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solver_misa.py
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solver_misa.py
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import os
import sys
import math
import copy
import random
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from sklearn.metrics import accuracy_score
import torch
import torch.nn as nn
import torchvision.models as models
from torch.autograd import Variable
from torch.optim.lr_scheduler import ReduceLROnPlateau
from networks.misa import MISA
from networks.misa_function import DiffLoss, MSE, CMD
class AR_MISA_solver(nn.Module):
def __init__(self, config):
super(AR_MISA_solver, self).__init__()
self.config = config
# Initiate the networks
self.model = MISA(config)
# Setup the optimizers and loss function
opt_params = list(self.model.parameters())
self.optimizer = torch.optim.AdamW(opt_params, lr=config.learning_rate, weight_decay=config.weight_decay)
self.scheduler = ReduceLROnPlateau(self.optimizer, mode='min', patience=config.when, factor=0.5, verbose=False)
self.criterion = nn.CrossEntropyLoss(reduction="mean")
self.domain_loss_criterion = nn.CrossEntropyLoss(reduction="mean")
self.sp_loss_criterion = nn.CrossEntropyLoss(reduction="mean")
self.loss_diff = DiffLoss()
self.loss_recon = MSE()
self.loss_cmd = CMD()
# Select the best ckpt
self.best_val_metric = 0.
def get_cmd_loss(self):
loss = self.loss_cmd(self.model.x_1_shared, self.model.x_2_shared, 5)
return loss
def get_diff_loss(self):
shared_1 = self.model.x_1_shared
shared_2 = self.model.x_2_shared
private_1 = self.model.x_1_private
private_2 = self.model.x_2_private
# Between private and shared
loss = self.loss_diff(private_1, shared_1)
loss += self.loss_diff(private_2, shared_2)
# Across privates
loss += self.loss_diff(private_1, private_2)
return loss
def get_recon_loss(self):
loss = self.loss_recon(self.model.x_1_recon, self.model.x_1_orig)
loss += self.loss_recon(self.model.x_2_recon, self.model.x_2_orig)
return loss/2.0
def update(self, rgb_frames, flow_frames, labels):
self.train()
self.optimizer.zero_grad()
rgb_frames, flow_frames, labels = rgb_frames.cuda(), flow_frames.cuda(), labels.cuda()
pred = self.model(rgb_frames, flow_frames)
cls_loss = self.criterion(pred, labels)
diff_loss = self.get_diff_loss()
recon_loss = self.get_recon_loss()
similarity_loss = self.get_cmd_loss()
loss = cls_loss + \
self.config.diff_weight * diff_loss + \
self.config.recon_weight * recon_loss + \
self.config.sim_weight * similarity_loss
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.config.clip)
self.optimizer.step()
def val(self, val_loader):
val_loss, val_acc = self.test(val_loader)
self.save_best_ckpt(val_acc)
self.scheduler.step(val_loss)
return val_loss, val_acc
def test(self, test_loader):
with torch.no_grad():
self.eval()
preds, gt = [], []
total_loss, total_samples = 0.0, 0
for (rgb_frames, flow_frames, labels) in test_loader:
rgb_frames, flow_frames, labels = rgb_frames.cuda(), flow_frames.cuda(), labels.cuda()
pred = self.model(rgb_frames, flow_frames)
loss = self.criterion(pred, labels)
_, pred = torch.max(pred, 1)
preds.append(pred)
gt.append(labels)
total_loss += loss.item()*labels.size(0)
total_samples += labels.size(0)
preds, gt = torch.cat(preds).cpu(), torch.cat(gt).cpu()
acc = accuracy_score(np.array(gt), np.array(preds))
loss = total_loss / total_samples
self.print_metric([loss, acc])
return loss, acc
def load_best_ckpt(self):
ckpt_name = os.path.join(self.config.ckpt_path, self.config.fusion + ".pt")
state_dict = torch.load(ckpt_name)
self.model.load_state_dict(state_dict['model'])
def save_best_ckpt(self, val_metric):
def update_metric(val_metric):
if val_metric > self.best_val_metric:
self.best_val_metric = val_metric
return True
return False
if update_metric(val_metric):
ckpt_name = os.path.join(self.config.ckpt_path, self.config.fusion +'.pt')
torch.save({'model': self.model.state_dict()}, ckpt_name)
def print_metric(self, metric):
print('Loss: %.4f Acc: %.3f'%(metric[0], metric[1]))
def run(self, train_loader, val_loader, test_loader):
best_val_loss = 1e8
patience = self.config.patience
for epochs in range(1, self.config.num_epochs+1):
print('Epoch: %d/%d' % (epochs, self.config.num_epochs))
for _, (rgb_frames, flow_frames, labels) in tqdm(enumerate(train_loader), total=len(train_loader)):
self.update(rgb_frames, flow_frames, labels)
# Validate model
val_loss, val_acc = self.val(val_loader)
if val_loss < best_val_loss:
patience = self.config.patience
best_val_loss = val_loss
else:
patience -= 1
if patience == 0:
break
# Test model
self.load_best_ckpt()
self.test(test_loader)