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Model2_SiamUnet_Encoder.py
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Model2_SiamUnet_Encoder.py
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# FORCE CPU
#import os
#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
#os.environ["CUDA_VISIBLE_DEVICES"] = ""
import Debugger, DataPreprocesser
import keras
from keras.layers import Input, Dense
from keras.models import Model
#from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.optimizers import Adam
from keras.utils import to_categorical
import numpy as np
from random import randint
from keras.layers import Conv2D
from keras.layers import Activation
from keras.models import Model
from segmentation_models.utils import freeze_model
from segmentation_models.utils import legacy_support
from segmentation_models.backbones import get_backbone, get_feature_layers
from segmentation_models.unet.blocks import Transpose2D_block
from segmentation_models.utils import get_layer_number, to_tuple
from loss_weighted_crossentropy import weighted_categorical_crossentropy
from keras.callbacks import ModelCheckpoint
from Model2_builder import SiameseUnet
from albumentations import (PadIfNeeded,
HorizontalFlip,
VerticalFlip,
CenterCrop,
Crop,
Compose,
Transpose,
RandomRotate90,
ElasticTransform,
GridDistortion,
OpticalDistortion,
RandomSizedCrop,
OneOf,
CLAHE,
RandomBrightnessContrast,
RandomGamma)
class Model2_SiamUnet_Encoder(object):
"""
An intermediate between the code and bunch of tester models.
"""
def __init__(self, settings, dataset):
self.settings = settings
self.dataset = dataset
self.dataPreprocesser = dataset.dataPreprocesser
self.debugger = Debugger.Debugger(settings)
self.use_sigmoid_or_softmax = 'softmax'
assert self.use_sigmoid_or_softmax == 'softmax'
#BACKBONE = 'resnet34'
#BACKBONE = 'resnet50' #batch 16
#BACKBONE = 'resnet101' #batch 8
BACKBONE = settings.model_backend
custom_weights_file = "imagenet"
#weights from imagenet finetuned on aerial data specific task - will it work? will it break?
#custom_weights_file = "/scratch/ruzicka/python_projects_large/AerialNet_VariousTasks/model_UNet-Resnet34_DSM_in01_95percOfTrain_8batch_100ep_dsm01proper.h5"
#resolution_of_input = self.dataset.datasetInstance.IMAGE_RESOLUTION
resolution_of_input = None
self.model = self.create_model(backbone=BACKBONE, custom_weights_file=custom_weights_file, input_size = resolution_of_input, channels = 3)
self.model.summary()
self.local_setting_batch_size = settings.train_batch #8 #32
self.local_setting_epochs = settings.train_epochs #100
self.train_data_augmentation = True
# saving paths for plots ...
self.save_plot_path = "plots/"
def train(self, show=True, save=False):
print("Train")
train_L, train_R, train_V = self.dataset.train
val_L, val_R, val_V = self.dataset.val
# 3 channels only - rgb
if train_L.shape[3] > 3:
train_L = train_L[:,:,:,1:4]
train_R = train_R[:,:,:,1:4]
val_L = val_L[:,:,:,1:4]
val_R = val_R[:,:,:,1:4]
# label also reshape
print("left images (train)")
self.debugger.explore_set_stats(train_L)
print("right images (train)")
self.debugger.explore_set_stats(train_R)
print("label images (train)")
self.debugger.explore_set_stats(train_V)
from albumentations.core.transforms_interface import DualTransform
class RandomRotate90x1(DualTransform):
def apply(self, img, factor=0, **params):
return np.ascontiguousarray(np.rot90(img, factor))
def get_params(self):
return {'factor': 1}
class RandomRotate90x2(DualTransform):
def apply(self, img, factor=0, **params):
return np.ascontiguousarray(np.rot90(img, factor))
def get_params(self):
return {'factor': 2}
class RandomRotate90x3(DualTransform):
def apply(self, img, factor=0, **params):
return np.ascontiguousarray(np.rot90(img, factor))
def get_params(self):
return {'factor': 3}
if self.train_data_augmentation:
# using the help of https://github.com/albu/albumentations/blob/master/notebooks/example_kaggle_salt.ipynb
# we can grow the training set, and then shuffle it
train_L
train_R
train_V
augmentations = []
augmentations.append( RandomRotate90x1(p=1) ) # 90, 180 or 270 <- we need the same for the same of l=r=y
augmentations.append( RandomRotate90x2(p=1) ) # 90, 180 or 270 <- we need the same for the same of l=r=y
augmentations.append( RandomRotate90x3(p=1) ) # 90, 180 or 270 <- we need the same for the same of l=r=y
augmentations.append( HorizontalFlip(p=1) ) # H reflection
augmentations.append( VerticalFlip(p=1) ) # V reflection
augmentations.append( Compose([VerticalFlip(p=1), RandomRotate90x1(p=1)]) ) # V reflection and then rotation
augmentations.append( Compose([HorizontalFlip(p=1), RandomRotate90x1(p=1)]) ) # H reflection and then rotation
# randomness inside the call breaks our case because we need to call it twice (two images and a mask)
# perhaps temporary concat in channels could be a workaround?
# include non-rigid transformations?
# Elastic def. = “Best Practices for Convolutional Neural Networks applied to Visual Document Analysis”
#augmentations.append(ElasticTransformDET(p=1, alpha=120, sigma=120 * 0.05, alpha_affine=120 * 0.03))
# more fancy ones ...
# isn't deterministic for 3 images ....
#original_height = original_width = self.dataset.datasetInstance.IMAGE_RESOLUTION
#augmentations.append(RandomSizedCrop(p=1, min_max_height=(int(original_height/2.0), int(original_height)), height=original_height, width=original_width))
# etc ...
aug_lefts = []
aug_rights = []
aug_ys = []
num_in_train = len(train_L)
for i in range(num_in_train):
#print(i)
image_l = train_L[i]
image_r = train_R[i]
mask = train_V[i]
if True:
# choose a random augmentation ... (we don't have mem for all of them!, solve by batches or smth ...)
aug_i = randint(0, len(augmentations)-1)
aug = augmentations[aug_i]
#for aug in augmentations:
augmented1 = aug(image=image_l, mask=mask)
augmented2 = aug(image=image_r, mask=mask)
aug_l = augmented1['image']
aug_y = augmented1['mask']
aug_lefts.append(np.asarray(aug_l))
aug_ys.append(np.asarray(aug_y))
aug_r = augmented2['image']
aug_rights.append(np.asarray(aug_r))
del aug_l
del aug_r
del aug_y
del augmented1
del augmented2
if False:
# for sake of showing:
aug_lefts_tmp, aug_rights_tmp = self.dataPreprocesser.postprocess_images(np.asarray(aug_lefts), np.asarray(aug_rights))
#self.debugger.viewTripples(aug_lefts, aug_rights, aug_ys)
by = 5
off = i * by
while off < len(aug_lefts):
self.debugger.viewTripples(aug_lefts_tmp, aug_rights_tmp, aug_ys, how_many=by, off=off)
off += by
aug_lefts = np.asarray(aug_lefts)
aug_rights = np.asarray(aug_rights)
aug_ys = np.asarray(aug_ys)
print("aug_lefts.shape", aug_lefts.shape)
print("aug_rights.shape", aug_rights.shape)
print("aug_ys.shape", aug_ys.shape)
# Adding them to the training set
train_L = np.append(train_L, aug_lefts, axis=0)
train_R = np.append(train_R, aug_rights, axis=0)
train_V = np.append(train_V, aug_ys, axis=0)
print("left images (aug train)")
self.debugger.explore_set_stats(train_L)
print("right images (aug train)")
self.debugger.explore_set_stats(train_R)
print("label images categorical (aug train)")
self.debugger.explore_set_stats(train_V)
# don't do this before Augmentation
train_V = train_V.reshape(train_V.shape + (1,))
val_V = val_V.reshape(val_V.shape + (1,))
if self.use_sigmoid_or_softmax == 'softmax':
train_V = to_categorical(train_V)
val_V = to_categorical(val_V)
print("label images categorical (train)")
self.debugger.explore_set_stats(train_V)
checkpoint = ModelCheckpoint("model2best_so_far_for_eastly_stops.h5", monitor='val_categorical_accuracy',
verbose=1, save_best_only=True, mode='max')
#callbacks = [checkpoint] # should we prefer the best model instead?? maybe no, the val is pretty small
callbacks = []
# Regular training:
history = self.model.fit([train_L, train_R], train_V, batch_size=self.local_setting_batch_size,
epochs=self.local_setting_epochs,
validation_data=([val_L, val_R], val_V), verbose=2,
callbacks=callbacks) # 2 ~ 1 line each ep
# print(history.history)
if self.use_sigmoid_or_softmax == 'sigmoid':
history.history["acc"] = history.history["binary_accuracy"] # we care about this one to show
history.history["val_acc"] = history.history["val_binary_accuracy"]
added_plots = []
else:
history.history["acc"] = history.history["categorical_accuracy"] # we care about this one to show
history.history["val_acc"] = history.history["val_categorical_accuracy"]
added_plots = []
print(history.history)
self.debugger.nice_plot_history(history,added_plots, save=save, show=show, name=self.save_plot_path+self.settings.run_name+"_training")
def save(self, path=""):
if path == "":
self.model.save_weights(self.settings.large_file_folder+"last_trained_model_weights.h5")
else:
self.model.save_weights(path)
print("Saved model weights.")
def load(self, path=""):
if path == "":
self.model.load_weights(self.settings.large_file_folder+"last_trained_model_weights.h5")
else:
self.model.load_weights(path)
print("Loaded model weights.")
def test(self, evaluator, show = True, save = False, threshold_fineness = 0.1):
print("Test")
test_L, test_R, test_V = self.dataset.test
if test_L.shape[3] > 3:
# 3 channels only - rgb
test_L = test_L[:,:,:,1:4]
test_R = test_R[:,:,:,1:4]
# label also reshape
if self.use_sigmoid_or_softmax == 'softmax':
test_V_cat = to_categorical(test_V)
else:
test_V_cat = test_V.reshape(test_V.shape + (1,))
predicted = self.model.predict(x=[test_L, test_R], batch_size=4)
metrics = self.model.evaluate(x=[test_L, test_R], y=test_V_cat, verbose=0, batch_size=4)
metrics_info = self.model.metrics_names
print(list(zip(metrics_info, metrics)))
kfold_txt = "MISSED_" + self.settings.model_backend + "_KFold_" + str(
self.settings.TestDataset_Fold_Index) + "z" + str(self.settings.TestDataset_K_Folds)
if self.use_sigmoid_or_softmax == 'softmax':
# with just 2 classes I can hax:
predicted = predicted[:, :, :, 1]
# else:
#predicted = np.argmax(predicted, axis=3)
#print("predicted.shape:", predicted.shape)
else:
# chop off that last dimension
predicted = predicted.reshape(predicted.shape[:-1])
# undo preprocessing steps
predicted = self.dataPreprocesser.postprocess_labels(predicted)
save_text_file = self.save_plot_path + kfold_txt + "_MASK_TXT.txt"
mask_best_thr, mask_recall, mask_precision, mask_accuracy, mask_f1 = evaluator.metrics_autothr_f1_max(predicted, test_V, jump_by = threshold_fineness, save_text_file=save_text_file)
mask_stats = mask_best_thr, mask_recall, mask_precision, mask_accuracy, mask_f1
tiles_stats = []
print("Threshold automatically chosen as", mask_best_thr)
# Adding evaluation from the standpoint of each tile.
Tile_Based_Evaluation = True
if Tile_Based_Evaluation:
chosen_threshold = mask_best_thr
test_classlabels = evaluator.mask_label_into_class_label(self.dataset.test[2])
#test_classlabels = self.dataset.datasetInstance.mask_label_into_class_label(self.dataset.test[2])
# This has to actually be thresholded before we calculate the tile label (we have to count occurance of 1s)
predictions_thresholded, _, _, _, _= evaluator.calculate_metrics(predicted, test_V, threshold=chosen_threshold)
predicted_classlabels = self.dataset.datasetInstance.mask_label_into_class_label(predictions_thresholded)
print(test_classlabels[0:20])
print(predicted_classlabels[0:20])
print("TILE based EVALUATION")
#evaluator.histogram_of_predictions(test_classlabels)
#evaluator.histogram_of_predictions(predicted_classlabels)
# print("trying thresholds ...")
# evaluator.try_all_thresholds(predicted_labels, test_class_Y, np.arange(0.0,1.0,0.01), title_txt="Labels (0/1) evaluated [Change Class]") #NoChange
print("threshold=",chosen_threshold)
save_text_file = self.save_plot_path+kfold_txt+"_TILES_TXT.txt"
_, tiles_recall, tiles_precision, tiles_accuracy, tiles_f1 = evaluator.calculate_metrics(predicted_classlabels, test_classlabels, threshold=chosen_threshold, save_text_file=save_text_file) # thr arbitrary no? we have only 0/1 in here already
tiles_stats = mask_best_thr, tiles_recall, tiles_precision, tiles_accuracy, tiles_f1
# Get indices of the misclassified samples
misclassified_indices = np.where(predicted_classlabels != test_classlabels)
misclassified_indices = misclassified_indices[0]
text_to_save_missclassifieds = ""
print("misclassified_indices:", misclassified_indices)
text_to_save_missclassifieds += "misclassified_indices:"+str(misclassified_indices)+"\n"
for ind in misclassified_indices:
#print("idx", ind, ":", predicted_classlabels[ind]," != ",test_classlabels[ind])
text_to_save_missclassifieds += "idx "+ str(ind)+ ": " +str( predicted_classlabels[ind])+" != "+str(test_classlabels[ind])+"\n"
if save:
path = self.save_plot_path+"MissedIndices.txt"
file = open(path, "w")
file.write(text_to_save_missclassifieds)
file.close()
test_L, test_R = self.dataPreprocesser.postprocess_images(test_L, test_R)
if test_L.shape[3] > 3:
# 3 channels only - rgb
test_L = test_L[:,:,:,1:4]
test_R = test_R[:,:,:,1:4]
print("left images (test)")
self.debugger.explore_set_stats(test_L)
print("right images (test)")
self.debugger.explore_set_stats(test_R)
print("label images (test)")
self.debugger.explore_set_stats(test_V)
print("predicted images (test)")
self.debugger.explore_set_stats(predicted)
if Tile_Based_Evaluation:
print("Misclassified samples (in total", len(misclassified_indices),"):")
if save:
off = 0
by = 4
by = min(by, len(misclassified_indices))
while off < len(misclassified_indices):
by_rem = min(by, len(misclassified_indices)-off)
#self.debugger.viewTripples(test_L, test_R, test_V, how_many=4, off=off)
self.debugger.viewQuadrupples(test_L[misclassified_indices], test_R[misclassified_indices], test_V[misclassified_indices], predicted[misclassified_indices], how_many=by_rem, off=off, show=show,save=save, name=self.save_plot_path+kfold_txt+"quad"+str(off)+"_"+self.settings.run_name)
off += by
if show:
off = 0
by = 4
by = min(by, len(test_L))
while off < len(predicted):
#self.debugger.viewTripples(test_L, test_R, test_V, how_many=4, off=off)
self.debugger.viewQuadrupples(test_L, test_R, test_V, predicted, how_many=by, off=off, show=show,save=save)
off += by
if save:
off = 0
by = 4
by = min(by, len(test_L))
until_n = min(by*8, len(test_L))
while off < until_n:
#self.debugger.viewTripples(test_L, test_R, test_V, how_many=4, off=off)
kfold_txt = self.settings.model_backend+"_KFold_" + str(self.settings.TestDataset_Fold_Index) + "z" + str(self.settings.TestDataset_K_Folds)
by_rem = min(by, until_n - off)
self.debugger.viewQuadrupples(test_L, test_R, test_V, predicted, how_many=by_rem, off=off, show=show,save=save, name=self.save_plot_path+kfold_txt+"quad"+str(off)+"_"+self.settings.run_name)
off += by
statistics = mask_stats, tiles_stats
return statistics
def test_on_specially_loaded_set(self, evaluator, show = True, save = False):
print("Test: Debug, showing performance on other loaded data!")
path_to_image_left = "/home/pf/pfstaff/projects/ruzicka/TiledDataset_6368x6368px_large/2012_strip2_6368tiles/strip2-2012_0.PNG"
path_to_image_right = "/home/pf/pfstaff/projects/ruzicka/TiledDataset_6368x6368px_large/2015_strip2_6368tiles/strip2-2015_0.PNG"
from skimage import io
def load_raster_image(filename):
img = io.imread(filename)
arr = np.asarray(img)
return arr
image_left = load_raster_image(path_to_image_left)
image_right = load_raster_image(path_to_image_right)
# show just small section of it ...
def crop_center(img, cropx, cropy):
y, x, ch = img.shape
startx = x // 2 - (cropx // 2)
starty = y // 2 - (cropy // 2)
return img[starty:starty + cropy, startx:startx + cropx, :]
SMALLER_SIZE = 2048
image_left = crop_center(image_left, SMALLER_SIZE,SMALLER_SIZE)
image_right = crop_center(image_right, SMALLER_SIZE,SMALLER_SIZE)
print("We have images of resolution:", image_left.shape, image_right.shape)
test = [image_left], [image_right], [image_right]
import copy
foo = copy.deepcopy(test)
A = self.dataPreprocesser.process_dataset(test, foo, foo)
test = A[0]
"""
# load data from folders
import DataLoader, Debugger, DatasetInstance_OurAerial
self.dataLoaderTMP = DataLoader.DataLoader(self.settings)
self.debugger = Debugger.Debugger(self.settings)
dataset_variant = "256_cleanManual"
dataset_variant = "6368_special"
self.datasetInstanceTMP= DatasetInstance_OurAerial.DatasetInstance_OurAerial(self.settings, self.dataLoaderTMP, dataset_variant)
self.dataPreprocesserTMP = DataPreprocesser.DataPreprocesser(self.settings, self.datasetInstanceTMP)
self.data, self.paths = self.datasetInstanceTMP.load_dataset() # this is a big file, even just loading takes a lot of time!
print("TMP Dataset loaded with", len(self.data[0]), "images.")
self.train, self.val, self.test = self.datasetInstanceTMP.split_train_val_test(self.data)
self.train_paths, self.val_paths, self.test_paths = self.datasetInstanceTMP.split_train_val_test(self.paths)
print("Has ", len(self.train[0]), "train, ", len(self.val[0]), "val, ", len(self.test[0]), "test, ")
# dataPreprocesser is the original one, while dataPreprocesserTMP is just the small test set dataset
# we want to use the original one for preprocessing of the images!
self.train, self.val, self.test = self.dataPreprocesser.process_dataset(self.train, self.val, self.test)
"""
print("Finally we have", len(test[0]), "test images.")
print("Predicting now ...")
test_L, test_R, test_V = test
if test_L.shape[3] > 3:
test_L = test_L[:,:,:,1:4]
test_R = test_R[:,:,:,1:4]
# OUCH MEMORY DIES FOR LARGE IMAGES - we will need to tile it ...
predicted = self.model.predict(x=[test_L, test_R], batch_size=1)
if self.use_sigmoid_or_softmax == 'softmax':
predicted = predicted[:, :, :, 1]
else:
predicted = predicted.reshape(predicted.shape[:-1])
predicted = self.dataPreprocesser.postprocess_labels(predicted)
test_L, test_R = self.dataPreprocesser.postprocess_images(test_L, test_R)
#test_L = test_L / 255.0
#test_R = test_R / 255.0
#predicted = predicted / 255.0
print("test_L shape:", test_L.shape)
print("test_R shape:", test_R.shape)
print("predicted shape:", predicted.shape)
print("test_L max, min:", np.max(test_L), np.min(test_L))
print("test_R max, min:", np.max(test_R), np.min(test_R))
print("pred max, min:", np.max(predicted), np.min(predicted))
"""
test_L shape: (1, 2048, 2048, 3)
test_R shape: (1, 2048, 2048, 3)
predicted shape: (1, 2048, 2048)
test_L max, min: 6.03318 -3.067333
test_R max, min: 4.3578467 -3.012093
pred max, min: 1.0 3.6731425e-09
"""
off = 0
by = 1
by = min(by, len(test_L))
while off < len(predicted):
#self.debugger.viewQuadrupples(predicted, predicted, predicted, predicted, how_many=by, off=off, show=show, save=save)
self.debugger.viewTripples(test_L, test_R, predicted, how_many=by, off=off)
off += by
print("TODO: Save as images ...")
def create_model(self, backbone='resnet34', custom_weights_file = "imagenet", input_size = 112, channels = 3):
model = SiameseUnet(backbone, encoder_weights=custom_weights_file, classes=2, activation='softmax',
input_shape=(input_size, input_size, channels), encoder_freeze=False)
print("Model loaded:")
print("model.input", model.input)
print("model.output", model.output)
# Loss and metrics:
loss = "categorical_crossentropy"
weights = [1, 3]
loss = weighted_categorical_crossentropy(weights)
metric = "categorical_accuracy"
model.compile(optimizer=Adam(lr=0.00001), loss=loss, metrics=[metric, 'mse'])
return model