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train.py
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import argparse
import os
import numpy as np
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.optim as optim
import json
import models.SVHNet as svhnet
import utils
from tqdm import tqdm
from eval import evaluate
from dataloader_utils import load_dataset
"""
A script to train models using checkpointing and the evaluation at fixed steps on val set.
The machinery has been borrowed from cs230 @ Stanford University.
"""
parser = argparse.ArgumentParser()
parser.add_argument('--param_path', default=None, help="Path to the folder having params.json")
parser.add_argument('--resume_path', default=None, help='Path to any previous saved checkpoint')
parser.add_argument('--teacher_checkpoint', default=None, help='Full Path to a trained teacher model')
def train(net, dataloader, loss_fn, params, metrics, optimizer):
"""
Train the net for one epoch i.e 1..len(dataloader)
net: The model to test
params: The hyperparams
loss_fn: The loss function
metrics: The metrics dictionary containing evaluation metrics.
"""
net.train()
summaries = []
loss_avg = utils.AverageMeter()
with tqdm(total=len(dataloader)) as t:
for i, (data_batch, label_batch) in enumerate(dataloader):
if params.cuda:
data_batch, label_batch = data_batch.cuda(), label_batch.cuda()
data_batch, label_batch = Variable(data_batch), Variable(label_batch)
# print(data_batch.size())
# print(label_batch.size())
output_batch = net(data_batch)
print(output_batch.size())
loss = loss_fn(output_batch, label_batch)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i % params.save_summary_steps == 0 :
out_np = output_batch.data.cpu().numpy()
label_np = label_batch.data.cpu().numpy()
batch_summary = {metric: metrics[metric](out_np, label_np) for metric in metrics}
batch_summary['loss'] = loss.data[0].cpu().item()
summaries.append(batch_summary)
loss_avg.update(loss.data[0].cpu().item())
t.set_postfix(loss='{:05.3f}'.format(loss_avg()))
t.update()
# compute mean of all the metrics
mean_metrics = {metric:np.mean([m[metric] for m in summaries]) for metric in summaries[0]}
metrics_string = " ; ".join("{}: {:05.3f}".format(k, v) for k, v in mean_metrics.items())
logging.info("Train Metrics: "+ metrics_string)
def train_and_eval(net, train_loader, val_loader, optimizer, loss_fn, metrics, params, model_dir, restore=None):
"""
Train and evaluate every epoch of a model.
net: The model.
train/val loader: The data loaders
params: The parameters parsed from JSON file
restore: if there is a checkpoint restore from that point.
"""
best_val_acc = 0.0
if restore is not None:
restore_file = os.path.join(args.param_path, args.resume_path + '_pth.tar')
logging.info("Loaded checkpoints from:{}".format(restore_file))
utils.load_checkpoint(restore_file, net, optimizer)
for ep in range(params.num_epochs):
logging.info("Running epoch: {}/{}".format(ep+1, params.num_epochs))
# train one epoch
train(net, train_loader, loss_fn, params, metrics, optimizer)
val_metrics = evaluate(net, val_loader, loss_fn, params, metrics)
val_acc = val_metrics['accuracy']
isbest = val_acc >= best_val_acc
utils.save_checkpoint({"epoch":ep, "state_dict":net.state_dict(), "optimizer":optimizer.state_dict()},
isBest=isbest, ckpt_dir=model_dir)
if isbest:
# if the accuracy is great save it to best.json
logging.info("New best accuracy found!")
best_val_acc = val_acc
best_json_path = os.path.join(model_dir, "best_model_params.json")
utils.save_dict_to_json(val_metrics, best_json_path)
last_acc_path = os.path.join(model_dir, 'last_acc_metrics.json')
utils.save_dict_to_json(val_metrics, last_acc_path)
if __name__ == '__main__':
args = parser.parse_args()
params = utils.ParamParser(os.path.join(args.param_path, 'params.json'))
params.cuda = torch.cuda.is_available()
utils.setLogger(os.path.join(args.param_path, "train.log"))
logging.info("Loading the datasets")
# TODO: Load the datasets
train_loader = load_dataset('train', 'cifar',params)
val_loader = load_dataset('val', 'cifar', params)
logging.info("finished loading the datasets")
if params.model_name == "base":
model = svhnet.BaseSVHNet(params.initial_channel, params.kernel_size, use_dropout=True).cuda() if params.cuda else svhnet.BaseSVHNet(params.initial_channel, params.kernel_size, use_dropout=True)
elif params.model_name == "stn":
spatial_dim = (params.height, params.width)
model = svhnet.STNSVHNet(spatial_dim, params.initial_channel, params.stn_kernel_size, params.kernel_size, use_dropout=True).cuda() if params.cuda else svhnet.STNSVHNet(spatial_dim, params.initial_channel, params.stn_kernel_size, params.kernel_size, use_dropout=True)
loss_fn = svhnet.loss_fn
metrics = svhnet.metrics
optimizer = optim.Adam(model.parameters(), lr=params.learning_rate)
logging.info("Started training for {} epochs".format(params.num_epochs))
train_and_eval(model, train_loader, val_loader, optimizer,loss_fn, metrics, params, args.param_path)
# loss_fn = net.loss_function
# metrics = net.metrics
# if params.model_name == "resnet18":
# model = resnet.ResNet18().cuda() if params.cuda else resnet.ResNet18(params)
# elif params.model_name == "cnn":
# model = net.CIFARNet(params).cuda() if params.cuda else net.CIFARNet(params)
# optimizer = optim.Adam(model.parameters(), lr=params.learning_rate)
# train_and_eval(model, train_loader, val_loader, optimizer,loss_fn, metrics, params, args.param_path)