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binary_fine_tuning.py
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binary_fine_tuning.py
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import copy
import os.path
import torch
import torch.utils.data
from tqdm import tqdm
import numpy as np
import data_preprocessing
import models
import context_handlers
import neural_evaluation
import neural_metrics
import backbone_pipeline
import neural_fine_tuning
import utils
import label
# All the label having sufficient result is saved in the file":
best_f1_label_file_name = 'best_f1_label_file_name.txt'
def save_label_with_good_f1_score(l: label.Label):
# Check if file exists (optional)
if os.path.exists(best_f1_label_file_name):
# Open the file in read mode ("r")
with open(best_f1_label_file_name, "r") as file:
# Read all lines into a set (efficient for checking membership)
existing_labels = set(line.strip() for line in file)
# Check if l.l_str is not present in the set
if l.l_str not in existing_labels:
# If not present, open in write mode ("a") to append
with open(best_f1_label_file_name, "a") as file:
file.write(f'{l.l_str}\n')
else:
# If file doesn't exist, create it and write (same as before)
with open(best_f1_label_file_name, "w") as file:
file.write(f'{l.l_str}\n')
def fine_tune_binary_model(data_str: str,
l: label.Label,
lr: float,
fine_tuner: models.FineTuner,
device: torch.device,
loaders: dict[str, torch.utils.data.DataLoader],
num_epochs: int,
positive_class_weight: list[float] = None,
save_files: bool = True,
evaluate_on_test: bool = True,
train_eval_split: float = None,
previous_f1_score: float = None):
fine_tuner.to(device)
fine_tuner.train()
train_loader = loaders['train']
num_batches = len(train_loader)
if positive_class_weight is not None:
positive_class_weight = torch.tensor(positive_class_weight).float().to(device)
criterion = torch.nn.BCEWithLogitsLoss(pos_weight=positive_class_weight)
else:
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(params=fine_tuner.parameters(),
lr=lr)
neural_fine_tuning.print_fine_tuning_initialization(
fine_tuner=fine_tuner,
num_epochs=num_epochs,
lr=lr,
device=device)
slicing_window = [0, 0]
best_fine_tuner = copy.deepcopy(fine_tuner)
print('#' * 100 + '\n')
for epoch in range(num_epochs):
with context_handlers.TimeWrapper():
total_running_loss = torch.Tensor([0.0]).to(device)
train_predictions = []
train_ground_truths = []
batches = tqdm(enumerate(train_loader, 0),
total=num_batches)
for batch_num, batch in batches:
with context_handlers.ClearCache(device=device):
X, Y = batch[0].to(device), batch[1].to(device)
Y_one_hot = torch.nn.functional.one_hot(Y, num_classes=2).float()
optimizer.zero_grad()
Y_pred = fine_tuner(X)
batch_total_loss = criterion(Y_pred, Y_one_hot)
# neural_metrics.print_post_batch_metrics(batch_num=batch_num,
# num_batches=num_batches,
# batch_total_loss=batch_total_loss.item())
batch_total_loss.backward()
optimizer.step()
total_running_loss += batch_total_loss.item()
predicted = torch.max(Y_pred, 1)[1]
train_predictions += predicted.tolist()
train_ground_truths += Y.tolist()
del X, Y, Y_pred
print(utils.blue_text(f'class is used for training and number of them '))
print(np.unique(train_ground_truths, return_counts=True))
neural_metrics.get_and_print_post_epoch_binary_metrics(
epoch=epoch,
num_epochs=num_epochs,
train_predictions=train_predictions,
train_ground_truths=train_ground_truths,
total_running_loss=total_running_loss.item()
)
if train_eval_split is not None:
_, _, f1 = neural_evaluation.run_binary_evaluating_pipeline(model_name=model_name_in_main,
l=l,
split='train_eval',
lr=lr,
loss='BCE',
num_epochs=num_epochs,
pretrained_fine_tuner=fine_tuner,
data_str=data_str,
train_eval_split=train_eval_split)
# Update slicing window, and break if the sum of current sliding window is smaller than previous one:
if f1 > slicing_window[1]:
print(utils.green_text(f'f1 of current fine_tuner is better. Update fine_tuner'))
best_fine_tuner = copy.deepcopy(fine_tuner)
current_sliding_window = [slicing_window[1], f1]
print(f'current sliding window is {current_sliding_window} and previous one is {slicing_window}')
if sum(slicing_window) > sum(current_sliding_window):
print(utils.green_text(f'finish training, stop criteria met'))
break
slicing_window = current_sliding_window
if evaluate_on_test:
_, _, test_f1 = neural_evaluation.run_binary_evaluating_pipeline(model_name=model_name_in_main,
l=l,
split='test',
lr=lr,
loss='BCE',
num_epochs=num_epochs,
pretrained_fine_tuner=best_fine_tuner,
data_str=data_str,
save_files=False)
if previous_f1_score is not None and test_f1 < previous_f1_score:
print(utils.red_text(f'previous f1 score is {previous_f1_score}, greater than current f1 score {test_f1}'
f'do not save model'))
save_files = False
elif test_f1 > 0.7:
print(utils.green_text(f'f1 score for {l} is sufficient on test: {test_f1}'))
save_label_with_good_f1_score(l=l)
else:
print(utils.red_text(f'f1 score for {l} is not sufficient on test: {test_f1}'))
save_files = False
print('#' * 100)
if save_files:
neural_evaluation.run_binary_evaluating_pipeline(model_name=model_name_in_main,
l=l,
split='test',
lr=lr,
loss='BCE',
num_epochs=num_epochs,
pretrained_fine_tuner=best_fine_tuner,
data_str=data_str,
save_files=True)
torch.save(best_fine_tuner.state_dict(),
f"models/binary_models/binary_{l}_{best_fine_tuner}_lr{lr}_loss_{loss}_e{num_epochs}.pth")
run_l_binary_evaluating_pipeline_from_train(data_str=data_str,
lr=lr,
num_epochs=num_epochs,
model_name=model_name_in_main,
l=l)
def run_l_binary_fine_tuning_pipeline(data_str: str,
model_name: str,
l: label.Label,
lr: float,
num_epochs: int,
train_eval_split: float = None,
save_files: bool = True,
previous_f1_score: float = None):
preprocessor, fine_tuners, loaders, devices = backbone_pipeline.initiate(
data_str=data_str,
lr=lr,
model_name=model_name,
train_eval_split=train_eval_split,
l=l)
for fine_tuner in fine_tuners:
fine_tune_binary_model(data_str=data_str,
l=l,
lr=lr,
fine_tuner=fine_tuner,
device=devices[0],
loaders=loaders,
num_epochs=num_epochs,
save_files=save_files,
train_eval_split=train_eval_split,
previous_f1_score=previous_f1_score
)
print('#' * 100)
def run_l_binary_evaluating_pipeline_from_train(data_str: str,
model_name: str,
l: label.Label,
lr: float,
num_epochs: int):
pretrained_path = f"models/binary_models/binary_{l}_{model_name}_lr{lr}_loss_{loss}_e{num_epochs}.pth"
try:
neural_evaluation.run_binary_evaluating_pipeline(model_name=model_name_in_main,
l=l,
split='train',
lr=lr,
loss='BCE',
num_epochs=num_epochs,
pretrained_path=pretrained_path,
data_str=data_str)
except FileNotFoundError:
print(f'There is no pretrained {model_name} model for {l}')
print('#' * 100)
if __name__ == '__main__':
# data_str_in_main = 'imagenet'
# num_epochs_in_main = 5
# lr_in_main = 0.000001
# model_name_in_main = 'dinov2_vits14'
# loss = 'BCE'
# train_eval_split_in_main = 0.8
# data_str_in_main = 'military_vehicles'
# num_epochs_in_main = 5
# lr_in_main = 0.0001
# model_name_in_main = 'vit_b_16'
# loss = 'BCE'
# train_eval_split_in_main = 0.8
data_str_in_main = 'openimage'
num_epochs_in_main = 5
lr_in_main = 0.000001
model_name_in_main = 'dinov2_vits14'
loss = 'BCE'
train_eval_split_in_main = 0.8
preprocessor_in_main = data_preprocessing.FineCoarseDataPreprocessor(data_str_in_main)
download_path = []
g_str = None
# for label_idx in range(len(preprocessor_in_main.coarse_grain_classes_str)):
# l_str = preprocessor_in_main.coarse_grain_classes_str[label_idx]
# l_in_main = preprocessor_in_main.coarse_grain_labels[l_str]
# g_str = 'coarse'
for label_idx in range(len(preprocessor_in_main.fine_grain_classes_str)):
l_str = preprocessor_in_main.fine_grain_classes_str[label_idx]
l_in_main = preprocessor_in_main.fine_grain_labels[l_str]
g_str = 'fine'
save_metric = neural_evaluation.evaluate_binary_models_from_files(data_str=data_str_in_main,
g_str=g_str,
test=True,
lr=lr_in_main,
num_epochs=num_epochs_in_main,
model_name=model_name_in_main,
l=l_in_main)
if save_metric is not None:
if save_metric[1] > 0.7:
print(f'binary model of class {l_in_main} is finished with sufficient f1 score {save_metric[1]}')
print(f'get prediction from train set)')
save_label_with_good_f1_score(l=l_in_main)
test_save_path = data_preprocessing.get_filepath(model_name=model_name_in_main,
l=l_in_main,
test=True,
loss=loss,
lr=lr_in_main,
pred=True,
epoch=num_epochs_in_main,
data_str=data_str_in_main)
train_save_path = data_preprocessing.get_filepath(model_name=model_name_in_main,
l=l_in_main,
test=False,
loss=loss,
lr=lr_in_main,
pred=True,
epoch=num_epochs_in_main,
data_str=data_str_in_main)
if os.path.exists(train_save_path):
print(utils.green_text(f'file {train_save_path} already exist, train prediction is checkout'))
download_path.append(train_save_path)
download_path.append(test_save_path)
else:
print(utils.red_text(f'file {train_save_path} do not exist, train prediction is created'))
run_l_binary_evaluating_pipeline_from_train(data_str=data_str_in_main,
lr=lr_in_main,
num_epochs=num_epochs_in_main,
model_name=model_name_in_main,
l=l_in_main)
continue
print(utils.red_text(f'Train model for {l_in_main} to get sufficient f1 score'))
run_l_binary_fine_tuning_pipeline(data_str=data_str_in_main,
model_name=model_name_in_main,
l=l_in_main,
lr=lr_in_main,
num_epochs=num_epochs_in_main,
save_files=True,
train_eval_split=train_eval_split_in_main,
previous_f1_score=save_metric[1] if save_metric is not None else None)
print(utils.green_text('#' * 50))
print(utils.green_text(f'Use these file for binary condition. This is for {data_str_in_main} dataset,'
f'model name {model_name_in_main} and {g_str} grain class'))
print(utils.green_text('#' * 50))
for file_path in download_path:
print(file_path)