import sys
sys.path.append("..")
import helpers
helpers.mask_busy_gpus(wait=False)
from keras import backend as K
import numpy as np
from skimage import transform
import cv2
from get_data import load_dataset, unpack
train_images, train_bboxes, train_shapes = load_dataset("data", "train")
val_images, val_bboxes, val_shapes = load_dataset("data", "val")
SAMPLE_SHAPE = (32, 32, 3)
from scores import iou_score # https://en.wikipedia.org/wiki/Jaccard_index
def is_negative_bbox(new_bbox, true_bboxes, eps=1e-1):
"""Check if new bbox not in true bbox list.
There bbox is 4 ints [min_row, min_col, max_row, max_col] without image index."""
for bbox in true_bboxes:
if iou_score(new_bbox, bbox) >= eps:
return False
return True
import torch
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import time
from get_data import load_dataset
# Hyper params
epochs = 12
train_batch_size = 30
test_batch_size = 1000
l_r = 0.0005
momentum = 0.5
# Retrieve the data
train_data, test_data = load_dataset(train_batch_size, test_batch_size)
# References for the main idea of building the __init__/forward functions:
# Training a Classifier — PyTorch Tutorials 1.7.1 documentation. 2020.
# [ONLINE] Available at: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html.
# [Accessed 17 December 2020].
# MNIST Handwritten Digit Recognition in PyTorch - Nextjournal. 2020.
# [ONLINE] Available at: https://nextjournal.com/gkoehler/pytorch-mnist
# [Accessed 17 December 2020].
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
# Set up the structure of layers for the network
# Takes 1-channel images
self.conv1 = nn.Conv2d(1, 10, 5)
import torchvision
import torch
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import time
from get_data import load_dataset
# Hyper params
n_epochs = 10
batch_size_train = 35
batch_size_test = 1000
learning_rate = 0.05
# Retrieve the data
train_loader, test_loader = load_dataset(batch_size_train, batch_size_test)
# References for the main idea of building the __init__/forward functions:
# Training a Classifier — PyTorch Tutorials 1.7.1 documentation. 2020.
# [ONLINE] Available at: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html.
# [Accessed 17 December 2020].
# MNIST Handwritten Digit Recognition in PyTorch - Nextjournal. 2020.
# [ONLINE] Available at: https://nextjournal.com/gkoehler/pytorch-mnist
# [Accessed 17 December 2020].
class SGD(nn.Module):
def __init__(self):
super(SGD, self).__init__()
# Set up the structure of layers for the network
self.fc1 = nn.Linear(784, 200)
self.fc2 = nn.Linear(200, 100)
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Dropout
from keras.layers import BatchNormalization
# from keras.optimizers import SGD
from keras.utils import np_utils
from keras.utils import to_categorical
from keras.models import model_from_json
from get_data import load_dataset
#load_data()
x_train, y_train, x_test, y_test, class_names = load_dataset('data')
num_classes = len(class_names)
image_size = 28
# Reshape and normalize
x_train = x_train.reshape(x_train.shape[0], image_size, image_size,
1).astype('float32')
x_test = x_test.reshape(x_test.shape[0], image_size, image_size,
1).astype('float32')
x_train /= 255.0
x_test /= 255.0
# Convert class vectors to class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
from Network import Network
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
from sklearn.metrics import plot_confusion_matrix
import seaborn as sns
import time
# you change the configuration for the network in the init of Network class
print('\n')
network = Network()
print("Network initialized")
print('\n')
# retrieve data
X_train, y_train, X_val, y_val, X_test, y_test = load_dataset()
print("Data has been loaded")
print("Number of images in dataset: ", len(X_train) + len(X_val))
print("Number of images to test on: ", len(X_test))
print('\n')
# parameters for the network
epochs = 30
minibatch_length = 35
print("Number of epochs: ", epochs)
print("Minibatch length: ", minibatch_length)
print('\n')
print("Epochs have started!")
print('\n')