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train_nus.py
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train_nus.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 8 21:56:19 2020
@author: naraysa & akshitac8
"""
import torch
import torch.backends.cudnn as cudnn
import torch.optim as optim
import model as model
import util_nus as util
from config import opt
import numpy as np
import random
import time
import os
import socket
import h5py
import pickle
import logging
from warmup_scheduler import GradualWarmupScheduler
## setting up the logs folder ##
if not os.path.exists("logs"):
os.mkdir("logs")
log_filename = os.path.join("logs",opt.SESSION + '.log')
logging.basicConfig(level=logging.INFO, filename=log_filename)
logging.info(("Process JOB ID :{}").format(opt.job_id))
print(opt)
logging.info(opt)
#############################################
#setting up seeds
if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
print("Random Seed: ", opt.manualSeed)
np.random.seed(opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
if opt.cuda:
torch.cuda.manual_seed(opt.manualSeed)
torch.cuda.manual_seed_all(opt.manualSeed)
torch.set_default_tensor_type('torch.FloatTensor')
cudnn.benchmark = True # For speed i.e, cudnn autotuner
########################################################
if torch.cuda.is_available() and not opt.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
name='NUS_WIDE_{}'.format(opt.SESSION)
opt.save_path += '/'+name
os.system("mkdir -p " + opt.save_path)
data = util.DATA_LOADER(opt) ### INTIAL DATALOADER ###
print('===> Loading datasets')
print(opt.src)
print('===> Result path ')
print(opt.save_path)
print('===> total samples')
print(data.ntrain)
logging.info('===> Loading datasets')
logging.info(opt.src)
logging.info('===> Result path ')
logging.info(opt.save_path)
logging.info('===> total samples')
logging.info(data.ntrain)
def train_sample():
#train dataloader
train_batch_feature, train_batch_labels = data.next_train_batch(opt.batch_size)
return train_batch_feature, train_batch_labels
def val_sample():
#val dataloader
val_feature, val_labels_925, val_labels_81 = data.next_val()
return val_feature, val_labels_925, val_labels_81
### Intialize attention model and global feature extractor ####
model_vgg = model.vgg_net()
model_biam = model.BiAM(opt, dim_feature=[196,512])
model_test = model.BiAM(opt, dim_feature=[196,512])
print(model_biam)
logging.info(model_biam)
## initialize optimizer ###
optimizer = torch.optim.Adam(model_biam.parameters(), opt.lr, weight_decay=0.0005, betas=(opt.beta1, 0.999))
start_epoch = 1
num_epochs = opt.nepoch+1
if opt.cosinelr_scheduler:
print("------------------------------------------------------------------")
print("USING LR SCHEDULER")
print("------------------------------------------------------------------")
######### Scheduler ###########
warmup_epochs = 3
scheduler_cosine = optim.lr_scheduler.CosineAnnealingLR(optimizer, opt.nepoch-warmup_epochs, eta_min=opt.lr_min)
scheduler = GradualWarmupScheduler(optimizer, multiplier=1, total_epoch=warmup_epochs, after_scheduler=scheduler_cosine)
scheduler.step()
print("initial learning rate", opt.lr)
logging.info(("initial learning rate {}".format(opt.lr)))
logger = util.Logger(cols=['index','mF1','mF1_u_val','mAP','lr','val_loss'],filename=opt.save_path+'/log.csv',is_save=True)
eval_interval = max((opt.eval_interval),2)
print(optimizer)
logging.info(optimizer)
if opt.cuda:
model_biam = model_biam.cuda()
model_test = model_test.cuda()
model_vgg.cuda()
model_vgg.eval()
data.vecs_81 = data.vecs_81.cuda()
data.vecs_925 = data.vecs_925.cuda()
gzsl_vecs = torch.cat([data.vecs_925,data.vecs_81],0)
## train function ###
def train(epoch):
print("TRAINING MODE")
logging.info("TRAINING MODE")
epoch_start_time = time.time()
mean_loss = 0
for i in range(0, data.ntrain, opt.batch_size):
optimizer.zero_grad()
train_inputs, train_labels = train_sample()
### remove empty label images while training ###
temp_label = torch.clamp(train_labels,0,1)
temp_seen_labels = temp_label.sum(1)
temp_label = temp_label[temp_seen_labels>0]
train_labels = train_labels[temp_seen_labels>0]
train_inputs = train_inputs[temp_seen_labels>0]
###
train_inputs = train_inputs.cuda()
train_labels = train_labels.cuda()
vgg_4096 = model_vgg(train_inputs)
vgg_4096 = vgg_4096.detach()
logits = model_biam(train_inputs, data.vecs_925, vgg_4096)
loss = model.ranking_lossT(logits, train_labels.float())
mean_loss += loss.item()
if torch.isnan(loss) or loss.item() > 100:
print('Unstable/High Loss:', loss)
import pdb; pdb.set_trace()
loss.backward()
optimizer.step()
mean_loss /= data.ntrain / opt.batch_size
if opt.cosinelr_scheduler:
learning_rate = scheduler.get_lr()[0]
else:
learning_rate = opt.lr
if opt.train:
print("------------------------------------------------------------------")
print("Epoch: {}/{} \tTime: {:.4f}\tLoss: {:.4f}\tLearningRate {:.6f}".format(epoch, num_epochs, time.time()-epoch_start_time,mean_loss, learning_rate))
print("------------------------------------------------------------------")
logging.info("------------------------------------------------------------------")
logging.info("Epoch: {}/{} \tTime: {:.4f}\tLoss: {:.4f}\tLearningRate {:.6f}".format(epoch, num_epochs, time.time()-epoch_start_time,mean_loss, learning_rate))
logging.info("------------------------------------------------------------------")
else:
learning_rate = opt.train_full_lr
print("------------------------------------------------------------------")
print("FINETUNING Epoch: {}/{} \tTime: {:.4f}\tLoss: {:.4f}\tLearningRate {:.6f}".format(epoch, num_epochs, time.time()-epoch_start_time,mean_loss, learning_rate))
print("------------------------------------------------------------------")
logging.info("------------------------------------------------------------------")
logging.info("FINETUNING Epoch: {}/{} \tTime: {:.4f}\tLoss: {:.4f}\tLearningRate {:.6f}".format(epoch, num_epochs, time.time()-epoch_start_time,mean_loss, learning_rate))
logging.info("------------------------------------------------------------------")
torch.save(model_biam.state_dict(), os.path.join(opt.save_path,("model_best_train_full_{}.pth").format(epoch)))
## validation function ###
def val(epoch):
print("validation mode")
logging.info("validation mode")
val_start_time = time.time()
mean_val_loss = 0
### load val data ###
seen_val_visual_features, seen_925_val_visual_labels, seen_81_val_visual_labels = val_sample()
seen_val_visual_features = seen_val_visual_features
seen_925_val_visual_labels = seen_925_val_visual_labels
seen_81_val_visual_labels = seen_81_val_visual_labels
prediction_81 = torch.empty(len(seen_81_val_visual_labels),81)
prediction_925 = torch.empty(len(seen_81_val_visual_labels),925)
val_batch_size = opt.val_batch_size
if model_vgg is not None:
model_vgg.eval()
for i in range(0, len(seen_81_val_visual_labels), val_batch_size):
strt = i
endt = min(i+val_batch_size, len(seen_81_val_visual_labels))
with torch.no_grad():
vgg_4096 = model_vgg(seen_val_visual_features[strt:endt,:,:].cuda()) #if model_vgg is not None else None
vgg_4096 = vgg_4096.detach() #check if this is needed
logits_81 = model_biam(seen_val_visual_features[strt:endt,:,:].cuda(), data.vecs_81, vgg_4096)
logits_925 = model_biam(seen_val_visual_features[strt:endt,:,:].cuda(), data.vecs_925, vgg_4096)
loss_925 = model.ranking_lossT(logits_925.cuda(), seen_925_val_visual_labels[strt:endt,:].cuda().float())
prediction_81[strt:endt,:] = logits_81
prediction_925[strt:endt,:] = logits_925
mean_val_loss += loss_925.item()
mean_val_loss /= len(seen_81_val_visual_labels) / val_batch_size
if opt.cosinelr_scheduler:
learning_rate = scheduler.get_lr()[0]
else:
learning_rate = opt.lr
print("------------------------------------------------------------------")
print("Epoch: {}/{} \tTime: {:.4f}\tLoss: {:.4f}\tLearningRate {:.6f}".format(epoch, num_epochs,time.time()-val_start_time, mean_val_loss, learning_rate))
print("------------------------------------------------------------------")
logging.info("------------------------------------------------------------------")
logging.info("Epoch: {}/{} \tTime: {:.4f}\tLoss: {:.4f}\tLearningRate {:.6f}".format(epoch, num_epochs,time.time()-val_start_time, mean_val_loss, learning_rate))
logging.info("------------------------------------------------------------------")
ap_val = util.compute_AP(prediction_925.cuda(), seen_925_val_visual_labels.cuda())
F1_val,P_val,R_val = util.compute_F1(prediction_925.cuda(), seen_925_val_visual_labels.cuda(), 'overall', k_val=5)
F1_u_val,P_u_val,R_u_val = util.compute_F1(prediction_81.cuda(), seen_81_val_visual_labels.cuda(), 'overall', k_val=5)
mF1_val,mP_val,mR_val,mAP_val = [torch.mean(F1_val),torch.mean(P_val),torch.mean(R_val),torch.mean(ap_val)]
mF1_u_val,mP_u_val,mR_u_val = [torch.mean(F1_u_val),torch.mean(P_u_val),torch.mean(R_u_val)]
print('SEEN AP',mAP_val.item())
print('k=5 AT 925',mF1_val.item(),mP_val.item(),mR_val.item())
print('k=5 AT 81 ',mF1_u_val.item(),mP_u_val.item(),mR_u_val.item())
logging.info('SEEN AP=%.4f',mAP_val.item())
logging.info('k=5 AT 925: %.4f,%.4f,%.4f',mF1_val.item(),mP_val.item(),mR_val.item())
logging.info('k=5 AT 81: %.4f,%.4f,%.4f ',mF1_u_val.item(),mP_u_val.item(),mR_u_val.item())
values = [epoch, mF1_val,mF1_u_val,mAP_val,learning_rate, mean_val_loss]
logger.add(values)
print('{} mF1: {} mF1_u_val: {} mAP: {} lr: {}'.format(*values))
print('Precision: {} Recall: {}'.format(mP_val,mR_val))
logging.info('{} mF1: {} mF1_u_val: {} mAP: {} lr: {}'.format(*values))
logging.info('Precision: {} Recall: {}'.format(mP_val,mR_val))
logger.save()
if mF1_val >= logger.get_max('mF1'):
print("model saved")
logging.info("model saved")
torch.save(model_biam.state_dict(), os.path.join(opt.save_path,"model_best.pth"))
torch.save(model_biam.state_dict(), os.path.join(opt.save_path,"model_latest.pth"))
def test(epoch):
print("=======================EVALUATION MODE=======================")
logging.info("=======================EVALUATION MODE=======================")
test_start_time = time.time()
if not opt.train:
model_path = os.path.join(opt.save_path, ('model_best_train_full_{}.pth').format(epoch))
else:
model_path = os.path.join(opt.save_path, 'model_best.pth')
print(model_path)
logging.info(model_path)
model_test.load_state_dict(torch.load(model_path))
model_test.eval()
src = opt.src
test_loc = os.path.join(src, 'NUS-WIDE','features', 'nus_wide_test.h5')
test_features = h5py.File(test_loc, 'r')
test_feature_keys = list(test_features.keys())
image_filenames = util.load_dict(os.path.join(src, 'NUS-WIDE', 'test_img_names.pkl'))
test_image_filenames = image_filenames['img_names']
ntest = len(test_image_filenames)
print(ntest)
logging.info(ntest)
prediction_81 = torch.empty(ntest,81)
prediction_1006 = torch.empty(ntest,1006)
lab_81 = torch.empty(ntest,81)
lab_1006 = torch.empty(ntest,1006)
test_batch_size = opt.test_batch_size
if model_vgg is not None:
logging.info("model vgg not none")
model_vgg.eval()
for m in range(0, ntest, test_batch_size):
strt = m
endt = min(m+test_batch_size, ntest)
bs = endt-strt
features, labels_1006, labels_81 = np.empty((bs,512,196)), np.empty((bs,1006)), np.empty((bs,81))
for i, key in enumerate(test_image_filenames[strt:endt]):
features[i,:,:] = np.float32(test_features.get(key+'-features'))
labels_1006[i,:] = np.int32(test_features.get(key+'-labels'))
labels_81[i,:] = np.int32(test_features.get(key+'-labels_81'))
features = torch.from_numpy(features).float()
labels_1006 = torch.from_numpy(labels_1006).long()
labels_81 = torch.from_numpy(labels_81).long()
with torch.no_grad():
vgg_4096 = model_vgg(features.cuda()) #if model_vgg is not None else None
vgg_4096 = vgg_4096.detach()
logits_81 = model_test(features.cuda(), data.vecs_81, vgg_4096)
logits_1006 = model_test(features.cuda(), gzsl_vecs, vgg_4096)
prediction_81[strt:endt,:] = logits_81
prediction_1006[strt:endt,:] = logits_1006
lab_81[strt:endt,:] = labels_81
lab_1006[strt:endt,:] = labels_1006
print("completed calculating predictions over all images")
logging.info("completed calculating predictions over all images")
logits_81_5 = prediction_81.clone()
ap_81 = util.compute_AP(prediction_81.cuda(), lab_81.cuda())
F1_3_81,P_3_81,R_3_81 = util.compute_F1(prediction_81.cuda(), lab_81.cuda(), 'overall', k_val=3)
F1_5_81,P_5_81,R_5_81 = util.compute_F1(logits_81_5.cuda(), lab_81.cuda(), 'overall', k_val=5)
print('ZSL AP',torch.mean(ap_81))
print('k=3',torch.mean(F1_3_81),torch.mean(P_3_81),torch.mean(R_3_81))
print('k=5',torch.mean(F1_5_81),torch.mean(P_5_81),torch.mean(R_5_81))
logging.info('ZSL AP: %.4f',torch.mean(ap_81))
logging.info('k=3: %.4f,%.4f,%.4f',torch.mean(F1_3_81),torch.mean(P_3_81),torch.mean(R_3_81))
logging.info('k=5: %.4f,%.4f,%.4f',torch.mean(F1_5_81),torch.mean(P_5_81),torch.mean(R_5_81))
logits_1006_5 = prediction_1006.clone()
ap_1006 = util.compute_AP(prediction_1006.cuda(), lab_1006.cuda())
F1_3_1006,P_3_1006,R_3_1006 = util.compute_F1(prediction_1006.cuda(), lab_1006.cuda(), 'overall', k_val=3)
F1_5_1006,P_5_1006,R_5_1006 = util.compute_F1(logits_1006_5.cuda(), lab_1006.cuda(), 'overall', k_val=5)
print('GZSL AP',torch.mean(ap_1006))
print('g_k=3',torch.mean(F1_3_1006), torch.mean(P_3_1006), torch.mean(R_3_1006))
print('g_k=5',torch.mean(F1_5_1006), torch.mean(P_5_1006), torch.mean(R_5_1006))
logging.info('GZSL AP:%.4f',torch.mean(ap_1006))
logging.info('g_k=3:%.4f,%.4f,%.4f',torch.mean(F1_3_1006), torch.mean(P_3_1006), torch.mean(R_3_1006))
logging.info('g_k=5:%.4f,%.4f,%.4f',torch.mean(F1_5_1006), torch.mean(P_5_1006), torch.mean(R_5_1006))
print("------------------------------------------------------------------")
print("TEST Time: {:.4f}".format(time.time()-test_start_time))
print("------------------------------------------------------------------")
logging.info("------------------------------------------------------------------")
logging.info("TEST Time: {:.4f}".format(time.time()-test_start_time))
logging.info("------------------------------------------------------------------")
if not opt.train_full_data:
for epoch in range(start_epoch, num_epochs): # loop over the dataset multiple times
train(epoch)
if (epoch > 3 and epoch % eval_interval == 0) or epoch == num_epochs - 1:
model_biam.eval()
val(epoch)
model_biam.train()
if opt.cosinelr_scheduler:
scheduler.step()
if (epoch > 3 and epoch % 10 == 0) or epoch == num_epochs-1:
test(epoch)
else:
src = ' results/NUS_WIDE_' + opt.pretrained_model + '/model_best.pth '
dst = os.path.join(opt.save_path, 'model_best.pth')
cmd = 'cp ' + src + ' ' + dst
print(cmd)
os.system(cmd)
## load the best model for training on full data
opt.train = False
data = util.DATA_LOADER(opt) ### INTIAL DATALOADER ###
print('===> total samples')
print(data.ntrain)
logging.info('===> total samples')
logging.info(data.ntrain)
optimizer = torch.optim.Adam(model_biam.parameters(), opt.train_full_lr, weight_decay=0.0005, betas=(opt.beta1, 0.999))
path_chk_rest = os.path.join(opt.save_path, 'model_best.pth')
print(path_chk_rest)
logging.info(path_chk_rest)
model_biam.load_state_dict(torch.load(path_chk_rest))
start_epoch = 1
if opt.cuda:
model_biam = model_biam.cuda()
model_test = model_test.cuda()
data.vecs_81 = data.vecs_81.cuda()
data.vecs_925 = data.vecs_925.cuda()
gzsl_vecs = torch.cat([data.vecs_925,data.vecs_81],0)
for epoch in range(start_epoch, start_epoch+5):
train(epoch)
test(epoch)