def test():
log_dir = './logdir'
snapshot_interval = 10000
snapshot_dir = './snapshot_dir'
max_iter = 100000
log_interval = 100
lr = 0.0005
file = 'data/test_32x32.mat'
X_raw, y_raw = getData(filename=file)
n_test = X_raw.shape[0]
y_raw[y_raw == 10] = 0
y_raw = np.reshape(y_raw, (n_test, ))
snapshot_file = './snapshot_dir/%s' % (snapshot_name)
with tf.Session() as sess:
X = tf.placeholder(tf.float32, shape=(None, 32, 32, 3))
y = tf.placeholder(tf.int32, shape=(None, ))
rcnn = RCNN(time=3, K=192, p=0.9, numclass=10, is_training=True)
_, _, _, preds = rcnn.buile_model(X, y)
snapshot_saver = tf.train.Saver(max_to_keep=None) # keep all snapshots
snapshot_saver.restore(sess, snapshot_file)
np.random.seed(0)
count = 0
start = datetime.datetime.now()
for i in range(n_test):
image = X_raw[i]
labels = y_raw[i]
# print(image.shape)
preds_each = sess.run(preds, feed_dict={X: image})
if preds_each == labels:
count += 1
else:
continue
acc = count / (n_test * 1.0)
end = datetime.datetime.now()
print("sum time: {}, acc: {}".format(end - start, acc))
print('Saved model checkpoint with val_acc: {}'.format(str(round(current_epoch_acc, 4))))
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
return model
''' ############################ Parameters ############################'''
lr = 0.001
batch_size = 32
# root_data_dir should contain 3 sub-folders: 'train' , 'validation' and 'test
root_data_dir = '/Users/royhirsch/Documents/GitHub/ProcessedData'
''' ############################ Main ############################'''
net = RCNN()
# weight_tensor = torch.tensor([1, 100]).float()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
# Decay LR by a factor of 0.1 * lr every n epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.0001)
# Create train and validation data loaders
trainDataLoader = BusDataLoader(root_dir=os.path.join(root_data_dir, 'train'),
data_loader_type='train',
BGpad=16,
outShape=224,
balance_data_size=1,
augment_pos=0)
def main():
log_dir = './logdir'
snapshot_interval = 10000
snapshot_dir = './snapshot_dir'
max_iter = 100000
log_interval = 100
lr = 0.0005
file = 'data/train_32x32.mat'
X_raw, y_raw = getData(filename=file)
n_train = X_raw.shape[0]
y_raw[y_raw == 10] = 0
y_raw = np.reshape(y_raw, (n_train, ))
with tf.Session() as sess:
X = tf.placeholder(tf.float32, shape=(None, 32, 32, 3))
y = tf.placeholder(tf.int32, shape=(None, ))
rcnn = RCNN(time=3, K=192, p=0.9, numclass=10, is_training=True)
loss, summary_op, acc, _ = rcnn.buile_model(X, y)
optimizer = tf.train.AdamOptimizer(learning_rate=lr,
beta1=0.9,
beta2=0.98,
epsilon=1e-8).minimize(loss)
init = tf.global_variables_initializer()
sess.run(init)
os.makedirs(snapshot_dir, exist_ok=True)
snapshot_saver = tf.train.Saver(max_to_keep=None) # keep all snapshots
writer = tf.summary.FileWriter(log_dir, sess.graph)
np.random.seed(0)
loss_mean = 0
acc_mean = 0
start = datetime.datetime.now()
for n_iter in range(max_iter):
index = np.random.choice(n_train, 64, replace=True)
image = X_raw[index]
labels = y_raw[index]
# print(image.shape)
loss_batch, summary_op_batch, acc_batch, _ = sess.run(
[loss, summary_op, acc, optimizer],
feed_dict={
X: image,
y: labels
})
loss_mean += loss_batch
acc_mean += acc_batch
if (n_iter + 1) % log_interval == 0 or (n_iter + 1) == max_iter:
loss_mean = loss_mean / (log_interval * 1.0)
acc_mean = acc_mean / (log_interval * 1.0)
batch_time = datetime.datetime.now()
print("time: {},iter = {}\n\tloss = {}, accuracy (cur) = {} ".
format(batch_time - start, n_iter + 1, loss_mean,
acc_mean))
loss_mean = 0
acc_mean = 0
writer.add_summary(summary_op_batch, global_step=n_iter)
if (n_iter + 1) % snapshot_interval == 0 or (n_iter +
1) == max_iter:
snapshot_file = os.path.join(snapshot_dir,
"%08d" % (n_iter + 1))
snapshot_saver.save(sess,
snapshot_file,
write_meta_graph=False)
print('snapshot saved to ' + snapshot_file)
end = datetime.datetime.now()
print("sum time: {}".format(end - start))
writer.close()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
return model
''' ############################ Parameters ############################'''
lr = 0.001
batch_size = 32
# root_data_dir should contain 3 sub-folders: 'train' , 'validation' and 'test
root_data_dir = '/Users/Mor\'s Yoga/Documents/GitHub/DetectionProject/ProcessedData'
''' ############################ Main ############################'''
# net = create_model()
net = RCNN(num_regressions=4, train_phase='regressions')
# net.load_state_dict(torch.load(PATH_TO_STATE_DICT_PR_FILE))
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(net.parameters(), lr=lr, weight_decay=1000)
# Decay LR by a factor of 0.1 * lr every n epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.0001)
# Create train and validation data loaders
trainDataLoader = BusDataLoader(root_dir=os.path.join(root_data_dir, 'train'),
data_loader_type='train',
BGpad=16,
outShape=224,
balance_data_size=1,
augment_pos=0)