def get_transform_fun(resized=False):
if resized == True:
transform_function = et.ExtCompose([
et.ExtRandomCrop(size=2048),
et.ExtRandomCrop(scale=0.7, size=None),
et.ExtEnhanceContrast(),
et.ExtRandomCrop(size=2048, pad_if_needed=True),
et.ExtResize(scale=0.5),
et.ExtRandomHorizontalFlip(p=0.5),
et.ExtRandomCrop(size=512),
et.ExtRandomVerticalFlip(p=0.5),
et.ExtToTensor()
])
else:
transform_function = et.ExtCompose([
et.ExtRandomCrop(size=256),
et.ExtRandomHorizontalFlip(p=0.5),
et.ExtRandomVerticalFlip(p=0.5),
et.ExtEnhanceContrast(),
et.ExtToTensor()
])
return transform_function
file_names_train = file_names_train[shuffled_index]
file_names_train = file_names_train[file_names_train != ".DS_S"]
file_names_val = np.array([
image_name[:-4] for image_name in os.listdir(path_val)
if image_name[-5] != "k"
])
N_files = len(file_names_val)
shuffled_index = np.random.permutation(len(file_names_val))
file_names_val = file_names_val[shuffled_index]
file_names_val = file_names_val[file_names_val != ".DS_S"]
# #FOR EXTENDED DATASET EXPERIMENT
transform_function = et.ExtCompose([
et.ExtRandomHorizontalFlip(p=0.5),
et.ExtRandomCrop(size=SIZE),
et.ExtEnhanceContrast(),
et.ExtRandomVerticalFlip(p=0.5),
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
if binary:
color_dict = data_loader.color_dict_binary
target_dict = data_loader.get_target_dict()
annotations_dict = data_loader.annotations_dict
train_dst = LeatherData(path_mask=path_train,
path_img=path_train,
list_of_filenames=file_names_train,
transform=transform_function,
if image_name[-5] != "k"
])
file_names_train = file_names_train[file_names_train != ".DS_S"]
file_names_val = np.array([
image_name[:-4] for image_name in os.listdir(path_val)
if image_name[-5] != "k"
])
file_names_val = file_names_val[file_names_val != ".DS_S"]
#transform_function = et.ExtCompose([et.ExtEnhanceContrast(), et.ExtRandomCrop((1000, 1000)), et.ExtToTensor(),
# et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
transform_function = et.ExtCompose([
et.ExtResize(scale=0.5),
et.ExtRandomCrop(size=512, semantic_evaluation_resize=True, scale=0.7),
et.ExtEnhanceContrast()
])
denorm = Denormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
if binary:
color_dict = data_loader.color_dict_binary
target_dict = data_loader.get_target_dict()
annotations_dict = data_loader.annotations_dict
else:
color_dict = data_loader.color_dict
target_dict = data_loader.get_target_dict(labels)
annotations_dict = data_loader.annotations_dict
train_dst = LeatherData(path_mask=path_train,
sys.path.append('/zhome/dd/4/128822/Bachelorprojekt/Bachelor-Criterion-AI')
import os
from data_import.data_loader import DataLoader
from data_import.data_pipeline import import_data_and_mask
from semantic_segmentation.DeepLabV3.utils import ext_transforms as et
import torch
import object_detect.helper.utils as utils
from semantic_segmentation.DeepLabV3.dataset_class import LeatherData
from data_import.data_loader import DataLoader
from torch.utils import data
import random
import numpy as np
transform_function = et.ExtCompose([
et.ExtRandomCrop(scale=0.7),
et.ExtRandomHorizontalFlip(p=0.5),
et.ExtRandomVerticalFlip(p=0.5),
et.ExtEnhanceContrast(),
et.ExtToTensor()
])
random_seed = 1
torch.manual_seed(random_seed)
np.random.seed(random_seed)
random.seed(random_seed)
device = torch.device('cpu')
lr = 0.01
layers_to_train = 'classifier'
num_epoch = 1