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viltkamera_classifier.py
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viltkamera_classifier.py
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import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
from torchvision import transforms
import time
import copy
import json
import torch.utils.data as data
from torch.utils.data import DataLoader, WeightedRandomSampler
import albumentations as A
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
from collections import defaultdict
import gc
# self defined functions
from fusion_model_v2 import FusionModel, EarlyFusionModel, MetadataModel
from conventional_models import *
# Dataset
from data.NINA_dataset_v2 import NINADataset
from data.NINA_meta_dataset import NINAMetaDataset
# reduces the size of the dataset, respective to the identifier
def reduce_sample_size(annotations, identifier, new_size):
"""
Reduces the data size of each individual identifier.
Reduces the size of the dataset by splitting the dataset into a dictionary
with each unique identifier as the key, and a list of all datapoints which
matches that identifier. Afterwards, each list is reduced to the size
len(samples_of_identifier)*new_size.
---------
Parameters:
annotations - list of all samples in dataset
identifier - unique key to split list by, use class label if unsure
new_size - float in range 0 - 1 determining the % size of the new dataset 1 means no samples are removed.
---------
Returns: size reduced annotations list
"""
d = {}
for entry in annotations:
if(entry[identifier] not in d.keys()):
d[entry[identifier]] = [entry]
else:
d[entry[identifier]].append(entry)
new_annotations = []
for key in d.keys():
samples = d[key]
sample_size = int(new_size * len(samples))
discard_size = len(samples) - sample_size
samples, _ = torch.utils.data.random_split(samples, [sample_size, discard_size]) # type: ignore
new_annotations += samples
return new_annotations
def confusion_matrix(model, dataloader, class_names, device):
n_classes = len(class_names)
conf_matrix = np.zeros((n_classes, n_classes), dtype=int)
with torch.no_grad():
for inputs, metadata, labels in dataloader:
inputs = inputs.to(device)
labels = labels.to(device)
metadata = metadata.to(device)
outputs = model(inputs, metadata)
_, preds = torch.max(outputs, 1)
for t, p in zip(labels.view(-1), preds.view(-1)):
try:
conf_matrix[t.long(), p.long()] += 1
except:
print(t.long(), p.long())
return conf_matrix
def create_model(model_type, num_meta_features, output_size):
if model_type == "ResNet18":
return ResNet18Model(num_meta_features, output_size)
elif model_type == "AlexNet":
return AlexNetModel(num_meta_features, output_size)
elif model_type == "Inception":
return InceptionModel(num_meta_features, output_size)
elif model_type == "ResNet50":
return ResNet50Model(num_meta_features, output_size)
elif model_type == "EfficientNet":
return EfficientNetModel(num_meta_features, output_size)
elif model_type == "EfficientNet1":
return EfficientNet2Model(num_meta_features, output_size)
elif model_type == "EfficientNet2":
return EfficientNet3Model(num_meta_features, output_size)
elif model_type == "EfficientNet3":
return EfficientNet4Model(num_meta_features, output_size)
elif model_type == "EfficientNet4":
return EfficientNet5Model(num_meta_features, output_size)
elif model_type == "EfficientNet5":
return EfficientNet6Model(num_meta_features, output_size)
elif model_type == "EfficientNet6":
return EfficientNet1Model(num_meta_features, output_size)
elif model_type == "EfficientNet7":
return EfficientNet7Model(num_meta_features, output_size)
elif model_type == "ViT":
return ViTModel(num_meta_features, output_size)
elif model_type == "MetadataModel":
return MetadataModel(num_meta_features, output_size)
elif model_type == "Fusion":
return FusionModel(num_meta_features, output_size)
elif model_type == "EarlyFusion":
return EarlyFusionModel(num_meta_features, output_size)
elif model_type == "MetadataModel":
return MetadataModel(num_meta_features, output_size)
else:
raise ValueError(f"Invalid model type: {model_type}")
def train_model(model, criterion, optimizer, scheduler, dataloaders, device, model_iteration, num_epochs=25, num_finished=0):
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
best_loss = 1e10
dataset_sizes = {x: len(dataloaders[x].dataset) for x in ['train', 'val']}
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch + 1, num_epochs))
print('-' * 10)
if(epoch < num_finished):
print("Skipping")
scheduler.step()
continue
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
for inputs, metadata, targets in dataloaders[phase]:
inputs = inputs.to(device)
metadata = metadata.to(device)
targets = targets.to(device)
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs, metadata)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, targets)
if phase == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * targets.size(0)
running_corrects += torch.sum(preds == targets.data)
if phase == 'train':
scheduler.step()
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects / dataset_sizes[phase]
print('{}; Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
if phase == 'val' and epoch_loss < best_loss:
best_loss = epoch_loss
best_model_wts = copy.deepcopy(model.state_dict())
torch.save(model.state_dict(), f"./weights_{model_iteration}.bin")
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(best_model_wts)
return model
def create_weighted_sampler(annotations, class_names):
class_counts = {class_name: 0 for class_name in class_names}
for ann in annotations:
class_counts[ann['Species']] += 1
weights = []
for ann in annotations:
weights.append(1.0 / class_counts[ann['Species']])
return WeightedRandomSampler(weights, len(annotations))
def get_samples(bp):
with open(bp + "metadata.json") as f:
metadata = json.load(f)
categories = metadata['categories']
annotations = metadata['annotations']
return categories, annotations
def define_transforms():
mean = np.array([0.5, 0.5, 0.5])
std = np.array([0.25, 0.25, 0.25])
transform = transforms.Compose([
transforms.ToTensor(),
# transforms.Resize((256, 256)), # only if 512x512 is too large for GPU
transforms.Normalize(mean, std)
])
albu_transform = A.Compose([
A.HorizontalFlip(p=0.5),
A.Rotate(limit=45, p=1.0),
A.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1, p=1.0),
A.augmentations.dropout.coarse_dropout.CoarseDropout (max_height=32, max_width=32, min_height=None, min_width=None)
])
return transform, albu_transform
def model_evaluation(matrices, models_run, categories):
model_iteration = 0 # used to save each model individually
for (conf_mat, model_name) in zip(matrices, models_run):
print(model_name)
f = open(f"./confusion_matrix_{model_iteration}.npy", "wb")
np.save(f, conf_mat)
f.close()
print("\nConfusion Matrix:")
for row in conf_mat:
print(str(list(np.round(row).astype(int))).replace("\n", "").replace(",", ""))
print("-"*80)
TP = np.diag(conf_mat)
FP = conf_mat.sum(axis=0) - TP
FN = conf_mat.sum(axis=1) - TP
TN = conf_mat.sum() - (FP + FN + TP)
accuracy = np.round((TP+TN)/(TP+FP+FN+TN), 3)
precision = np.round(TP/(TP+FP), 3)
recall = np.round(TP/(TP+FN), 3)
f1 = np.round(2 * precision*recall/(precision+recall), 3)
kappa = np.round(cohen_kappa_score(conf_mat), 3)
file = open(f"Evaluation_{model_iteration}.txt", "w")
s = "Name".ljust(15) + " | " + "Accuracy".ljust(9) + " | " + "Precision".ljust(9) + " | " + "Recall".ljust(9) + " | " + "f1-Score".ljust(9) + " | " + "Kappa".ljust(9) + " |"
print(s)
file.write(s + "\n")
for c, a, p, r, f in zip(categories, accuracy, precision, recall, f1):
s = str(c).ljust(15) + " | " + str(a).ljust(9) + " | " + str(p).ljust(9) + " | " + str(r).ljust(9) + " | " + str(f).ljust(9) + " | " + str(kappa).ljust(9) + " |"
print(s)
file.write(s + "\n")
file.close()
print("="*80, "\n")
model_iteration += 1
def remove_classes(annotations, c):
new_annotations = []
for anot in annotations:
if (anot['Species'] not in c):
new_annotations.append(anot)
return new_annotations
def keep_classes(annotations, c):
new_annotations = []
for anot in annotations:
if (anot['Species'] in c):
new_annotations.append(anot)
return new_annotations
def combine_change_class(annotations, src, dst):
print("Replacing:", src, "with:", dst)
new_anot = []
for anot in annotations:
if(anot['Species'] == src):
anot['Species'] = dst
new_anot.append(anot)
return new_anot
def cohen_kappa_score(conf_matrix):
n_classes = conf_matrix.shape[0]
# Calculate observed accuracy (Po)
Po = np.trace(conf_matrix) / conf_matrix.sum()
# Calculate expected accuracy (Pe)
Pe = sum((conf_matrix.sum(axis=0)[i] * conf_matrix.sum(axis=1)[i]) for i in range(n_classes)) / (conf_matrix.sum() ** 2)
# Calculate Cohen's Kappa score (k)
k = (Po - Pe) / (1 - Pe)
return k
def main():
# Data loading
bp = "/cluster/home/aslakto/ondemand/data/viltkamera/NINA/Images/"
categories, annotations = get_samples(bp)
# some class combination to reduce classes
annotations = combine_change_class(annotations, "Squirrel", "Rodent")
annotations = combine_change_class(annotations, "Rabbit", "Rodent")
# for anot in annotations:
# if(anot['Species'] != "Deer"):
# anot['Species'] = "Not Deer"
categories = {}
for anot in annotations:
c = anot['Species']
if c not in categories.keys():
categories[c] = 1
else:
categories[c] += 1
class_names = [cat for cat in categories.keys()]
for anot in annotations:
anot['Species_ID'] = class_names.index(anot['Species'])
# transforms used
transform, albu_transform = define_transforms()
# reduce size of whole dataset for trial runs (range 0-1, lower number means less data)
# annotations = reduce_sample_size(annotations, 'Species_ID', 0.005)
# model configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
DL_models = ["EfficientNet7", "EfficientNet6", "EfficientNet5", "EfficientNet4", "EfficientNet3", "EfficientNet2", "EfficientNet1"]
DL_models = ["EfficientNet7"]
losses = [nn.CrossEntropyLoss()]
confusion_matrices = []
models_run = []
model_iteration = 0 # used to save each model individually
# data splitting and dataloader creation
annot_train, annot_test= train_test_split(annotations, test_size=0.1)
annot_train, annot_val = train_test_split(annot_train, test_size=0.1)
dataset_train = NINADataset(annot_train, bp, transform, augment_transform=albu_transform, augment=True)
dataset_val = NINADataset(annot_val, bp, transform)
dataset_test = NINADataset(annot_test, bp, transform)
wrs = create_weighted_sampler(annot_train, list(categories.keys()))
bz = 12
dataloaders = {
'train': DataLoader(dataset_train, batch_size=bz, sampler=wrs, num_workers=8), # training dataset augmented
'val': DataLoader(dataset_val, batch_size=bz, num_workers=8),
'test': DataLoader(dataset_test, batch_size=bz, num_workers=8)
}
# get length of metadata vector
img, meta_features, _ = dataset_train[15]
print(f"Device: {device}")
print(len(class_names), categories)
print(f"Full size: {len(annotations)}, Train size: {len(dataset_train)}, validation size: {len(dataset_val)}, test size: {len(dataset_test)}")
for model_string in DL_models:
print(model_string)
for criterion in losses:
model = create_model(model_string, len(meta_features), len(class_names))
model = model.to(device)
print("Loading model")
model.load_state_dict(torch.load('./weights_0.bin'))
optimizer_conv = optim.Adam(model.parameters(), lr=1e-4)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)
model = train_model(model, criterion, optimizer_conv, exp_lr_scheduler, dataloaders, device, model_iteration, num_epochs=25, num_finished=18)
conf_mat = confusion_matrix(model, dataloaders['test'], class_names, device)
confusion_matrices.append(conf_mat)
models_run.append(model_string)
# Delete model and optimizer
del model, optimizer_conv
# Call garbage collector
gc.collect()
# Empty cuda cache
torch.cuda.empty_cache()
model_iteration += 1
model_evaluation(confusion_matrices, models_run, class_names)
if __name__ == "__main__":
main()