File size: 7,251 Bytes
002bd9b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
import sys
sys.path.append(".")
import gradio as gr
from src.models.sam_captioner import SAMCaptionerConfig, SAMCaptionerModel, SAMCaptionerProcessor
import torch
from PIL import Image
import requests
import numpy as np
import time
from transformers import CLIPProcessor, CLIPModel
cache_dir = ".model.cache"
device = "cuda" if torch.cuda.is_available() else "cpu"
sam_model = "facebook/sam-vit-huge"
# captioner_model = "Salesforce/blip-image-captioning-base"
# captioner_model = "microsoft/git-large"
captioner_model = "Salesforce/blip2-opt-2.7b"
clip_model = "openai/clip-vit-base-patch32"
img_url = "https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg"
raw_image = Image.open(requests.get(img_url, stream=True).raw)
model = SAMCaptionerModel.from_sam_captioner_pretrained(sam_model, captioner_model, cache_dir=cache_dir).to(device)
processor = SAMCaptionerProcessor.from_sam_captioner_pretrained(sam_model, captioner_model, cache_dir=cache_dir)
sam_processor = processor.sam_processor
captioner_processor = processor.captioner_processor
clip = CLIPModel.from_pretrained(clip_model, cache_dir=cache_dir).to(device)
clip_processor = CLIPProcessor.from_pretrained(clip_model, cache_dir=cache_dir)
# NOTE(xiaoke): in original clip, dtype is float16, here we use float32 as hf default
dtype = clip.dtype
NUM_OUTPUT_HEADS = 3
LIBRARIES = ["multimask_output", "return_patches"]
DEFAULT_LIBRARIES = ["multimask_output", "return_patches"]
def click_and_assign(args, visual_prompt_mode, input_point_text, input_boxes_text, evt: gr.SelectData):
x, y = evt.index
if visual_prompt_mode == "point":
input_point_text = f"{x},{y}"
elif visual_prompt_mode == "box":
if len(input_boxes_text.split(",")) == 2:
input_boxes_text = f"{input_boxes_text},{x},{y}"
else:
input_boxes_text = f"{x},{y}"
return input_point_text, input_boxes_text
def box_and_run(input_image, args, input_boxes_text):
x, y, x2, y2 = list(map(int, input_boxes_text.split(",")))
input_boxes = [[[x, y, x2, y2]]]
return run(args, input_image, input_boxes=input_boxes)
def point_and_run(input_image, args, input_point_text):
x, y = list(map(int, input_point_text.split(",")))
input_points = [[[[x, y]]]]
return run(args, input_image, input_points=input_points)
def run(args, input_image, input_points=None, input_boxes=None):
if input_points is None and input_boxes is None:
raise ValueError("input_points and input_boxes cannot be both None")
multimask_output = "multimask_output" in args
return_patches = "return_patches" in args
inputs = processor(input_image, input_points=input_points, input_boxes=input_boxes, return_tensors="pt")
for k, v in inputs.items():
if isinstance(v, torch.Tensor):
# NOTE(xiaoke): in original clip, dtype is float16
inputs[k] = v.to(device, dtype if v.dtype == torch.float32 else v.dtype)
tic = time.perf_counter()
with torch.inference_mode():
model_outputs = model.generate(
**inputs,
multimask_output=multimask_output,
return_patches=return_patches,
return_dict_in_generate=True,
)
toc = time.perf_counter()
print(f"Time taken: {(toc - tic)*1000:0.4f} ms")
batch_size, num_masks, num_heads, num_tokens = model_outputs.sequences.shape
if batch_size != 1 or num_masks != 1:
raise ValueError("batch_size and num_masks must be 1")
captions = captioner_processor.batch_decode(
model_outputs.sequences.reshape(-1, num_tokens), skip_special_tokens=True
)
masks = sam_processor.post_process_masks(
model_outputs.pred_masks, inputs["original_sizes"], inputs["reshaped_input_sizes"]
) # List[(num_masks, num_heads, H, W)]
iou_scores = model_outputs.iou_scores # (batch_size, num_masks, num_heads)
patches = model_outputs.patches # List[List[Image.Image]]
outputs = []
# Tuple[numpy.ndarray | PIL.Image | str, List[Tuple[numpy.ndarray | Tuple[int, int, int, int], str]]]
# (batch_size(1), region_size(1), num_heads)
iou_scores = iou_scores[0][0]
for i in range(num_heads):
output = [input_image, [[masks[0][:, i].cpu().numpy(), f"{captions[i]}|iou:{iou_scores[i]:.4f}"]]]
outputs.append(output)
for i in range(num_heads, NUM_OUTPUT_HEADS):
output = [np.ones((1, 1)), []]
outputs.append(output)
if return_patches:
# (batch_size(1), region_size(1), num_heads)
patches = patches[0][0]
num_patches = len(patches)
for i in range(num_patches):
patch = patches[i]
caption = captions[i]
# https://huggingface.co/openai/clip-vit-base-patch32
clip_inputs = clip_processor(text=[caption], images=[patch], return_tensors="pt", padding=True).to(device)
clip_outputs = clip(**clip_inputs)
logits_per_image = clip_outputs.logits_per_image
output = [patches[i], [[[0, 0, 0, 0], f"{caption}|clip{logits_per_image.item():.4f}"]]]
outputs.append(output)
for i in range(num_patches, NUM_OUTPUT_HEADS):
output = [np.ones((1, 1)), []]
outputs.append(output)
else:
for i in range(NUM_OUTPUT_HEADS):
output = [np.ones((1, 1)), []]
outputs.append(output)
return outputs
def fake_click_and_run(input_image, args, evt: gr.SelectData):
outputs = []
# Tuple[numpy.ndarray | PIL.Image | str, List[Tuple[numpy.ndarray | Tuple[int, int, int, int], str]]]
num_heads = 1
for i in range(num_heads):
output = [input_image, []]
outputs.append(output)
for i in range(num_heads, NUM_OUTPUT_HEADS):
output = [input_image, []]
outputs.append(output)
return outputs
with gr.Blocks() as demo:
input_image = gr.Image(value=raw_image, label="Input Image", interactive=True, type="pil", height=500)
visual_prompt_mode = gr.Radio(choices=["point", "box"], value="point", label="Visual Prompt Mode")
args = gr.CheckboxGroup(choices=LIBRARIES, value=DEFAULT_LIBRARIES, label="SAM Captioner Arguments")
input_point_text = gr.Textbox(lines=1, label="Input Points (x,y)", value="0,0")
input_point_button = gr.Button(value="Run with Input Points")
input_boxes_text = gr.Textbox(lines=1, label="Input Boxes (x,y,x2,y2)", value="0,0,100,100")
input_boxes_button = gr.Button(value="Run with Input Boxes")
output_images = []
with gr.Row():
for i in range(NUM_OUTPUT_HEADS):
output_images.append(gr.AnnotatedImage(label=f"Output Image {i}", height=500))
with gr.Row():
for i in range(NUM_OUTPUT_HEADS):
output_images.append(gr.AnnotatedImage(label=f"Output Image {i}", height=500))
input_image.select(
click_and_assign,
[args, visual_prompt_mode, input_point_text, input_boxes_text],
[input_point_text, input_boxes_text],
)
input_point_button.click(point_and_run, [input_image, args, input_point_text], [*output_images])
input_boxes_button.click(box_and_run, [input_image, args, input_boxes_text], [*output_images])
demo.launch()
|