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run_training_save_acc.py
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run_training_save_acc.py
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import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from net.st_gcn import Model
import random
from scipy import stats
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
###### **model parameters**
W = 50 # window size
TS = 64 # number of voters per test subject
###### **training parameters**
LR = 0.001 # learning rate
batch_size = 64
criterion = nn.BCELoss() # CrossEntropyLoss()
#optimizer = optim.Adam(net.parameters(), lr=LR, weight_decay=0.001)
# state_dict = torch.load('checkpoint.pth')
# net.load_state_dict(state_dict)
train_data = np.load('data/train_data_1200_1.npy')
train_label = np.load('data/train_label_1200_1.npy')
test_data = np.load('data/test_data_1200_1.npy')
test_label = np.load('data/test_label_1200_1.npy')
print(train_data.shape)
###### start training model
training_loss = 0.0
for window_size in [128]:#[50, 64, 75, 100, 128, 256, 1200]:
W = window_size
final_testing_accuracy = 0
testing_acc_curr_fold = []
#print('-'*80)
#print("Window Size {}".format(W))
#print('-'*80)
for fold in range(1, 6):
print('-'*80)
print("Window Size {}, Fold {}".format(W, fold))
print('-'*80)
best_test_acc_curr_fold = 0
best_test_epoch_curr_fold = 0
best_edge_imp_curr_fold = []
train_data = np.load('data/train_data_1200_'+str(fold)+'.npy')
train_label = np.load('data/train_label_1200_'+str(fold)+'.npy')
test_data = np.load('data/test_data_1200_'+str(fold)+'.npy')
test_label = np.load('data/test_label_1200_'+str(fold)+'.npy')
net = Model(1, 1, None, True)
net.to(device)
optimizer = optim.Adam(net.parameters(), lr=LR, weight_decay=0.001)
for epoch in range(60001): # number of mini-batches
# select a random sub-set of subjects
idx_batch = np.random.permutation(int(train_data.shape[0]))
idx_batch = idx_batch[:int(batch_size)]
# construct a mini-batch by sampling a window W for each subject
train_data_batch = np.zeros((batch_size, 1, W, 22, 1))
train_label_batch = train_label[idx_batch]
for i in range(batch_size):
r1 = random.randint(0, train_data.shape[2] - W)
train_data_batch[i] = train_data[idx_batch[i], :, r1:r1 + W, :, :]
train_data_batch_dev = torch.from_numpy(train_data_batch).float().to(device)
train_label_batch_dev = torch.from_numpy(train_label_batch).float().to(device)
#train_data_batch_dev = train_data_batch_dev.squeeze()
# forward + backward + optimize
optimizer.zero_grad()
#net.hidden = net.init_hidden(batch_size)
outputs = net(train_data_batch_dev)
loss = criterion(outputs, train_label_batch_dev)
loss.backward()
optimizer.step()
# print training statistics
training_loss += loss.item()
if epoch % 1000 == 0: # print every T mini-batches
# print(outputs)
outputs = outputs.data.cpu().numpy() > 0.5
train_acc = sum(outputs[:, 0] == train_label_batch) / train_label_batch.shape[0]
print('[%d] training loss: %.3f training batch acc %f' % (epoch + 1, training_loss/1000, train_acc))
training_loss = 0.0
# validate on test subjects by voting
if epoch % 1000 == 0: # print every K mini-batches
idx_batch = np.random.permutation(int(test_data.shape[0]))
idx_batch = idx_batch[:int(batch_size)]
test_label_batch = test_label[idx_batch]
prediction = np.zeros((test_data.shape[0],))
voter = np.zeros((test_data.shape[0],))
for v in range(TS):
idx = np.random.permutation(int(test_data.shape[0]))
# testing also performed batch by batch (otherwise it produces error)
for k in range(int(test_data.shape[0] / batch_size)):
idx_batch = idx[int(batch_size * k):int(batch_size * (k + 1))]
# construct random sub-sequences from a batch of test subjects
test_data_batch = np.zeros((batch_size, 1, W, 22, 1))
for i in range(64):
r1 = random.randint(0, test_data.shape[2] - W)
test_data_batch[i] = test_data[idx_batch[i], :, r1:r1 + W, :, :]
test_data_batch_dev = torch.from_numpy(test_data_batch).float().to(device)
outputs = net(test_data_batch_dev)
outputs = outputs.data.cpu().numpy()
prediction[idx_batch] = prediction[idx_batch] + outputs[:, 0];
voter[idx_batch] = voter[idx_batch] + 1;
# average voting
prediction = prediction / voter;
# print(prediction)
test_acc = sum((prediction > 0.5) == test_label) / test_label.shape[0]
print(sum((prediction > 0.5) == test_label) / test_label.shape[0])
with open('output/testing_acc_st_gcn_folds/windowsize_'+str(W)+'_fold_'+str(fold)+'.txt', 'a') as f:
f.write("Epoch {}, Accuracy {}\n".format(epoch, test_acc))
if test_acc > best_test_acc_curr_fold:
best_test_acc_curr_fold = test_acc
best_test_epoch_curr_fold = epoch
tmp_edge_imp = []
for edge_importances in net.edge_importance:
edge_imp = torch.squeeze(edge_importances.data).cpu().numpy()
tmp_edge_imp.append(edge_imp)
best_edge_imp_curr_fold = tmp_edge_imp
torch.save(net.state_dict(), 'checkpoint.pth')
print("Best accuracy for window {} and fold {} = {} at epoch = {}".format(W, fold, best_test_acc_curr_fold, best_test_epoch_curr_fold))
testing_acc_curr_fold.append(best_test_acc_curr_fold)
layer_num = 1
for edge_imp in best_edge_imp_curr_fold:
filename = "output/edge_importance/edge_imp_layer_" + str(layer_num) + "_WS_" + str(W) + "_fold_"+str(fold)
np.save(filename, edge_imp)
layer_num += 1
print("Window size {} completed. Final testing accuracy = {}".format(W, np.mean(np.array(testing_acc_curr_fold))))
with open('output/testing_acc_st_gcn_final.txt', 'a') as f:
f.write("Window size {} completed. Final testing accuracy = {}\n".format(W, np.mean(np.array(testing_acc_curr_fold))))