85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
import torch
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import matplotlib.pyplot as plt
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from confic import OUTDIR
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class Averager:
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def __init__(self):
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self.current_total = 0.0
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self.iterations = 0.0
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def send(self, value):
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self.current_total += value
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self.iterations += 1
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@property
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def value(self):
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if self.iterations == 0:
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return 0
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else:
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return 1.0 * self.current_total / self.iterations
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def reset(self):
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self.current_total = 0.0
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self.iterations = 0.0
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class SaveBestModel:
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"""
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Class to save the best model while training. If the current epoch's
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validation loss is less than the previous least less, then save the
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model state.
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"""
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def __init__(
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self, best_valid_loss=float('inf')
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):
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self.best_valid_loss = best_valid_loss
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def __call__(
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self, current_valid_loss,
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epoch, model, optimizer
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):
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if current_valid_loss < self.best_valid_loss:
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self.best_valid_loss = current_valid_loss
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print(f"\nBest validation loss: {self.best_valid_loss}")
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print(f"\nSaving best model for epoch: {epoch + 1}\n")
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torch.save({
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'epoch': epoch + 1,
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'model_state_dict': model.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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}, f'./{OUTDIR}/best_model.pth')
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def collate_fn(batch):
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"""
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To handle the data loading as different images may have different number
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of objects and to handle varying size tensors as well.
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"""
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return tuple(zip(*batch))
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def save_model(epoch, model, optimizer):
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"""
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Function to save the trained model till current epoch, or whenver called
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"""
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torch.save({
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'epoch': epoch+1,
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'model_state_dict': model.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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}, f'./{OUTDIR}/last_model.pth')
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def save_loss_plot(OUT_DIR, train_loss, val_loss):
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figure_1, train_ax = plt.subplots()
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figure_2, valid_ax = plt.subplots()
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train_ax.plot(train_loss, color='tab:blue')
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train_ax.set_xlabel('iterations')
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train_ax.set_ylabel('train loss')
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valid_ax.plot(val_loss, color='tab:red')
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valid_ax.set_xlabel('iterations')
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valid_ax.set_ylabel('validation loss')
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figure_1.savefig(f"{OUT_DIR}/train_loss.png")
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figure_2.savefig(f"{OUT_DIR}/valid_loss.png")
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print('SAVING PLOTS COMPLETE...')
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plt.close('all')
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