train_lstm_cse.py

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import torch.nn.init as init
from torchvision import models, transforms
from torch.utils.data import Dataset, DataLoader
from torch.nn import DataParallel
import os
from PIL import Image, ImageOps
import time
import pickle
import numpy as np
from torchvision.transforms import Lambda
import argparse
import copy
import random
import numbers

parser = argparse.ArgumentParser(description='lstm training')
parser.add_argument('-g', '--gpu', default=[2], nargs='+', type=int, help='index of gpu to use, default 2')
parser.add_argument('-s', '--seq', default=4, type=int, help='sequence length, default 4')
parser.add_argument('-t', '--train', default=100, type=int, help='train batch size, default 100')
parser.add_argument('-v', '--val', default=8, type=int, help='valid batch size, default 8')
parser.add_argument('-o', '--opt', default=1, type=int, help='0 for sgd 1 for adam, default 1')
parser.add_argument('-m', '--multi', default=1, type=int, help='0 for single opt, 1 for multi opt, default 1')
parser.add_argument('-e', '--epo', default=25, type=int, help='epochs to train and val, default 25')
parser.add_argument('-w', '--work', default=2, type=int, help='num of workers to use, default 2')
parser.add_argument('-f', '--flip', default=0, type=int, help='0 for not flip, 1 for flip, default 0')
parser.add_argument('-c', '--crop', default=1, type=int, help='0 rand, 1 cent, 5 five_crop, 10 ten_crop, default 1')
parser.add_argument('-l', '--lr', default=1e-3, type=float, help='learning rate for optimizer, default 1e-3')
parser.add_argument('--momentum', default=0.9, type=float, help='momentum for sgd, default 0.9')
parser.add_argument('--weightdecay', default=0, type=float, help='weight decay for sgd, default 0')
parser.add_argument('--dampening', default=0, type=float, help='dampening for sgd, default 0')
parser.add_argument('--nesterov', default=False, type=bool, help='nesterov momentum, default False')
parser.add_argument('--sgdadjust', default=1, type=int, help='sgd method adjust lr 0 for step 1 for min, default 1')
parser.add_argument('--sgdstep', default=5, type=int, help='number of steps to adjust lr for sgd, default 5')
parser.add_argument('--sgdgamma', default=0.1, type=float, help='gamma of steps to adjust lr for sgd, default 0.1')


args = parser.parse_args()

gpu_usg = ",".join(list(map(str, args.gpu)))
sequence_length = args.seq
train_batch_size = args.train
val_batch_size = args.val
optimizer_choice = args.opt
multi_optim = args.multi
epochs = args.epo
workers = args.work
use_flip = args.flip
crop_type = args.crop
learning_rate = args.lr
momentum = args.momentum
weight_decay = args.weightdecay
dampening = args.dampening
use_nesterov = args.nesterov

sgd_adjust_lr = args.sgdadjust
sgd_step = args.sgdstep
sgd_gamma = args.sgdgamma

os.environ["CUDA_VISIBLE_DEVICES"] = gpu_usg
num_gpu = torch.cuda.device_count()
use_gpu = torch.cuda.is_available()

print('number of gpu   : {:6d}'.format(num_gpu))
print('sequence length : {:6d}'.format(sequence_length))
print('train batch size: {:6d}'.format(train_batch_size))
print('valid batch size: {:6d}'.format(val_batch_size))
print('optimizer choice: {:6d}'.format(optimizer_choice))
print('multiple optim  : {:6d}'.format(multi_optim))
print('num of epochs   : {:6d}'.format(epochs))
print('num of workers  : {:6d}'.format(workers))
print('test crop type  : {:6d}'.format(crop_type))
print('whether to flip : {:6d}'.format(use_flip))
print('learning rate   : {:.4f}'.format(learning_rate))
print('momentum for sgd: {:.4f}'.format(momentum))
print('weight decay    : {:.4f}'.format(weight_decay))
print('dampening       : {:.4f}'.format(dampening))
print('use nesterov    : {:6d}'.format(use_nesterov))
print('method for sgd  : {:6d}'.format(sgd_adjust_lr))
print('step for sgd    : {:6d}'.format(sgd_step))
print('gamma for sgd   : {:.4f}'.format(sgd_gamma))


def pil_loader(path):
    with open(path, 'rb') as f:
        with Image.open(f) as img:
            return img.convert('RGB')


class RandomCrop(object):

    def __init__(self, size, padding=0):
        if isinstance(size, numbers.Number):
            self.size = (int(size), int(size))
        else:
            self.size = size
        self.padding = padding
        self.count = 0

    def __call__(self, img):

        if self.padding > 0:
            img = ImageOps.expand(img, border=self.padding, fill=0)

        w, h = img.size
        th, tw = self.size
        if w == tw and h == th:
            return img

        random.seed(self.count // sequence_length)
        x1 = random.randint(0, w - tw)
        y1 = random.randint(0, h - th)
        # print(self.count, x1, y1)
        self.count += 1
        return img.crop((x1, y1, x1 + tw, y1 + th))


class RandomHorizontalFlip(object):
    def __init__(self):
        self.count = 0

    def __call__(self, img):
        seed = self.count // sequence_length
        random.seed(seed)
        prob = random.random()
        self.count += 1
        # print(self.count, seed, prob)
        if prob < 0.5:
            return img.transpose(Image.FLIP_LEFT_RIGHT)
        return img


class CholecDataset(Dataset):
    def __init__(self, file_paths, file_labels, transform=None,
                 loader=pil_loader):
        self.file_paths = file_paths
        self.file_labels_1 = file_labels[:, range(7)]
        self.file_labels_2 = file_labels[:, -1]
        self.transform = transform
        self.loader = loader

    def __getitem__(self, index):
        img_names = self.file_paths[index]
        labels_1 = self.file_labels_1[index]
        labels_2 = self.file_labels_2[index]
        imgs = self.loader(img_names)
        if self.transform is not None:
            imgs = self.transform(imgs)

        return imgs, labels_1, labels_2

    def __len__(self):
        return len(self.file_paths)


class resnet_lstm(torch.nn.Module):
    def __init__(self):
        super(resnet_lstm, self).__init__()
        resnet = models.resnet50(pretrained=True)
        self.share = torch.nn.Sequential()
        self.share.add_module("conv1", resnet.conv1)
        self.share.add_module("bn1", resnet.bn1)
        self.share.add_module("relu", resnet.relu)
        self.share.add_module("maxpool", resnet.maxpool)
        self.share.add_module("layer1", resnet.layer1)
        self.share.add_module("layer2", resnet.layer2)
        self.share.add_module("layer3", resnet.layer3)
        self.share.add_module("layer4", resnet.layer4)
        self.share.add_module("avgpool", resnet.avgpool)
        self.lstm = nn.LSTM(2048, 512, batch_first=True)
        self.fc = nn.Linear(512, 7)

        init.xavier_normal(self.lstm.all_weights[0][0])
        init.xavier_normal(self.lstm.all_weights[0][1])
        init.xavier_uniform(self.fc.weight)

    def forward(self, x):
        x = self.share.forward(x)
        x = x.view(-1, 2048)
        x = x.view(-1, sequence_length, 2048)
        self.lstm.flatten_parameters()
        y, _ = self.lstm(x)
        y = y.contiguous().view(-1, 512)
        y = self.fc(y)
        return y


def get_useful_start_idx(sequence_length, list_each_length):
    count = 0
    idx = []
    for i in range(len(list_each_length)):
        for j in range(count, count + (list_each_length[i] + 1 - sequence_length)):
            idx.append(j)
        count += list_each_length[i]
    return idx


def get_data(data_path):
    with open(data_path, 'rb') as f:
        train_test_paths_labels = pickle.load(f)
    train_paths = train_test_paths_labels[0]
    val_paths = train_test_paths_labels[1]
    test_paths = train_test_paths_labels[2]
    train_labels = train_test_paths_labels[3]
    val_labels = train_test_paths_labels[4]
    test_labels = train_test_paths_labels[5]
    train_num_each = train_test_paths_labels[6]
    val_num_each = train_test_paths_labels[7]
    test_num_each = train_test_paths_labels[8]

    print('train_paths  : {:6d}'.format(len(train_paths)))
    print('train_labels : {:6d}'.format(len(train_labels)))
    print('valid_paths  : {:6d}'.format(len(val_paths)))
    print('valid_labels : {:6d}'.format(len(val_labels)))
    print('test_paths   : {:6d}'.format(len(test_paths)))
    print('test_labels  : {:6d}'.format(len(test_labels)))

    train_labels = np.asarray(train_labels, dtype=np.int64)
    val_labels = np.asarray(val_labels, dtype=np.int64)
    test_labels = np.asarray(test_labels, dtype=np.int64)

    if use_flip == 0:
        train_transforms = transforms.Compose([
            RandomCrop(224),
            transforms.ToTensor(),
            transforms.Normalize([0.3456, 0.2281, 0.2233], [0.2528, 0.2135, 0.2104])
        ])
    elif use_flip == 1:
        train_transforms = transforms.Compose([
            RandomCrop(224),
            RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize([0.3456, 0.2281, 0.2233], [0.2528, 0.2135, 0.2104])
        ])

    if crop_type == 0:
        test_transforms = transforms.Compose([
            RandomCrop(224),
            transforms.ToTensor(),
            transforms.Normalize([0.3456, 0.2281, 0.2233], [0.2528, 0.2135, 0.2104])
        ])
    elif crop_type == 1:
        test_transforms = transforms.Compose([
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize([0.3456, 0.2281, 0.2233], [0.2528, 0.2135, 0.2104])
        ])
    elif crop_type == 5:
        test_transforms = transforms.Compose([
            transforms.FiveCrop(224),
            Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),
            Lambda(
                lambda crops: torch.stack(
                    [transforms.Normalize([0.3456, 0.2281, 0.2233], [0.2528, 0.2135, 0.2104])(crop) for crop in crops]))
        ])
    elif crop_type == 10:
        test_transforms = transforms.Compose([
            transforms.TenCrop(224),
            Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),
            Lambda(
                lambda crops: torch.stack(
                    [transforms.Normalize([0.3456, 0.2281, 0.2233], [0.2528, 0.2135, 0.2104])(crop) for crop in crops]))
        ])

    train_dataset = CholecDataset(train_paths, train_labels, train_transforms)
    val_dataset = CholecDataset(val_paths, val_labels, test_transforms)
    test_dataset = CholecDataset(test_paths, test_labels, test_transforms)

    return train_dataset, train_num_each, val_dataset, val_num_each, test_dataset, test_num_each


def train_model(train_dataset, train_num_each, val_dataset, val_num_each):
    num_train = len(train_dataset)
    num_val = len(val_dataset)

    train_useful_start_idx = get_useful_start_idx(sequence_length, train_num_each)
    val_useful_start_idx = get_useful_start_idx(sequence_length, val_num_each)

    num_train_we_use = len(train_useful_start_idx) // num_gpu * num_gpu
    num_val_we_use = len(val_useful_start_idx) // num_gpu * num_gpu
    # num_train_we_use = 8000
    # num_val_we_use = 800

    train_we_use_start_idx = train_useful_start_idx[0:num_train_we_use]
    val_we_use_start_idx = val_useful_start_idx[0:num_val_we_use]

    #    np.random.seed(0)
    # np.random.shuffle(train_we_use_start_idx)
    train_idx = []
    for i in range(num_train_we_use):
        for j in range(sequence_length):
            train_idx.append(train_we_use_start_idx[i] + j)

    val_idx = []
    for i in range(num_val_we_use):
        for j in range(sequence_length):
            val_idx.append(val_we_use_start_idx[i] + j)

    num_train_all = len(train_idx)
    num_val_all = len(val_idx)
    print('num of train dataset: {:6d}'.format(num_train))
    print('num train start idx : {:6d}'.format(len(train_useful_start_idx)))
    print('last idx train start: {:6d}'.format(train_useful_start_idx[-1]))
    print('num of train we use : {:6d}'.format(num_train_we_use))
    print('num of all train use: {:6d}'.format(num_train_all))
    print('num of valid dataset: {:6d}'.format(num_val))
    print('num valid start idx : {:6d}'.format(len(val_useful_start_idx)))
    print('last idx valid start: {:6d}'.format(val_useful_start_idx[-1]))
    print('num of valid we use : {:6d}'.format(num_val_we_use))
    print('num of all valid use: {:6d}'.format(num_val_all))

    train_loader = DataLoader(
        train_dataset,
        batch_size=train_batch_size,
        sampler=train_idx,
        num_workers=workers,
        pin_memory=False
    )
    val_loader = DataLoader(
        val_dataset,
        batch_size=val_batch_size,
        sampler=val_idx,
        num_workers=workers,
        pin_memory=False
    )
    model = resnet_lstm()
    if use_gpu:
        model = model.cuda()

    model = DataParallel(model)
    criterion = nn.CrossEntropyLoss(size_average=False)

    if multi_optim == 0:
        if optimizer_choice == 0:
            optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum, dampening=dampening,
                                  weight_decay=weight_decay, nesterov=use_nesterov)
            if sgd_adjust_lr == 0:
                exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=sgd_adjust_lr, gamma=sgd_gamma)
            elif sgd_adjust_lr == 1:
                exp_lr_scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
        elif optimizer_choice == 1:
            optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    elif multi_optim == 1:
        if optimizer_choice == 0:
            optimizer = optim.SGD([
                {'params': model.module.share.parameters()},
                {'params': model.module.lstm.parameters(), 'lr': learning_rate},
                {'params': model.module.fc.parameters(), 'lr': learning_rate},
            ], lr=learning_rate / 10, momentum=momentum, dampening=dampening,
                weight_decay=weight_decay, nesterov=use_nesterov)
            if sgd_adjust_lr == 0:
                exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=sgd_adjust_lr, gamma=sgd_gamma)
            elif sgd_adjust_lr == 1:
                exp_lr_scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
        elif optimizer_choice == 1:
            optimizer = optim.Adam([
                {'params': model.module.share.parameters()},
                {'params': model.module.lstm.parameters(), 'lr': learning_rate},
                {'params': model.module.fc.parameters(), 'lr': learning_rate},
            ], lr=learning_rate / 10)

    best_model_wts = copy.deepcopy(model.state_dict())
    best_val_accuracy = 0.0
    correspond_train_acc = 0.0

    record_np = np.zeros([epochs, 4])

    for epoch in range(epochs):
        # np.random.seed(epoch)
        np.random.shuffle(train_we_use_start_idx)
        train_idx = []
        for i in range(num_train_we_use):
            for j in range(sequence_length):
                train_idx.append(train_we_use_start_idx[i] + j)

        train_loader = DataLoader(
            train_dataset,
            batch_size=train_batch_size,
            sampler=train_idx,
            num_workers=workers,
            pin_memory=False
        )

        model.train()
        train_loss = 0.0
        train_corrects = 0
        train_start_time = time.time()
        for data in train_loader:
            inputs, labels_1, labels_2 = data
            if use_gpu:
                inputs = Variable(inputs.cuda())
                labels = Variable(labels_2.cuda())
            else:
                inputs = Variable(inputs)
                labels = Variable(labels_2)
            optimizer.zero_grad()
            outputs = model.forward(inputs)
            _, preds = torch.max(outputs.data, 1)

            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            train_loss += loss.data[0]
            train_corrects += torch.sum(preds == labels.data)
        train_elapsed_time = time.time() - train_start_time
        train_accuracy = train_corrects / num_train_all
        train_average_loss = train_loss / num_train_all

        # begin eval
        model.eval()
        val_loss = 0.0
        val_corrects = 0
        val_start_time = time.time()
        for data in val_loader:
            inputs, labels_1, labels_2 = data
            labels_2 = labels_2[(sequence_length - 1)::sequence_length]
            if use_gpu:
                inputs = Variable(inputs.cuda(), volatile=True)
                labels = Variable(labels_2.cuda(), volatile=True)
            else:
                inputs = Variable(inputs, volatile=True)
                labels = Variable(labels_2, volatile=True)

            if crop_type == 0 or crop_type == 1:
                outputs = model.forward(inputs)
            elif crop_type == 5:
                inputs = inputs.permute(1, 0, 2, 3, 4).contiguous()
                inputs = inputs.view(-1, 3, 224, 224)
                outputs = model.forward(inputs)
                outputs = outputs.view(5, -1, 7)
                outputs = torch.mean(outputs, 0)
            elif crop_type == 10:
                inputs = inputs.permute(1, 0, 2, 3, 4).contiguous()
                inputs = inputs.view(-1, 3, 224, 224)
                outputs = model.forward(inputs)
                outputs = outputs.view(10, -1, 7)
                outputs = torch.mean(outputs, 0)

            outputs = outputs[sequence_length - 1::sequence_length]

            _, preds = torch.max(outputs.data, 1)

            loss = criterion(outputs, labels)
            val_loss += loss.data[0]
            val_corrects += torch.sum(preds == labels.data)
        val_elapsed_time = time.time() - val_start_time
        val_accuracy = val_corrects / num_val_we_use
        val_average_loss = val_loss / num_val_we_use
        print('epoch: {:4d}'
              ' train in: {:2.0f}m{:2.0f}s'
              ' train loss: {:4.4f}'
              ' train accu: {:.4f}'
              ' valid in: {:2.0f}m{:2.0f}s'
              ' valid loss: {:4.4f}'
              ' valid accu: {:.4f}'
              .format(epoch,
                      train_elapsed_time // 60,
                      train_elapsed_time % 60,
                      train_average_loss,
                      train_accuracy,
                      val_elapsed_time // 60,
                      val_elapsed_time % 60,
                      val_average_loss,
                      val_accuracy))

        if optimizer_choice == 0:
            if sgd_adjust_lr == 0:
                exp_lr_scheduler.step()
            elif sgd_adjust_lr == 1:
                exp_lr_scheduler.step(val_average_loss)

        if val_accuracy > best_val_accuracy:
            best_val_accuracy = val_accuracy
            correspond_train_acc = train_accuracy
            best_model_wts = copy.deepcopy(model.state_dict())
        if val_accuracy == best_val_accuracy:
            if train_accuracy > correspond_train_acc:
                correspond_train_acc = train_accuracy
                best_model_wts = copy.deepcopy(model.state_dict())

        record_np[epoch, 0] = train_accuracy
        record_np[epoch, 1] = train_average_loss
        record_np[epoch, 2] = val_accuracy
        record_np[epoch, 3] = val_average_loss

    print('best accuracy: {:.4f} cor train accu: {:.4f}'.format(best_val_accuracy, correspond_train_acc))

    save_val = int("{:4.0f}".format(best_val_accuracy * 10000))
    save_train = int("{:4.0f}".format(correspond_train_acc * 10000))
    public_name = "lstm" \
                  + "_epoch_" + str(epochs) \
                  + "_length_" + str(sequence_length) \
                  + "_opt_" + str(optimizer_choice) \
                  + "_mulopt_" + str(multi_optim) \
                  + "_flip_" + str(use_flip) \
                  + "_crop_" + str(crop_type) \
                  + "_batch_" + str(train_batch_size) \
                  + "_train_" + str(save_train) \
                  + "_val_" + str(save_val)
    model_name = public_name + ".pth"
    torch.save(best_model_wts, model_name)

    record_name = public_name + ".npy"
    np.save(record_name, record_np)


def main():
    train_dataset, train_num_each, val_dataset, val_num_each, _, _ = get_data('train_val_test_paths_labels.pkl')
    train_model(train_dataset, train_num_each, val_dataset, val_num_each)


if __name__ == "__main__":
    main()

print('Done')
print()