Merge branch 'det1' of https://github.com/juncaipeng/PaddleRS into det1
juncaipeng
3 years ago
commit
806335ad29
11 changed files with 358 additions and 16 deletions
@ -1,2 +1,3 @@ |
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from .voc import VOCDetection |
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from .seg_dataset import SegDataset |
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from .raster import Raster |
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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|
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import os.path as osp |
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import numpy as np |
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from typing import List, Tuple, Union |
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from paddlers.utils import raster2uint8 |
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|
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try: |
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from osgeo import gdal |
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except: |
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import gdal |
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|
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class Raster: |
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def __init__(self, |
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path: str, |
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band_list: Union[List[int], Tuple[int], None]=None, |
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to_uint8: bool=False) -> None: |
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""" Class of read raster. |
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|
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Args: |
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path (str): The path of raster. |
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band_list (Union[List[int], Tuple[int], None], optional): |
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band list (start with 1) or None (all of bands). Defaults to None. |
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to_uint8 (bool, optional): |
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Convert uint8 or return raw data. Defaults to False. |
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""" |
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super(Raster, self).__init__() |
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if osp.exists(path): |
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self.path = path |
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self.__src_data = np.load(path) if path.split(".")[-1] == "npy" \ |
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else gdal.Open(path) |
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self.__getInfo() |
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self.to_uint8 = to_uint8 |
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self.setBands(band_list) |
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else: |
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raise ValueError("The path {0} not exists.".format(path)) |
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|
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def setBands(self, |
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band_list: Union[List[int], Tuple[int], None]) -> None: |
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""" Set band of data. |
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|
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Args: |
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band_list (Union[List[int], Tuple[int], None]): |
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band list (start with 1) or None (all of bands). |
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""" |
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if band_list is not None: |
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if len(band_list) > self.bands: |
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raise ValueError("The lenght of band_list must be less than {0}.".format(str(self.bands))) |
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if max(band_list) > self.bands or min(band_list) < 1: |
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raise ValueError("The range of band_list must within [1, {0}].".format(str(self.bands))) |
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self.band_list = band_list |
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|
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def getArray(self, |
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start_loc: Union[List[int], Tuple[int], None]=None, |
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block_size: Union[List[int], Tuple[int]]=[512, 512]) -> np.ndarray: |
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""" Get ndarray data |
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|
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Args: |
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start_loc (Union[List[int], Tuple[int], None], optional): |
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Coordinates of the upper left corner of the block, if None means return full image. |
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block_size (Union[List[int], Tuple[int]], optional): |
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Block size. Defaults to [512, 512]. |
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|
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Returns: |
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np.ndarray: data's ndarray. |
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""" |
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if start_loc is None: |
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return self.__getAarray() |
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else: |
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return self.__getBlock(start_loc, block_size) |
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|
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def __getInfo(self) -> None: |
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self.bands = self.__src_data.RasterCount |
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self.width = self.__src_data.RasterXSize |
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self.height = self.__src_data.RasterYSize |
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|
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def __getAarray(self, window: Union[None, List[int], Tuple[int]]=None) -> np.ndarray: |
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if window is not None: |
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xoff, yoff, xsize, ysize = window |
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if self.band_list is None: |
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if window is None: |
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ima = self.__src_data.ReadAsArray() |
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else: |
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ima = self.__src_data.ReadAsArray(xoff, yoff, xsize, ysize) |
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else: |
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band_array = [] |
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for b in self.band_list: |
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if window is None: |
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band_i = self.__src_data.GetRasterBand(b).ReadAsArray() |
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else: |
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band_i = self.__src_data.GetRasterBand(b).ReadAsArray(xoff, yoff, xsize, ysize) |
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band_array.append(band_i) |
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ima = np.stack(band_array, axis=0) |
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if self.bands == 1: |
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# the type is complex means this is a SAR data |
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if isinstance(type(ima[0, 0]), complex): |
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ima = abs(ima) |
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else: |
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ima = ima.transpose((1, 2, 0)) |
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if self.to_uint8 is True: |
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ima = raster2uint8(ima) |
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return ima |
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|
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def __getBlock(self, |
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start_loc: Union[List[int], Tuple[int]], |
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block_size: Union[List[int], Tuple[int]]=[512, 512]) -> np.ndarray: |
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if len(start_loc) != 2 or len(block_size) != 2: |
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raise ValueError("The length start_loc/block_size must be 2.") |
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xoff, yoff = start_loc |
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xsize, ysize = block_size |
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if (xoff < 0 or xoff > self.width) or (yoff < 0 or yoff > self.height): |
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raise ValueError( |
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"start_loc must be within [0-{0}, 0-{1}].".format(str(self.width), str(self.height))) |
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if xoff + xsize > self.width: |
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xsize = self.width - xoff |
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if yoff + ysize > self.height: |
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ysize = self.height - yoff |
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ima = self.__getAarray([int(xoff), int(yoff), int(xsize), int(ysize)]) |
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h, w = ima.shape[:2] if len(ima.shape) == 3 else ima.shape |
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if self.bands != 1: |
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tmp = np.zeros((block_size[0], block_size[1], self.bands), dtype=ima.dtype) |
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tmp[:h, :w, :] = ima |
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else: |
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tmp = np.zeros((block_size[0], block_size[1]), dtype=ima.dtype) |
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tmp[:h, :w] = ima |
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return tmp |
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@ -0,0 +1,95 @@ |
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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|
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import numpy as np |
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import operator |
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from functools import reduce |
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def raster2uint8(image: np.ndarray) -> np.ndarray: |
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""" Convert raster to uint8. |
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Args: |
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image (np.ndarray): image. |
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Returns: |
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np.ndarray: image on uint8. |
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""" |
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dtype = image.dtype.name |
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dtypes = ["uint8", "uint16", "float32"] |
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if dtype not in dtypes: |
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raise ValueError(f"'dtype' must be uint8/uint16/float32, not {dtype}.") |
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if dtype == "uint8": |
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return image |
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else: |
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if dtype == "float32": |
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image = _sample_norm(image) |
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return _two_percentLinear(image) |
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# 2% linear stretch |
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def _two_percentLinear(image: np.ndarray, max_out: int=255, min_out: int=0) -> np.ndarray: |
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def _gray_process(gray, maxout=max_out, minout=min_out): |
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# get the corresponding gray level at 98% histogram |
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high_value = np.percentile(gray, 98) |
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low_value = np.percentile(gray, 2) |
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truncated_gray = np.clip(gray, a_min=low_value, a_max=high_value) |
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processed_gray = ((truncated_gray - low_value) / (high_value - low_value)) * (maxout - minout) |
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return processed_gray |
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if len(image.shape) == 3: |
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processes = [] |
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for b in range(image.shape[-1]): |
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processes.append(_gray_process(image[:, :, b])) |
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result = np.stack(processes, axis=2) |
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else: # if len(image.shape) == 2 |
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result = _gray_process(image) |
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return np.uint8(result) |
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|
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# simple image standardization |
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def _sample_norm(image: np.ndarray, NUMS: int=65536) -> np.ndarray: |
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stretches = [] |
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if len(image.shape) == 3: |
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for b in range(image.shape[-1]): |
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stretched = _stretch(image[:, :, b], NUMS) |
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stretched /= float(NUMS) |
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stretches.append(stretched) |
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stretched_img = np.stack(stretches, axis=2) |
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else: # if len(image.shape) == 2 |
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stretched_img = _stretch(image, NUMS) |
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return np.uint8(stretched_img * 255) |
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|
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# histogram equalization |
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def _stretch(ima: np.ndarray, NUMS: int) -> np.ndarray: |
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hist = _histogram(ima, NUMS) |
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lut = [] |
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for bt in range(0, len(hist), NUMS): |
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# step size |
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step = reduce(operator.add, hist[bt : bt + NUMS]) / (NUMS - 1) |
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# create balanced lookup table |
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n = 0 |
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for i in range(NUMS): |
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lut.append(n / step) |
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n += hist[i + bt] |
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np.take(lut, ima, out=ima) |
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return ima |
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|
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|
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# calculate histogram |
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def _histogram(ima: np.ndarray, NUMS: int) -> np.ndarray: |
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bins = list(range(0, NUMS)) |
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flat = ima.flat |
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n = np.searchsorted(np.sort(flat), bins) |
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n = np.concatenate([n, [len(flat)]]) |
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hist = n[1:] - n[:-1] |
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return hist |
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@ -0,0 +1,53 @@ |
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# 使用教程——训练模型 |
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|
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本目录下整理了使用PaddleRS训练模型的示例代码，代码中均提供了示例数据的自动下载，并均使用单张GPU卡进行训练。 |
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|
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|代码 | 模型任务 | 数据 | |
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|------|--------|---------| |
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|object_detection/ppyolo.py | 目标检测PPYOLO | 昆虫检测 | |
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|semantic_segmentation/deeplabv3p_resnet50_vd.py | 语义分割DeepLabV3 | 视盘分割 | |
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|
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<!-- 可参考API接口说明了解示例代码中的API： |
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* [数据集读取API](../../docs/apis/datasets.md) |
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* [数据预处理和数据增强API](../../docs/apis/transforms/transforms.md) |
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* [模型API/模型加载API](../../docs/apis/models/README.md) |
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* [预测结果可视化API](../../docs/apis/visualize.md) --> |
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# 环境准备 |
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- [PaddlePaddle安装](https://www.paddlepaddle.org.cn/install/quick) |
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* 版本要求：PaddlePaddle>=2.1.0 |
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<!-- - [PaddleRS安装](../../docs/install.md) --> |
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|
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## 开始训练 |
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* 修改tutorials/train/semantic_segmentation/deeplabv3p_resnet50_vd.py中sys.path路径 |
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``` |
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sys.path.append("your/PaddleRS/path") |
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``` |
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|
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* 在安装PaddleRS后，使用如下命令开始训练，代码会自动下载训练数据, 并均使用单张GPU卡进行训练。 |
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|
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```commandline |
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export CUDA_VISIBLE_DEVICES=0 |
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python tutorials/train/semantic_segmentation/deeplabv3p_resnet50_vd.py |
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``` |
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|
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* 若需使用多张GPU卡进行训练，例如使用2张卡时执行： |
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|
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```commandline |
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python -m paddle.distributed.launch --gpus 0,1 tutorials/train/semantic_segmentation/deeplabv3p_resnet50_vd.py |
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``` |
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使用多卡时，参考[训练参数调整](../../docs/parameters.md)调整学习率和批量大小。 |
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|
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## VisualDL可视化训练指标 |
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在模型训练过程，在`train`函数中，将`use_vdl`设为True，则训练过程会自动将训练日志以VisualDL的格式打点在`save_dir`（用户自己指定的路径）下的`vdl_log`目录，用户可以使用如下命令启动VisualDL服务，查看可视化指标 |
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```commandline |
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visualdl --logdir output/deeplabv3p_resnet50_vd/vdl_log --port 8001 |
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``` |
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服务启动后，使用浏览器打开 https://0.0.0.0:8001 或 https://localhost:8001 |
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import sys |
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sys.path.append("/ssd2/pengjuncai/PaddleRS") |
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import paddlers as pdrs |
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from paddlers import transforms as T |
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train_transforms = T.Compose([ |
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T.MixupImage(mixup_epoch=-1), T.RandomDistort(), |
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T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(), |
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T.RandomHorizontalFlip(), T.BatchRandomResize( |
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target_sizes=[320, 352, 384, 416, 448, 480, 512, 544, 576, 608], |
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interp='RANDOM'), T.Normalize( |
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
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]) |
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|
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eval_transforms = T.Compose([ |
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T.Resize( |
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target_size=608, interp='CUBIC'), T.Normalize( |
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
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]) |
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train_dataset = pdrs.datasets.VOCDetection( |
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data_dir='insect_det', |
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file_list='insect_det/train_list.txt', |
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label_list='insect_det/labels.txt', |
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transforms=train_transforms, |
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shuffle=True) |
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|
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eval_dataset = pdrs.datasets.VOCDetection( |
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data_dir='insect_det', |
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file_list='insect_det/val_list.txt', |
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label_list='insect_det/labels.txt', |
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transforms=eval_transforms, |
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shuffle=False) |
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num_classes = len(train_dataset.labels) |
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model = pdrs.tasks.det.PPYOLO(num_classes=num_classes, backbone='ResNet50_vd_dcn') |
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model.train( |
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num_epochs=200, |
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train_dataset=train_dataset, |
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train_batch_size=8, |
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eval_dataset=eval_dataset, |
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pretrain_weights='COCO', |
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learning_rate=0.005 / 12, |
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warmup_steps=500, |
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warmup_start_lr=0.0, |
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save_interval_epochs=5, |
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lr_decay_epochs=[85, 135], |
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save_dir='output/ppyolo_r50vd_dcn', |
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use_vdl=True) |
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