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abctrain.py
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# -*- coding: utf-8 -*-
"""abctrain.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1mQS5klYhGTAVBisii1RU71vXdVe7zjgK
"""
import torch
import torch.nn as nn
# import torch.nn.functional as F
# # import torchvision.transforms as transforms
# # import torchvision.models as models
# import torch.optim as optim
# import matplotlib.pyplot as plt
# import numpy as np
def evaluate_conv_deconv(conv, deconv, validate_loader,criterion):
val_loss = 0.0
# evaluate model at end of epoch
for i, data in enumerate(validate_loader, 0):
# Get inputs and labels
inputs, labels = data
features, pool_size = conv(inputs)
outputs = deconv(features, pool_size )
# Compute loss
loss = criterion(outputs, inputs)
val_loss += loss.item()
return val_loss
# Evaluate the model
def evaluate_predict(predict_model, head_model, validate_loader,criterion, factor):
with torch.no_grad():
total_loss = 0
total_samples = 0
for data in validate_loader:
inputs, labels = data
features, pool_size = head_model(inputs)
outputs = predict_model(features)
# loss = criterion(torch.exp(outputs), torch.exp(labels))
loss = criterion(outputs*factor, labels*factor)
total_loss += loss.item() * inputs.size(0)
total_samples += inputs.size(0)
avg_loss = total_loss / total_samples
print('Average test loss: %.3f' % avg_loss)
return total_loss
def evaluate_model(model, validate_loader,criterion, factor):
# Evaluate the model
with torch.no_grad():
total_loss = 0
total_samples = 0
for data in validate_loader:
inputs, labels = data
outputs = model(inputs)
loss = criterion(outputs*factor, labels*factor)
# loss = criterion(torch.exp(outputs), torch.exp(labels))
total_loss += loss.item() * inputs.size(0)
total_samples += inputs.size(0)
avg_loss = total_loss / total_samples
print('Average test loss: %.3f' % avg_loss)
return total_loss
def train_conv_deconv(conv_model, deconv_model, train_loader, validate_loader, criterion, optimizer, num_epochs):
num_epochs = 5
for epoch in range(num_epochs):
running_loss = 0.0
train_loss = 0.0
for i, data in enumerate(train_loader, 0):
# Get inputs and labels
inputs, labels = data
# Zero the parameter gradients
optimizer.zero_grad()
# Forward pass
#
features, pool_size = conv_model(inputs)
outputs = deconv_model(features, pool_size )
# outputs =conv_deconv(inputs)
# Compute loss
loss = criterion(outputs, inputs)
# Backward pass and optimization
loss.backward()
# update weights
optimizer.step()
# Print statistics
running_loss += loss.item()
if i % 100 == 99: # Print every 100 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss))
train_loss += running_loss
running_loss = 0.0
val_loss = evaluate_conv_deconv(conv_model, deconv_model, validate_loader,criterion)
print(f'the training loss is {train_loss}, the validation loss is {val_loss}')
# # save each
# torch.save({
# 'epoch': epoch,
# 'model_state_dict': conv_model.state_dict(),
# 'optimizer_state_dict': optimizer.state_dict(),
# 'loss': val_loss,
# }, f'/path/conv_statedict_epoch_{epoch}_loss_{val_loss}')
# save the whole model
# torch.save(conv_model, f'/path/Conv_{epoch}_loss_{val_loss}')
def train_predict(head_model, predict_model, criterion, optimizer, train_loader, validate_loader, num_epochs, factor):
for epoch in range(num_epochs):
running_loss = 0.0
train_loss = 0.0
for i, data in enumerate(train_loader, 0):
# Get inputs and labels
inputs, labels = data
# Zero the parameter gradients
optimizer.zero_grad()
# Forward pass
features, pool_size = head_model(inputs)
outputs = predict_model(features)
# Compute loss
loss = criterion(outputs, labels)
# Backward pass and optimization
loss.backward()
# update weights
optimizer.step()
# Print statistics
running_loss += loss.item()
if i % 100 == 99: # Print every 100 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss))
train_loss +=running_loss
running_loss = 0.0
# validate the model
val_loss = evaluate_predict(predict_model, head_model, validate_loader,criterion, factor)
print(f'the training loss is {train_loss}, the validation loss is {val_loss}')
# # save each
# torch.save(predict_model.state_dict(),f'/path/predict_epoch_{epoch}_loss_{val_loss}.pth')
# torch.save({
# 'epoch': epoch,
# 'model_state_dict': predict_model.state_dict(),
# 'optimizer_state_dict': optimizer.state_dict(),
# 'loss': val_loss,
# }, f'/path/predict_epoch_{epoch}_loss_{val_loss}')
# # save the whole model
# torch.save(predict_model, f'/path/Predict_model_epoch_{epoch}_loss_{val_loss}')
def train_model(model, criterion, optimizer, train_loader, validate_loader, num_epochs, factor):
# net = net.float()
num_epochs = 5
for epoch in range(num_epochs): # loop over the dataset multiple times
train_loss = 0.0
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# print(inputs.dtype)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
# running_loss += loss.item()
running_loss = loss.item()
if i % 100 == 0: # print every 2000 mini-batches
print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss:.3f}')
print(outputs.flatten())
print(labels.flatten())
train_loss += running_loss
running_loss = 0.0
val_loss = evaluate_model(model, validate_loader,criterion, factor)
print(f'the training loss is {train_loss}, the validation loss is {val_loss}')
# # save each
# torch.save({
# 'epoch': epoch,
# 'model_state_dict': model.state_dict(),
# 'optimizer_state_dict': optimizer.state_dict(),
# 'loss': val_loss,
# }, f'/path/predict_epoch_{epoch}_loss_{val_loss}')
# # save the whole model
# torch.save(predict_model, f'/path/Predict_model_epoch_{epoch}_loss_{val_loss}')