 * math.pi
+ self.scale = scale
+
+ def forward(self, tensor_list: NestedTensor):
+ x = tensor_list.tensors
+ mask = tensor_list.mask
+ assert mask is not None
+ not_mask = ~mask
+ y_embed = not_mask.cumsum(1, dtype=torch.float32)
+ x_embed = not_mask.cumsum(2, dtype=torch.float32)
+ if self.normalize:
+ eps = 1e-6
+ # if os.environ.get("SHILONG_AMP", None) == '1':
+ # eps = 1e-4
+ # else:
+ # eps = 1e-6
+ y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+ x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+ dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+ dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+ pos_x = x_embed[:, :, :, None] / dim_t
+ pos_y = y_embed[:, :, :, None] / dim_t
+ pos_x = torch.stack(
+ (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+ ).flatten(3)
+ pos_y = torch.stack(
+ (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+ ).flatten(3)
+ pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+ return pos
+
+
+class PositionEmbeddingSineHW(nn.Module):
+ """
+ This is a more standard version of the position embedding, very similar to the one
+ used by the Attention is all you need paper, generalized to work on images.
+ """
+
+ def __init__(
+ self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None
+ ):
+ super().__init__()
+ self.num_pos_feats = num_pos_feats
+ self.temperatureH = temperatureH
+ self.temperatureW = temperatureW
+ self.normalize = normalize
+ if scale is not None and normalize is False:
+ raise ValueError("normalize should be True if scale is passed")
+ if scale is None:
+ scale = 2 * math.pi
+ self.scale = scale
+
+ def forward(self, tensor_list: NestedTensor):
+ x = tensor_list.tensors
+ mask = tensor_list.mask
+ assert mask is not None
+ not_mask = ~mask
+ y_embed = not_mask.cumsum(1, dtype=torch.float32)
+ x_embed = not_mask.cumsum(2, dtype=torch.float32)
+
+ # import ipdb; ipdb.set_trace()
+
+ if self.normalize:
+ eps = 1e-6
+ y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+ x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+ dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+ dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats)
+ pos_x = x_embed[:, :, :, None] / dim_tx
+
+ dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+ dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats)
+ pos_y = y_embed[:, :, :, None] / dim_ty
+
+ pos_x = torch.stack(
+ (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+ ).flatten(3)
+ pos_y = torch.stack(
+ (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+ ).flatten(3)
+ pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+
+ # import ipdb; ipdb.set_trace()
+
+ return pos
+
+
+class PositionEmbeddingLearned(nn.Module):
+ """
+ Absolute pos embedding, learned.
+ """
+
+ def __init__(self, num_pos_feats=256):
+ super().__init__()
+ self.row_embed = nn.Embedding(50, num_pos_feats)
+ self.col_embed = nn.Embedding(50, num_pos_feats)
+ self.reset_parameters()
+
+ def reset_parameters(self):
+ nn.init.uniform_(self.row_embed.weight)
+ nn.init.uniform_(self.col_embed.weight)
+
+ def forward(self, tensor_list: NestedTensor):
+ x = tensor_list.tensors
+ h, w = x.shape[-2:]
+ i = torch.arange(w, device=x.device)
+ j = torch.arange(h, device=x.device)
+ x_emb = self.col_embed(i)
+ y_emb = self.row_embed(j)
+ pos = (
+ torch.cat(
+ [
+ x_emb.unsqueeze(0).repeat(h, 1, 1),
+ y_emb.unsqueeze(1).repeat(1, w, 1),
+ ],
+ dim=-1,
+ )
+ .permute(2, 0, 1)
+ .unsqueeze(0)
+ .repeat(x.shape[0], 1, 1, 1)
+ )
+ return pos
+
+
+def build_position_encoding(args):
+ N_steps = args.hidden_dim // 2
+ if args.position_embedding in ("v2", "sine"):
+ # TODO find a better way of exposing other arguments
+ position_embedding = PositionEmbeddingSineHW(
+ N_steps,
+ temperatureH=args.pe_temperatureH,
+ temperatureW=args.pe_temperatureW,
+ normalize=True,
+ )
+ elif args.position_embedding in ("v3", "learned"):
+ position_embedding = PositionEmbeddingLearned(N_steps)
+ else:
+ raise ValueError(f"not supported {args.position_embedding}")
+
+ return position_embedding
diff --git a/groundingdino/models/GroundingDINO/backbone/swin_transformer.py b/groundingdino/models/GroundingDINO/backbone/swin_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c66194deb5dd370e797e57e2712f44303e568cc
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/backbone/swin_transformer.py
@@ -0,0 +1,802 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# --------------------------------------------------------
+# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
+# --------------------------------------------------------
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+from groundingdino.util.misc import NestedTensor
+
+
+class Mlp(nn.Module):
+ """Multilayer perceptron."""
+
+ def __init__(
+ self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0
+ ):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+def window_partition(x, window_size):
+ """
+ Args:
+ x: (B, H, W, C)
+ window_size (int): window size
+ Returns:
+ windows: (num_windows*B, window_size, window_size, C)
+ """
+ B, H, W, C = x.shape
+ x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+ windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+ return windows
+
+
+def window_reverse(windows, window_size, H, W):
+ """
+ Args:
+ windows: (num_windows*B, window_size, window_size, C)
+ window_size (int): Window size
+ H (int): Height of image
+ W (int): Width of image
+ Returns:
+ x: (B, H, W, C)
+ """
+ B = int(windows.shape[0] / (H * W / window_size / window_size))
+ x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+ return x
+
+
+class WindowAttention(nn.Module):
+ """Window based multi-head self attention (W-MSA) module with relative position bias.
+ It supports both of shifted and non-shifted window.
+ Args:
+ dim (int): Number of input channels.
+ window_size (tuple[int]): The height and width of the window.
+ num_heads (int): Number of attention heads.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+ """
+
+ def __init__(
+ self,
+ dim,
+ window_size,
+ num_heads,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ ):
+
+ super().__init__()
+ self.dim = dim
+ self.window_size = window_size # Wh, Ww
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ self.scale = qk_scale or head_dim**-0.5
+
+ # define a parameter table of relative position bias
+ self.relative_position_bias_table = nn.Parameter(
+ torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
+ ) # 2*Wh-1 * 2*Ww-1, nH
+
+ # get pair-wise relative position index for each token inside the window
+ coords_h = torch.arange(self.window_size[0])
+ coords_w = torch.arange(self.window_size[1])
+ coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
+ coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
+ relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
+ relative_coords[:, :, 1] += self.window_size[1] - 1
+ relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+ relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
+ self.register_buffer("relative_position_index", relative_position_index)
+
+ 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)
+
+ trunc_normal_(self.relative_position_bias_table, std=0.02)
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, x, mask=None):
+ """Forward function.
+ Args:
+ x: input features with shape of (num_windows*B, N, C)
+ mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+ """
+ B_, N, C = x.shape
+ qkv = (
+ self.qkv(x)
+ .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
+ .permute(2, 0, 3, 1, 4)
+ )
+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
+
+ q = q * self.scale
+ attn = q @ k.transpose(-2, -1)
+
+ relative_position_bias = self.relative_position_bias_table[
+ self.relative_position_index.view(-1)
+ ].view(
+ self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
+ ) # Wh*Ww,Wh*Ww,nH
+ relative_position_bias = relative_position_bias.permute(
+ 2, 0, 1
+ ).contiguous() # nH, Wh*Ww, Wh*Ww
+ attn = attn + relative_position_bias.unsqueeze(0)
+
+ if mask is not None:
+ nW = mask.shape[0]
+ attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+ attn = attn.view(-1, self.num_heads, N, N)
+ attn = self.softmax(attn)
+ else:
+ attn = self.softmax(attn)
+
+ attn = self.attn_drop(attn)
+
+ x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+
+class SwinTransformerBlock(nn.Module):
+ """Swin Transformer Block.
+ Args:
+ dim (int): Number of input channels.
+ num_heads (int): Number of attention heads.
+ window_size (int): Window size.
+ shift_size (int): Shift size for SW-MSA.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float, optional): Stochastic depth rate. Default: 0.0
+ act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+
+ def __init__(
+ self,
+ dim,
+ num_heads,
+ window_size=7,
+ shift_size=0,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ drop=0.0,
+ attn_drop=0.0,
+ drop_path=0.0,
+ act_layer=nn.GELU,
+ norm_layer=nn.LayerNorm,
+ ):
+ super().__init__()
+ self.dim = dim
+ self.num_heads = num_heads
+ self.window_size = window_size
+ self.shift_size = shift_size
+ self.mlp_ratio = mlp_ratio
+ assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+ self.norm1 = norm_layer(dim)
+ self.attn = WindowAttention(
+ dim,
+ window_size=to_2tuple(self.window_size),
+ num_heads=num_heads,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ attn_drop=attn_drop,
+ proj_drop=drop,
+ )
+
+ self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(
+ in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop
+ )
+
+ self.H = None
+ self.W = None
+
+ def forward(self, x, mask_matrix):
+ """Forward function.
+ Args:
+ x: Input feature, tensor size (B, H*W, C).
+ H, W: Spatial resolution of the input feature.
+ mask_matrix: Attention mask for cyclic shift.
+ """
+ B, L, C = x.shape
+ H, W = self.H, self.W
+ assert L == H * W, "input feature has wrong size"
+
+ shortcut = x
+ x = self.norm1(x)
+ x = x.view(B, H, W, C)
+
+ # pad feature maps to multiples of window size
+ pad_l = pad_t = 0
+ pad_r = (self.window_size - W % self.window_size) % self.window_size
+ pad_b = (self.window_size - H % self.window_size) % self.window_size
+ x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+ _, Hp, Wp, _ = x.shape
+
+ # cyclic shift
+ if self.shift_size > 0:
+ shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+ attn_mask = mask_matrix
+ else:
+ shifted_x = x
+ attn_mask = None
+
+ # partition windows
+ x_windows = window_partition(
+ shifted_x, self.window_size
+ ) # nW*B, window_size, window_size, C
+ x_windows = x_windows.view(
+ -1, self.window_size * self.window_size, C
+ ) # nW*B, window_size*window_size, C
+
+ # W-MSA/SW-MSA
+ attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
+
+ # merge windows
+ attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+ shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
+
+ # reverse cyclic shift
+ if self.shift_size > 0:
+ x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+ else:
+ x = shifted_x
+
+ if pad_r > 0 or pad_b > 0:
+ x = x[:, :H, :W, :].contiguous()
+
+ x = x.view(B, H * W, C)
+
+ # FFN
+ x = shortcut + self.drop_path(x)
+ x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+ return x
+
+
+class PatchMerging(nn.Module):
+ """Patch Merging Layer
+ Args:
+ dim (int): Number of input channels.
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+
+ def __init__(self, dim, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.dim = dim
+ self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+ self.norm = norm_layer(4 * dim)
+
+ def forward(self, x, H, W):
+ """Forward function.
+ Args:
+ x: Input feature, tensor size (B, H*W, C).
+ H, W: Spatial resolution of the input feature.
+ """
+ B, L, C = x.shape
+ assert L == H * W, "input feature has wrong size"
+
+ x = x.view(B, H, W, C)
+
+ # padding
+ pad_input = (H % 2 == 1) or (W % 2 == 1)
+ if pad_input:
+ x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+ x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
+ x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
+ x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
+ x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
+ x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
+ x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
+
+ x = self.norm(x)
+ x = self.reduction(x)
+
+ return x
+
+
+class BasicLayer(nn.Module):
+ """A basic Swin Transformer layer for one stage.
+ Args:
+ dim (int): Number of feature channels
+ depth (int): Depths of this stage.
+ num_heads (int): Number of attention head.
+ window_size (int): Local window size. Default: 7.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ """
+
+ def __init__(
+ self,
+ dim,
+ depth,
+ num_heads,
+ window_size=7,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ drop=0.0,
+ attn_drop=0.0,
+ drop_path=0.0,
+ norm_layer=nn.LayerNorm,
+ downsample=None,
+ use_checkpoint=False,
+ ):
+ super().__init__()
+ self.window_size = window_size
+ self.shift_size = window_size // 2
+ self.depth = depth
+ self.use_checkpoint = use_checkpoint
+
+ # build blocks
+ self.blocks = nn.ModuleList(
+ [
+ SwinTransformerBlock(
+ dim=dim,
+ num_heads=num_heads,
+ window_size=window_size,
+ shift_size=0 if (i % 2 == 0) else window_size // 2,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ drop=drop,
+ attn_drop=attn_drop,
+ drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+ norm_layer=norm_layer,
+ )
+ for i in range(depth)
+ ]
+ )
+
+ # patch merging layer
+ if downsample is not None:
+ self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+ else:
+ self.downsample = None
+
+ def forward(self, x, H, W):
+ """Forward function.
+ Args:
+ x: Input feature, tensor size (B, H*W, C).
+ H, W: Spatial resolution of the input feature.
+ """
+
+ # calculate attention mask for SW-MSA
+ Hp = int(np.ceil(H / self.window_size)) * self.window_size
+ Wp = int(np.ceil(W / self.window_size)) * self.window_size
+ img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
+ h_slices = (
+ slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None),
+ )
+ w_slices = (
+ slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None),
+ )
+ cnt = 0
+ for h in h_slices:
+ for w in w_slices:
+ img_mask[:, h, w, :] = cnt
+ cnt += 1
+
+ mask_windows = window_partition(
+ img_mask, self.window_size
+ ) # nW, window_size, window_size, 1
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+ attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
+ attn_mask == 0, float(0.0)
+ )
+
+ for blk in self.blocks:
+ blk.H, blk.W = H, W
+ if self.use_checkpoint:
+ x = checkpoint.checkpoint(blk, x, attn_mask)
+ else:
+ x = blk(x, attn_mask)
+ if self.downsample is not None:
+ x_down = self.downsample(x, H, W)
+ Wh, Ww = (H + 1) // 2, (W + 1) // 2
+ return x, H, W, x_down, Wh, Ww
+ else:
+ return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+ """Image to Patch Embedding
+ Args:
+ patch_size (int): Patch token size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
+ """
+
+ def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ patch_size = to_2tuple(patch_size)
+ self.patch_size = patch_size
+
+ self.in_chans = in_chans
+ self.embed_dim = embed_dim
+
+ self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+ if norm_layer is not None:
+ self.norm = norm_layer(embed_dim)
+ else:
+ self.norm = None
+
+ def forward(self, x):
+ """Forward function."""
+ # padding
+ _, _, H, W = x.size()
+ if W % self.patch_size[1] != 0:
+ x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+ if H % self.patch_size[0] != 0:
+ x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+ x = self.proj(x) # B C Wh Ww
+ if self.norm is not None:
+ Wh, Ww = x.size(2), x.size(3)
+ x = x.flatten(2).transpose(1, 2)
+ x = self.norm(x)
+ x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+ return x
+
+
+class SwinTransformer(nn.Module):
+ """Swin Transformer backbone.
+ A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
+ https://arxiv.org/pdf/2103.14030
+ Args:
+ pretrain_img_size (int): Input image size for training the pretrained model,
+ used in absolute postion embedding. Default 224.
+ patch_size (int | tuple(int)): Patch size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ depths (tuple[int]): Depths of each Swin Transformer stage.
+ num_heads (tuple[int]): Number of attention head of each stage.
+ window_size (int): Window size. Default: 7.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+ qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+ drop_rate (float): Dropout rate.
+ attn_drop_rate (float): Attention dropout rate. Default: 0.
+ drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+ norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+ ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+ patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+ out_indices (Sequence[int]): Output from which stages.
+ frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+ -1 means not freezing any parameters.
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ dilation (bool): if True, the output size if 16x downsample, ow 32x downsample.
+ """
+
+ def __init__(
+ self,
+ pretrain_img_size=224,
+ patch_size=4,
+ in_chans=3,
+ embed_dim=96,
+ depths=[2, 2, 6, 2],
+ num_heads=[3, 6, 12, 24],
+ window_size=7,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.2,
+ norm_layer=nn.LayerNorm,
+ ape=False,
+ patch_norm=True,
+ out_indices=(0, 1, 2, 3),
+ frozen_stages=-1,
+ dilation=False,
+ use_checkpoint=False,
+ ):
+ super().__init__()
+
+ self.pretrain_img_size = pretrain_img_size
+ self.num_layers = len(depths)
+ self.embed_dim = embed_dim
+ self.ape = ape
+ self.patch_norm = patch_norm
+ self.out_indices = out_indices
+ self.frozen_stages = frozen_stages
+ self.dilation = dilation
+
+ # if use_checkpoint:
+ # print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!")
+
+ # split image into non-overlapping patches
+ self.patch_embed = PatchEmbed(
+ patch_size=patch_size,
+ in_chans=in_chans,
+ embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None,
+ )
+
+ # absolute position embedding
+ if self.ape:
+ pretrain_img_size = to_2tuple(pretrain_img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [
+ pretrain_img_size[0] // patch_size[0],
+ pretrain_img_size[1] // patch_size[1],
+ ]
+
+ self.absolute_pos_embed = nn.Parameter(
+ torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
+ )
+ trunc_normal_(self.absolute_pos_embed, std=0.02)
+
+ self.pos_drop = nn.Dropout(p=drop_rate)
+
+ # stochastic depth
+ dpr = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+ ] # stochastic depth decay rule
+
+ # build layers
+ self.layers = nn.ModuleList()
+ # prepare downsample list
+ downsamplelist = [PatchMerging for i in range(self.num_layers)]
+ downsamplelist[-1] = None
+ num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
+ if self.dilation:
+ downsamplelist[-2] = None
+ num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2
+ for i_layer in range(self.num_layers):
+ layer = BasicLayer(
+ # dim=int(embed_dim * 2 ** i_layer),
+ dim=num_features[i_layer],
+ depth=depths[i_layer],
+ num_heads=num_heads[i_layer],
+ window_size=window_size,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ drop=drop_rate,
+ attn_drop=attn_drop_rate,
+ drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
+ norm_layer=norm_layer,
+ # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+ downsample=downsamplelist[i_layer],
+ use_checkpoint=use_checkpoint,
+ )
+ self.layers.append(layer)
+
+ # num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
+ self.num_features = num_features
+
+ # add a norm layer for each output
+ for i_layer in out_indices:
+ layer = norm_layer(num_features[i_layer])
+ layer_name = f"norm{i_layer}"
+ self.add_module(layer_name, layer)
+
+ self._freeze_stages()
+
+ def _freeze_stages(self):
+ if self.frozen_stages >= 0:
+ self.patch_embed.eval()
+ for param in self.patch_embed.parameters():
+ param.requires_grad = False
+
+ if self.frozen_stages >= 1 and self.ape:
+ self.absolute_pos_embed.requires_grad = False
+
+ if self.frozen_stages >= 2:
+ self.pos_drop.eval()
+ for i in range(0, self.frozen_stages - 1):
+ m = self.layers[i]
+ m.eval()
+ for param in m.parameters():
+ param.requires_grad = False
+
+ # def init_weights(self, pretrained=None):
+ # """Initialize the weights in backbone.
+ # Args:
+ # pretrained (str, optional): Path to pre-trained weights.
+ # Defaults to None.
+ # """
+
+ # def _init_weights(m):
+ # if isinstance(m, nn.Linear):
+ # trunc_normal_(m.weight, std=.02)
+ # if isinstance(m, nn.Linear) and m.bias is not None:
+ # nn.init.constant_(m.bias, 0)
+ # elif isinstance(m, nn.LayerNorm):
+ # nn.init.constant_(m.bias, 0)
+ # nn.init.constant_(m.weight, 1.0)
+
+ # if isinstance(pretrained, str):
+ # self.apply(_init_weights)
+ # logger = get_root_logger()
+ # load_checkpoint(self, pretrained, strict=False, logger=logger)
+ # elif pretrained is None:
+ # self.apply(_init_weights)
+ # else:
+ # raise TypeError('pretrained must be a str or None')
+
+ def forward_raw(self, x):
+ """Forward function."""
+ x = self.patch_embed(x)
+
+ Wh, Ww = x.size(2), x.size(3)
+ if self.ape:
+ # interpolate the position embedding to the corresponding size
+ absolute_pos_embed = F.interpolate(
+ self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
+ )
+ x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
+ else:
+ x = x.flatten(2).transpose(1, 2)
+ x = self.pos_drop(x)
+
+ outs = []
+ for i in range(self.num_layers):
+ layer = self.layers[i]
+ x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+ # import ipdb; ipdb.set_trace()
+
+ if i in self.out_indices:
+ norm_layer = getattr(self, f"norm{i}")
+ x_out = norm_layer(x_out)
+
+ out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+ outs.append(out)
+ # in:
+ # torch.Size([2, 3, 1024, 1024])
+ # outs:
+ # [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
+ # torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
+ return tuple(outs)
+
+ def forward(self, tensor_list: NestedTensor):
+ x = tensor_list.tensors
+
+ """Forward function."""
+ x = self.patch_embed(x)
+
+ Wh, Ww = x.size(2), x.size(3)
+ if self.ape:
+ # interpolate the position embedding to the corresponding size
+ absolute_pos_embed = F.interpolate(
+ self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
+ )
+ x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
+ else:
+ x = x.flatten(2).transpose(1, 2)
+ x = self.pos_drop(x)
+
+ outs = []
+ for i in range(self.num_layers):
+ layer = self.layers[i]
+ x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+ if i in self.out_indices:
+ norm_layer = getattr(self, f"norm{i}")
+ x_out = norm_layer(x_out)
+
+ out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+ outs.append(out)
+ # in:
+ # torch.Size([2, 3, 1024, 1024])
+ # out:
+ # [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
+ # torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
+
+ # collect for nesttensors
+ outs_dict = {}
+ for idx, out_i in enumerate(outs):
+ m = tensor_list.mask
+ assert m is not None
+ mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0]
+ outs_dict[idx] = NestedTensor(out_i, mask)
+
+ return outs_dict
+
+ def train(self, mode=True):
+ """Convert the model into training mode while keep layers freezed."""
+ super(SwinTransformer, self).train(mode)
+ self._freeze_stages()
+
+
+def build_swin_transformer(modelname, pretrain_img_size, **kw):
+ assert modelname in [
+ "swin_T_224_1k",
+ "swin_B_224_22k",
+ "swin_B_384_22k",
+ "swin_L_224_22k",
+ "swin_L_384_22k",
+ ]
+
+ model_para_dict = {
+ "swin_T_224_1k": dict(
+ embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7
+ ),
+ "swin_B_224_22k": dict(
+ embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7
+ ),
+ "swin_B_384_22k": dict(
+ embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12
+ ),
+ "swin_L_224_22k": dict(
+ embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7
+ ),
+ "swin_L_384_22k": dict(
+ embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12
+ ),
+ }
+ kw_cgf = model_para_dict[modelname]
+ kw_cgf.update(kw)
+ model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf)
+ return model
+
+
+if __name__ == "__main__":
+ model = build_swin_transformer("swin_L_384_22k", 384, dilation=True)
+ x = torch.rand(2, 3, 1024, 1024)
+ y = model.forward_raw(x)
+ import ipdb
+
+ ipdb.set_trace()
+ x = torch.rand(2, 3, 384, 384)
+ y = model.forward_raw(x)
diff --git a/groundingdino/models/GroundingDINO/bertwarper.py b/groundingdino/models/GroundingDINO/bertwarper.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0cf9779b270e1aead32845006f8b881fcba37ad
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/bertwarper.py
@@ -0,0 +1,273 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from torch import Tensor, nn
+from torchvision.ops.boxes import nms
+from transformers import BertConfig, BertModel, BertPreTrainedModel
+from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
+
+
+class BertModelWarper(nn.Module):
+ def __init__(self, bert_model):
+ super().__init__()
+ # self.bert = bert_modelc
+
+ self.config = bert_model.config
+ self.embeddings = bert_model.embeddings
+ self.encoder = bert_model.encoder
+ self.pooler = bert_model.pooler
+
+ self.get_extended_attention_mask = bert_model.get_extended_attention_mask
+ self.invert_attention_mask = bert_model.invert_attention_mask
+ self.get_head_mask = bert_model.get_head_mask
+
+ def forward(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ token_type_ids=None,
+ position_ids=None,
+ head_mask=None,
+ inputs_embeds=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ past_key_values=None,
+ use_cache=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ r"""
+ encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+ the model is configured as a decoder.
+ encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+ Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+ the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+ Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+ If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+ (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+ instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+ use_cache (:obj:`bool`, `optional`):
+ If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+ decoding (see :obj:`past_key_values`).
+ """
+ output_attentions = (
+ output_attentions if output_attentions is not None else self.config.output_attentions
+ )
+ output_hidden_states = (
+ output_hidden_states
+ if output_hidden_states is not None
+ else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if self.config.is_decoder:
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ else:
+ use_cache = False
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = input_ids.size()
+ batch_size, seq_length = input_shape
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ batch_size, seq_length = input_shape
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ # past_key_values_length
+ past_key_values_length = (
+ past_key_values[0][0].shape[2] if past_key_values is not None else 0
+ )
+
+ if attention_mask is None:
+ attention_mask = torch.ones(
+ ((batch_size, seq_length + past_key_values_length)), device=device
+ )
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+ # ourselves in which case we just need to make it broadcastable to all heads.
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
+ attention_mask, input_shape, device
+ )
+
+ # If a 2D or 3D attention mask is provided for the cross-attention
+ # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+ if self.config.is_decoder and encoder_hidden_states is not None:
+ encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+ encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+ if encoder_attention_mask is None:
+ encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+ encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+ else:
+ encoder_extended_attention_mask = None
+ # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+ # import ipdb; ipdb.set_trace()
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids,
+ position_ids=position_ids,
+ token_type_ids=token_type_ids,
+ inputs_embeds=inputs_embeds,
+ past_key_values_length=past_key_values_length,
+ )
+
+ encoder_outputs = self.encoder(
+ embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_extended_attention_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+ if not return_dict:
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPoolingAndCrossAttentions(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ past_key_values=encoder_outputs.past_key_values,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ cross_attentions=encoder_outputs.cross_attentions,
+ )
+
+
+class TextEncoderShell(nn.Module):
+ def __init__(self, text_encoder):
+ super().__init__()
+ self.text_encoder = text_encoder
+ self.config = self.text_encoder.config
+
+ def forward(self, **kw):
+ # feed into text encoder
+ return self.text_encoder(**kw)
+
+
+def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer):
+ """Generate attention mask between each pair of special tokens
+ Args:
+ input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
+ special_tokens_mask (list): special tokens mask.
+ Returns:
+ torch.Tensor: attention mask between each special tokens.
+ """
+ input_ids = tokenized["input_ids"]
+ bs, num_token = input_ids.shape
+ # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
+ special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
+ for special_token in special_tokens_list:
+ special_tokens_mask |= input_ids == special_token
+
+ # idxs: each row is a list of indices of special tokens
+ idxs = torch.nonzero(special_tokens_mask)
+
+ # generate attention mask and positional ids
+ attention_mask = (
+ torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
+ )
+ position_ids = torch.zeros((bs, num_token), device=input_ids.device)
+ previous_col = 0
+ for i in range(idxs.shape[0]):
+ row, col = idxs[i]
+ if (col == 0) or (col == num_token - 1):
+ attention_mask[row, col, col] = True
+ position_ids[row, col] = 0
+ else:
+ attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
+ position_ids[row, previous_col + 1 : col + 1] = torch.arange(
+ 0, col - previous_col, device=input_ids.device
+ )
+
+ previous_col = col
+
+ # # padding mask
+ # padding_mask = tokenized['attention_mask']
+ # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
+
+ return attention_mask, position_ids.to(torch.long)
+
+
+def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer):
+ """Generate attention mask between each pair of special tokens
+ Args:
+ input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
+ special_tokens_mask (list): special tokens mask.
+ Returns:
+ torch.Tensor: attention mask between each special tokens.
+ """
+ input_ids = tokenized["input_ids"]
+ bs, num_token = input_ids.shape
+ # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
+ special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
+ for special_token in special_tokens_list:
+ special_tokens_mask |= input_ids == special_token
+
+ # idxs: each row is a list of indices of special tokens
+ idxs = torch.nonzero(special_tokens_mask)
+
+ # generate attention mask and positional ids
+ attention_mask = (
+ torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
+ )
+ position_ids = torch.zeros((bs, num_token), device=input_ids.device)
+ cate_to_token_mask_list = [[] for _ in range(bs)]
+ previous_col = 0
+ for i in range(idxs.shape[0]):
+ row, col = idxs[i]
+ if (col == 0) or (col == num_token - 1):
+ attention_mask[row, col, col] = True
+ position_ids[row, col] = 0
+ else:
+ attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
+ position_ids[row, previous_col + 1 : col + 1] = torch.arange(
+ 0, col - previous_col, device=input_ids.device
+ )
+ c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()
+ c2t_maski[previous_col + 1 : col] = True
+ cate_to_token_mask_list[row].append(c2t_maski)
+ previous_col = col
+
+ cate_to_token_mask_list = [
+ torch.stack(cate_to_token_mask_listi, dim=0)
+ for cate_to_token_mask_listi in cate_to_token_mask_list
+ ]
+
+ # # padding mask
+ # padding_mask = tokenized['attention_mask']
+ # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
+
+ return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list
diff --git a/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h b/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h
new file mode 100644
index 0000000000000000000000000000000000000000..c7408eba007b424194618baa63726657e36875e3
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h
@@ -0,0 +1,64 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#pragma once
+
+#include "ms_deform_attn_cpu.h"
+
+#ifdef WITH_CUDA
+#include "ms_deform_attn_cuda.h"
+#endif
+
+namespace groundingdino {
+
+at::Tensor
+ms_deform_attn_forward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const int im2col_step)
+{
+ if (value.type().is_cuda())
+ {
+#ifdef WITH_CUDA
+ return ms_deform_attn_cuda_forward(
+ value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);
+#else
+ AT_ERROR("Not compiled with GPU support");
+#endif
+ }
+ AT_ERROR("Not implemented on the CPU");
+}
+
+std::vector<at::Tensor>
+ms_deform_attn_backward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const at::Tensor &grad_output,
+ const int im2col_step)
+{
+ if (value.type().is_cuda())
+ {
+#ifdef WITH_CUDA
+ return ms_deform_attn_cuda_backward(
+ value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);
+#else
+ AT_ERROR("Not compiled with GPU support");
+#endif
+ }
+ AT_ERROR("Not implemented on the CPU");
+}
+
+} // namespace groundingdino
\ No newline at end of file
diff --git a/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp b/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..551243fdadfd1682b5dc6628623b67a79b3f6c74
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp
@@ -0,0 +1,43 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#include <vector>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+namespace groundingdino {
+
+at::Tensor
+ms_deform_attn_cpu_forward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const int im2col_step)
+{
+ AT_ERROR("Not implement on cpu");
+}
+
+std::vector<at::Tensor>
+ms_deform_attn_cpu_backward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const at::Tensor &grad_output,
+ const int im2col_step)
+{
+ AT_ERROR("Not implement on cpu");
+}
+
+} // namespace groundingdino
diff --git a/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h b/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h
new file mode 100644
index 0000000000000000000000000000000000000000..b2b88e8c46f19b6db0933163e57ccdb51180f517
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h
@@ -0,0 +1,35 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#pragma once
+#include <torch/extension.h>
+
+namespace groundingdino {
+
+at::Tensor
+ms_deform_attn_cpu_forward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const int im2col_step);
+
+std::vector<at::Tensor>
+ms_deform_attn_cpu_backward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const at::Tensor &grad_output,
+ const int im2col_step);
+
+} // namespace groundingdino
diff --git a/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.cu b/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.cu
new file mode 100644
index 0000000000000000000000000000000000000000..d04fae8a9a45c11e4e74f3035e94762796da4096
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.cu
@@ -0,0 +1,156 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#include <vector>
+#include "ms_deform_im2col_cuda.cuh"
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+namespace groundingdino {
+
+at::Tensor ms_deform_attn_cuda_forward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const int im2col_step)
+{
+ AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+ AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+ AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+ AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
+ AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
+
+ AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
+ AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
+ AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
+ AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
+ AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
+
+ const int batch = value.size(0);
+ const int spatial_size = value.size(1);
+ const int num_heads = value.size(2);
+ const int channels = value.size(3);
+
+ const int num_levels = spatial_shapes.size(0);
+
+ const int num_query = sampling_loc.size(1);
+ const int num_point = sampling_loc.size(4);
+
+ const int im2col_step_ = std::min(batch, im2col_step);
+
+ AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+
+ auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());
+
+ const int batch_n = im2col_step_;
+ auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
+ auto per_value_size = spatial_size * num_heads * channels;
+ auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+ auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+ for (int n = 0; n < batch/im2col_step_; ++n)
+ {
+ auto columns = output_n.select(0, n);
+ AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] {
+ ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),
+ value.data<scalar_t>() + n * im2col_step_ * per_value_size,
+ spatial_shapes.data<int64_t>(),
+ level_start_index.data<int64_t>(),
+ sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+ attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
+ batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+ columns.data<scalar_t>());
+
+ }));
+ }
+
+ output = output.view({batch, num_query, num_heads*channels});
+
+ return output;
+}
+
+
+std::vector<at::Tensor> ms_deform_attn_cuda_backward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const at::Tensor &grad_output,
+ const int im2col_step)
+{
+
+ AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+ AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+ AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+ AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
+ AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
+ AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
+
+ AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
+ AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
+ AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
+ AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
+ AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
+ AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor");
+
+ const int batch = value.size(0);
+ const int spatial_size = value.size(1);
+ const int num_heads = value.size(2);
+ const int channels = value.size(3);
+
+ const int num_levels = spatial_shapes.size(0);
+
+ const int num_query = sampling_loc.size(1);
+ const int num_point = sampling_loc.size(4);
+
+ const int im2col_step_ = std::min(batch, im2col_step);
+
+ AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+
+ auto grad_value = at::zeros_like(value);
+ auto grad_sampling_loc = at::zeros_like(sampling_loc);
+ auto grad_attn_weight = at::zeros_like(attn_weight);
+
+ const int batch_n = im2col_step_;
+ auto per_value_size = spatial_size * num_heads * channels;
+ auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+ auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+ auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
+
+ for (int n = 0; n < batch/im2col_step_; ++n)
+ {
+ auto grad_output_g = grad_output_n.select(0, n);
+ AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] {
+ ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),
+ grad_output_g.data<scalar_t>(),
+ value.data<scalar_t>() + n * im2col_step_ * per_value_size,
+ spatial_shapes.data<int64_t>(),
+ level_start_index.data<int64_t>(),
+ sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+ attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
+ batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+ grad_value.data<scalar_t>() + n * im2col_step_ * per_value_size,
+ grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+ grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size);
+
+ }));
+ }
+
+ return {
+ grad_value, grad_sampling_loc, grad_attn_weight
+ };
+}
+
+} // namespace groundingdino
\ No newline at end of file
diff --git a/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h b/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h
new file mode 100644
index 0000000000000000000000000000000000000000..ad1311a78f61303616504eb991aaa9c4a93d9948
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h
@@ -0,0 +1,33 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#pragma once
+#include <torch/extension.h>
+
+namespace groundingdino {
+
+at::Tensor ms_deform_attn_cuda_forward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const int im2col_step);
+
+std::vector<at::Tensor> ms_deform_attn_cuda_backward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const at::Tensor &grad_output,
+ const int im2col_step);
+
+} // namespace groundingdino
\ No newline at end of file
diff --git a/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_im2col_cuda.cuh b/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_im2col_cuda.cuh
new file mode 100644
index 0000000000000000000000000000000000000000..6bc2acb7aea0eab2e9e91e769a16861e1652c284
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_im2col_cuda.cuh
@@ -0,0 +1,1327 @@
+/*!
+**************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************
+* Modified from DCN (https://github.com/msracver/Deformable-ConvNets)
+* Copyright (c) 2018 Microsoft
+**************************************************************************
+*/
+
+#include <cstdio>
+#include <algorithm>
+#include <cstring>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <THC/THCAtomics.cuh>
+
+#define CUDA_KERNEL_LOOP(i, n) \
+ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
+ i < (n); \
+ i += blockDim.x * gridDim.x)
+
+const int CUDA_NUM_THREADS = 1024;
+inline int GET_BLOCKS(const int N, const int num_threads)
+{
+ return (N + num_threads - 1) / num_threads;
+}
+
+
+template <typename scalar_t>
+__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data,
+ const int &height, const int &width, const int &nheads, const int &channels,
+ const scalar_t &h, const scalar_t &w, const int &m, const int &c)
+{
+ const int h_low = floor(h);
+ const int w_low = floor(w);
+ const int h_high = h_low + 1;
+ const int w_high = w_low + 1;
+
+ const scalar_t lh = h - h_low;
+ const scalar_t lw = w - w_low;
+ const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+ const int w_stride = nheads * channels;
+ const int h_stride = width * w_stride;
+ const int h_low_ptr_offset = h_low * h_stride;
+ const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+ const int w_low_ptr_offset = w_low * w_stride;
+ const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+ const int base_ptr = m * channels + c;
+
+ scalar_t v1 = 0;
+ if (h_low >= 0 && w_low >= 0)
+ {
+ const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+ v1 = bottom_data[ptr1];
+ }
+ scalar_t v2 = 0;
+ if (h_low >= 0 && w_high <= width - 1)
+ {
+ const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+ v2 = bottom_data[ptr2];
+ }
+ scalar_t v3 = 0;
+ if (h_high <= height - 1 && w_low >= 0)
+ {
+ const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+ v3 = bottom_data[ptr3];
+ }
+ scalar_t v4 = 0;
+ if (h_high <= height - 1 && w_high <= width - 1)
+ {
+ const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+ v4 = bottom_data[ptr4];
+ }
+
+ const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+
+ const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+ return val;
+}
+
+
+template <typename scalar_t>
+__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data,
+ const int &height, const int &width, const int &nheads, const int &channels,
+ const scalar_t &h, const scalar_t &w, const int &m, const int &c,
+ const scalar_t &top_grad,
+ const scalar_t &attn_weight,
+ scalar_t* &grad_value,
+ scalar_t* grad_sampling_loc,
+ scalar_t* grad_attn_weight)
+{
+ const int h_low = floor(h);
+ const int w_low = floor(w);
+ const int h_high = h_low + 1;
+ const int w_high = w_low + 1;
+
+ const scalar_t lh = h - h_low;
+ const scalar_t lw = w - w_low;
+ const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+ const int w_stride = nheads * channels;
+ const int h_stride = width * w_stride;
+ const int h_low_ptr_offset = h_low * h_stride;
+ const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+ const int w_low_ptr_offset = w_low * w_stride;
+ const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+ const int base_ptr = m * channels + c;
+
+ const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+ const scalar_t top_grad_value = top_grad * attn_weight;
+ scalar_t grad_h_weight = 0, grad_w_weight = 0;
+
+ scalar_t v1 = 0;
+ if (h_low >= 0 && w_low >= 0)
+ {
+ const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+ v1 = bottom_data[ptr1];
+ grad_h_weight -= hw * v1;
+ grad_w_weight -= hh * v1;
+ atomicAdd(grad_value+ptr1, w1*top_grad_value);
+ }
+ scalar_t v2 = 0;
+ if (h_low >= 0 && w_high <= width - 1)
+ {
+ const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+ v2 = bottom_data[ptr2];
+ grad_h_weight -= lw * v2;
+ grad_w_weight += hh * v2;
+ atomicAdd(grad_value+ptr2, w2*top_grad_value);
+ }
+ scalar_t v3 = 0;
+ if (h_high <= height - 1 && w_low >= 0)
+ {
+ const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+ v3 = bottom_data[ptr3];
+ grad_h_weight += hw * v3;
+ grad_w_weight -= lh * v3;
+ atomicAdd(grad_value+ptr3, w3*top_grad_value);
+ }
+ scalar_t v4 = 0;
+ if (h_high <= height - 1 && w_high <= width - 1)
+ {
+ const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+ v4 = bottom_data[ptr4];
+ grad_h_weight += lw * v4;
+ grad_w_weight += lh * v4;
+ atomicAdd(grad_value+ptr4, w4*top_grad_value);
+ }
+
+ const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+ *grad_attn_weight = top_grad * val;
+ *grad_sampling_loc = width * grad_w_weight * top_grad_value;
+ *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value;
+}
+
+
+template <typename scalar_t>
+__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data,
+ const int &height, const int &width, const int &nheads, const int &channels,
+ const scalar_t &h, const scalar_t &w, const int &m, const int &c,
+ const scalar_t &top_grad,
+ const scalar_t &attn_weight,
+ scalar_t* &grad_value,
+ scalar_t* grad_sampling_loc,
+ scalar_t* grad_attn_weight)
+{
+ const int h_low = floor(h);
+ const int w_low = floor(w);
+ const int h_high = h_low + 1;
+ const int w_high = w_low + 1;
+
+ const scalar_t lh = h - h_low;
+ const scalar_t lw = w - w_low;
+ const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+ const int w_stride = nheads * channels;
+ const int h_stride = width * w_stride;
+ const int h_low_ptr_offset = h_low * h_stride;
+ const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+ const int w_low_ptr_offset = w_low * w_stride;
+ const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+ const int base_ptr = m * channels + c;
+
+ const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+ const scalar_t top_grad_value = top_grad * attn_weight;
+ scalar_t grad_h_weight = 0, grad_w_weight = 0;
+
+ scalar_t v1 = 0;
+ if (h_low >= 0 && w_low >= 0)
+ {
+ const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+ v1 = bottom_data[ptr1];
+ grad_h_weight -= hw * v1;
+ grad_w_weight -= hh * v1;
+ atomicAdd(grad_value+ptr1, w1*top_grad_value);
+ }
+ scalar_t v2 = 0;
+ if (h_low >= 0 && w_high <= width - 1)
+ {
+ const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+ v2 = bottom_data[ptr2];
+ grad_h_weight -= lw * v2;
+ grad_w_weight += hh * v2;
+ atomicAdd(grad_value+ptr2, w2*top_grad_value);
+ }
+ scalar_t v3 = 0;
+ if (h_high <= height - 1 && w_low >= 0)
+ {
+ const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+ v3 = bottom_data[ptr3];
+ grad_h_weight += hw * v3;
+ grad_w_weight -= lh * v3;
+ atomicAdd(grad_value+ptr3, w3*top_grad_value);
+ }
+ scalar_t v4 = 0;
+ if (h_high <= height - 1 && w_high <= width - 1)
+ {
+ const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+ v4 = bottom_data[ptr4];
+ grad_h_weight += lw * v4;
+ grad_w_weight += lh * v4;
+ atomicAdd(grad_value+ptr4, w4*top_grad_value);
+ }
+
+ const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+ atomicAdd(grad_attn_weight, top_grad * val);
+ atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value);
+ atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value);
+}
+
+
+template <typename scalar_t>
+__global__ void ms_deformable_im2col_gpu_kernel(const int n,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *data_col)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ scalar_t *data_col_ptr = data_col + index;
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+ scalar_t col = 0;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride);
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight;
+ }
+
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ }
+ }
+ *data_col_ptr = col;
+ }
+}
+
+template <typename scalar_t, unsigned int blockSize>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n,
+ const scalar_t *grad_col,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *grad_value,
+ scalar_t *grad_sampling_loc,
+ scalar_t *grad_attn_weight)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
+ __shared__ scalar_t cache_grad_attn_weight[blockSize];
+ unsigned int tid = threadIdx.x;
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ const scalar_t top_grad = grad_col[index];
+
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int grad_sampling_ptr = data_weight_ptr;
+ grad_sampling_loc += grad_sampling_ptr << 1;
+ grad_attn_weight += grad_sampling_ptr;
+ const int grad_weight_stride = 1;
+ const int grad_loc_stride = 2;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+ const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+ scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+ *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+ *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+ *(cache_grad_attn_weight+threadIdx.x)=0;
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ ms_deform_attn_col2im_bilinear(
+ data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+ top_grad, weight, grad_value_ptr,
+ cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+ }
+
+ __syncthreads();
+ if (tid == 0)
+ {
+ scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];
+ int sid=2;
+ for (unsigned int tid = 1; tid < blockSize; ++tid)
+ {
+ _grad_w += cache_grad_sampling_loc[sid];
+ _grad_h += cache_grad_sampling_loc[sid + 1];
+ _grad_a += cache_grad_attn_weight[tid];
+ sid += 2;
+ }
+
+
+ *grad_sampling_loc = _grad_w;
+ *(grad_sampling_loc + 1) = _grad_h;
+ *grad_attn_weight = _grad_a;
+ }
+ __syncthreads();
+
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ grad_attn_weight += grad_weight_stride;
+ grad_sampling_loc += grad_loc_stride;
+ }
+ }
+ }
+}
+
+
+template <typename scalar_t, unsigned int blockSize>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n,
+ const scalar_t *grad_col,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *grad_value,
+ scalar_t *grad_sampling_loc,
+ scalar_t *grad_attn_weight)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
+ __shared__ scalar_t cache_grad_attn_weight[blockSize];
+ unsigned int tid = threadIdx.x;
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ const scalar_t top_grad = grad_col[index];
+
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int grad_sampling_ptr = data_weight_ptr;
+ grad_sampling_loc += grad_sampling_ptr << 1;
+ grad_attn_weight += grad_sampling_ptr;
+ const int grad_weight_stride = 1;
+ const int grad_loc_stride = 2;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+ const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+ scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+ *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+ *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+ *(cache_grad_attn_weight+threadIdx.x)=0;
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ ms_deform_attn_col2im_bilinear(
+ data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+ top_grad, weight, grad_value_ptr,
+ cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+ }
+
+ __syncthreads();
+
+ for (unsigned int s=blockSize/2; s>0; s>>=1)
+ {
+ if (tid < s) {
+ const unsigned int xid1 = tid << 1;
+ const unsigned int xid2 = (tid + s) << 1;
+ cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+ cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+ cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+ }
+ __syncthreads();
+ }
+
+ if (tid == 0)
+ {
+ *grad_sampling_loc = cache_grad_sampling_loc[0];
+ *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
+ *grad_attn_weight = cache_grad_attn_weight[0];
+ }
+ __syncthreads();
+
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ grad_attn_weight += grad_weight_stride;
+ grad_sampling_loc += grad_loc_stride;
+ }
+ }
+ }
+}
+
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n,
+ const scalar_t *grad_col,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *grad_value,
+ scalar_t *grad_sampling_loc,
+ scalar_t *grad_attn_weight)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ extern __shared__ int _s[];
+ scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
+ scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+ unsigned int tid = threadIdx.x;
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ const scalar_t top_grad = grad_col[index];
+
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int grad_sampling_ptr = data_weight_ptr;
+ grad_sampling_loc += grad_sampling_ptr << 1;
+ grad_attn_weight += grad_sampling_ptr;
+ const int grad_weight_stride = 1;
+ const int grad_loc_stride = 2;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+ const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+ scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+ *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+ *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+ *(cache_grad_attn_weight+threadIdx.x)=0;
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ ms_deform_attn_col2im_bilinear(
+ data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+ top_grad, weight, grad_value_ptr,
+ cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+ }
+
+ __syncthreads();
+ if (tid == 0)
+ {
+ scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];
+ int sid=2;
+ for (unsigned int tid = 1; tid < blockDim.x; ++tid)
+ {
+ _grad_w += cache_grad_sampling_loc[sid];
+ _grad_h += cache_grad_sampling_loc[sid + 1];
+ _grad_a += cache_grad_attn_weight[tid];
+ sid += 2;
+ }
+
+
+ *grad_sampling_loc = _grad_w;
+ *(grad_sampling_loc + 1) = _grad_h;
+ *grad_attn_weight = _grad_a;
+ }
+ __syncthreads();
+
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ grad_attn_weight += grad_weight_stride;
+ grad_sampling_loc += grad_loc_stride;
+ }
+ }
+ }
+}
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n,
+ const scalar_t *grad_col,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *grad_value,
+ scalar_t *grad_sampling_loc,
+ scalar_t *grad_attn_weight)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ extern __shared__ int _s[];
+ scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
+ scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+ unsigned int tid = threadIdx.x;
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ const scalar_t top_grad = grad_col[index];
+
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int grad_sampling_ptr = data_weight_ptr;
+ grad_sampling_loc += grad_sampling_ptr << 1;
+ grad_attn_weight += grad_sampling_ptr;
+ const int grad_weight_stride = 1;
+ const int grad_loc_stride = 2;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+ const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+ scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+ *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+ *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+ *(cache_grad_attn_weight+threadIdx.x)=0;
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ ms_deform_attn_col2im_bilinear(
+ data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+ top_grad, weight, grad_value_ptr,
+ cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+ }
+
+ __syncthreads();
+
+ for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)
+ {
+ if (tid < s) {
+ const unsigned int xid1 = tid << 1;
+ const unsigned int xid2 = (tid + s) << 1;
+ cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+ cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+ cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+ if (tid + (s << 1) < spre)
+ {
+ cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
+ cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
+ cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
+ }
+ }
+ __syncthreads();
+ }
+
+ if (tid == 0)
+ {
+ *grad_sampling_loc = cache_grad_sampling_loc[0];
+ *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
+ *grad_attn_weight = cache_grad_attn_weight[0];
+ }
+ __syncthreads();
+
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ grad_attn_weight += grad_weight_stride;
+ grad_sampling_loc += grad_loc_stride;
+ }
+ }
+ }
+}
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n,
+ const scalar_t *grad_col,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *grad_value,
+ scalar_t *grad_sampling_loc,
+ scalar_t *grad_attn_weight)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ extern __shared__ int _s[];
+ scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
+ scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+ unsigned int tid = threadIdx.x;
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ const scalar_t top_grad = grad_col[index];
+
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int grad_sampling_ptr = data_weight_ptr;
+ grad_sampling_loc += grad_sampling_ptr << 1;
+ grad_attn_weight += grad_sampling_ptr;
+ const int grad_weight_stride = 1;
+ const int grad_loc_stride = 2;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+ const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+ scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+ *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+ *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+ *(cache_grad_attn_weight+threadIdx.x)=0;
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ ms_deform_attn_col2im_bilinear(
+ data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+ top_grad, weight, grad_value_ptr,
+ cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+ }
+
+ __syncthreads();
+
+ for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)
+ {
+ if (tid < s) {
+ const unsigned int xid1 = tid << 1;
+ const unsigned int xid2 = (tid + s) << 1;
+ cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+ cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+ cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+ if (tid + (s << 1) < spre)
+ {
+ cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
+ cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
+ cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
+ }
+ }
+ __syncthreads();
+ }
+
+ if (tid == 0)
+ {
+ atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]);
+ atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]);
+ atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]);
+ }
+ __syncthreads();
+
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ grad_attn_weight += grad_weight_stride;
+ grad_sampling_loc += grad_loc_stride;
+ }
+ }
+ }
+}
+
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n,
+ const scalar_t *grad_col,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *grad_value,
+ scalar_t *grad_sampling_loc,
+ scalar_t *grad_attn_weight)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ const scalar_t top_grad = grad_col[index];
+
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int grad_sampling_ptr = data_weight_ptr;
+ grad_sampling_loc += grad_sampling_ptr << 1;
+ grad_attn_weight += grad_sampling_ptr;
+ const int grad_weight_stride = 1;
+ const int grad_loc_stride = 2;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+ const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+ scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ ms_deform_attn_col2im_bilinear_gm(
+ data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+ top_grad, weight, grad_value_ptr,
+ grad_sampling_loc, grad_attn_weight);
+ }
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ grad_attn_weight += grad_weight_stride;
+ grad_sampling_loc += grad_loc_stride;
+ }
+ }
+ }
+}
+
+
+template <typename scalar_t>
+void ms_deformable_im2col_cuda(cudaStream_t stream,
+ const scalar_t* data_value,
+ const int64_t* data_spatial_shapes,
+ const int64_t* data_level_start_index,
+ const scalar_t* data_sampling_loc,
+ const scalar_t* data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t* data_col)
+{
+ const int num_kernels = batch_size * num_query * num_heads * channels;
+ const int num_actual_kernels = batch_size * num_query * num_heads * channels;
+ const int num_threads = CUDA_NUM_THREADS;
+ ms_deformable_im2col_gpu_kernel<scalar_t>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ 0, stream>>>(
+ num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight,
+ batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col);
+
+ cudaError_t err = cudaGetLastError();
+ if (err != cudaSuccess)
+ {
+ printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err));
+ }
+
+}
+
+template <typename scalar_t>
+void ms_deformable_col2im_cuda(cudaStream_t stream,
+ const scalar_t* grad_col,
+ const scalar_t* data_value,
+ const int64_t * data_spatial_shapes,
+ const int64_t * data_level_start_index,
+ const scalar_t * data_sampling_loc,
+ const scalar_t * data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t* grad_value,
+ scalar_t* grad_sampling_loc,
+ scalar_t* grad_attn_weight)
+{
+ const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels;
+ const int num_kernels = batch_size * num_query * num_heads * channels;
+ const int num_actual_kernels = batch_size * num_query * num_heads * channels;
+ if (channels > 1024)
+ {
+ if ((channels & 1023) == 0)
+ {
+ ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks<scalar_t>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ num_threads*3*sizeof(scalar_t), stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ }
+ else
+ {
+ ms_deformable_col2im_gpu_kernel_gm<scalar_t>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ 0, stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ }
+ }
+ else{
+ switch(channels)
+ {
+ case 1:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 1>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ 0, stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 2:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 2>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ 0, stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 4:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 4>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ 0, stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 8:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 8>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ 0, stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 16:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 16>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ 0, stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 32:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 32>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ 0, stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 64:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 64>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ 0, stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 128:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 128>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ 0, stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 256:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 256>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ 0, stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 512:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 512>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ 0, stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 1024:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 1024>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ 0, stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ default:
+ if (channels < 64)
+ {
+ ms_deformable_col2im_gpu_kernel_shm_reduce_v1<scalar_t>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ num_threads*3*sizeof(scalar_t), stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ }
+ else
+ {
+ ms_deformable_col2im_gpu_kernel_shm_reduce_v2<scalar_t>
+ <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+ num_threads*3*sizeof(scalar_t), stream>>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ }
+ }
+ }
+ cudaError_t err = cudaGetLastError();
+ if (err != cudaSuccess)
+ {
+ printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err));
+ }
+
+}
\ No newline at end of file
diff --git a/groundingdino/models/GroundingDINO/csrc/cuda_version.cu b/groundingdino/models/GroundingDINO/csrc/cuda_version.cu
new file mode 100644
index 0000000000000000000000000000000000000000..64569e34ffb250964de27e33e7a53f3822270b9e
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/csrc/cuda_version.cu
@@ -0,0 +1,7 @@
+#include <cuda_runtime_api.h>
+
+namespace groundingdino {
+int get_cudart_version() {
+ return CUDART_VERSION;
+}
+} // namespace groundingdino
diff --git a/groundingdino/models/GroundingDINO/csrc/vision.cpp b/groundingdino/models/GroundingDINO/csrc/vision.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..c1f2c50c82909bbd5492c163d634af77a3ba1781
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/csrc/vision.cpp
@@ -0,0 +1,58 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+#include "MsDeformAttn/ms_deform_attn.h"
+
+namespace groundingdino {
+
+#ifdef WITH_CUDA
+extern int get_cudart_version();
+#endif
+
+std::string get_cuda_version() {
+#ifdef WITH_CUDA
+ std::ostringstream oss;
+
+ // copied from
+ // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231
+ auto printCudaStyleVersion = [&](int v) {
+ oss << (v / 1000) << "." << (v / 10 % 100);
+ if (v % 10 != 0) {
+ oss << "." << (v % 10);
+ }
+ };
+ printCudaStyleVersion(get_cudart_version());
+ return oss.str();
+#else
+ return std::string("not available");
+#endif
+}
+
+// similar to
+// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp
+std::string get_compiler_version() {
+ std::ostringstream ss;
+#if defined(__GNUC__)
+#ifndef __clang__
+ { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; }
+#endif
+#endif
+
+#if defined(__clang_major__)
+ {
+ ss << "clang " << __clang_major__ << "." << __clang_minor__ << "."
+ << __clang_patchlevel__;
+ }
+#endif
+
+#if defined(_MSC_VER)
+ { ss << "MSVC " << _MSC_FULL_VER; }
+#endif
+ return ss.str();
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+ m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
+ m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
+}
+
+} // namespace groundingdino
\ No newline at end of file
diff --git a/groundingdino/models/GroundingDINO/fuse_modules.py b/groundingdino/models/GroundingDINO/fuse_modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..2753b3ddee43c7a9fe28d1824db5d786e7e1ad59
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/fuse_modules.py
@@ -0,0 +1,297 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.models.layers import DropPath
+
+
+class FeatureResizer(nn.Module):
+ """
+ This class takes as input a set of embeddings of dimension C1 and outputs a set of
+ embedding of dimension C2, after a linear transformation, dropout and normalization (LN).
+ """
+
+ def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True):
+ super().__init__()
+ self.do_ln = do_ln
+ # Object feature encoding
+ self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True)
+ self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12)
+ self.dropout = nn.Dropout(dropout)
+
+ def forward(self, encoder_features):
+ x = self.fc(encoder_features)
+ if self.do_ln:
+ x = self.layer_norm(x)
+ output = self.dropout(x)
+ return output
+
+
+def l1norm(X, dim, eps=1e-8):
+ """L1-normalize columns of X"""
+ norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps
+ X = torch.div(X, norm)
+ return X
+
+
+def l2norm(X, dim, eps=1e-8):
+ """L2-normalize columns of X"""
+ norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
+ X = torch.div(X, norm)
+ return X
+
+
+def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8):
+ """
+ query: (n_context, queryL, d)
+ context: (n_context, sourceL, d)
+ """
+ batch_size_q, queryL = query.size(0), query.size(1)
+ batch_size, sourceL = context.size(0), context.size(1)
+
+ # Get attention
+ # --> (batch, d, queryL)
+ queryT = torch.transpose(query, 1, 2)
+
+ # (batch, sourceL, d)(batch, d, queryL)
+ # --> (batch, sourceL, queryL)
+ attn = torch.bmm(context, queryT)
+ if raw_feature_norm == "softmax":
+ # --> (batch*sourceL, queryL)
+ attn = attn.view(batch_size * sourceL, queryL)
+ attn = nn.Softmax()(attn)
+ # --> (batch, sourceL, queryL)
+ attn = attn.view(batch_size, sourceL, queryL)
+ elif raw_feature_norm == "l2norm":
+ attn = l2norm(attn, 2)
+ elif raw_feature_norm == "clipped_l2norm":
+ attn = nn.LeakyReLU(0.1)(attn)
+ attn = l2norm(attn, 2)
+ else:
+ raise ValueError("unknown first norm type:", raw_feature_norm)
+ # --> (batch, queryL, sourceL)
+ attn = torch.transpose(attn, 1, 2).contiguous()
+ # --> (batch*queryL, sourceL)
+ attn = attn.view(batch_size * queryL, sourceL)
+ attn = nn.Softmax()(attn * smooth)
+ # --> (batch, queryL, sourceL)
+ attn = attn.view(batch_size, queryL, sourceL)
+ # --> (batch, sourceL, queryL)
+ attnT = torch.transpose(attn, 1, 2).contiguous()
+
+ # --> (batch, d, sourceL)
+ contextT = torch.transpose(context, 1, 2)
+ # (batch x d x sourceL)(batch x sourceL x queryL)
+ # --> (batch, d, queryL)
+ weightedContext = torch.bmm(contextT, attnT)
+ # --> (batch, queryL, d)
+ weightedContext = torch.transpose(weightedContext, 1, 2)
+
+ return weightedContext, attnT
+
+
+class BiMultiHeadAttention(nn.Module):
+ def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None):
+ super(BiMultiHeadAttention, self).__init__()
+
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.head_dim = embed_dim // num_heads
+ self.v_dim = v_dim
+ self.l_dim = l_dim
+
+ assert (
+ self.head_dim * self.num_heads == self.embed_dim
+ ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
+ self.scale = self.head_dim ** (-0.5)
+ self.dropout = dropout
+
+ self.v_proj = nn.Linear(self.v_dim, self.embed_dim)
+ self.l_proj = nn.Linear(self.l_dim, self.embed_dim)
+ self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim)
+ self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim)
+
+ self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim)
+ self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim)
+
+ self.stable_softmax_2d = True
+ self.clamp_min_for_underflow = True
+ self.clamp_max_for_overflow = True
+
+ self._reset_parameters()
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def _reset_parameters(self):
+ nn.init.xavier_uniform_(self.v_proj.weight)
+ self.v_proj.bias.data.fill_(0)
+ nn.init.xavier_uniform_(self.l_proj.weight)
+ self.l_proj.bias.data.fill_(0)
+ nn.init.xavier_uniform_(self.values_v_proj.weight)
+ self.values_v_proj.bias.data.fill_(0)
+ nn.init.xavier_uniform_(self.values_l_proj.weight)
+ self.values_l_proj.bias.data.fill_(0)
+ nn.init.xavier_uniform_(self.out_v_proj.weight)
+ self.out_v_proj.bias.data.fill_(0)
+ nn.init.xavier_uniform_(self.out_l_proj.weight)
+ self.out_l_proj.bias.data.fill_(0)
+
+ def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
+ """_summary_
+
+ Args:
+ v (_type_): bs, n_img, dim
+ l (_type_): bs, n_text, dim
+ attention_mask_v (_type_, optional): _description_. bs, n_img
+ attention_mask_l (_type_, optional): _description_. bs, n_text
+
+ Returns:
+ _type_: _description_
+ """
+ # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+ # import ipdb; ipdb.set_trace()
+ bsz, tgt_len, _ = v.size()
+
+ query_states = self.v_proj(v) * self.scale
+ key_states = self._shape(self.l_proj(l), -1, bsz)
+ value_v_states = self._shape(self.values_v_proj(v), -1, bsz)
+ value_l_states = self._shape(self.values_l_proj(l), -1, bsz)
+
+ proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+ query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
+ key_states = key_states.view(*proj_shape)
+ value_v_states = value_v_states.view(*proj_shape)
+ value_l_states = value_l_states.view(*proj_shape)
+
+ src_len = key_states.size(1)
+ attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) # bs*nhead, nimg, ntxt
+
+ if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+ )
+
+ if self.stable_softmax_2d:
+ attn_weights = attn_weights - attn_weights.max()
+
+ if self.clamp_min_for_underflow:
+ attn_weights = torch.clamp(
+ attn_weights, min=-50000
+ ) # Do not increase -50000, data type half has quite limited range
+ if self.clamp_max_for_overflow:
+ attn_weights = torch.clamp(
+ attn_weights, max=50000
+ ) # Do not increase 50000, data type half has quite limited range
+
+ attn_weights_T = attn_weights.transpose(1, 2)
+ attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]
+ if self.clamp_min_for_underflow:
+ attn_weights_l = torch.clamp(
+ attn_weights_l, min=-50000
+ ) # Do not increase -50000, data type half has quite limited range
+ if self.clamp_max_for_overflow:
+ attn_weights_l = torch.clamp(
+ attn_weights_l, max=50000
+ ) # Do not increase 50000, data type half has quite limited range
+
+ # mask vison for language
+ if attention_mask_v is not None:
+ attention_mask_v = (
+ attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+ )
+ attn_weights_l.masked_fill_(attention_mask_v, float("-inf"))
+
+ attn_weights_l = attn_weights_l.softmax(dim=-1)
+
+ # mask language for vision
+ if attention_mask_l is not None:
+ attention_mask_l = (
+ attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+ )
+ attn_weights.masked_fill_(attention_mask_l, float("-inf"))
+ attn_weights_v = attn_weights.softmax(dim=-1)
+
+ attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training)
+ attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training)
+
+ attn_output_v = torch.bmm(attn_probs_v, value_l_states)
+ attn_output_l = torch.bmm(attn_probs_l, value_v_states)
+
+ if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}"
+ )
+
+ if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}"
+ )
+
+ attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim)
+ attn_output_v = attn_output_v.transpose(1, 2)
+ attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim)
+
+ attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim)
+ attn_output_l = attn_output_l.transpose(1, 2)
+ attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim)
+
+ attn_output_v = self.out_v_proj(attn_output_v)
+ attn_output_l = self.out_l_proj(attn_output_l)
+
+ return attn_output_v, attn_output_l
+
+
+# Bi-Direction MHA (text->image, image->text)
+class BiAttentionBlock(nn.Module):
+ def __init__(
+ self,
+ v_dim,
+ l_dim,
+ embed_dim,
+ num_heads,
+ dropout=0.1,
+ drop_path=0.0,
+ init_values=1e-4,
+ cfg=None,
+ ):
+ """
+ Inputs:
+ embed_dim - Dimensionality of input and attention feature vectors
+ hidden_dim - Dimensionality of hidden layer in feed-forward network
+ (usually 2-4x larger than embed_dim)
+ num_heads - Number of heads to use in the Multi-Head Attention block
+ dropout - Amount of dropout to apply in the feed-forward network
+ """
+ super(BiAttentionBlock, self).__init__()
+
+ # pre layer norm
+ self.layer_norm_v = nn.LayerNorm(v_dim)
+ self.layer_norm_l = nn.LayerNorm(l_dim)
+ self.attn = BiMultiHeadAttention(
+ v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout
+ )
+
+ # add layer scale for training stability
+ self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+ self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True)
+ self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True)
+
+ def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
+ v = self.layer_norm_v(v)
+ l = self.layer_norm_l(l)
+ delta_v, delta_l = self.attn(
+ v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l
+ )
+ # v, l = v + delta_v, l + delta_l
+ v = v + self.drop_path(self.gamma_v * delta_v)
+ l = l + self.drop_path(self.gamma_l * delta_l)
+ return v, l
+
+ # def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None)
diff --git a/groundingdino/models/GroundingDINO/groundingdino.py b/groundingdino/models/GroundingDINO/groundingdino.py
new file mode 100644
index 0000000000000000000000000000000000000000..052df6220595a1b39b7e2aea37ca4872d113dfd2
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/groundingdino.py
@@ -0,0 +1,395 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR model and criterion classes.
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Modified from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# ------------------------------------------------------------------------
+import copy
+from typing import List
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torchvision.ops.boxes import nms
+from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
+
+from groundingdino.util import box_ops, get_tokenlizer
+from groundingdino.util.misc import (
+ NestedTensor,
+ accuracy,
+ get_world_size,
+ interpolate,
+ inverse_sigmoid,
+ is_dist_avail_and_initialized,
+ nested_tensor_from_tensor_list,
+)
+from groundingdino.util.utils import get_phrases_from_posmap
+from groundingdino.util.visualizer import COCOVisualizer
+from groundingdino.util.vl_utils import create_positive_map_from_span
+
+from ..registry import MODULE_BUILD_FUNCS
+from .backbone import build_backbone
+from .bertwarper import (
+ BertModelWarper,
+ generate_masks_with_special_tokens,
+ generate_masks_with_special_tokens_and_transfer_map,
+)
+from .transformer import build_transformer
+from .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss
+
+
+class GroundingDINO(nn.Module):
+ """This is the Cross-Attention Detector module that performs object detection"""
+
+ def __init__(
+ self,
+ backbone,
+ transformer,
+ num_queries,
+ aux_loss=False,
+ iter_update=False,
+ query_dim=2,
+ num_feature_levels=1,
+ nheads=8,
+ # two stage
+ two_stage_type="no", # ['no', 'standard']
+ dec_pred_bbox_embed_share=True,
+ two_stage_class_embed_share=True,
+ two_stage_bbox_embed_share=True,
+ num_patterns=0,
+ dn_number=100,
+ dn_box_noise_scale=0.4,
+ dn_label_noise_ratio=0.5,
+ dn_labelbook_size=100,
+ text_encoder_type="bert-base-uncased",
+ sub_sentence_present=True,
+ max_text_len=256,
+ ):
+ """Initializes the model.
+ Parameters:
+ backbone: torch module of the backbone to be used. See backbone.py
+ transformer: torch module of the transformer architecture. See transformer.py
+ num_queries: number of object queries, ie detection slot. This is the maximal number of objects
+ Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
+ aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
+ """
+ super().__init__()
+ self.num_queries = num_queries
+ self.transformer = transformer
+ self.hidden_dim = hidden_dim = transformer.d_model
+ self.num_feature_levels = num_feature_levels
+ self.nheads = nheads
+ self.max_text_len = 256
+ self.sub_sentence_present = sub_sentence_present
+
+ # setting query dim
+ self.query_dim = query_dim
+ assert query_dim == 4
+
+ # for dn training
+ self.num_patterns = num_patterns
+ self.dn_number = dn_number
+ self.dn_box_noise_scale = dn_box_noise_scale
+ self.dn_label_noise_ratio = dn_label_noise_ratio
+ self.dn_labelbook_size = dn_labelbook_size
+
+ # bert
+ self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)
+ self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
+ self.bert.pooler.dense.weight.requires_grad_(False)
+ self.bert.pooler.dense.bias.requires_grad_(False)
+ self.bert = BertModelWarper(bert_model=self.bert)
+
+ self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
+ nn.init.constant_(self.feat_map.bias.data, 0)
+ nn.init.xavier_uniform_(self.feat_map.weight.data)
+ # freeze
+
+ # special tokens
+ self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
+
+ # prepare input projection layers
+ if num_feature_levels > 1:
+ num_backbone_outs = len(backbone.num_channels)
+ input_proj_list = []
+ for _ in range(num_backbone_outs):
+ in_channels = backbone.num_channels[_]
+ input_proj_list.append(
+ nn.Sequential(
+ nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
+ nn.GroupNorm(32, hidden_dim),
+ )
+ )
+ for _ in range(num_feature_levels - num_backbone_outs):
+ input_proj_list.append(
+ nn.Sequential(
+ nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
+ nn.GroupNorm(32, hidden_dim),
+ )
+ )
+ in_channels = hidden_dim
+ self.input_proj = nn.ModuleList(input_proj_list)
+ else:
+ assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
+ self.input_proj = nn.ModuleList(
+ [
+ nn.Sequential(
+ nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
+ nn.GroupNorm(32, hidden_dim),
+ )
+ ]
+ )
+
+ self.backbone = backbone
+ self.aux_loss = aux_loss
+ self.box_pred_damping = box_pred_damping = None
+
+ self.iter_update = iter_update
+ assert iter_update, "Why not iter_update?"
+
+ # prepare pred layers
+ self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
+ # prepare class & box embed
+ _class_embed = ContrastiveEmbed()
+
+ _bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
+ nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)
+ nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
+
+ if dec_pred_bbox_embed_share:
+ box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
+ else:
+ box_embed_layerlist = [
+ copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)
+ ]
+ class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
+ self.bbox_embed = nn.ModuleList(box_embed_layerlist)
+ self.class_embed = nn.ModuleList(class_embed_layerlist)
+ self.transformer.decoder.bbox_embed = self.bbox_embed
+ self.transformer.decoder.class_embed = self.class_embed
+
+ # two stage
+ self.two_stage_type = two_stage_type
+ assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
+ two_stage_type
+ )
+ if two_stage_type != "no":
+ if two_stage_bbox_embed_share:
+ assert dec_pred_bbox_embed_share
+ self.transformer.enc_out_bbox_embed = _bbox_embed
+ else:
+ self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
+
+ if two_stage_class_embed_share:
+ assert dec_pred_bbox_embed_share
+ self.transformer.enc_out_class_embed = _class_embed
+ else:
+ self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
+
+ self.refpoint_embed = None
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ # init input_proj
+ for proj in self.input_proj:
+ nn.init.xavier_uniform_(proj[0].weight, gain=1)
+ nn.init.constant_(proj[0].bias, 0)
+
+ def init_ref_points(self, use_num_queries):
+ self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)
+
+ def forward(self, samples: NestedTensor, targets: List = None, **kw):
+ """The forward expects a NestedTensor, which consists of:
+ - samples.tensor: batched images, of shape [batch_size x 3 x H x W]
+ - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
+
+ It returns a dict with the following elements:
+ - "pred_logits": the classification logits (including no-object) for all queries.
+ Shape= [batch_size x num_queries x num_classes]
+ - "pred_boxes": The normalized boxes coordinates for all queries, represented as
+ (center_x, center_y, width, height). These values are normalized in [0, 1],
+ relative to the size of each individual image (disregarding possible padding).
+ See PostProcess for information on how to retrieve the unnormalized bounding box.
+ - "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of
+ dictionnaries containing the two above keys for each decoder layer.
+ """
+ if targets is None:
+ captions = kw["captions"]
+ else:
+ captions = [t["caption"] for t in targets]
+ len(captions)
+
+ # encoder texts
+ tokenized = self.tokenizer(captions, padding="longest", return_tensors="pt").to(
+ samples.device
+ )
+ (
+ text_self_attention_masks,
+ position_ids,
+ cate_to_token_mask_list,
+ ) = generate_masks_with_special_tokens_and_transfer_map(
+ tokenized, self.specical_tokens, self.tokenizer
+ )
+
+ if text_self_attention_masks.shape[1] > self.max_text_len:
+ text_self_attention_masks = text_self_attention_masks[
+ :, : self.max_text_len, : self.max_text_len
+ ]
+ position_ids = position_ids[:, : self.max_text_len]
+ tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
+ tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
+ tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
+
+ # extract text embeddings
+ if self.sub_sentence_present:
+ tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"}
+ tokenized_for_encoder["attention_mask"] = text_self_attention_masks
+ tokenized_for_encoder["position_ids"] = position_ids
+ else:
+ # import ipdb; ipdb.set_trace()
+ tokenized_for_encoder = tokenized
+
+ bert_output = self.bert(**tokenized_for_encoder) # bs, 195, 768
+
+ encoded_text = self.feat_map(bert_output["last_hidden_state"]) # bs, 195, d_model
+ text_token_mask = tokenized.attention_mask.bool() # bs, 195
+ # text_token_mask: True for nomask, False for mask
+ # text_self_attention_masks: True for nomask, False for mask
+
+ if encoded_text.shape[1] > self.max_text_len:
+ encoded_text = encoded_text[:, : self.max_text_len, :]
+ text_token_mask = text_token_mask[:, : self.max_text_len]
+ position_ids = position_ids[:, : self.max_text_len]
+ text_self_attention_masks = text_self_attention_masks[
+ :, : self.max_text_len, : self.max_text_len
+ ]
+
+ text_dict = {
+ "encoded_text": encoded_text, # bs, 195, d_model
+ "text_token_mask": text_token_mask, # bs, 195
+ "position_ids": position_ids, # bs, 195
+ "text_self_attention_masks": text_self_attention_masks, # bs, 195,195
+ }
+
+ # import ipdb; ipdb.set_trace()
+
+ if isinstance(samples, (list, torch.Tensor)):
+ samples = nested_tensor_from_tensor_list(samples)
+ features, poss = self.backbone(samples)
+
+ srcs = []
+ masks = []
+ for l, feat in enumerate(features):
+ src, mask = feat.decompose()
+ srcs.append(self.input_proj[l](src))
+ masks.append(mask)
+ assert mask is not None
+ if self.num_feature_levels > len(srcs):
+ _len_srcs = len(srcs)
+ for l in range(_len_srcs, self.num_feature_levels):
+ if l == _len_srcs:
+ src = self.input_proj[l](features[-1].tensors)
+ else:
+ src = self.input_proj[l](srcs[-1])
+ m = samples.mask
+ mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]
+ pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)
+ srcs.append(src)
+ masks.append(mask)
+ poss.append(pos_l)
+
+ input_query_bbox = input_query_label = attn_mask = dn_meta = None
+ hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(
+ srcs, masks, input_query_bbox, poss, input_query_label, attn_mask, text_dict
+ )
+
+ # deformable-detr-like anchor update
+ outputs_coord_list = []
+ for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_hs) in enumerate(
+ zip(reference[:-1], self.bbox_embed, hs)
+ ):
+ layer_delta_unsig = layer_bbox_embed(layer_hs)
+ layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig)
+ layer_outputs_unsig = layer_outputs_unsig.sigmoid()
+ outputs_coord_list.append(layer_outputs_unsig)
+ outputs_coord_list = torch.stack(outputs_coord_list)
+
+ # output
+ outputs_class = torch.stack(
+ [
+ layer_cls_embed(layer_hs, text_dict)
+ for layer_cls_embed, layer_hs in zip(self.class_embed, hs)
+ ]
+ )
+ out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord_list[-1]}
+
+ # # for intermediate outputs
+ # if self.aux_loss:
+ # out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord_list)
+
+ # # for encoder output
+ # if hs_enc is not None:
+ # # prepare intermediate outputs
+ # interm_coord = ref_enc[-1]
+ # interm_class = self.transformer.enc_out_class_embed(hs_enc[-1], text_dict)
+ # out['interm_outputs'] = {'pred_logits': interm_class, 'pred_boxes': interm_coord}
+ # out['interm_outputs_for_matching_pre'] = {'pred_logits': interm_class, 'pred_boxes': init_box_proposal}
+
+ return out
+
+ @torch.jit.unused
+ def _set_aux_loss(self, outputs_class, outputs_coord):
+ # this is a workaround to make torchscript happy, as torchscript
+ # doesn't support dictionary with non-homogeneous values, such
+ # as a dict having both a Tensor and a list.
+ return [
+ {"pred_logits": a, "pred_boxes": b}
+ for a, b in zip(outputs_class[:-1], outputs_coord[:-1])
+ ]
+
+
+@MODULE_BUILD_FUNCS.registe_with_name(module_name="groundingdino")
+def build_groundingdino(args):
+
+ backbone = build_backbone(args)
+ transformer = build_transformer(args)
+
+ dn_labelbook_size = args.dn_labelbook_size
+ dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share
+ sub_sentence_present = args.sub_sentence_present
+
+ model = GroundingDINO(
+ backbone,
+ transformer,
+ num_queries=args.num_queries,
+ aux_loss=True,
+ iter_update=True,
+ query_dim=4,
+ num_feature_levels=args.num_feature_levels,
+ nheads=args.nheads,
+ dec_pred_bbox_embed_share=dec_pred_bbox_embed_share,
+ two_stage_type=args.two_stage_type,
+ two_stage_bbox_embed_share=args.two_stage_bbox_embed_share,
+ two_stage_class_embed_share=args.two_stage_class_embed_share,
+ num_patterns=args.num_patterns,
+ dn_number=0,
+ dn_box_noise_scale=args.dn_box_noise_scale,
+ dn_label_noise_ratio=args.dn_label_noise_ratio,
+ dn_labelbook_size=dn_labelbook_size,
+ text_encoder_type=args.text_encoder_type,
+ sub_sentence_present=sub_sentence_present,
+ max_text_len=args.max_text_len,
+ )
+
+ return model
diff --git a/groundingdino/models/GroundingDINO/ms_deform_attn.py b/groundingdino/models/GroundingDINO/ms_deform_attn.py
new file mode 100644
index 0000000000000000000000000000000000000000..489d501bef364020212306d81e9b85c8daa27491
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/ms_deform_attn.py
@@ -0,0 +1,413 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from:
+# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/functions/ms_deform_attn_func.py
+# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py
+# https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/multi_scale_deform_attn.py
+# ------------------------------------------------------------------------------------------------
+
+import math
+import warnings
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Function
+from torch.autograd.function import once_differentiable
+from torch.nn.init import constant_, xavier_uniform_
+
+try:
+ from groundingdino import _C
+except:
+ warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only!")
+
+
+# helpers
+def _is_power_of_2(n):
+ if (not isinstance(n, int)) or (n < 0):
+ raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
+ return (n & (n - 1) == 0) and n != 0
+
+
+class MultiScaleDeformableAttnFunction(Function):
+ @staticmethod
+ def forward(
+ ctx,
+ value,
+ value_spatial_shapes,
+ value_level_start_index,
+ sampling_locations,
+ attention_weights,
+ im2col_step,
+ ):
+ ctx.im2col_step = im2col_step
+ output = _C.ms_deform_attn_forward(
+ value,
+ value_spatial_shapes,
+ value_level_start_index,
+ sampling_locations,
+ attention_weights,
+ ctx.im2col_step,
+ )
+ ctx.save_for_backward(
+ value,
+ value_spatial_shapes,
+ value_level_start_index,
+ sampling_locations,
+ attention_weights,
+ )
+ return output
+
+ @staticmethod
+ @once_differentiable
+ def backward(ctx, grad_output):
+ (
+ value,
+ value_spatial_shapes,
+ value_level_start_index,
+ sampling_locations,
+ attention_weights,
+ ) = ctx.saved_tensors
+ grad_value, grad_sampling_loc, grad_attn_weight = _C.ms_deform_attn_backward(
+ value,
+ value_spatial_shapes,
+ value_level_start_index,
+ sampling_locations,
+ attention_weights,
+ grad_output,
+ ctx.im2col_step,
+ )
+
+ return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None
+
+
+def multi_scale_deformable_attn_pytorch(
+ value: torch.Tensor,
+ value_spatial_shapes: torch.Tensor,
+ sampling_locations: torch.Tensor,
+ attention_weights: torch.Tensor,
+) -> torch.Tensor:
+
+ bs, _, num_heads, embed_dims = value.shape
+ _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
+ value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
+ sampling_grids = 2 * sampling_locations - 1
+ sampling_value_list = []
+ for level, (H_, W_) in enumerate(value_spatial_shapes):
+ # bs, H_*W_, num_heads, embed_dims ->
+ # bs, H_*W_, num_heads*embed_dims ->
+ # bs, num_heads*embed_dims, H_*W_ ->
+ # bs*num_heads, embed_dims, H_, W_
+ value_l_ = (
+ value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_)
+ )
+ # bs, num_queries, num_heads, num_points, 2 ->
+ # bs, num_heads, num_queries, num_points, 2 ->
+ # bs*num_heads, num_queries, num_points, 2
+ sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1)
+ # bs*num_heads, embed_dims, num_queries, num_points
+ sampling_value_l_ = F.grid_sample(
+ value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
+ )
+ sampling_value_list.append(sampling_value_l_)
+ # (bs, num_queries, num_heads, num_levels, num_points) ->
+ # (bs, num_heads, num_queries, num_levels, num_points) ->
+ # (bs, num_heads, 1, num_queries, num_levels*num_points)
+ attention_weights = attention_weights.transpose(1, 2).reshape(
+ bs * num_heads, 1, num_queries, num_levels * num_points
+ )
+ output = (
+ (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
+ .sum(-1)
+ .view(bs, num_heads * embed_dims, num_queries)
+ )
+ return output.transpose(1, 2).contiguous()
+
+
+class MultiScaleDeformableAttention(nn.Module):
+ """Multi-Scale Deformable Attention Module used in Deformable-DETR
+
+ `Deformable DETR: Deformable Transformers for End-to-End Object Detection.
+ <https://arxiv.org/pdf/2010.04159.pdf>`_.
+
+ Args:
+ embed_dim (int): The embedding dimension of Attention. Default: 256.
+ num_heads (int): The number of attention heads. Default: 8.
+ num_levels (int): The number of feature map used in Attention. Default: 4.
+ num_points (int): The number of sampling points for each query
+ in each head. Default: 4.
+ img2col_steps (int): The step used in image_to_column. Defualt: 64.
+ dropout (float): Dropout layer used in output. Default: 0.1.
+ batch_first (bool): if ``True``, then the input and output tensor will be
+ provided as `(bs, n, embed_dim)`. Default: False. `(n, bs, embed_dim)`
+ """
+
+ def __init__(
+ self,
+ embed_dim: int = 256,
+ num_heads: int = 8,
+ num_levels: int = 4,
+ num_points: int = 4,
+ img2col_step: int = 64,
+ batch_first: bool = False,
+ ):
+ super().__init__()
+ if embed_dim % num_heads != 0:
+ raise ValueError(
+ "embed_dim must be divisible by num_heads, but got {} and {}".format(
+ embed_dim, num_heads
+ )
+ )
+ head_dim = embed_dim // num_heads
+
+ self.batch_first = batch_first
+
+ if not _is_power_of_2(head_dim):
+ warnings.warn(
+ """
+ You'd better set d_model in MSDeformAttn to make sure that
+ each dim of the attention head a power of 2, which is more efficient.
+ """
+ )
+
+ self.im2col_step = img2col_step
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.num_levels = num_levels
+ self.num_points = num_points
+ self.sampling_offsets = nn.Linear(embed_dim, num_heads * num_levels * num_points * 2)
+ self.attention_weights = nn.Linear(embed_dim, num_heads * num_levels * num_points)
+ self.value_proj = nn.Linear(embed_dim, embed_dim)
+ self.output_proj = nn.Linear(embed_dim, embed_dim)
+
+ self.init_weights()
+
+ def _reset_parameters(self):
+ return self.init_weights()
+
+ def init_weights(self):
+ """
+ Default initialization for Parameters of Module.
+ """
+ constant_(self.sampling_offsets.weight.data, 0.0)
+ thetas = torch.arange(self.num_heads, dtype=torch.float32) * (
+ 2.0 * math.pi / self.num_heads
+ )
+ grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
+ grid_init = (
+ (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
+ .view(self.num_heads, 1, 1, 2)
+ .repeat(1, self.num_levels, self.num_points, 1)
+ )
+ for i in range(self.num_points):
+ grid_init[:, :, i, :] *= i + 1
+ with torch.no_grad():
+ self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
+ constant_(self.attention_weights.weight.data, 0.0)
+ constant_(self.attention_weights.bias.data, 0.0)
+ xavier_uniform_(self.value_proj.weight.data)
+ constant_(self.value_proj.bias.data, 0.0)
+ xavier_uniform_(self.output_proj.weight.data)
+ constant_(self.output_proj.bias.data, 0.0)
+
+ def freeze_sampling_offsets(self):
+ print("Freeze sampling offsets")
+ self.sampling_offsets.weight.requires_grad = False
+ self.sampling_offsets.bias.requires_grad = False
+
+ def freeze_attention_weights(self):
+ print("Freeze attention weights")
+ self.attention_weights.weight.requires_grad = False
+ self.attention_weights.bias.requires_grad = False
+
+ def forward(
+ self,
+ query: torch.Tensor,
+ key: Optional[torch.Tensor] = None,
+ value: Optional[torch.Tensor] = None,
+ query_pos: Optional[torch.Tensor] = None,
+ key_padding_mask: Optional[torch.Tensor] = None,
+ reference_points: Optional[torch.Tensor] = None,
+ spatial_shapes: Optional[torch.Tensor] = None,
+ level_start_index: Optional[torch.Tensor] = None,
+ **kwargs
+ ) -> torch.Tensor:
+
+ """Forward Function of MultiScaleDeformableAttention
+
+ Args:
+ query (torch.Tensor): Query embeddings with shape
+ `(num_query, bs, embed_dim)`
+ key (torch.Tensor): Key embeddings with shape
+ `(num_key, bs, embed_dim)`
+ value (torch.Tensor): Value embeddings with shape
+ `(num_key, bs, embed_dim)`
+ query_pos (torch.Tensor): The position embedding for `query`. Default: None.
+ key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape `(bs, num_key)`,
+ indicating which elements within `key` to be ignored in attention.
+ reference_points (torch.Tensor): The normalized reference points
+ with shape `(bs, num_query, num_levels, 2)`,
+ all elements is range in [0, 1], top-left (0, 0),
+ bottom-right (1, 1), including padding are.
+ or `(N, Length_{query}, num_levels, 4)`, add additional
+ two dimensions `(h, w)` to form reference boxes.
+ spatial_shapes (torch.Tensor): Spatial shape of features in different levels.
+ With shape `(num_levels, 2)`, last dimension represents `(h, w)`.
+ level_start_index (torch.Tensor): The start index of each level. A tensor with
+ shape `(num_levels, )` which can be represented as
+ `[0, h_0 * w_0, h_0 * w_0 + h_1 * w_1, ...]`.
+
+ Returns:
+ torch.Tensor: forward results with shape `(num_query, bs, embed_dim)`
+ """
+
+ if value is None:
+ value = query
+
+ if query_pos is not None:
+ query = query + query_pos
+
+ if not self.batch_first:
+ # change to (bs, num_query ,embed_dims)
+ query = query.permute(1, 0, 2)
+ value = value.permute(1, 0, 2)
+
+ bs, num_query, _ = query.shape
+ bs, num_value, _ = value.shape
+
+ assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value
+
+ value = self.value_proj(value)
+ if key_padding_mask is not None:
+ value = value.masked_fill(key_padding_mask[..., None], float(0))
+ value = value.view(bs, num_value, self.num_heads, -1)
+ sampling_offsets = self.sampling_offsets(query).view(
+ bs, num_query, self.num_heads, self.num_levels, self.num_points, 2
+ )
+ attention_weights = self.attention_weights(query).view(
+ bs, num_query, self.num_heads, self.num_levels * self.num_points
+ )
+ attention_weights = attention_weights.softmax(-1)
+ attention_weights = attention_weights.view(
+ bs,
+ num_query,
+ self.num_heads,
+ self.num_levels,
+ self.num_points,
+ )
+
+ # bs, num_query, num_heads, num_levels, num_points, 2
+ if reference_points.shape[-1] == 2:
+ offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
+ sampling_locations = (
+ reference_points[:, :, None, :, None, :]
+ + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
+ )
+ elif reference_points.shape[-1] == 4:
+ sampling_locations = (
+ reference_points[:, :, None, :, None, :2]
+ + sampling_offsets
+ / self.num_points
+ * reference_points[:, :, None, :, None, 2:]
+ * 0.5
+ )
+ else:
+ raise ValueError(
+ "Last dim of reference_points must be 2 or 4, but get {} instead.".format(
+ reference_points.shape[-1]
+ )
+ )
+
+ if torch.cuda.is_available() and value.is_cuda:
+ halffloat = False
+ if value.dtype == torch.float16:
+ halffloat = True
+ value = value.float()
+ sampling_locations = sampling_locations.float()
+ attention_weights = attention_weights.float()
+
+ output = MultiScaleDeformableAttnFunction.apply(
+ value,
+ spatial_shapes,
+ level_start_index,
+ sampling_locations,
+ attention_weights,
+ self.im2col_step,
+ )
+
+ if halffloat:
+ output = output.half()
+ else:
+ output = multi_scale_deformable_attn_pytorch(
+ value, spatial_shapes, sampling_locations, attention_weights
+ )
+
+ output = self.output_proj(output)
+
+ if not self.batch_first:
+ output = output.permute(1, 0, 2)
+
+ return output
+
+
+def create_dummy_class(klass, dependency, message=""):
+ """
+ When a dependency of a class is not available, create a dummy class which throws ImportError
+ when used.
+
+ Args:
+ klass (str): name of the class.
+ dependency (str): name of the dependency.
+ message: extra message to print
+ Returns:
+ class: a class object
+ """
+ err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, klass)
+ if message:
+ err = err + " " + message
+
+ class _DummyMetaClass(type):
+ # throw error on class attribute access
+ def __getattr__(_, __): # noqa: B902
+ raise ImportError(err)
+
+ class _Dummy(object, metaclass=_DummyMetaClass):
+ # throw error on constructor
+ def __init__(self, *args, **kwargs):
+ raise ImportError(err)
+
+ return _Dummy
+
+
+def create_dummy_func(func, dependency, message=""):
+ """
+ When a dependency of a function is not available, create a dummy function which throws
+ ImportError when used.
+
+ Args:
+ func (str): name of the function.
+ dependency (str or list[str]): name(s) of the dependency.
+ message: extra message to print
+ Returns:
+ function: a function object
+ """
+ err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, func)
+ if message:
+ err = err + " " + message
+
+ if isinstance(dependency, (list, tuple)):
+ dependency = ",".join(dependency)
+
+ def _dummy(*args, **kwargs):
+ raise ImportError(err)
+
+ return _dummy
diff --git a/groundingdino/models/GroundingDINO/transformer.py b/groundingdino/models/GroundingDINO/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcb8742dbdde6e80fd38b11d064211f6935aae76
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/transformer.py
@@ -0,0 +1,959 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR Transformer class.
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Modified from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+from typing import Optional
+
+import torch
+import torch.utils.checkpoint as checkpoint
+from torch import Tensor, nn
+
+from groundingdino.util.misc import inverse_sigmoid
+
+from .fuse_modules import BiAttentionBlock
+from .ms_deform_attn import MultiScaleDeformableAttention as MSDeformAttn
+from .transformer_vanilla import TransformerEncoderLayer
+from .utils import (
+ MLP,
+ _get_activation_fn,
+ _get_clones,
+ gen_encoder_output_proposals,
+ gen_sineembed_for_position,
+ get_sine_pos_embed,
+)
+
+
+class Transformer(nn.Module):
+ def __init__(
+ self,
+ d_model=256,
+ nhead=8,
+ num_queries=300,
+ num_encoder_layers=6,
+ num_unicoder_layers=0,
+ num_decoder_layers=6,
+ dim_feedforward=2048,
+ dropout=0.0,
+ activation="relu",
+ normalize_before=False,
+ return_intermediate_dec=False,
+ query_dim=4,
+ num_patterns=0,
+ # for deformable encoder
+ num_feature_levels=1,
+ enc_n_points=4,
+ dec_n_points=4,
+ # init query
+ learnable_tgt_init=False,
+ # two stage
+ two_stage_type="no", # ['no', 'standard', 'early', 'combine', 'enceachlayer', 'enclayer1']
+ embed_init_tgt=False,
+ # for text
+ use_text_enhancer=False,
+ use_fusion_layer=False,
+ use_checkpoint=False,
+ use_transformer_ckpt=False,
+ use_text_cross_attention=False,
+ text_dropout=0.1,
+ fusion_dropout=0.1,
+ fusion_droppath=0.0,
+ ):
+ super().__init__()
+ self.num_feature_levels = num_feature_levels
+ self.num_encoder_layers = num_encoder_layers
+ self.num_unicoder_layers = num_unicoder_layers
+ self.num_decoder_layers = num_decoder_layers
+ self.num_queries = num_queries
+ assert query_dim == 4
+
+ # choose encoder layer type
+ encoder_layer = DeformableTransformerEncoderLayer(
+ d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points
+ )
+
+ if use_text_enhancer:
+ text_enhance_layer = TransformerEncoderLayer(
+ d_model=d_model,
+ nhead=nhead // 2,
+ dim_feedforward=dim_feedforward // 2,
+ dropout=text_dropout,
+ )
+ else:
+ text_enhance_layer = None
+
+ if use_fusion_layer:
+ feature_fusion_layer = BiAttentionBlock(
+ v_dim=d_model,
+ l_dim=d_model,
+ embed_dim=dim_feedforward // 2,
+ num_heads=nhead // 2,
+ dropout=fusion_dropout,
+ drop_path=fusion_droppath,
+ )
+ else:
+ feature_fusion_layer = None
+
+ encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
+ assert encoder_norm is None
+ self.encoder = TransformerEncoder(
+ encoder_layer,
+ num_encoder_layers,
+ d_model=d_model,
+ num_queries=num_queries,
+ text_enhance_layer=text_enhance_layer,
+ feature_fusion_layer=feature_fusion_layer,
+ use_checkpoint=use_checkpoint,
+ use_transformer_ckpt=use_transformer_ckpt,
+ )
+
+ # choose decoder layer type
+ decoder_layer = DeformableTransformerDecoderLayer(
+ d_model,
+ dim_feedforward,
+ dropout,
+ activation,
+ num_feature_levels,
+ nhead,
+ dec_n_points,
+ use_text_cross_attention=use_text_cross_attention,
+ )
+
+ decoder_norm = nn.LayerNorm(d_model)
+ self.decoder = TransformerDecoder(
+ decoder_layer,
+ num_decoder_layers,
+ decoder_norm,
+ return_intermediate=return_intermediate_dec,
+ d_model=d_model,
+ query_dim=query_dim,
+ num_feature_levels=num_feature_levels,
+ )
+
+ self.d_model = d_model
+ self.nhead = nhead
+ self.dec_layers = num_decoder_layers
+ self.num_queries = num_queries # useful for single stage model only
+ self.num_patterns = num_patterns
+ if not isinstance(num_patterns, int):
+ Warning("num_patterns should be int but {}".format(type(num_patterns)))
+ self.num_patterns = 0
+
+ if num_feature_levels > 1:
+ if self.num_encoder_layers > 0:
+ self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
+ else:
+ self.level_embed = None
+
+ self.learnable_tgt_init = learnable_tgt_init
+ assert learnable_tgt_init, "why not learnable_tgt_init"
+ self.embed_init_tgt = embed_init_tgt
+ if (two_stage_type != "no" and embed_init_tgt) or (two_stage_type == "no"):
+ self.tgt_embed = nn.Embedding(self.num_queries, d_model)
+ nn.init.normal_(self.tgt_embed.weight.data)
+ else:
+ self.tgt_embed = None
+
+ # for two stage
+ self.two_stage_type = two_stage_type
+ assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
+ two_stage_type
+ )
+ if two_stage_type == "standard":
+ # anchor selection at the output of encoder
+ self.enc_output = nn.Linear(d_model, d_model)
+ self.enc_output_norm = nn.LayerNorm(d_model)
+ self.two_stage_wh_embedding = None
+
+ if two_stage_type == "no":
+ self.init_ref_points(num_queries) # init self.refpoint_embed
+
+ self.enc_out_class_embed = None
+ self.enc_out_bbox_embed = None
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+ for m in self.modules():
+ if isinstance(m, MSDeformAttn):
+ m._reset_parameters()
+ if self.num_feature_levels > 1 and self.level_embed is not None:
+ nn.init.normal_(self.level_embed)
+
+ def get_valid_ratio(self, mask):
+ _, H, W = mask.shape
+ valid_H = torch.sum(~mask[:, :, 0], 1)
+ valid_W = torch.sum(~mask[:, 0, :], 1)
+ valid_ratio_h = valid_H.float() / H
+ valid_ratio_w = valid_W.float() / W
+ valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
+ return valid_ratio
+
+ def init_ref_points(self, use_num_queries):
+ self.refpoint_embed = nn.Embedding(use_num_queries, 4)
+
+ def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, text_dict=None):
+ """
+ Input:
+ - srcs: List of multi features [bs, ci, hi, wi]
+ - masks: List of multi masks [bs, hi, wi]
+ - refpoint_embed: [bs, num_dn, 4]. None in infer
+ - pos_embeds: List of multi pos embeds [bs, ci, hi, wi]
+ - tgt: [bs, num_dn, d_model]. None in infer
+
+ """
+ # prepare input for encoder
+ src_flatten = []
+ mask_flatten = []
+ lvl_pos_embed_flatten = []
+ spatial_shapes = []
+ for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
+ bs, c, h, w = src.shape
+ spatial_shape = (h, w)
+ spatial_shapes.append(spatial_shape)
+
+ src = src.flatten(2).transpose(1, 2) # bs, hw, c
+ mask = mask.flatten(1) # bs, hw
+ pos_embed = pos_embed.flatten(2).transpose(1, 2) # bs, hw, c
+ if self.num_feature_levels > 1 and self.level_embed is not None:
+ lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
+ else:
+ lvl_pos_embed = pos_embed
+ lvl_pos_embed_flatten.append(lvl_pos_embed)
+ src_flatten.append(src)
+ mask_flatten.append(mask)
+ src_flatten = torch.cat(src_flatten, 1) # bs, \sum{hxw}, c
+ mask_flatten = torch.cat(mask_flatten, 1) # bs, \sum{hxw}
+ lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) # bs, \sum{hxw}, c
+ spatial_shapes = torch.as_tensor(
+ spatial_shapes, dtype=torch.long, device=src_flatten.device
+ )
+ level_start_index = torch.cat(
+ (spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])
+ )
+ valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
+
+ # two stage
+ enc_topk_proposals = enc_refpoint_embed = None
+
+ #########################################################
+ # Begin Encoder
+ #########################################################
+ memory, memory_text = self.encoder(
+ src_flatten,
+ pos=lvl_pos_embed_flatten,
+ level_start_index=level_start_index,
+ spatial_shapes=spatial_shapes,
+ valid_ratios=valid_ratios,
+ key_padding_mask=mask_flatten,
+ memory_text=text_dict["encoded_text"],
+ text_attention_mask=~text_dict["text_token_mask"],
+ # we ~ the mask . False means use the token; True means pad the token
+ position_ids=text_dict["position_ids"],
+ text_self_attention_masks=text_dict["text_self_attention_masks"],
+ )
+ #########################################################
+ # End Encoder
+ # - memory: bs, \sum{hw}, c
+ # - mask_flatten: bs, \sum{hw}
+ # - lvl_pos_embed_flatten: bs, \sum{hw}, c
+ # - enc_intermediate_output: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
+ # - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
+ #########################################################
+ text_dict["encoded_text"] = memory_text
+ # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
+ # if memory.isnan().any() | memory.isinf().any():
+ # import ipdb; ipdb.set_trace()
+
+ if self.two_stage_type == "standard":
+ output_memory, output_proposals = gen_encoder_output_proposals(
+ memory, mask_flatten, spatial_shapes
+ )
+ output_memory = self.enc_output_norm(self.enc_output(output_memory))
+
+ if text_dict is not None:
+ enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict)
+ else:
+ enc_outputs_class_unselected = self.enc_out_class_embed(output_memory)
+
+ topk_logits = enc_outputs_class_unselected.max(-1)[0]
+ enc_outputs_coord_unselected = (
+ self.enc_out_bbox_embed(output_memory) + output_proposals
+ ) # (bs, \sum{hw}, 4) unsigmoid
+ topk = self.num_queries
+
+ topk_proposals = torch.topk(topk_logits, topk, dim=1)[1] # bs, nq
+
+ # gather boxes
+ refpoint_embed_undetach = torch.gather(
+ enc_outputs_coord_unselected, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
+ ) # unsigmoid
+ refpoint_embed_ = refpoint_embed_undetach.detach()
+ init_box_proposal = torch.gather(
+ output_proposals, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
+ ).sigmoid() # sigmoid
+
+ # gather tgt
+ tgt_undetach = torch.gather(
+ output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)
+ )
+ if self.embed_init_tgt:
+ tgt_ = (
+ self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
+ ) # nq, bs, d_model
+ else:
+ tgt_ = tgt_undetach.detach()
+
+ if refpoint_embed is not None:
+ refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
+ tgt = torch.cat([tgt, tgt_], dim=1)
+ else:
+ refpoint_embed, tgt = refpoint_embed_, tgt_
+
+ elif self.two_stage_type == "no":
+ tgt_ = (
+ self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
+ ) # nq, bs, d_model
+ refpoint_embed_ = (
+ self.refpoint_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
+ ) # nq, bs, 4
+
+ if refpoint_embed is not None:
+ refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
+ tgt = torch.cat([tgt, tgt_], dim=1)
+ else:
+ refpoint_embed, tgt = refpoint_embed_, tgt_
+
+ if self.num_patterns > 0:
+ tgt_embed = tgt.repeat(1, self.num_patterns, 1)
+ refpoint_embed = refpoint_embed.repeat(1, self.num_patterns, 1)
+ tgt_pat = self.patterns.weight[None, :, :].repeat_interleave(
+ self.num_queries, 1
+ ) # 1, n_q*n_pat, d_model
+ tgt = tgt_embed + tgt_pat
+
+ init_box_proposal = refpoint_embed_.sigmoid()
+
+ else:
+ raise NotImplementedError("unknown two_stage_type {}".format(self.two_stage_type))
+ #########################################################
+ # End preparing tgt
+ # - tgt: bs, NQ, d_model
+ # - refpoint_embed(unsigmoid): bs, NQ, d_model
+ #########################################################
+
+ #########################################################
+ # Begin Decoder
+ #########################################################
+ hs, references = self.decoder(
+ tgt=tgt.transpose(0, 1),
+ memory=memory.transpose(0, 1),
+ memory_key_padding_mask=mask_flatten,
+ pos=lvl_pos_embed_flatten.transpose(0, 1),
+ refpoints_unsigmoid=refpoint_embed.transpose(0, 1),
+ level_start_index=level_start_index,
+ spatial_shapes=spatial_shapes,
+ valid_ratios=valid_ratios,
+ tgt_mask=attn_mask,
+ memory_text=text_dict["encoded_text"],
+ text_attention_mask=~text_dict["text_token_mask"],
+ # we ~ the mask . False means use the token; True means pad the token
+ )
+ #########################################################
+ # End Decoder
+ # hs: n_dec, bs, nq, d_model
+ # references: n_dec+1, bs, nq, query_dim
+ #########################################################
+
+ #########################################################
+ # Begin postprocess
+ #########################################################
+ if self.two_stage_type == "standard":
+ hs_enc = tgt_undetach.unsqueeze(0)
+ ref_enc = refpoint_embed_undetach.sigmoid().unsqueeze(0)
+ else:
+ hs_enc = ref_enc = None
+ #########################################################
+ # End postprocess
+ # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or (n_enc, bs, nq, d_model) or None
+ # ref_enc: (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or (n_enc, bs, nq, d_model) or None
+ #########################################################
+
+ return hs, references, hs_enc, ref_enc, init_box_proposal
+ # hs: (n_dec, bs, nq, d_model)
+ # references: sigmoid coordinates. (n_dec+1, bs, bq, 4)
+ # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or None
+ # ref_enc: sigmoid coordinates. \
+ # (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or None
+
+
+class TransformerEncoder(nn.Module):
+ def __init__(
+ self,
+ encoder_layer,
+ num_layers,
+ d_model=256,
+ num_queries=300,
+ enc_layer_share=False,
+ text_enhance_layer=None,
+ feature_fusion_layer=None,
+ use_checkpoint=False,
+ use_transformer_ckpt=False,
+ ):
+ """_summary_
+
+ Args:
+ encoder_layer (_type_): _description_
+ num_layers (_type_): _description_
+ norm (_type_, optional): _description_. Defaults to None.
+ d_model (int, optional): _description_. Defaults to 256.
+ num_queries (int, optional): _description_. Defaults to 300.
+ enc_layer_share (bool, optional): _description_. Defaults to False.
+
+ """
+ super().__init__()
+ # prepare layers
+ self.layers = []
+ self.text_layers = []
+ self.fusion_layers = []
+ if num_layers > 0:
+ self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)
+
+ if text_enhance_layer is not None:
+ self.text_layers = _get_clones(
+ text_enhance_layer, num_layers, layer_share=enc_layer_share
+ )
+ if feature_fusion_layer is not None:
+ self.fusion_layers = _get_clones(
+ feature_fusion_layer, num_layers, layer_share=enc_layer_share
+ )
+ else:
+ self.layers = []
+ del encoder_layer
+
+ if text_enhance_layer is not None:
+ self.text_layers = []
+ del text_enhance_layer
+ if feature_fusion_layer is not None:
+ self.fusion_layers = []
+ del feature_fusion_layer
+
+ self.query_scale = None
+ self.num_queries = num_queries
+ self.num_layers = num_layers
+ self.d_model = d_model
+
+ self.use_checkpoint = use_checkpoint
+ self.use_transformer_ckpt = use_transformer_ckpt
+
+ @staticmethod
+ def get_reference_points(spatial_shapes, valid_ratios, device):
+ reference_points_list = []
+ for lvl, (H_, W_) in enumerate(spatial_shapes):
+
+ ref_y, ref_x = torch.meshgrid(
+ torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
+ torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),
+ )
+ ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
+ ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
+ ref = torch.stack((ref_x, ref_y), -1)
+ reference_points_list.append(ref)
+ reference_points = torch.cat(reference_points_list, 1)
+ reference_points = reference_points[:, :, None] * valid_ratios[:, None]
+ return reference_points
+
+ def forward(
+ self,
+ # for images
+ src: Tensor,
+ pos: Tensor,
+ spatial_shapes: Tensor,
+ level_start_index: Tensor,
+ valid_ratios: Tensor,
+ key_padding_mask: Tensor,
+ # for texts
+ memory_text: Tensor = None,
+ text_attention_mask: Tensor = None,
+ pos_text: Tensor = None,
+ text_self_attention_masks: Tensor = None,
+ position_ids: Tensor = None,
+ ):
+ """
+ Input:
+ - src: [bs, sum(hi*wi), 256]
+ - pos: pos embed for src. [bs, sum(hi*wi), 256]
+ - spatial_shapes: h,w of each level [num_level, 2]
+ - level_start_index: [num_level] start point of level in sum(hi*wi).
+ - valid_ratios: [bs, num_level, 2]
+ - key_padding_mask: [bs, sum(hi*wi)]
+
+ - memory_text: bs, n_text, 256
+ - text_attention_mask: bs, n_text
+ False for no padding; True for padding
+ - pos_text: bs, n_text, 256
+
+ - position_ids: bs, n_text
+ Intermedia:
+ - reference_points: [bs, sum(hi*wi), num_level, 2]
+ Outpus:
+ - output: [bs, sum(hi*wi), 256]
+ """
+
+ output = src
+
+ # preparation and reshape
+ if self.num_layers > 0:
+ reference_points = self.get_reference_points(
+ spatial_shapes, valid_ratios, device=src.device
+ )
+
+ if self.text_layers:
+ # generate pos_text
+ bs, n_text, text_dim = memory_text.shape
+ if pos_text is None and position_ids is None:
+ pos_text = (
+ torch.arange(n_text, device=memory_text.device)
+ .float()
+ .unsqueeze(0)
+ .unsqueeze(-1)
+ .repeat(bs, 1, 1)
+ )
+ pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False)
+ if position_ids is not None:
+ pos_text = get_sine_pos_embed(
+ position_ids[..., None], num_pos_feats=256, exchange_xy=False
+ )
+
+ # main process
+ for layer_id, layer in enumerate(self.layers):
+ # if output.isnan().any() or memory_text.isnan().any():
+ # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+ # import ipdb; ipdb.set_trace()
+ if self.fusion_layers:
+ if self.use_checkpoint:
+ output, memory_text = checkpoint.checkpoint(
+ self.fusion_layers[layer_id],
+ output,
+ memory_text,
+ key_padding_mask,
+ text_attention_mask,
+ )
+ else:
+ output, memory_text = self.fusion_layers[layer_id](
+ v=output,
+ l=memory_text,
+ attention_mask_v=key_padding_mask,
+ attention_mask_l=text_attention_mask,
+ )
+
+ if self.text_layers:
+ memory_text = self.text_layers[layer_id](
+ src=memory_text.transpose(0, 1),
+ src_mask=~text_self_attention_masks, # note we use ~ for mask here
+ src_key_padding_mask=text_attention_mask,
+ pos=(pos_text.transpose(0, 1) if pos_text is not None else None),
+ ).transpose(0, 1)
+
+ # main process
+ if self.use_transformer_ckpt:
+ output = checkpoint.checkpoint(
+ layer,
+ output,
+ pos,
+ reference_points,
+ spatial_shapes,
+ level_start_index,
+ key_padding_mask,
+ )
+ else:
+ output = layer(
+ src=output,
+ pos=pos,
+ reference_points=reference_points,
+ spatial_shapes=spatial_shapes,
+ level_start_index=level_start_index,
+ key_padding_mask=key_padding_mask,
+ )
+
+ return output, memory_text
+
+
+class TransformerDecoder(nn.Module):
+ def __init__(
+ self,
+ decoder_layer,
+ num_layers,
+ norm=None,
+ return_intermediate=False,
+ d_model=256,
+ query_dim=4,
+ num_feature_levels=1,
+ ):
+ super().__init__()
+ if num_layers > 0:
+ self.layers = _get_clones(decoder_layer, num_layers)
+ else:
+ self.layers = []
+ self.num_layers = num_layers
+ self.norm = norm
+ self.return_intermediate = return_intermediate
+ assert return_intermediate, "support return_intermediate only"
+ self.query_dim = query_dim
+ assert query_dim in [2, 4], "query_dim should be 2/4 but {}".format(query_dim)
+ self.num_feature_levels = num_feature_levels
+
+ self.ref_point_head = MLP(query_dim // 2 * d_model, d_model, d_model, 2)
+ self.query_pos_sine_scale = None
+
+ self.query_scale = None
+ self.bbox_embed = None
+ self.class_embed = None
+
+ self.d_model = d_model
+
+ self.ref_anchor_head = None
+
+ def forward(
+ self,
+ tgt,
+ memory,
+ tgt_mask: Optional[Tensor] = None,
+ memory_mask: Optional[Tensor] = None,
+ tgt_key_padding_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ refpoints_unsigmoid: Optional[Tensor] = None, # num_queries, bs, 2
+ # for memory
+ level_start_index: Optional[Tensor] = None, # num_levels
+ spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2
+ valid_ratios: Optional[Tensor] = None,
+ # for text
+ memory_text: Optional[Tensor] = None,
+ text_attention_mask: Optional[Tensor] = None,
+ ):
+ """
+ Input:
+ - tgt: nq, bs, d_model
+ - memory: hw, bs, d_model
+ - pos: hw, bs, d_model
+ - refpoints_unsigmoid: nq, bs, 2/4
+ - valid_ratios/spatial_shapes: bs, nlevel, 2
+ """
+ output = tgt
+
+ intermediate = []
+ reference_points = refpoints_unsigmoid.sigmoid()
+ ref_points = [reference_points]
+
+ for layer_id, layer in enumerate(self.layers):
+
+ if reference_points.shape[-1] == 4:
+ reference_points_input = (
+ reference_points[:, :, None]
+ * torch.cat([valid_ratios, valid_ratios], -1)[None, :]
+ ) # nq, bs, nlevel, 4
+ else:
+ assert reference_points.shape[-1] == 2
+ reference_points_input = reference_points[:, :, None] * valid_ratios[None, :]
+ query_sine_embed = gen_sineembed_for_position(
+ reference_points_input[:, :, 0, :]
+ ) # nq, bs, 256*2
+
+ # conditional query
+ raw_query_pos = self.ref_point_head(query_sine_embed) # nq, bs, 256
+ pos_scale = self.query_scale(output) if self.query_scale is not None else 1
+ query_pos = pos_scale * raw_query_pos
+ # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
+ # if query_pos.isnan().any() | query_pos.isinf().any():
+ # import ipdb; ipdb.set_trace()
+
+ # main process
+ output = layer(
+ tgt=output,
+ tgt_query_pos=query_pos,
+ tgt_query_sine_embed=query_sine_embed,
+ tgt_key_padding_mask=tgt_key_padding_mask,
+ tgt_reference_points=reference_points_input,
+ memory_text=memory_text,
+ text_attention_mask=text_attention_mask,
+ memory=memory,
+ memory_key_padding_mask=memory_key_padding_mask,
+ memory_level_start_index=level_start_index,
+ memory_spatial_shapes=spatial_shapes,
+ memory_pos=pos,
+ self_attn_mask=tgt_mask,
+ cross_attn_mask=memory_mask,
+ )
+ if output.isnan().any() | output.isinf().any():
+ print(f"output layer_id {layer_id} is nan")
+ try:
+ num_nan = output.isnan().sum().item()
+ num_inf = output.isinf().sum().item()
+ print(f"num_nan {num_nan}, num_inf {num_inf}")
+ except Exception as e:
+ print(e)
+ # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
+ # import ipdb; ipdb.set_trace()
+
+ # iter update
+ if self.bbox_embed is not None:
+ # box_holder = self.bbox_embed(output)
+ # box_holder[..., :self.query_dim] += inverse_sigmoid(reference_points)
+ # new_reference_points = box_holder[..., :self.query_dim].sigmoid()
+
+ reference_before_sigmoid = inverse_sigmoid(reference_points)
+ delta_unsig = self.bbox_embed[layer_id](output)
+ outputs_unsig = delta_unsig + reference_before_sigmoid
+ new_reference_points = outputs_unsig.sigmoid()
+
+ reference_points = new_reference_points.detach()
+ # if layer_id != self.num_layers - 1:
+ ref_points.append(new_reference_points)
+
+ intermediate.append(self.norm(output))
+
+ return [
+ [itm_out.transpose(0, 1) for itm_out in intermediate],
+ [itm_refpoint.transpose(0, 1) for itm_refpoint in ref_points],
+ ]
+
+
+class DeformableTransformerEncoderLayer(nn.Module):
+ def __init__(
+ self,
+ d_model=256,
+ d_ffn=1024,
+ dropout=0.1,
+ activation="relu",
+ n_levels=4,
+ n_heads=8,
+ n_points=4,
+ ):
+ super().__init__()
+
+ # self attention
+ self.self_attn = MSDeformAttn(
+ embed_dim=d_model,
+ num_levels=n_levels,
+ num_heads=n_heads,
+ num_points=n_points,
+ batch_first=True,
+ )
+ self.dropout1 = nn.Dropout(dropout)
+ self.norm1 = nn.LayerNorm(d_model)
+
+ # ffn
+ self.linear1 = nn.Linear(d_model, d_ffn)
+ self.activation = _get_activation_fn(activation, d_model=d_ffn)
+ self.dropout2 = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(d_ffn, d_model)
+ self.dropout3 = nn.Dropout(dropout)
+ self.norm2 = nn.LayerNorm(d_model)
+
+ @staticmethod
+ def with_pos_embed(tensor, pos):
+ return tensor if pos is None else tensor + pos
+
+ def forward_ffn(self, src):
+ src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
+ src = src + self.dropout3(src2)
+ src = self.norm2(src)
+ return src
+
+ def forward(
+ self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None
+ ):
+ # self attention
+ # import ipdb; ipdb.set_trace()
+ src2 = self.self_attn(
+ query=self.with_pos_embed(src, pos),
+ reference_points=reference_points,
+ value=src,
+ spatial_shapes=spatial_shapes,
+ level_start_index=level_start_index,
+ key_padding_mask=key_padding_mask,
+ )
+ src = src + self.dropout1(src2)
+ src = self.norm1(src)
+
+ # ffn
+ src = self.forward_ffn(src)
+
+ return src
+
+
+class DeformableTransformerDecoderLayer(nn.Module):
+ def __init__(
+ self,
+ d_model=256,
+ d_ffn=1024,
+ dropout=0.1,
+ activation="relu",
+ n_levels=4,
+ n_heads=8,
+ n_points=4,
+ use_text_feat_guide=False,
+ use_text_cross_attention=False,
+ ):
+ super().__init__()
+
+ # cross attention
+ self.cross_attn = MSDeformAttn(
+ embed_dim=d_model,
+ num_levels=n_levels,
+ num_heads=n_heads,
+ num_points=n_points,
+ batch_first=True,
+ )
+ self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+ self.norm1 = nn.LayerNorm(d_model)
+
+ # cross attention text
+ if use_text_cross_attention:
+ self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
+ self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+ self.catext_norm = nn.LayerNorm(d_model)
+
+ # self attention
+ self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
+ self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+ self.norm2 = nn.LayerNorm(d_model)
+
+ # ffn
+ self.linear1 = nn.Linear(d_model, d_ffn)
+ self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1)
+ self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+ self.linear2 = nn.Linear(d_ffn, d_model)
+ self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+ self.norm3 = nn.LayerNorm(d_model)
+
+ self.key_aware_proj = None
+ self.use_text_feat_guide = use_text_feat_guide
+ assert not use_text_feat_guide
+ self.use_text_cross_attention = use_text_cross_attention
+
+ def rm_self_attn_modules(self):
+ self.self_attn = None
+ self.dropout2 = None
+ self.norm2 = None
+
+ @staticmethod
+ def with_pos_embed(tensor, pos):
+ return tensor if pos is None else tensor + pos
+
+ def forward_ffn(self, tgt):
+ with torch.cuda.amp.autocast(enabled=False):
+ tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
+ tgt = tgt + self.dropout4(tgt2)
+ tgt = self.norm3(tgt)
+ return tgt
+
+ def forward(
+ self,
+ # for tgt
+ tgt: Optional[Tensor], # nq, bs, d_model
+ tgt_query_pos: Optional[Tensor] = None, # pos for query. MLP(Sine(pos))
+ tgt_query_sine_embed: Optional[Tensor] = None, # pos for query. Sine(pos)
+ tgt_key_padding_mask: Optional[Tensor] = None,
+ tgt_reference_points: Optional[Tensor] = None, # nq, bs, 4
+ memory_text: Optional[Tensor] = None, # bs, num_token, d_model
+ text_attention_mask: Optional[Tensor] = None, # bs, num_token
+ # for memory
+ memory: Optional[Tensor] = None, # hw, bs, d_model
+ memory_key_padding_mask: Optional[Tensor] = None,
+ memory_level_start_index: Optional[Tensor] = None, # num_levels
+ memory_spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2
+ memory_pos: Optional[Tensor] = None, # pos for memory
+ # sa
+ self_attn_mask: Optional[Tensor] = None, # mask used for self-attention
+ cross_attn_mask: Optional[Tensor] = None, # mask used for cross-attention
+ ):
+ """
+ Input:
+ - tgt/tgt_query_pos: nq, bs, d_model
+ -
+ """
+ assert cross_attn_mask is None
+
+ # self attention
+ if self.self_attn is not None:
+ # import ipdb; ipdb.set_trace()
+ q = k = self.with_pos_embed(tgt, tgt_query_pos)
+ tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0]
+ tgt = tgt + self.dropout2(tgt2)
+ tgt = self.norm2(tgt)
+
+ if self.use_text_cross_attention:
+ tgt2 = self.ca_text(
+ self.with_pos_embed(tgt, tgt_query_pos),
+ memory_text.transpose(0, 1),
+ memory_text.transpose(0, 1),
+ key_padding_mask=text_attention_mask,
+ )[0]
+ tgt = tgt + self.catext_dropout(tgt2)
+ tgt = self.catext_norm(tgt)
+
+ tgt2 = self.cross_attn(
+ query=self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1),
+ reference_points=tgt_reference_points.transpose(0, 1).contiguous(),
+ value=memory.transpose(0, 1),
+ spatial_shapes=memory_spatial_shapes,
+ level_start_index=memory_level_start_index,
+ key_padding_mask=memory_key_padding_mask,
+ ).transpose(0, 1)
+ tgt = tgt + self.dropout1(tgt2)
+ tgt = self.norm1(tgt)
+
+ # ffn
+ tgt = self.forward_ffn(tgt)
+
+ return tgt
+
+
+def build_transformer(args):
+ return Transformer(
+ d_model=args.hidden_dim,
+ dropout=args.dropout,
+ nhead=args.nheads,
+ num_queries=args.num_queries,
+ dim_feedforward=args.dim_feedforward,
+ num_encoder_layers=args.enc_layers,
+ num_decoder_layers=args.dec_layers,
+ normalize_before=args.pre_norm,
+ return_intermediate_dec=True,
+ query_dim=args.query_dim,
+ activation=args.transformer_activation,
+ num_patterns=args.num_patterns,
+ num_feature_levels=args.num_feature_levels,
+ enc_n_points=args.enc_n_points,
+ dec_n_points=args.dec_n_points,
+ learnable_tgt_init=True,
+ # two stage
+ two_stage_type=args.two_stage_type, # ['no', 'standard', 'early']
+ embed_init_tgt=args.embed_init_tgt,
+ use_text_enhancer=args.use_text_enhancer,
+ use_fusion_layer=args.use_fusion_layer,
+ use_checkpoint=args.use_checkpoint,
+ use_transformer_ckpt=args.use_transformer_ckpt,
+ use_text_cross_attention=args.use_text_cross_attention,
+ text_dropout=args.text_dropout,
+ fusion_dropout=args.fusion_dropout,
+ fusion_droppath=args.fusion_droppath,
+ )
diff --git a/groundingdino/models/GroundingDINO/transformer_vanilla.py b/groundingdino/models/GroundingDINO/transformer_vanilla.py
new file mode 100644
index 0000000000000000000000000000000000000000..10c0920c1a217af5bb3e1b13077568035ab3b7b5
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/transformer_vanilla.py
@@ -0,0 +1,123 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+DETR Transformer class.
+
+Copy-paste from torch.nn.Transformer with modifications:
+ * positional encodings are passed in MHattention
+ * extra LN at the end of encoder is removed
+ * decoder returns a stack of activations from all decoding layers
+"""
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+from .utils import (
+ MLP,
+ _get_activation_fn,
+ _get_clones,
+ gen_encoder_output_proposals,
+ gen_sineembed_for_position,
+ sigmoid_focal_loss,
+)
+
+
+class TextTransformer(nn.Module):
+ def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1):
+ super().__init__()
+ self.num_layers = num_layers
+ self.d_model = d_model
+ self.nheads = nheads
+ self.dim_feedforward = dim_feedforward
+ self.norm = None
+
+ single_encoder_layer = TransformerEncoderLayer(
+ d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout
+ )
+ self.layers = _get_clones(single_encoder_layer, num_layers)
+
+ def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor):
+ """
+
+ Args:
+ text_attention_mask: bs, num_token
+ memory_text: bs, num_token, d_model
+
+ Raises:
+ RuntimeError: _description_
+
+ Returns:
+ output: bs, num_token, d_model
+ """
+
+ output = memory_text.transpose(0, 1)
+
+ for layer in self.layers:
+ output = layer(output, src_key_padding_mask=text_attention_mask)
+
+ if self.norm is not None:
+ output = self.norm(output)
+
+ return output.transpose(0, 1)
+
+
+class TransformerEncoderLayer(nn.Module):
+ def __init__(
+ self,
+ d_model,
+ nhead,
+ dim_feedforward=2048,
+ dropout=0.1,
+ activation="relu",
+ normalize_before=False,
+ ):
+ super().__init__()
+ self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+ # Implementation of Feedforward model
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.norm1 = nn.LayerNorm(d_model)
+ self.norm2 = nn.LayerNorm(d_model)
+ self.dropout1 = nn.Dropout(dropout)
+ self.dropout2 = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+ self.nhead = nhead
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward(
+ self,
+ src,
+ src_mask: Optional[Tensor] = None,
+ src_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ ):
+ # repeat attn mask
+ if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:
+ # bs, num_q, num_k
+ src_mask = src_mask.repeat(self.nhead, 1, 1)
+
+ q = k = self.with_pos_embed(src, pos)
+
+ src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0]
+
+ # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
+ src = src + self.dropout1(src2)
+ src = self.norm1(src)
+ src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
+ src = src + self.dropout2(src2)
+ src = self.norm2(src)
+ return src
diff --git a/groundingdino/models/GroundingDINO/utils.py b/groundingdino/models/GroundingDINO/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bd18f70225e12b2e27fdb4eabcde91d959f8e31
--- /dev/null
+++ b/groundingdino/models/GroundingDINO/utils.py
@@ -0,0 +1,268 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+
+import copy
+import math
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+
+def _get_clones(module, N, layer_share=False):
+ # import ipdb; ipdb.set_trace()
+ if layer_share:
+ return nn.ModuleList([module for i in range(N)])
+ else:
+ return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+def get_sine_pos_embed(
+ pos_tensor: torch.Tensor,
+ num_pos_feats: int = 128,
+ temperature: int = 10000,
+ exchange_xy: bool = True,
+):
+ """generate sine position embedding from a position tensor
+ Args:
+ pos_tensor (torch.Tensor): shape: [..., n].
+ num_pos_feats (int): projected shape for each float in the tensor.
+ temperature (int): temperature in the sine/cosine function.
+ exchange_xy (bool, optional): exchange pos x and pos y. \
+ For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True.
+ Returns:
+ pos_embed (torch.Tensor): shape: [..., n*num_pos_feats].
+ """
+ scale = 2 * math.pi
+ dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
+ dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
+
+ def sine_func(x: torch.Tensor):
+ sin_x = x * scale / dim_t
+ sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
+ return sin_x
+
+ pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]
+ if exchange_xy:
+ pos_res[0], pos_res[1] = pos_res[1], pos_res[0]
+ pos_res = torch.cat(pos_res, dim=-1)
+ return pos_res
+
+
+def gen_encoder_output_proposals(
+ memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None
+):
+ """
+ Input:
+ - memory: bs, \sum{hw}, d_model
+ - memory_padding_mask: bs, \sum{hw}
+ - spatial_shapes: nlevel, 2
+ - learnedwh: 2
+ Output:
+ - output_memory: bs, \sum{hw}, d_model
+ - output_proposals: bs, \sum{hw}, 4
+ """
+ N_, S_, C_ = memory.shape
+ proposals = []
+ _cur = 0
+ for lvl, (H_, W_) in enumerate(spatial_shapes):
+ mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(N_, H_, W_, 1)
+ valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
+ valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
+
+ # import ipdb; ipdb.set_trace()
+
+ grid_y, grid_x = torch.meshgrid(
+ torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
+ torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device),
+ )
+ grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) # H_, W_, 2
+
+ scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
+ grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
+
+ if learnedwh is not None:
+ # import ipdb; ipdb.set_trace()
+ wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0**lvl)
+ else:
+ wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)
+
+ # scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1)
+ # grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
+ # wh = torch.ones_like(grid) / scale
+ proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
+ proposals.append(proposal)
+ _cur += H_ * W_
+ # import ipdb; ipdb.set_trace()
+ output_proposals = torch.cat(proposals, 1)
+ output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(
+ -1, keepdim=True
+ )
+ output_proposals = torch.log(output_proposals / (1 - output_proposals)) # unsigmoid
+ output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf"))
+ output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))
+
+ output_memory = memory
+ output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
+ output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
+
+ # output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
+ # output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf'))
+
+ return output_memory, output_proposals
+
+
+class RandomBoxPerturber:
+ def __init__(
+ self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2
+ ) -> None:
+ self.noise_scale = torch.Tensor(
+ [x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale]
+ )
+
+ def __call__(self, refanchors: Tensor) -> Tensor:
+ nq, bs, query_dim = refanchors.shape
+ device = refanchors.device
+
+ noise_raw = torch.rand_like(refanchors)
+ noise_scale = self.noise_scale.to(device)[:query_dim]
+
+ new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale)
+ return new_refanchors.clamp_(0, 1)
+
+
+def sigmoid_focal_loss(
+ inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False
+):
+ """
+ Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
+ Args:
+ inputs: A float tensor of arbitrary shape.
+ The predictions for each example.
+ targets: A float tensor with the same shape as inputs. Stores the binary
+ classification label for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ alpha: (optional) Weighting factor in range (0,1) to balance
+ positive vs negative examples. Default = -1 (no weighting).
+ gamma: Exponent of the modulating factor (1 - p_t) to
+ balance easy vs hard examples.
+ Returns:
+ Loss tensor
+ """
+ prob = inputs.sigmoid()
+ ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
+ p_t = prob * targets + (1 - prob) * (1 - targets)
+ loss = ce_loss * ((1 - p_t) ** gamma)
+
+ if alpha >= 0:
+ alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
+ loss = alpha_t * loss
+
+ if no_reduction:
+ return loss
+
+ return loss.mean(1).sum() / num_boxes
+
+
+class MLP(nn.Module):
+ """Very simple multi-layer perceptron (also called FFN)"""
+
+ def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+ super().__init__()
+ self.num_layers = num_layers
+ h = [hidden_dim] * (num_layers - 1)
+ self.layers = nn.ModuleList(
+ nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
+ )
+
+ def forward(self, x):
+ for i, layer in enumerate(self.layers):
+ x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+ return x
+
+
+def _get_activation_fn(activation, d_model=256, batch_dim=0):
+ """Return an activation function given a string"""
+ if activation == "relu":
+ return F.relu
+ if activation == "gelu":
+ return F.gelu
+ if activation == "glu":
+ return F.glu
+ if activation == "prelu":
+ return nn.PReLU()
+ if activation == "selu":
+ return F.selu
+
+ raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
+
+
+def gen_sineembed_for_position(pos_tensor):
+ # n_query, bs, _ = pos_tensor.size()
+ # sineembed_tensor = torch.zeros(n_query, bs, 256)
+ scale = 2 * math.pi
+ dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)
+ dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode='floor')) / 128)
+ x_embed = pos_tensor[:, :, 0] * scale
+ y_embed = pos_tensor[:, :, 1] * scale
+ pos_x = x_embed[:, :, None] / dim_t
+ pos_y = y_embed[:, :, None] / dim_t
+ pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
+ pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
+ if pos_tensor.size(-1) == 2:
+ pos = torch.cat((pos_y, pos_x), dim=2)
+ elif pos_tensor.size(-1) == 4:
+ w_embed = pos_tensor[:, :, 2] * scale
+ pos_w = w_embed[:, :, None] / dim_t
+ pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)
+
+ h_embed = pos_tensor[:, :, 3] * scale
+ pos_h = h_embed[:, :, None] / dim_t
+ pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)
+
+ pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
+ else:
+ raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1)))
+ return pos
+
+
+class ContrastiveEmbed(nn.Module):
+ def __init__(self, max_text_len=256):
+ """
+ Args:
+ max_text_len: max length of text.
+ """
+ super().__init__()
+ self.max_text_len = max_text_len
+
+ def forward(self, x, text_dict):
+ """_summary_
+
+ Args:
+ x (_type_): _description_
+ text_dict (_type_): _description_
+ {
+ 'encoded_text': encoded_text, # bs, 195, d_model
+ 'text_token_mask': text_token_mask, # bs, 195
+ # True for used tokens. False for padding tokens
+ }
+ Returns:
+ _type_: _description_
+ """
+ assert isinstance(text_dict, dict)
+
+ y = text_dict["encoded_text"]
+ text_token_mask = text_dict["text_token_mask"]
+
+ res = x @ y.transpose(-1, -2)
+ res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))
+
+ # padding to max_text_len
+ new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
+ new_res[..., : res.shape[-1]] = res
+
+ return new_res
diff --git a/groundingdino/models/__init__.py b/groundingdino/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3413961d1d184b99835eb1e919b052d70298bc6
--- /dev/null
+++ b/groundingdino/models/__init__.py
@@ -0,0 +1,18 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+from .GroundingDINO import build_groundingdino
+
+
+def build_model(args):
+ # we use register to maintain models from catdet6 on.
+ from .registry import MODULE_BUILD_FUNCS
+
+ assert args.modelname in MODULE_BUILD_FUNCS._module_dict
+ build_func = MODULE_BUILD_FUNCS.get(args.modelname)
+ model = build_func(args)
+ return model
diff --git a/groundingdino/models/__pycache__/__init__.cpython-310.pyc b/groundingdino/models/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a1d9cf742697ed4f5b743c2ef27b0d202afa699d
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diff --git a/groundingdino/models/__pycache__/registry.cpython-310.pyc b/groundingdino/models/__pycache__/registry.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8f7596016cbc327e0fa8a0b623fada9eaf836411
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diff --git a/groundingdino/models/registry.py b/groundingdino/models/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d22a59eec79a2a19b83fa1779f2adaf5753aec6
--- /dev/null
+++ b/groundingdino/models/registry.py
@@ -0,0 +1,66 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# -*- coding: utf-8 -*-
+# @Author: Yihao Chen
+# @Date: 2021-08-16 16:03:17
+# @Last Modified by: Shilong Liu
+# @Last Modified time: 2022-01-23 15:26
+# modified from mmcv
+
+import inspect
+from functools import partial
+
+
+class Registry(object):
+ def __init__(self, name):
+ self._name = name
+ self._module_dict = dict()
+
+ def __repr__(self):
+ format_str = self.__class__.__name__ + "(name={}, items={})".format(
+ self._name, list(self._module_dict.keys())
+ )
+ return format_str
+
+ def __len__(self):
+ return len(self._module_dict)
+
+ @property
+ def name(self):
+ return self._name
+
+ @property
+ def module_dict(self):
+ return self._module_dict
+
+ def get(self, key):
+ return self._module_dict.get(key, None)
+
+ def registe_with_name(self, module_name=None, force=False):
+ return partial(self.register, module_name=module_name, force=force)
+
+ def register(self, module_build_function, module_name=None, force=False):
+ """Register a module build function.
+ Args:
+ module (:obj:`nn.Module`): Module to be registered.
+ """
+ if not inspect.isfunction(module_build_function):
+ raise TypeError(
+ "module_build_function must be a function, but got {}".format(
+ type(module_build_function)
+ )
+ )
+ if module_name is None:
+ module_name = module_build_function.__name__
+ if not force and module_name in self._module_dict:
+ raise KeyError("{} is already registered in {}".format(module_name, self.name))
+ self._module_dict[module_name] = module_build_function
+
+ return module_build_function
+
+
+MODULE_BUILD_FUNCS = Registry("model build functions")
diff --git a/groundingdino/util/__init__.py b/groundingdino/util/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..168f9979a4623806934b0ff1102ac166704e7dec
--- /dev/null
+++ b/groundingdino/util/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
diff --git a/groundingdino/util/__pycache__/__init__.cpython-310.pyc b/groundingdino/util/__pycache__/__init__.cpython-310.pyc
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diff --git a/groundingdino/util/__pycache__/box_ops.cpython-310.pyc b/groundingdino/util/__pycache__/box_ops.cpython-310.pyc
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diff --git a/groundingdino/util/box_ops.py b/groundingdino/util/box_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..781068d294e576954edb4bd07b6e0f30e4e1bcd9
--- /dev/null
+++ b/groundingdino/util/box_ops.py
@@ -0,0 +1,140 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Utilities for bounding box manipulation and GIoU.
+"""
+import torch
+from torchvision.ops.boxes import box_area
+
+
+def box_cxcywh_to_xyxy(x):
+ x_c, y_c, w, h = x.unbind(-1)
+ b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
+ return torch.stack(b, dim=-1)
+
+
+def box_xyxy_to_cxcywh(x):
+ x0, y0, x1, y1 = x.unbind(-1)
+ b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
+ return torch.stack(b, dim=-1)
+
+
+# modified from torchvision to also return the union
+def box_iou(boxes1, boxes2):
+ area1 = box_area(boxes1)
+ area2 = box_area(boxes2)
+
+ # import ipdb; ipdb.set_trace()
+ lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
+ rb = torch.min(boxes1[:, None, 2:], boxes2[:, 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
+
+ iou = inter / (union + 1e-6)
+ return iou, union
+
+
+def generalized_box_iou(boxes1, boxes2):
+ """
+ Generalized IoU from https://giou.stanford.edu/
+
+ The boxes should be in [x0, y0, x1, y1] format
+
+ Returns a [N, M] pairwise matrix, where N = len(boxes1)
+ and M = len(boxes2)
+ """
+ # degenerate boxes gives inf / nan results
+ # so do an early check
+ assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
+ assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
+ # except:
+ # import ipdb; ipdb.set_trace()
+ iou, union = box_iou(boxes1, boxes2)
+
+ lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
+ rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
+
+ wh = (rb - lt).clamp(min=0) # [N,M,2]
+ area = wh[:, :, 0] * wh[:, :, 1]
+
+ return iou - (area - union) / (area + 1e-6)
+
+
+# modified from torchvision to also return the union
+def box_iou_pairwise(boxes1, boxes2):
+ area1 = box_area(boxes1)
+ area2 = box_area(boxes2)
+
+ lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2]
+ rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2]
+
+ wh = (rb - lt).clamp(min=0) # [N,2]
+ inter = wh[:, 0] * wh[:, 1] # [N]
+
+ union = area1 + area2 - inter
+
+ iou = inter / union
+ return iou, union
+
+
+def generalized_box_iou_pairwise(boxes1, boxes2):
+ """
+ Generalized IoU from https://giou.stanford.edu/
+
+ Input:
+ - boxes1, boxes2: N,4
+ Output:
+ - giou: N, 4
+ """
+ # degenerate boxes gives inf / nan results
+ # so do an early check
+ assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
+ assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
+ assert boxes1.shape == boxes2.shape
+ iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4
+
+ lt = torch.min(boxes1[:, :2], boxes2[:, :2])
+ rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])
+
+ wh = (rb - lt).clamp(min=0) # [N,2]
+ area = wh[:, 0] * wh[:, 1]
+
+ return iou - (area - union) / area
+
+
+def masks_to_boxes(masks):
+ """Compute the bounding boxes around the provided masks
+
+ The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
+
+ Returns a [N, 4] tensors, with the boxes in xyxy format
+ """
+ if masks.numel() == 0:
+ return torch.zeros((0, 4), device=masks.device)
+
+ h, w = masks.shape[-2:]
+
+ y = torch.arange(0, h, dtype=torch.float)
+ x = torch.arange(0, w, dtype=torch.float)
+ y, x = torch.meshgrid(y, x)
+
+ x_mask = masks * x.unsqueeze(0)
+ x_max = x_mask.flatten(1).max(-1)[0]
+ x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+ y_mask = masks * y.unsqueeze(0)
+ y_max = y_mask.flatten(1).max(-1)[0]
+ y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+ return torch.stack([x_min, y_min, x_max, y_max], 1)
+
+
+if __name__ == "__main__":
+ x = torch.rand(5, 4)
+ y = torch.rand(3, 4)
+ iou, union = box_iou(x, y)
+ import ipdb
+
+ ipdb.set_trace()
diff --git a/groundingdino/util/get_tokenlizer.py b/groundingdino/util/get_tokenlizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7dcf7e95f03f95b20546b26442a94225924618b
--- /dev/null
+++ b/groundingdino/util/get_tokenlizer.py
@@ -0,0 +1,26 @@
+from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
+
+
+def get_tokenlizer(text_encoder_type):
+ if not isinstance(text_encoder_type, str):
+ # print("text_encoder_type is not a str")
+ if hasattr(text_encoder_type, "text_encoder_type"):
+ text_encoder_type = text_encoder_type.text_encoder_type
+ elif text_encoder_type.get("text_encoder_type", False):
+ text_encoder_type = text_encoder_type.get("text_encoder_type")
+ else:
+ raise ValueError(
+ "Unknown type of text_encoder_type: {}".format(type(text_encoder_type))
+ )
+ print("final text_encoder_type: {}".format(text_encoder_type))
+
+ tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)
+ return tokenizer
+
+
+def get_pretrained_language_model(text_encoder_type):
+ if text_encoder_type == "bert-base-uncased":
+ return BertModel.from_pretrained(text_encoder_type)
+ if text_encoder_type == "roberta-base":
+ return RobertaModel.from_pretrained(text_encoder_type)
+ raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type))
diff --git a/groundingdino/util/inference.py b/groundingdino/util/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe3ac64f094c7ab525d940e7f4381f8c13039dd2
--- /dev/null
+++ b/groundingdino/util/inference.py
@@ -0,0 +1,244 @@
+from typing import Tuple, List
+
+import cv2
+import numpy as np
+import supervision as sv
+import torch
+from PIL import Image
+from torchvision.ops import box_convert
+
+import groundingdino.datasets.transforms as T
+from groundingdino.models import build_model
+from groundingdino.util.misc import clean_state_dict
+from groundingdino.util.slconfig import SLConfig
+from groundingdino.util.utils import get_phrases_from_posmap
+
+# ----------------------------------------------------------------------------------------------------------------------
+# OLD API
+# ----------------------------------------------------------------------------------------------------------------------
+
+
+def preprocess_caption(caption: str) -> str:
+ result = caption.lower().strip()
+ if result.endswith("."):
+ return result
+ return result + "."
+
+
+def load_model(model_config_path: str, model_checkpoint_path: str, device: str = "cuda"):
+ args = SLConfig.fromfile(model_config_path)
+ args.device = device
+ model = build_model(args)
+ checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
+ model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
+ model.eval()
+ return model
+
+
+def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:
+ transform = T.Compose(
+ [
+ T.RandomResize([800], max_size=1333),
+ T.ToTensor(),
+ T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+ ]
+ )
+ image_source = Image.open(image_path).convert("RGB")
+ image = np.asarray(image_source)
+ image_transformed, _ = transform(image_source, None)
+ return image, image_transformed
+
+
+def predict(
+ model,
+ image: torch.Tensor,
+ caption: str,
+ box_threshold: float,
+ text_threshold: float,
+ device: str = "cuda"
+) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:
+ caption = preprocess_caption(caption=caption)
+
+ model = model.to(device)
+ image = image.to(device)
+
+ with torch.no_grad():
+ outputs = model(image[None], captions=[caption])
+
+ prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0] # prediction_logits.shape = (nq, 256)
+ prediction_boxes = outputs["pred_boxes"].cpu()[0] # prediction_boxes.shape = (nq, 4)
+
+ mask = prediction_logits.max(dim=1)[0] > box_threshold
+ logits = prediction_logits[mask] # logits.shape = (n, 256)
+ boxes = prediction_boxes[mask] # boxes.shape = (n, 4)
+
+ tokenizer = model.tokenizer
+ tokenized = tokenizer(caption)
+
+ phrases = [
+ get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '')
+ for logit
+ in logits
+ ]
+
+ return boxes, logits.max(dim=1)[0], phrases
+
+
+def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str]) -> np.ndarray:
+ h, w, _ = image_source.shape
+ boxes = boxes * torch.Tensor([w, h, w, h])
+ xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
+ detections = sv.Detections(xyxy=xyxy)
+
+ labels = [
+ f"{phrase} {logit:.2f}"
+ for phrase, logit
+ in zip(phrases, logits)
+ ]
+
+ box_annotator = sv.BoxAnnotator()
+ annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR)
+ annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
+ return annotated_frame
+
+
+# ----------------------------------------------------------------------------------------------------------------------
+# NEW API
+# ----------------------------------------------------------------------------------------------------------------------
+
+
+class Model:
+
+ def __init__(
+ self,
+ model_config_path: str,
+ model_checkpoint_path: str,
+ device: str = "cuda"
+ ):
+ self.model = load_model(
+ model_config_path=model_config_path,
+ model_checkpoint_path=model_checkpoint_path,
+ device=device
+ ).to(device)
+ self.device = device
+
+ def predict_with_caption(
+ self,
+ image: np.ndarray,
+ caption: str,
+ box_threshold: float = 0.35,
+ text_threshold: float = 0.25
+ ) -> Tuple[sv.Detections, List[str]]:
+ """
+ import cv2
+
+ image = cv2.imread(IMAGE_PATH)
+
+ model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH)
+ detections, labels = model.predict_with_caption(
+ image=image,
+ caption=caption,
+ box_threshold=BOX_THRESHOLD,
+ text_threshold=TEXT_THRESHOLD
+ )
+
+ import supervision as sv
+
+ box_annotator = sv.BoxAnnotator()
+ annotated_image = box_annotator.annotate(scene=image, detections=detections, labels=labels)
+ """
+ processed_image = Model.preprocess_image(image_bgr=image).to(self.device)
+ boxes, logits, phrases = predict(
+ model=self.model,
+ image=processed_image,
+ caption=caption,
+ box_threshold=box_threshold,
+ text_threshold=text_threshold,
+ device=self.device)
+ source_h, source_w, _ = image.shape
+ detections = Model.post_process_result(
+ source_h=source_h,
+ source_w=source_w,
+ boxes=boxes,
+ logits=logits)
+ return detections, phrases
+
+ def predict_with_classes(
+ self,
+ image: np.ndarray,
+ classes: List[str],
+ box_threshold: float,
+ text_threshold: float
+ ) -> sv.Detections:
+ """
+ import cv2
+
+ image = cv2.imread(IMAGE_PATH)
+
+ model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH)
+ detections = model.predict_with_classes(
+ image=image,
+ classes=CLASSES,
+ box_threshold=BOX_THRESHOLD,
+ text_threshold=TEXT_THRESHOLD
+ )
+
+
+ import supervision as sv
+
+ box_annotator = sv.BoxAnnotator()
+ annotated_image = box_annotator.annotate(scene=image, detections=detections)
+ """
+ caption = ". ".join(classes)
+ processed_image = Model.preprocess_image(image_bgr=image).to(self.device)
+ boxes, logits, phrases = predict(
+ model=self.model,
+ image=processed_image,
+ caption=caption,
+ box_threshold=box_threshold,
+ text_threshold=text_threshold,
+ device=self.device)
+ source_h, source_w, _ = image.shape
+ detections = Model.post_process_result(
+ source_h=source_h,
+ source_w=source_w,
+ boxes=boxes,
+ logits=logits)
+ class_id = Model.phrases2classes(phrases=phrases, classes=classes)
+ detections.class_id = class_id
+ return detections
+
+ @staticmethod
+ def preprocess_image(image_bgr: np.ndarray) -> torch.Tensor:
+ transform = T.Compose(
+ [
+ T.RandomResize([800], max_size=1333),
+ T.ToTensor(),
+ T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+ ]
+ )
+ image_pillow = Image.fromarray(cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB))
+ image_transformed, _ = transform(image_pillow, None)
+ return image_transformed
+
+ @staticmethod
+ def post_process_result(
+ source_h: int,
+ source_w: int,
+ boxes: torch.Tensor,
+ logits: torch.Tensor
+ ) -> sv.Detections:
+ boxes = boxes * torch.Tensor([source_w, source_h, source_w, source_h])
+ xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
+ confidence = logits.numpy()
+ return sv.Detections(xyxy=xyxy, confidence=confidence)
+
+ @staticmethod
+ def phrases2classes(phrases: List[str], classes: List[str]) -> np.ndarray:
+ class_ids = []
+ for phrase in phrases:
+ try:
+ class_ids.append(classes.index(phrase))
+ except ValueError:
+ class_ids.append(None)
+ return np.array(class_ids)
diff --git a/groundingdino/util/logger.py b/groundingdino/util/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..18145f54c927abd59b95f3fa6e6da8002bc2ce97
--- /dev/null
+++ b/groundingdino/util/logger.py
@@ -0,0 +1,93 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+import functools
+import logging
+import os
+import sys
+
+from termcolor import colored
+
+
+class _ColorfulFormatter(logging.Formatter):
+ def __init__(self, *args, **kwargs):
+ self._root_name = kwargs.pop("root_name") + "."
+ self._abbrev_name = kwargs.pop("abbrev_name", "")
+ if len(self._abbrev_name):
+ self._abbrev_name = self._abbrev_name + "."
+ super(_ColorfulFormatter, self).__init__(*args, **kwargs)
+
+ def formatMessage(self, record):
+ record.name = record.name.replace(self._root_name, self._abbrev_name)
+ log = super(_ColorfulFormatter, self).formatMessage(record)
+ if record.levelno == logging.WARNING:
+ prefix = colored("WARNING", "red", attrs=["blink"])
+ elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:
+ prefix = colored("ERROR", "red", attrs=["blink", "underline"])
+ else:
+ return log
+ return prefix + " " + log
+
+
+# so that calling setup_logger multiple times won't add many handlers
+@functools.lru_cache()
+def setup_logger(output=None, distributed_rank=0, *, color=True, name="imagenet", abbrev_name=None):
+ """
+ Initialize the detectron2 logger and set its verbosity level to "INFO".
+
+ Args:
+ output (str): a file name or a directory to save log. If None, will not save log file.
+ If ends with ".txt" or ".log", assumed to be a file name.
+ Otherwise, logs will be saved to `output/log.txt`.
+ name (str): the root module name of this logger
+
+ Returns:
+ logging.Logger: a logger
+ """
+ logger = logging.getLogger(name)
+ logger.setLevel(logging.DEBUG)
+ logger.propagate = False
+
+ if abbrev_name is None:
+ abbrev_name = name
+
+ plain_formatter = logging.Formatter(
+ "[%(asctime)s.%(msecs)03d]: %(message)s", datefmt="%m/%d %H:%M:%S"
+ )
+ # stdout logging: master only
+ if distributed_rank == 0:
+ ch = logging.StreamHandler(stream=sys.stdout)
+ ch.setLevel(logging.DEBUG)
+ if color:
+ formatter = _ColorfulFormatter(
+ colored("[%(asctime)s.%(msecs)03d]: ", "green") + "%(message)s",
+ datefmt="%m/%d %H:%M:%S",
+ root_name=name,
+ abbrev_name=str(abbrev_name),
+ )
+ else:
+ formatter = plain_formatter
+ ch.setFormatter(formatter)
+ logger.addHandler(ch)
+
+ # file logging: all workers
+ if output is not None:
+ if output.endswith(".txt") or output.endswith(".log"):
+ filename = output
+ else:
+ filename = os.path.join(output, "log.txt")
+ if distributed_rank > 0:
+ filename = filename + f".rank{distributed_rank}"
+ os.makedirs(os.path.dirname(filename), exist_ok=True)
+
+ fh = logging.StreamHandler(_cached_log_stream(filename))
+ fh.setLevel(logging.DEBUG)
+ fh.setFormatter(plain_formatter)
+ logger.addHandler(fh)
+
+ return logger
+
+
+# cache the opened file object, so that different calls to `setup_logger`
+# with the same file name can safely write to the same file.
+@functools.lru_cache(maxsize=None)
+def _cached_log_stream(filename):
+ return open(filename, "a")
diff --git a/groundingdino/util/misc.py b/groundingdino/util/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..d64b84ef24bea0c98e76824feb1903f6bfebe7a5
--- /dev/null
+++ b/groundingdino/util/misc.py
@@ -0,0 +1,717 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Misc functions, including distributed helpers.
+
+Mostly copy-paste from torchvision references.
+"""
+import colorsys
+import datetime
+import functools
+import io
+import json
+import os
+import pickle
+import subprocess
+import time
+from collections import OrderedDict, defaultdict, deque
+from typing import List, Optional
+
+import numpy as np
+import torch
+import torch.distributed as dist
+
+# needed due to empty tensor bug in pytorch and torchvision 0.5
+import torchvision
+from torch import Tensor
+
+__torchvision_need_compat_flag = float(torchvision.__version__.split(".")[1]) < 7
+if __torchvision_need_compat_flag:
+ from torchvision.ops import _new_empty_tensor
+ from torchvision.ops.misc import _output_size
+
+
+class SmoothedValue(object):
+ """Track a series of values and provide access to smoothed values over a
+ window or the global series average.
+ """
+
+ def __init__(self, window_size=20, fmt=None):
+ if fmt is None:
+ fmt = "{median:.4f} ({global_avg:.4f})"
+ self.deque = deque(maxlen=window_size)
+ self.total = 0.0
+ self.count = 0
+ self.fmt = fmt
+
+ def update(self, value, n=1):
+ self.deque.append(value)
+ self.count += n
+ self.total += value * n
+
+ def synchronize_between_processes(self):
+ """
+ Warning: does not synchronize the deque!
+ """
+ if not is_dist_avail_and_initialized():
+ return
+ t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
+ dist.barrier()
+ dist.all_reduce(t)
+ t = t.tolist()
+ self.count = int(t[0])
+ self.total = t[1]
+
+ @property
+ def median(self):
+ d = torch.tensor(list(self.deque))
+ if d.shape[0] == 0:
+ return 0
+ return d.median().item()
+
+ @property
+ def avg(self):
+ d = torch.tensor(list(self.deque), dtype=torch.float32)
+ return d.mean().item()
+
+ @property
+ def global_avg(self):
+ if os.environ.get("SHILONG_AMP", None) == "1":
+ eps = 1e-4
+ else:
+ eps = 1e-6
+ return self.total / (self.count + eps)
+
+ @property
+ def max(self):
+ return max(self.deque)
+
+ @property
+ def value(self):
+ return self.deque[-1]
+
+ def __str__(self):
+ return self.fmt.format(
+ median=self.median,
+ avg=self.avg,
+ global_avg=self.global_avg,
+ max=self.max,
+ value=self.value,
+ )
+
+
+@functools.lru_cache()
+def _get_global_gloo_group():
+ """
+ Return a process group based on gloo backend, containing all the ranks
+ The result is cached.
+ """
+
+ if dist.get_backend() == "nccl":
+ return dist.new_group(backend="gloo")
+
+ return dist.group.WORLD
+
+
+def all_gather_cpu(data):
+ """
+ Run all_gather on arbitrary picklable data (not necessarily tensors)
+ Args:
+ data: any picklable object
+ Returns:
+ list[data]: list of data gathered from each rank
+ """
+
+ world_size = get_world_size()
+ if world_size == 1:
+ return [data]
+
+ cpu_group = _get_global_gloo_group()
+
+ buffer = io.BytesIO()
+ torch.save(data, buffer)
+ data_view = buffer.getbuffer()
+ device = "cuda" if cpu_group is None else "cpu"
+ tensor = torch.ByteTensor(data_view).to(device)
+
+ # obtain Tensor size of each rank
+ local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)
+ size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]
+ if cpu_group is None:
+ dist.all_gather(size_list, local_size)
+ else:
+ print("gathering on cpu")
+ dist.all_gather(size_list, local_size, group=cpu_group)
+ size_list = [int(size.item()) for size in size_list]
+ max_size = max(size_list)
+ assert isinstance(local_size.item(), int)
+ local_size = int(local_size.item())
+
+ # receiving Tensor from all ranks
+ # we pad the tensor because torch all_gather does not support
+ # gathering tensors of different shapes
+ tensor_list = []
+ for _ in size_list:
+ tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))
+ if local_size != max_size:
+ padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)
+ tensor = torch.cat((tensor, padding), dim=0)
+ if cpu_group is None:
+ dist.all_gather(tensor_list, tensor)
+ else:
+ dist.all_gather(tensor_list, tensor, group=cpu_group)
+
+ data_list = []
+ for size, tensor in zip(size_list, tensor_list):
+ tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]
+ buffer = io.BytesIO(tensor.cpu().numpy())
+ obj = torch.load(buffer)
+ data_list.append(obj)
+
+ return data_list
+
+
+def all_gather(data):
+ """
+ Run all_gather on arbitrary picklable data (not necessarily tensors)
+ Args:
+ data: any picklable object
+ Returns:
+ list[data]: list of data gathered from each rank
+ """
+
+ if os.getenv("CPU_REDUCE") == "1":
+ return all_gather_cpu(data)
+
+ world_size = get_world_size()
+ if world_size == 1:
+ return [data]
+
+ # serialized to a Tensor
+ buffer = pickle.dumps(data)
+ storage = torch.ByteStorage.from_buffer(buffer)
+ tensor = torch.ByteTensor(storage).to("cuda")
+
+ # obtain Tensor size of each rank
+ local_size = torch.tensor([tensor.numel()], device="cuda")
+ size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
+ dist.all_gather(size_list, local_size)
+ size_list = [int(size.item()) for size in size_list]
+ max_size = max(size_list)
+
+ # receiving Tensor from all ranks
+ # we pad the tensor because torch all_gather does not support
+ # gathering tensors of different shapes
+ tensor_list = []
+ for _ in size_list:
+ tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
+ if local_size != max_size:
+ padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
+ tensor = torch.cat((tensor, padding), dim=0)
+ dist.all_gather(tensor_list, tensor)
+
+ data_list = []
+ for size, tensor in zip(size_list, tensor_list):
+ buffer = tensor.cpu().numpy().tobytes()[:size]
+ data_list.append(pickle.loads(buffer))
+
+ return data_list
+
+
+def reduce_dict(input_dict, average=True):
+ """
+ Args:
+ input_dict (dict): all the values will be reduced
+ average (bool): whether to do average or sum
+ Reduce the values in the dictionary from all processes so that all processes
+ have the averaged results. Returns a dict with the same fields as
+ input_dict, after reduction.
+ """
+ world_size = get_world_size()
+ if world_size < 2:
+ return input_dict
+ with torch.no_grad():
+ names = []
+ values = []
+ # sort the keys so that they are consistent across processes
+ for k in sorted(input_dict.keys()):
+ names.append(k)
+ values.append(input_dict[k])
+ values = torch.stack(values, dim=0)
+ dist.all_reduce(values)
+ if average:
+ values /= world_size
+ reduced_dict = {k: v for k, v in zip(names, values)}
+ return reduced_dict
+
+
+class MetricLogger(object):
+ def __init__(self, delimiter="\t"):
+ self.meters = defaultdict(SmoothedValue)
+ self.delimiter = delimiter
+
+ def update(self, **kwargs):
+ for k, v in kwargs.items():
+ if isinstance(v, torch.Tensor):
+ v = v.item()
+ assert isinstance(v, (float, int))
+ self.meters[k].update(v)
+
+ def __getattr__(self, attr):
+ if attr in self.meters:
+ return self.meters[attr]
+ if attr in self.__dict__:
+ return self.__dict__[attr]
+ raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
+
+ def __str__(self):
+ loss_str = []
+ for name, meter in self.meters.items():
+ # print(name, str(meter))
+ # import ipdb;ipdb.set_trace()
+ if meter.count > 0:
+ loss_str.append("{}: {}".format(name, str(meter)))
+ return self.delimiter.join(loss_str)
+
+ def synchronize_between_processes(self):
+ for meter in self.meters.values():
+ meter.synchronize_between_processes()
+
+ def add_meter(self, name, meter):
+ self.meters[name] = meter
+
+ def log_every(self, iterable, print_freq, header=None, logger=None):
+ if logger is None:
+ print_func = print
+ else:
+ print_func = logger.info
+
+ i = 0
+ if not header:
+ header = ""
+ start_time = time.time()
+ end = time.time()
+ iter_time = SmoothedValue(fmt="{avg:.4f}")
+ data_time = SmoothedValue(fmt="{avg:.4f}")
+ space_fmt = ":" + str(len(str(len(iterable)))) + "d"
+ if torch.cuda.is_available():
+ log_msg = self.delimiter.join(
+ [
+ header,
+ "[{0" + space_fmt + "}/{1}]",
+ "eta: {eta}",
+ "{meters}",
+ "time: {time}",
+ "data: {data}",
+ "max mem: {memory:.0f}",
+ ]
+ )
+ else:
+ log_msg = self.delimiter.join(
+ [
+ header,
+ "[{0" + space_fmt + "}/{1}]",
+ "eta: {eta}",
+ "{meters}",
+ "time: {time}",
+ "data: {data}",
+ ]
+ )
+ MB = 1024.0 * 1024.0
+ for obj in iterable:
+ data_time.update(time.time() - end)
+ yield obj
+ # import ipdb; ipdb.set_trace()
+ iter_time.update(time.time() - end)
+ if i % print_freq == 0 or i == len(iterable) - 1:
+ eta_seconds = iter_time.global_avg * (len(iterable) - i)
+ eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+ if torch.cuda.is_available():
+ print_func(
+ log_msg.format(
+ i,
+ len(iterable),
+ eta=eta_string,
+ meters=str(self),
+ time=str(iter_time),
+ data=str(data_time),
+ memory=torch.cuda.max_memory_allocated() / MB,
+ )
+ )
+ else:
+ print_func(
+ log_msg.format(
+ i,
+ len(iterable),
+ eta=eta_string,
+ meters=str(self),
+ time=str(iter_time),
+ data=str(data_time),
+ )
+ )
+ i += 1
+ end = time.time()
+ total_time = time.time() - start_time
+ total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+ print_func(
+ "{} Total time: {} ({:.4f} s / it)".format(
+ header, total_time_str, total_time / len(iterable)
+ )
+ )
+
+
+def get_sha():
+ cwd = os.path.dirname(os.path.abspath(__file__))
+
+ def _run(command):
+ return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
+
+ sha = "N/A"
+ diff = "clean"
+ branch = "N/A"
+ try:
+ sha = _run(["git", "rev-parse", "HEAD"])
+ subprocess.check_output(["git", "diff"], cwd=cwd)
+ diff = _run(["git", "diff-index", "HEAD"])
+ diff = "has uncommited changes" if diff else "clean"
+ branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
+ except Exception:
+ pass
+ message = f"sha: {sha}, status: {diff}, branch: {branch}"
+ return message
+
+
+def collate_fn(batch):
+ # import ipdb; ipdb.set_trace()
+ batch = list(zip(*batch))
+ batch[0] = nested_tensor_from_tensor_list(batch[0])
+ return tuple(batch)
+
+
+def _max_by_axis(the_list):
+ # type: (List[List[int]]) -> List[int]
+ maxes = the_list[0]
+ for sublist in the_list[1:]:
+ for index, item in enumerate(sublist):
+ maxes[index] = max(maxes[index], item)
+ return maxes
+
+
+class NestedTensor(object):
+ def __init__(self, tensors, mask: Optional[Tensor]):
+ self.tensors = tensors
+ self.mask = mask
+ if mask == "auto":
+ self.mask = torch.zeros_like(tensors).to(tensors.device)
+ if self.mask.dim() == 3:
+ self.mask = self.mask.sum(0).to(bool)
+ elif self.mask.dim() == 4:
+ self.mask = self.mask.sum(1).to(bool)
+ else:
+ raise ValueError(
+ "tensors dim must be 3 or 4 but {}({})".format(
+ self.tensors.dim(), self.tensors.shape
+ )
+ )
+
+ def imgsize(self):
+ res = []
+ for i in range(self.tensors.shape[0]):
+ mask = self.mask[i]
+ maxH = (~mask).sum(0).max()
+ maxW = (~mask).sum(1).max()
+ res.append(torch.Tensor([maxH, maxW]))
+ return res
+
+ def to(self, device):
+ # type: (Device) -> NestedTensor # noqa
+ cast_tensor = self.tensors.to(device)
+ mask = self.mask
+ if mask is not None:
+ assert mask is not None
+ cast_mask = mask.to(device)
+ else:
+ cast_mask = None
+ return NestedTensor(cast_tensor, cast_mask)
+
+ def to_img_list_single(self, tensor, mask):
+ assert tensor.dim() == 3, "dim of tensor should be 3 but {}".format(tensor.dim())
+ maxH = (~mask).sum(0).max()
+ maxW = (~mask).sum(1).max()
+ img = tensor[:, :maxH, :maxW]
+ return img
+
+ def to_img_list(self):
+ """remove the padding and convert to img list
+
+ Returns:
+ [type]: [description]
+ """
+ if self.tensors.dim() == 3:
+ return self.to_img_list_single(self.tensors, self.mask)
+ else:
+ res = []
+ for i in range(self.tensors.shape[0]):
+ tensor_i = self.tensors[i]
+ mask_i = self.mask[i]
+ res.append(self.to_img_list_single(tensor_i, mask_i))
+ return res
+
+ @property
+ def device(self):
+ return self.tensors.device
+
+ def decompose(self):
+ return self.tensors, self.mask
+
+ def __repr__(self):
+ return str(self.tensors)
+
+ @property
+ def shape(self):
+ return {"tensors.shape": self.tensors.shape, "mask.shape": self.mask.shape}
+
+
+def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
+ # TODO make this more general
+ if tensor_list[0].ndim == 3:
+ if torchvision._is_tracing():
+ # nested_tensor_from_tensor_list() does not export well to ONNX
+ # call _onnx_nested_tensor_from_tensor_list() instead
+ return _onnx_nested_tensor_from_tensor_list(tensor_list)
+
+ # TODO make it support different-sized images
+ max_size = _max_by_axis([list(img.shape) for img in tensor_list])
+ # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
+ batch_shape = [len(tensor_list)] + max_size
+ b, c, h, w = batch_shape
+ dtype = tensor_list[0].dtype
+ device = tensor_list[0].device
+ tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
+ mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
+ for img, pad_img, m in zip(tensor_list, tensor, mask):
+ pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+ m[: img.shape[1], : img.shape[2]] = False
+ else:
+ raise ValueError("not supported")
+ return NestedTensor(tensor, mask)
+
+
+# _onnx_nested_tensor_from_tensor_list() is an implementation of
+# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
+@torch.jit.unused
+def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
+ max_size = []
+ for i in range(tensor_list[0].dim()):
+ max_size_i = torch.max(
+ torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
+ ).to(torch.int64)
+ max_size.append(max_size_i)
+ max_size = tuple(max_size)
+
+ # work around for
+ # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+ # m[: img.shape[1], :img.shape[2]] = False
+ # which is not yet supported in onnx
+ padded_imgs = []
+ padded_masks = []
+ for img in tensor_list:
+ padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
+ padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
+ padded_imgs.append(padded_img)
+
+ m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
+ padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
+ padded_masks.append(padded_mask.to(torch.bool))
+
+ tensor = torch.stack(padded_imgs)
+ mask = torch.stack(padded_masks)
+
+ return NestedTensor(tensor, mask=mask)
+
+
+def setup_for_distributed(is_master):
+ """
+ This function disables printing when not in master process
+ """
+ import builtins as __builtin__
+
+ builtin_print = __builtin__.print
+
+ def print(*args, **kwargs):
+ force = kwargs.pop("force", False)
+ if is_master or force:
+ builtin_print(*args, **kwargs)
+
+ __builtin__.print = print
+
+
+def is_dist_avail_and_initialized():
+ if not dist.is_available():
+ return False
+ if not dist.is_initialized():
+ return False
+ return True
+
+
+def get_world_size():
+ if not is_dist_avail_and_initialized():
+ return 1
+ return dist.get_world_size()
+
+
+def get_rank():
+ if not is_dist_avail_and_initialized():
+ return 0
+ return dist.get_rank()
+
+
+def is_main_process():
+ return get_rank() == 0
+
+
+def save_on_master(*args, **kwargs):
+ if is_main_process():
+ torch.save(*args, **kwargs)
+
+
+def init_distributed_mode(args):
+ if "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "": # 'RANK' in os.environ and
+ args.rank = int(os.environ["RANK"])
+ args.world_size = int(os.environ["WORLD_SIZE"])
+ args.gpu = args.local_rank = int(os.environ["LOCAL_RANK"])
+
+ # launch by torch.distributed.launch
+ # Single node
+ # python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 1 --rank 0 ...
+ # Multi nodes
+ # python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 0 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
+ # python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 1 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
+ # args.rank = int(os.environ.get('OMPI_COMM_WORLD_RANK'))
+ # local_world_size = int(os.environ['GPU_PER_NODE_COUNT'])
+ # args.world_size = args.world_size * local_world_size
+ # args.gpu = args.local_rank = int(os.environ['LOCAL_RANK'])
+ # args.rank = args.rank * local_world_size + args.local_rank
+ print(
+ "world size: {}, rank: {}, local rank: {}".format(
+ args.world_size, args.rank, args.local_rank
+ )
+ )
+ print(json.dumps(dict(os.environ), indent=2))
+ elif "SLURM_PROCID" in os.environ:
+ args.rank = int(os.environ["SLURM_PROCID"])
+ args.gpu = args.local_rank = int(os.environ["SLURM_LOCALID"])
+ args.world_size = int(os.environ["SLURM_NPROCS"])
+
+ print(
+ "world size: {}, world rank: {}, local rank: {}, device_count: {}".format(
+ args.world_size, args.rank, args.local_rank, torch.cuda.device_count()
+ )
+ )
+ else:
+ print("Not using distributed mode")
+ args.distributed = False
+ args.world_size = 1
+ args.rank = 0
+ args.local_rank = 0
+ return
+
+ print("world_size:{} rank:{} local_rank:{}".format(args.world_size, args.rank, args.local_rank))
+ args.distributed = True
+ torch.cuda.set_device(args.local_rank)
+ args.dist_backend = "nccl"
+ print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True)
+
+ torch.distributed.init_process_group(
+ backend=args.dist_backend,
+ world_size=args.world_size,
+ rank=args.rank,
+ init_method=args.dist_url,
+ )
+
+ print("Before torch.distributed.barrier()")
+ torch.distributed.barrier()
+ print("End torch.distributed.barrier()")
+ setup_for_distributed(args.rank == 0)
+
+
+@torch.no_grad()
+def accuracy(output, target, topk=(1,)):
+ """Computes the precision@k for the specified values of k"""
+ if target.numel() == 0:
+ return [torch.zeros([], device=output.device)]
+ maxk = max(topk)
+ batch_size = target.size(0)
+
+ _, pred = output.topk(maxk, 1, True, True)
+ pred = pred.t()
+ correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+ res = []
+ for k in topk:
+ correct_k = correct[:k].view(-1).float().sum(0)
+ res.append(correct_k.mul_(100.0 / batch_size))
+ return res
+
+
+@torch.no_grad()
+def accuracy_onehot(pred, gt):
+ """_summary_
+
+ Args:
+ pred (_type_): n, c
+ gt (_type_): n, c
+ """
+ tp = ((pred - gt).abs().sum(-1) < 1e-4).float().sum()
+ acc = tp / gt.shape[0] * 100
+ return acc
+
+
+def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
+ # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
+ """
+ Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
+ This will eventually be supported natively by PyTorch, and this
+ class can go away.
+ """
+ if __torchvision_need_compat_flag < 0.7:
+ if input.numel() > 0:
+ return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
+
+ output_shape = _output_size(2, input, size, scale_factor)
+ output_shape = list(input.shape[:-2]) + list(output_shape)
+ return _new_empty_tensor(input, output_shape)
+ else:
+ return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
+
+
+class color_sys:
+ def __init__(self, num_colors) -> None:
+ self.num_colors = num_colors
+ colors = []
+ for i in np.arange(0.0, 360.0, 360.0 / num_colors):
+ hue = i / 360.0
+ lightness = (50 + np.random.rand() * 10) / 100.0
+ saturation = (90 + np.random.rand() * 10) / 100.0
+ colors.append(
+ tuple([int(j * 255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)])
+ )
+ self.colors = colors
+
+ def __call__(self, idx):
+ return self.colors[idx]
+
+
+def inverse_sigmoid(x, eps=1e-3):
+ x = x.clamp(min=0, max=1)
+ x1 = x.clamp(min=eps)
+ x2 = (1 - x).clamp(min=eps)
+ return torch.log(x1 / x2)
+
+
+def clean_state_dict(state_dict):
+ new_state_dict = OrderedDict()
+ for k, v in state_dict.items():
+ if k[:7] == "module.":
+ k = k[7:] # remove `module.`
+ new_state_dict[k] = v
+ return new_state_dict
diff --git a/groundingdino/util/slconfig.py b/groundingdino/util/slconfig.py
new file mode 100644
index 0000000000000000000000000000000000000000..672e72ed0b68a54c13ade66c9f146d2d542e97c6
--- /dev/null
+++ b/groundingdino/util/slconfig.py
@@ -0,0 +1,427 @@
+# ==========================================================
+# Modified from mmcv
+# ==========================================================
+import ast
+import os
+import os.path as osp
+import shutil
+import sys
+import tempfile
+from argparse import Action
+from importlib import import_module
+
+from addict import Dict
+from yapf.yapflib.yapf_api import FormatCode
+
+BASE_KEY = "_base_"
+DELETE_KEY = "_delete_"
+RESERVED_KEYS = ["filename", "text", "pretty_text", "get", "dump", "merge_from_dict"]
+
+
+def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
+ if not osp.isfile(filename):
+ raise FileNotFoundError(msg_tmpl.format(filename))
+
+
+class ConfigDict(Dict):
+ def __missing__(self, name):
+ raise KeyError(name)
+
+ def __getattr__(self, name):
+ try:
+ value = super(ConfigDict, self).__getattr__(name)
+ except KeyError:
+ ex = AttributeError(f"'{self.__class__.__name__}' object has no " f"attribute '{name}'")
+ except Exception as e:
+ ex = e
+ else:
+ return value
+ raise ex
+
+
+class SLConfig(object):
+ """
+ config files.
+ only support .py file as config now.
+
+ ref: mmcv.utils.config
+
+ Example:
+ >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
+ >>> cfg.a
+ 1
+ >>> cfg.b
+ {'b1': [0, 1]}
+ >>> cfg.b.b1
+ [0, 1]
+ >>> cfg = Config.fromfile('tests/data/config/a.py')
+ >>> cfg.filename
+ "/home/kchen/projects/mmcv/tests/data/config/a.py"
+ >>> cfg.item4
+ 'test'
+ >>> cfg
+ "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: "
+ "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}"
+ """
+
+ @staticmethod
+ def _validate_py_syntax(filename):
+ with open(filename) as f:
+ content = f.read()
+ try:
+ ast.parse(content)
+ except SyntaxError:
+ raise SyntaxError("There are syntax errors in config " f"file {filename}")
+
+ @staticmethod
+ def _file2dict(filename):
+ filename = osp.abspath(osp.expanduser(filename))
+ check_file_exist(filename)
+ if filename.lower().endswith(".py"):
+ with tempfile.TemporaryDirectory() as temp_config_dir:
+ temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=".py")
+ temp_config_name = osp.basename(temp_config_file.name)
+ if os.name == 'nt':
+ temp_config_file.close()
+ shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name))
+ temp_module_name = osp.splitext(temp_config_name)[0]
+ sys.path.insert(0, temp_config_dir)
+ SLConfig._validate_py_syntax(filename)
+ mod = import_module(temp_module_name)
+ sys.path.pop(0)
+ cfg_dict = {
+ name: value for name, value in mod.__dict__.items() if not name.startswith("__")
+ }
+ # delete imported module
+ del sys.modules[temp_module_name]
+ # close temp file
+ temp_config_file.close()
+ elif filename.lower().endswith((".yml", ".yaml", ".json")):
+ from .slio import slload
+
+ cfg_dict = slload(filename)
+ else:
+ raise IOError("Only py/yml/yaml/json type are supported now!")
+
+ cfg_text = filename + "\n"
+ with open(filename, "r") as f:
+ cfg_text += f.read()
+
+ # parse the base file
+ if BASE_KEY in cfg_dict:
+ cfg_dir = osp.dirname(filename)
+ base_filename = cfg_dict.pop(BASE_KEY)
+ base_filename = base_filename if isinstance(base_filename, list) else [base_filename]
+
+ cfg_dict_list = list()
+ cfg_text_list = list()
+ for f in base_filename:
+ _cfg_dict, _cfg_text = SLConfig._file2dict(osp.join(cfg_dir, f))
+ cfg_dict_list.append(_cfg_dict)
+ cfg_text_list.append(_cfg_text)
+
+ base_cfg_dict = dict()
+ for c in cfg_dict_list:
+ if len(base_cfg_dict.keys() & c.keys()) > 0:
+ raise KeyError("Duplicate key is not allowed among bases")
+ # TODO Allow the duplicate key while warnning user
+ base_cfg_dict.update(c)
+
+ base_cfg_dict = SLConfig._merge_a_into_b(cfg_dict, base_cfg_dict)
+ cfg_dict = base_cfg_dict
+
+ # merge cfg_text
+ cfg_text_list.append(cfg_text)
+ cfg_text = "\n".join(cfg_text_list)
+
+ return cfg_dict, cfg_text
+
+ @staticmethod
+ def _merge_a_into_b(a, b):
+ """merge dict `a` into dict `b` (non-inplace).
+ values in `a` will overwrite `b`.
+ copy first to avoid inplace modification
+
+ Args:
+ a ([type]): [description]
+ b ([type]): [description]
+
+ Returns:
+ [dict]: [description]
+ """
+ # import ipdb; ipdb.set_trace()
+ if not isinstance(a, dict):
+ return a
+
+ b = b.copy()
+ for k, v in a.items():
+ if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False):
+
+ if not isinstance(b[k], dict) and not isinstance(b[k], list):
+ # if :
+ # import ipdb; ipdb.set_trace()
+ raise TypeError(
+ f"{k}={v} in child config cannot inherit from base "
+ f"because {k} is a dict in the child config but is of "
+ f"type {type(b[k])} in base config. You may set "
+ f"`{DELETE_KEY}=True` to ignore the base config"
+ )
+ b[k] = SLConfig._merge_a_into_b(v, b[k])
+ elif isinstance(b, list):
+ try:
+ _ = int(k)
+ except:
+ raise TypeError(
+ f"b is a list, " f"index {k} should be an int when input but {type(k)}"
+ )
+ b[int(k)] = SLConfig._merge_a_into_b(v, b[int(k)])
+ else:
+ b[k] = v
+
+ return b
+
+ @staticmethod
+ def fromfile(filename):
+ cfg_dict, cfg_text = SLConfig._file2dict(filename)
+ return SLConfig(cfg_dict, cfg_text=cfg_text, filename=filename)
+
+ def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
+ if cfg_dict is None:
+ cfg_dict = dict()
+ elif not isinstance(cfg_dict, dict):
+ raise TypeError("cfg_dict must be a dict, but " f"got {type(cfg_dict)}")
+ for key in cfg_dict:
+ if key in RESERVED_KEYS:
+ raise KeyError(f"{key} is reserved for config file")
+
+ super(SLConfig, self).__setattr__("_cfg_dict", ConfigDict(cfg_dict))
+ super(SLConfig, self).__setattr__("_filename", filename)
+ if cfg_text:
+ text = cfg_text
+ elif filename:
+ with open(filename, "r") as f:
+ text = f.read()
+ else:
+ text = ""
+ super(SLConfig, self).__setattr__("_text", text)
+
+ @property
+ def filename(self):
+ return self._filename
+
+ @property
+ def text(self):
+ return self._text
+
+ @property
+ def pretty_text(self):
+
+ indent = 4
+
+ def _indent(s_, num_spaces):
+ s = s_.split("\n")
+ if len(s) == 1:
+ return s_
+ first = s.pop(0)
+ s = [(num_spaces * " ") + line for line in s]
+ s = "\n".join(s)
+ s = first + "\n" + s
+ return s
+
+ def _format_basic_types(k, v, use_mapping=False):
+ if isinstance(v, str):
+ v_str = f"'{v}'"
+ else:
+ v_str = str(v)
+
+ if use_mapping:
+ k_str = f"'{k}'" if isinstance(k, str) else str(k)
+ attr_str = f"{k_str}: {v_str}"
+ else:
+ attr_str = f"{str(k)}={v_str}"
+ attr_str = _indent(attr_str, indent)
+
+ return attr_str
+
+ def _format_list(k, v, use_mapping=False):
+ # check if all items in the list are dict
+ if all(isinstance(_, dict) for _ in v):
+ v_str = "[\n"
+ v_str += "\n".join(
+ f"dict({_indent(_format_dict(v_), indent)})," for v_ in v
+ ).rstrip(",")
+ if use_mapping:
+ k_str = f"'{k}'" if isinstance(k, str) else str(k)
+ attr_str = f"{k_str}: {v_str}"
+ else:
+ attr_str = f"{str(k)}={v_str}"
+ attr_str = _indent(attr_str, indent) + "]"
+ else:
+ attr_str = _format_basic_types(k, v, use_mapping)
+ return attr_str
+
+ def _contain_invalid_identifier(dict_str):
+ contain_invalid_identifier = False
+ for key_name in dict_str:
+ contain_invalid_identifier |= not str(key_name).isidentifier()
+ return contain_invalid_identifier
+
+ def _format_dict(input_dict, outest_level=False):
+ r = ""
+ s = []
+
+ use_mapping = _contain_invalid_identifier(input_dict)
+ if use_mapping:
+ r += "{"
+ for idx, (k, v) in enumerate(input_dict.items()):
+ is_last = idx >= len(input_dict) - 1
+ end = "" if outest_level or is_last else ","
+ if isinstance(v, dict):
+ v_str = "\n" + _format_dict(v)
+ if use_mapping:
+ k_str = f"'{k}'" if isinstance(k, str) else str(k)
+ attr_str = f"{k_str}: dict({v_str}"
+ else:
+ attr_str = f"{str(k)}=dict({v_str}"
+ attr_str = _indent(attr_str, indent) + ")" + end
+ elif isinstance(v, list):
+ attr_str = _format_list(k, v, use_mapping) + end
+ else:
+ attr_str = _format_basic_types(k, v, use_mapping) + end
+
+ s.append(attr_str)
+ r += "\n".join(s)
+ if use_mapping:
+ r += "}"
+ return r
+
+ cfg_dict = self._cfg_dict.to_dict()
+ text = _format_dict(cfg_dict, outest_level=True)
+ # copied from setup.cfg
+ yapf_style = dict(
+ based_on_style="pep8",
+ blank_line_before_nested_class_or_def=True,
+ split_before_expression_after_opening_paren=True,
+ )
+ text, _ = FormatCode(text, style_config=yapf_style, verify=True)
+
+ return text
+
+ def __repr__(self):
+ return f"Config (path: {self.filename}): {self._cfg_dict.__repr__()}"
+
+ def __len__(self):
+ return len(self._cfg_dict)
+
+ def __getattr__(self, name):
+ # # debug
+ # print('+'*15)
+ # print('name=%s' % name)
+ # print("addr:", id(self))
+ # # print('type(self):', type(self))
+ # print(self.__dict__)
+ # print('+'*15)
+ # if self.__dict__ == {}:
+ # raise ValueError
+
+ return getattr(self._cfg_dict, name)
+
+ def __getitem__(self, name):
+ return self._cfg_dict.__getitem__(name)
+
+ def __setattr__(self, name, value):
+ if isinstance(value, dict):
+ value = ConfigDict(value)
+ self._cfg_dict.__setattr__(name, value)
+
+ def __setitem__(self, name, value):
+ if isinstance(value, dict):
+ value = ConfigDict(value)
+ self._cfg_dict.__setitem__(name, value)
+
+ def __iter__(self):
+ return iter(self._cfg_dict)
+
+ def dump(self, file=None):
+ # import ipdb; ipdb.set_trace()
+ if file is None:
+ return self.pretty_text
+ else:
+ with open(file, "w") as f:
+ f.write(self.pretty_text)
+
+ def merge_from_dict(self, options):
+ """Merge list into cfg_dict
+
+ Merge the dict parsed by MultipleKVAction into this cfg.
+
+ Examples:
+ >>> options = {'model.backbone.depth': 50,
+ ... 'model.backbone.with_cp':True}
+ >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet'))))
+ >>> cfg.merge_from_dict(options)
+ >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
+ >>> assert cfg_dict == dict(
+ ... model=dict(backbone=dict(depth=50, with_cp=True)))
+
+ Args:
+ options (dict): dict of configs to merge from.
+ """
+ option_cfg_dict = {}
+ for full_key, v in options.items():
+ d = option_cfg_dict
+ key_list = full_key.split(".")
+ for subkey in key_list[:-1]:
+ d.setdefault(subkey, ConfigDict())
+ d = d[subkey]
+ subkey = key_list[-1]
+ d[subkey] = v
+
+ cfg_dict = super(SLConfig, self).__getattribute__("_cfg_dict")
+ super(SLConfig, self).__setattr__(
+ "_cfg_dict", SLConfig._merge_a_into_b(option_cfg_dict, cfg_dict)
+ )
+
+ # for multiprocess
+ def __setstate__(self, state):
+ self.__init__(state)
+
+ def copy(self):
+ return SLConfig(self._cfg_dict.copy())
+
+ def deepcopy(self):
+ return SLConfig(self._cfg_dict.deepcopy())
+
+
+class DictAction(Action):
+ """
+ argparse action to split an argument into KEY=VALUE form
+ on the first = and append to a dictionary. List options should
+ be passed as comma separated values, i.e KEY=V1,V2,V3
+ """
+
+ @staticmethod
+ def _parse_int_float_bool(val):
+ try:
+ return int(val)
+ except ValueError:
+ pass
+ try:
+ return float(val)
+ except ValueError:
+ pass
+ if val.lower() in ["true", "false"]:
+ return True if val.lower() == "true" else False
+ if val.lower() in ["none", "null"]:
+ return None
+ return val
+
+ def __call__(self, parser, namespace, values, option_string=None):
+ options = {}
+ for kv in values:
+ key, val = kv.split("=", maxsplit=1)
+ val = [self._parse_int_float_bool(v) for v in val.split(",")]
+ if len(val) == 1:
+ val = val[0]
+ options[key] = val
+ setattr(namespace, self.dest, options)
diff --git a/groundingdino/util/slio.py b/groundingdino/util/slio.py
new file mode 100644
index 0000000000000000000000000000000000000000..72c1f0f7b82cdc931d381feef64fe15815ba657e
--- /dev/null
+++ b/groundingdino/util/slio.py
@@ -0,0 +1,177 @@
+# ==========================================================
+# Modified from mmcv
+# ==========================================================
+
+import json
+import pickle
+from abc import ABCMeta, abstractmethod
+from pathlib import Path
+
+import yaml
+
+try:
+ from yaml import CLoader as Loader, CDumper as Dumper
+except ImportError:
+ from yaml import Loader, Dumper
+
+
+# ===========================
+# Rigister handler
+# ===========================
+
+
+class BaseFileHandler(metaclass=ABCMeta):
+ @abstractmethod
+ def load_from_fileobj(self, file, **kwargs):
+ pass
+
+ @abstractmethod
+ def dump_to_fileobj(self, obj, file, **kwargs):
+ pass
+
+ @abstractmethod
+ def dump_to_str(self, obj, **kwargs):
+ pass
+
+ def load_from_path(self, filepath, mode="r", **kwargs):
+ with open(filepath, mode) as f:
+ return self.load_from_fileobj(f, **kwargs)
+
+ def dump_to_path(self, obj, filepath, mode="w", **kwargs):
+ with open(filepath, mode) as f:
+ self.dump_to_fileobj(obj, f, **kwargs)
+
+
+class JsonHandler(BaseFileHandler):
+ def load_from_fileobj(self, file):
+ return json.load(file)
+
+ def dump_to_fileobj(self, obj, file, **kwargs):
+ json.dump(obj, file, **kwargs)
+
+ def dump_to_str(self, obj, **kwargs):
+ return json.dumps(obj, **kwargs)
+
+
+class PickleHandler(BaseFileHandler):
+ def load_from_fileobj(self, file, **kwargs):
+ return pickle.load(file, **kwargs)
+
+ def load_from_path(self, filepath, **kwargs):
+ return super(PickleHandler, self).load_from_path(filepath, mode="rb", **kwargs)
+
+ def dump_to_str(self, obj, **kwargs):
+ kwargs.setdefault("protocol", 2)
+ return pickle.dumps(obj, **kwargs)
+
+ def dump_to_fileobj(self, obj, file, **kwargs):
+ kwargs.setdefault("protocol", 2)
+ pickle.dump(obj, file, **kwargs)
+
+ def dump_to_path(self, obj, filepath, **kwargs):
+ super(PickleHandler, self).dump_to_path(obj, filepath, mode="wb", **kwargs)
+
+
+class YamlHandler(BaseFileHandler):
+ def load_from_fileobj(self, file, **kwargs):
+ kwargs.setdefault("Loader", Loader)
+ return yaml.load(file, **kwargs)
+
+ def dump_to_fileobj(self, obj, file, **kwargs):
+ kwargs.setdefault("Dumper", Dumper)
+ yaml.dump(obj, file, **kwargs)
+
+ def dump_to_str(self, obj, **kwargs):
+ kwargs.setdefault("Dumper", Dumper)
+ return yaml.dump(obj, **kwargs)
+
+
+file_handlers = {
+ "json": JsonHandler(),
+ "yaml": YamlHandler(),
+ "yml": YamlHandler(),
+ "pickle": PickleHandler(),
+ "pkl": PickleHandler(),
+}
+
+# ===========================
+# load and dump
+# ===========================
+
+
+def is_str(x):
+ """Whether the input is an string instance.
+
+ Note: This method is deprecated since python 2 is no longer supported.
+ """
+ return isinstance(x, str)
+
+
+def slload(file, file_format=None, **kwargs):
+ """Load data from json/yaml/pickle files.
+
+ This method provides a unified api for loading data from serialized files.
+
+ Args:
+ file (str or :obj:`Path` or file-like object): Filename or a file-like
+ object.
+ file_format (str, optional): If not specified, the file format will be
+ inferred from the file extension, otherwise use the specified one.
+ Currently supported formats include "json", "yaml/yml" and
+ "pickle/pkl".
+
+ Returns:
+ The content from the file.
+ """
+ if isinstance(file, Path):
+ file = str(file)
+ if file_format is None and is_str(file):
+ file_format = file.split(".")[-1]
+ if file_format not in file_handlers:
+ raise TypeError(f"Unsupported format: {file_format}")
+
+ handler = file_handlers[file_format]
+ if is_str(file):
+ obj = handler.load_from_path(file, **kwargs)
+ elif hasattr(file, "read"):
+ obj = handler.load_from_fileobj(file, **kwargs)
+ else:
+ raise TypeError('"file" must be a filepath str or a file-object')
+ return obj
+
+
+def sldump(obj, file=None, file_format=None, **kwargs):
+ """Dump data to json/yaml/pickle strings or files.
+
+ This method provides a unified api for dumping data as strings or to files,
+ and also supports custom arguments for each file format.
+
+ Args:
+ obj (any): The python object to be dumped.
+ file (str or :obj:`Path` or file-like object, optional): If not
+ specified, then the object is dump to a str, otherwise to a file
+ specified by the filename or file-like object.
+ file_format (str, optional): Same as :func:`load`.
+
+ Returns:
+ bool: True for success, False otherwise.
+ """
+ if isinstance(file, Path):
+ file = str(file)
+ if file_format is None:
+ if is_str(file):
+ file_format = file.split(".")[-1]
+ elif file is None:
+ raise ValueError("file_format must be specified since file is None")
+ if file_format not in file_handlers:
+ raise TypeError(f"Unsupported format: {file_format}")
+
+ handler = file_handlers[file_format]
+ if file is None:
+ return handler.dump_to_str(obj, **kwargs)
+ elif is_str(file):
+ handler.dump_to_path(obj, file, **kwargs)
+ elif hasattr(file, "write"):
+ handler.dump_to_fileobj(obj, file, **kwargs)
+ else:
+ raise TypeError('"file" must be a filename str or a file-object')
diff --git a/groundingdino/util/time_counter.py b/groundingdino/util/time_counter.py
new file mode 100644
index 0000000000000000000000000000000000000000..0aedb2e4d61bfbe7571dca9d50053f0fedaa1359
--- /dev/null
+++ b/groundingdino/util/time_counter.py
@@ -0,0 +1,62 @@
+import json
+import time
+
+
+class TimeCounter:
+ def __init__(self) -> None:
+ pass
+
+ def clear(self):
+ self.timedict = {}
+ self.basetime = time.perf_counter()
+
+ def timeit(self, name):
+ nowtime = time.perf_counter() - self.basetime
+ self.timedict[name] = nowtime
+ self.basetime = time.perf_counter()
+
+
+class TimeHolder:
+ def __init__(self) -> None:
+ self.timedict = {}
+
+ def update(self, _timedict: dict):
+ for k, v in _timedict.items():
+ if k not in self.timedict:
+ self.timedict[k] = AverageMeter(name=k, val_only=True)
+ self.timedict[k].update(val=v)
+
+ def final_res(self):
+ return {k: v.avg for k, v in self.timedict.items()}
+
+ def __str__(self):
+ return json.dumps(self.final_res(), indent=2)
+
+
+class AverageMeter(object):
+ """Computes and stores the average and current value"""
+
+ def __init__(self, name, fmt=":f", val_only=False):
+ self.name = name
+ self.fmt = fmt
+ self.val_only = val_only
+ self.reset()
+
+ def reset(self):
+ self.val = 0
+ self.avg = 0
+ self.sum = 0
+ self.count = 0
+
+ def update(self, val, n=1):
+ self.val = val
+ self.sum += val * n
+ self.count += n
+ self.avg = self.sum / self.count
+
+ def __str__(self):
+ if self.val_only:
+ fmtstr = "{name} {val" + self.fmt + "}"
+ else:
+ fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})"
+ return fmtstr.format(**self.__dict__)
diff --git a/groundingdino/util/utils.py b/groundingdino/util/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9f0318e306fa04bff0ada70486b41aaa69b07c8
--- /dev/null
+++ b/groundingdino/util/utils.py
@@ -0,0 +1,608 @@
+import argparse
+import json
+import warnings
+from collections import OrderedDict
+from copy import deepcopy
+from typing import Any, Dict, List
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer
+
+from groundingdino.util.slconfig import SLConfig
+
+
+def slprint(x, name="x"):
+ if isinstance(x, (torch.Tensor, np.ndarray)):
+ print(f"{name}.shape:", x.shape)
+ elif isinstance(x, (tuple, list)):
+ print("type x:", type(x))
+ for i in range(min(10, len(x))):
+ slprint(x[i], f"{name}[{i}]")
+ elif isinstance(x, dict):
+ for k, v in x.items():
+ slprint(v, f"{name}[{k}]")
+ else:
+ print(f"{name}.type:", type(x))
+
+
+def clean_state_dict(state_dict):
+ new_state_dict = OrderedDict()
+ for k, v in state_dict.items():
+ if k[:7] == "module.":
+ k = k[7:] # remove `module.`
+ new_state_dict[k] = v
+ return new_state_dict
+
+
+def renorm(
+ img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+) -> torch.FloatTensor:
+ # img: tensor(3,H,W) or tensor(B,3,H,W)
+ # return: same as img
+ assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
+ if img.dim() == 3:
+ assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
+ img.size(0),
+ str(img.size()),
+ )
+ img_perm = img.permute(1, 2, 0)
+ mean = torch.Tensor(mean)
+ std = torch.Tensor(std)
+ img_res = img_perm * std + mean
+ return img_res.permute(2, 0, 1)
+ else: # img.dim() == 4
+ assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
+ img.size(1),
+ str(img.size()),
+ )
+ img_perm = img.permute(0, 2, 3, 1)
+ mean = torch.Tensor(mean)
+ std = torch.Tensor(std)
+ img_res = img_perm * std + mean
+ return img_res.permute(0, 3, 1, 2)
+
+
+class CocoClassMapper:
+ def __init__(self) -> None:
+ self.category_map_str = {
+ "1": 1,
+ "2": 2,
+ "3": 3,
+ "4": 4,
+ "5": 5,
+ "6": 6,
+ "7": 7,
+ "8": 8,
+ "9": 9,
+ "10": 10,
+ "11": 11,
+ "13": 12,
+ "14": 13,
+ "15": 14,
+ "16": 15,
+ "17": 16,
+ "18": 17,
+ "19": 18,
+ "20": 19,
+ "21": 20,
+ "22": 21,
+ "23": 22,
+ "24": 23,
+ "25": 24,
+ "27": 25,
+ "28": 26,
+ "31": 27,
+ "32": 28,
+ "33": 29,
+ "34": 30,
+ "35": 31,
+ "36": 32,
+ "37": 33,
+ "38": 34,
+ "39": 35,
+ "40": 36,
+ "41": 37,
+ "42": 38,
+ "43": 39,
+ "44": 40,
+ "46": 41,
+ "47": 42,
+ "48": 43,
+ "49": 44,
+ "50": 45,
+ "51": 46,
+ "52": 47,
+ "53": 48,
+ "54": 49,
+ "55": 50,
+ "56": 51,
+ "57": 52,
+ "58": 53,
+ "59": 54,
+ "60": 55,
+ "61": 56,
+ "62": 57,
+ "63": 58,
+ "64": 59,
+ "65": 60,
+ "67": 61,
+ "70": 62,
+ "72": 63,
+ "73": 64,
+ "74": 65,
+ "75": 66,
+ "76": 67,
+ "77": 68,
+ "78": 69,
+ "79": 70,
+ "80": 71,
+ "81": 72,
+ "82": 73,
+ "84": 74,
+ "85": 75,
+ "86": 76,
+ "87": 77,
+ "88": 78,
+ "89": 79,
+ "90": 80,
+ }
+ self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()}
+ self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()}
+
+ def origin2compact(self, idx):
+ return self.origin2compact_mapper[int(idx)]
+
+ def compact2origin(self, idx):
+ return self.compact2origin_mapper[int(idx)]
+
+
+def to_device(item, device):
+ if isinstance(item, torch.Tensor):
+ return item.to(device)
+ elif isinstance(item, list):
+ return [to_device(i, device) for i in item]
+ elif isinstance(item, dict):
+ return {k: to_device(v, device) for k, v in item.items()}
+ else:
+ raise NotImplementedError(
+ "Call Shilong if you use other containers! type: {}".format(type(item))
+ )
+
+
+#
+def get_gaussian_mean(x, axis, other_axis, softmax=True):
+ """
+
+ Args:
+ x (float): Input images(BxCxHxW)
+ axis (int): The index for weighted mean
+ other_axis (int): The other index
+
+ Returns: weighted index for axis, BxC
+
+ """
+ mat2line = torch.sum(x, axis=other_axis)
+ # mat2line = mat2line / mat2line.mean() * 10
+ if softmax:
+ u = torch.softmax(mat2line, axis=2)
+ else:
+ u = mat2line / (mat2line.sum(2, keepdim=True) + 1e-6)
+ size = x.shape[axis]
+ ind = torch.linspace(0, 1, size).to(x.device)
+ batch = x.shape[0]
+ channel = x.shape[1]
+ index = ind.repeat([batch, channel, 1])
+ mean_position = torch.sum(index * u, dim=2)
+ return mean_position
+
+
+def get_expected_points_from_map(hm, softmax=True):
+ """get_gaussian_map_from_points
+ B,C,H,W -> B,N,2 float(0, 1) float(0, 1)
+ softargmax function
+
+ Args:
+ hm (float): Input images(BxCxHxW)
+
+ Returns:
+ weighted index for axis, BxCx2. float between 0 and 1.
+
+ """
+ # hm = 10*hm
+ B, C, H, W = hm.shape
+ y_mean = get_gaussian_mean(hm, 2, 3, softmax=softmax) # B,C
+ x_mean = get_gaussian_mean(hm, 3, 2, softmax=softmax) # B,C
+ # return torch.cat((x_mean.unsqueeze(-1), y_mean.unsqueeze(-1)), 2)
+ return torch.stack([x_mean, y_mean], dim=2)
+
+
+# Positional encoding (section 5.1)
+# borrow from nerf
+class Embedder:
+ def __init__(self, **kwargs):
+ self.kwargs = kwargs
+ self.create_embedding_fn()
+
+ def create_embedding_fn(self):
+ embed_fns = []
+ d = self.kwargs["input_dims"]
+ out_dim = 0
+ if self.kwargs["include_input"]:
+ embed_fns.append(lambda x: x)
+ out_dim += d
+
+ max_freq = self.kwargs["max_freq_log2"]
+ N_freqs = self.kwargs["num_freqs"]
+
+ if self.kwargs["log_sampling"]:
+ freq_bands = 2.0 ** torch.linspace(0.0, max_freq, steps=N_freqs)
+ else:
+ freq_bands = torch.linspace(2.0**0.0, 2.0**max_freq, steps=N_freqs)
+
+ for freq in freq_bands:
+ for p_fn in self.kwargs["periodic_fns"]:
+ embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq))
+ out_dim += d
+
+ self.embed_fns = embed_fns
+ self.out_dim = out_dim
+
+ def embed(self, inputs):
+ return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
+
+
+def get_embedder(multires, i=0):
+ import torch.nn as nn
+
+ if i == -1:
+ return nn.Identity(), 3
+
+ embed_kwargs = {
+ "include_input": True,
+ "input_dims": 3,
+ "max_freq_log2": multires - 1,
+ "num_freqs": multires,
+ "log_sampling": True,
+ "periodic_fns": [torch.sin, torch.cos],
+ }
+
+ embedder_obj = Embedder(**embed_kwargs)
+ embed = lambda x, eo=embedder_obj: eo.embed(x)
+ return embed, embedder_obj.out_dim
+
+
+class APOPMeter:
+ def __init__(self) -> None:
+ self.tp = 0
+ self.fp = 0
+ self.tn = 0
+ self.fn = 0
+
+ def update(self, pred, gt):
+ """
+ Input:
+ pred, gt: Tensor()
+ """
+ assert pred.shape == gt.shape
+ self.tp += torch.logical_and(pred == 1, gt == 1).sum().item()
+ self.fp += torch.logical_and(pred == 1, gt == 0).sum().item()
+ self.tn += torch.logical_and(pred == 0, gt == 0).sum().item()
+ self.tn += torch.logical_and(pred == 1, gt == 0).sum().item()
+
+ def update_cm(self, tp, fp, tn, fn):
+ self.tp += tp
+ self.fp += fp
+ self.tn += tn
+ self.tn += fn
+
+
+def inverse_sigmoid(x, eps=1e-5):
+ x = x.clamp(min=0, max=1)
+ x1 = x.clamp(min=eps)
+ x2 = (1 - x).clamp(min=eps)
+ return torch.log(x1 / x2)
+
+
+def get_raw_dict(args):
+ """
+ return the dicf contained in args.
+
+ e.g:
+ >>> with open(path, 'w') as f:
+ json.dump(get_raw_dict(args), f, indent=2)
+ """
+ if isinstance(args, argparse.Namespace):
+ return vars(args)
+ elif isinstance(args, dict):
+ return args
+ elif isinstance(args, SLConfig):
+ return args._cfg_dict
+ else:
+ raise NotImplementedError("Unknown type {}".format(type(args)))
+
+
+def stat_tensors(tensor):
+ assert tensor.dim() == 1
+ tensor_sm = tensor.softmax(0)
+ entropy = (tensor_sm * torch.log(tensor_sm + 1e-9)).sum()
+
+ return {
+ "max": tensor.max(),
+ "min": tensor.min(),
+ "mean": tensor.mean(),
+ "var": tensor.var(),
+ "std": tensor.var() ** 0.5,
+ "entropy": entropy,
+ }
+
+
+class NiceRepr:
+ """Inherit from this class and define ``__nice__`` to "nicely" print your
+ objects.
+
+ Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function
+ Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.
+ If the inheriting class has a ``__len__``, method then the default
+ ``__nice__`` method will return its length.
+
+ Example:
+ >>> class Foo(NiceRepr):
+ ... def __nice__(self):
+ ... return 'info'
+ >>> foo = Foo()
+ >>> assert str(foo) == '<Foo(info)>'
+ >>> assert repr(foo).startswith('<Foo(info) at ')
+
+ Example:
+ >>> class Bar(NiceRepr):
+ ... pass
+ >>> bar = Bar()
+ >>> import pytest
+ >>> with pytest.warns(None) as record:
+ >>> assert 'object at' in str(bar)
+ >>> assert 'object at' in repr(bar)
+
+ Example:
+ >>> class Baz(NiceRepr):
+ ... def __len__(self):
+ ... return 5
+ >>> baz = Baz()
+ >>> assert str(baz) == '<Baz(5)>'
+ """
+
+ def __nice__(self):
+ """str: a "nice" summary string describing this module"""
+ if hasattr(self, "__len__"):
+ # It is a common pattern for objects to use __len__ in __nice__
+ # As a convenience we define a default __nice__ for these objects
+ return str(len(self))
+ else:
+ # In all other cases force the subclass to overload __nice__
+ raise NotImplementedError(f"Define the __nice__ method for {self.__class__!r}")
+
+ def __repr__(self):
+ """str: the string of the module"""
+ try:
+ nice = self.__nice__()
+ classname = self.__class__.__name__
+ return f"<{classname}({nice}) at {hex(id(self))}>"
+ except NotImplementedError as ex:
+ warnings.warn(str(ex), category=RuntimeWarning)
+ return object.__repr__(self)
+
+ def __str__(self):
+ """str: the string of the module"""
+ try:
+ classname = self.__class__.__name__
+ nice = self.__nice__()
+ return f"<{classname}({nice})>"
+ except NotImplementedError as ex:
+ warnings.warn(str(ex), category=RuntimeWarning)
+ return object.__repr__(self)
+
+
+def ensure_rng(rng=None):
+ """Coerces input into a random number generator.
+
+ If the input is None, then a global random state is returned.
+
+ If the input is a numeric value, then that is used as a seed to construct a
+ random state. Otherwise the input is returned as-is.
+
+ Adapted from [1]_.
+
+ Args:
+ rng (int | numpy.random.RandomState | None):
+ if None, then defaults to the global rng. Otherwise this can be an
+ integer or a RandomState class
+ Returns:
+ (numpy.random.RandomState) : rng -
+ a numpy random number generator
+
+ References:
+ .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270 # noqa: E501
+ """
+
+ if rng is None:
+ rng = np.random.mtrand._rand
+ elif isinstance(rng, int):
+ rng = np.random.RandomState(rng)
+ else:
+ rng = rng
+ return rng
+
+
+def random_boxes(num=1, scale=1, rng=None):
+ """Simple version of ``kwimage.Boxes.random``
+
+ Returns:
+ Tensor: shape (n, 4) in x1, y1, x2, y2 format.
+
+ References:
+ https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390
+
+ Example:
+ >>> num = 3
+ >>> scale = 512
+ >>> rng = 0
+ >>> boxes = random_boxes(num, scale, rng)
+ >>> print(boxes)
+ tensor([[280.9925, 278.9802, 308.6148, 366.1769],
+ [216.9113, 330.6978, 224.0446, 456.5878],
+ [405.3632, 196.3221, 493.3953, 270.7942]])
+ """
+ rng = ensure_rng(rng)
+
+ tlbr = rng.rand(num, 4).astype(np.float32)
+
+ tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])
+ tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])
+ br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])
+ br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])
+
+ tlbr[:, 0] = tl_x * scale
+ tlbr[:, 1] = tl_y * scale
+ tlbr[:, 2] = br_x * scale
+ tlbr[:, 3] = br_y * scale
+
+ boxes = torch.from_numpy(tlbr)
+ return boxes
+
+
+class ModelEma(torch.nn.Module):
+ def __init__(self, model, decay=0.9997, device=None):
+ super(ModelEma, self).__init__()
+ # make a copy of the model for accumulating moving average of weights
+ self.module = deepcopy(model)
+ self.module.eval()
+
+ # import ipdb; ipdb.set_trace()
+
+ self.decay = decay
+ self.device = device # perform ema on different device from model if set
+ if self.device is not None:
+ self.module.to(device=device)
+
+ def _update(self, model, update_fn):
+ with torch.no_grad():
+ for ema_v, model_v in zip(
+ self.module.state_dict().values(), model.state_dict().values()
+ ):
+ if self.device is not None:
+ model_v = model_v.to(device=self.device)
+ ema_v.copy_(update_fn(ema_v, model_v))
+
+ def update(self, model):
+ self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m)
+
+ def set(self, model):
+ self._update(model, update_fn=lambda e, m: m)
+
+
+class BestMetricSingle:
+ def __init__(self, init_res=0.0, better="large") -> None:
+ self.init_res = init_res
+ self.best_res = init_res
+ self.best_ep = -1
+
+ self.better = better
+ assert better in ["large", "small"]
+
+ def isbetter(self, new_res, old_res):
+ if self.better == "large":
+ return new_res > old_res
+ if self.better == "small":
+ return new_res < old_res
+
+ def update(self, new_res, ep):
+ if self.isbetter(new_res, self.best_res):
+ self.best_res = new_res
+ self.best_ep = ep
+ return True
+ return False
+
+ def __str__(self) -> str:
+ return "best_res: {}\t best_ep: {}".format(self.best_res, self.best_ep)
+
+ def __repr__(self) -> str:
+ return self.__str__()
+
+ def summary(self) -> dict:
+ return {
+ "best_res": self.best_res,
+ "best_ep": self.best_ep,
+ }
+
+
+class BestMetricHolder:
+ def __init__(self, init_res=0.0, better="large", use_ema=False) -> None:
+ self.best_all = BestMetricSingle(init_res, better)
+ self.use_ema = use_ema
+ if use_ema:
+ self.best_ema = BestMetricSingle(init_res, better)
+ self.best_regular = BestMetricSingle(init_res, better)
+
+ def update(self, new_res, epoch, is_ema=False):
+ """
+ return if the results is the best.
+ """
+ if not self.use_ema:
+ return self.best_all.update(new_res, epoch)
+ else:
+ if is_ema:
+ self.best_ema.update(new_res, epoch)
+ return self.best_all.update(new_res, epoch)
+ else:
+ self.best_regular.update(new_res, epoch)
+ return self.best_all.update(new_res, epoch)
+
+ def summary(self):
+ if not self.use_ema:
+ return self.best_all.summary()
+
+ res = {}
+ res.update({f"all_{k}": v for k, v in self.best_all.summary().items()})
+ res.update({f"regular_{k}": v for k, v in self.best_regular.summary().items()})
+ res.update({f"ema_{k}": v for k, v in self.best_ema.summary().items()})
+ return res
+
+ def __repr__(self) -> str:
+ return json.dumps(self.summary(), indent=2)
+
+ def __str__(self) -> str:
+ return self.__repr__()
+
+
+def targets_to(targets: List[Dict[str, Any]], device):
+ """Moves the target dicts to the given device."""
+ excluded_keys = [
+ "questionId",
+ "tokens_positive",
+ "strings_positive",
+ "tokens",
+ "dataset_name",
+ "sentence_id",
+ "original_img_id",
+ "nb_eval",
+ "task_id",
+ "original_id",
+ "token_span",
+ "caption",
+ "dataset_type",
+ ]
+ return [
+ {k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets
+ ]
+
+
+def get_phrases_from_posmap(
+ posmap: torch.BoolTensor, tokenized: Dict, tokenizer: AutoTokenizer
+):
+ assert isinstance(posmap, torch.Tensor), "posmap must be torch.Tensor"
+ if posmap.dim() == 1:
+ non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist()
+ token_ids = [tokenized["input_ids"][i] for i in non_zero_idx]
+ return tokenizer.decode(token_ids)
+ else:
+ raise NotImplementedError("posmap must be 1-dim")
diff --git a/groundingdino/util/visualizer.py b/groundingdino/util/visualizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a1b7b101e9b73f75f9136bc67f2063c7c1cf1c1
--- /dev/null
+++ b/groundingdino/util/visualizer.py
@@ -0,0 +1,318 @@
+# -*- coding: utf-8 -*-
+"""
+@File : visualizer.py
+@Time : 2022/04/05 11:39:33
+@Author : Shilong Liu
+@Contact : slongliu86@gmail.com
+"""
+
+import datetime
+import os
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from matplotlib import transforms
+from matplotlib.collections import PatchCollection
+from matplotlib.patches import Polygon
+from pycocotools import mask as maskUtils
+
+
+def renorm(
+ img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+) -> torch.FloatTensor:
+ # img: tensor(3,H,W) or tensor(B,3,H,W)
+ # return: same as img
+ assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
+ if img.dim() == 3:
+ assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
+ img.size(0),
+ str(img.size()),
+ )
+ img_perm = img.permute(1, 2, 0)
+ mean = torch.Tensor(mean)
+ std = torch.Tensor(std)
+ img_res = img_perm * std + mean
+ return img_res.permute(2, 0, 1)
+ else: # img.dim() == 4
+ assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
+ img.size(1),
+ str(img.size()),
+ )
+ img_perm = img.permute(0, 2, 3, 1)
+ mean = torch.Tensor(mean)
+ std = torch.Tensor(std)
+ img_res = img_perm * std + mean
+ return img_res.permute(0, 3, 1, 2)
+
+
+class ColorMap:
+ def __init__(self, basergb=[255, 255, 0]):
+ self.basergb = np.array(basergb)
+
+ def __call__(self, attnmap):
+ # attnmap: h, w. np.uint8.
+ # return: h, w, 4. np.uint8.
+ assert attnmap.dtype == np.uint8
+ h, w = attnmap.shape
+ res = self.basergb.copy()
+ res = res[None][None].repeat(h, 0).repeat(w, 1) # h, w, 3
+ attn1 = attnmap.copy()[..., None] # h, w, 1
+ res = np.concatenate((res, attn1), axis=-1).astype(np.uint8)
+ return res
+
+
+def rainbow_text(x, y, ls, lc, **kw):
+ """
+ Take a list of strings ``ls`` and colors ``lc`` and place them next to each
+ other, with text ls[i] being shown in color lc[i].
+
+ This example shows how to do both vertical and horizontal text, and will
+ pass all keyword arguments to plt.text, so you can set the font size,
+ family, etc.
+ """
+ t = plt.gca().transData
+ fig = plt.gcf()
+ plt.show()
+
+ # horizontal version
+ for s, c in zip(ls, lc):
+ text = plt.text(x, y, " " + s + " ", color=c, transform=t, **kw)
+ text.draw(fig.canvas.get_renderer())
+ ex = text.get_window_extent()
+ t = transforms.offset_copy(text._transform, x=ex.width, units="dots")
+
+ # #vertical version
+ # for s,c in zip(ls,lc):
+ # text = plt.text(x,y," "+s+" ",color=c, transform=t,
+ # rotation=90,va='bottom',ha='center',**kw)
+ # text.draw(fig.canvas.get_renderer())
+ # ex = text.get_window_extent()
+ # t = transforms.offset_copy(text._transform, y=ex.height, units='dots')
+
+
+class COCOVisualizer:
+ def __init__(self, coco=None, tokenlizer=None) -> None:
+ self.coco = coco
+
+ def visualize(self, img, tgt, caption=None, dpi=180, savedir="vis"):
+ """
+ img: tensor(3, H, W)
+ tgt: make sure they are all on cpu.
+ must have items: 'image_id', 'boxes', 'size'
+ """
+ plt.figure(dpi=dpi)
+ plt.rcParams["font.size"] = "5"
+ ax = plt.gca()
+ img = renorm(img).permute(1, 2, 0)
+ # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+ # import ipdb; ipdb.set_trace()
+ ax.imshow(img)
+
+ self.addtgt(tgt)
+
+ if tgt is None:
+ image_id = 0
+ elif "image_id" not in tgt:
+ image_id = 0
+ else:
+ image_id = tgt["image_id"]
+
+ if caption is None:
+ savename = "{}/{}-{}.png".format(
+ savedir, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
+ )
+ else:
+ savename = "{}/{}-{}-{}.png".format(
+ savedir, caption, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
+ )
+ print("savename: {}".format(savename))
+ os.makedirs(os.path.dirname(savename), exist_ok=True)
+ plt.savefig(savename)
+ plt.close()
+
+ def addtgt(self, tgt):
+ """ """
+ if tgt is None or not "boxes" in tgt:
+ ax = plt.gca()
+
+ if "caption" in tgt:
+ ax.set_title(tgt["caption"], wrap=True)
+
+ ax.set_axis_off()
+ return
+
+ ax = plt.gca()
+ H, W = tgt["size"]
+ numbox = tgt["boxes"].shape[0]
+
+ color = []
+ polygons = []
+ boxes = []
+ for box in tgt["boxes"].cpu():
+ unnormbbox = box * torch.Tensor([W, H, W, H])
+ unnormbbox[:2] -= unnormbbox[2:] / 2
+ [bbox_x, bbox_y, bbox_w, bbox_h] = unnormbbox.tolist()
+ boxes.append([bbox_x, bbox_y, bbox_w, bbox_h])
+ poly = [
+ [bbox_x, bbox_y],
+ [bbox_x, bbox_y + bbox_h],
+ [bbox_x + bbox_w, bbox_y + bbox_h],
+ [bbox_x + bbox_w, bbox_y],
+ ]
+ np_poly = np.array(poly).reshape((4, 2))
+ polygons.append(Polygon(np_poly))
+ c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
+ color.append(c)
+
+ p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.1)
+ ax.add_collection(p)
+ p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
+ ax.add_collection(p)
+
+ if "strings_positive" in tgt and len(tgt["strings_positive"]) > 0:
+ assert (
+ len(tgt["strings_positive"]) == numbox
+ ), f"{len(tgt['strings_positive'])} = {numbox}, "
+ for idx, strlist in enumerate(tgt["strings_positive"]):
+ cate_id = int(tgt["labels"][idx])
+ _string = str(cate_id) + ":" + " ".join(strlist)
+ bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
+ # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
+ ax.text(
+ bbox_x,
+ bbox_y,
+ _string,
+ color="black",
+ bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
+ )
+
+ if "box_label" in tgt:
+ assert len(tgt["box_label"]) == numbox, f"{len(tgt['box_label'])} = {numbox}, "
+ for idx, bl in enumerate(tgt["box_label"]):
+ _string = str(bl)
+ bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
+ # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
+ ax.text(
+ bbox_x,
+ bbox_y,
+ _string,
+ color="black",
+ bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
+ )
+
+ if "caption" in tgt:
+ ax.set_title(tgt["caption"], wrap=True)
+ # plt.figure()
+ # rainbow_text(0.0,0.0,"all unicorns poop rainbows ! ! !".split(),
+ # ['red', 'orange', 'brown', 'green', 'blue', 'purple', 'black'])
+
+ if "attn" in tgt:
+ # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+ # import ipdb; ipdb.set_trace()
+ if isinstance(tgt["attn"], tuple):
+ tgt["attn"] = [tgt["attn"]]
+ for item in tgt["attn"]:
+ attn_map, basergb = item
+ attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min() + 1e-3)
+ attn_map = (attn_map * 255).astype(np.uint8)
+ cm = ColorMap(basergb)
+ heatmap = cm(attn_map)
+ ax.imshow(heatmap)
+ ax.set_axis_off()
+
+ def showAnns(self, anns, draw_bbox=False):
+ """
+ Display the specified annotations.
+ :param anns (array of object): annotations to display
+ :return: None
+ """
+ if len(anns) == 0:
+ return 0
+ if "segmentation" in anns[0] or "keypoints" in anns[0]:
+ datasetType = "instances"
+ elif "caption" in anns[0]:
+ datasetType = "captions"
+ else:
+ raise Exception("datasetType not supported")
+ if datasetType == "instances":
+ ax = plt.gca()
+ ax.set_autoscale_on(False)
+ polygons = []
+ color = []
+ for ann in anns:
+ c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
+ if "segmentation" in ann:
+ if type(ann["segmentation"]) == list:
+ # polygon
+ for seg in ann["segmentation"]:
+ poly = np.array(seg).reshape((int(len(seg) / 2), 2))
+ polygons.append(Polygon(poly))
+ color.append(c)
+ else:
+ # mask
+ t = self.imgs[ann["image_id"]]
+ if type(ann["segmentation"]["counts"]) == list:
+ rle = maskUtils.frPyObjects(
+ [ann["segmentation"]], t["height"], t["width"]
+ )
+ else:
+ rle = [ann["segmentation"]]
+ m = maskUtils.decode(rle)
+ img = np.ones((m.shape[0], m.shape[1], 3))
+ if ann["iscrowd"] == 1:
+ color_mask = np.array([2.0, 166.0, 101.0]) / 255
+ if ann["iscrowd"] == 0:
+ color_mask = np.random.random((1, 3)).tolist()[0]
+ for i in range(3):
+ img[:, :, i] = color_mask[i]
+ ax.imshow(np.dstack((img, m * 0.5)))
+ if "keypoints" in ann and type(ann["keypoints"]) == list:
+ # turn skeleton into zero-based index
+ sks = np.array(self.loadCats(ann["category_id"])[0]["skeleton"]) - 1
+ kp = np.array(ann["keypoints"])
+ x = kp[0::3]
+ y = kp[1::3]
+ v = kp[2::3]
+ for sk in sks:
+ if np.all(v[sk] > 0):
+ plt.plot(x[sk], y[sk], linewidth=3, color=c)
+ plt.plot(
+ x[v > 0],
+ y[v > 0],
+ "o",
+ markersize=8,
+ markerfacecolor=c,
+ markeredgecolor="k",
+ markeredgewidth=2,
+ )
+ plt.plot(
+ x[v > 1],
+ y[v > 1],
+ "o",
+ markersize=8,
+ markerfacecolor=c,
+ markeredgecolor=c,
+ markeredgewidth=2,
+ )
+
+ if draw_bbox:
+ [bbox_x, bbox_y, bbox_w, bbox_h] = ann["bbox"]
+ poly = [
+ [bbox_x, bbox_y],
+ [bbox_x, bbox_y + bbox_h],
+ [bbox_x + bbox_w, bbox_y + bbox_h],
+ [bbox_x + bbox_w, bbox_y],
+ ]
+ np_poly = np.array(poly).reshape((4, 2))
+ polygons.append(Polygon(np_poly))
+ color.append(c)
+
+ # p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
+ # ax.add_collection(p)
+ p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
+ ax.add_collection(p)
+ elif datasetType == "captions":
+ for ann in anns:
+ print(ann["caption"])
diff --git a/groundingdino/util/vl_utils.py b/groundingdino/util/vl_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c91bb02f584398f08a28e6b7719e2b99f6e28616
--- /dev/null
+++ b/groundingdino/util/vl_utils.py
@@ -0,0 +1,100 @@
+import os
+import random
+from typing import List
+
+import torch
+
+
+def create_positive_map_from_span(tokenized, token_span, max_text_len=256):
+ """construct a map such that positive_map[i,j] = True iff box i is associated to token j
+ Input:
+ - tokenized:
+ - input_ids: Tensor[1, ntokens]
+ - attention_mask: Tensor[1, ntokens]
+ - token_span: list with length num_boxes.
+ - each item: [start_idx, end_idx]
+ """
+ positive_map = torch.zeros((len(token_span), max_text_len), dtype=torch.float)
+ for j, tok_list in enumerate(token_span):
+ for (beg, end) in tok_list:
+ beg_pos = tokenized.char_to_token(beg)
+ end_pos = tokenized.char_to_token(end - 1)
+ if beg_pos is None:
+ try:
+ beg_pos = tokenized.char_to_token(beg + 1)
+ if beg_pos is None:
+ beg_pos = tokenized.char_to_token(beg + 2)
+ except:
+ beg_pos = None
+ if end_pos is None:
+ try:
+ end_pos = tokenized.char_to_token(end - 2)
+ if end_pos is None:
+ end_pos = tokenized.char_to_token(end - 3)
+ except:
+ end_pos = None
+ if beg_pos is None or end_pos is None:
+ continue
+
+ assert beg_pos is not None and end_pos is not None
+ if os.environ.get("SHILONG_DEBUG_ONLY_ONE_POS", None) == "TRUE":
+ positive_map[j, beg_pos] = 1
+ break
+ else:
+ positive_map[j, beg_pos : end_pos + 1].fill_(1)
+
+ return positive_map / (positive_map.sum(-1)[:, None] + 1e-6)
+
+
+def build_captions_and_token_span(cat_list, force_lowercase):
+ """
+ Return:
+ captions: str
+ cat2tokenspan: dict
+ {
+ 'dog': [[0, 2]],
+ ...
+ }
+ """
+
+ cat2tokenspan = {}
+ captions = ""
+ for catname in cat_list:
+ class_name = catname
+ if force_lowercase:
+ class_name = class_name.lower()
+ if "/" in class_name:
+ class_name_list: List = class_name.strip().split("/")
+ class_name_list.append(class_name)
+ class_name: str = random.choice(class_name_list)
+
+ tokens_positive_i = []
+ subnamelist = [i.strip() for i in class_name.strip().split(" ")]
+ for subname in subnamelist:
+ if len(subname) == 0:
+ continue
+ if len(captions) > 0:
+ captions = captions + " "
+ strat_idx = len(captions)
+ end_idx = strat_idx + len(subname)
+ tokens_positive_i.append([strat_idx, end_idx])
+ captions = captions + subname
+
+ if len(tokens_positive_i) > 0:
+ captions = captions + " ."
+ cat2tokenspan[class_name] = tokens_positive_i
+
+ return captions, cat2tokenspan
+
+
+def build_id2posspan_and_caption(category_dict: dict):
+ """Build id2pos_span and caption from category_dict
+
+ Args:
+ category_dict (dict): category_dict
+ """
+ cat_list = [item["name"].lower() for item in category_dict]
+ id2catname = {item["id"]: item["name"].lower() for item in category_dict}
+ caption, cat2posspan = build_captions_and_token_span(cat_list, force_lowercase=True)
+ id2posspan = {catid: cat2posspan[catname] for catid, catname in id2catname.items()}
+ return id2posspan, caption
diff --git a/groundingdino/version.py b/groundingdino/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..b794fd409a5e3b3b65ad76a43d6a01a318877640
--- /dev/null
+++ b/groundingdino/version.py
@@ -0,0 +1 @@
+__version__ = '0.1.0'
diff --git a/images/bags.jpeg b/images/bags.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..180791dad85e2b17f87f91b01f16082a36ba316d
Binary files /dev/null and b/images/bags.jpeg differ
diff --git a/images/car.jpeg b/images/car.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..bdc747018b84c79164b40b2e39c4e98b5557e886
Binary files /dev/null and b/images/car.jpeg differ
diff --git a/images/deer.jpeg b/images/deer.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..510bd77199ddd0cfe0f7f238a9380769f621276a
Binary files /dev/null and b/images/deer.jpeg differ
diff --git a/images/penn.jpeg b/images/penn.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..a8960c85a13a4f15cc87fefbbe1b76d21ae72aa8
Binary files /dev/null and b/images/penn.jpeg differ
diff --git a/images/tiger.jpeg b/images/tiger.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..737db30cea25b45f2aa2c69701298143b422a849
Binary files /dev/null and b/images/tiger.jpeg differ
diff --git a/pre-requirements.txt b/pre-requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..401d733ae8ccbc30362ebf6cbfbfb8f6dbf9c31f
--- /dev/null
+++ b/pre-requirements.txt
@@ -0,0 +1,3 @@
+torch==1.12.1
+torchvision==0.13.1
+torchaudio==0.12.1
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..20f6d4e41401942235b7235acbfe5c4600acbf47
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,15 @@
+git+https://github.com/facebookresearch/segment-anything.git
+opencv-python
+pycocotools
+matplotlib
+onnxruntime
+onnx
+setuptools==65.3.0
+packaging==21.3
+transformers
+numpy
+addict
+yapf
+gradio
+supervision
+timm
\ No newline at end of file
diff --git a/weights/groundingdino_swint_ogc.pth b/weights/groundingdino_swint_ogc.pth
new file mode 100644
index 0000000000000000000000000000000000000000..5cdf6bcd10d491abf170a78eca4fcebf76aa791a
--- /dev/null
+++ b/weights/groundingdino_swint_ogc.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b3ca2563c77c69f651d7bd133e97139c186df06231157a64c507099c52bc799
+size 693997677
diff --git a/weights/sam_vit_b.pth b/weights/sam_vit_b.pth
new file mode 100644
index 0000000000000000000000000000000000000000..ab7d111e57bd052a76fe669986560e3555e9c8f6
--- /dev/null
+++ b/weights/sam_vit_b.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ec2df62732614e57411cdcf32a23ffdf28910380d03139ee0f4fcbe91eb8c912
+size 375042383
diff --git a/weights/sam_vit_h.pth b/weights/sam_vit_h.pth
new file mode 100644
index 0000000000000000000000000000000000000000..8523acce9ddab1cf7e355628a08b1aab8ce08a72
--- /dev/null
+++ b/weights/sam_vit_h.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7bf3b02f3ebf1267aba913ff637d9a2d5c33d3173bb679e46d9f338c26f262e
+size 2564550879