def train(args):
data_loader = DataLoader(args.batch_size, args.seq_length, args.data_scale)
with open(os.path.join('save', 'config.pkl'), 'w') as f:
cPickle.dump(args, f)
model = Model(args)
with tf.Session() as sess:
tf.initialize_all_variables().run()
saver = tf.train.Saver(tf.all_variables())
for e in xrange(args.num_epochs):
sess.run(tf.assign(model.lr, args.learning_rate * (args.decay_rate ** e)))
data_loader.reset_batch_pointer()
state = model.initial_state.eval()
for b in xrange(data_loader.num_batches):
start = time.time()
x, y = data_loader.next_batch()
feed = {model.input_data: x, model.target_data: y, model.initial_state: state}
train_loss, state, _ = sess.run([model.cost, model.final_state, model.train_op], feed)
end = time.time()
print "{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}" \
.format(e * data_loader.num_batches + b,
args.num_epochs * data_loader.num_batches,
e, train_loss, end - start)
if (e * data_loader.num_batches + b) % args.save_every == 0 and ((e * data_loader.num_batches + b) > 0):
checkpoint_path = os.path.join('save', 'model.ckpt')
saver.save(sess, checkpoint_path, global_step = e * data_loader.num_batches + b)
print "model saved to {}".format(checkpoint_path)
def train(args):
dim = 6
data_loader = DataLoader(dim, args.batch_size, args.seq_length, reprocess=args.reprocess)
x, y = data_loader.next_batch()
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
with open(os.path.join(args.save_dir, 'config.pkl'), 'w') as f:
cPickle.dump(args, f)
model = Model(dim, args)
with tf.Session() as sess:
tf.initialize_all_variables().run()
ts = TrainingStatus(sess, args.num_epochs, data_loader.num_batches, save_interval = args.save_every, graph_def = sess.graph_def, save_dir = args.save_dir)
for e in xrange(ts.startEpoch, args.num_epochs):
sess.run(tf.assign(model.lr, args.learning_rate * (args.decay_rate ** e)))
data_loader.reset_batch_pointer()
state = model.initial_state.eval()
for b in xrange(data_loader.num_batches):
ts.tic()
x, y = data_loader.next_batch()
feed = {model.input_data: x, model.target_data: y, model.initial_state: state}
summary, train_loss, state, _ = sess.run([model.summary, model.cost, model.final_state, model.train_op], feed)
print ts.tocBatch(summary, e, b, train_loss)
ts.tocEpoch(sess, e)
def evals(net, adata ,alabel, batch_size):
hidden = net.begin_state(func=mx.nd.zeros,batch_size = batch_size,ctx=mx.cpu())
dataLoader = DataLoader(adata, alabel)
tl = 0
for data, label in dataLoader.dataIter(batch_size):
label = nd.array(label)
#label = nd.ones(shape=(5,batch_size)) * label
#label = label.reshape((-1,))
dd = nd.array(data.reshape((batch_size,5,11)).swapaxes(0,1))
#hidden = detach(hidden)
output,hidden = net(dd, hidden)
output = output.reshape((5,batch_size,1))
output = nd.sum(output,axis=0)/5
lv = loss(output, label)
tl += nd.sum(lv).asscalar()
return tl / len(adata)
related_path,
antonyms_path,
_embedding_size=EMBEDDING_SIZE)
lang.load_gensim_word2vec(embeddings_path) # load pre-trained embeddings
# this creates TF matrix of embeddings
embeddings_matrix = lang.create_embeddings()
#embeddings_matrix = lang.create_temp_embeddings()
# this loads a matrix of on_sense -> related words mappings
related = load_related(related_path)
# load tf_records
tf_records_files = [
path_to_tf_records + f for f in os.listdir(path_to_tf_records)
if f[0] != "."
]
data_loader = DataLoader()
dataset = data_loader.make_dataset(tf_records_files, batch_size=BATCH_SIZE)
eval_data_loader = EvalDataLoader()
validation_dataset = eval_data_loader.make_dataset([path_to_valid_records],
batch_size=BATCH_SIZE)
test_dataset = eval_data_loader.make_dataset([path_to_test_records],
batch_size=BATCH_SIZE)
train(embedding_matrix=embeddings_matrix,
related_matrix=related,
dataset=dataset,
validation_dataset=validation_dataset,
test_dataset=test_dataset,
encoder_hidden_size=ENCODER_HIDDEN_SIZE,
encoder_embedding_size=EMBEDDING_SIZE,
_learning_rate=LEARNING_RATE,
def train(args):
# Construct the DataLoader object
dataloader = DataLoader(args.batch_size, args.seq_length + 1, args.train_dataset, forcePreProcess=True)
# Construct the ST-graph object
stgraph = ST_GRAPH(1, args.seq_length + 1)
# Log directory
log_directory = 'log/trainedOn_'+ str(args.train_dataset)
if not os.path.exists(log_directory):
os.makedirs(log_directory)
# Logging file
log_file_curve = open(os.path.join(log_directory, 'log_curve.txt'), 'w')
log_file = open(os.path.join(log_directory, 'val.txt'), 'w')
# Save directory for saving the model
save_directory = 'save/trainedOn_'+str(args.train_dataset)
if not os.path.exists(save_directory):
os.makedirs(save_directory)
# Open the configuration file
with open(os.path.join(save_directory, 'config.pkl'), 'wb') as f:
pickle.dump(args, f) #store arguments from parser
# Path to store the checkpoint file
def checkpoint_path(x):
return os.path.join(save_directory, 'srnn_model_'+str(x)+'.tar')
# Initialize net
net = SRNN(args)
if args.use_cuda:
net = net.cuda()
# optimizer = torch.optim.Adam(net.parameters(), lr=args.learning_rate)
# optimizer = torch.optim.RMSprop(net.parameters(), lr=args.learning_rate, momentum=0.0001, centered=True)
optimizer = torch.optim.Adagrad(net.parameters())
learning_rate = args.learning_rate
print('Training begin')
best_val_loss = 100
best_epoch = 0
# Training
for epoch in range(args.num_epochs):
dataloader.reset_batch_pointer(valid=False)
loss_epoch = 0
# For each batch
for batch in range(dataloader.num_batches):
start = time.time()
# Get batch data
x, _, _, d = dataloader.next_batch(randomUpdate=True)
# Loss for this batch
loss_batch = 0
# For each sequence in the batch
for sequence in range(dataloader.batch_size):
# Construct the graph for the current sequence
stgraph.readGraph([x[sequence]])
nodes, edges, nodesPresent, edgesPresent = stgraph.getSequence()
# Convert to cuda variables
nodes = Variable(torch.from_numpy(nodes).float())
if args.use_cuda:
nodes = nodes.cuda()
edges = Variable(torch.from_numpy(edges).float())
if args.use_cuda:
edges = edges.cuda()
# Define hidden states
numNodes = nodes.size()[1]
hidden_states_node_RNNs = Variable(torch.zeros(numNodes, args.human_node_rnn_size))
if args.use_cuda:
hidden_states_node_RNNs = hidden_states_node_RNNs.cuda()
hidden_states_edge_RNNs = Variable(torch.zeros(numNodes*numNodes, args.human_human_edge_rnn_size))
if args.use_cuda:
hidden_states_edge_RNNs = hidden_states_edge_RNNs.cuda()
cell_states_node_RNNs = Variable(torch.zeros(numNodes, args.human_node_rnn_size))
if args.use_cuda:
cell_states_node_RNNs = cell_states_node_RNNs.cuda()
cell_states_edge_RNNs = Variable(torch.zeros(numNodes*numNodes, args.human_human_edge_rnn_size))
if args.use_cuda:
cell_states_edge_RNNs = cell_states_edge_RNNs.cuda()
# Zero out the gradients
net.zero_grad()
optimizer.zero_grad()
# Forward prop
outputs, _, _, _, _, _ = net(nodes[:args.seq_length], edges[:args.seq_length], nodesPresent[:-1], edgesPresent[:-1], hidden_states_node_RNNs, hidden_states_edge_RNNs, cell_states_node_RNNs, cell_states_edge_RNNs)
# Compute loss
loss = Gaussian2DLikelihood(outputs, nodes[1:], nodesPresent[1:], args.pred_length)
loss_batch += loss.data[0]
# Compute gradients
loss.backward()
# Clip gradients
torch.nn.utils.clip_grad_norm(net.parameters(), args.grad_clip)
# Update parameters
optimizer.step()
# Reset the stgraph
stgraph.reset()
end = time.time()
loss_batch = loss_batch / dataloader.batch_size
loss_epoch += loss_batch
print(
'{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}'.format(epoch * dataloader.num_batches + batch,
args.num_epochs * dataloader.num_batches,
epoch,
loss_batch, end - start))
# Compute loss for the entire epoch
loss_epoch /= dataloader.num_batches
# Log it
log_file_curve.write(str(epoch)+','+str(loss_epoch)+',')
# Validation
dataloader.reset_batch_pointer(valid=True)
loss_epoch = 0
for batch in range(dataloader.valid_num_batches):
# Get batch data
x, _, d = dataloader.next_valid_batch(randomUpdate=False)
# Loss for this batch
loss_batch = 0
for sequence in range(dataloader.batch_size):
stgraph.readGraph([x[sequence]])
nodes, edges, nodesPresent, edgesPresent = stgraph.getSequence()
# Convert to cuda variables
nodes = Variable(torch.from_numpy(nodes).float())
if args.use_cuda:
nodes = nodes.cuda()
edges = Variable(torch.from_numpy(edges).float())
if args.use_cuda:
edges = edges.cuda()
# Define hidden states
numNodes = nodes.size()[1]
hidden_states_node_RNNs = Variable(torch.zeros(numNodes, args.human_node_rnn_size))
if args.use_cuda:
hidden_states_node_RNNs = hidden_states_node_RNNs.cuda()
hidden_states_edge_RNNs = Variable(torch.zeros(numNodes*numNodes, args.human_human_edge_rnn_size))
if args.use_cuda:
hidden_states_edge_RNNs = hidden_states_edge_RNNs.cuda()
cell_states_node_RNNs = Variable(torch.zeros(numNodes, args.human_node_rnn_size))
if args.use_cuda:
cell_states_node_RNNs = cell_states_node_RNNs.cuda()
cell_states_edge_RNNs = Variable(torch.zeros(numNodes*numNodes, args.human_human_edge_rnn_size))
if args.use_cuda:
cell_states_edge_RNNs = cell_states_edge_RNNs.cuda()
outputs, _, _, _, _, _ = net(nodes[:args.seq_length], edges[:args.seq_length], nodesPresent[:-1], edgesPresent[:-1],
hidden_states_node_RNNs, hidden_states_edge_RNNs,
cell_states_node_RNNs, cell_states_edge_RNNs)
# Compute loss
loss = Gaussian2DLikelihood(outputs, nodes[1:], nodesPresent[1:], args.pred_length)
loss_batch += loss.data[0]
# Reset the stgraph
stgraph.reset()
loss_batch = loss_batch / dataloader.batch_size
loss_epoch += loss_batch
loss_epoch = loss_epoch / dataloader.valid_num_batches
#Saving the model\
if loss_epoch < best_val_loss or args.save_every:
print('Saving model')
torch.save({
'epoch': epoch,
'state_dict': net.state_dict(),
'optimizer_state_dict': optimizer.state_dict()
}, checkpoint_path(epoch))
#save_best_model overwriting the earlier file
#if loss_epoch < best_val_loss:
# Update best validation loss until now
if loss_epoch < best_val_loss:
best_val_loss = loss_epoch
best_epoch = epoch
# Record best epoch and best validation loss
print('(epoch {}), valid_loss = {:.3f}'.format(epoch, loss_epoch))
print('Best epoch {}, Best validation loss {}'.format(best_epoch, best_val_loss))
# Log it
log_file_curve.write(str(loss_epoch)+'\n')
# Record the best epoch and best validation loss overall
print('Best epoch {}, Best validation loss {}'.format(best_epoch, best_val_loss))
# Log it
log_file.write(str(best_epoch)+','+str(best_val_loss))
# Close logging files
log_file.close()
log_file_curve.close()
def train(args):
prefix = ''
f_prefix = '.'
if not os.path.isdir("log/"):
print("Directory creation script is running...")
subprocess.call([f_prefix+'/make_directories.sh'])
args.freq_validation = np.clip(args.freq_validation, 0, args.num_epochs)
validation_epoch_list = list(range(args.freq_validation, args.num_epochs+1, args.freq_validation))
validation_epoch_list[-1]-=1
# Create the data loader object. This object would preprocess the data in terms of
# batches each of size args.batch_size, of length args.seq_length
dataloader = DataLoader(f_prefix, args.batch_size, args.seq_length, args.num_validation, forcePreProcess=True)
method_name = "VANILLALSTM"
model_name = "LSTM"
save_tar_name = method_name+"_lstm_model_"
if args.gru:
model_name = "GRU"
save_tar_name = method_name+"_gru_model_"
# Log directory
log_directory = os.path.join(prefix, 'log/')
plot_directory = os.path.join(prefix, 'plot/', method_name, model_name)
plot_train_file_directory = 'validation'
# Logging files
log_file_curve = open(os.path.join(log_directory, method_name, model_name,'log_curve.txt'), 'w+')
log_file = open(os.path.join(log_directory, method_name, model_name, 'val.txt'), 'w+')
# model directory
save_directory = os.path.join(f_prefix, 'model')
# Save the arguments int the config file
with open(os.path.join(save_directory, method_name, model_name,'config.pkl'), 'wb') as f:
pickle.dump(args, f)
# Path to store the checkpoint file
def checkpoint_path(x):
return os.path.join(save_directory, method_name, model_name, save_tar_name+str(x)+'.tar')
# model creation
net = LSTM(args)
if args.use_cuda:
net = net.cuda()
# optimizer = torch.optim.Adagrad(net.parameters(), weight_decay=args.lambda_param)
optimizer = torch.optim.RMSprop(net.parameters(), lr=args.learning_rate)
loss_f = torch.nn.MSELoss()
learning_rate = args.learning_rate
best_val_loss = 100
best_val_data_loss = 100
smallest_err_val = 100000
smallest_err_val_data = 100000
best_epoch_val = 0
best_epoch_val_data = 0
best_err_epoch_val = 0
best_err_epoch_val_data = 0
all_epoch_results = []
grids = []
num_batch = 0
# Training
for epoch in range(args.num_epochs):
print('****************Training epoch beginning******************')
if dataloader.additional_validation and (epoch-1) in validation_epoch_list:
dataloader.switch_to_dataset_type(True)
dataloader.reset_batch_pointer(valid=False)
loss_epoch = 0
# For each batch
# num_batches 資料可以被分多少批 要跑幾個iter
for batch in range(dataloader.num_batches):
start = time.time()
# print(dataloader.num_batches, dataloader.batch_size)
# Get batch data
x, y, d = dataloader.next_batch(randomUpdate=False)
loss_batch = 0
# x_cat = Variable(torch.from_numpy(np.array(x[0])).float())
x_seq = np.array(x)
y_seq = np.array(y)
x_seq = Variable(torch.from_numpy(x_seq).float())
y_seq = Variable(torch.from_numpy(y_seq).float())
temp = x_seq[:,:,-2:]
x_seq = x_seq[:,:,:-2]
y_seq = y_seq[:,:,:3]
hidden_states = Variable(torch.zeros(x_seq.size()[0], args.rnn_size))
cell_states = Variable(torch.zeros(x_seq.size()[0], args.rnn_size))
if args.use_cuda:
x_seq = x_seq.cuda()
y_seq = y_seq.cuda()
temp = temp.cuda()
hidden_states = hidden_states.cuda()
cell_states = cell_states.cuda()
# Zero out gradients
net.zero_grad()
optimizer.zero_grad()
outputs, _, _ = net(x_seq, temp, hidden_states, cell_states)
loss = loss_f(outputs, y_seq)
loss_batch = loss.detach().item()
# Compute gradients
loss.backward()
# Clip gradients
torch.nn.utils.clip_grad_norm_(net.parameters(), args.grad_clip)
# Update parameters
optimizer.step()
end = time.time()
loss_epoch += loss_batch
print('{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}'.format((batch+1) * dataloader.batch_size,
dataloader.num_batches * dataloader.batch_size,
epoch,
loss_batch, end - start))
loss_epoch /= dataloader.num_batches
print("Training epoch: "+str(epoch)+" loss: "+str(loss_epoch))
#Log loss values
log_file_curve.write("Training epoch: "+str(epoch)+" loss: "+str(loss_epoch)+'\n')
# Validation dataset
if dataloader.additional_validation and (epoch) in validation_epoch_list:
dataloader.switch_to_dataset_type()
print('****************Validation with dataset epoch beginning******************')
dataloader.reset_batch_pointer(valid=False)
dataset_pointer_ins = dataloader.dataset_pointer
validation_dataset_executed = True
loss_epoch = 0
err_epoch = 0
num_of_batch = 0
smallest_err = 100000
#results of one epoch for all validation datasets
epoch_result = []
#results of one validation dataset
results = []
# For each batch
for batch in range(dataloader.num_batches):
# Get batch data
x, y, d = dataloader.next_batch(randomUpdate=False)
# Loss for this batch
loss_batch = 0
err_batch = 0
# For each sequence
for sequence in range(len(x)):
# Get the sequence
x_seq = x[sequence]
y_seq = y[sequence]
x_seq= np.array(x_seq)
y_seq= np.array(y_seq)[:,:3]
x_seq = Variable(torch.from_numpy(x_seq).float())
y_seq = Variable(torch.from_numpy(y_seq).float())
temp = x_seq[:,-2:]
x_seq = x_seq[:,:-2]
y_seq = y_seq[:,:3]
if args.use_cuda:
x_seq = x_seq.cuda()
y_seq = y_seq.cuda()
temp = temp.cuda()
#will be used for error calculation
orig_x_seq = y_seq.clone()
# print(x_seq.size(), args.seq_length)
with torch.no_grad():
hidden_states = Variable(torch.zeros(1, args.rnn_size))
cell_states = Variable(torch.zeros(1, args.rnn_size))
ret_x_seq = Variable(torch.zeros(args.seq_length, net.input_size))
# all_outputs = Variable(torch.zeros(1, args.seq_length, net.input_size))
# Initialize the return data structure
if args.use_cuda:
ret_x_seq = ret_x_seq.cuda()
hidden_states = hidden_states.cuda()
cell_states = cell_states.cuda()
total_loss = 0
# For the observed part of the trajectory
for tstep in range(args.seq_length):
outputs, hidden_states, cell_states = net(x_seq[tstep].view(1, 1, net.input_size), temp[tstep].view(1, 1, temp.size()[-1]), hidden_states, cell_states)
ret_x_seq[tstep, 0] = outputs[0,0,0]
ret_x_seq[tstep, 1] = outputs[0,0,1]
ret_x_seq[tstep, 2] = outputs[0,0,2]
print(outputs.size(), )
loss = loss_f(outputs, y_seq[tstep].view(1, 1, y_seq.size()[1]))
total_loss += loss
total_loss = total_loss / args.seq_length
#get mean and final error
# print(ret_x_seq.size(), y_seq.size())
err = get_mean_error(ret_x_seq.data, y_seq.data, args.use_cuda)
loss_batch += total_loss.item()
err_batch += err
print('Current file : ',' Batch : ', batch+1, ' Sequence: ', sequence+1, ' Sequence mean error: ', err, 'valid_loss: ',total_loss.item())
results.append((y_seq.data.cpu().numpy(), ret_x_seq.data.cpu().numpy()))
loss_batch = loss_batch / dataloader.batch_size
err_batch = err_batch / dataloader.batch_size
num_of_batch += 1
loss_epoch += loss_batch
err_epoch += err_batch
epoch_result.append(results)
all_epoch_results.append(epoch_result)
if dataloader.num_batches != 0:
loss_epoch = loss_epoch / dataloader.num_batches
err_epoch = err_epoch / dataloader.num_batches
# avarage_err = (err_epoch + f_err_epoch)/2
# Update best validation loss until now
if loss_epoch < best_val_data_loss:
best_val_data_loss = loss_epoch
best_epoch_val_data = epoch
if err_epoch<smallest_err_val_data:
# Save the model after each epoch
print('Saving model')
torch.save({
'epoch': epoch,
'state_dict': net.state_dict(),
'optimizer_state_dict': optimizer.state_dict()
}, checkpoint_path(epoch))
smallest_err_val_data = err_epoch
best_err_epoch_val_data = epoch
print('(epoch {}), valid_loss = {:.3f}, valid_mean_err = {:.3f}'.format(epoch, loss_epoch, err_epoch))
print('Best epoch', best_epoch_val_data, 'Best validation loss', best_val_data_loss, 'Best error epoch',best_err_epoch_val_data, 'Best error', smallest_err_val_data)
log_file_curve.write("Validation dataset epoch: "+str(epoch)+" loss: "+str(loss_epoch)+" mean_err: "+str(err_epoch.data.cpu().numpy())+'\n')
optimizer = time_lr_scheduler(optimizer, epoch, lr_decay_epoch = args.freq_optimizer)
if dataloader.valid_num_batches != 0:
print('Best epoch', best_epoch_val, 'Best validation Loss', best_val_loss, 'Best error epoch',best_err_epoch_val, 'Best error', smallest_err_val)
# Log the best epoch and best validation loss
log_file.write('Validation Best epoch:'+str(best_epoch_val_data)+','+' Best validation Loss: '+str(best_val_data_loss))
if dataloader.additional_validation:
print('Best epoch acording to validation dataset', best_epoch_val_data, 'Best validation Loss', best_val_data_loss, 'Best error epoch',best_err_epoch_val_data, 'Best error', smallest_err_val_data)
log_file.write("Validation dataset Best epoch: "+str(best_epoch_val_data)+','+' Best validation Loss: '+str(best_val_data_loss)+'Best error epoch: ',str(best_err_epoch_val_data),'\n')
#dataloader.write_to_plot_file(all_epoch_results[best_epoch_val_data], plot_directory)
#elif dataloader.valid_num_batches != 0:
# dataloader.write_to_plot_file(all_epoch_results[best_epoch_val], plot_directory)
#else:
if validation_dataset_executed:
dataloader.switch_to_dataset_type(load_data=False)
create_directories(plot_directory, [plot_train_file_directory])
dataloader.write_to_plot_file(all_epoch_results[len(all_epoch_results)-1], os.path.join(plot_directory, plot_train_file_directory))
# Close logging files
log_file.close()
log_file_curve.close()
class data_augmentator():
# class for data augmentation
def __init__(self,f_prefix, num_of_data, seq_length, val_percent):
self.base_train_path = 'data/train/'
self.base_validation_path = 'data/validation/'
# list of angles will be use for rotation
self.angles = list(range(0,360,30))
self.num_of_data = np.clip(num_of_data, 0, len(self.angles) -1)
self.num_validation_data = math.ceil(self.num_of_data * val_percent) # number of validation dataset
self.num_train_data = self.num_of_data - self.num_validation_data # number of train dataset
print("For each dataset -----> Number of additional training dataset: ", self.num_train_data, " Number of validation dataset: ", self.num_validation_data)
self.num_validation_data =+1
self.seq_length = seq_length
self.val_percent = val_percent
self.f_prefix = f_prefix
self.dataloader = DataLoader(f_prefix, 1, seq_length , 0 ,forcePreProcess = True, infer = False, generate=True)
# noise parameter definition
self.noise_std_min = 0.05
self.noise_std_max = 0.15
self.noise_std = random.uniform(self.noise_std_min, self.noise_std_max)
self.noise_mean = 0.0
# remove datasets from directories for new creation
self.clear_directories(self.base_train_path)
self.clear_directories(self.base_validation_path, True)
self.random_dataset_creation()
def random_dataset_creation(self):
self.dataloader.reset_batch_pointer(valid=False)
dataset_pointer_ins = self.dataloader.dataset_pointer
dataset_instances = {}
whole_dataset = []
random_angles = random.sample(self.angles, self.num_of_data)
file_name = self.dataloader.get_file_name()
print("Dataset creation for: ", file_name, " angles: ", random_angles)
for batch in range(self.dataloader.num_batches):
start = time.time()
# Get data
x, y, d , numPedsList, PedsList, _= self.dataloader.next_batch()
dir_name = self.dataloader.get_directory_name()
file_name = self.dataloader.get_file_name()
# Get the sequence
x_seq,d_seq ,numPedsList_seq, PedsList_seq = x[0], d[0], numPedsList[0], PedsList[0]
# convert dense vector
x_seq , lookup_seq = self.dataloader.convert_proper_array(x_seq, numPedsList_seq, PedsList_seq)
if dataset_pointer_ins is not self.dataloader.dataset_pointer:
if self.dataloader.dataset_pointer is not 0:
whole_dataset.append(dataset_instances)
dataset_instances = {}
random_angles = random.sample(self.angles, self.num_of_data) # sample new angle
self.noise_std = random.uniform(self.noise_std_min, self.noise_std_max) #sample new noise
print("Dataset creation for: ", file_name, " angles: ", random_angles)
dataset_pointer_ins = self.dataloader.dataset_pointer
for index, angle in enumerate(random_angles):
self.noise_std = random.uniform(self.noise_std_min, self.noise_std_max)
# modify and preprocess dataset
modified_x_seq = self.submision_seq_preprocess(self.handle_seq(x_seq, lookup_seq, PedsList_seq, angle), self.seq_length, lookup_seq)
# store modified data points to dict
self.dataloader.add_element_to_dict(dataset_instances, (dir_name, file_name, index), modified_x_seq)
end = time.time()
print('Current file : ', file_name,' Processed trajectory number : ', batch+1, 'out of', self.dataloader.num_batches, 'trajectories in time', end - start)
# write modified datapoints to txt files
whole_dataset.append(dataset_instances)
createDirectories(os.path.join(self.f_prefix, self.base_validation_path), self.dataloader.get_all_directory_namelist())
self.write_modified_datasets(whole_dataset)
def handle_seq(self, x_seq, lookup_seq, PedsList_seq, angle):
# add noise and rotate a trajectory
vectorized_x_seq, first_values_dict = vectorizeSeq(x_seq, PedsList_seq, lookup_seq)
modified_x_seq = vectorized_x_seq.clone()
mean = torch.FloatTensor([self.noise_mean, self.noise_mean])
stddev =torch.FloatTensor([self.noise_std, self.noise_std])
origin = (0, 0)
for ind, frame in enumerate(vectorized_x_seq):
for ped in PedsList_seq[ind]:
selected_point = frame[lookup_seq[ped], :]
# rotate a frame point
rotated_point = rotate(origin, selected_point, math.radians(angle))
noise = torch.normal(mean, stddev).clone()
# add random noise
modified_x_seq[ind, lookup_seq[ped], 0] = rotated_point[0] + noise[0]
modified_x_seq[ind, lookup_seq[ped], 1] = rotated_point[1] + noise[1]
#modified_x_seq[ind, lookup_seq[ped], :] = torch.cat(rotate(origin, first_values_dict[ped], math.radians(angle))) + modified_x_seq[ind, lookup_seq[ped], :]
#roatate first frame value as well and add it back to get absoute coordinates
modified_x_seq[ind, lookup_seq[ped], 0] = (rotate(origin, first_values_dict[ped], math.radians(angle)))[0] + modified_x_seq[ind, lookup_seq[ped], 0]
modified_x_seq[ind, lookup_seq[ped], 1] = (rotate(origin, first_values_dict[ped], math.radians(angle)))[1] + modified_x_seq[ind, lookup_seq[ped], 1]
return modified_x_seq
def submision_seq_preprocess(self, x_seq, seq_lenght, lookup_seq):
# create original txt structure for modified datapoints
ret_x_seq_c = x_seq.data.numpy()
ped_ids = self.dataloader.get_id_sequence(seq_lenght)
positions_of_peds = [lookup_seq[ped] for ped in ped_ids]
ret_x_seq_c = ret_x_seq_c[:, positions_of_peds, :]
ret_x_seq_c_selected = ret_x_seq_c[:,0,:]
ret_x_seq_c_selected[:,[0,1]] = ret_x_seq_c_selected[:,[1,0]]
frame_numbers = self.dataloader.get_frame_sequence(seq_lenght)
id_integrated_seq = np.append(np.array(ped_ids)[:,None], ret_x_seq_c_selected, axis=1)
frame_integrated_seq = np.append(frame_numbers[:, None], id_integrated_seq, axis=1)
return frame_integrated_seq
def write_modified_datasets(self, dataset_instances_store):
# write constructed txt structure to txt file
self.dataloader.reset_batch_pointer()
for dataset_index in range(self.dataloader.numDatasets):
dataset_instances = dataset_instances_store[dataset_index]
train_sub_instances = dict(random.sample(dataset_instances.items(), self.num_train_data))
validation_sub_instances = {k: v for k, v in dataset_instances.items() if k not in train_sub_instances}
print("*********************************************************************************")
print("Training datasets are writing for: ", self.dataloader.get_file_name(dataset_index))
self.write_dict(train_sub_instances, self.base_train_path)
print("*********************************************************************************")
print("Validation datasets are writing for: ", self.dataloader.get_file_name(dataset_index))
self.write_dict(validation_sub_instances, self.base_validation_path)
def write_dict(self, dict, base_path):
cleared_direcories = []
for key, value in dict.items():
path = os.path.join(self.f_prefix, base_path, key[0])
ext_removed_file_name = removeFileExtention(key[1])
file_name = ext_removed_file_name + "_" + str(key[2])
file_name = addFileExtention(file_name, 'txt')
self.dataloader.write_dataset(value, file_name, path)
def clear_directories(self, base_path, delete_all = False):
# delete all files from a directory
print("Clearing directories...")
dir_names = self.dataloader.get_all_directory_namelist()
base_path = os.path.join(self.f_prefix, base_path)
for dir_ in dir_names:
dir_path = os.path.join(base_path, dir_)
file_names = getAllFileNames(dir_path)
if delete_all:
base_file_names = []
else:
base_file_names = self.dataloader.get_base_file_name(dir_)
[deleteFile(dir_path, [file_name]) for file_name in file_names if file_name not in base_file_names]
# %% Imports
from utils import DataLoader
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score, accuracy_score
from interpret.blackbox import LimeTabular
from interpret import show
# %% Load and preprocess data
data_loader = DataLoader()
data_loader.load_dataset()
data_loader.preprocess_data()
# Split the data for evaluation
X_train, X_test, y_train, y_test = data_loader.get_data_split()
# Oversample the train data
X_train, y_train = data_loader.oversample(X_train, y_train)
print(X_train.shape)
print(X_test.shape)
# %% Fit blackbox model
rf = RandomForestClassifier()
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
print(f"F1 Score {f1_score(y_test, y_pred, average='macro')}")
print(f"Accuracy {accuracy_score(y_test, y_pred)}")
# %% Apply lime
# Initilize Lime for Tabular data
lime = LimeTabular(predict_fn=rf.predict_proba, data=X_train, random_state=1)
# Get local explanations
lime_local = lime.explain_local(X_test[-20:], y_test[-20:], name='LIME')
save_every = args.save_every
num_epochs = args.num_epochs
workdir = args.workdir
is_verbose = args.verbose
use_pretrained_model = args.pretrained_embedding
learning_rate = args.lr
logpath = os.path.abspath(os.path.join(workdir, args.logfile))
log_reset(logpath)
# logpath = os.path.abspath(os.path.join(workdir, "runs"))
# with subprocess.Popen(['tensorboard', '--logdir', logpath]):
"""Loading datasets"""
dataloader_train = DataLoader('dataset/sentences.train',
vocab_size,
max_size,
workdir=workdir)
dataloader_eval = DataLoader('dataset/sentences.eval',
vocab_size,
max_size,
workdir=workdir)
"""Get the vocab and save it to vocab.dat.
If method not called, the model tries to open vocab.dat
Otherwise if no savefile is given, just loads the vocab
directly.
Uncomment to generate the vocab.dat file"""
# dataloader_train.compute_vocab(savefile='vocab.dat')
"""Let's do some test on the dataloader..."""
# Get the word to index correspondance for the embedding.
if __name__ == '__main__':
from utils import loadDataLabel3, DataLoader
a,b,c,d = loadDataLabel3('three_pt',0.7)
print(d.shape)
rnn = RNN("rnn_tanh",11,100,2)
rnn.collect_params().initialize(mx.init.Xavier())
batch_size = 256
clipping_norm = 0.1
num_steps = 5
loss = mx.gluon.loss.L2Loss()
trainer = mx.gluon.Trainer(rnn.collect_params(),'sgd',{
'learning_rate':0.5,
"wd":0.00
})
dataLoader = DataLoader(a,b)
trl = []
tel = []
for epoch in range(500):
total_L = 0.0
hidden = rnn.begin_state(func=mx.nd.zeros,batch_size = batch_size,ctx=mx.cpu())
for data,label in dataLoader.dataIter(batch_size):
label = nd.array(label)
# print("label shape" ,label.shape)
#label = nd.ones(shape=(5,32)) * label
#label = label.reshape((-1,))
dd = nd.array(data.reshape((batch_size,5,11)).swapaxes(0,1))
hidden = detach(hidden)
with mx.autograd.record():
output, hidden = rnn(dd,hidden)
print('test prediction gt = {}'.format(test_gt))
# define testing generator function and
# in testing, only generator networks, there is no discriminator networks and flownet.
with tf.variable_scope('generator', reuse=None):
print('testing = {}'.format(tf.get_variable_scope().name))
test_outputs = generator(test_inputs, layers=const.LAYERS, output_channel=3)
test_psnr_error = psnr_error(gen_frames=test_outputs, gt_frames=test_gt)
diff_mask_tensor = diff_mask(test_outputs, test_gt)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
# dataset
data_loader = DataLoader(test_folder, height, width)
# initialize weights
sess.run(tf.global_variables_initializer())
print('Init global successfully!')
# tf saver
saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=None)
restore_var = [v for v in tf.global_variables()]
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, snapshot_dir)
videos_info = data_loader.videos
num_videos = len(videos_info.keys())
print('Num videos: ', num_videos)
total = 0
def dataload(self, train_data, train_label, test_data, test_label):
self.train_data = train_data
self.train_label = train_label
self.test_data = test_data
self.test_label = test_label
self.dataLoader = DataLoader(self.train_data, self.train_label)
class MLPTrainer(object):
paramsFloder = './params/MLP'
def __init__(self,
labelName,
loss,
net='mlp',
bn=False,
dropout=None,
all=False):
self.all = all
self.labelName = labelName
self.labelIndex = OutputData.colName().index(labelName)
self.bn = bn
self.dropout = dropout
if net == 'mlp':
self.net = MLP(bn, dropout)
elif net == 'smlp':
self.net = SampleMLP(bn, dropout)
#self.net = MLP(bn,dropout)
self.loss = loss
def initnet(self, lr, wd, opt='adam', init='x'):
self.lr = lr
self.wd = wd
if init == 'x':
self.net.collect_params().initialize(mx.init.Xavier(),
force_reinit=True)
else:
self.net.initialize()
self.trainer = gluon.Trainer(self.net.collect_params(), opt, {
'learning_rate': self.lr,
'wd': self.wd
})
def dataload(self, train_data, train_label, test_data, test_label):
self.train_data = train_data
self.train_label = train_label
self.test_data = test_data
self.test_label = test_label
self.dataLoader = DataLoader(self.train_data, self.train_label)
def train(self, epochs, batch_size, con=False, ctx=None):
self.train_loss = []
self.test_loss = []
if con:
self.load(ctx=ctx)
t1 = time.time()
for epoch in range(epochs):
train_loss = 0.0
if is_sigint_up:
break
for data, label in self.dataLoader.dataIter(batch_size):
with autograd.record():
output = self.net(data)
lossv = self.loss(output, label)
lossv.backward()
self.trainer.step(batch_size)
train_loss += nd.sum(lossv).asscalar()
ptrain_loss = self.eval(self.train_data, self.train_label)
ptest_loss = self.eval(self.test_data, self.test_label)
self.test_loss.append(ptest_loss / len(self.test_data))
self.train_loss.append(ptrain_loss / len(self.train_data))
#train_acc = self.accuracy(self.train_data,self.train_label)
#test_acc = self.accuracy(self.test_data, self.test_label)
# train_acc = 0.0
# test_acc = 0.0
#print('Epoch %d : Train loss -> %f Test loss -> %f Train acc -> %f Test acc -> %f' % (epoch, self.train_loss[-1], self.test_loss[-1],
# train_acc, test_acc))
print("'Epoch %d : Train loss -> %f Test loss -> %f" %
(epoch, self.train_loss[-1], self.test_loss[-1]))
t2 = time.time()
print("Time : ", (t2 - t1) / epochs)
p = input('plot ? (y/n)')
if p.lower() == 'y':
self.plot()
r = input('save params ? (y/n)')
if r.lower() == 'y':
self.save()
def plot(self):
plt.figure(figsize=(8, 6))
plt.plot(self.train_loss)
plt.plot(self.test_loss)
plt.legend(['train', 'test'])
plt.xlabel('Epoch')
plt.ylabel('Loss Value')
plt.show()
def eval(self, data, label):
data = nd.array(data)
label = nd.array(label)
#print(output[:,0]-test_label)
#print(data.shape,label.shape)
output = self.net(data)
return nd.sum(self.loss(output, label)).asscalar()
#loss = nd.sqrt(2*nd.sum(nd.power(output.reshape(label.shape) - label,2))).asscalar()
#return loss
def predict(self, x):
x = nd.array(x)
l = self.net(x)
#print('x : ',x.shape)
return l.argmax(axis=1)
def save(self):
loss_folder = "./loss"
if not os.path.exists(loss_folder):
os.makedirs(loss_folder)
with open(os.path.join(loss_folder,
self.labelName + str(self.lr) + 'txt'),
'w',
newline="") as f:
writer = csv.writer(f)
#print(self.train_loss)
writer.writerows([self.train_loss, self.test_loss])
name = self.labelName + str(self.lr) + '.txt'
paramsFile = os.path.join(self.paramsFloder, name)
self.net.save_params(paramsFile)
def load(self, ctx=None):
name = self.labelName + str(self.lr) + '.txt'
paramsFile = os.path.join(self.paramsFloder, name)
self.net.load_params(paramsFile, ctx)
def accuracy(self, data, label):
output = self.net(data)
#print(output.argmax(axis=1))
return nd.mean(output.argmax(axis=1) == label).asscalar()
from __future__ import division
from __future__ import print_function
from utils import DataLoader
import numpy as np
from sklearn import metrics
import pandas
import tensorflow as tf
from tensorflow.contrib import learn
### Training data
print("LOAD")
loader = DataLoader('data/tweets', 5, 144)
x, y = loader.get_xy()
split = int(len(x) * 0.8)
X_train, X_test = x[:split], x[split:]
y_train, y_test = pandas.Series(y[:split]), pandas.Series(y[split:])
y_train = y_train.convert_objects(convert_numeric=True)
y_test = y_test.convert_objects(convert_numeric=True)
### Process vocabulary
MAX_DOCUMENT_LENGTH = 144
print("PREPROCESS")
char_processor = learn.preprocessing.ByteProcessor(MAX_DOCUMENT_LENGTH)
X_train = np.array(list(char_processor.fit_transform(X_train)))
X_test = np.array(list(char_processor.transform(X_test)))
def train(args):
data_loader = DataLoader(args.batch_size, args.seq_length, args.data_scale)
if args.model_dir != '' and not os.path.exists(args.model_dir):
os.makedirs(args.model_dir)
with open(os.path.join(args.model_dir, 'config.pkl'), 'wb') as f:
pickle.dump(args, f)
model = Model(args)
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(
os.path.join(args.model_dir, 'log'), sess.graph)
tf.global_variables_initializer().run()
saver = tf.train.Saver(tf.global_variables())
for e in range(args.num_epochs):
sess.run(tf.assign(model.lr,
args.learning_rate * (args.decay_rate ** e)))
data_loader.reset_batch_pointer()
v_x, v_y = data_loader.validation_data()
valid_feed = {
model.input_data: v_x,
model.target_data: v_y,
model.state_in: model.state_in.eval()}
state = model.state_in.eval()
for b in range(data_loader.num_batches):
ith_train_step = e * data_loader.num_batches + b
start = time.time()
x, y = data_loader.next_batch()
feed = {
model.input_data: x,
model.target_data: y,
model.state_in: state}
train_loss_summary, train_loss, state, _ = sess.run(
[model.train_loss_summary, model.cost, model.state_out, model.train_op], feed)
summary_writer.add_summary(train_loss_summary, ith_train_step)
valid_loss_summary, valid_loss, = sess.run(
[model.valid_loss_summary, model.cost], valid_feed)
summary_writer.add_summary(valid_loss_summary, ith_train_step)
end = time.time()
print(
"{}/{} (epoch {}), train_loss = {:.3f}, valid_loss = {:.3f}, time/batch = {:.3f}"
.format(
ith_train_step,
args.num_epochs * data_loader.num_batches,
e,
train_loss,
valid_loss,
end - start))
if (ith_train_step %
args.save_every == 0) and (ith_train_step > 0):
checkpoint_path = os.path.join(
args.model_dir, 'model.ckpt')
saver.save(
sess,
checkpoint_path,
global_step=ith_train_step)
print("model saved to {}".format(checkpoint_path))
else:
gpus = ""
for i in range(len(args.gpus)):
gpus = gpus + args.gpus[i] + ","
os.environ["CUDA_VISIBLE_DEVICES"] = gpus[:-1]
#os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2'
torch.backends.cudnn.enabled = True # make sure to use cudnn for computational performance 确保使用cudnn
train_folder = args.dataset_path + args.dataset_type + "/training/frames/"
#train_folder = args.dataset_path + args.dataset_type + "/videos/training_frames/"
test_folder = args.dataset_path + args.dataset_type + "/testing/frames/"
train_dataset = DataLoader(train_folder,
transforms.Compose([
transforms.ToTensor(),
]),
resize_height=args.h,
resize_width=args.w,
time_step=args.t_length - 1)
# transforms.ToTensor() 将numpy的ndarray或PIL.Image读的图片转换成形状为(C,H, W)的Tensor格式，且/255归一化到[0,1.0]之间
train_size = len(train_dataset)
print(train_size)
split = int(np.floor(args.vali * train_size))
# 在训练模型时使用到此函数，用来把训练数据分成多个小组，此函数每次抛出一组数据。直至把所有的数据都抛出。就是做一个数据的初始化
val_size = int(args.vali * len(train_dataset)) #验证用20%的训练集
train_size = int(len(train_dataset) - val_size) #训练集就是剩下80%
train_dataset, valid_dataset = torch.utils.data.random_split(
train_dataset, [train_size, val_size])
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
def train(args):
origin = (0, 0)
reference_point = (0, 1)
validation_dataset_executed = False
prefix = ''
f_prefix = '.'
if args.drive is True:
prefix = 'drive/semester_project/social_lstm_final/'
f_prefix = 'drive/semester_project/social_lstm_final'
if not os.path.isdir("log/"):
print("Directory creation script is running...")
subprocess.call([f_prefix + '/make_directories.sh'])
args.freq_validation = np.clip(args.freq_validation, 0, args.num_epochs)
validation_epoch_list = list(
range(args.freq_validation, args.num_epochs + 1, args.freq_validation))
validation_epoch_list[-1] -= 1
# Create the data loader object. This object would preprocess the data in terms of
# batches each of size args.batch_size, of length args.seq_length
dataloader = DataLoader(f_prefix,
args.batch_size,
args.seq_length,
args.num_validation,
forcePreProcess=True)
method_name = "OBSTACLELSTM"
model_name = "LSTM"
save_tar_name = method_name + "_lstm_model_"
if args.gru:
model_name = "GRU"
save_tar_name = method_name + "_gru_model_"
# Log directory
log_directory = os.path.join(prefix, 'log/')
plot_directory = os.path.join(prefix, 'plot/', method_name, model_name)
plot_train_file_directory = 'validation'
# Logging files
log_file_curve = open(
os.path.join(log_directory, method_name, model_name, 'log_curve.txt'),
'w+')
log_file = open(
os.path.join(log_directory, method_name, model_name, 'val.txt'), 'w+')
# model directory
save_directory = os.path.join(prefix, 'model/')
# save_directory += str(args.leaveDataset) + '/'
# Save the arguments in the config file
with open(
os.path.join(save_directory, method_name, model_name,
'config.pkl'), 'wb') as f:
pickle.dump(args, f)
# Path to store the checkpoint file
def checkpoint_path(x):
return os.path.join(save_directory, method_name, model_name,
save_tar_name + str(x) + '.tar')
# model creation
net = OLSTMModel(args)
if args.use_cuda:
net = net.cuda()
# optimizer = torch.optim.RMSprop(net.parameters(), lr=args.learning_rate)
optimizer = torch.optim.Adagrad(net.parameters(),
weight_decay=args.lambda_param)
# optimizer = torch.optim.Adam(net.parameters(), weight_decay=args.lambda_param)
learning_rate = args.learning_rate
best_val_loss = 100
best_val_data_loss = 100
smallest_err_val = 100000
smallest_err_val_data = 100000
best_epoch_val = 0
best_epoch_val_data = 0
best_err_epoch_val = 0
best_err_epoch_val_data = 0
all_epoch_results = []
grids = []
num_batch = 0
dataset_pointer_ins_grid = -1
[grids.append([]) for dataset in range(dataloader.get_len_of_dataset())]
# Training
for epoch in range(args.num_epochs):
print('****************Training epoch beginning******************')
if dataloader.additional_validation and (epoch -
1) in validation_epoch_list:
dataloader.switch_to_dataset_type(True)
dataloader.reset_batch_pointer(valid=False)
loss_epoch = 0
# For each batch
for batch in range(dataloader.num_batches):
start = time.time()
# Get batch data
x, y, d, numPedsList, PedsList, target_ids = dataloader.next_batch(
)
loss_batch = 0
# if we are in a new dataset, zero the counter of batch
if dataset_pointer_ins_grid is not dataloader.dataset_pointer and epoch is not 0:
num_batch = 0
dataset_pointer_ins_grid = dataloader.dataset_pointer
# For each sequence
for sequence in range(dataloader.batch_size):
# Get the data corresponding to the current sequence
x_seq, _, d_seq, numPedsList_seq, PedsList_seq = x[
sequence], y[sequence], d[sequence], numPedsList[
sequence], PedsList[sequence]
target_id = target_ids[sequence]
# get processing file name and then get dimensions of file
folder_name = dataloader.get_directory_name_with_pointer(d_seq)
dataset_data = dataloader.get_dataset_dimension(
folder_name
) #dataloader.get_dataset_dimension(folder_name)
# dense vector creation
x_seq, lookup_seq = dataloader.convert_proper_array(
x_seq, numPedsList_seq, PedsList_seq)
# print("LOOKUP SEQ = " ,lookup_seq)
#print("TARGET_ID = " , target_id)
target_id_values = x_seq[0][lookup_seq[target_id], 0:2] #:2
# Compute grid masks
if (args.grid):
if (epoch is 0):
grid_seq = getSequenceGridMask(x_seq, dataset_data,
PedsList_seq,
args.neighborhood_size,
args.grid_size,
args.use_cuda, True)
grids[dataloader.dataset_pointer].append(grid_seq)
else:
grid_seq = grids[dataloader.dataset_pointer][
(num_batch * dataloader.batch_size) + sequence]
else:
grid_seq = getSequenceGridMask(x_seq, dataset_data,
PedsList_seq,
args.neighborhood_size,
args.grid_size,
args.use_cuda, True)
# vectorize trajectories in sequence
x_seq, _ = vectorize_seq(x_seq, PedsList_seq, lookup_seq)
if args.use_cuda:
x_seq = x_seq.cuda()
# number of peds in this sequence per frame
numNodes = len(lookup_seq)
hidden_states = Variable(torch.zeros(numNodes, args.rnn_size))
if args.use_cuda:
hidden_states = hidden_states.cuda()
cell_states = Variable(torch.zeros(numNodes, args.rnn_size))
if args.use_cuda:
cell_states = cell_states.cuda()
# Zero out gradients
net.zero_grad()
optimizer.zero_grad()
# Forward prop
outputs, _, _ = net(x_seq, grid_seq, hidden_states,
cell_states, PedsList_seq, numPedsList_seq,
dataloader, lookup_seq)
# Compute loss
loss = Gaussian2DLikelihood(outputs, x_seq, PedsList_seq,
lookup_seq)
loss_batch += loss.item()
# Compute gradients
loss.backward()
# Clip gradients
torch.nn.utils.clip_grad_norm_(net.parameters(),
args.grad_clip)
# Update parameters
optimizer.step()
end = time.time()
loss_batch = loss_batch / dataloader.batch_size
loss_epoch += loss_batch
num_batch += 1
print('{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}'.
format(epoch * dataloader.num_batches + batch,
args.num_epochs * dataloader.num_batches, epoch,
loss_batch, end - start))
loss_epoch /= dataloader.num_batches
# Log loss values
log_file_curve.write("Training epoch: " + str(epoch) + " loss: " +
str(loss_epoch) + '\n')
if dataloader.valid_num_batches > 0:
print(
'****************Validation epoch beginning******************')
# Validation
dataloader.reset_batch_pointer(valid=True)
loss_epoch = 0
err_epoch = 0
# For each batch
for batch in range(dataloader.valid_num_batches):
# Get batch data
x, y, d, numPedsList, PedsList, target_ids = dataloader.next_valid_batch(
)
# Loss for this batch
loss_batch = 0
err_batch = 0
# For each sequence
for sequence in range(dataloader.batch_size):
# Get data corresponding to the current sequence
x_seq, _, d_seq, numPedsList_seq, PedsList_seq = x[
sequence], y[sequence], d[sequence], numPedsList[
sequence], PedsList[sequence]
target_id = target_ids[sequence]
# get processing file name and then get dimensions of file
folder_name = dataloader.get_directory_name_with_pointer(
d_seq)
dataset_data = dataloader.get_dataset_dimension(
folder_name)
# dense vector creation
x_seq, lookup_seq = dataloader.convert_proper_array(
x_seq, numPedsList_seq, PedsList_seq)
target_id_values = x_seq[0][lookup_seq[target_id], 0:2]
# Compute grid masks
if (args.grid):
if (epoch is 0):
grid_seq = getSequenceGridMask(
x_seq, dataset_data, PedsList_seq,
args.neighborhood_size, args.grid_size,
args.use_cuda, True)
grids[dataloader.dataset_pointer].append(grid_seq)
else:
grid_seq = grids[dataloader.dataset_pointer][
(num_batch * dataloader.batch_size) + sequence]
else:
grid_seq = getSequenceGridMask(x_seq, dataset_data,
PedsList_seq,
args.neighborhood_size,
args.grid_size,
args.use_cuda, True)
# vectorize trajectories in sequence
x_seq, _ = vectorize_seq(x_seq, PedsList_seq, lookup_seq)
if args.use_cuda:
x_seq = x_seq.cuda()
# number of peds in this sequence per frame
numNodes = len(lookup_seq)
hidden_states = Variable(
torch.zeros(numNodes, args.rnn_size))
if args.use_cuda:
hidden_states = hidden_states.cuda()
cell_states = Variable(torch.zeros(numNodes,
args.rnn_size))
if args.use_cuda:
cell_states = cell_states.cuda()
# Forward prop
outputs, _, _ = net(x_seq[:-1], grid_seq[:-1],
hidden_states, cell_states,
PedsList_seq[:-1], numPedsList_seq,
dataloader, lookup_seq)
# Compute loss
loss = Gaussian2DLikelihood(outputs, x_seq[1:],
PedsList_seq[1:], lookup_seq)
# Extract the mean, std and corr of the bivariate Gaussian
mux, muy, sx, sy, corr = getCoef(outputs)
# Sample from the bivariate Gaussian
next_x, next_y = sample_gaussian_2d(
mux.data, muy.data, sx.data, sy.data, corr.data,
PedsList_seq[-1], lookup_seq)
next_vals = torch.FloatTensor(1, numNodes, 2)
next_vals[:, :, 0] = next_x
next_vals[:, :, 1] = next_y
err = get_mean_error(next_vals, x_seq[-1].data[None, :, :],
[PedsList_seq[-1]],
[PedsList_seq[-1]], args.use_cuda,
lookup_seq)
loss_batch += loss.item()
err_batch += err
loss_batch = loss_batch / dataloader.batch_size
err_batch = err_batch / dataloader.batch_size
loss_epoch += loss_batch
err_epoch += err_batch
if dataloader.valid_num_batches != 0:
loss_epoch = loss_epoch / dataloader.valid_num_batches
err_epoch = err_epoch / dataloader.num_batches
# Update best validation loss until now
if loss_epoch < best_val_loss:
best_val_loss = loss_epoch
best_epoch_val = epoch
if err_epoch < smallest_err_val:
smallest_err_val = err_epoch
best_err_epoch_val = epoch
print('(epoch {}), valid_loss = {:.3f}, valid_err = {:.3f}'.
format(epoch, loss_epoch, err_epoch))
print('Best epoch', best_epoch_val, 'Best validation loss',
best_val_loss, 'Best error epoch', best_err_epoch_val,
'Best error', smallest_err_val)
log_file_curve.write("Validation epoch: " + str(epoch) +
" loss: " + str(loss_epoch) + " err: " +
str(err_epoch) + '\n')
# Validation dataset
if dataloader.additional_validation and (
epoch) in validation_epoch_list:
dataloader.switch_to_dataset_type()
print(
'****************Validation with dataset epoch beginning******************'
)
dataloader.reset_batch_pointer(valid=False)
dataset_pointer_ins = dataloader.dataset_pointer
validation_dataset_executed = True
loss_epoch = 0
err_epoch = 0
f_err_epoch = 0
num_of_batch = 0
smallest_err = 100000
# results of one epoch for all validation datasets
epoch_result = []
# results of one validation dataset
results = []
# For each batch
for batch in range(dataloader.num_batches):
# Get batch data
x, y, d, numPedsList, PedsList, target_ids = dataloader.next_batch(
)
if dataset_pointer_ins is not dataloader.dataset_pointer:
if dataloader.dataset_pointer is not 0:
print('Finished prosessed file : ',
dataloader.get_file_name(-1),
' Avarage error : ', err_epoch / num_of_batch)
num_of_batch = 0
epoch_result.append(results)
dataset_pointer_ins = dataloader.dataset_pointer
results = []
# Loss for this batch
loss_batch = 0
err_batch = 0
f_err_batch = 0
# For each sequence
for sequence in range(dataloader.batch_size):
# Get data corresponding to the current sequence
x_seq, _, d_seq, numPedsList_seq, PedsList_seq = x[
sequence], y[sequence], d[sequence], numPedsList[
sequence], PedsList[sequence]
target_id = target_ids[sequence]
# get processing file name and then get dimensions of file
folder_name = dataloader.get_directory_name_with_pointer(
d_seq)
dataset_data = dataloader.get_dataset_dimension(
folder_name)
# dense vector creation
x_seq, lookup_seq = dataloader.convert_proper_array(
x_seq, numPedsList_seq, PedsList_seq)
# will be used for error calculation
orig_x_seq = x_seq.clone()
target_id_values = orig_x_seq[0][lookup_seq[target_id],
0:2]
# grid mask calculation
grid_seq = getSequenceGridMask(x_seq, dataset_data,
PedsList_seq,
args.neighborhood_size,
args.grid_size,
args.use_cuda, True)
# vectorize datapoints
x_seq, first_values_dict = vectorize_seq(
x_seq, PedsList_seq, lookup_seq)
if args.use_cuda:
x_seq = x_seq.cuda()
# sample predicted points from model
ret_x_seq, loss = sample_validation_data(
x_seq, PedsList_seq, grid_seq, args, net, lookup_seq,
numPedsList_seq, dataloader)
# revert the points back to original space
ret_x_seq = revert_seq(ret_x_seq, PedsList_seq, lookup_seq,
first_values_dict)
# get mean and final error
err = get_mean_error(ret_x_seq.data, orig_x_seq.data,
PedsList_seq, PedsList_seq,
args.use_cuda, lookup_seq)
f_err = get_final_error(ret_x_seq.data, orig_x_seq.data,
PedsList_seq, PedsList_seq,
lookup_seq)
loss_batch += loss.item()
err_batch += err
f_err_batch += f_err
print('Current file : ', dataloader.get_file_name(0),
' Batch : ', batch + 1, ' Sequence: ', sequence + 1,
' Sequence mean error: ', err,
' Sequence final error: ', f_err, ' time: ',
end - start)
results.append(
(orig_x_seq.data.cpu().numpy(),
ret_x_seq.data.cpu().numpy(), PedsList_seq,
lookup_seq,
dataloader.get_frame_sequence(args.seq_length),
target_id))
loss_batch = loss_batch / dataloader.batch_size
err_batch = err_batch / dataloader.batch_size
f_err_batch = f_err_batch / dataloader.batch_size
num_of_batch += 1
loss_epoch += loss_batch
err_epoch += err_batch
f_err_epoch += f_err_batch
epoch_result.append(results)
all_epoch_results.append(epoch_result)
if dataloader.num_batches != 0:
loss_epoch = loss_epoch / dataloader.num_batches
err_epoch = err_epoch / dataloader.num_batches
f_err_epoch = f_err_epoch / dataloader.num_batches
# Update best validation loss until now
if loss_epoch < best_val_data_loss:
best_val_data_loss = loss_epoch
best_epoch_val_data = epoch
if err_epoch < smallest_err_val_data:
smallest_err_val_data = err_epoch
best_err_epoch_val_data = epoch
print(
'(epoch {}), valid_loss = {:.3f}, valid_mean_err = {:.3f}, valid_final_err = {:.3f}'
.format(epoch, loss_epoch, err_epoch, f_err_epoch))
print('Best epoch', best_epoch_val_data,
'Best validation loss', best_val_data_loss,
'Best error epoch', best_err_epoch_val_data,
'Best error', smallest_err_val_data)
log_file_curve.write("Validation dataset epoch: " +
str(epoch) + " loss: " + str(loss_epoch) +
" mean_err: " + str(err_epoch) +
'final_err: ' + str(f_err_epoch) + '\n')
optimizer = time_lr_scheduler(optimizer,
epoch,
lr_decay_epoch=args.freq_optimizer)
# Save the model after each epoch
print('Saving model')
torch.save(
{
'epoch': epoch,
'state_dict': net.state_dict(),
'optimizer_state_dict': optimizer.state_dict()
}, checkpoint_path(epoch))
if dataloader.valid_num_batches != 0:
print('Best epoch', best_epoch_val, 'Best validation Loss',
best_val_loss, 'Best error epoch', best_err_epoch_val,
'Best error', smallest_err_val)
# Log the best epoch and best validation loss
log_file.write('Validation Best epoch:' + str(best_epoch_val) + ',' +
' Best validation Loss: ' + str(best_val_loss))
if dataloader.additional_validation:
print('Best epoch acording to validation dataset', best_epoch_val_data,
'Best validation Loss', best_val_data_loss, 'Best error epoch',
best_err_epoch_val_data, 'Best error', smallest_err_val_data)
log_file.write("Validation dataset Best epoch: " +
str(best_epoch_val_data) + ',' +
' Best validation Loss: ' + str(best_val_data_loss) +
'\n')
# dataloader.write_to_plot_file(all_epoch_results[best_epoch_val_data], plot_directory)
# elif dataloader.valid_num_batches != 0:
# dataloader.write_to_plot_file(all_epoch_results[best_epoch_val], plot_directory)
# else:
if validation_dataset_executed: # if we executed a validation epoch, write last epoch file
dataloader.switch_to_dataset_type(load_data=False)
create_directories(plot_directory, [plot_train_file_directory])
dataloader.write_to_plot_file(
all_epoch_results[len(all_epoch_results) - 1],
os.path.join(plot_directory, plot_train_file_directory))
# Close logging files
log_file.close()
log_file_curve.close()
def main(unused_argv):
# Initialise learning rate
eff_rate = FLAGS.learning_rate
# extract the vocabulary from training sentendes
with open("vocabulary.pickle", "rb") as f:
vocabulary = pickle.load(f)
# load training data
if FLAGS.do_train:
train_loader = DataLoader(FLAGS.train_file_path,
vocabulary, do_shuffle=True)
batches_train = train_loader.batch_iterator(FLAGS.num_epochs,
FLAGS.batch_size)
# load validation data
if FLAGS.do_eval:
eval_loader = DataLoader(FLAGS.eval_file_path,
vocabulary, do_shuffle=False)
batches_eval = eval_loader.batch_iterator(num_epochs=1,
batch_size=1000)
# Load continuation data
if FLAGS.do_generate:
gen_loader = DataLoader(FLAGS.generate_file_path,
vocabulary, do_shuffle=False, is_partial=True)
batches_gen = gen_loader.batch_iterator(num_epochs=1, batch_size=1000)
# Create the graph
global_counter = tf.Variable(0, trainable=False)
if FLAGS.task == "A":
# For a) the embedding is a matrix to be learned from scratch
embedding_matrix = tf.get_variable(
name="embedding_matrix",
shape=[FLAGS.vocab_size, FLAGS.embedding_size],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
else:
# For simplicity we do not use the code given but use the given
# embeddings directly.
# TODO confirm that this is okay for the tutors.
keys = []
emb = []
ext_emb = csv.reader(open(FLAGS.embedding), delimiter=' ')
for line in ext_emb:
keys += [line[0]]
emb += [list(map(float, line[1:]))]
given_emb = dict(zip(keys, emb))
external_embedding = np.zeros(shape=(FLAGS.vocab_size,
FLAGS.embedding_size))
for k, v in vocabulary.get_vocabulary_as_dict().items():
try:
external_embedding[v, :] = given_emb[k]
except KeyError:
print("Unmatched: %s" % k)
external_embedding[v, :] = \
np.random.uniform(low=-0.25,
high=0.25,
size=FLAGS.embedding_size)
embedding_matrix = tf.Variable(external_embedding, dtype=tf.float32)
input_words = tf.placeholder(tf.int32, [None, FLAGS.sentence_length])
# add to collection for usage from restored model
tf.add_to_collection("input_words", input_words)
embedded_words = tf.nn.embedding_lookup(embedding_matrix, input_words)
# RNN graph
lstm = tf.contrib.rnn.BasicLSTMCell(FLAGS.lstm_size)
# Somehow lstm has a touple of states instead of just one.
# We learn sensible initial states as well.
# The states seem to correspond to the LSTM cell state and the hidden layer output
# see http://stackoverflow.com/questions/41789133/c-state-and-m-state-in-tensorflow-lstm
# https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/LSTMStateTuple
lstm_zero_c = \
tf.get_variable("zero_state_c",
shape=[1, FLAGS.lstm_size],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
lstm_zero_h = \
tf.get_variable("zero_state_h",
shape=[1, FLAGS.lstm_size],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
lstm_state = (tf.tile(lstm_zero_c, [tf.shape(input_words)[0], 1]),
tf.tile(lstm_zero_h, [tf.shape(input_words)[0], 1]))
lstm_state_g = (tf.tile(lstm_zero_c, [tf.shape(input_words)[0], 1]),
tf.tile(lstm_zero_h, [tf.shape(input_words)[0], 1]))
if not FLAGS.task == "C":
out_to_logit_w = tf.get_variable(
name="output_weights",
shape=[FLAGS.lstm_size, FLAGS.vocab_size],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
out_to_logit_b = tf.get_variable("output_bias",
shape=[FLAGS.vocab_size])
else:
inter_w = tf.get_variable("intermediate_weights",
shape=[FLAGS.lstm_size,
FLAGS.intermediate_size],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
inter_b = tf.get_variable("intermediate_biases",
shape=[FLAGS.intermediate_size])
out_to_logit_w = tf.get_variable(
name="output_weights",
shape=[FLAGS.intermediate_size, FLAGS.vocab_size],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
out_to_logit_b = tf.get_variable("output_bias",
shape=[FLAGS.vocab_size])
# initialize
lstm_outputs = []
# add summaries for tensorboard
with tf.variable_scope("RNN"):
for time_step in range(FLAGS.sentence_length):
if time_step > 0:
tf.get_variable_scope().reuse_variables()
lstm_out, lstm_state = lstm(embedded_words[:, time_step, :],
lstm_state)
lstm_outputs.append(lstm_out)
output = tf.concat(axis=0, values=lstm_outputs)
if not FLAGS.task == "C":
logits = tf.matmul(output, out_to_logit_w) + out_to_logit_b
l2_loss = tf.nn.l2_loss(out_to_logit_w) * FLAGS.lambda_l2
else:
logits = tf.matmul(tf.matmul(output, inter_w) + inter_b,
out_to_logit_w) + out_to_logit_b
l2_loss = (tf.nn.l2_loss(out_to_logit_w) + tf.nn.l2_loss(
inter_w)) * FLAGS.lambda_l2
logits_reshaped = tf.transpose(tf.reshape(logits,
[FLAGS.sentence_length, -1,
FLAGS.vocab_size]), [1, 0, 2])
best_pred = tf.arg_max(logits_reshaped, 2)
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=input_words[:, 1:],
logits=logits_reshaped[:, :-1, :]) / np.log(2) * \
tf.to_float(tf.not_equal(input_words[:, 1:],
vocabulary.dict[vocabulary.PADDING]))
# Sanity check
any_word = input_words[10, 5]
any_word_pre = input_words[10, 4]
any_word_probs = tf.nn.softmax(logits_reshaped[10, 5, :])
any_word_max_prob = tf.reduce_max(any_word_probs)
any_word_prediction = tf.argmax(any_word_probs, dimension=0)
any_word_real_perp = 1 / any_word_probs[any_word]
any_word_probs2 = tf.nn.softmax(logits_reshaped[11, 6, :])
any_word_prediction2 = tf.argmax(any_word_probs2, dimension=0)
# output of the last layer in the unrolled LSTM
last_output = lstm_outputs[-1]
last_prob = tf.nn.softmax(logits_reshaped[:, -1, :])
# add to collection for re-use in task 1.2
tf.add_to_collection("last_output", last_output)
tf.add_to_collection("last_prob", last_prob)
tf.summary.histogram("last_prob", last_prob)
# define perplexity and add to collection to provide access when reloading the model elsewhere
# add a summary scalar for tensorboard
# TODO Confirm that the elementwise crossentropy is -p(w_t|w_1,...,w_{t-1})
mean_loss = tf.reduce_sum(loss) / tf.reduce_sum(
tf.to_float(tf.not_equal(input_words[:, 1:],
vocabulary.dict[vocabulary.PADDING]))) + l2_loss
tf.summary.scalar('loss', mean_loss)
perplexity = tf.pow(2.0, mean_loss)
tf.add_to_collection("perplexity", perplexity)
tf.summary.scalar('perplexity', perplexity)
nominator = tf.reduce_sum(loss, axis=1)
denominator = tf.reduce_sum(tf.to_float(
tf.not_equal(input_words[:, 1:],
vocabulary.dict[vocabulary.PADDING])), axis=1)
sentence_perplexity = \
tf.pow(2.0, tf.reduce_sum(loss, axis=1) / tf.reduce_sum(tf.to_float(
tf.not_equal(input_words[:, 1:],
vocabulary.dict[vocabulary.PADDING])), axis=1))
tf.summary.histogram('sPerplexity', sentence_perplexity)
# TODO add learning_rate to summaries
optimiser = tf.train.AdamOptimizer(FLAGS.learning_rate)
gradients, v = zip(*optimiser.compute_gradients(mean_loss))
# TODO from the doc it looks as if we actually wanted to set use_norm to 10 instead. Confirm!
# https://www.tensorflow.org/api_docs/python/tf/clip_by_global_norm
clipped_gradients, _ = tf.clip_by_global_norm(gradients, 10)
train_op = optimiser.apply_gradients(zip(clipped_gradients, v),
global_step=global_counter)
# initialize the Variables
init_op = tf.global_variables_initializer()
# Saver to save model checkpoints
saver = tf.train.Saver(max_to_keep=FLAGS.num_checkpoints,
keep_checkpoint_every_n_hours=2)
# Get an idea of the overall size of the model
total_parameters = 0
for variable in tf.trainable_variables():
shape = variable.get_shape()
print(shape)
variable_parametes = 1
for dim in shape:
variable_parametes *= dim.value
total_parameters += variable_parametes
print("Built a graph with a total of %d trainable parameters" % (
total_parameters))
merged_summaries = tf.summary.merge_all()
# loop over training batches
if FLAGS.do_train:
with tf.Session() as sess:
# Summary Filewriter
train_summary_dir = os.path.join(FLAGS.model_dir, "summary",
"train")
train_summary_writer = tf.summary.FileWriter(train_summary_dir,
sess.graph)
# Restoring or initialising session
if not FLAGS.force_init:
try:
saver.restore(sess,
tf.train.latest_checkpoint(FLAGS.model_dir))
print("Recovered Session")
except: # TODO find name for this exception (it does not accept the NotFoundError displayed if it does not find the save)
sess.run(init_op)
print("Unexpectedly initialised session")
else:
sess.run(init_op)
print("Initialised session")
print("Start training")
for data_train in batches_train:
gc_ = 0
if (gc_ % FLAGS.evaluate_every) == 0 or gc_ == 1:
ms_, gc_, pp_, last_out_, last_prob_, _, \
word, max_p, pred, perp_of_true, word2, word_pre = \
sess.run([merged_summaries, global_counter, perplexity,
last_output, last_prob, train_op,
any_word, any_word_max_prob, any_word_prediction,
any_word_real_perp, any_word_prediction2,
any_word_pre],
feed_dict={input_words: data_train})
else:
_, gc_ = sess.run([train_op, global_counter],
feed_dict={input_words: data_train})
if gc_ > FLAGS.no_output_before_n:
train_summary_writer.add_summary(ms_, gc_)
if (gc_ % FLAGS.evaluate_every) == 0 or gc_ == 1:
print("Iteration %s: Perplexity is %s" % (gc_, pp_))
if (gc_ % FLAGS.checkpoint_every == 0) and gc_ > 0:
ckpt_path = saver.save(sess, os.path.join(FLAGS.model_dir,
'model'), gc_)
print("Model saved in file: %s" % ckpt_path)
if gc_ % FLAGS.hlave_lr_every == 0 & gc_ > 0:
eff_rate /= 2
print("Adjusted learning rate")
print(eff_rate)
if gc_ % 250 == 0:
print(
"Target: %s, Perplexity of target: %s, "
"max prob: %s, predicted: %s, second_word: %s,"
"Previous word: %s" % (word, perp_of_true,
max_p, pred, word2,
word_pre))
if FLAGS.do_eval:
with tf.Session() as sess:
# Restoring or initialising session
saver.restore(sess,
tf.train.latest_checkpoint(FLAGS.model_dir))
print("Recovered Session")
out_pp = np.empty(0)
for data_eval in batches_eval:
out_pp = np.concatenate((out_pp, sess.run(sentence_perplexity,
feed_dict={
input_words: data_eval})))
np.savetxt(
FLAGS.output_dir + "/group25.perplexity" + FLAGS.task,
np.array(out_pp),
fmt="%4.8f",
delimiter=',')
if FLAGS.do_generate:
with tf.Session() as sess:
# Restoring or initialising session
saver.restore(sess,
tf.train.latest_checkpoint(FLAGS.model_dir))
print("Recovered Session")
sentences = []
for data_gen in batches_gen:
input = data_gen
for t in range(FLAGS.sentence_length - 1):
best = sess.run(best_pred, feed_dict={input_words: input})
if t < (FLAGS.sentence_length - 1):
next_available = data_gen[:, t+1] != vocabulary.dict[vocabulary.PADDING]
input[:, t + 1] = next_available * data_gen[:, t+1] + \
(1-next_available) * best[:, t]
sentences.append(input)
translator = vocabulary.get_inverse_voc_dict()
sentence_together = np.vstack(sentences)
out_sentences = np.array([translator[x] for x in sentence_together.reshape([-1])]).reshape([-1, FLAGS.sentence_length])
tt = clean_and_cut_sentences(out_sentences)
np.savetxt(
FLAGS.output_dir + "/group25.continuation" + FLAGS.task,
np.array(tt),
fmt="%s",
delimiter='\n')
def main():
parser = argparse.ArgumentParser()
# Model to be loaded
parser.add_argument('--epoch',
type=int,
default=15,
help='Epoch of model to be loaded')
parser.add_argument('--seq_length',
type=int,
default=20,
help='RNN sequence length')
parser.add_argument('--use_cuda',
action="store_true",
default=False,
help='Use GPU or not')
parser.add_argument('--drive',
action="store_true",
default=False,
help='Use Google drive or not')
# Size of neighborhood to be considered parameter
parser.add_argument(
'--neighborhood_size',
type=int,
default=32,
help='Neighborhood size to be considered for social grid')
# Size of the social grid parameter
parser.add_argument('--grid_size',
type=int,
default=4,
help='Grid size of the social grid')
# number of validation will be used
parser.add_argument(
'--num_validation',
type=int,
default=5,
help='Total number of validation dataset will be visualized')
# gru support
parser.add_argument('--gru',
action="store_true",
default=False,
help='True : GRU cell, False: LSTM cell')
# method selection
parser.add_argument(
'--method',
type=int,
default=1,
help=
'Method of lstm will be used (1 = social lstm, 2 = obstacle lstm, 3 = vanilla lstm)'
)
# Parse the parameters
sample_args = parser.parse_args()
# for drive run
prefix = ''
f_prefix = '.'
if sample_args.drive is True:
prefix = 'drive/semester_project/social_lstm_final/'
f_prefix = 'drive/semester_project/social_lstm_final'
method_name = get_method_name(sample_args.method)
model_name = "LSTM"
save_tar_name = method_name + "_lstm_model_"
if sample_args.gru:
model_name = "GRU"
save_tar_name = method_name + "_gru_model_"
# Save directory
save_directory = os.path.join(f_prefix, 'model/', method_name, model_name)
# plot directory for plotting in the future
plot_directory = os.path.join(f_prefix, 'plot/', method_name, model_name)
plot_validation_file_directory = 'validation'
# Define the path for the config file for saved args
with open(os.path.join(save_directory, 'config.pkl'), 'rb') as f:
saved_args = pickle.load(f)
origin = (0, 0)
reference_point = (0, 1)
net = get_model(sample_args.method, saved_args, True)
if sample_args.use_cuda:
net = net.cuda()
# Get the checkpoint path
checkpoint_path = os.path.join(
save_directory, save_tar_name + str(sample_args.epoch) + '.tar')
if os.path.isfile(checkpoint_path):
print('Loading checkpoint')
checkpoint = torch.load(checkpoint_path)
model_epoch = checkpoint['epoch']
net.load_state_dict(checkpoint['state_dict'])
print('Loaded checkpoint at epoch', model_epoch)
# Create the DataLoader object
dataloader = DataLoader(f_prefix,
1,
sample_args.seq_length,
num_of_validation=sample_args.num_validation,
forcePreProcess=True,
infer=True)
create_directories(plot_directory, [plot_validation_file_directory])
dataloader.reset_batch_pointer()
print(
'****************Validation dataset batch processing******************'
)
dataloader.reset_batch_pointer(valid=False)
dataset_pointer_ins = dataloader.dataset_pointer
loss_epoch = 0
err_epoch = 0
f_err_epoch = 0
num_of_batch = 0
smallest_err = 100000
# results of one epoch for all validation datasets
epoch_result = []
# results of one validation dataset
results = []
# For each batch
for batch in range(dataloader.num_batches):
start = time.time()
# Get batch data
x, y, d, numPedsList, PedsList, target_ids = dataloader.next_batch()
if dataset_pointer_ins is not dataloader.dataset_pointer:
if dataloader.dataset_pointer is not 0:
print('Finished prosessed file : ',
dataloader.get_file_name(-1), ' Avarage error : ',
err_epoch / num_of_batch)
num_of_batch = 0
epoch_result.append(results)
dataset_pointer_ins = dataloader.dataset_pointer
results = []
# Loss for this batch
loss_batch = 0
err_batch = 0
f_err_batch = 0
# For each sequence
for sequence in range(dataloader.batch_size):
# Get data corresponding to the current sequence
x_seq, _, d_seq, numPedsList_seq, PedsList_seq = x[sequence], y[
sequence], d[sequence], numPedsList[sequence], PedsList[
sequence]
target_id = target_ids[sequence]
folder_name = dataloader.get_directory_name_with_pointer(d_seq)
dataset_data = dataloader.get_dataset_dimension(folder_name)
# dense vector creation
x_seq, lookup_seq = dataloader.convert_proper_array(
x_seq, numPedsList_seq, PedsList_seq)
# will be used for error calculation
orig_x_seq = x_seq.clone()
target_id_values = x_seq[0][lookup_seq[target_id], 0:2]
# grid mask calculation
if sample_args.method == 2: # obstacle lstm
grid_seq = getSequenceGridMask(x_seq, dataset_data,
PedsList_seq,
saved_args.neighborhood_size,
saved_args.grid_size,
saved_args.use_cuda, True)
elif sample_args.method == 1: # social lstm
grid_seq = getSequenceGridMask(x_seq, dataset_data,
PedsList_seq,
saved_args.neighborhood_size,
saved_args.grid_size,
saved_args.use_cuda)
# vectorize datapoints
x_seq, first_values_dict = vectorize_seq(x_seq, PedsList_seq,
lookup_seq)
# <---------------- Experimental block (may need update in methods)----------------------->
# x_seq = translate(x_seq, PedsList_seq, lookup_seq ,target_id_values)
# angle = angle_between(reference_point, (x_seq[1][lookup_seq[target_id], 0].data.numpy(), x_seq[1][lookup_seq[target_id], 1].data.numpy()))
# x_seq = rotate_traj_with_target_ped(x_seq, angle, PedsList_seq, lookup_seq)
# # Compute grid masks
# grid_seq = getSequenceGridMask(x_seq, dataset_data, PedsList_seq, sample_args.neighborhood_size, sample_args.grid_size, sample_args.use_cuda)
# x_seq, first_values_dict = vectorize_seq(x_seq, PedsList_seq, lookup_seq)
if sample_args.use_cuda:
x_seq = x_seq.cuda()
if sample_args.method == 3: # vanilla lstm
ret_x_seq, loss = sample_validation_data_vanilla(
x_seq, PedsList_seq, sample_args, net, lookup_seq,
numPedsList_seq, dataloader)
else:
ret_x_seq, loss = sample_validation_data(
x_seq, PedsList_seq, grid_seq, sample_args, net,
lookup_seq, numPedsList_seq, dataloader)
# <---------------------Experimental inverse block -------------->
# ret_x_seq = revert_seq(ret_x_seq, PedsList_seq, lookup_seq, target_id_values, first_values_dict)
# ret_x_seq = rotate_traj_with_target_ped(ret_x_seq, -angle, PedsList_seq, lookup_seq)
# ret_x_seq = translate(ret_x_seq, PedsList_seq, lookup_seq ,-target_id_values)
# revert the points back to original space
ret_x_seq = revert_seq(ret_x_seq, PedsList_seq, lookup_seq,
first_values_dict)
err = get_mean_error(ret_x_seq.data, orig_x_seq.data, PedsList_seq,
PedsList_seq, sample_args.use_cuda,
lookup_seq)
f_err = get_final_error(ret_x_seq.data, orig_x_seq.data,
PedsList_seq, PedsList_seq, lookup_seq)
loss_batch += loss
err_batch += err
f_err_batch += f_err
results.append(
(orig_x_seq.data.cpu().numpy(), ret_x_seq.data.cpu().numpy(),
PedsList_seq, lookup_seq,
dataloader.get_frame_sequence(sample_args.seq_length),
target_id))
end = time.time()
print('Current file : ', dataloader.get_file_name(0), ' Batch : ',
batch + 1, ' Sequence: ', sequence + 1, ' Sequence mean error: ',
err, ' Sequence final error: ', f_err, ' time: ', end - start)
loss_batch = loss_batch / dataloader.batch_size
err_batch = err_batch / dataloader.batch_size
f_err_batch = f_err_batch / dataloader.batch_size
num_of_batch += 1
loss_epoch += loss_batch.item()
err_epoch += err_batch
f_err_epoch += f_err_batch
epoch_result.append(results)
if dataloader.num_batches != 0:
loss_epoch = loss_epoch / dataloader.num_batches
err_epoch = err_epoch / dataloader.num_batches
f_err_epoch = f_err_epoch / dataloader.num_batches
print(
'valid_loss = {:.3f}, valid_mean_err = {:.3f}, valid_final_err = {:.3f}'
.format(loss_epoch, err_epoch, f_err_epoch))
dataloader.write_to_plot_file(
epoch_result,
os.path.join(plot_directory, plot_validation_file_directory))
sample_args = parser.parse_args()
'''KITTI Training Setting'''
save_directory = '/home/serene/Documents/KITTIData/GT/save/pixel_data/1'
with open(os.path.join(save_directory, 'config.pkl'), 'rb') as f:
saved_args = pickle.load(f)
f = open('/home/serene/Documents/KITTIData/GT/save/pixel_data/1/results.pkl',
'rb')
results = pickle.load(f)
dataset = [sample_args.visual_dataset]
data_loader = DataLoader(1,
sample_args.pred_length + sample_args.obs_length,
dataset,
True,
infer=True)
#print(data_loader.data[0][0].shape)
# for j in range(len(data_loader.data[0])):
#
# sourceFileName = "/home/hesl/PycharmProjects/social-lstm-tf-HW/data/KITTI-17/img1/" + str(j + 1).zfill(6) + ".jpg"
#
# avatar= cv2.imread(sourceFileName)
# #drawAvatar= ImageDraw.Draw(avatar)
# #print(avatar.shape)
# xSize = avatar.shape[1]
# ySize = avatar.shape[0]
# #print(data_loader.data[0][0][0])
# for i in range(20):
optimizer = RMSprop(lr=0.003)
model.compile(loss=negative_log_likelihood_sequences, optimizer=optimizer)
yPadded = np.pad(y, ((0, 0), (0, 0), (0, 3)),
'constant',
constant_values=(0., 0.)) # the values is not important
print('yPadded', yPadded.shape)
history = model.fit(X, yPadded, batch_size=50, epochs=1, verbose=2)
# model.save('many_to_many.h5')
return history
seqLength = 20
obsLength = 8
predLength = 12
data_loader = DataLoader(seq_length=seqLength) # , forcePreProcess=True)
trajectoriesTrain, trajectoriesTest = data_loader.get_data(test_dataset=4)
xA = np.array(trajectoriesTrain, dtype='float32')
print('training set shape', xA.shape)
X = xA[:, 0:obsLength, :]
y = xA[:, obsLength:seqLength, :]
xOneFrame = xA[:, 0, :]
yOneFrame = xA[:, obsLength, :]
hist = train_many2one(X, yOneFrame)
# hist = train_many2many(X, y)
import matplotlib.pyplot as plt
#print(hist.history['loss'])
def train(args):
print("INPUT SEQUENCE LENGTH: {}".format(args.seq_length))
print("OUTPUT SEQUENCE LENGTH: {}".format(args.pred_length))
# Construct the DataLoader object
dataloader = DataLoader(args.batch_size,
args.seq_length + 1,
forcePreProcess=False)
# Construct the ST-graph object
stgraph = ST_GRAPH(1, args.seq_length + 1)
# Log directory
log_directory = "../log/"
# Logging file
log_file_curve = open(os.path.join(log_directory, "log_curve.txt"), "w")
log_file = open(os.path.join(log_directory, "val.txt"), "w")
# Save directory
save_directory = "../../save_weight/"
# Open the configuration file # 현재 argument 세팅 저장.
with open(os.path.join(save_directory, "config.pkl"), "wb") as f:
pickle.dump(args, f)
# Path to store the checkpoint file
def checkpoint_path(x):
return os.path.join(save_directory, "4Dgraph.S-{}.P-{}.srnn_model_"\
.format(args.seq_length, args.pred_length)\
+ str(x) + ".tar")
# Initialize net
net = SRNN(args)
if args.use_cuda:
net = net.cuda()
optimizer = torch.optim.Adam(net.parameters(), weight_decay=1e-5)
# learning_rate = args.learning_rate
logging.info("Training begin")
best_val_loss = 100
best_epoch = 0
# Training
for epoch in range(args.num_epochs):
dataloader.reset_batch_pointer(valid=False) # Initialization
loss_epoch = 0
# For each batch
# dataloader.num_batches = 10. 1 epoch have 10 batches
for batch in range(dataloader.num_batches):
start = time.time()
# Get batch data, mini-batch
x, _, _, d = dataloader.next_batch(randomUpdate=True)
# Loss for this batch
loss_batch = 0
# For each sequence in the batch
for sequence in range(
dataloader.batch_size): #미니 배치에 있는 각 sequence 데이터에 대한 처리.
# Construct the graph for the current sequence in {nodes, edges}
stgraph.readGraph([x[sequence]])
nodes, edges, nodesPresent, edgesPresent = stgraph.getSequence(
) #미니 배치에 있는 각 sequence의 graph 정보.
##### Convert to cuda variables #####
nodes = Variable(torch.from_numpy(nodes).float())
# nodes[0] represent all the person(object)'s corrdinate show up in frame 0.
if args.use_cuda:
nodes = nodes.cuda()
edges = Variable(torch.from_numpy(edges).float())
if args.use_cuda:
edges = edges.cuda()
# Define hidden states
numNodes = nodes.size()[1] #numNodes
hidden_states_node_RNNs = Variable(
torch.zeros(numNodes, args.node_rnn_size))
if args.use_cuda:
hidden_states_node_RNNs = hidden_states_node_RNNs.cuda()
hidden_states_edge_RNNs = Variable(
torch.zeros(numNodes * numNodes, args.edge_rnn_size))
if args.use_cuda:
hidden_states_edge_RNNs = hidden_states_edge_RNNs.cuda()
cell_states_node_RNNs = Variable(
torch.zeros(numNodes, args.node_rnn_size))
if args.use_cuda:
cell_states_node_RNNs = cell_states_node_RNNs.cuda()
cell_states_edge_RNNs = Variable(
torch.zeros(numNodes * numNodes, args.edge_rnn_size))
if args.use_cuda:
cell_states_edge_RNNs = cell_states_edge_RNNs.cuda()
hidden_states_super_node_RNNs = Variable( #NOTE: 0 for peds., 1 for Bic., 2 for Veh.
torch.zeros(3, args.node_rnn_size))
if args.use_cuda:
hidden_states_super_node_RNNs = hidden_states_super_node_RNNs.cuda(
)
cell_states_super_node_RNNs = Variable(
torch.zeros(3, args.node_rnn_size))
if args.use_cuda:
cell_states_super_node_RNNs = cell_states_super_node_RNNs.cuda(
)
hidden_states_super_node_Edge_RNNs = Variable(
torch.zeros(3, args.edge_rnn_size))
if args.use_cuda:
hidden_states_super_node_Edge_RNNs = (
hidden_states_super_node_Edge_RNNs.cuda())
cell_states_super_node_Edge_RNNs = Variable(
torch.zeros(3, args.edge_rnn_size))
if args.use_cuda:
cell_states_super_node_Edge_RNNs = (
cell_states_super_node_Edge_RNNs.cuda())
# Zero out the gradients // Initialization Step
net.zero_grad()
optimizer.zero_grad()
# Forward prop
outputs, _, _, _, _, _, _, _, _, _ = net(
nodes[:args.seq_length],
edges[:args.seq_length],
nodesPresent[:-1],
edgesPresent[:-1],
hidden_states_node_RNNs,
hidden_states_edge_RNNs,
cell_states_node_RNNs,
cell_states_edge_RNNs,
hidden_states_super_node_RNNs,
hidden_states_super_node_Edge_RNNs,
cell_states_super_node_RNNs,
cell_states_super_node_Edge_RNNs,
)
# Compute loss
loss = Gaussian2DLikelihood(outputs, nodes[1:],
nodesPresent[1:], args.pred_length)
loss_batch += loss.item()
# embed()
# Compute gradients
loss.backward()
# Clip gradients
torch.nn.utils.clip_grad_norm(net.parameters(), args.grad_clip)
# Update parameters
optimizer.step()
# Reset the stgraph
stgraph.reset()
end = time.time()
loss_batch = loss_batch / dataloader.batch_size ##### NOTE: Expected Loss; E[L]
loss_epoch += loss_batch
logging.info(
"{}/{} (epoch {}), train_loss = {:.12f}, time/batch = {:.12f}".
format(
epoch * dataloader.num_batches + batch,
args.num_epochs * dataloader.num_batches,
epoch,
loss_batch,
end - start,
))
# Compute loss for the entire epoch
loss_epoch /= dataloader.num_batches
# Log it
log_file_curve.write(str(epoch) + "," + str(loss_epoch) + ",")
##################### Validation Part #####################
dataloader.reset_batch_pointer(valid=True)
loss_epoch = 0
for batch in range(dataloader.valid_num_batches):
# Get batch data
x, _, d = dataloader.next_valid_batch(randomUpdate=False)
# Loss for this batch
loss_batch = 0
for sequence in range(dataloader.batch_size):
stgraph.readGraph([x[sequence]])
nodes, edges, nodesPresent, edgesPresent = stgraph.getSequence(
)
# Convert to cuda variables
nodes = Variable(torch.from_numpy(nodes).float())
if args.use_cuda:
nodes = nodes.cuda()
edges = Variable(torch.from_numpy(edges).float())
if args.use_cuda:
edges = edges.cuda()
# Define hidden states
numNodes = nodes.size()[1]
hidden_states_node_RNNs = Variable(
torch.zeros(numNodes, args.node_rnn_size))
if args.use_cuda:
hidden_states_node_RNNs = hidden_states_node_RNNs.cuda()
hidden_states_edge_RNNs = Variable(
torch.zeros(numNodes * numNodes, args.edge_rnn_size))
if args.use_cuda:
hidden_states_edge_RNNs = hidden_states_edge_RNNs.cuda()
cell_states_node_RNNs = Variable(
torch.zeros(numNodes, args.node_rnn_size))
if args.use_cuda:
cell_states_node_RNNs = cell_states_node_RNNs.cuda()
cell_states_edge_RNNs = Variable(
torch.zeros(numNodes * numNodes, args.edge_rnn_size))
if args.use_cuda:
cell_states_edge_RNNs = cell_states_edge_RNNs.cuda()
hidden_states_super_node_RNNs = Variable(
torch.zeros(3, args.node_rnn_size))
if args.use_cuda:
hidden_states_super_node_RNNs = hidden_states_super_node_RNNs.cuda(
)
cell_states_super_node_RNNs = Variable(
torch.zeros(3, args.node_rnn_size))
if args.use_cuda:
cell_states_super_node_RNNs = cell_states_super_node_RNNs.cuda(
)
hidden_states_super_node_Edge_RNNs = Variable(
torch.zeros(3, args.edge_rnn_size))
if args.use_cuda:
hidden_states_super_node_Edge_RNNs = (
hidden_states_super_node_Edge_RNNs.cuda())
cell_states_super_node_Edge_RNNs = Variable(
torch.zeros(3, args.edge_rnn_size))
if args.use_cuda:
cell_states_super_node_Edge_RNNs = (
cell_states_super_node_Edge_RNNs.cuda())
outputs, _, _, _, _, _, _, _, _, _ = net(
nodes[:args.seq_length],
edges[:args.seq_length],
nodesPresent[:-1],
edgesPresent[:-1],
hidden_states_node_RNNs,
hidden_states_edge_RNNs,
cell_states_node_RNNs,
cell_states_edge_RNNs,
hidden_states_super_node_RNNs,
hidden_states_super_node_Edge_RNNs,
cell_states_super_node_RNNs,
cell_states_super_node_Edge_RNNs,
)
# Compute loss
loss = Gaussian2DLikelihood(outputs, nodes[1:],
nodesPresent[1:], args.pred_length)
loss_batch += loss.item()
# Reset the stgraph
stgraph.reset()
loss_batch = loss_batch / dataloader.batch_size #### NOTE: Expected Loss; E[L]
loss_epoch += loss_batch
loss_epoch = loss_epoch / dataloader.valid_num_batches
# Update best validation loss until now
if loss_epoch < best_val_loss:
best_val_loss = loss_epoch
best_epoch = epoch
# Record best epoch and best validation loss
logging.info("(epoch {}), valid_loss = {:.3f}".format(
epoch, loss_epoch))
logging.info("Best epoch {}, Best validation loss {}".format(
best_epoch, best_val_loss))
# Log it
log_file_curve.write(str(loss_epoch) + "\n")
# Save the model after each epoch
logging.info("Saving model")
torch.save(
{
"epoch": epoch,
"state_dict": net.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
},
checkpoint_path(epoch),
)
# Record the best epoch and best validation loss overall
logging.info("Best epoch {}, Best validation loss {}".format(
best_epoch, best_val_loss))
# Log it
log_file.write(str(best_epoch) + "," + str(best_val_loss))
# Close logging files
log_file.close()
log_file_curve.close()
def train(args):
data_loader = DataLoader(args.batch_size, args.seq_length, args.data_scale)
if args.model_dir != '' and not os.path.exists(args.model_dir):
os.makedirs(args.model_dir)
with open(os.path.join(args.model_dir, 'config.pkl'), 'wb') as f:
pickle.dump(args, f)
model = Model(args)
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(
os.path.join(args.model_dir, 'log'), sess.graph)
tf.global_variables_initializer().run()
saver = tf.train.Saver(tf.global_variables(), max_to_keep=0)
for e in range(args.num_epochs):
sess.run(
tf.assign(model.lr, args.learning_rate * (args.decay_rate**e)))
data_loader.reset_batch_pointer()
v_x, v_y = data_loader.validation_data()
valid_feed = {
model.input_data: v_x,
model.target_data: v_y,
model.state_in: model.state_in.eval()
}
state = model.state_in.eval()
for b in range(data_loader.num_batches):
ith_train_step = e * data_loader.num_batches + b
start = time.time()
x, y = data_loader.next_batch()
feed = {
model.input_data: x,
model.target_data: y,
model.state_in: state
}
train_loss_summary, train_loss, state, _ = sess.run([
model.train_loss_summary, model.cost, model.state_out,
model.train_op
], feed)
summary_writer.add_summary(train_loss_summary, ith_train_step)
valid_loss_summary, valid_loss, = sess.run(
[model.valid_loss_summary, model.cost], valid_feed)
summary_writer.add_summary(valid_loss_summary, ith_train_step)
end = time.time()
print(
"{}/{} (epoch {}), train_loss = {:.3f}, valid_loss = {:.3f}, time/batch = {:.3f}"
.format(ith_train_step,
args.num_epochs * data_loader.num_batches, e,
train_loss, valid_loss, end - start))
if (ith_train_step % args.save_every
== 0) and (ith_train_step > 0):
checkpoint_path = os.path.join(args.model_dir,
'model.ckpt')
saver.save(sess,
checkpoint_path,
global_step=ith_train_step)
print("model saved to {}".format(checkpoint_path))
def main():
# Define the parser
parser = argparse.ArgumentParser()
# Observed length of the trajectory parameter
parser.add_argument('--obs_length',
type=int,
default=5,
help='Observed length of the trajectory')
# Predicted length of the trajectory parameter
parser.add_argument('--pred_length',
type=int,
default=5,
help='Predicted length of the trajectory')
# Test dataset
parser.add_argument('--test_dataset',
type=int,
default=3,
help='Dataset to be tested on')
# Read the arguments
sample_args = parser.parse_args()
sample_args.train_logs = os.path.join('..', 'train_logs', 'lstm')
# Load the saved arguments to the model from the config file
with open(os.path.join(sample_args.train_logs, 'save', 'config.pkl'),
'rb') as f:
saved_args = pickle.load(f)
# Initialize with the saved args
model = Model(saved_args, True)
# Initialize TensorFlow session
sess = tf.InteractiveSession()
# Initialize TensorFlow saver
saver = tf.train.Saver()
# Get the checkpoint state to load the model from
ckpt = tf.train.get_checkpoint_state(
os.path.join(sample_args.train_logs, 'model'))
print('loading model: ', ckpt.model_checkpoint_path)
# Restore the model at the checpoint
saver.restore(sess, ckpt.model_checkpoint_path)
# Dataset to get data from
dataset = [sample_args.test_dataset]
# Initialize the dataloader object to
# Get sequences of length obs_length+pred_length
data_loader = DataLoader(1,
sample_args.pred_length + sample_args.obs_length,
dataset, True)
# Reset the data pointers of the data loader object
data_loader.reset_batch_pointer()
# Maintain the total_error until now
total_error = 0.
counter = 0.
for b in range(data_loader.num_batches):
# Get the source, target data for the next batch
x, y = data_loader.next_batch()
# The observed part of the trajectory
obs_traj = x[0][:sample_args.obs_length]
# Get the complete trajectory with both the observed and the predicted part from the model
complete_traj = model.sample(sess,
obs_traj,
x[0],
num=sample_args.pred_length)
# Compute the mean error between the predicted part and the true trajectory
total_error += get_mean_error(complete_traj, x[0],
sample_args.obs_length)
print("Processed trajectory number : ", b, "out of ",
data_loader.num_batches, " trajectories")
# Print the mean error across all the batches
print("Total mean error of the model is ",
total_error / data_loader.num_batches)
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import experiments.Hybird_tVAE_test as T
import utils.DataLoader as D
import utils.Utils as U
from models.Hybird_tVAE import Hybird_tVAE
flags = tf.app.flags
# data_loader
data_loader = D.DataLoader(D.Vocab('europarl_hybird_tvae', D.Level.CHAR))
# ptb_loader = D.DataLoader(D.Vocab('ptb_hybird_tvae', D.Level.CHAR))
# hybird_tvae config
flags.DEFINE_string('model_name', 'Hybird_tVAE', '')
flags.DEFINE_string('ckpt_path', './results/Hybird_tVAE/ckpt/', '')
flags.DEFINE_string('logs_path', './results/Hybird_tVAE/logs/', '')
flags.DEFINE_integer('batch_size', 32, '')
flags.DEFINE_integer('steps', U.epoch_to_step(10, data_loader.train_size, batch_size=32), '')
flags.DEFINE_integer('lr', 0.001, 'learning rate')
flags.DEFINE_integer('z_size', 32, '')
flags.DEFINE_integer('seq_len', 60, '')
flags.DEFINE_integer('rnn_num', 1, '')
flags.DEFINE_integer('embed_size', 80, '')
flags.DEFINE_integer('vocab_size', data_loader.vocab_size, '')
def main():
'''
主要目的為 計算測試資料的 error rate
'''
parser = argparse.ArgumentParser()
# Observed length of the trajectory parameter
parser.add_argument('--obs_length',
type=int,
default=180,
help='Observed length of the trajectory')
# Predicted length of the trajectory parameter
# parser.add_argument('--pred_length', type=int, default=378-60-1,
# help='Predicted length of the trajectory')
parser.add_argument('--pred_length',
type=int,
default=240,
help='Predicted length of the trajectory')
# Model to be loaded
parser.add_argument('--epoch',
type=int,
default=199,
help='Epoch of model to be loaded')
# cuda support
parser.add_argument('--use_cuda',
action="store_true",
default=False,
help='Use GPU or not')
# gru model
parser.add_argument('--gru',
action="store_true",
default=False,
help='True : GRU cell, False: LSTM cell')
# method selection
parser.add_argument(
'--method',
type=int,
default=1,
help=
'Method of lstm will be used (1 = social lstm, 2 = obstacle lstm, 3 = vanilla lstm)'
)
# Parse the parameters
sample_args = parser.parse_args()
# for drive run
prefix = ''
f_prefix = '.'
method_name = "OLSTM"
model_name = "LSTM"
save_tar_name = method_name + "_lstm_model_"
if sample_args.gru:
model_name = "GRU"
save_tar_name = method_name + "_gru_model_"
print("Selected method name: ", method_name, " model name: ", model_name)
# Save directory
save_directory = os.path.join(f_prefix, 'model/', method_name, model_name)
# plot directory for plotting in the future
plot_directory = os.path.join(f_prefix, 'plot/', method_name, model_name)
result_directory = os.path.join(f_prefix, 'result/', method_name)
plot_test_file_directory = 'test'
# Define the path for the config file for saved args
with open(os.path.join(save_directory, 'config.pkl'), 'rb') as f:
saved_args = pickle.load(f)
seq_lenght = sample_args.pred_length + sample_args.obs_length
# Create the DataLoader object
dataloader = DataLoader(f_prefix,
1,
sample_args.pred_length + sample_args.obs_length,
forcePreProcess=True,
infer=True)
create_directories(os.path.join(result_directory, model_name),
dataloader.get_all_directory_namelist())
create_directories(plot_directory, [plot_test_file_directory])
dataloader.reset_batch_pointer(valid=False)
dataset_pointer_ins = dataloader.dataset_pointer
smallest_err = 100000
smallest_err_iter_num = -1
origin = (0, 0)
reference_point = (0, 1)
submission_store = [] # store submission data points (txt)
result_store = [] # store points for plotting
# Initialize net
net = OLSTM(saved_args, True)
if sample_args.use_cuda:
net = net.cuda()
# Get the checkpoint path
checkpoint_path = os.path.join(
save_directory, save_tar_name + str(sample_args.epoch) + '.tar')
if os.path.isfile(checkpoint_path):
print('Loading checkpoint')
checkpoint = torch.load(checkpoint_path)
model_epoch = checkpoint['epoch']
net.load_state_dict(checkpoint['state_dict'])
print('Loaded checkpoint at epoch', model_epoch)
results_it = []
for iterator in range(50):
x_seq_arr = []
ret_x_seq_arr = []
error_arr = []
expected_day_arr = []
predicted_day_arr = []
total_error = 0
for batch in range(dataloader.num_batches):
# Get data
x, y, d = dataloader.next_batch(randomUpdate=False)
# Get the sequence
x_seq, y_seq, d_seq = x[0], y[0], d[0]
x_seq = np.array(x_seq)
'''
x_seq = dataloader.inverse_transform_MinMaxScaler(x_seq)
print('{}/{}'.format(batch, dataloader.num_batches))
x_seq[sample_args.obs_length:,-2]= 17
x_seq[sample_args.obs_length:,-1]= 28
x_seq = dataloader.fit_transform_MinMaxScaler(x_seq)
'''
x_seq = Variable(torch.from_numpy(x_seq).float())
if sample_args.use_cuda:
x_seq = x_seq.cuda()
obs_data = x_seq[:sample_args.obs_length]
ret_x_seq = sample(sample_args, x_seq, net)
error = get_mean_error(x_seq[sample_args.obs_length:],
ret_x_seq[sample_args.obs_length:], False)
total_error += error
# 顯示預測
# x_seq = result[0]
x_seq = x_seq.data.cpu().numpy()
x_seq = np.reshape(x_seq, (x_seq.shape[0], saved_args.input_size))
x_seq = dataloader.inverse_transform_MinMaxScaler(x_seq)
# ret_x_seq = result[1]
ret_x_seq = ret_x_seq.data.cpu().numpy()
ret_x_seq = np.reshape(ret_x_seq,
(ret_x_seq.shape[0], saved_args.input_size))
ret_x_seq = dataloader.inverse_transform_MinMaxScaler(ret_x_seq)
gt = (x_seq[:, 0] - x_seq[:, 2]) / (x_seq[:, 1] - x_seq[:, 0])
pred = (ret_x_seq[:, 0] - ret_x_seq[:, 2]) / (ret_x_seq[:, 1] -
ret_x_seq[:, 0])
gt2 = gt[sample_args.obs_length:]
pred2 = pred[sample_args.obs_length:]
expected_day = np.mean(gt2)
predicted_day = np.mean(pred2)
# print(expected_day, predicted_day, expected_day-predicted_day)
# print('Error: ',error)
expected_day = np.mean(gt2)
predicted_day = np.mean(pred2)
x_seq_arr.append(x_seq)
ret_x_seq_arr.append(ret_x_seq)
error_arr.append(error.data.cpu().numpy())
expected_day_arr.append(expected_day)
predicted_day_arr.append(predicted_day)
# fig, axs = plt.subplots(6, 1)
# axs[0].plot(ret_x_seq[:,0], color = 'blue' , label = 'Predict h1', linestyle='--', marker='^')
# axs[0].plot(x_seq[:,0], color = 'red', label = 'Real h1', linestyle='-', marker='.')
# axs[1].plot(ret_x_seq[:,1], color = 'blue' , label = 'Predict h2', linestyle='--', marker='^')
# axs[1].plot(x_seq[:,1], color = 'red', label = 'Real h2', linestyle='-', marker='.')
# axs[2].plot(ret_x_seq[:,2], color = 'blue' , label = 'Predict h3', linestyle='--', marker='^')
# axs[2].plot(x_seq[:,2], color = 'red', label = 'Real h3', linestyle='-', marker='.')
# axs[3].plot(pred, color = 'blue' , label = 'Predict h3', linestyle='--', marker='^')
# axs[3].plot(gt, color = 'red', label = 'Real h3', linestyle='-', marker='.')
# axs[4].plot(ret_x_seq[:,-2], color = 'blue' , label = 'Predict Tevwi', linestyle='--', marker='^')
# axs[4].plot(x_seq[:,-2], color = 'red', label = 'Real Tevwi', linestyle='-', marker='.')
# axs[5].plot(ret_x_seq[:,-1], color = 'blue' , label = 'Predict Tcdwi', linestyle='--', marker='^')
# axs[5].plot(x_seq[:,-1], color = 'red', label = 'Real Tcdwi', linestyle='-', marker='.')
# for ax in axs:
# ax.legend()
# ax.grid()
# plt.show()
total_error = total_error / dataloader.num_batches
if total_error < smallest_err:
print("**********************************************************")
print('Best iteration has been changed. Previous best iteration: ',
smallest_err_iter_num, 'Error: ', smallest_err)
print('New best iteration : ', iterator, 'Error: ', total_error)
smallest_err_iter_num = iterator
smallest_err = total_error
results_it.append((sample_args.pred_length, sample_args.obs_length,
x_seq_arr, ret_x_seq_arr, error_arr))
dataloader.write_to_plot_file([results_it[smallest_err_iter_num]],
os.path.join(plot_directory,
plot_test_file_directory))
alpha_num = const.ALPHA_NUM
lam_lp = const.LAM_LP
lam_gdl = const.LAM_GDL
lam_adv = const.LAM_ADV
lam_flow = const.LAM_FLOW
adversarial = (lam_adv != 0)
summary_dir = const.SUMMARY_DIR
snapshot_dir = const.SNAPSHOT_DIR
print(const)
# define dataset
with tf.name_scope('dataset'):
train_loader = DataLoader(train_folder,
resize_height=height,
resize_width=width)
train_dataset = train_loader(batch_size=batch_size,
time_steps=num_his,
num_pred=1)
train_it = train_dataset.make_one_shot_iterator()
train_videos_clips_tensor = train_it.get_next()
train_videos_clips_tensor.set_shape(
[batch_size, height, width, 3 * (num_his + 1)])
train_inputs = train_videos_clips_tensor[..., 0:num_his * 3]
train_gt = train_videos_clips_tensor[..., -3:]
print('train inputs = {}'.format(train_inputs))
print('train prediction gt = {}'.format(train_gt))
model.add(Reshape((-1, 3)))
model.summary()
return model
if __name__ == '__main__':
train_size = 1054 * 5
valid_size = 1186 * 5
batch_size = 72
epochs = 100
train_loader = DataLoader(
file_glob_pattern='no_compensate_feature/*.train.*.npy',
batch_size=batch_size)
valid_loader = DataLoader(
file_glob_pattern='no_compensate_feature/*.valid.*.npy',
batch_size=batch_size)
model_ckpt = ModelCheckpoint('model/no_compensate.deconv.model.keras.h5',
verbose=1,
save_best_only=True)
tensorboard = TensorBoard(log_dir='./logs/deconv_no_compensate',
histogram_freq=0,
write_graph=True,
write_images=False)
model = build_model()
# model.compile(loss = 'categorical_crossentropy', optimizer = Adam(lr = 1e-4), metrics = ['accuracy'])
# history = model.fit_generator(train_loader, steps_per_epoch = train_size // batch_size, epochs = epochs, validation_data = valid_loader, validation_steps = valid_size // batch_size
"""
In these experiments we will try logistic regression, svms, ridge, decision trees, naive bayes and random forests classifiers across a wide variety of parameters for each algorithm and test them via nested cross validation method.
"""
from utils import DataLoader, nested_cross_val
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression, RidgeClassifier
from sklearn.naive_bayes import MultinomialNB, BernoulliNB
from sklearn.model_selection import KFold, GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import make_scorer, confusion_matrix, f1_score, accuracy_score, precision_score, recall_score
dl = DataLoader('data/Ethos_Dataset_Binary.csv')
X, y = dl.get_data()
f = open("res/setA.txt", "w+")
f.write("{: <7} | {: <7} {: <7} {: <7} {: <7} {: <7} {: <7} {: <7} {: <7} \n".
format('Method', 'Duration', 'scoreTi', 'F1', 'Prec.', 'Recall',
'Acc.', 'Spec.', 'Sens.'))
f.write(
"=========================================================================\n"
)
f.close()
"""""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """
Run Naive Bayes
""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """"""
mNB = MultinomialNB()
vec = TfidfVectorizer(analyzer='word')
pipe = Pipeline(steps=[('vec', vec), ('mNB', mNB)])
def train(args):
## 19th Feb : move training files for 3 && 4 to fine_obstacle under copy_2
# os.chdir('/home/serene/PycharmProjects/srnn-pytorch-master/')
# context_factor is an experiment for including potential destinations of pedestrians in the graph.
# did not yield good improvement
os.chdir('/home/serene/Documents/copy_srnn_pytorch/srnn-pytorch-master')
# os.chdir('/home/siri0005/srnn-pytorch-master')
# base_dir = '../fine_obstacle/prelu/p_02/'
base_dir = '../fine_obstacle/prelu/p_02/'
# '../MultiNodeAttn_HH/'
# os.chdir('/home/serene/Documents/KITTIData/GT')
datasets = [i for i in [0,1,2,3,4]]
# Remove the leave out dataset from the datasets
datasets.remove(args.leaveDataset)
# datasets = [0]
# args.leaveDataset = 0
# Construct the DataLoader object
dataloader = DataLoader(args.batch_size, args.seq_length + 1, datasets, forcePreProcess=True)
# Construct the ST-graph object
stgraph = ST_GRAPH(1, args.seq_length + 1)
# Log directory
# log_directory = './log/world_data/normalized_01/'
log_directory = base_dir+'log/'
log_directory += str(args.leaveDataset) + '/'
log_directory += 'log_attention/'
# Logging file
log_file_curve = open(os.path.join(log_directory, 'log_curve.txt'), 'w')
log_file = open(os.path.join(log_directory, 'val.txt'), 'w')
# Save directory
# save_directory = './save/world_data/normalized_01/'
save_directory = base_dir+'save/'
save_directory += str(args.leaveDataset)+'/'
save_directory += 'save_attention/'
# log RELU parameter
param_log_dir = save_directory + 'param_log.txt'
param_log = open(param_log_dir , 'w')
# Open the configuration file
with open(os.path.join(save_directory, 'config.pkl'), 'wb') as f:
pickle.dump(args, f)
# Path to store the checkpoint file
def checkpoint_path(x):
return os.path.join(save_directory, 'srnn_model_'+str(x)+'.tar')
# Initialize net
net = SRNN(args)
# srnn_model = SRNN(args)
# net = torch.nn.DataParallel(srnn_model)
# CUDA_VISIBLE_DEVICES = 1
net.cuda()
optimizer = torch.optim.Adam(net.parameters(), lr=args.learning_rate)
# optimizer = torch.optim.RMSprop(net.parameters(), lr=args.learning_rate, momentum=0.0001, centered=True)
# learning_rate = args.learning_rate
print('Training begin')
best_val_loss = 100
best_epoch = 0
# start_epoch = 0
# if args.leaveDataset == 3:
# start_epoch =checkpoint_path(0)
# ckp = torch.load(checkpoint_path(3))
# net.load_state_dict(ckp['state_dict'])
# optimizer.load_state_dict(ckp['optimizer_state_dict'])
# ckp = torch.load(checkpoint_path(4))
# net.load_state_dict(ckp['state_dict'])
# optimizer.load_state_dict(ckp['optimizer_state_dict'])
# last_epoch = ckp['epoch']
# rang = range(last_epoch, args.num_epochs, 1)
rang = range(args.num_epochs)
# Training
for epoch in rang:
dataloader.reset_batch_pointer(valid=False)
loss_epoch = 0
# if args.leaveDataset == 2 and epoch == 0:
# dataloader.num_batches = dataloader.num_batches + start_epoch
# epoch += start_epoch
# For each batch
stateful = True # flag that controls transmission of previous hidden states to current hidden states vectors.
# Part of statefulness
for batch in range( dataloader.num_batches):
start = time.time()
# Get batch data
x, _, _, d = dataloader.next_batch(randomUpdate=True) ## shuffling input + stateless lstm
# Loss for this batch
loss_batch = 0
# For each sequence in the batch
for sequence in range(dataloader.batch_size):
# Construct the graph for the current sequence
stgraph.readGraph([x[sequence]],d, args.distance_thresh)
nodes, edges, nodesPresent, edgesPresent,obsNodes, obsEdges, obsNodesPresent, obsEdgesPresent = stgraph.getSequence() #
# Convert to cuda variables
nodes = Variable(torch.from_numpy(nodes).float()).cuda()
edges = Variable(torch.from_numpy(edges).float()).cuda()
obsNodes = Variable(torch.from_numpy(obsNodes).float()).cuda()
obsEdges = Variable(torch.from_numpy(obsEdges).float()).cuda()
## Modification : reset hidden and cell states only once after every batch ; keeping states updated during sequences 31st JAN
# Define hidden states
numNodes = nodes.size()[1]
numObsNodes = obsNodes.size()[1]
# if not stateful:
hidden_states_node_RNNs = Variable(torch.zeros(numNodes, args.human_node_rnn_size)).cuda()
hidden_states_edge_RNNs = Variable(torch.zeros(numNodes * numNodes, args.human_human_edge_rnn_size)).cuda()
cell_states_node_RNNs = Variable(torch.zeros(numNodes, args.human_node_rnn_size)).cuda()
cell_states_edge_RNNs = Variable(torch.zeros(numNodes * numNodes, args.human_human_edge_rnn_size)).cuda()
## new update : 25th JAN , let the hidden state transition begin with negative ones
## such initialization did not lead to any new learning results, network converged quickly and loss did not decrease below
# -6
hidden_states_obs_node_RNNs = Variable(torch.zeros(numObsNodes, args.obs_node_rnn_size)).cuda()
hidden_states_obs_edge_RNNs = Variable(torch.zeros(numNodes * numNodes, args.human_obstacle_edge_rnn_size)).cuda()
cell_states_obs_node_RNNs = Variable(torch.zeros(numObsNodes, args.obs_node_rnn_size)).cuda()
cell_states_obs_edge_RNNs = Variable(torch.zeros(numNodes * numNodes, args.human_obstacle_edge_rnn_size)).cuda()
net.zero_grad()
optimizer.zero_grad()
# Forward prop
# _ = \
outputs, h_node_rnn, h_edge_rnn, cell_node_rnn, cell_edge_rnn,o_h_node_rnn, o_h_edge_rnn, o_cell_node_rnn, o_cell_edge_rnn, _ = \
net(nodes[:args.seq_length], edges[:args.seq_length], nodesPresent[:-1], edgesPresent[:-1],
hidden_states_node_RNNs, hidden_states_edge_RNNs, cell_states_node_RNNs, cell_states_edge_RNNs,
obsNodes[:args.seq_length], obsEdges[:args.seq_length], obsNodesPresent[:-1],
obsEdgesPresent[:-1]
,hidden_states_obs_node_RNNs, hidden_states_obs_edge_RNNs, cell_states_obs_node_RNNs,
cell_states_obs_edge_RNNs)
# # else:
# # if len(nodes) == len(hidden_states_node_RNNs): # no additional nodes introduced in graph
# # hidden_states_node_RNNs = Variable(h_node_rnn).cuda()
# # cell_states_node_RNNs = Variable(cell_node_rnn).cuda()
# # else: # for now number of nodes is only increasing in time as new pedestrians are detected in the scene
# # pad_size = len(nodes) - len(hidden_states_node_RNNs)
# cell_states_node_RNNs = Variable(np.pad(cell_node_rnn, pad_size)).cuda()
# if sequence > 0:
# hidden_states_node_RNNs = Variable(h_node_rnn).resize(hidden_states_node_RNNs.cpu().size())
# hidden_states_edge_RNNs = h_edge_rnn
# cell_states_node_RNNs = cell_node_rnn
# cell_states_edge_RNNs = cell_edge_rnn
# new_num_nodes = h_node_rnn.size()[0] - hidden_states_node_RNNs.size()[0]
# if h_node_rnn.size()[0] - hidden_states_node_RNNs.size()[0] >=1:
# np.pad(hidden_states_node_RNNs.cpu() , new_num_nodes, mode='constant').cuda()
# hidden_states_obs_node_RNNs = o_h_node_rnn
# hidden_states_obs_edge_RNNs = o_h_edge_rnn
# cell_states_obs_node_RNNs = o_cell_node_rnn
# cell_states_obs_edge_RNNs = o_cell_edge_rnn
# Zero out the gradients
# Compute loss
loss = Gaussian2DLikelihood(outputs, nodes[1:], nodesPresent[1:], args.pred_length)
loss_batch += loss.data[0]
# Compute gradients
loss.backward(retain_variables=True)
param_log.write(str(net.alpha.data[0]) + '\n')
# Clip gradients
torch.nn.utils.clip_grad_norm(net.parameters(), args.grad_clip)
# Update parameters
optimizer.step()
# Reset the stgraph
stgraph.reset()
end = time.time()
loss_batch = loss_batch / dataloader.batch_size
loss_epoch += loss_batch
print(
'{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}'.format(epoch * dataloader.num_batches + batch,
args.num_epochs * dataloader.num_batches,
epoch,
loss_batch, end - start))
# Compute loss for the entire epoch
loss_epoch /= dataloader.num_batches
# Log it
log_file_curve.write(str(epoch)+','+str(loss_epoch)+',')
# Validation
dataloader.reset_batch_pointer(valid=True)
loss_epoch = 0
# dataloader.valid_num_batches = dataloader.valid_num_batches + start_epoch
for batch in range(dataloader.valid_num_batches):
# Get batch data
x, _, d = dataloader.next_valid_batch(randomUpdate=False)## stateless lstm without shuffling
# Loss for this batch
loss_batch = 0
for sequence in range(dataloader.batch_size):
stgraph.readGraph([x[sequence]], d, args.distance_thresh)
nodes, edges, nodesPresent, edgesPresent,obsNodes, obsEdges, obsNodesPresent, obsEdgesPresent = stgraph.getSequence() #
# Convert to cuda variables
nodes = Variable(torch.from_numpy(nodes).float()).cuda()
edges = Variable(torch.from_numpy(edges).float()).cuda()
obsNodes = Variable(torch.from_numpy(obsNodes).float()).cuda()
obsEdges = Variable(torch.from_numpy(obsEdges).float()).cuda()
# Define hidden states
numNodes = nodes.size()[1]
hidden_states_node_RNNs = Variable(torch.zeros(numNodes, args.human_node_rnn_size)).cuda()
hidden_states_edge_RNNs = Variable(torch.zeros(numNodes * numNodes, args.human_human_edge_rnn_size)).cuda()
cell_states_node_RNNs = Variable(torch.zeros(numNodes, args.human_node_rnn_size)).cuda()
cell_states_edge_RNNs = Variable(torch.zeros(numNodes * numNodes, args.human_human_edge_rnn_size)).cuda()
numObsNodes = obsNodes.size()[1]
hidden_states_obs_node_RNNs = Variable(torch.zeros(numObsNodes, args.obs_node_rnn_size)).cuda()
hidden_states_obs_edge_RNNs = Variable(torch.zeros(numNodes * numNodes, args.human_obstacle_edge_rnn_size)).cuda()
cell_states_obs_node_RNNs = Variable(torch.zeros(numObsNodes, args.obs_node_rnn_size)).cuda()
cell_states_obs_edge_RNNs = Variable(torch.zeros(numNodes * numNodes, args.human_obstacle_edge_rnn_size)).cuda()
#
outputs, h_node_rnn, h_edge_rnn, cell_node_rnn, cell_edge_rnn,o_h_node_rnn ,o_h_edge_rnn, o_cell_node_rnn, o_cell_edge_rnn, _= net(nodes[:args.seq_length], edges[:args.seq_length], nodesPresent[:-1],
edgesPresent[:-1], hidden_states_node_RNNs, hidden_states_edge_RNNs,
cell_states_node_RNNs, cell_states_edge_RNNs
, obsNodes[:args.seq_length], obsEdges[:args.seq_length],
obsNodesPresent[:-1], obsEdgesPresent[:-1]
,hidden_states_obs_node_RNNs, hidden_states_obs_edge_RNNs,
cell_states_obs_node_RNNs, cell_states_obs_edge_RNNs)
# Compute loss
loss = Gaussian2DLikelihood(outputs, nodes[1:], nodesPresent[1:], args.pred_length)
loss_batch += loss.data[0]
# Reset the stgraph
stgraph.reset()
loss_batch = loss_batch / dataloader.batch_size
loss_epoch += loss_batch
loss_epoch = loss_epoch / dataloader.valid_num_batches
# Update best validation loss until now
if loss_epoch < best_val_loss:
best_val_loss = loss_epoch
best_epoch = epoch
# Record best epoch and best validation loss
print('(epoch {}), valid_loss = {:.3f}'.format(epoch, loss_epoch))
print('Best epoch {}, Best validation loss {}'.format(best_epoch, best_val_loss))
# Log it
log_file_curve.write(str(loss_epoch) + '\n')
# Save the model after each epoch
print('Saving model')
torch.save({
'epoch': epoch,
'state_dict': net.state_dict(),
'optimizer_state_dict': optimizer.state_dict()
}, checkpoint_path(epoch))
# Record the best epoch and best validation loss overall
print('Best epoch {}, Best validation loss {}'.format(best_epoch, best_val_loss))
# Log it
log_file.write(str(best_epoch) + ',' + str(best_val_loss))
# Close logging files
log_file.close()
log_file_curve.close()
param_log.close()
def main():
# os.chdir('/home/serene/Documents/KITTIData/GT/')
# os.chdir('/home/siri0005/copy_srnn_pytorch/srnn-pytorch-master/')#/srnn-pytorch-master
os.chdir('/home/serene/Documents/copy_srnn_pytorch/srnn-pytorch-master')
H_path = ['./pedestrians/ewap_dataset/seq_eth/H.txt',
'./pedestrians/ewap_dataset/seq_hotel/H.txt',
'./pedestrians/ucy_crowd/data_zara01/H.txt',
'./pedestrians/ucy_crowd/data_zara02/H.txt',
'./pedestrians/ucy_crowd/data_students03/H.txt']
# ,1,2,3,
base_dir = '../fine_obstacle/prelu/p_02/'
for i in [4]:#,1,2,3,4
avg = []
ade = []
with open(base_dir+'log/{0}/log_attention/val.txt'.format(i)) as val_f:
if i == 1:
e = 99
else:
best_val = val_f.readline()
[e, val] = best_val.split(',')
parser = argparse.ArgumentParser()
# Observed length of the trajectory parameter
parser.add_argument('--obs_length', type=int, default=8,
help='Observed length of the trajectory')
# Predicted length of the trajectory parameter
parser.add_argument('--pred_length', type=int, default=12,
help='Predicted length of the trajectory')
# Test dataset
parser.add_argument('--test_dataset', type=int, default=i,
help='Dataset to be tested on')
# Model to be loaded
parser.add_argument('--epoch', type=int, default=e,
help='Epoch of model to be loaded')
# Parse the parameters
sample_args = parser.parse_args()
# Save directory
save_directory = base_dir+'save/{0}/save_attention'.format(i)
plot_directory = base_dir + '/selected_plots/' #'plot_1/'
# Define the path for the config file for saved args
with open(os.path.join(save_directory, 'config.pkl'), 'rb') as f:
saved_args = pickle.load(f)
# Initialize net
net = SRNN(saved_args, sample_args.test_dataset, True)
net.cuda()
## TODO: visualize trained weights
# plt.imshow(net.humanNodeRNN.edge_embed.weight)
# plt.colorbar()
# plt.show()
checkpoint_path = os.path.join(save_directory, 'srnn_model_'+str(sample_args.epoch)+'.tar')
if os.path.isfile(checkpoint_path):
print('Loading checkpoint')
checkpoint = torch.load(checkpoint_path)
# model_iteration = checkpoint['iteration']
model_epoch = checkpoint['epoch']
net.load_state_dict(checkpoint['state_dict'])
print('Loaded checkpoint at {}'.format(model_epoch))
H_mat = np.loadtxt(H_path[i])
avg = []
ade = []
# Dataset to get data from
dataset = [sample_args.test_dataset]
sample_ade_error_arr = []
sample_fde_error_arr = []
num_nodes = 0
inner_num_nodes_1= 0
inner_num_nodes_2= 0
ade_sum = 0
fde_sum = 0
dataloader = DataLoader(1, sample_args.pred_length + sample_args.obs_length, dataset, True, infer=True)
dataloader.reset_batch_pointer()
# Construct the ST-graph object
stgraph = ST_GRAPH(1, sample_args.pred_length + sample_args.obs_length)
results = []
# Variable to maintain total error
total_error = 0
final_error = 0
# TRY this code version
for batch in range(dataloader.num_batches):
sample_fde_error = []
sample_ade_error = []
running_time_sample = []
c = 0
x, _, frameIDs, d = dataloader.next_batch(randomUpdate=False)
# Construct ST graph
stgraph.readGraph(x, ds_ptr=d, threshold=1.0)
nodes, edges, nodesPresent, edgesPresent, obsNodes, obsEdges, obsNodesPresent, obsEdgesPresent = stgraph.getSequence()
nodes = Variable(torch.from_numpy(nodes).float(), volatile=True).cuda()
edges = Variable(torch.from_numpy(edges).float(), volatile=True).cuda()
obsNodes = Variable(torch.from_numpy(obsNodes).float()).cuda()
obsEdges = Variable(torch.from_numpy(obsEdges).float()).cuda()
# Separate out the observed part of the trajectory
obs_nodes, obs_edges, obs_nodesPresent, obs_edgesPresent = nodes[:sample_args.obs_length], edges[:sample_args.obs_length], nodesPresent[:sample_args.obs_length], edgesPresent[:sample_args.obs_length]
# Separate out the observed obstacles in a given sequence
obsnodes_v, obsEdges_v, obsNodesPresent_v, obsEdgesPresent_v = obsNodes[:sample_args.obs_length], obsEdges[:sample_args.obs_length], obsNodesPresent[:sample_args.obs_length], obsEdgesPresent[:sample_args.obs_length]
# if c == 0:
# num_nodes += np.shape(nodes)[1]
for c in range(10):
num_nodes += np.shape(nodes)[1]
start = time.time()
# Sample function
ret_nodes, ret_attn = sample(obs_nodes, obs_edges, obs_nodesPresent, obs_edgesPresent, obsnodes_v,
obsEdges_v, obsNodesPresent_v,obsEdgesPresent_v, sample_args, net, nodes, edges, nodesPresent)
end = time.time()
running_time_sample.append((end-start))
# print('One-time Sampling took = ', (end - start), ' seconds.')
# Compute mean and final displacement error
total_error , _ = get_mean_error(ret_nodes[sample_args.obs_length:].data, nodes[sample_args.obs_length:].data,
nodesPresent[sample_args.obs_length - 1],
nodesPresent[sample_args.obs_length:], H_mat, i)
# print("ADE errors:", total_error)
inner_num_nodes_1 += _
sample_ade_error.append(total_error)
final_error , _ = get_final_error(ret_nodes[sample_args.obs_length:].data, nodes[sample_args.obs_length:].data,
nodesPresent[sample_args.obs_length - 1],
nodesPresent[sample_args.obs_length:], H_mat, i)
# print("final errors:", final_error)
sample_fde_error.append(final_error)
results.append((nodes.data.cpu().numpy(), ret_nodes.data.cpu().numpy(), nodesPresent, sample_args.obs_length, ret_attn, frameIDs))
stgraph.reset()
sample_ade_error_arr.append(np.sum(sample_ade_error))
sample_fde_error_arr.append(np.sum(sample_fde_error))
sample_ade_error = np.sum(sample_ade_error, 0)
if len(sample_ade_error):
# sample_ade_error /= 10
sample_ade_error = torch.min(sample_ade_error)
ade_sum += sample_ade_error
ade.append(ade_sum)
# for non-rectangular tensors
for (e, idx) in zip(sample_fde_error , range(len(sample_fde_error))):
if int(len(e)) > 0 :
l = int(len(e))
sample_fde_error[idx] = np.sum(e) #/l
else:
del sample_fde_error[idx]
print(sample_fde_error)
if (np.ndim(sample_fde_error) == 1 and len(sample_fde_error)) or \
(np.ndim(sample_fde_error) > 1 and np.all([True for x in sample_fde_error if len(x) > 0] == True)):
sample_fde_error = np.min(sample_fde_error)
fde_sum += sample_fde_error
avg.append(fde_sum)
with open(os.path.join(save_directory, 'results.pkl'), 'wb') as f:
pickle.dump(results, f)
print('SUMMARY **************************//')
print('One-time Sampling took = ', np.average(running_time_sample), ' seconds.')
print(np.sum(ade) , ' ' , np.sum(avg))
print('average ADE', np.sum(ade) / (sample_args.pred_length * num_nodes))#
print('average FDE', np.sum(avg) / (num_nodes*10))#
with open(os.path.join(save_directory, 'sampling_results.txt'), 'wb') as o:
np.savetxt(os.path.join(save_directory, 'sampling_results.txt'), (np.sum(ade) / (sample_args.pred_length * num_nodes),
np.sum(avg) / inner_num_nodes_1))
class DCGAN():
def __init__(self):
# Input shape
self.channels = 3
self.img_size = 64
self.latent_dim = 100
self.time = time()
self.dataset_name = 'vdsr'
self.learning_rate = 1e-4
optimizer = Adam(self.learning_rate, beta_1=0.5, decay=0.00005)
self.gf = 64 # filter size of generator's last layer
self.df = 64 # filter size of discriminator's first layer
# Configure data loader
self.data_loader = DataLoader(dataset_name=self.dataset_name,
img_res=(self.img_size, self.img_size),
mem_load=True)
self.n_data = self.data_loader.get_n_data()
self.generator = self.build_generator()
print(
"---------------------generator summary----------------------------"
)
self.generator.summary()
self.generator.compile(loss='mse',
optimizer=optimizer,
metrics=['mse'])
# Build and compile the discriminator
self.discriminator = self.build_discriminator()
print(
"\n---------------------discriminator summary----------------------------"
)
self.discriminator.summary()
self.discriminator.compile(loss='mse',
optimizer=optimizer,
metrics=['accuracy'])
make_trainable(self.discriminator, False)
z = Input(shape=(self.latent_dim, ))
fake_img = self.generator(z)
# for the combined model, we only train ganerator
self.discriminator.trainable = False
validity = self.discriminator(fake_img)
self.combined = Model([z], [validity])
print(
"\n---------------------combined summary----------------------------"
)
self.combined.summary()
self.combined.compile(loss=['binary_crossentropy'],
optimizer=optimizer)
def build_generator(self):
noise = Input(shape=(self.latent_dim, ))
def deconv2d(layer_input,
filters=256,
kernel_size=(5, 5),
strides=(2, 2),
bn_relu=True):
"""Layers used during upsampling"""
# u = UpSampling2D(size=2)(layer_input)
u = Conv2DTranspose(filters,
kernel_size=kernel_size,
strides=strides,
padding='same')(layer_input)
if bn_relu:
u = BatchNormalization(momentum=0.9)(u)
u = Activation('relu')(u)
return u
generator = Dense(16 * self.gf * self.img_size // 16 * self.img_size //
16,
activation="relu")(noise)
generator = Reshape((self.img_size // 16, self.img_size // 16,
self.gf * 16))(generator)
generator = BatchNormalization()(generator)
generator = Activation('relu')(generator)
generator = deconv2d(generator, filters=self.gf * 8)
generator = deconv2d(generator, filters=self.gf * 4)
generator = deconv2d(generator, filters=self.gf * 2)
generator = deconv2d(generator, filters=self.gf)
generator = deconv2d(generator,
filters=self.channels,
kernel_size=(3, 3),
strides=(1, 1),
bn_relu=False)
gen_img = Activation('tanh')(generator)
return Model(noise, gen_img)
def build_discriminator(self):
def d_block(layer_input, filters, strides=1, bn=True):
"""Discriminator layer"""
d = Conv2D(filters, kernel_size=3, strides=strides,
padding='same')(layer_input)
if bn:
d = BatchNormalization(momentum=0.9)(d)
d = LeakyReLU(alpha=0.2)(d)
return d
# Input img = generated image
d0 = Input(shape=(self.img_size, self.img_size, self.channels))
d = d_block(d0, self.df, strides=2, bn=False)
d = d_block(d, self.df * 2, strides=2)
d = d_block(d, self.df * 4, strides=2)
d = d_block(d, self.df * 8, strides=2)
d = Flatten()(d)
validity = Dense(1, activation='sigmoid')(d)
return Model(d0, validity)
def train(self, epochs, batch_size, sample_interval):
def named_logs(model, logs):
result = {}
for l in zip(model.metrics_names, logs):
result[l[0]] = l[1]
return result
start_time = datetime.datetime.now()
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
max_iter = int(self.n_data / batch_size)
os.makedirs('./logs/%s' % self.time, exist_ok=True)
tensorboard = TensorBoard('./logs/%s' % self.time)
tensorboard.set_model(self.generator)
os.makedirs('models/%s' % self.time, exist_ok=True)
with open('models/%s/%s_architecture.json' % (self.time, 'generator'),
'w') as f:
f.write(self.generator.to_json())
print(
"\nbatch size : %d | num_data : %d | max iteration : %d | time : %s \n"
% (batch_size, self.n_data, max_iter, self.time))
for epoch in range(1, epochs + 1):
for iter in range(max_iter):
# ------------------
# Train Generator
# ------------------
ref_imgs = self.data_loader.load_data(batch_size)
noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
gen_imgs = self.generator.predict(noise)
make_trainable(self.discriminator, True)
d_loss_real = self.discriminator.train_on_batch(
ref_imgs, valid * 0.9) # label smoothing
d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
make_trainable(self.discriminator, False)
logs = self.combined.train_on_batch([noise], [valid])
tensorboard.on_epoch_end(iter,
named_logs(self.combined, [logs]))
if iter % (sample_interval // 10) == 0:
elapsed_time = datetime.datetime.now() - start_time
print(
"epoch:%d | iter : %d / %d | time : %10s | g_loss : %15s | d_loss : %s "
% (epoch, iter, max_iter, elapsed_time, logs, d_loss))
if (iter + 1) % sample_interval == 0:
self.sample_images(epoch, iter + 1)
# save weights after every epoch
self.generator.save_weights('models/%s/%s_epoch%d_weights.h5' %
(self.time, 'generator', epoch))
def sample_images(self, epoch, iter):
os.makedirs('samples/%s' % self.time, exist_ok=True)
r, c = 5, 5
noise = np.random.normal(0, 1, (r * c, self.latent_dim))
gen_img = self.generator.predict(noise)
# Rescale images 0 - 1
gen_img = 0.5 * gen_img + 0.5
# Save generated images and the high resolution originals
fig, axs = plt.subplots(r, c)
for row in range(r):
for col in range(c):
axs[row, col].imshow(gen_img[5 * row + col])
axs[row, col].axis('off')
fig.savefig("samples/%s/e%d-i%d.png" % (self.time, epoch, iter),
bbox_inches='tight',
dpi=100)
plt.close()
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from utils import Utils as U, DataLoader as D
from Hybird_tVAE import Hybird_tVAE
flags = tf.app.flags
# data_loader
# data_loader = D.DataLoader(D.Vocab('europarl_hybird_tvae', D.Level.CHAR))
data_loader = D.DataLoader(D.Vocab('train128', D.Level.CHAR))
# hybird_tvae config
flags.DEFINE_string('model_name', 'Hybird_tVAE', '')
flags.DEFINE_string('ckpt_path', './results/Hybird_tVAE/ckpt_f5_1/', '')
flags.DEFINE_string('logs_path', './results/Hybird_tVAE/logs_f5_1/', '')
flags.DEFINE_integer('batch_size', 32, '')
flags.DEFINE_integer('steps',
U.epoch_to_step(5, data_loader.train_size, batch_size=64),
'')
flags.DEFINE_integer('lr', 0.001, 'learning rate')
flags.DEFINE_integer('z_size', 32, '')
flags.DEFINE_integer('seq_len', 129, '')
flags.DEFINE_integer('rnn_num', 2, '')
flags.DEFINE_integer('embed_size', 80, '')
flags.DEFINE_integer('vocab_size', data_loader.vocab_size, '')
def sample(self, sess):
def get_pi_idx(x, pdf):
N = pdf.size
accumulate = 0
for i in range(0, N):
accumulate += pdf[i]
if (accumulate >= x):
return i
print('error with sampling ensemble')
return -1
# def sample_gaussian_2d(mu1, mu2, s1, s2, rho):
# mean = [mu1, mu2]
# cov = [[s1*s1, rho*s1*s2], [rho*s1*s2, s2*s2]]
# x = np.random.multivariate_normal(mean, cov, 1)
# return x[0][0], x[0][1]
def sample_gaussian_2d(mu, s):
mean = mu
sigma = s
x = np.random.normal(mean, sigma)
print(x.shape)
return x
prev_x = np.random.rand(1, 90, 3 * 25)
prev_state = sess.run(self.cell.zero_state(1, tf.float32))
dance = np.zeros((self.args.seq_length * 4, 3 * 25), dtype=np.float32)
mixture_params = []
data_loader = DataLoader('data', 'data_files.list', 'output_body.h5')
mini, maxi = data_loader.load_preprocessed('true')
for i in range(self.args.seq_length * 4):
feed = {self.input_data: prev_x, self.state_in: prev_state}
[o_pi, o_mu, o_sigma, next_state
] = sess.run([self.pi, self.mu, self.sigma, self.state_out], feed)
idx = get_pi_idx(random.random(), o_pi[0])
next_x1 = sample_gaussian_2d(o_mu[0, :, idx], o_sigma[0, :, idx])
dance[i, :] = next_x1
params = [o_pi[0], o_mu[0], o_sigma[0]]
mixture_params.append(params)
prev_x = np.zeros((1, 90, 3 * 25), dtype=np.float32)
prev_x[0, 0:] = np.array([next_x1], dtype=np.float32)
prev_state = next_state
if i == 355:
# archivo = open("archivo.txt","w")
# archivo.write(str((dance)))
# archivo.close()
for j, sequence in enumerate(dance):
print('Maxi,mini: ')
print(maxi)
print(mini)
dance[j] = sequence * (maxi - mini) + mini
np.savetxt('fooTODO.csv', dance, delimiter=",")
return dance, mixture_params
from __future__ import division
from __future__ import print_function
from utils import DataLoader
import numpy as np
from sklearn import metrics
import pandas
import tensorflow as tf
from tensorflow.contrib import learn
### Training data
print("LOAD")
loader = DataLoader('data/tweets',5,144)
x,y = loader.get_xy()
split = int(len(x)*0.8)
X_train,X_test = x[:split],x[split:]
y_train,y_test = pandas.Series(y[:split]),pandas.Series(y[split:])
y_train = y_train.convert_objects(convert_numeric=True)
y_test = y_test.convert_objects(convert_numeric=True)
### Process vocabulary
MAX_DOCUMENT_LENGTH = 144
print("PREPROCESS")
char_processor = learn.preprocessing.ByteProcessor(MAX_DOCUMENT_LENGTH)
X_train = np.array(list(char_processor.fit_transform(X_train)))
X_test = np.array(list(char_processor.transform(X_test)))
def train(args):
data_loader = DataLoader(args.batch_size, args.data_scale,
args.bptt_length)
data_loader.reset_batch_pointer()
# model needs to know the dim of one-hot vectors
args.char_vec_dim = data_loader.char_vec_dim
# also the max length of char vector
args.max_char_len = data_loader.max_char_len
if args.model_dir != '' and not os.path.exists(args.model_dir):
os.makedirs(args.model_dir)
with open(os.path.join(args.model_dir, 'config.pkl'), 'wb') as f:
pickle.dump(args, f)
print("hyperparam. saved.")
model = Model(args)
# training
with tf.Session() as sess:
tf.global_variables_initializer().run()
saver = tf.train.Saver()
if args.load_model is not None:
saver.restore(sess, args.load_model)
_, ep_start = args.load_model.rsplit("-", 1)
ep_start = int(ep_start)
model_steps = int(ep_start * data_loader.num_batches)
else:
ep_start = 0
model_steps = last_model_steps = 0
last_time = time.time()
for ep in range(ep_start, args.num_epochs):
ep_loss = []
sess.run(
tf.assign(model.lr,
args.learning_rate * (args.decay_rate**ep)))
for i in range(int(data_loader.num_sequences / args.batch_size)):
idx = ep * data_loader.num_sequences + i * args.batch_size
start = time.time()
x, y, w, c = data_loader.next_batch()
loss_list, model_steps = model.train(
sess=sess,
sequence=x,
targets=y,
weights=w,
texts=c,
subseq_length=args.bptt_length,
step_count=model_steps)
ep_loss += loss_list
if model_steps - last_model_steps >= 100:
last_model_steps = model_steps
new_time = time.time()
print(
"Sequence %d/%d (epoch %d), batch %d, train_loss = %.3f, time/(100*batch) = %.3f"
% (idx, args.num_epochs * data_loader.num_sequences,
ep, model_steps, np.mean(loss_list),
new_time - last_time),
flush=True)
last_time = new_time
print("Epoch %d completed, average train loss %.6f" %
(ep, np.mean(ep_loss)))
if not os.path.isdir(args.model_dir):
os.makedirs(args.model_dir)
if (ep + 1) % args.save_every == 0:
checkpoint_path = os.path.join(args.model_dir, 'model.ckpt')
saver.save(sess,
save_path=checkpoint_path,
global_step=(ep + 1))
print("model saved.")