<reponame>njoel-ethz/saliency-rl<filename>run_train.py
import sys
import os
import random
import numpy as np
import cv2
import time
from datetime import timedelta
import torch
from model import TASED_v2
from loss import KLDLoss
from dataset import DHF1KDataset, InfiniteDataLoader
from itertools import islice
import matplotlib.pyplot as plt
from PIL import Image
from scipy.ndimage import gaussian_filter
import csv

def main():
    ''' concise script for training '''
    # optional two command-line arguments
    path_indata = 'Atari_dataset'
    path_output = 'output'
    shuffle_data = True

    if len(sys.argv) > 1:
        path_indata = sys.argv[1]
        if len(sys.argv) > 2:
            path_output = sys.argv[2]

    # we checked that using only 2 gpus is enough to produce similar results
    num_gpu = 1 #2 on Server
    pile = 5
    batch_size = 1 #6 on Server
    num_iters = 1000
    len_temporal = 32
    file_weight = 'TASED_updated.pt'
    path_output = os.path.join(path_output, time.strftime("%m-%d_%H-%M-%S"))
    if not os.path.isdir(path_output):
        os.makedirs(path_output)
    if shuffle_data or not os.path.isfile('Atari_num_frame_train.csv'):
        split_train_test_set()

    model = TASED_v2()

    # load the weight file and copy the parameters
    if os.path.isfile(file_weight):
        print ('loading weight file')
        weight_dict = torch.load(file_weight)
        model_dict = model.state_dict()
        for name, param in weight_dict.items():
            if 'module' in name:
                name = '.'.join(name.split('.')[1:])
            if 'base.' in name:
                bn = int(name.split('.')[1])
                sn_list = [0, 5, 8, 14]
                sn = sn_list[0]
                if bn >= sn_list[1] and bn < sn_list[2]:
                    sn = sn_list[1]
                elif bn >= sn_list[2] and bn < sn_list[3]:
                    sn = sn_list[2]
                elif bn >= sn_list[3]:
                    sn = sn_list[3]
                name = '.'.join(name.split('.')[2:])
                name = 'base%d.%d.'%(sn_list.index(sn)+1, bn-sn)+name
            if name in model_dict:
                if param.size() == model_dict[name].size():
                    model_dict[name].copy_(param)
                else:
                    print (' size? ' + name, param.size(), model_dict[name].size())
            else:
                print (' name? ' + name)

        print (' loaded')
    else:
        print ('weight file?')

    # parameter setting for fine-tuning
    params = []
    lr1 = 0.002 #0.0001 David
    lr2 = 0.05 #0.01 David
    for key, value in dict(model.named_parameters()).items():
        if 'convtsp' in key:
            params += [{'params':[value], 'key':key+'(new)'}]
        else:
            params += [{'params':[value], 'lr':lr1, 'key':key}] #0.001

    optimizer = torch.optim.SGD(params, lr=lr2, momentum=0.9, weight_decay=2e-7) #lr = 0.1
    lr_adaption = 0.1
    criterion = KLDLoss()

    model = model.cuda()
    model = torch.nn.DataParallel(model, device_ids=range(num_gpu))
    torch.backends.cudnn.benchmark = False
    model.train()

    train_loader = InfiniteDataLoader(DHF1KDataset(path_indata, len_temporal), batch_size=batch_size, shuffle=True, num_workers=0) #was 24, 0 means that data is loaded in the main process

    loss_statistic = []
    averaged_loss_statistic = []
    index_statistic = []

    i, step = 0, 0
    loss_sum = 0
    start_time = time.time()
    for clip, annt, (file_name, picture_name) in islice(train_loader, num_iters*pile):
        with torch.set_grad_enabled(True):
            output = model(clip.cuda())
            loss = criterion(output, annt.cuda())

        loss_sum += loss.item()
        loss.backward()
        if (i+1) % pile == 0:
            optimizer.step()
            optimizer.zero_grad()
            step += 1

            # whole process takes less than 3 hours
            print ('iteration: [%4d/%4d], loss: %.4f, %s' % (step, num_iters, loss_sum/pile, timedelta(seconds=int(time.time()-start_time))), flush=True)

            loss_statistic.append(loss_sum/pile)

            visualize(output, path_indata, file_name[0], picture_name[0], step)
            plt.ylabel('Loss')
            plt.xlabel(path_indata + ', weights: ' + file_weight + ', lr/optim = ' + str((lr1, lr2)))
            plt.plot(loss_statistic, color='b')
            if step==1:
                plt.show(block=False)
            else:
                plt.draw()
            plt.pause(0.00001)

            if step%10==0:
                averaged_loss_statistic.append(sum(loss_statistic[step-10:step])/10)
                index_statistic.append(step)

            loss_sum = 0
            # adjust learning rate
            if step in [250, 750, 950]:
                for opt in optimizer.param_groups:
                    if 'new' in opt['key']:
                        opt['lr'] *= lr_adaption   #0.1

            if step % 25 == 0:
                try:
                    if not os.path.exists(os.path.join(path_output, 'weights')):
                        os.makedirs(os.path.join(path_output, 'weights'))
                except OSError:
                    print('Error: Creating directory of data')
                torch.save(model.state_dict(), os.path.join(path_output, 'weights', 'iter_%04d.pt' % step))

        i += 1
    torch.save(model.state_dict(), os.path.join(path_indata, 'produced_weight_file.pt'))

    print('plotten')
    #plt.plot(loss_statistic)
    plt.savefig(os.path.join(path_indata, "loss.png"))

    plt.plot(index_statistic, averaged_loss_statistic, color='r')
    plt.ylabel('Averaged loss')
    plt.xlabel(path_indata + ', weights: ' + file_weight + ', lr/optim = ' + str((lr1, lr2)))
    plt.savefig(os.path.join(path_indata, "averaged_loss.png"))

    os.system('python run_saliency_metrics.py')

def split_train_test_set():
    path_full = 'Atari_num_frame_FullData.csv'
    path_train = 'Atari_num_frame_train.csv'
    path_test = 'Atari_num_frame_testing.csv'

    list_num_frame = [int(row[0]) for row in csv.reader(open(path_full, 'r'))]
    total_len = len(list_num_frame)
    half_len = int(np.floor(total_len/2)) #for 50/50 split
    idx = range(1, total_len+1)
    z = list(zip(list_num_frame, idx))

    #comment this out for training on ~ highscore data
    random.shuffle(z)

    list_num_frame, idx = zip(*z)

    test_list, test_idx = list_num_frame[:half_len], idx[:half_len]
    train_list, train_idx = list_num_frame[half_len:], idx[half_len:]
    train_strings = []
    test_strings = []

    for i in range(len(train_list)):
        train_strings.append(str(train_list[i])+',%04d'%train_idx[i])
    for i in range(len(test_list)):
        test_strings.append(str(test_list[i])+',%04d'%test_idx[i])

    with open(path_train, 'w') as file:
        for line in train_strings:
            file.write(line)
            file.write('\n')
    with open(path_test, 'w') as file:
        for line in test_strings:
            file.write(line)
            file.write('\n')

    return 0

def visualize(output, path_indata, file_name, picture_name, step):
    # print(output.shape)  ---> torch.Size([1, 224, 384])
    # print(output.cpu().data.numpy())
    np_array = (output.cpu().data[0].numpy()*255.).astype(np.int)/255.
    # print(np_array.shape)    ---> (224, 384)
    np_array = gaussian_filter(np_array, sigma=7)
    np_array = (np_array/np.max(np_array)*255.).astype(np.uint8)
    mask = Image.fromarray(np_array)  # gives a 384x224 Image object
    path_to_clip = os.path.join(path_indata, 'video', file_name, picture_name)
    video_img = cv2.resize(cv2.imread(path_to_clip, cv2.IMREAD_COLOR), (384, 224))
    video_img = Image.fromarray(video_img)
    red_img = Image.new('RGB', (384, 224), (0, 0, 255))
    video_img.paste(red_img, mask=mask)

    open_cv_image = cv2.resize(np.array(video_img), (160, 210))

    output_path = os.path.join(path_indata, 'training_visualized')
    try:
        if not os.path.exists(output_path):
            os.makedirs(output_path)
    except OSError:
        print('Error: Creating directory of data')
    cv2.imwrite(os.path.join(output_path, '%06d.png' % (step)), open_cv_image)
    """cv2.imshow('step', open_cv_image)
    cv2.waitKey(3000)
    cv2.destroyAllWindows()"""
    return 0

if __name__ == '__main__':
    main()
import sys
import os
import random
import numpy as np
import cv2
import time
from datetime import timedelta
import torch
from model import TASED_v2
from loss import KLDLoss
from dataset import DHF1KDataset, InfiniteDataLoader
from itertools import islice
import matplotlib.pyplot as plt
from PIL import Image
from scipy.ndimage import gaussian_filter
import csv

def main():
    ''' concise script for training '''
    # optional two command-line arguments
    path_indata = 'Atari_dataset'
    path_output = 'output'
    shuffle_data = True

    if len(sys.argv) > 1:
        path_indata = sys.argv[1]
        if len(sys.argv) > 2:
            path_output = sys.argv[2]

    # we checked that using only 2 gpus is enough to produce similar results
    num_gpu = 1 #2 on Server
    pile = 5
    batch_size = 1 #6 on Server
    num_iters = 1000
    len_temporal = 32
    file_weight = 'TASED_updated.pt'
    path_output = os.path.join(path_output, time.strftime("%m-%d_%H-%M-%S"))
    if not os.path.isdir(path_output):
        os.makedirs(path_output)
    if shuffle_data or not os.path.isfile('Atari_num_frame_train.csv'):
        split_train_test_set()

    model = TASED_v2()

    # load the weight file and copy the parameters
    if os.path.isfile(file_weight):
        print ('loading weight file')
        weight_dict = torch.load(file_weight)
        model_dict = model.state_dict()
        for name, param in weight_dict.items():
            if 'module' in name:
                name = '.'.join(name.split('.')[1:])
            if 'base.' in name:
                bn = int(name.split('.')[1])
                sn_list = [0, 5, 8, 14]
                sn = sn_list[0]
                if bn >= sn_list[1] and bn < sn_list[2]:
                    sn = sn_list[1]
                elif bn >= sn_list[2] and bn < sn_list[3]:
                    sn = sn_list[2]
                elif bn >= sn_list[3]:
                    sn = sn_list[3]
                name = '.'.join(name.split('.')[2:])
                name = 'base%d.%d.'%(sn_list.index(sn)+1, bn-sn)+name
            if name in model_dict:
                if param.size() == model_dict[name].size():
                    model_dict[name].copy_(param)
                else:
                    print (' size? ' + name, param.size(), model_dict[name].size())
            else:
                print (' name? ' + name)

        print (' loaded')
    else:
        print ('weight file?')

    # parameter setting for fine-tuning
    params = []
    lr1 = 0.002 #0.0001 David
    lr2 = 0.05 #0.01 David
    for key, value in dict(model.named_parameters()).items():
        if 'convtsp' in key:
            params += [{'params':[value], 'key':key+'(new)'}]
        else:
            params += [{'params':[value], 'lr':lr1, 'key':key}] #0.001

    optimizer = torch.optim.SGD(params, lr=lr2, momentum=0.9, weight_decay=2e-7) #lr = 0.1
    lr_adaption = 0.1
    criterion = KLDLoss()

    model = model.cuda()
    model = torch.nn.DataParallel(model, device_ids=range(num_gpu))
    torch.backends.cudnn.benchmark = False
    model.train()

    train_loader = InfiniteDataLoader(DHF1KDataset(path_indata, len_temporal), batch_size=batch_size, shuffle=True, num_workers=0) #was 24, 0 means that data is loaded in the main process

    loss_statistic = []
    averaged_loss_statistic = []
    index_statistic = []

    i, step = 0, 0
    loss_sum = 0
    start_time = time.time()
    for clip, annt, (file_name, picture_name) in islice(train_loader, num_iters*pile):
        with torch.set_grad_enabled(True):
            output = model(clip.cuda())
            loss = criterion(output, annt.cuda())

        loss_sum += loss.item()
        loss.backward()
        if (i+1) % pile == 0:
            optimizer.step()
            optimizer.zero_grad()
            step += 1

            # whole process takes less than 3 hours
            print ('iteration: [%4d/%4d], loss: %.4f, %s' % (step, num_iters, loss_sum/pile, timedelta(seconds=int(time.time()-start_time))), flush=True)

            loss_statistic.append(loss_sum/pile)

            visualize(output, path_indata, file_name[0], picture_name[0], step)
            plt.ylabel('Loss')
            plt.xlabel(path_indata + ', weights: ' + file_weight + ', lr/optim = ' + str((lr1, lr2)))
            plt.plot(loss_statistic, color='b')
            if step==1:
                plt.show(block=False)
            else:
                plt.draw()
            plt.pause(0.00001)

            if step%10==0:
                averaged_loss_statistic.append(sum(loss_statistic[step-10:step])/10)
                index_statistic.append(step)

            loss_sum = 0
            # adjust learning rate
            if step in [250, 750, 950]:
                for opt in optimizer.param_groups:
                    if 'new' in opt['key']:
                        opt['lr'] *= lr_adaption   #0.1

            if step % 25 == 0:
                try:
                    if not os.path.exists(os.path.join(path_output, 'weights')):
                        os.makedirs(os.path.join(path_output, 'weights'))
                except OSError:
                    print('Error: Creating directory of data')
                torch.save(model.state_dict(), os.path.join(path_output, 'weights', 'iter_%04d.pt' % step))

        i += 1
    torch.save(model.state_dict(), os.path.join(path_indata, 'produced_weight_file.pt'))

    print('plotten')
    #plt.plot(loss_statistic)
    plt.savefig(os.path.join(path_indata, "loss.png"))

    plt.plot(index_statistic, averaged_loss_statistic, color='r')
    plt.ylabel('Averaged loss')
    plt.xlabel(path_indata + ', weights: ' + file_weight + ', lr/optim = ' + str((lr1, lr2)))
    plt.savefig(os.path.join(path_indata, "averaged_loss.png"))

    os.system('python run_saliency_metrics.py')

def split_train_test_set():
    path_full = 'Atari_num_frame_FullData.csv'
    path_train = 'Atari_num_frame_train.csv'
    path_test = 'Atari_num_frame_testing.csv'

    list_num_frame = [int(row[0]) for row in csv.reader(open(path_full, 'r'))]
    total_len = len(list_num_frame)
    half_len = int(np.floor(total_len/2)) #for 50/50 split
    idx = range(1, total_len+1)
    z = list(zip(list_num_frame, idx))

    #comment this out for training on ~ highscore data
    random.shuffle(z)

    list_num_frame, idx = zip(*z)

    test_list, test_idx = list_num_frame[:half_len], idx[:half_len]
    train_list, train_idx = list_num_frame[half_len:], idx[half_len:]
    train_strings = []
    test_strings = []

    for i in range(len(train_list)):
        train_strings.append(str(train_list[i])+',%04d'%train_idx[i])
    for i in range(len(test_list)):
        test_strings.append(str(test_list[i])+',%04d'%test_idx[i])

    with open(path_train, 'w') as file:
        for line in train_strings:
            file.write(line)
            file.write('\n')
    with open(path_test, 'w') as file:
        for line in test_strings:
            file.write(line)
            file.write('\n')

    return 0

def visualize(output, path_indata, file_name, picture_name, step):
    # print(output.shape)  ---> torch.Size([1, 224, 384])
    # print(output.cpu().data.numpy())
    np_array = (output.cpu().data[0].numpy()*255.).astype(np.int)/255.
    # print(np_array.shape)    ---> (224, 384)
    np_array = gaussian_filter(np_array, sigma=7)
    np_array = (np_array/np.max(np_array)*255.).astype(np.uint8)
    mask = Image.fromarray(np_array)  # gives a 384x224 Image object
    path_to_clip = os.path.join(path_indata, 'video', file_name, picture_name)
    video_img = cv2.resize(cv2.imread(path_to_clip, cv2.IMREAD_COLOR), (384, 224))
    video_img = Image.fromarray(video_img)
    red_img = Image.new('RGB', (384, 224), (0, 0, 255))
    video_img.paste(red_img, mask=mask)

    open_cv_image = cv2.resize(np.array(video_img), (160, 210))

    output_path = os.path.join(path_indata, 'training_visualized')
    try:
        if not os.path.exists(output_path):
            os.makedirs(output_path)
    except OSError:
        print('Error: Creating directory of data')
    cv2.imwrite(os.path.join(output_path, '%06d.png' % (step)), open_cv_image)
    """cv2.imshow('step', open_cv_image)
    cv2.waitKey(3000)
    cv2.destroyAllWindows()"""
    return 0

if __name__ == '__main__':
    main()
concise script for training # optional two command-line arguments # we checked that using only 2 gpus is enough to produce similar results #2 on Server #6 on Server # load the weight file and copy the parameters # parameter setting for fine-tuning #0.0001 David #0.01 David #0.001 #lr = 0.1 #was 24, 0 means that data is loaded in the main process # whole process takes less than 3 hours # adjust learning rate #0.1 #plt.plot(loss_statistic) #for 50/50 split #comment this out for training on ~ highscore data # print(output.shape)  ---> torch.Size([1, 224, 384]) # print(output.cpu().data.numpy()) # print(np_array.shape)    ---> (224, 384) # gives a 384x224 Image object cv2.imshow('step', open_cv_image)     cv2.waitKey(3000)     cv2.destroyAllWindows()