[Enhancement] Add type hints for mmcv/arraymisc and mmcv/video (#1950)

* Add type hints

* Add type hints

* Fix int float about scalar

* Add type hints for mmcv/tensorrt

* Update mmcv/tensorrt/tensorrt_utils.py

Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>

* Update mmcv/arraymisc/quantization.py

Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>

* Ignore type hint for dtype

Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>

mmcv/arraymisc/quantization.py

@@ -1,14 +1,20 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from typing import Union
+
 import numpy as np
 
 
-def quantize(arr, min_val, max_val, levels, dtype=np.int64):
+def quantize(arr: np.ndarray,
+             min_val: Union[int, float],
+             max_val: Union[int, float],
+             levels: int,
+             dtype=np.int64) -> tuple:
     """Quantize an array of (-inf, inf) to [0, levels-1].
 
     Args:
         arr (ndarray): Input array.
-        min_val (scalar): Minimum value to be clipped.
-        max_val (scalar): Maximum value to be clipped.
+        min_val (int or float): Minimum value to be clipped.
+        max_val (int or float): Maximum value to be clipped.
         levels (int): Quantization levels.
         dtype (np.type): The type of the quantized array.
 
@@ -29,13 +35,17 @@ def quantize(arr, min_val, max_val, levels, dtype=np.int64):
     return quantized_arr
 
 
-def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
+def dequantize(arr: np.ndarray,
+               min_val: Union[int, float],
+               max_val: Union[int, float],
+               levels: int,
+               dtype=np.float64) -> tuple:
     """Dequantize an array.
 
     Args:
         arr (ndarray): Input array.
-        min_val (scalar): Minimum value to be clipped.
-        max_val (scalar): Maximum value to be clipped.
+        min_val (int or float): Minimum value to be clipped.
+        max_val (int or float): Maximum value to be clipped.
         levels (int): Quantization levels.
         dtype (np.type): The type of the dequantized array.
 

mmcv/tensorrt/init_plugins.py

@@ -5,7 +5,7 @@ import os
 import warnings
 
 
-def get_tensorrt_op_path():
+def get_tensorrt_op_path() -> str:
     """Get TensorRT plugins library path."""
     # Following strings of text style are from colorama package
     bright_style, reset_style = '\x1b[1m', '\x1b[0m'
@@ -31,7 +31,7 @@ def get_tensorrt_op_path():
 plugin_is_loaded = False
 
 
-def is_tensorrt_plugin_loaded():
+def is_tensorrt_plugin_loaded() -> bool:
     """Check if TensorRT plugins library is loaded or not.
 
     Returns:
@@ -54,7 +54,7 @@ def is_tensorrt_plugin_loaded():
     return plugin_is_loaded
 
 
-def load_tensorrt_plugin():
+def load_tensorrt_plugin() -> None:
     """load TensorRT plugins library."""
 
     # Following strings of text style are from colorama package

mmcv/tensorrt/preprocess.py

@@ -5,7 +5,7 @@ import numpy as np
 import onnx
 
 
-def preprocess_onnx(onnx_model):
+def preprocess_onnx(onnx_model: onnx.ModelProto) -> onnx.ModelProto:
     """Modify onnx model to match with TensorRT plugins in mmcv.
 
     There are some conflict between onnx node definition and TensorRT limit.

mmcv/tensorrt/tensorrt_utils.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import warnings
+from typing import Union
 
 import onnx
 import tensorrt as trt
@@ -8,12 +9,12 @@ import torch
 from .preprocess import preprocess_onnx
 
 
-def onnx2trt(onnx_model,
-             opt_shape_dict,
-             log_level=trt.Logger.ERROR,
-             fp16_mode=False,
-             max_workspace_size=0,
-             device_id=0):
+def onnx2trt(onnx_model: Union[str, onnx.ModelProto],
+             opt_shape_dict: dict,
+             log_level: trt.ILogger.Severity = trt.Logger.ERROR,
+             fp16_mode: bool = False,
+             max_workspace_size: int = 0,
+             device_id: int = 0) -> trt.ICudaEngine:
     """Convert onnx model to tensorrt engine.
 
     Arguments:
@@ -100,7 +101,7 @@ def onnx2trt(onnx_model,
     return engine
 
 
-def save_trt_engine(engine, path):
+def save_trt_engine(engine: trt.ICudaEngine, path: str) -> None:
     """Serialize TensorRT engine to disk.
 
     Arguments:
@@ -124,7 +125,7 @@ def save_trt_engine(engine, path):
         f.write(bytearray(engine.serialize()))
 
 
-def load_trt_engine(path):
+def load_trt_engine(path: str) -> trt.ICudaEngine:
     """Deserialize TensorRT engine from disk.
 
     Arguments:
@@ -153,7 +154,7 @@ def load_trt_engine(path):
         return engine
 
 
-def torch_dtype_from_trt(dtype):
+def torch_dtype_from_trt(dtype: trt.DataType) -> Union[torch.dtype, TypeError]:
     """Convert pytorch dtype to TensorRT dtype."""
     if dtype == trt.bool:
         return torch.bool
@@ -169,7 +170,8 @@ def torch_dtype_from_trt(dtype):
         raise TypeError('%s is not supported by torch' % dtype)
 
 
-def torch_device_from_trt(device):
+def torch_device_from_trt(
+        device: trt.TensorLocation) -> Union[torch.device, TypeError]:
     """Convert pytorch device to TensorRT device."""
     if device == trt.TensorLocation.DEVICE:
         return torch.device('cuda')

mmcv/video/io.py

@@ -272,14 +272,14 @@ class VideoReader:
         self._vcap.release()
 
 
-def frames2video(frame_dir,
-                 video_file,
-                 fps=30,
-                 fourcc='XVID',
-                 filename_tmpl='{:06d}.jpg',
-                 start=0,
-                 end=0,
-                 show_progress=True):
+def frames2video(frame_dir: str,
+                 video_file: str,
+                 fps: float = 30,
+                 fourcc: str = 'XVID',
+                 filename_tmpl: str = '{:06d}.jpg',
+                 start: int = 0,
+                 end: int = 0,
+                 show_progress: bool = True) -> None:
     """Read the frame images from a directory and join them as a video.
 
     Args:

mmcv/video/optflow.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import warnings
+from typing import Tuple, Union
 
 import cv2
 import numpy as np
@@ -9,7 +10,11 @@ from mmcv.image import imread, imwrite
 from mmcv.utils import is_str
 
 
-def flowread(flow_or_path, quantize=False, concat_axis=0, *args, **kwargs):
+def flowread(flow_or_path: Union[np.ndarray, str],
+             quantize: bool = False,
+             concat_axis: int = 0,
+             *args,
+             **kwargs) -> np.ndarray:
     """Read an optical flow map.
 
     Args:
@@ -58,7 +63,12 @@ def flowread(flow_or_path, quantize=False, concat_axis=0, *args, **kwargs):
     return flow.astype(np.float32)
 
 
-def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs):
+def flowwrite(flow: np.ndarray,
+              filename: str,
+              quantize: bool = False,
+              concat_axis: int = 0,
+              *args,
+              **kwargs) -> None:
     """Write optical flow to file.
 
     If the flow is not quantized, it will be saved as a .flo file losslessly,
@@ -88,7 +98,9 @@ def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs):
         imwrite(dxdy, filename)
 
 
-def quantize_flow(flow, max_val=0.02, norm=True):
+def quantize_flow(flow: np.ndarray,
+                  max_val: float = 0.02,
+                  norm: bool = True) -> tuple:
     """Quantize flow to [0, 255].
 
     After this step, the size of flow will be much smaller, and can be
@@ -116,7 +128,10 @@ def quantize_flow(flow, max_val=0.02, norm=True):
     return tuple(flow_comps)
 
 
-def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
+def dequantize_flow(dx: np.ndarray,
+                    dy: np.ndarray,
+                    max_val: float = 0.02,
+                    denorm: bool = True) -> np.ndarray:
     """Recover from quantized flow.
 
     Args:
@@ -140,12 +155,15 @@ def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
     return flow
 
 
-def flow_warp(img, flow, filling_value=0, interpolate_mode='nearest'):
+def flow_warp(img: np.ndarray,
+              flow: np.ndarray,
+              filling_value: int = 0,
+              interpolate_mode: str = 'nearest') -> np.ndarray:
     """Use flow to warp img.
 
     Args:
-        img (ndarray, float or uint8): Image to be warped.
-        flow (ndarray, float): Optical Flow.
+        img (ndarray): Image to be warped.
+        flow (ndarray): Optical Flow.
         filling_value (int): The missing pixels will be set with filling_value.
         interpolate_mode (str): bilinear -> Bilinear Interpolation;
                                 nearest -> Nearest Neighbor.
@@ -201,7 +219,7 @@ def flow_warp(img, flow, filling_value=0, interpolate_mode='nearest'):
     return output.astype(img.dtype)
 
 
-def flow_from_bytes(content):
+def flow_from_bytes(content: bytes) -> np.ndarray:
     """Read dense optical flow from bytes.
 
     .. note::
@@ -231,7 +249,7 @@ def flow_from_bytes(content):
     return flow
 
 
-def sparse_flow_from_bytes(content):
+def sparse_flow_from_bytes(content: bytes) -> Tuple[np.ndarray, np.ndarray]:
     """Read the optical flow in KITTI datasets from bytes.
 
     This function is modified from RAFT load the `KITTI datasets