[Other] Integrate paddle2coreml tool into FastDeploy (#1460)

Integration of paddle2coreml tool

tools/common_tools/common_tools.py

@@ -6,7 +6,7 @@ import uvicorn
 def argsparser():
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument(
-        'tools', choices=['compress', 'convert', 'simple_serving'])
+        'tools', choices=['compress', 'convert', 'simple_serving', 'paddle2coreml'])
     ## argumentments for auto compression
     parser.add_argument(
         '--config_path',
@@ -84,6 +84,49 @@ def argsparser():
         help="Simple serving host IP address")
     parser.add_argument(
         "--port", type=int, default=8000, help="Simple serving host port")
+    ## arguments for paddle2coreml
+    parser.add_argument(
+        "--p2c_paddle_model_dir",
+        type=str,
+        default=None,
+        required=True,
+        help="define paddle model path")
+    parser.add_argument(
+        "--p2c_coreml_model_dir",
+        type=str,
+        default=None,
+        required=True,
+        help="define generated coreml model path")
+    parser.add_argument(
+        "--p2c_coreml_model_name",
+        type=str,
+        default="coreml_model",
+        required=False,
+        help="define generated coreml model name")
+    parser.add_argument(
+        "--p2c_input_names",
+        type=str,
+        default=None,
+        required=True,
+        help="define input names")
+    parser.add_argument(
+        "--p2c_input_dtypes",
+        type=str,
+        default="float32",
+        required=True,
+        help="define input dtypes")
+    parser.add_argument(
+        "--p2c_input_shapes",
+        type=str,
+        default=None,
+        required=True,
+        help="define input shapes") 
+    parser.add_argument(
+        "--p2c_output_names",
+        type=str,
+        default=None,
+        required=True,
+        help="define output names")  
     ## arguments for other tools
     return parser
 
@@ -170,6 +213,51 @@ def main():
                     port=args.port,
                     app_dir='.',
                     log_config=custom_logging_config)
+    if args.tools == "paddle2coreml":
+        import coremltools as ct
+        import os
+        import numpy as np
+        def type_to_np_dtype(dtype):
+            if dtype == 'float32':
+                return np.float32
+            elif dtype == 'float64':
+                return np.float64
+            elif dtype == 'int32':
+                return np.int32
+            elif dtype == 'int64':
+                return np.int64
+            elif dtype == 'uint8':
+                return np.uint8
+            elif dtype == 'uint16':
+                return np.uint16
+            elif dtype == 'uint32':
+                return np.uint32
+            elif dtype == 'uint64':
+                return np.uint64
+            elif dtype == 'int8':
+                return np.int8
+            elif dtype == 'int16':
+                return np.int16
+            else:
+                raise Exception("Unsupported dtype: {}".format(dtype))
+        input_names = args.p2c_input_names.split(' ')
+        input_shapes = [[int(i) for i in shape.split(',')] for shape in args.p2c_input_shapes.split(' ')]
+        input_dtypes = map(type_to_np_dtype, args.p2c_input_dtypes.split(' '))
+        output_names = args.p2c_output_names.split(' ')
+        sample_input = [ct.TensorType(
+                                name=k,
+                                shape=s,
+                                dtype=d,
+                            ) for k, s, d in zip(input_names, input_shapes, input_dtypes)]
+
+        coreml_model = ct.convert(
+            args.p2c_paddle_model_dir,
+            convert_to="mlprogram",
+            minimum_deployment_target=ct.target.macOS13,
+            inputs=sample_input,
+            outputs=[ct.TensorType(name=name) for name in output_names],
+        )
+        coreml_model.save(os.path.join(args.p2c_coreml_model_dir, args.p2c_coreml_model_name))        
 
 
 if __name__ == '__main__':