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config_multitask.py
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config_multitask.py
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import importlib
import random
import cv2
import numpy as np
import tensorflow as tf
from tensorpack import imgaug
from loader.augs import (BinarizeLabel, GaussianBlur, GenInstanceDistance,
GenInstanceHV, MedianBlur, GenInstanceUnetMap,
GenInstanceContourMap)
from definitions import ROOT_DIR
class Config(object):
def __init__(self, ):
# Defined static settings for all tasks
self.seed = 10
mode = 'hover'
self.model_type = 'np_hv'
#### Dynamically setting the config file into variable
if mode == 'hover':
config_file = importlib.import_module('opt.hover_multitask') # np_hv, np_dist
print('(Config) Using Multi-Task Settings..')
else:
config_file = importlib.import_module('opt.other') # fcn8, dcan, etc.
config_dict = config_file.__getattribute__(self.model_type)
for variable, value in config_dict.items():
self.__setattr__(variable, value)
#### Training data
# patches are stored as numpy arrays with N channels
# ordering as [Image][Nuclei Pixels][Nuclei Type][Additional Map]
# Ex: with type_classification=True
# HoVer-Net: RGB - Nuclei Pixels - Type Map - Horizontal and Vertical Map
# Ex: with type_classification=False
# Dist : RGB - Nuclei Pixels - Distance Map
data_code_dict = {
'unet' : '536x536_84x84',
'dist' : '536x536_84x84',
'fcn8' : '512x512_256x256',
'dcan' : '512x512_256x256',
'segnet' : '512x512_256x256',
'micronet' : '504x504_252x252',
'np_hv' : '540x540_80x80',
'np_dist' : '540x540_80x80',
}
self.data_ext = '.npy'
# list of directories containing validation patches.
# For both train and valid directories, a comma separated list of directories can be used
# number of processes for parallel processing input
self.nr_procs_train = 8
self.nr_procs_valid = 4
self.input_norm = True # normalize RGB to 0-1 range
####
# v1_multitask, v2_multitask, v2_multitask_large_batch_high_learn,
exp_id = 'v2_multitask_slow'
model_id = '%s' % self.model_type
self.model_name = '%s/%s' % (exp_id, model_id)
# loading chkpts in tensorflow, the path must not contain extra '/'
self.log_path = ROOT_DIR + '/../' # log root path - modify according to needs
self.save_dir = '%s/%s' % (self.log_path, self.model_name) # log file destination
#### Info for running inference
self.inf_auto_find_chkpt = True
# path to checkpoints will be used for inference, replace accordingly
self.inf_model_path = self.save_dir + '/model-19640.index'
# output will have channel ordering as [Nuclei Type][Nuclei Pixels][Additional]
# where [Nuclei Type] will be used for getting the type of each instance
# while [Nuclei Pixels][Additional] will be used for extracting instances
self.inf_imgs_ext = '.png'
self.inf_data_dir = ROOT_DIR + '/../MoNuSAC_processed/Valid_Images/'
self.inf_output_dir = ROOT_DIR + '/../MoNuSAC_processed/Overlay/%s/%s' % (exp_id, model_id)
# for inference during evalutaion mode i.e run by infer.py
self.eval_inf_input_tensor_names = ['images']
self.eval_inf_output_tensor_names = ['predmap-coded']
# for inference during training mode i.e run by trainer.py
self.train_inf_output_tensor_names = ['predmap-coded', 'truemap-coded']
def get_model(self):
if self.model_type == 'np_hv':
model_constructor = importlib.import_module('model.graph_multitask')
model_constructor = model_constructor.Model_NP_HV
elif self.model_type == 'np_dist':
model_constructor = importlib.import_module('model.graph')
model_constructor = model_constructor.Model_NP_DIST
else:
model_constructor = importlib.import_module('model.%s' % self.model_type)
model_constructor = model_constructor.Graph
return model_constructor # NOTE return alias, not object
# refer to https://tensorpack.readthedocs.io/modules/dataflow.imgaug.html for
# information on how to modify the augmentation parameters
def get_train_augmentors(self, input_shape, output_shape, type_classification, view=False):
print('GET_TRAIN_AUGMENTORS Parameters: ',input_shape, output_shape, type_classification)
shape_augs = [
imgaug.Affine(
shear=5, # in degree
scale=(0.8, 1.2),
rotate_max_deg=179,
translate_frac=(0.01, 0.01),
interp=cv2.INTER_NEAREST,
border=cv2.BORDER_CONSTANT),
imgaug.Flip(vert=True),
imgaug.Flip(horiz=True),
imgaug.CenterCrop(input_shape),
]
input_augs = [
imgaug.RandomApplyAug(
imgaug.RandomChooseAug(
[
GaussianBlur(),
MedianBlur(),
imgaug.GaussianNoise(),
]
), 0.5),
# standard color augmentation
imgaug.RandomOrderAug(
[imgaug.Hue((-8, 8), rgb=True),
imgaug.Saturation(0.2, rgb=True),
imgaug.Brightness(26, clip=True),
imgaug.Contrast((0.75, 1.25), clip=True),
]),
imgaug.ToUint8(),
]
label_augs = []
if self.model_type == 'unet' or self.model_type == 'micronet':
label_augs =[GenInstanceUnetMap(crop_shape=output_shape)]
if self.model_type == 'dcan':
label_augs =[GenInstanceContourMap(crop_shape=output_shape)]
if self.model_type == 'dist':
label_augs = [GenInstanceDistance(crop_shape=output_shape, inst_norm=False)]
if self.model_type == 'np_hv':
label_augs = [GenInstanceHV(crop_shape=output_shape)]
if self.model_type == 'np_dist':
label_augs = [GenInstanceDistance(crop_shape=output_shape, inst_norm=True)]
if not type_classification:
label_augs.append(BinarizeLabel())
if not view:
label_augs.append(imgaug.CenterCrop(output_shape))
return shape_augs, input_augs, label_augs
def get_valid_augmentors(self, input_shape, output_shape, view=False):
print(input_shape, output_shape)
shape_augs = [
imgaug.CenterCrop(input_shape),
]
input_augs = None
label_augs = []
if self.model_type == 'unet' or self.model_type == 'micronet':
label_augs =[GenInstanceUnetMap(crop_shape=output_shape)]
if self.model_type == 'dcan':
label_augs =[GenInstanceContourMap(crop_shape=output_shape)]
if self.model_type == 'dist':
label_augs = [GenInstanceDistance(crop_shape=output_shape, inst_norm=False)]
if self.model_type == 'np_hv':
label_augs = [GenInstanceHV(crop_shape=output_shape)]
if self.model_type == 'np_dist':
label_augs = [GenInstanceDistance(crop_shape=output_shape, inst_norm=True)]
label_augs.append(BinarizeLabel())
if not view:
label_augs.append(imgaug.CenterCrop(output_shape))
return shape_augs, input_augs, label_augs