refactor: remove unuseful app_helpers.get_inference_model_by_args(args, inference_decorator=SPACES_GPU), fix logs, initialize gpu within infer_lisa_gradio()

.idea/vcs.xml

@@ -2,5 +2,6 @@
 <project version="4">
   <component name="VcsDirectoryMappings">
     <mapping directory="" vcs="Git" />
+    <mapping directory="$PROJECT_DIR$/sam-quantized" vcs="Git" />
   </component>
 </project>

app.py

@@ -5,6 +5,7 @@ import uuid
 from typing import Callable, NoReturn
 
 import gradio as gr
+import spaces
 import uvicorn
 from fastapi import FastAPI, HTTPException, Request, status
 from fastapi.exceptions import RequestValidationError
@@ -13,8 +14,6 @@ from fastapi.staticfiles import StaticFiles
 from fastapi.templating import Jinja2Templates
 from lisa_on_cuda.utils import app_helpers, frontend_builder, create_folders_and_variables_if_not_exists
 from pydantic import ValidationError
-from spaces import GPU as SPACES_GPU
-
 from samgis_core.utilities.fastapi_logger import setup_logging
 from samgis_lisa_on_zero import PROJECT_ROOT_FOLDER, WORKDIR
 from samgis_lisa_on_zero.utilities.type_hints import ApiRequestBody, StringPromptApiRequestBody
@@ -31,6 +30,11 @@ FASTAPI_TITLE = "samgis-lisa-on-zero"
 app = FastAPI(title=FASTAPI_TITLE, version="1.0")
 
 
+@spaces.GPU
+def gpu_initialization() -> None:
+    app_logger.info("GPU initialization...")
+
+
 def get_gradio_interface_geojson(
         fn_inference: Callable
 ):
@@ -143,13 +147,16 @@ def infer_lisa_gradio(request_input: StringPromptApiRequestBody) -> JSONResponse
         time_start_run = time.time()
         body_request = get_parsed_bbox_points_with_string_prompt(request_input)
         app_logger.info(f"lisa body_request:{body_request}.")
-        app_logger.info(f"lisa module:{lisa}.")
         try:
-            source_name = get_source_name(request_input.source_type)
-            app_logger.info(f"source_name = {source_name}.")
+            source = body_request["source"]
+            source_name = body_request["source_name"]
+            app_logger.debug(f"body_request:type(source):{type(source)}, source:{source}.")
+            app_logger.debug(f"body_request:type(source_name):{type(source_name)}, source_name:{source_name}.")
+            app_logger.debug(f"lisa module:{lisa}.")
+            gpu_initialization()
             output = lisa.lisa_predict(
                 bbox=body_request["bbox"], prompt=body_request["prompt"], zoom=body_request["zoom"],
-                source=body_request["source"], source_name=source_name, inference_function_name_key=LISA_INFERENCE_FN
+                source=source, source_name=source_name, inference_function_name_key=LISA_INFERENCE_FN
             )
             duration_run = time.time() - time_start_run
             app_logger.info(f"duration_run:{duration_run}.")
@@ -157,9 +164,10 @@ def infer_lisa_gradio(request_input: StringPromptApiRequestBody) -> JSONResponse
                 "duration_run": duration_run,
                 "output": output
             }
-            app_logger.info(f"json.dumps(body):{json.dumps(body)}.")
-            # return JSONResponse(status_code=200, content={"body": json.dumps(body)})
-            return json.dumps(body)
+            dumped = json.dumps(body)
+            app_logger.info(f"json.dumps(body) type:{type(dumped)}, len:{len(dumped)}.")
+            app_logger.debug(f"complete json.dumps(body):{dumped}.")
+            return dumped
         except Exception as inference_exception:
             handle_exception_response(inference_exception)
     except ValidationError as va1:
@@ -187,7 +195,7 @@ def infer_samgis(request_input: ApiRequestBody) -> JSONResponse:
         body_request = get_parsed_bbox_points_with_dictlist_prompt(request_input)
         app_logger.info(f"body_request:{body_request}.")
         try:
-            source_name = get_source_name(request_input.source_type)
+            source_name = body_request["source_name"]
             app_logger.info(f"source_name = {source_name}.")
             output = predictors.samexporter_predict(
                 bbox=body_request["bbox"], prompt=body_request["prompt"], zoom=body_request["zoom"],
@@ -296,7 +304,7 @@ async def lisa() -> FileResponse:
     return FileResponse(path=static_dist_folder / "lisa.html", media_type="text/html")
 
 
-# # index.html (lisa.html copy)
+# index.html (lisa.html copy)
 app.mount(VITE_INDEX_URL, StaticFiles(directory=static_dist_folder, html=True), name="index")
 
 
@@ -305,12 +313,7 @@ async def index() -> FileResponse:
     return FileResponse(path=static_dist_folder / "index.html", media_type="text/html")
 
 
-args = app_helpers.parse_args([])
-app_helpers.app_logger.info(f"prepared default arguments:{args}.")
-inference_fn = app_helpers.get_inference_model_by_args(args, inference_decorator=SPACES_GPU)
-
 app_helpers.app_logger.info(f"prepared inference_fn function:{inference_fn.__name__}, creating gradio interface...")
-# io_package = app_helpers.get_gradio_interface(inference_fn)
 io = get_gradio_interface_geojson(infer_lisa_gradio)
 app_helpers.app_logger.info(
     f"created gradio interface, mounting gradio app on url {VITE_GRADIO_URL} within FastAPI...")

samgis_lisa_on_zero/io_package/wrappers_helpers.py

@@ -83,7 +83,8 @@ def get_parsed_bbox_points_with_string_prompt(request_input: StringPromptApiRequ
         "bbox": [ne_latlng, sw_latlng],
         "prompt": cleaned_prompt,
         "zoom": new_zoom,
-        "source": get_url_tile(request_input.source_type)
+        "source": get_url_tile(request_input.source_type),
+        "source_name": get_source_name(request_input.source_type)
     }
 
 
@@ -119,7 +120,8 @@ def get_parsed_bbox_points_with_dictlist_prompt(request_input: ApiRequestBody) -
         "bbox": [ne_latlng, sw_latlng],
         "prompt": new_prompt_list,
         "zoom": new_zoom,
-        "source": get_url_tile(request_input.source_type)
+        "source": get_url_tile(request_input.source_type),
+        "source_name": get_source_name(request_input.source_type)
     }
 
 

samgis_lisa_on_zero/prediction_api/lisa.py

@@ -16,7 +16,9 @@ def load_model_and_inference_fn(inference_function_name_key: str):
     from samgis_lisa_on_zero.prediction_api.global_models import models_dict
 
     if models_dict[inference_function_name_key]["inference"] is None:
-        app_logger.info(f"missing inference function {inference_function_name_key}, instantiating it now using inference_decorator {SPACES_GPU}!")
+        msg = f"missing inference function {inference_function_name_key}, "
+        msg += f"instantiating it now using inference_decorator {SPACES_GPU}!"
+        app_logger.info(msg)
         parsed_args = app_helpers.parse_args([])
         inference_fn = app_helpers.get_inference_model_by_args(
             parsed_args,
@@ -57,10 +59,17 @@ def lisa_predict(
     from samgis_lisa_on_zero import app_logger
     from samgis_lisa_on_zero.prediction_api.global_models import models_dict
 
+    if source_name is None:
+        source_name = str(source)
+
     app_logger.info("start lisa inference...")
+    app_logger.debug(f"type(source):{type(source)}, source:{source},")
+    app_logger.debug(f"type(source_name):{type(source_name)}, source_name:{source_name}.")
+
     load_model_and_inference_fn(inference_function_name_key)
-    app_logger.debug(f"using a {inference_function_name_key} instance model...")
+    app_logger.debug(f"using a '{inference_function_name_key}' instance model...")
     inference_fn = models_dict[inference_function_name_key]["inference"]
+    app_logger.info(f"loaded inference function '{inference_fn.__name__}'.")
 
     pt0, pt1 = bbox
     app_logger.info(f"tile_source: {source}: downloading geo-referenced raster with bbox {bbox}, zoom {zoom}.")
@@ -80,15 +89,14 @@ def lisa_predict(
         app_logger.info("keep all temp data in memory...")
 
     app_logger.info(f"lisa_zero, source_name:{source_name}, source_name type:{type(source_name)}.")
-    app_logger.info(f"lisa_zero, prompt tpye:{type(prompt)}.")
+    app_logger.info(f"lisa_zero, prompt type:{type(prompt)}.")
     app_logger.info(f"lisa_zero, prompt:{prompt}.")
     prompt_str = str(prompt)
-    app_logger.info(f"lisa_zero, img tpye:{type(img)}.")
+    app_logger.info(f"lisa_zero, img type:{type(img)}.")
     embedding_key = f"{source_name}_z{zoom}_{prefix}"
     _, mask, output_string = inference_fn(input_str=prompt_str, input_image=img, embedding_key=embedding_key)
-    app_logger.info(f"lisa_zero, output_string tpye:{type(output_string)}.")
-    app_logger.info(f"lisa_zero, output_string:{output_string}.")
-    app_logger.info(f"lisa_zero, mask_output tpye:{type(mask)}.")
+    app_logger.info(f"lisa_zero, output_string type:{type(output_string)}.")
+    app_logger.info(f"lisa_zero, mask_output type:{type(mask)}.")
     app_logger.info(f"created output_string '{output_string}', preparing conversion to geojson...")
     return {
         "output_string": output_string,