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s1s2_evaluator_prg.py
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from ntpath import join
import os, glob
from cv2 import _InputArray_OPENGL_BUFFER
import matplotlib.pyplot as plt
from imageio import imread, imsave
import torch
import numpy as np
from tqdm import tqdm
from pathlib import Path
import tifffile as tiff
import smp
from easydict import EasyDict as edict
from smp.base.modules import Activation
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
# from utils.GeoTIFF import GeoTIFF
# geotiff = GeoTIFF()
import wandb
def image_padding(img, patchsize):
def zero_padding(arr, patchsize):
# print("zero_padding patchsize: {}".format(patchsize))
(c, h, w) = arr.shape
pad_h = (1 + np.floor(h/patchsize)) * patchsize - h
pad_w = (1 + np.floor(w/patchsize)) * patchsize - w
arr_pad = np.pad(arr, ((0, 0), (0, int(pad_h)), (0, int(pad_w))), mode='symmetric')
return arr_pad
padSize = int(patchsize/2)
img_pad0 = zero_padding(img, patchsize) # pad img into a shape: (m*PATCHSIZE, n*PATCHSIZE)
img_pad = np.pad(img_pad0, ((0, 0), (padSize, padSize), (padSize, padSize)), mode='symmetric')
return img_pad
def get_band_index_dict(cfg):
ALL_BANDS = cfg.DATA.ALL_BANDS
INPUT_BANDS = cfg.DATA.INPUT_BANDS
def get_band_index(sat):
all_bands = list(ALL_BANDS[sat])
input_bands = list(INPUT_BANDS[sat])
band_index = []
for band in input_bands:
band_index.append(all_bands.index(band))
return band_index
band_index_dict = {}
for sat in ['S1', 'ALOS', 'S2']:
band_index_dict[sat] = get_band_index(sat)
return band_index_dict
def inference(model, test_dir, test_id, cfg):
patchsize = cfg.EVAL.PATCHSIZE
NUM_CLASS = cfg.MODEL.NUM_CLASSES
# model.cpu()
model.to("cuda")
input_tensors = []
for sat in cfg.DATA.SATELLITES:
post_url = test_dir / sat / "post" / f"{test_id}.tif"
post_image = tiff.imread(post_url).transpose(2,0,1) # C*H*W
post_image = np.nan_to_num(post_image, 0)
# print(f"post: {post_image.shape}")
# print(post_image.min(), post_image.max())
post_image = post_image[cfg.band_index_dict[sat],] # select bands
if sat in ['S1', 'ALOS']: post_image = (np.clip(post_image, -30, 0) + 30) / 30
post_image_pad = image_padding(post_image, patchsize)
# img_preprocessed = self.preprocessing_fn(img_pad)
post_image_tensor = torch.from_numpy(post_image_pad).unsqueeze(0) # n * C * H * W
if 'pre' in cfg.DATA.PREPOST:
pre_folder = test_dir / sat / "pre"
# pre_url = pre_folder / os.listdir(pre_folder)[0]
query = test_id.split("_")[-1]
pre_url = glob.glob(str(pre_folder / f"*_{query}.tif"))[0]
print("pre_image: ", os.path.split(pre_url)[-1])
pre_image = tiff.imread(pre_url).transpose(2,0,1)
pre_image = np.nan_to_num(pre_image, 0)
pre_image = pre_image[cfg.band_index_dict[sat],] # select bands
if sat in ['S1', 'ALOS']: pre_image = (np.clip(pre_image, -30, 0) + 30) / 30
pre_image_pad = image_padding(pre_image, patchsize)
pre_image_tensor = torch.from_numpy(pre_image_pad).unsqueeze(0) # n * C * H * W
input_tensors.append((pre_image_tensor, post_image_tensor))
else:
input_tensors.append(post_image_tensor)
C, H, W = post_image.shape
_, _, Height, Width = input_tensors[0][0].shape
pred_mask_pad = np.zeros((Height, Width))
# prob_mask_pad = np.zeros((NUM_CLASS, Height, Width))
input_patchsize = 2 * patchsize
padSize = int(patchsize/2)
for i in tqdm(range(0, Height - input_patchsize + 1, patchsize)):
for j in range(0, Width - input_patchsize + 1, patchsize):
# print(i, i+input_patchsize, j, j+input_patchsize)
''' ------------> tile input data <---------- '''
input_patchs = []
for sat_tensor in input_tensors:
post_patch = (sat_tensor[1][..., i:i+input_patchsize, j:j+input_patchsize]).type(torch.cuda.FloatTensor)
if 'pre' in cfg.DATA.PREPOST:
pre_patch = (sat_tensor[0][..., i:i+input_patchsize, j:j+input_patchsize]).type(torch.cuda.FloatTensor)
if cfg.DATA.STACKING:
inputPatch = torch.cat([pre_patch, post_patch], dim=1) # stacked inputs
input_patchs.append(inputPatch)
else:
input_patchs += [pre_patch, post_patch]
else:
input_patchs.append(post_patch)
if 'distill_unet' == cfg.MODEL.ARCH:
if cfg.MODEL.DISTILL:
out = model.forward(input_patchs[:1])[-1] # ONLY USE S1 sensor in distill mode.
else:
out = model.forward(input_patchs)[-1] # USE all data in pretrain mode.
elif 'UNet_resnet' in cfg.MODEL.ARCH:
out = model.forward(torch.cat(input_patchs, dim=1))
# elif 'SiamResUNet' in cfg.MODEL.ARCH:
# out, decoder_out = model.forward(input_patchs, False)
# elif 'cdc_unet' in cfg.MODEL.ARCH:
# out, decoder_out = model.forward(input_patchs, False)
else: # UNet, SiamUnet
# NEW: input_patchs should be a list or tuple, the last one is the wanted output.
out = model.forward(input_patchs)[-1]
''' ------------------------------------------ '''
activation = Activation(name=cfg.MODEL.ACTIVATION)
predPatch = activation(out) #NCWH for sigmoid, NWH for argmax, N=1, C=1
predPatch = predPatch.squeeze() #1x1xWxH or 1xWxH -> WxH
predPatch = predPatch.cpu().detach().numpy()
if 'sigmoid' == cfg.MODEL.ACTIVATION:
predLabel = np.round(predPatch) # binarized with 0.5
else: # 'argmax'
predLabel = predPatch
''' save predicted tile '''
# prob_mask_pad[i+padSize:i+padSize+patchsize, j+padSize:j+padSize+patchsize] = predPatch[padSize:padSize+patchsize, padSize:padSize+patchsize]
pred_mask_pad[i+padSize:i+padSize+patchsize, j+padSize:j+padSize+patchsize] = predLabel[padSize:padSize+patchsize, padSize:padSize+patchsize]
''' clip back into original shape '''
# prob_mask = prob_mask_pad[padSize:padSize+H, padSize:padSize+W] # clip back to original shape
pred_mask = pred_mask_pad[padSize:padSize+H, padSize:padSize+W] # clip back to original shape
return pred_mask
def gen_errMap(grouthTruth, preMap, save_url=False):
errMap = np.zeros(preMap.shape)
# errMap[np.where((OptREF==0) & (SARREF==0))] = 0
errMap[np.where((grouthTruth==1) & (preMap==1))] = 1.0 # TP, dark red
errMap[np.where((grouthTruth==1) & (preMap==0))] = 2.0 # FN, light red
errMap[np.where((grouthTruth==0) & (preMap==1))] = 3.0 # FP, green
num_color = int(1 + max(np.unique(errMap)))
# color_tuple = ([1,1,1], [0.6,0,0], [0,0.8,0], [1, 0.6, 0.6])
color_tuple = ([1,1,1], [0.6,0,0], [1, 0.6, 0.6], [0,0.8,0])
my_cmap = ListedColormap(color_tuple[:num_color])
# plt.figure(figsize=(15, 15))
# plt.imshow(errMap, cmap=my_cmap)
if save_url:
plt.imsave(save_url, errMap, cmap=my_cmap)
saveName = os.path.split(save_url)[-1].split('.')[0]
errMap_rgb = imread(save_url)
wandb.log({f"test_errMap/{saveName}": wandb.Image(errMap_rgb)})
return errMap
def apply_model_on_event(model, test_id, output_dir, cfg):
output_dir = Path(output_dir)
output_dir.mkdir(exist_ok=True)
data_dir = Path(cfg.DATA.DIR) #/ "test_images"
# orbKeyLen = len(test_id.split("_")[-1]) + 1
# event = test_id[:-orbKeyLen]
event = test_id
print(event)
print(f"------------------> {test_id} <-------------------")
predMask = inference(model, data_dir, event, cfg)
print(f"predMask shape: {predMask.shape}, unique: {np.unique(predMask)}")
# print(f"probMask: [{probMask.min()}, {probMask.max()}]")
# # mtbs_palette = ["000000", "006400","7fffd4","ffff00","ff0000","7fff00"]
# # [0,100/255,0]
# mtbs_palette = [[0,100/255,0], [127/255,1,212/255], [1,1,0], [1,0,0], [127/255,1,0], [1,1,1]]
plt.imsave(output_dir / f"{test_id}_predLabel.png", predMask, cmap='gray', vmin=0, vmax=1)
# errMap_rgb = imread(save_url)
wandb.log({f"predMap/{test_id}": wandb.Image(predMask)})
# plt.imsave(output_dir / f"{test_id}_probMap.png", predMask, cmap='gray', vmin=0, vmax=1)
# # read and save true labels
# if os.path.isfile(data_dir / "mask" / "poly" / f"{event}.tif"):
# trueLabel = tiff.imread(data_dir / "mask" / "poly" / f"{event}.tif")
# # _, _, trueLabel = geotiff.read(data_dir / "mask" / "poly" / f"{event}.tif")
# # geotiff.save(output_dir / f"{test_id}_predLabel.tif", predMask[np.newaxis,])
# trueLabel = trueLabel.squeeze()
# # print(trueLabel.shape, predMask.shape)
# plt.imsave(output_dir / f"{test_id}_trueLabel.png", trueLabel, cmap='gray', vmin=0, vmax=1)
# gen_errMap(trueLabel, predMask, save_url=output_dir / f"{test_id}.png")
def evaluate_model(cfg, model_url, output_dir):
# import json
# json_url = Path(cfg.data.dir) / "train_test.json"
# with open(json_url) as json_file:
# split_dict = json.load(json_file)
# test_id_list = split_dict['test']['sarname']
test_id_list = [filename[:-4] for filename in os.listdir(Path(cfg.DATA.DIR) / "S1" / "post")]
model = torch.load(model_url)
# output_dir = Path(SegModel.project_dir) / 'outputs'
output_dir.mkdir(exist_ok=True)
band_index_dict = get_band_index_dict(cfg)
cfg = edict(cfg)
cfg.update({"band_index_dict": band_index_dict})
for test_id in test_id_list:
apply_model_on_event(model, test_id, output_dir, cfg)
import hydra
import wandb
from omegaconf import DictConfig, OmegaConf
@hydra.main(config_path="./config", config_name="s1s2_cfg_prg")
def run_app(cfg : DictConfig) -> None:
print(OmegaConf.to_yaml(cfg))
# wandb.init(config=cfg, project=cfg.project.name, name=cfg.experiment.name)
wandb.init(config=cfg, project=cfg.PROJECT.NAME, entity=cfg.PROJECT.ENTITY, name=cfg.EXP.NAME)
# project_dir = Path(hydra.utils.get_original_cwd())
#########################################################################
# # load test_id list
# import json
# json_url = "D:\wildfire-s1s2-dataset-ak-tiles/train_test.json"
# with open(json_url) as json_file:
# split_dict = json.load(json_file)
# test_id_list = split_dict['test']['sarname']
# model = torch.load("G:/PyProjects/smp-seg-pytorch/outputs/best_model_mse.pth")
# output_dir = Path(f"G:/PyProjects/smp-seg-pytorch/outputs/test_output_mse")
# for test_id in test_id_list:
# apply_model_on_event(model, test_id, output_dir, satellites=['S1', 'S2'])
model_url = "/home/p/u/puzhao/smp-seg-pytorch/outputs-igarss/run_s1s2_UNet_['S1']_EF_20220116T212807/model.pth"
# output_dir = Path("/cephyr/NOBACKUP/groups/snic2021-7-104/puzhao-snic-500G/smp-seg-pytorch/outputs") / "errMap"
output_dir = Path("/home/p/u/puzhao/wildfire-progression-dataset/CA_2021_Kamloops/outputs/pred_small")
evaluate_model(cfg, model_url, output_dir)
#########################################################################
wandb.finish()
if __name__ == "__main__":
run_app()
# model = torch.load("G:/PyProjects/smp-seg-pytorch/outputs/best_model_s1s2.pth")
# output_dir = Path(f"G:/PyProjects/smp-seg-pytorch/outputs/test_output_s1s2_")
# for test_id in test_id_list:
# apply_model_on_event(model, test_id, output_dir, satellites=['S1', 'S2'])
# scp -r [email protected]:/cephyr/NOBACKUP/groups/snic2021-7-104/puzhao-snic-500G/CA_2021_Kamloops/S1/pre pre/