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testing_19_classes_single_script.py
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import warnings
warnings.filterwarnings("ignore", message="Mean of empty slice")
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
import numpy as np
import random
from IPython.display import clear_output
import os.path
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler
import torch.nn.init
from torch.autograd import Variable
from torch import sigmoid
from tqdm import tqdm
from dataset import BigEarthNet_Dataset
from evaluation import testing, validation, accuracy
from helper import mkdir, save_checkpoint, show_time, load_checkpoint
import datetime
# =============================================================================
# HYPER_PARAMETERS
# =============================================================================
Batch_size = 256
Model_name = []
epoch_weight_path = []
Model_name.append('base')
epoch_weight_path.append('base/epoch_191_val_acc_0.807210_.pkl')
Model_name.append('weighted_loss_from_the_weights_of_base_low_lr')
epoch_weight_path.append('weighted_loss_from_the_weights_of_base_low_lr/epoch_191_val_acc_0.696328_.pkl')
Model_name.append('weighted_loss_from_the_weights_of_base')
epoch_weight_path.append('weighted_loss_from_the_weights_of_base/epoch_191_val_acc_0.688985_.pkl')
Model_name.append('weighted_sampler_from_the_weights_of_base')
epoch_weight_path.append('weighted_sampler_from_the_weights_of_base/epoch_178_val_acc_0.802138_.pkl')
Model_name.append('weighted_sampler_from_the_weights_of_base')
epoch_weight_path.append('weighted_sampler_from_the_weights_of_base/epoch_179_val_acc_0.802008_.pkl')
Model_name.append('weighted_sampler_from_the_weights_of_base')
epoch_weight_path.append('weighted_sampler_from_the_weights_of_base/epoch_183_val_acc_0.801986_.pkl')
Model_name.append('weighted_sampler_from_the_weights_of_base')
epoch_weight_path.append('weighted_sampler_from_the_weights_of_base/epoch_185_val_acc_0.801947_.pkl' )
Model_name.append('weighted_sampler_and_loss_from_the_weights_of_base')
epoch_weight_path.append('weighted_sampler_and_loss_from_the_weights_of_base/epoch_179_val_acc_0.696578_.pkl')
Model_name.append('weighted_sampler_and_loss_from_the_weights_of_base')
epoch_weight_path.append('weighted_sampler_and_loss_from_the_weights_of_base/epoch_180_val_acc_0.695897_.pkl')
Model_name.append('weighted_sampler_and_loss_from_the_weights_of_base')
epoch_weight_path.append('weighted_sampler_and_loss_from_the_weights_of_base/epoch_192_val_acc_0.695803_.pkl')
Model_name.append('weighted_sampler_and_loss_from_the_weights_of_base')
epoch_weight_path.append('weighted_sampler_and_loss_from_the_weights_of_base/epoch_194_val_acc_0.695327_.pkl')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# =============================================================================
# LOAD THE MODEL
# =============================================================================
def load_model():
from torchvision import models
model = models.resnet50(pretrained=False)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs,19)# len (LABELS))
model.conv1 = nn.Conv2d(12, 64, kernel_size=7, stride=2, padding = 3, bias = False)
print('Model Loaded')
return model#.cuda()
# =============================================================================
# DATA_LOADER
# =============================================================================
def data_loader(BATCH_SIZE = Batch_size):
img_folder = '../BEN/Images/'
test_img_path = img_folder + 'Test/'
lab_folder = '../BEN/GT/'
test_lab_path = lab_folder + 'Test/'
#tar = pd.read_csv('E:\Biglabelsjustclassnum.csv')
test_data = BigEarthNet_Dataset(test_img_path,test_lab_path)
test_loader=torch.utils.data.DataLoader(test_data,batch_size=BATCH_SIZE, num_workers=16,pin_memory = True)
return {'test': test_loader}
#def main():
#TODO will change every processing into one main function
if __name__ == '__main__':
#TODO write a function for seeding
torch.manual_seed(0)
np.random.seed(0)
print('pytorch version', torch.__version__)
torch.cuda.manual_seed(0)
np.random.seed(0)
random.seed(0)
loader = data_loader()
#model.load_state_dict(torch.load('./BEN_models/base/epoch94_acc_0.747304'))
for step in range(len(epoch_weight_path)):
model = load_model()
mkdir(os.path.join('./BEN_models', Model_name[step]))
load_checkpoint(model = model,
checkpoint_path = os.path.join('./BEN_models', epoch_weight_path[step]))
model.to(device)
test_accuracy, conf = testing(model, loader['test'], epoch_weight_path[step][:-5])
del model