unet bug fix master
modified: official/cv/unet/postprocess.py
This commit is contained in:
zhangxiaoxiao
parent
e0adfec9a9
commit
deb38e76a8
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@ -18,75 +18,24 @@ import cv2
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import numpy as np
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from src.model_utils.config import config
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class dice_coeff():
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def __init__(self):
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self.clear()
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def clear(self):
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self._dice_coeff_sum = 0
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self._iou_sum = 0
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self._samples_num = 0
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def update(self, *inputs):
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if len(inputs) != 2:
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raise ValueError('Need 2 inputs ((y_softmax, y_argmax), y), but got {}'.format(len(inputs)))
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y = np.array(inputs[1])
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self._samples_num += y.shape[0]
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y = y.transpose(0, 2, 3, 1)
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b, h, w, c = y.shape
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if b != 1:
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raise ValueError('Batch size should be 1 when in evaluation.')
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y = y.reshape((h, w, c))
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if config.eval_activate.lower() == "softmax":
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y_softmax = np.squeeze(inputs[0][0], axis=0)
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if config.eval_resize:
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y_pred = []
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for m in range(config.num_classes):
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y_pred.append(cv2.resize(np.uint8(y_softmax[:, :, m] * 255), (w, h)) / 255)
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y_pred = np.stack(y_pred, axis=-1)
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else:
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y_pred = y_softmax
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elif config.eval_activate.lower() == "argmax":
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y_argmax = np.squeeze(inputs[0][1], axis=0)
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y_pred = []
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for n in range(config.num_classes):
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if config.eval_resize:
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y_pred.append(cv2.resize(np.uint8(y_argmax == n), (w, h), interpolation=cv2.INTER_NEAREST))
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else:
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y_pred.append(np.float32(y_argmax == n))
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y_pred = np.stack(y_pred, axis=-1)
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else:
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raise ValueError('config eval_activate should be softmax or argmax.')
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y_pred = y_pred.astype(np.float32)
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inter = np.dot(y_pred.flatten(), y.flatten())
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union = np.dot(y_pred.flatten(), y_pred.flatten()) + np.dot(y.flatten(), y.flatten())
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single_dice_coeff = 2*float(inter)/float(union+1e-6)
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single_iou = single_dice_coeff / (2 - single_dice_coeff)
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print("single dice coeff is: {}, IOU is: {}".format(single_dice_coeff, single_iou))
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self._dice_coeff_sum += single_dice_coeff
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self._iou_sum += single_iou
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def eval(self):
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if self._samples_num == 0:
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raise RuntimeError('Total samples num must not be 0.')
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return (self._dice_coeff_sum / float(self._samples_num), self._iou_sum / float(self._samples_num))
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from src.utils import dice_coeff
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if __name__ == '__main__':
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rst_path = config.rst_path
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metrics = dice_coeff()
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eval_activate = config.eval_activate.lower()
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if eval_activate not in ("softmax", "argmax"):
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raise ValueError("eval_activate only support 'softmax' or 'argmax'")
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if hasattr(config, "dataset") and config.dataset == "Cell_nuclei":
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img_size = tuple(config.image_size)
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for i, bin_name in enumerate(os.listdir('./preprocess_Result/')):
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f = bin_name.replace(".png", "")
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bin_name_softmax = f + "_0.bin"
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bin_name_argmax = f + "_1.bin"
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file_name_sof = rst_path + bin_name_softmax
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file_name_arg = rst_path + bin_name_argmax
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softmax_out = np.fromfile(file_name_sof, np.float32).reshape(1, 96, 96, 2)
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argmax_out = np.fromfile(file_name_arg, np.float32).reshape(1, 96, 96)
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file_name = rst_path + f + "_0.bin"
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if eval_activate == "softmax":
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rst_out = np.fromfile(file_name, np.float32).reshape(1, 96, 96, 2)
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else:
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rst_out = np.fromfile(file_name, np.int32).reshape(1, 96, 96)
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mask = cv2.imread(os.path.join(config.data_path, f, "mask.png"), cv2.IMREAD_GRAYSCALE)
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mask = cv2.resize(mask, img_size)
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mask = mask.astype(np.float32) / 255
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@ -94,16 +43,17 @@ if __name__ == '__main__':
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mask = (np.arange(2) == mask[..., None]).astype(int)
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mask = mask.transpose(2, 0, 1).astype(np.float32)
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label = mask.reshape(1, 2, 96, 96)
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metrics.update((softmax_out, argmax_out), label)
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metrics.update(rst_out, label)
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else:
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label_list = np.load('label.npy')
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for j in range(len(os.listdir('./preprocess_Result/'))):
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file_name_sof = rst_path + "ISBI_test_bs_1_" + str(j) + "_0" + ".bin"
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file_name_arg = rst_path + "ISBI_test_bs_1_" + str(j) + "_1" + ".bin"
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softmax_out = np.fromfile(file_name_sof, np.float32).reshape(1, 576, 576, 2)
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argmax_out = np.fromfile(file_name_arg, np.float32).reshape(1, 576, 576)
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file_name = rst_path + "ISBI_test_bs_1_" + str(j) + "_0" + ".bin"
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if eval_activate == "softmax":
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rst_out = np.fromfile(file_name, np.float32).reshape(1, 576, 576, 2)
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else:
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rst_out = np.fromfile(file_name, np.int32).reshape(1, 576, 576)
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label = label_list[j]
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metrics.update((softmax_out, argmax_out), label)
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metrics.update(rst_out, label)
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eval_score = metrics.eval()
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print("============== Cross valid dice coeff is:", eval_score[0])
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@ -44,7 +44,6 @@ class CellNucleiDataset:
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self.data_dir = data_dir
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self.img_ids = sorted(next(os.walk(self.data_dir))[1])
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self.train_ids = self.img_ids[:int(len(self.img_ids) * split)] * repeat
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np.random.shuffle(self.train_ids)
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self.val_ids = self.img_ids[int(len(self.img_ids) * split):]
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self.is_train = is_train
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self.result_path = result_path
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