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liuyizhang
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•
058f70d
1
Parent(s):
68b6cad
update app.py && app_cli.py
Browse files- app.py +10 -8
- app_cli.py +6 -4
app.py
CHANGED
@@ -15,7 +15,6 @@ if run_gradio:
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os.system("pip install gradio==3.50.2")
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import gradio as gr
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-
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from loguru import logger
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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@@ -24,6 +23,8 @@ if os.environ.get('IS_MY_DEBUG') is None:
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result = subprocess.run(['pip', 'install', '-e', 'GroundingDINO'], check=True)
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print(f'pip install GroundingDINO = {result}')
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# result = subprocess.run(['pip', 'list'], check=True)
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# print(f'pip list = {result}')
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@@ -188,7 +189,6 @@ def plot_boxes_to_image(image_pil, tgt):
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mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6)
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-
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return image_pil, mask
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def load_image(image_path):
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@@ -290,19 +290,20 @@ def mix_masks(imgs):
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re_img = 1 - re_img
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return Image.fromarray(np.uint8(255*re_img))
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-
def set_device():
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if os.environ.get('IS_MY_DEBUG') is None:
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device =
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else:
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device = 'cpu'
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print(f'device={device}')
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return device
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def load_groundingdino_model(device):
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# initialize groundingdino model
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logger.info(f"initialize groundingdino model...")
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groundingdino_model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae, device=device) #'cpu')
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-
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def get_sam_vit_h_4b8939():
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if not os.path.exists('./sam_vit_h_4b8939.pth'):
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@@ -1104,13 +1105,14 @@ if __name__ == "__main__":
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parser.add_argument("--debug", action="store_true", help="using debug mode")
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parser.add_argument("--share", action="store_true", help="share the app")
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parser.add_argument("--port", "-p", type=int, default=7860, help="port")
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args, _ = parser.parse_known_args()
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print(f'args = {args}')
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if os.environ.get('IS_MY_DEBUG') is None:
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os.system("pip list")
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-
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if device == 'cpu':
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kosmos_enable = False
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@@ -1118,7 +1120,7 @@ if __name__ == "__main__":
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kosmos_model, kosmos_processor = load_kosmos_model(device)
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if groundingdino_enable:
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-
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if sam_enable:
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load_sam_model(device)
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os.system("pip install gradio==3.50.2")
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import gradio as gr
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from loguru import logger
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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result = subprocess.run(['pip', 'install', '-e', 'GroundingDINO'], check=True)
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print(f'pip install GroundingDINO = {result}')
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+
logger.info(f"Start app...")
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+
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# result = subprocess.run(['pip', 'list'], check=True)
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# print(f'pip list = {result}')
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mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6)
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return image_pil, mask
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def load_image(image_path):
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re_img = 1 - re_img
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return Image.fromarray(np.uint8(255*re_img))
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+
def set_device(args):
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global device
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if os.environ.get('IS_MY_DEBUG') is None:
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device = args.cuda if torch.cuda.is_available() else 'cpu'
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else:
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device = 'cpu'
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print(f'device={device}')
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def load_groundingdino_model(device):
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# initialize groundingdino model
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global groundingdino_model
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logger.info(f"initialize groundingdino model...")
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groundingdino_model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae, device=device) #'cpu')
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logger.info(f"initialize groundingdino model...{type(groundingdino_model)}")
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def get_sam_vit_h_4b8939():
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if not os.path.exists('./sam_vit_h_4b8939.pth'):
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parser.add_argument("--debug", action="store_true", help="using debug mode")
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parser.add_argument("--share", action="store_true", help="share the app")
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parser.add_argument("--port", "-p", type=int, default=7860, help="port")
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parser.add_argument("--cuda", "-c", type=str, default='cuda:0', help="cuda")
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args, _ = parser.parse_known_args()
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print(f'args = {args}')
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if os.environ.get('IS_MY_DEBUG') is None:
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os.system("pip list")
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set_device(args)
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if device == 'cpu':
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kosmos_enable = False
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kosmos_model, kosmos_processor = load_kosmos_model(device)
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if groundingdino_enable:
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load_groundingdino_model('cpu')
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if sam_enable:
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load_sam_model(device)
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app_cli.py
CHANGED
@@ -53,7 +53,7 @@ from io import BytesIO
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from diffusers import StableDiffusionInpaintPipeline
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from huggingface_hub import hf_hub_download
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-
from
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# relate anything
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from ram_utils import iou, sort_and_deduplicate, relation_classes, MLP, show_anns, ram_show_mask
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from ram_train_eval import RamModel,RamPredictor
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@@ -85,6 +85,7 @@ def get_args():
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argparser.add_argument("--input_image", "-i", type=str, default="", help="")
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argparser.add_argument("--text", "-t", type=str, default="", help="")
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argparser.add_argument("--output_image", "-o", type=str, default="", help="")
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args = argparser.parse_args()
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return args
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@@ -96,8 +97,8 @@ if __name__ == '__main__':
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logger.info(f'\nargs={args}\n')
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logger.info(f'loading models ... ')
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# set_device() # If you have enough GPUs, you can open this comment
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-
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load_sam_model(device)
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# load_sd_model(device)
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load_lama_cleaner_model(device)
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@@ -105,7 +106,7 @@ if __name__ == '__main__':
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input_image = Image.open(args.input_image)
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text_prompt = args.text,
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task_type = 'remove',
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inpaint_prompt = '',
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@@ -120,6 +121,7 @@ if __name__ == '__main__':
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kosmos_input = None,
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cleaner_size_limit = -1,
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)
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if len(output_images) > 0:
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logger.info(f'save result to {args.output_image} ... ')
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output_images[-1].save(args.output_image)
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from diffusers import StableDiffusionInpaintPipeline
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from huggingface_hub import hf_hub_download
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from util_computer import computer_info
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# relate anything
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from ram_utils import iou, sort_and_deduplicate, relation_classes, MLP, show_anns, ram_show_mask
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from ram_train_eval import RamModel,RamPredictor
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argparser.add_argument("--input_image", "-i", type=str, default="", help="")
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argparser.add_argument("--text", "-t", type=str, default="", help="")
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argparser.add_argument("--output_image", "-o", type=str, default="", help="")
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argparser.add_argument("--cuda", "-c", type=str, default='cpu', help="cuda")
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args = argparser.parse_args()
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return args
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logger.info(f'\nargs={args}\n')
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logger.info(f'loading models ... ')
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# set_device(args) # If you have enough GPUs, you can open this comment
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load_groundingdino_model('cpu')
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load_sam_model(device)
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# load_sd_model(device)
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load_lama_cleaner_model(device)
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input_image = Image.open(args.input_image)
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run_rets = run_anything_task(input_image = input_image,
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text_prompt = args.text,
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task_type = 'remove',
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inpaint_prompt = '',
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kosmos_input = None,
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cleaner_size_limit = -1,
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)
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output_images = run_rets[0]
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if len(output_images) > 0:
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logger.info(f'save result to {args.output_image} ... ')
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output_images[-1].save(args.output_image)
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