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app_gradio.py
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251 lines (227 loc) Β· 11.6 KB
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# ------------------------------------------------------------------------
# Copyright (c) 2023-present, BAAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------
"""Gradio application."""
import argparse
import multiprocessing as mp
import os
import time
import numpy as np
import torch
from tokenize_anything import engine
from tokenize_anything.utils.image import im_rescale
from tokenize_anything.utils.image import im_vstack
def parse_args():
"""Parse arguments."""
parser = argparse.ArgumentParser(description="Launch gradio application")
parser.add_argument("--model-type", type=str, required=True, help="Model type")
parser.add_argument("--checkpoint", type=str, required=True, help="Model checkpoint")
parser.add_argument("--concept", type=str, required=True, help="Concept weights")
parser.add_argument("--device", nargs="+", type=int, default=[0], help="Index of devices")
parser.add_argument("--port", type=int, default=2030, help="Server port")
return parser.parse_args()
class Predictor(object):
"""Predictor."""
def __init__(self, model, kwargs):
self.model = model
self.kwargs = kwargs
self.batch_size = kwargs.get("batch_size", 256)
self.model.concept_projector.reset_weights(kwargs["concept_weights"])
self.model.text_decoder.reset_cache(max_batch_size=self.batch_size)
def preprocess_images(self, imgs):
"""Preprocess the inference images."""
im_batch, im_shapes, im_scales = [], [], []
for img in imgs:
scaled_imgs, scales = im_rescale(img, scales=[1024])
im_batch += scaled_imgs
im_scales += scales
im_shapes += [x.shape[:2] for x in scaled_imgs]
im_batch = im_vstack(im_batch, self.model.pixel_mean_value, size=(1024, 1024))
im_shapes = np.array(im_shapes)
im_scales = np.array(im_scales).reshape((len(im_batch), -1))
im_info = np.hstack([im_shapes, im_scales]).astype("float32")
return im_batch, im_info
@torch.inference_mode()
def get_results(self, examples):
"""Return the results."""
# Preprocess images and prompts.
imgs = [example["img"] for example in examples]
points = np.concatenate([example["points"] for example in examples])
im_batch, im_info = self.preprocess_images(imgs)
num_prompts = points.shape[0] if len(points.shape) > 2 else 1
batch_shape = im_batch.shape[0], num_prompts // im_batch.shape[0]
batch_points = points.reshape(batch_shape + (-1, 3))
batch_points[:, :, :, :2] *= im_info[:, None, None, 2:4]
batch_points = batch_points.reshape(points.shape)
# Predict tokens and masks.
inputs = self.model.get_inputs({"img": im_batch})
inputs.update(self.model.get_features(inputs))
outputs = self.model.get_outputs(dict(**inputs, **{"points": batch_points}))
# Select final mask.
iou_pred = outputs["iou_pred"].cpu().numpy()
point_score = batch_points[:, 0, 2].__eq__(2).__sub__(0.5)[:, None]
rank_scores = iou_pred + point_score * ([1000] + [0] * (iou_pred.shape[1] - 1))
mask_index = np.arange(rank_scores.shape[0]), rank_scores.argmax(1)
iou_scores = outputs["iou_pred"][mask_index].cpu().numpy().reshape(batch_shape)
# Upscale masks to the original image resolution.
mask_pred = outputs["mask_pred"][mask_index].unsqueeze_(1)
mask_pred = self.model.upscale_masks(mask_pred, im_batch.shape[1:-1])
mask_pred = mask_pred.view(batch_shape + mask_pred.shape[2:])
# Predict concepts.
concepts, scores = self.model.predict_concept(outputs["sem_embeds"][mask_index])
concepts, scores = [x.reshape(batch_shape) for x in (concepts, scores)]
# Generate captions.
sem_tokens = outputs["sem_tokens"][mask_index]
captions = self.model.generate_text(sem_tokens).reshape(batch_shape)
# Postprocess results.
results = []
for i in range(batch_shape[0]):
pred_h, pred_w = im_info[i, :2].astype("int")
masks = mask_pred[i : i + 1, :, :pred_h, :pred_w]
masks = self.model.upscale_masks(masks, imgs[i].shape[:2]).flatten(0, 1)
results.append(
{
"scores": np.stack([iou_scores[i], scores[i]], axis=-1),
"masks": masks.gt(0).cpu().numpy().astype("uint8"),
"concepts": concepts[i],
"captions": captions[i],
}
)
return results
class ServingCommand(object):
"""Command to run serving."""
def __init__(self, output_queue):
self.output_queue = output_queue
self.output_dict = mp.Manager().dict()
self.output_index = mp.Value("i", 0)
def postprocess_outputs(self, outputs):
"""Main the detection objects."""
scores, masks = outputs["scores"], outputs["masks"]
concepts, captions = outputs["concepts"], outputs["captions"]
text_template = "{} ({:.2f}, {:.2f}): {}"
text_contents = concepts, scores[:, 0], scores[:, 1], captions
texts = np.array([text_template.format(*vals) for vals in zip(*text_contents)])
return masks, texts
def run(self):
"""Main loop to make the serving outputs."""
while True:
img_id, outputs = self.output_queue.get()
self.output_dict[img_id] = self.postprocess_outputs(outputs)
def build_gradio_app(queues, command):
"""Build the gradio application."""
import gradio as gr
import gradio_image_prompter as gr_ext
title = "Tokenize Anything"
header = (
"<div align='center'>"
"<h1>Tokenize Anything via Prompting</h1>"
"<h3><a href='https://arxiv.org/abs/2312.09128' target='_blank' rel='noopener'>[paper]</a>"
"<a href='https://github.com/baaivision/tokenize-anything' target='_blank' rel='noopener'>[code]</a></h3>" # noqa
"<h3>A promptable model capable of simultaneous segmentation, recognition and caption.</h3>" # noqa
"</div>"
)
theme = "soft"
css = """#anno-img .mask {opacity: 0.5; transition: all 0.2s ease-in-out;}
#anno-img .mask.active {opacity: 0.7}"""
def get_click_examples():
assets_dir = os.path.join(os.path.dirname(__file__), "../assets")
app_images = list(filter(lambda x: x.startswith("app_image"), os.listdir(assets_dir)))
app_images.sort()
return [{"image": os.path.join(assets_dir, x)} for x in app_images]
def on_reset_btn():
click_img, draw_img = gr.Image(None), gr.ImageEditor(None)
anno_img = gr.AnnotatedImage(None)
return click_img, draw_img, anno_img
def on_submit_btn(click_img, mask_img, prompt, multipoint):
img, points = None, np.array([[[0, 0, 4]]])
if prompt == 0 and click_img is not None:
img, points = click_img["image"], click_img["points"]
points = np.array(points).reshape((-1, 2, 3))
if multipoint == 1:
points = points.reshape((-1, 3))
lt = points[np.where(points[:, 2] == 2)[0]][None, :, :]
rb = points[np.where(points[:, 2] == 3)[0]][None, :, :]
poly = points[np.where(points[:, 2] <= 1)[0]][None, :, :]
points = [lt, rb, poly] if len(lt) > 0 else [poly, np.array([[[0, 0, 4]]])]
points = np.concatenate(points, axis=1)
elif prompt == 1 and mask_img is not None:
img, points = mask_img["background"], []
for layer in mask_img["layers"]:
ys, xs = np.nonzero(layer[:, :, 0])
if len(ys) > 0:
keep = np.linspace(0, ys.shape[0], 11, dtype="int64")[1:-1]
points.append(np.stack([xs[keep][None, :], ys[keep][None, :]], 2))
if len(points) > 0:
points = np.concatenate(points).astype("float32")
points = np.pad(points, [(0, 0), (0, 0), (0, 1)], constant_values=1)
pad_points = np.array([[[0, 0, 4]]], "float32").repeat(points.shape[0], 0)
points = np.concatenate([points, pad_points], axis=1)
img = img[:, :, (2, 1, 0)] if img is not None else img
img = np.zeros((480, 640, 3), dtype="uint8") if img is None else img
points = np.array([[[0, 0, 4]]]) if (len(points) == 0 or points.size == 0) else points
inputs = {"img": img, "points": points.astype("float32")}
with command.output_index.get_lock():
command.output_index.value += 1
img_id = command.output_index.value
queues[img_id % len(queues)].put((img_id, inputs))
while img_id not in command.output_dict:
time.sleep(0.005)
masks, texts = command.output_dict.pop(img_id)
annotations = [(x, y) for x, y in zip(masks, texts)]
return inputs["img"][:, :, ::-1], annotations
app, _ = gr.Blocks(title=title, theme=theme, css=css).__enter__(), gr.Markdown(header)
container, column = gr.Row().__enter__(), gr.Column().__enter__()
click_tab, click_img = gr.Tab("Point+Box").__enter__(), gr_ext.ImagePrompter(show_label=False)
interactions = "LeftClick (FG) | MiddleClick (BG) | PressMove (Box)"
gr.Markdown("<h3 style='text-align: center'>[π±οΈ | ποΈ]: ππ {} ππ </h3>".format(interactions))
point_opt = gr.Radio(["Batch", "Ensemble"], label="Multipoint", type="index", value="Batch")
gr.Examples(get_click_examples(), inputs=[click_img])
_, draw_tab = click_tab.__exit__(), gr.Tab("Sketch").__enter__()
draw_img, _ = gr.ImageEditor(show_label=False), draw_tab.__exit__()
prompt_opt = gr.Radio(["Click", "Draw"], type="index", visible=False, value="Click")
row, reset_btn, submit_btn = gr.Row().__enter__(), gr.Button("Reset"), gr.Button("Execute")
_, _, column = row.__exit__(), column.__exit__(), gr.Column().__enter__()
anno_img = gr.AnnotatedImage(elem_id="anno-img", show_label=False)
reset_btn.click(on_reset_btn, [], [click_img, draw_img, anno_img])
submit_btn.click(on_submit_btn, [click_img, draw_img, prompt_opt, point_opt], [anno_img])
click_tab.select(lambda: "Click", [], [prompt_opt])
draw_tab.select(lambda: "Draw", [], [prompt_opt])
column.__exit__(), container.__exit__(), app.__exit__()
return app
if __name__ == "__main__":
args = parse_args()
queues = [mp.Queue(1024) for _ in range(len(args.device) + 1)]
commands = [
engine.InferenceCommand(
queues[i],
queues[-1],
kwargs={
"model_type": args.model_type,
"weights": args.checkpoint,
"concept_weights": args.concept,
"device": args.device[i],
"predictor_type": Predictor,
"verbose": i == 0,
},
)
for i in range(len(args.device))
]
commands += [ServingCommand(queues[-1])]
actors = [mp.Process(target=command.run, daemon=True) for command in commands]
for actor in actors:
actor.start()
app = build_gradio_app(queues[:-1], commands[-1])
app.queue()
app.launch(server_port=args.port, share=False)