def degree_vaccination(infile, outfile, k):
"""
Change the state (to VACCINATED) of the top k (%) highly-connected
individuals in population contained in infile, and save the altered
population into outfile.
"""
fh = open(infile, "r")
params = pickle.load(fh)
population = pickle.load(fh)
fh.close()
net = network.build_network(population, params["network_topology"],
params["network_params"])
L = networkx.degree_centrality(net.get_structure()).items()
L = sorted(L, key=operator.itemgetter(1), reverse=True)
n = int(k / 100.0 * len(population))
for i in range(0, n):
person = population[L[i][0]]
person.set_state(Player.VACCINATED)
sl = person.get_state_list()
sl[len(sl) - 1] = Player.VACCINATED
person.set_infection(0.0)
il = person.get_infection_list()
il[len(il) - 1] = 0.0
person.set_c(0.0)
cl = person.get_c_list()
cl[len(cl) - 1] = 0.0
fh = open(outfile, "w")
pickle.dump(params, fh)
pickle.dump(population, fh)
fh.close()
def degree_vaccination(infile, outfile, k):
"""
Change the state (to VACCINATED) of the top k (%) highly-connected
individuals in population contained in infile, and save the altered
population into outfile.
"""
fh = open(infile, "r")
params = pickle.load(fh)
population = pickle.load(fh)
fh.close()
net = network.build_network(population, params["network_topology"],
params["network_params"])
L = networkx.degree_centrality(net.get_structure()).items()
L = sorted(L, key = operator.itemgetter(1), reverse = True)
n = int(k / 100.0 * len(population))
for i in range(0, n):
person = population[L[i][0]]
person.set_state(Player.VACCINATED)
sl = person.get_state_list()
sl[len(sl) - 1] = Player.VACCINATED
person.set_infection(0.0)
il = person.get_infection_list()
il[len(il) - 1] = 0.0
person.set_c(0.0)
cl = person.get_c_list()
cl[len(cl) - 1] = 0.0
fh = open(outfile, "w")
pickle.dump(params, fh)
pickle.dump(population, fh)
fh.close()
def random_vaccination(infile, outfile, k):
"""
Change the state (to VACCINATED) of k (%) individuals in population
contained in infile, and save the altered population into outfile. Each of
these individuals is randomly picked.
"""
fh = open(infile, "r")
params = pickle.load(fh)
population = pickle.load(fh)
fh.close()
net = network.build_network(population, params["network_topology"],
params["network_params"])
m = int(k / 100.0 * len(population))
i = 0
while i < m:
person = net.get_random_vertex()
if person == None or person.get_state() == Player.VACCINATED:
continue
person.set_state(Player.VACCINATED)
sl = person.get_state_list()
sl[len(sl) - 1] = Player.VACCINATED
person.set_infection(0.0)
il = person.get_infection_list()
il[len(il) - 1] = 0.0
person.set_c(0.0)
cl = person.get_c_list()
cl[len(cl) - 1] = 0.0
i += 1
fh = open(outfile, "w")
pickle.dump(params, fh)
pickle.dump(population, fh)
fh.close()
def random_vaccination(infile, outfile, k):
"""
Change the state (to VACCINATED) of k (%) individuals in population
contained in infile, and save the altered population into outfile. Each of
these individuals is randomly picked.
"""
fh = open(infile, "r")
params = pickle.load(fh)
population = pickle.load(fh)
fh.close()
net = network.build_network(population, params["network_topology"],
params["network_params"])
m = int(k / 100.0 * len(population))
i = 0
while i < m:
person = net.get_random_vertex()
if person == None or person.get_state() == Player.VACCINATED:
continue
person.set_state(Player.VACCINATED)
sl = person.get_state_list()
sl[len(sl) - 1] = Player.VACCINATED
person.set_infection(0.0)
il = person.get_infection_list()
il[len(il) - 1] = 0.0
person.set_c(0.0)
cl = person.get_c_list()
cl[len(cl) - 1] = 0.0
i += 1
fh = open(outfile, "w")
pickle.dump(params, fh)
pickle.dump(population, fh)
fh.close()
def train(args, params):
print("Loading training set...")
train = load.load_dataset(params['train'])
print("Loading dev set...")
dev = load.load_dataset(params['dev'])
print("Building preprocessor...")
preproc = load.Preproc(*train)
print("train_set_classes:", preproc.classes)
print("Training size: " + str(len(train[0])) + " examples.")
print("Dev size: " + str(len(dev[0])) + " examples.")
save_dir = make_save_dir(params['save_dir'], args.experiment)
util.save(preproc, save_dir)
params.update({
"input_shape": [None, 1],
"num_categories": len(preproc.classes)
})
model = network.build_network(**params)
stopping = keras.callbacks.EarlyStopping(patience=30)
reduce_lr = keras.callbacks.ReduceLROnPlateau(
factor=0.1, patience=2, min_lr=params["learning_rate"] * 0.001)
checkpointer = keras.callbacks.ModelCheckpoint(
filepath=get_filename_for_saving(save_dir), save_best_only=False)
batch_size = params.get("batch_size", 32)
# summary = str(model.summary(print_fn=lambda x: fh.write(x + '\n')))
# out = open("/content/ecg/report.txt",'w')
# out.write(summary)
# out.close
if params.get("generator", False):
train_gen = load.data_generator(batch_size, preproc, *train)
dev_gen = load.data_generator(batch_size, preproc, *dev)
model.fit_generator(train_gen,
steps_per_epoch=int(len(train[0]) / batch_size),
epochs=MAX_EPOCHS,
validation_data=dev_gen,
validation_steps=int(len(dev[0]) / batch_size),
callbacks=[checkpointer, reduce_lr, stopping])
# util.learning_curve(history)
else:
train_x, train_y = preproc.process(*train)
dev_x, dev_y = preproc.process(*dev)
model.fit(train_x,
train_y,
batch_size=batch_size,
epochs=MAX_EPOCHS,
validation_data=(dev_x, dev_y),
callbacks=[checkpointer, reduce_lr, stopping])
def main(restored_model=None, test_model=None, phase='training'):
source_train_list = _read_lists(source_train_fid)
source_val_list = _read_lists(source_val_fid)
target_train_list = _read_lists(target_train_fid)
target_val_list = _read_lists(target_val_fid)
network = build_network(channels=3,
n_class=5,
batch_size=1,
cost_kwargs=cost_kwargs,
network_config=network_config,
opt_kwargs=opt_kwargs)
logging.info("Network built")
trainer = Trainer(net = network,\
source_train_list = source_train_list,\
source_val_list = source_val_list,\
target_train_list = target_train_list,\
target_val_list = target_val_list, \
output_path = output_path, \
opt_kwargs = opt_kwargs,\
num_epochs = 750,\
checkpoint_space = 300)
# start tensorboard before getting started
command2 = "fuser " + opt_kwargs["port"] + "/tcp -k"
os.system(command2)
command1 = "tensorboard --logdir=" + output_path + " --port=" + opt_kwargs[
"port"] + " &"
os.system(command1)
if phase == 'training':
trainer.train_segmenter(restored_model=restored_model)
if phase == 'testing':
# here are for the testing phase
test_list_fid = "/home/chenxi/dlummkd/test_mr_list"
test_nii_list_fid = "/home/chenxi/dlummkd/test_mr_nii_list"
part = "source"
logging.info('performance on source ...')
source_dice = trainer.test(test_model=test_model,
part=part,
test_list_fid=test_list_fid,
test_nii_list_fid=test_nii_list_fid)
test_list_fid = "/home/chenxi/dlummkd/test_ct_list"
test_nii_list_fid = "/home/chenxi/dlummkd/test_ct_nii_list"
part = "target"
logging.info('performance on target ...')
target_dice = trainer.test(test_model=test_model,
part=part,
test_list_fid=test_list_fid,
test_nii_list_fid=test_nii_list_fid)
return source_dice, target_dice
def static_nem_iterations(input_data, target_data, binary, k):
# Get dimensions
input_shape = tf.shape(input_data)
assert input_shape.get_shape()[0].value == 6, "Requires 6D input (T, B, K, W, H, C) but {}".format(input_shape.get_shape()[0].value)
W, H, C = (x.value for x in input_data.get_shape()[-3:])
# set pixel distribution
pixel_dist = 'bernoulli' if binary else 'gaussian'
# set up inner cells and nem cells
inner_cell = build_network(W * H * C, output_dist=pixel_dist)
nem_cell = NEMCell(inner_cell, input_shape=(W, H, C), distribution=pixel_dist)
# compute prior
prior = compute_prior(distribution=pixel_dist)
# get state initializer
with tf.name_scope('initial_state'):
hidden_state = nem_cell.init_state(input_shape[1], k, dtype=tf.float32)
# build static iterations
outputs = [hidden_state]
total_losses, upper_bound_losses, other_losses = [], [], []
loss_step_weights = get_loss_step_weights()
with tf.variable_scope('NEM') as varscope:
for t, loss_weight in enumerate(loss_step_weights):
varscope.reuse_variables() if t > 0 else None # share weights across time
with tf.name_scope('step_{}'.format(t)):
# run nem cell
inputs = (input_data[t], target_data[t+1])
hidden_state, output = nem_cell(inputs, hidden_state)
theta, pred, gamma = output
# compute nem losses
total_loss, intra_loss, inter_loss = compute_outer_loss(
pred, gamma, target_data[t+1], prior, pixel_distribution=pixel_dist)
# compute estimated loss upper bound (which doesn't use E-step)
loss_upper_bound = compute_loss_upper_bound(pred, target_data[t+1], pixel_dist)
total_losses.append(loss_weight * total_loss)
upper_bound_losses.append(loss_upper_bound)
other_losses.append(tf.stack([total_loss, intra_loss, inter_loss]))
outputs.append(output)
# collect outputs
with tf.name_scope('collect_outputs'):
thetas, preds, gammas = zip(*outputs)
thetas = tf.stack(thetas) # (T, 1, B*K, M)
preds = tf.stack(preds) # (T, B, K, W, H, C)
gammas = tf.stack(gammas) # (T, B, K, W, H, C)
other_losses = tf.stack(other_losses) # (T, 3)
upper_bound_losses = tf.stack(upper_bound_losses) # (T,)
with tf.name_scope('total_loss'):
total_loss = tf.reduce_sum(tf.stack(total_losses))
return total_loss, thetas, preds, gammas, other_losses, upper_bound_losses
def __init__(self, board_state, batch_size=256):
'''
Constructor for DQNAgent
Input: board_state, (for getting the input_shape of teh network we will train)
'''
num_cols = board_state.shape[-1]
self.n_actions = num_cols
self.net = build_network(input_shape=board_state.shape,
num_outputs=self.n_actions)
self.target_net = build_network(input_shape=board_state.shape,
num_outputs=self.n_actions)
self.target_net.set_weights(self.net.get_weights())
self.net_optimizer = tf.train.AdamOptimizer()
self.memory = Memory(batch_size)
self.curr_obs = None
self.curr_action = None
def _build_graph(self):
encoder = build_network(inputs=self.datapipe.next_element,
model_specs=self.network_specs['encoder'],
latent_dim=self.latent_dim,
name='encoder')
self.mu, self.logvar = tf.split(encoder,
[self.latent_dim, self.latent_dim],
axis=1)
self.z_samples = misc.sampler_normal(self.mu, self.logvar)
self._build_decoder(self.z_samples)
def train(args, params):
print("Loading training set...")
train = load.load_dataset(params['train'])
print("Loading dev set...")
dev = load.load_dataset(params['dev'])
print("Building preprocessor...")
preproc = load.Preproc(*train)
print("Training size: " + str(len(train[0])) + " examples.")
print("Dev size: " + str(len(dev[0])) + " examples.")
save_dir = make_save_dir(params['save_dir'], args.experiment)
util.save(preproc, save_dir)
params.update({
"input_shape": [None, 1],
"num_categories": len(preproc.classes)
})
print(params)
model = network.build_network(**params)
stopping = keras.callbacks.EarlyStopping(patience=8)
reduce_lr = keras.callbacks.ReduceLROnPlateau(
factor=0.1, patience=2, min_lr=params["learning_rate"] * 0.001)
checkpointer = keras.callbacks.ModelCheckpoint(
filepath=get_filename_for_saving(save_dir), save_best_only=False)
ckpt_best = keras.callbacks.ModelCheckpoint(os.path.join(
save_dir, 'best.hdf5'),
save_best_only=True)
batch_size = params.get("batch_size", 32)
if params.get("generator", False):
train_gen = load.data_generator(batch_size, preproc, *train)
dev_gen = load.data_generator(batch_size, preproc, *dev)
model.fit_generator(
train_gen,
steps_per_epoch=int(len(train[0]) / batch_size),
epochs=MAX_EPOCHS,
validation_data=dev_gen,
validation_steps=int(len(dev[0]) / batch_size),
callbacks=[checkpointer, ckpt_best, reduce_lr, stopping])
else:
train_x, train_y = preproc.process(*train)
dev_x, dev_y = preproc.process(*dev)
model.fit(train_x,
train_y,
batch_size=batch_size,
epochs=MAX_EPOCHS,
validation_data=(dev_x, dev_y),
callbacks=[checkpointer, ckpt_best, reduce_lr, stopping])
def train(args, params):
print("Loading training set...")
train = load.load_dataset(params['train'])
print("Loading dev set...")
dev = load.load_dataset(params['dev'])
print("Building preprocessor...")
preproc = load.Preproc(*train)
print("Training size: " + str(len(train[0])) + " examples.")
print("Dev size: " + str(len(dev[0])) + " examples.")
save_dir = make_save_dir(params['save_dir'], args.experiment)
util.save(preproc, save_dir)
params.update({
"input_shape": [None, 1],
"num_categories": len(preproc.classes)
})
model = network.build_network(**params)
stopping = keras.callbacks.EarlyStopping(patience=8)
reduce_lr = keras.callbacks.ReduceLROnPlateau(
factor=0.1,
patience=2,
min_lr=params["learning_rate"] * 0.001)
checkpointer = keras.callbacks.ModelCheckpoint(
filepath=get_filename_for_saving(save_dir),
save_best_only=False)
batch_size = params.get("batch_size", 32)
if params.get("generator", False):
train_gen = load.data_generator(batch_size, preproc, *train)
dev_gen = load.data_generator(batch_size, preproc, *dev)
model.fit_generator(
train_gen,
steps_per_epoch=int(len(train[0]) / batch_size),
epochs=MAX_EPOCHS,
validation_data=dev_gen,
validation_steps=int(len(dev[0]) / batch_size),
callbacks=[checkpointer, reduce_lr, stopping])
else:
train_x, train_y = preproc.process(*train)
dev_x, dev_y = preproc.process(*dev)
model.fit(
train_x, train_y,
batch_size=batch_size,
epochs=MAX_EPOCHS,
validation_data=(dev_x, dev_y),
callbacks=[checkpointer, reduce_lr, stopping])
def _build_encoder(self, inputs):
encoder = build_network(inputs=inputs,
model_specs=self.network_specs['encoder'],
latent_dim=self.network_specs['latent_dim'],
name='encoder')
z_mean, z_logvar = tf.split(encoder, [
self.network_specs['latent_dim'], self.network_specs['latent_dim']
],
axis=1)
z_samples = sampler_normal(z_mean, z_logvar)
return z_mean, z_logvar, z_samples
def _build_decoder(self, inputs):
logits = build_network(inputs=inputs,
model_specs=self.network_specs['decoder'],
num_channel=self.network_specs['num_channel'],
name='decoder')
re_mask, re_image_mean = tf.split(logits, [1, 3], axis=-1)
# i think this should be trained with tf.nn.sigmoid
# because the means should be between 0 and 1
if self.sigmoid_output:
re_image_mean = tf.nn.sigmoid(re_image_mean)
return re_mask, re_image_mean
def main():
communities_t0, number_nodes_t0, nodes_t0 = build_communities()
build_network(communities_t0, number_nodes_t0, nodes_t0)
if write_gephi:
write_links_csv(nodes_t0)
for Ts.timestamp in range(1, TIMESTEPS+1):
communities_t1, number_nodes_t1, nodes_t1 = build_communities()
# initialize confusion matrix with deaths and births
confusion_matrix = events_communities(communities_t0, nodes_t0, communities_t1, nodes_t1, number_nodes_t1)
# find most similar transition
score, global_solution = transition_t0_t1(communities_t0, communities_t1)
# finalize confusion matrix
flow_nodes(communities_t0, communities_t1, nodes_t1, global_solution, confusion_matrix)
match_communities(communities_t0, communities_t1, confusion_matrix, sigma, jaccard_null_model)
build_network(communities_t1, number_nodes_t1, nodes_t1)
if write_gephi:
write_links_csv(nodes_t1)
printouts(confusion_matrix, score, communities_t0, communities_t1)
communities_t0, nodes_t0 = turnover_communities(communities_t1, nodes_t1)
# [node.reset_node_links() for node in nodes_t1]
if write_gephi:
write_nodes_csv()
if write_netgram:
write_nodes_netgram()
def mypredict(args):
# ecg = load.load_ecg(record +".mat")
if args.data_fname[-3:] == "npy":
x_test = np.load(args.data_fname)
elif args.data_fname[-3:] == "csv":
x_test = pd.read_csv(args.data_fname)
x_test.drop(['id'], axis=1)
x_test = x_test.fillna(0)
x_test = x_test.values
np.save(args.data_fname[:-4], x_test)
print("data loading done")
x_test_list = []
for i in range(len(x_test)):
x_i_end = x_test[i].nonzero()[0][-1] + 1
x_i_end = STEP * int((x_i_end) / STEP)
# print(x_i_end)
x_test_list.append(x_test[i][:x_i_end])
preproc = util.load(args.prep_fname)
x_test = preproc.process_x(x_test_list)
params = json.load(open(args.config_fname))
params.update({
"compile": False,
"input_shape": [None, 1],
"num_categories": len(preproc.classes)
})
model = network.build_network(**params)
model.load_weights(args.model_fname)
# print(x_test.shape)
probs = model.predict(x_test)
preds = np.argmax(probs, axis=2)
results = -np.ones((len(x_test), ), dtype=np.int64)
for i in range(len(x_test)):
# print(len(x_test_list[i])//STEP)
# print(preds[i, :len(x_test_list[i])//STEP])
# print(sst.mode(preds[i, :len(x_test_list[i])//STEP])[0][0])
results[i] = sst.mode(preds[i, :len(x_test_list[i]) // STEP])[0][0]
# print(probs.shape)
# prediction = sst.mode(np.argmax(probs, axis=2).squeeze())[0][0]
print(Counter(results))
sample = pd.read_csv(args.sample_fname)
sample['y'] = results
sample.to_csv(args.submit_fname, index=False)
return
def __init__(self, inputSimulator, preprocessor, pretrained):
self.iS = inputSimulator
self.pP = preprocessor
self.input_var = T.tensor4('input')
self.network = build_network(6, self.input_var, (20,20))
self.prediction = lasagne.layers.get_output(self.network, deterministic=True)
self.x = np.zeros((1,4,20,20),np.int8)
if pretrained:
self.loadParams()
print("Compiling network")
self.pred_fn = theano.function([self.input_var], self.prediction, allow_input_downcast=True)
print("Compiling done")
def __init__(self, W, H, n_input, lr, n_h1=25, n_h2=20, l2=10, name='deep_irl_fc'):
self.n_input = n_input
self.lr = lr
self.n_h1 = n_h1
self.n_h2 = n_h2
self.name = name
self.networks = build_network(W, H)
# self.network = self.network.cuda()
self.finetune(allow=True)
# self.sess = tf.Session()
# self.input_s, self.reward, self.theta = self.build_network(self.name)
self.optimizer = optim.SGD(self.networks.parameters(), lr=0.001)
# self.optimizer = tf.train.GradientDescentOptimizer(lr)
self.l2_loss = nn.MSELoss()
def predict(record):
ecg = load.load_ecg(record +".mat")
preproc = util.load(".")
x = preproc.process_x([ecg])
params = json.load(open("config.json"))
params.update({
"compile" : False,
"input_shape": [None, 1],
"num_categories": len(preproc.classes)
})
model = network.build_network(**params)
model.load_weights('model.hdf5')
probs = model.predict(x)
prediction = sst.mode(np.argmax(probs, axis=2).squeeze())[0][0]
return preproc.int_to_class[prediction]
def train(session, dataset):
network = network_mod.build_network(dataset.image_shape,
dataset.num_classes)
session.run(tf.global_variables_initializer())
file_writer = tf.summary.FileWriter("logs/", graph=session.graph)
saver = tf.train.Saver()
ts = TrainingSession(
session=session,
network=network,
dataset=dataset,
file_writer=file_writer,
saver=saver,
learning_rate=config.INITIAL_LEARNING_RATE,
)
for epoch_idx in range(1, config.NUM_EPOCHS + 1):
ts = train_epoch(ts, epoch_idx)
def mylimepredict(ecg):
preproc = util.load(".")
x = preproc.process_x(ecg)
params = json.load(open("config.json"))
params.update({
"compile": False,
"input_shape": [None, 1],
"num_categories": len(preproc.classes)
})
model = network.build_network(**params)
model.load_weights('model.hdf5')
probs = model.predict(x)
classprobs = np.amax(probs, axis=1)
prediction = sst.mode(np.argmax(probs, axis=2).squeeze())[0][0]
# predictionclass = preproc.int_to_class[prediction]
# print prediction, predictionclass
return classprobs
train_word_y = np.expand_dims(data["train_word"][1], -1)
train_orth_char_x = data["orth_char"][0]
train_orth_x = data["train_orth"]
n_train_examples = len(train_word_x)
dev_char_x = data["char"][1]
dev_word_x = data["dev_word"][0]
dev_word_y = data["dev_word"][1]
dev_mask = data["dev_word"][2]
dev_orth_char_x = data["orth_char"][1]
dev_orth_x = data["dev_orth"]
n_dev_examples = len(dev_word_x)
# Build network
logger.info("Building network")
model = network.build_network(args, data)
num_labels = data["label_collection"].size() - 1
# Compiling model
logger.info("Compiling model")
model.compile(optimizer="adam",
loss=model.layers[-1].loss,
metrics=["accuracy"])
# Callbacks
loss_train = []
acc_train = []
loss_history = keras.callbacks.LambdaCallback(
on_epoch_end=lambda epoch, logs: loss_train.append(logs['loss']))
acc_history = keras.callbacks.LambdaCallback(
on_epoch_end=lambda epoch, logs: acc_train.append(logs['acc'] * 100.0))
def test():
tf.reset_default_graph()
infer_graph = tf.Graph()
with infer_graph.as_default():
encoder_outputs_t, inputs_t = build_cnn(False, flags.batch_size,
flags.height, flags.width,
flags.channels)
_, _, pred_ids, logits_t, decoder_inputs_t, \
_, _ ,keep_prob_t= build_network(encoder_outputs_t,
True,
flags.batch_size,
flags.decoder_length,
flags.tgt_vocab_size,
flags.attn_num_hidden,
flags.encoder_length,
flags.max_gradient_norm
)
infer_saver = tf.train.Saver()
infer_sess = tf.Session(graph=infer_graph)
model_file = tf.train.latest_checkpoint(flags.load_dir)
infer_saver.restore(infer_sess, model_file)
with open(flags.test_txt) as f:
test = [line.rstrip() for line in f]
test_len = len(test)
test = np.array(test)
data_test = Dataset(test)
if flags.lex_txt != None:
with open(flags.lex_txt) as f:
lex = [line.rstrip().lower() for line in f]
ti = int(test_len / flags.batch_size)
rest = test_len % flags.batch_size
gt = []
predict = []
for t in range(ti):
batch_test = data_test.next_batch(flags.batch_size)
path = []
texts = []
for line in batch_test:
path.append(line.split(' ', 1)[0])
texts.append(line.split(' ', 1)[1])
images = load_img(path, flags.height, flags.width)
testing_decoder_inputs = np.zeros(
(flags.decoder_length, flags.batch_size), dtype=float)
feed_dict_t = {
inputs_t: images[:, :, :, np.newaxis],
decoder_inputs_t: testing_decoder_inputs,
keep_prob_t: 1
}
q = infer_sess.run(pred_ids, feed_dict=feed_dict_t)
for j in range(flags.batch_size):
gt.append(texts[j])
ans = np.array(q).T[j]
pd = []
for c in ans:
if c != -1:
character = tools.idx_to_word[c]
if character != '<EOS>':
pd.append(character)
predict.append(''.join(pd))
batch_test = data_test.next_batch(flags.batch_size)
path = []
texts = []
for line in batch_test:
path.append(line.split(' ', 1)[0])
texts.append(line.split(' ', 1)[1])
images = load_img(path, flags.height, flags.width)
feed_dict_t = {
inputs_t: images[:, :, :, np.newaxis],
decoder_inputs_t: testing_decoder_inputs,
keep_prob_t: 1
}
q = infer_sess.run(pred_ids, feed_dict=feed_dict_t)
for k in range(rest):
gt.append(texts[k])
ans = np.array(q).T[k]
pd = []
for c in ans:
if c != -1:
character = tools.idx_to_word[c]
if character != '<EOS>':
pd.append(character)
predict.append(''.join(pd))
correct = float(0)
cnt = 0
acc_s = 0
for l in range(len(gt)):
cnt = cnt + 1
if gt[l] == predict[l]:
correct = correct + 1
acc_s = correct / cnt
if flags.lex_txt != None:
correct_l = float(0)
cnt = 0
for l in range(len(gt)):
cnt = cnt + 1
lexicon = lex[l].split(',')
dt = editdistance.eval(predict[l], lexicon[0])
pl = lexicon[0]
for ll in lexicon[1:]:
dt_temp = editdistance.eval(predict[l], ll)
if dt_temp < dt:
dt = dt_temp
pl = ll
if pl == gt[l]:
correct_l = correct_l + 1
acc_l = correct_l / cnt
print('accuracy: ', acc_s)
if flags.lex_txt != None:
print('accuracy with lexicon: ', acc_l)
def train():
f_size = int(flags.img_size / 8)
encoder_length = f_size * f_size
with open(flags.train_txt) as f:
sample = [line.rstrip() for line in f]
sample = np.array(sample)
iteration = len(sample) // flags.batch_size
data = Dataset(sample)
tf.reset_default_graph()
train_graph = tf.Graph()
infer_graph = tf.Graph()
start = time.time()
with train_graph.as_default():
c, inputs = build_cnn(is_training=True,
batch_size=flags.batch_size,
img_size=flags.img_size,
channels=flags.channels)
deconv_outputs = build_deconv(True, c, flags.batch_size)
x = np.linspace(-0.5, 0.5, f_size)
x = np.tile(x, (f_size, 1))
y = np.transpose(x)
x = np.expand_dims(x, axis=2)
y = np.expand_dims(y, axis=2)
m = np.concatenate((x, y), axis=2)
m = np.expand_dims(m, axis=0)
m = np.repeat(m, flags.batch_size, axis=0)
m = tf.convert_to_tensor(m, np.float32)
encoder_outputs = tf.concat([c, m], -1)
encoder_outputs = tf.reshape(encoder_outputs,
shape=(-1, f_size * f_size, 258))
encoder_outputs = tf.transpose(encoder_outputs, [1, 0, 2])
train_op, loss , sample_ids,logits, decoder_inputs, \
target_labels, learning_rate,attention_weights_history,att_label,lamda,att_mask,input_seg= build_network(encoder_outputs,
False,
flags.batch_size,
flags.decoder_length,
flags.tgt_vocab_size,
flags.attn_num_hidden,
encoder_length,
flags.max_gradient_norm,
f_size,
flags.att_loss,
flags.img_size,
deconv_outputs
)
initializer = tf.global_variables_initializer()
train_saver = tf.train.Saver()
train_sess = tf.Session(graph=train_graph)
train_sess.run(initializer)
with infer_graph.as_default():
c_t, inputs_t = build_cnn(is_training=False,
batch_size=flags.batch_size,
img_size=flags.img_size,
channels=flags.channels)
deconv_outputs_t = build_deconv(False, c_t, flags.batch_size)
x_t = np.linspace(-0.5, 0.5, f_size)
x_t = np.tile(x_t, (f_size, 1))
y_t = np.transpose(x_t)
x_t = np.expand_dims(x_t, axis=2)
y_t = np.expand_dims(y_t, axis=2)
m_t = np.concatenate((x_t, y_t), axis=2)
m_t = np.expand_dims(m_t, axis=0)
m_t = np.repeat(m_t, flags.batch_size, axis=0)
m_t = tf.convert_to_tensor(m_t, np.float32)
encoder_outputs_t = tf.concat([c_t, m_t], -1)
encoder_outputs_t = tf.reshape(encoder_outputs_t,
shape=(-1, f_size * f_size, 258))
encoder_outputs_t = tf.transpose(encoder_outputs_t, [1, 0, 2])
_, _ , pred_ids,logits_t, decoder_inputs_t, \
_, _,_,_,_,_,_= build_network(encoder_outputs_t,
True,
flags.batch_size,
flags.decoder_length,
flags.tgt_vocab_size,
flags.attn_num_hidden,
encoder_length,
flags.max_gradient_norm,
f_size,
flags.att_loss,
flags.img_size,
deconv_outputs_t
)
infer_saver = tf.train.Saver()
infer_sess = tf.Session(graph=infer_graph)
# Training
la = 10
acc_log = 0
count = 0
lr = flags.learning_rate
for h in range(flags.epoch):
for i in range(iteration):
batch_train = data.next_batch(flags.batch_size)
path = []
texts = []
for line in batch_train:
path.append(line.split(' ')[0])
texts.append(line.split(' ')[1])
if flags.att_loss:
images, npy, mask, seg = load_img_label(
path, flags.img_size, flags.decoder_length)
else:
images = load_img(path, flags.img_size)
training_target_labels = get_label(texts, flags.decoder_length)
training_decoder_inputs = np.delete(training_target_labels,
-1,
axis=1)
training_decoder_inputs = np.c_[
np.zeros(training_decoder_inputs.shape[0]),
training_decoder_inputs].T
feed_dict = {
inputs: images,
decoder_inputs: training_decoder_inputs,
target_labels: training_target_labels,
learning_rate: lr
}
if flags.att_loss:
feed_dict[att_label] = npy
feed_dict[att_mask] = mask
feed_dict[input_seg] = seg[:, :, :, np.newaxis]
feed_dict[lamda] = la
_, loss_value, att = train_sess.run(
[train_op, loss, attention_weights_history],
feed_dict=feed_dict)
step = float(i + 1)
if step % flags.display_step == 0:
now = time.time()
print(step, now - start, loss_value)
start = now
if step % flags.eval_step == 0:
train_saver.save(train_sess, flags.save_dir)
model_file = tf.train.latest_checkpoint(
flags.save_dir.rsplit('/', 1)[0])
infer_saver.restore(infer_sess, model_file)
gt = []
predict = []
images = load_img(path, flags.img_size)
testing_decoder_inputs = np.zeros(
(flags.decoder_length, flags.batch_size), dtype=float)
feed_dict_t = {
inputs_t: images,
decoder_inputs_t: testing_decoder_inputs
}
q = infer_sess.run(pred_ids, feed_dict=feed_dict_t)
for j in range(flags.batch_size):
gt.append(texts[j])
ans = np.array(q).T[j]
pd = []
for c in ans:
if c != -1:
character = tools.idx_to_word[c]
if character != '<EOS>':
pd.append(character)
predict.append(''.join(pd))
correct = float(0)
cnt = 0
acc_s = 0
for l in range(len(gt)):
cnt = cnt + 1
if gt[l] == predict[l]:
correct = correct + 1
count = count + 1
acc_s = correct / cnt
if acc_s > acc_log:
acc_log = acc_s
count = 0
if count == (iteration // flags.eval_step):
lr = lr / 5
def train(args, params):
print("Loading training set...")
train = load.load_dataset(params['train'])
print("Loading dev set...")
dev = load.load_dataset(params['dev'])
print("Building preprocessor...")
preproc = load.Preproc(*train)
print("Training size: " + str(len(train[0])) + " examples.")
print("Dev size: " + str(len(dev[0])) + " examples.")
preproc.set_window_size(params['window_size'])
save_dir = make_save_dir(params['save_dir'], args.experiment)
util.save(preproc, save_dir)
print('classes', preproc.classes)
print("Skip Init initial Alpha : {}, L2 : {}, Lr : {}, batch_size : {}".
format(params["skip_init_a"], params["conv_l2"],
params["learning_rate"], params["batch_size"]))
params.update({
"input_shape": [None, 1],
"num_categories": len(preproc.classes)
})
model = network.build_network(**params)
print(model.summary())
if params.get('is_regular_conv', False):
plot_model(model, to_file='model_regular_conv.png', show_shapes=True)
else:
if params.get('conv_batch_norm'):
plot_model(model,
to_file='model_residual_conv_bn.png',
show_shapes=True)
else:
plot_model(model,
to_file='model_residual_conv_nobn.png',
show_shapes=True)
stopping = keras.callbacks.EarlyStopping(patience=8)
reduce_lr = keras.callbacks.ReduceLROnPlateau(
factor=0.1, patience=2, min_lr=params["learning_rate"] * 0.001)
# checkpointer = keras.callbacks.ModelCheckpoint(
# filepath=get_filename_for_saving(save_dir),
# save_best_only=False, save_weights_only=False)
checkpointer = keras.callbacks.ModelCheckpoint(
filepath=get_filename_for_saving(save_dir),
save_weights_only=False,
save_best_only=True,
monitor='val_accuracy',
mode='max',
)
batch_size = params.get("batch_size", 32)
if params.get("generator", False):
train_gen = load.data_generator(batch_size, preproc, *train)
dev_gen = load.data_generator(batch_size, preproc, *dev)
train_gen = np.array(train_gen)
dev_gen = np.array(dev_gen)
# print('train_gen m, s: ', train_gen.mean(), train_gen.std())
# print('train_gen min, max: ', train_gen.min(), train_gen.max())
print('train_gen shape : ', train_gen.shape)
print('dev_gen shape : ', train_gen.shape)
model.fit_generator(train_gen,
steps_per_epoch=int(len(train[0]) / batch_size),
epochs=params.get("epoch", 10),
validation_data=dev_gen,
validation_steps=int(len(dev[0]) / batch_size),
callbacks=[checkpointer, reduce_lr, stopping])
else:
train_x, train_y = preproc.process(*train)
dev_x, dev_y = preproc.process(*dev)
print('train_x original shape : ', train_x.shape)
print('train_y original shape : ', train_y.shape)
print('test_x original shape : ', dev_x.shape)
print('test_y original shape : ', dev_y.shape)
# train_x = np.array(train_x)
# train_y = np.array(train_y)
# test_x = np.array(dev_x)
# test_y = np.array(dev_y)
window_size = 256
n_sample = 40
r_i = 0
r_length = 1
# print('random number : ', np.random.choice(test_x.shape[1] // window_size, 1)[0])
# train_x = train_x[:, 256 * r_i:256 * (r_i+1)* r_length, :]
# train_y = train_y[:, r_i, :]
#
# test_x = test_x[:, 256 * r_i:256 * (r_i+1) * r_length, :]
# test_y = test_y[:, r_i, :]
# train_x = np.array(all_train_x)
# train_y = np.array(all_train_y)
# FOR PLAINTEXT conversion start
# get 8 windows
# train_x = train_x[:, :window_size * n_sample, :]
# train_y = train_y[:, r_i, :]
# test_x = test_x[:, :window_size * n_sample, :]
# test_y = test_y[:, r_i, :]
# test_x = np.array(random_test_x)
# train_x = np.array(train_x)
#
# train_x = np.squeeze(train_x, axis=(2,))
# test_x = np.squeeze(test_x, axis=(2,))
#
# print('train_x m, s: ', train_x.mean(), train_x.std())
# print('train_x min, max: ', train_x.min(), train_x.max())
#
# print('train_x shape : ', train_x.shape)
# print('train_y shape : ', train_y.shape)
# print('test_x shape : ', test_x.shape)
# print('test_y shape : ', test_y.shape)
# gt = np.argmax(test_y, axis=1)
# print('0:', len(gt[gt == 0]))
# print('1:', len(gt[gt == 1]))
# print('2:', len(gt[gt == 2]))
# print('3:', len(gt[gt == 3]))
# print('gt shape:', gt.shape)
# data_dir = 'processed_data_2560'
# data_dir = 'processed_data_5120'
#
# if not os.path.exists(data_dir):
# os.makedirs(data_dir)
# train_file_suffix = 'train'
# test_file_suffix = 'test'
#
# file_name_train_x = 'X{}'.format(train_file_suffix)
# file_name_train_y = 'y{}'.format(train_file_suffix)
# file_name_test_x = 'X{}'.format(test_file_suffix)
# file_name_test_y = 'y{}'.format(test_file_suffix)
#
# np.savetxt('{}/{}'.format(data_dir, file_name_train_x), train_x, delimiter=',', fmt='%1.8f')
# np.savetxt('{}/{}'.format(data_dir, file_name_train_y), train_y, delimiter=',', fmt='%1.8f')
# np.savetxt('{}/{}'.format(data_dir, file_name_test_x), test_x, delimiter=',', fmt='%1.8f')
# np.savetxt('{}/{}'.format(data_dir, file_name_test_y), test_y, delimiter=',', fmt='%1.8f')
# print('train_x shape : ', train_x.shape)
# print('train_y shape : ', train_y.shape)
# print('test_x shape : ', test_x.shape)
# print('test_y shape : ', test_y.shape)
model.fit(train_x,
train_y,
batch_size=batch_size,
epochs=params.get("epoch", 10),
validation_data=(dev_x, dev_y),
callbacks=[checkpointer, reduce_lr, stopping])
def build_model(self, reuse, device):
with tf.variable_scope(self.name) and tf.device(device):
if reuse:
tf.get_variable_scope().reuse_variables()
# placeholder for inputs of network
self.screen_ph = tf.placeholder(tf.float32, [
None,
U.screen_channel(), self.screen_dimensions,
self.screen_dimensions
],
name='screen')
self.minimap_ph = tf.placeholder(tf.float32, [
None,
U.minimap_channel(), self.minimap_dimensions,
self.minimap_dimensions
],
name='minimap')
self.structured_ph = tf.placeholder(
tf.float32, [None, self.structured_dimensions],
name='structured')
# build network
network = build_network(self.structured_ph, self.screen_ph,
self.minimap_ph, len(actions.FUNCTIONS))
self.non_spatial_action, self.spatial_action, self.value = network
# placeholder for targets and masks
self.valid_non_spatial_action_ph = tf.placeholder(
tf.float32, [None, len(actions.FUNCTIONS)],
name='valid_non_spatial_action')
self.sample_non_spatial_action_ph = tf.placeholder(
tf.float32, [None, len(actions.FUNCTIONS)],
name='sample_non_spatial_action')
self.valid_spatial_action_ph = tf.placeholder(
tf.float32, [None], name='valid_spatial_action')
self.sample_spatial_action_ph = tf.placeholder(
tf.float32, [None, self.minimap_dimensions**2],
name='sample_spatial_action')
self.target_value_ph = tf.placeholder(tf.float32, [None],
name='target_value')
# compute log probability
valid_non_spatial_action_prob = tf.reduce_sum(
self.non_spatial_action * self.valid_non_spatial_action_ph,
axis=1)
valid_non_spatial_action_prob = tf.clip_by_value(
valid_non_spatial_action_prob, 1e-10, 1.)
non_spatial_action_prob = tf.reduce_sum(
self.non_spatial_action * self.sample_non_spatial_action_ph,
axis=1)
non_spatial_action_prob /= valid_non_spatial_action_prob
non_spatial_action_log_prob = tf.log(
tf.clip_by_value(non_spatial_action_prob, 1e-10, 1.))
spatial_action_prob = tf.reduce_sum(self.spatial_action *
self.sample_spatial_action_ph,
axis=1)
spatial_action_log_prob = tf.log(
tf.clip_by_value(spatial_action_prob, 1e-10, 1.))
self.summary.append(
tf.summary.histogram('non_spatial_action_prob',
non_spatial_action_prob))
self.summary.append(
tf.summary.histogram('spatial_action_prob',
spatial_action_prob))
# compute loss
action_log_prob = self.valid_spatial_action_ph * spatial_action_log_prob + non_spatial_action_log_prob
advantage = tf.stop_gradient(self.target_value_ph - self.value)
policy_loss = -tf.reduce_mean(action_log_prob * advantage)
value_loss = -tf.reduce_mean(self.value * advantage)
loss = policy_loss + value_loss
self.summary.append(tf.summary.scalar('policy_loss', policy_loss))
self.summary.append(tf.summary.scalar('value_loss', value_loss))
# optimizer
self.learning_rate_ph = tf.placeholder(tf.float32,
None,
name='learning_rate')
optimizer = tf.train.RMSPropOptimizer(self.learning_rate_ph,
decay=0.99,
epsilon=1e-10)
grads = optimizer.compute_gradients(loss)
clipped_grads = []
for grad, var in grads:
self.summary.append(tf.summary.histogram(var.op.name, var))
self.summary.append(
tf.summary.histogram(var.op.name + '/grad', grad))
grad = tf.clip_by_norm(grad, 10.0)
clipped_grads.append([grad, var])
self.train_op = optimizer.apply_gradients(clipped_grads)
self.summary_op = tf.summary.merge(self.summary)
self.saver = tf.train.Saver(max_to_keep=None)
def build_model(self, reuse, device):
with tf.variable_scope(self.name) and tf.device(device):
# tf.device:指定用於新創建的操作的默認設備。
if reuse:
tf.get_variable_scope().reuse_variables()
"""
達到重複利用變量的效果,
要使用tf.variable_scope(),
並搭配tf.get_variable() 這種方式產生和提取變量.
不像tf.Variable() 每次都會產生新的變量,
tf.get_variable () 如果遇到了同樣名字的變量時,
它會單純的提取這個同樣名字的變量(避免產生新變量).
而在重複使用的時候,
一定要在代碼中強調scope.reuse_variables(),
否則係統將會報錯, 以為你只是單純的不小心重複使用到了一個變量
"""
self.screen = tf.placeholder(tf.float32, [None, utils.screen(), self.resolution, self.resolution], name="screen")
self.minimap = tf.placeholder(tf.float32, [None, utils.minimap(), self.resolution, self.resolution], name="minimap")
self.structured = tf.placeholder(tf.float32, [None, self.structured_dimensions], name="structured")
# build network
network = build_network(self.structured, self.screen, self.minimap, len(actions.FUNCTIONS))
self.non_spatial_action, self.spatial_action, self.value = network
self.valid_non_spatial_action = tf.placeholder(tf.float32, [None, len(actions.FUNCTIONS)], name="valid_non_spatial_action")
self.non_spatial_action_selected = tf.placeholder(tf.float32, [None, len(actions.FUNCTIONS)], name="non_spatial_action_selected")
self.valid_spatial_action = tf.placeholder(tf.float32, [None], name="valid_spatial_action")
self.spatial_action_selected = tf.placeholder(tf.float32, [None, self.resolution ** 2], name="spatial_action_selected")
self.target_value = tf.placeholder(tf.float32, [None], name="target_value")
# compute log probability
valid_non_spatial_action_prob = tf.reduce_sum(self.non_spatial_action * self.valid_non_spatial_action, axis=1)
# tf.reduce_sum:計算張量維度的元素總和。
valid_non_spatial_action_prob = tf.clip_by_value(valid_non_spatial_action_prob, 1e-5, 1.)
# tf.clip_by_value: 輸入一個張量A，把A中的每一個元素的值都壓縮在min和max之間。小於min的讓它等於min，大於max的元素的值等於max。
non_spatial_action_prob = tf.reduce_sum(self.non_spatial_action * self.non_spatial_action_selected, axis=1)
non_spatial_action_prob /= valid_non_spatial_action_prob
non_spatial_action_log_prob = tf.log(tf.clip_by_value(non_spatial_action_prob, 1e-5, 1.))
spatial_action_prob = tf.reduce_sum(self.spatial_action * self.spatial_action_selected, axis=1)
spatial_action_log_prob = tf.log(tf.clip_by_value(spatial_action_prob, 1e-5, 1.))
self.summary.append(tf.summary.histogram('non_spatial_action_prob', non_spatial_action_prob))
self.summary.append(tf.summary.histogram('spatial_action_prob', spatial_action_prob))
# compute loss !! 尚未加入entropy以及loss_weight
action_log_prob = self.valid_spatial_action * spatial_action_log_prob + non_spatial_action_log_prob
advantage = tf.stop_gradient(self.target_value - self.value)
policy_loss = -tf.reduce_mean(action_log_prob * advantage)
# value_loss = -tf.reduce_mean(self.value * advantage)
value_loss = tf.reduce_mean(tf.square(self.target_value - self.value) / 2.)
entropy = 0
if self.mode == 'original_ac3':
entropy = -tf.reduce_sum(self.non_spatial_action * tf.log(self.non_spatial_action + 1e-10))
loss = policy_loss + value_loss * self.value_loss_weight - entropy * self.entropy_weight
loss = policy_loss + value_loss * self.value_loss_weight - entropy * self.entropy_weight
self.summary.append(tf.summary.scalar('policy_loss', policy_loss))
self.summary.append(tf.summary.scalar('value_loss', value_loss))
self.summary.append(tf.summary.scalar('value', tf.reduce_mean(self.value)))
self.summary.append(tf.summary.scalar('advantage', tf.reduce_mean(advantage)))
self.summary.append(tf.summary.scalar('returns', tf.reduce_mean(self.target_value)))
# optimizer
self.learning_rate = tf.placeholder(tf.float32, None, name="learning_rate")
optimizer = tf.train.RMSPropOptimizer(self.learning_rate, decay=0.99, epsilon=1e-10)
grads = optimizer.compute_gradients(loss)
clipped_grads = []
for grad, var in grads:
self.summary.append(tf.summary.histogram(var.op.name, var))
self.summary.append(tf.summary.histogram(var.op.name + '/grad', grad))
grad = tf.clip_by_norm(grad, 10.0)
clipped_grads.append([grad, var])
self.train_op = optimizer.apply_gradients(clipped_grads)
self.summary_op = tf.summary.merge(self.summary)
self.saver = tf.train.Saver()
masses = np.random.uniform(0.1, 10.0, (N_PARTICLES))
# get a target scaling
targets = np.random.uniform(10.0, 100.0, (1, N_PARTICLES, 1))
def random_four_vectors(n_data=256):
momenta = np.random.normal(0.0, 1.0, (n_data, N_PARTICLES, 3))
energies = np.sqrt(np.sum(momenta**2, axis=-1) + np.sum(masses**2))
vectors = np.concatenate((np.expand_dims(energies, axis=-1), momenta),
axis=-1)
return np.reshape(vectors, (-1, N_PARTICLES, 4))
network = build_network(n_particles=N_PARTICLES,
batch_size=BATCH_SIZE,
built_in_noise=BUILT_IN_NOISE)
# Some information about the network
print(network.summary())
print("-" * 20)
print("Trainable Variables:")
for layer in network.layers:
print("-" * 20)
print("{}:".format(layer.name))
print("Trainable Variables:")
print(layer.trainable_variables)
data = random_four_vectors(n_data=N_DATA)
noise = tf.random.normal((N_DATA, N_PARTICLES, 4), 0.0, 1.0)
print("SHAPE:")
print("Loading training set...")
train = load.load_dataset(params['train'])
print("Loading dev set...")
dev = load.load_dataset(params['dev'])
print("Building preprocessor...")
preproc = load.Preproc(*train)
print("Training size: " + str(len(train[0])) + " examples.")
print("Dev size: " + str(len(dev[0])) + " examples.")
params.update({
"input_shape": [8000, 1],
"num_categories": len(preproc.classes)
})
#create the cl-pcg-net
model = network.build_network(**params)
#learning rate reduce strategy
def scheduler(epoch):
if epoch % 80 == 0 and epoch != 0:
lr = K.get_value(model.optimizer.lr)
model.load_weights(save_dir + 'best.hdf5')
K.set_value(model.optimizer.lr, lr * 0.1)
print("lr changed to {}".format(lr * 0.1))
return K.get_value(model.optimizer.lr)
reduce_lr = LearningRateScheduler(scheduler)
#choose best model to save
checkpointer = keras.callbacks.ModelCheckpoint(mode='max',
monitor='val_acc',
def test():
tf.reset_default_graph()
infer_graph = tf.Graph()
with infer_graph.as_default():
_, _, pred_ids,pred_logits , inputs_t, decoder_inputs_t, decoder_lengths_t, \
_ = build_network(is_training=False,
batch_size=FLAGS.batch_size,
height=FLAGS.height,
width=FLAGS.width,
channels=FLAGS.channels,
decoder_length=FLAGS.decoder_length,
tgt_vocab_size=FLAGS.tgt_vocab_size,
num_units=FLAGS.num_units,
beam_width=FLAGS.beam_width,
encoder_length=FLAGS.encoder_length,
max_gradient_norm=FLAGS.max_gradient_norm,
embedding_size=FLAGS.embedding_size)
infer_saver = tf.train.Saver()
infer_sess = tf.Session(graph=infer_graph)
with open(FLAGS.test_txt) as f:
test = [line.rstrip() for line in f]
test_len = len(test)
test = np.array(test)
data_test = Dataset_test(test)
if FLAGS.lex_txt != None:
with open(FLAGS.lex_txt) as f:
lex = [line.rstrip().lower() for line in f]
ti = int(test_len / FLAGS.batch_size)
rest = test_len % FLAGS.batch_size
gt = []
predict = []
model_file=tf.train.latest_checkpoint(FLAGS.load_dir)
infer_saver.restore(infer_sess, model_file)
for t in range(ti):
batch_test = data_test.next_batch(FLAGS.batch_size)
path = []
texts = []
for line in batch_test:
path.append(line.split(' ',1)[0])
texts.append(line.split(' ',1)[1])
images = load_img(path,FLAGS.height,FLAGS.width)
feed_dict_t = {inputs_t:images[:, :, :, np.newaxis],
decoder_lengths_t:np.ones((FLAGS.batch_size), \
dtype=int) * FLAGS.decoder_length
}
q= infer_sess.run( pred_ids,feed_dict=feed_dict_t)
for j in range(len(texts)):
gt.append(texts[j])
ans = q[j].T[0]
pd = []
for c in ans:
if c != -1:
character = tools.idx_to_word[c]
if character != '<EOS>':
pd.append(character)
predict.append(''.join(pd))
batch_test = data_test.next_batch(FLAGS.batch_size)
path = []
texts = []
for line in batch_test:
path.append(line.split(' ',1)[0])
texts.append(line.split(' ',1)[1])
images = load_img(path,FLAGS.height,FLAGS.width)
feed_dict_t = {inputs_t:images[:, :, :, np.newaxis],
decoder_lengths_t:np.ones((FLAGS.batch_size), \
dtype=int) * FLAGS.decoder_length
}
q = infer_sess.run( pred_ids,feed_dict=feed_dict_t)
for k in range(rest):
gt.append(texts[k])
ans = q[k].T[0]
pd = []
for c in ans:
if c != -1:
character = tools.idx_to_word[c]
if character != '<EOS>':
pd.append(character)
predict.append(''.join(pd))
correct = float(0)
cnt = 0
acc_s = 0
for l in range(len(gt)):
cnt =cnt + 1
if gt[l] == predict[l]:
correct = correct + 1
acc_s = correct / cnt
if FLAGS.lex_txt != None:
correct_l = float(0)
cnt = 0
for l in range(len(gt)):
cnt =cnt + 1
lexicon = lex[l].split(',')
dt = distance.levenshtein(predict[l], lexicon[0])
pl = lexicon[0]
for ll in lexicon[1:]:
dt_temp = distance.levenshtein(predict[l], ll)
if dt_temp < dt:
dt = dt_temp
pl = ll
if pl == gt[l]:
correct_l = correct_l + 1
acc_l = correct_l / cnt
print('accuracy: ', acc_s)
if FLAGS.lex_txt != None:
print('accuracy with lexicon: ', acc_l)
def dynamic_nem_iteration(input_data,
target_data,
h_old,
preds_old,
gamma_old,
k,
collisions=None,
actions=None):
# Get dimensions
input_shape = tf.shape(input_data)
assert input_shape.get_shape(
)[0].value == 5, "Requires 5D input (B, K, W, H, C) but {}".format(
input_shape.get_shape()[0].value)
W, H, C = (x.value for x in input_data.get_shape()[-3:])
# set pixel distribution
pixel_dist = 'bernoulli'
# set up inner cells and nem cells
inner_cell = build_network(K=k)
nem_cell = NEMCell(inner_cell,
input_shape=(W, H, C),
distribution=pixel_dist)
# compute prior
prior = compute_prior(distribution=pixel_dist)
# build dynamic iteration
with tf.variable_scope('R-RNNEM') as varscope:
with tf.name_scope('step_{}'.format(0)):
# compute inputs
inputs = (input_data, target_data)
# feed action through hidden state
if actions is not None:
h_old = {'state': h_old, 'action': actions}
hidden_state = (h_old, preds_old, gamma_old)
# run hidden cell
hidden_state, output = nem_cell(inputs, hidden_state)
theta, pred, gamma = output
# set collision
collision = tf.zeros(
(1, 1, 1, 1, 1)) if collisions is None else collisions
# compute nem losses
total_loss, intra_loss, inter_loss, r_total_loss, r_intra_loss, r_inter_loss = compute_outer_loss(
pred,
gamma,
target_data,
prior,
pixel_distribution=pixel_dist,
collision=collision)
# compute estimated loss upper bound (which doesn't use E-step)
total_ub_loss, intra_ub_loss, inter_ub_loss, r_total_ub_loss, r_intra_ub_loss, r_inter_ub_loss = \
compute_outer_ub_loss(pred, target_data, prior, pixel_distribution=pixel_dist, collision=collision)
other_losses = tf.stack([total_loss, intra_loss, inter_loss])
other_ub_losses = tf.stack(
[total_ub_loss, intra_ub_loss, inter_ub_loss])
r_other_losses = tf.stack([r_total_loss, r_intra_loss, r_inter_loss])
r_other_ub_losses = tf.stack(
[r_total_ub_loss, r_intra_ub_loss, r_inter_ub_loss])
return total_loss, total_ub_loss, r_total_loss, r_total_ub_loss, theta, pred, gamma, other_losses, \
other_ub_losses, r_other_losses, r_other_ub_losses
def static_nem_iterations(input_data,
target_data,
k,
collisions=None,
actions=None):
# Get dimensions
input_shape = tf.shape(input_data)
assert input_shape.get_shape(
)[0].value == 6, "Requires 6D input (T, B, K, W, H, C) but {}".format(
input_shape.get_shape()[0].value)
W, H, C = (x.value for x in input_data.get_shape()[-3:])
# set pixel distribution
pixel_dist = 'bernoulli'
# set up inner cells and nem cells
inner_cell = build_network(K=k)
nem_cell = NEMCell(inner_cell,
input_shape=(W, H, C),
distribution=pixel_dist)
# compute prior
prior = compute_prior(distribution=pixel_dist)
# get state initializer
with tf.name_scope('initial_state'):
hidden_state = nem_cell.init_state(input_shape[1], k, dtype=tf.float32)
# build static iterations
outputs = [hidden_state]
total_losses, total_ub_losses, r_total_losses, r_total_ub_losses, other_losses, other_ub_losses, r_other_losses, r_other_ub_losses = [], [], [], [], [], [], [], []
loss_step_weights = get_loss_step_weights()
with tf.variable_scope('R-RNNEM') as varscope:
for t, loss_weight in enumerate(loss_step_weights):
varscope.reuse_variables() if t > 0 else None
with tf.name_scope('step_{}'.format(t)):
# compute inputs
inputs = (input_data[t], target_data[t + 1])
# feed action through hidden state
if actions is not None:
h_old, preds_old, gamma_old = hidden_state
h_old = {'state': h_old, 'action': actions[t]}
hidden_state = (h_old, preds_old, gamma_old)
# run hidden cell
hidden_state, output = nem_cell(inputs, hidden_state)
theta, pred, gamma = output
# set collision
collision = tf.zeros(
(1, 1, 1, 1, 1)) if collisions is None else collisions[t]
# compute nem losses
total_loss, intra_loss, inter_loss, r_total_loss, r_intra_loss, r_inter_loss = compute_outer_loss(
pred,
gamma,
target_data[t + 1],
prior,
pixel_distribution=pixel_dist,
collision=collision)
# compute estimated loss upper bound (which doesn't use E-step)
total_ub_loss, intra_ub_loss, inter_ub_loss, r_total_ub_loss, r_intra_ub_loss, r_inter_ub_loss = \
compute_outer_ub_loss(pred, target_data[t+1], prior, pixel_distribution=pixel_dist, collision=collision)
total_losses.append(loss_weight * total_loss)
total_ub_losses.append(loss_weight * total_ub_loss)
r_total_losses.append(loss_weight * r_total_loss)
r_total_ub_losses.append(loss_weight * r_total_ub_loss)
other_losses.append(tf.stack([total_loss, intra_loss, inter_loss]))
other_ub_losses.append(
tf.stack([total_ub_loss, intra_ub_loss, inter_ub_loss]))
r_other_losses.append(
tf.stack([r_total_loss, r_intra_loss, r_inter_loss]))
r_other_ub_losses.append(
tf.stack([r_total_ub_loss, r_intra_ub_loss, r_inter_ub_loss]))
outputs.append(output)
# collect outputs
with tf.name_scope('collect_outputs'):
thetas, preds, gammas = zip(*outputs)
thetas = tf.stack(thetas) # (T, 1, B*K, M)
preds = tf.stack(preds) # (T, B, K, W, H, C)
gammas = tf.stack(gammas) # (T, B, K, W, H, C)
other_losses = tf.stack(other_losses) # (T, 3)
other_ub_losses = tf.stack(other_ub_losses) # (T, 3)
r_other_losses = tf.stack(r_other_losses)
r_other_ub_losses = tf.stack(r_other_ub_losses)
with tf.name_scope('total_loss'):
total_loss = tf.reduce_sum(
tf.stack(total_losses)) / np.sum(loss_step_weights)
with tf.name_scope('total_ub_loss'):
total_ub_loss = tf.reduce_sum(
tf.stack(total_ub_losses)) / np.sum(loss_step_weights)
with tf.name_scope('r_total_loss'):
r_total_loss = tf.reduce_sum(
tf.stack(r_total_losses)) / np.sum(loss_step_weights)
with tf.name_scope('r_total_ub_loss'):
r_total_ub_loss = tf.reduce_sum(
tf.stack(r_total_ub_losses)) / np.sum(loss_step_weights)
return total_loss, total_ub_loss, r_total_loss, r_total_ub_loss, thetas, preds, gammas, other_losses, \
other_ub_losses, r_other_losses, r_other_ub_losses
def train():
with open(flags.train_txt) as f:
sample = [line.rstrip() for line in f]
sample = np.array(sample)
iteration = len(sample) // flags.batch_size
data = Dataset(sample)
tf.reset_default_graph()
train_graph = tf.Graph()
infer_graph = tf.Graph()
with train_graph.as_default():
encoder_outputs, inputs = build_cnn(True, flags.batch_size,
flags.height, flags.width,
flags.channels)
train_op, loss, sample_ids, logits, decoder_inputs, \
target_labels, learning_rate,keep_prob = build_network(encoder_outputs,
False,
flags.batch_size,
flags.decoder_length,
flags.tgt_vocab_size,
flags.attn_num_hidden,
flags.encoder_length,
flags.max_gradient_norm
)
initializer = tf.global_variables_initializer()
train_saver = tf.train.Saver()
train_sess = tf.Session(graph=train_graph)
train_sess.run(initializer)
with infer_graph.as_default():
encoder_outputs_t, inputs_t = build_cnn(False, flags.batch_size,
flags.height, flags.width,
flags.channels)
_, _, pred_ids, logits_t, decoder_inputs_t, \
_, _ ,keep_prob_t= build_network(encoder_outputs_t,
True,
flags.batch_size,
flags.decoder_length,
flags.tgt_vocab_size,
flags.attn_num_hidden,
flags.encoder_length,
flags.max_gradient_norm
)
infer_saver = tf.train.Saver()
infer_sess = tf.Session(graph=infer_graph)
# Training
start = time.time()
acc_log = 0
count = 0
lr = flags.learning_rate
for h in range(flags.epoch):
for i in range(iteration):
batch_train = data.next_batch(flags.batch_size)
path = []
texts = []
for line in batch_train:
path.append(line.split(' ')[0])
texts.append(line.split(' ')[1])
images = load_img(path, flags.height, flags.width)
training_target_labels = get_label(texts, flags.decoder_length)
training_decoder_inputs = np.delete(training_target_labels,
-1,
axis=1)
training_decoder_inputs = np.c_[
np.zeros(training_decoder_inputs.shape[0]),
training_decoder_inputs].T
feed_dict = {
inputs: images[:, :, :, np.newaxis],
decoder_inputs: training_decoder_inputs,
target_labels: training_target_labels,
learning_rate: lr,
keep_prob: 0.5
}
_, loss_value = train_sess.run([train_op, loss],
feed_dict=feed_dict)
step = float(i + 1)
if step % flags.display_step == 0:
now = time.time()
print(step, now - start, loss_value)
start = now
if step % flags.eval_step == 0:
train_saver.save(train_sess, flags.save_dir)
model_file = tf.train.latest_checkpoint(
flags.save_dir.rsplit('/', 1)[0])
infer_saver.restore(infer_sess, model_file)
gt = []
predict = []
images = load_img(path, flags.height, flags.width)
testing_decoder_inputs = np.zeros(
(flags.decoder_length, flags.batch_size), dtype=float)
feed_dict_t = {
inputs_t: images[:, :, :, np.newaxis],
decoder_inputs_t: testing_decoder_inputs,
keep_prob_t: 1
}
q = infer_sess.run(pred_ids, feed_dict=feed_dict_t)
for j in range(flags.batch_size):
gt.append(texts[j])
ans = np.array(q).T[j]
pd = []
for c in ans:
if c != -1:
character = tools.idx_to_word[c]
if character != '<EOS>':
pd.append(character)
predict.append(''.join(pd))
correct = float(0)
cnt = 0
acc_s = 0
for l in range(len(gt)):
cnt = cnt + 1
if gt[l] == predict[l]:
correct = correct + 1
count = count + 1
acc_s = correct / cnt
if acc_s > acc_log:
acc_log = acc_s
count = 0
if count == (iteration // flags.eval_step):
lr = lr / 5
def PolarOffsetMain(args, cfg):
if args.launcher == None:
dist_train = False
else:
args.batch_size, cfg.LOCAL_RANK = getattr(
common_utils, 'init_dist_%s' % args.launcher)(args.batch_size,
args.tcp_port,
args.local_rank,
backend='nccl')
dist_train = True
cfg['DIST_TRAIN'] = dist_train
output_dir = os.path.join('./output', args.tag)
ckpt_dir = os.path.join(output_dir, 'ckpt')
tmp_dir = os.path.join(output_dir, 'tmp')
summary_dir = os.path.join(output_dir, 'summary')
if not os.path.exists(output_dir):
os.makedirs(output_dir, exist_ok=True)
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir, exist_ok=True)
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir, exist_ok=True)
if not os.path.exists(summary_dir):
os.makedirs(summary_dir, exist_ok=True)
if args.onlyval and args.saveval:
results_dir = os.path.join(output_dir, 'test', 'sequences')
if not os.path.exists(results_dir):
os.makedirs(results_dir, exist_ok=True)
for i in range(8, 9):
sub_dir = os.path.join(results_dir, str(i).zfill(2), 'predictions')
if not os.path.exists(sub_dir):
os.makedirs(sub_dir, exist_ok=True)
if args.onlytest:
results_dir = os.path.join(output_dir, 'test', 'sequences')
if not os.path.exists(results_dir):
os.makedirs(results_dir, exist_ok=True)
for i in range(11, 22):
sub_dir = os.path.join(results_dir, str(i).zfill(2), 'predictions')
if not os.path.exists(sub_dir):
os.makedirs(sub_dir, exist_ok=True)
log_file = os.path.join(
output_dir, ('log_train_%s.txt' %
datetime.datetime.now().strftime('%Y%m%d-%H%M%S')))
logger = common_utils.create_logger(log_file, rank=cfg.LOCAL_RANK)
logger.info('**********************Start logging**********************')
gpu_list = os.environ[
'CUDA_VISIBLE_DEVICES'] if 'CUDA_VISIBLE_DEVICES' in os.environ.keys(
) else 'ALL'
logger.info('CUDA_VISIBLE_DEVICES=%s' % gpu_list)
if dist_train:
total_gpus = dist.get_world_size()
logger.info('total_batch_size: %d' % (total_gpus * args.batch_size))
for key, val in vars(args).items():
logger.info('{:16} {}'.format(key, val))
log_config_to_file(cfg, logger=logger)
if cfg.LOCAL_RANK == 0:
os.system('cp %s %s' % (args.config, output_dir))
### create dataloader
if (not args.onlytest) and (not args.onlyval):
train_dataset_loader = build_dataloader(args,
cfg,
split='train',
logger=logger)
val_dataset_loader = build_dataloader(args,
cfg,
split='val',
logger=logger,
no_shuffle=True,
no_aug=True)
elif args.onlyval:
val_dataset_loader = build_dataloader(args,
cfg,
split='val',
logger=logger,
no_shuffle=True,
no_aug=True)
else:
test_dataset_loader = build_dataloader(args,
cfg,
split='test',
logger=logger,
no_shuffle=True,
no_aug=True)
### create model
model = build_network(cfg)
model.cuda()
### create optimizer
optimizer = train_utils.build_optimizer(model, cfg)
### load ckpt
ckpt_fname = os.path.join(ckpt_dir, args.ckpt_name)
epoch = -1
other_state = {}
if args.pretrained_ckpt is not None and os.path.exists(ckpt_fname):
logger.info(
"Now in pretrain mode and loading ckpt: {}".format(ckpt_fname))
if not args.nofix:
if args.fix_semantic_instance:
logger.info(
"Freezing backbone, semantic and instance part of the model."
)
model.fix_semantic_instance_parameters()
else:
logger.info(
"Freezing semantic and backbone part of the model.")
model.fix_semantic_parameters()
optimizer = train_utils.build_optimizer(model, cfg)
epoch, other_state = train_utils.load_params_with_optimizer_otherstate(
model,
ckpt_fname,
to_cpu=dist_train,
optimizer=optimizer,
logger=logger) # new feature
logger.info("Loaded Epoch: {}".format(epoch))
elif args.pretrained_ckpt is not None:
train_utils.load_pretrained_model(model,
args.pretrained_ckpt,
to_cpu=dist_train,
logger=logger)
if not args.nofix:
if args.fix_semantic_instance:
logger.info(
"Freezing backbone, semantic and instance part of the model."
)
model.fix_semantic_instance_parameters()
else:
logger.info(
"Freezing semantic and backbone part of the model.")
model.fix_semantic_parameters()
else:
logger.info("No Freeze.")
optimizer = train_utils.build_optimizer(model, cfg)
elif os.path.exists(ckpt_fname):
epoch, other_state = train_utils.load_params_with_optimizer_otherstate(
model,
ckpt_fname,
to_cpu=dist_train,
optimizer=optimizer,
logger=logger) # new feature
logger.info("Loaded Epoch: {}".format(epoch))
if other_state is None:
other_state = {}
### create optimizer and scheduler
lr_scheduler = None
if lr_scheduler == None:
logger.info('Not using lr scheduler')
model.train(
) # before wrap to DistributedDataParallel to support fixed some parameters
if dist_train:
model = nn.parallel.DistributedDataParallel(
model,
device_ids=[cfg.LOCAL_RANK % torch.cuda.device_count()],
find_unused_parameters=True)
logger.info(model)
if cfg.LOCAL_RANK == 0:
writer = SummaryWriter(log_dir=summary_dir)
logger.info('**********************Start Training**********************')
rank = cfg.LOCAL_RANK
best_before_iou = -1 if 'best_before_iou' not in other_state else other_state[
'best_before_iou']
best_pq = -1 if 'best_pq' not in other_state else other_state['best_pq']
best_after_iou = -1 if 'best_after_iou' not in other_state else other_state[
'best_after_iou']
global_iter = 0 if 'global_iter' not in other_state else other_state[
'global_iter']
val_global_iter = 0 if 'val_global_iter' not in other_state else other_state[
'val_global_iter']
best_tracking_loss = 10086 if 'best_tracking_loss' not in other_state else other_state[
'best_tracking_loss']
### test
if args.onlytest:
logger.info('----EPOCH {} Testing----'.format(epoch))
model.eval()
if rank == 0:
vbar = tqdm(total=len(test_dataset_loader), dynamic_ncols=True)
for i_iter, inputs in enumerate(test_dataset_loader):
with torch.no_grad():
if cfg.MODEL.NAME.startswith(
'PolarOffsetSpconvPytorchMeanshiftTracking'
) or cfg.MODEL.NAME.startswith('PolarOffsetSpconvTracking'):
ret_dict = model(inputs,
is_test=True,
merge_evaluator_list=None,
merge_evaluator_window_k_list=None,
require_cluster=True)
else:
ret_dict = model(inputs,
is_test=True,
require_cluster=True,
require_merge=True)
common_utils.save_test_results(ret_dict, results_dir, inputs)
if rank == 0:
vbar.set_postfix({
'fname':
'/'.join(inputs['pcd_fname'][0].split('/')[-3:])
})
vbar.update(1)
if rank == 0:
vbar.close()
logger.info("----Testing Finished----")
return
### evaluate
if args.onlyval:
logger.info('----EPOCH {} Evaluating----'.format(epoch))
model.eval()
min_points = 50 # according to SemanticKITTI official rule
if cfg.MODEL.NAME.startswith(
'PolarOffsetSpconvPytorchMeanshiftTracking'
) or cfg.MODEL.NAME.startswith('PolarOffsetSpconvTracking'):
merge_evaluator_list = []
merge_evaluator_window_k_list = []
for k in [1, 5, 10, 15]:
merge_evaluator_list.append(init_eval(min_points))
merge_evaluator_window_k_list.append(k)
else:
before_merge_evaluator = init_eval(min_points=min_points)
after_merge_evaluator = init_eval(min_points=min_points)
if rank == 0:
vbar = tqdm(total=len(val_dataset_loader), dynamic_ncols=True)
for i_iter, inputs in enumerate(val_dataset_loader):
inputs['i_iter'] = i_iter
# torch.cuda.empty_cache()
with torch.no_grad():
if cfg.MODEL.NAME.startswith(
'PolarOffsetSpconvPytorchMeanshiftTracking'
) or cfg.MODEL.NAME.startswith('PolarOffsetSpconvTracking'):
ret_dict = model(inputs,
is_test=True,
merge_evaluator_list=merge_evaluator_list,
merge_evaluator_window_k_list=
merge_evaluator_window_k_list,
require_cluster=True)
else:
ret_dict = model(
inputs,
is_test=True,
before_merge_evaluator=before_merge_evaluator,
after_merge_evaluator=after_merge_evaluator,
require_cluster=True)
#########################
# with open('./ipnb/{}_matching_list.pkl'.format(i_iter), 'wb') as fd:
# pickle.dump(ret_dict['matching_list'], fd)
#########################
if args.saveval:
common_utils.save_test_results(ret_dict, results_dir,
inputs)
if rank == 0:
vbar.set_postfix({
'loss':
ret_dict['loss'].item(),
'fname':
'/'.join(inputs['pcd_fname'][0].split('/')[-3:]),
'ins_num':
-1 if 'ins_num' not in ret_dict else ret_dict['ins_num']
})
vbar.update(1)
if dist_train:
if cfg.MODEL.NAME.startswith(
'PolarOffsetSpconvPytorchMeanshiftTracking'
) or cfg.MODEL.NAME.startswith('PolarOffsetSpconvTracking'):
pass
else:
before_merge_evaluator = common_utils.merge_evaluator(
before_merge_evaluator, tmp_dir)
dist.barrier()
after_merge_evaluator = common_utils.merge_evaluator(
after_merge_evaluator, tmp_dir)
if rank == 0:
vbar.close()
if rank == 0:
## print results
if cfg.MODEL.NAME.startswith(
'PolarOffsetSpconvPytorchMeanshiftTracking'
) or cfg.MODEL.NAME.startswith('PolarOffsetSpconvTracking'):
for evaluate, window_k in zip(merge_evaluator_list,
merge_evaluator_window_k_list):
logger.info("Current Window K: {}".format(window_k))
printResults(evaluate, logger=logger)
else:
logger.info("Before Merge Semantic Scores")
before_merge_results = printResults(before_merge_evaluator,
logger=logger,
sem_only=True)
logger.info("After Merge Panoptic Scores")
after_merge_results = printResults(after_merge_evaluator,
logger=logger)
logger.info("----Evaluating Finished----")
return
### train
while True:
epoch += 1
if 'MAX_EPOCH' in cfg.OPTIMIZE.keys():
if epoch > cfg.OPTIMIZE.MAX_EPOCH:
break
### train one epoch
logger.info('----EPOCH {} Training----'.format(epoch))
loss_acc = 0
if rank == 0:
pbar = tqdm(total=len(train_dataset_loader), dynamic_ncols=True)
for i_iter, inputs in enumerate(train_dataset_loader):
# torch.cuda.empty_cache()
torch.autograd.set_detect_anomaly(True)
model.train()
optimizer.zero_grad()
inputs['i_iter'] = i_iter
inputs['rank'] = rank
ret_dict = model(inputs)
if args.pretrained_ckpt is not None and not args.fix_semantic_instance: # training offset
if args.nofix:
loss = ret_dict['loss']
elif len(ret_dict['offset_loss_list']) > 0:
loss = sum(ret_dict['offset_loss_list'])
else:
loss = torch.tensor(0.0, requires_grad=True) #mock pbar
ret_dict['offset_loss_list'] = [loss] #mock writer
elif args.pretrained_ckpt is not None and args.fix_semantic_instance and cfg.MODEL.NAME == 'PolarOffsetSpconvPytorchMeanshift': # training dynamic shifting
loss = sum(ret_dict['meanshift_loss'])
elif cfg.MODEL.NAME.startswith(
'PolarOffsetSpconvPytorchMeanshiftTracking'
) or cfg.MODEL.NAME.startswith('PolarOffsetSpconvTracking'):
loss = sum(ret_dict['tracking_loss'])
#########################
# with open('./ipnb/{}_matching_list.pkl'.format(i_iter), 'wb') as fd:
# pickle.dump(ret_dict['matching_list'], fd)
#########################
else:
loss = ret_dict['loss']
loss.backward()
optimizer.step()
if rank == 0:
try:
cur_lr = float(optimizer.lr)
except:
cur_lr = optimizer.param_groups[0]['lr']
loss_acc += loss.item()
pbar.set_postfix({
'loss': loss.item(),
'lr': cur_lr,
'mean_loss': loss_acc / float(i_iter + 1)
})
pbar.update(1)
writer.add_scalar('Train/01_Loss', ret_dict['loss'].item(),
global_iter)
writer.add_scalar('Train/02_SemLoss',
ret_dict['sem_loss'].item(), global_iter)
if 'offset_loss_list' in ret_dict and sum(
ret_dict['offset_loss_list']).item() > 0:
writer.add_scalar('Train/03_InsLoss',
sum(ret_dict['offset_loss_list']).item(),
global_iter)
writer.add_scalar('Train/04_LR', cur_lr, global_iter)
writer_acc = 5
if 'meanshift_loss' in ret_dict:
writer.add_scalar('Train/05_DSLoss',
sum(ret_dict['meanshift_loss']).item(),
global_iter)
writer_acc += 1
if 'tracking_loss' in ret_dict:
writer.add_scalar('Train/06_TRLoss',
sum(ret_dict['tracking_loss']).item(),
global_iter)
writer_acc += 1
more_keys = []
for k, _ in ret_dict.items():
if k.find('summary') != -1:
more_keys.append(k)
for ki, k in enumerate(more_keys):
if k == 'bandwidth_weight_summary':
continue
ki += writer_acc
writer.add_scalar(
'Train/{}_{}'.format(str(ki).zfill(2), k), ret_dict[k],
global_iter)
global_iter += 1
if rank == 0:
pbar.close()
### evaluate after each epoch
logger.info('----EPOCH {} Evaluating----'.format(epoch))
model.eval()
min_points = 50
before_merge_evaluator = init_eval(min_points=min_points)
after_merge_evaluator = init_eval(min_points=min_points)
tracking_loss = 0
if rank == 0:
vbar = tqdm(total=len(val_dataset_loader), dynamic_ncols=True)
for i_iter, inputs in enumerate(val_dataset_loader):
# torch.cuda.empty_cache()
inputs['i_iter'] = i_iter
inputs['rank'] = rank
with torch.no_grad():
if cfg.MODEL.NAME.startswith(
'PolarOffsetSpconvPytorchMeanshiftTracking'
) or cfg.MODEL.NAME.startswith('PolarOffsetSpconvTracking'):
ret_dict = model(inputs,
is_test=True,
merge_evaluator_list=None,
merge_evaluator_window_k_list=None,
require_cluster=True)
else:
ret_dict = model(
inputs,
is_test=True,
before_merge_evaluator=before_merge_evaluator,
after_merge_evaluator=after_merge_evaluator,
require_cluster=True)
if rank == 0:
vbar.set_postfix({'loss': ret_dict['loss'].item()})
vbar.update(1)
writer.add_scalar('Val/01_Loss', ret_dict['loss'].item(),
val_global_iter)
writer.add_scalar('Val/02_SemLoss',
ret_dict['sem_loss'].item(), val_global_iter)
if 'offset_loss_list' in ret_dict and sum(
ret_dict['offset_loss_list']).item() > 0:
writer.add_scalar('Val/03_InsLoss',
sum(ret_dict['offset_loss_list']).item(),
val_global_iter)
if 'tracking_loss' in ret_dict:
writer.add_scalar('Val/06_TRLoss',
sum(ret_dict['tracking_loss']).item(),
global_iter)
tracking_loss += sum(ret_dict['tracking_loss']).item()
more_keys = []
for k, _ in ret_dict.items():
if k.find('summary') != -1:
more_keys.append(k)
for ki, k in enumerate(more_keys):
if k == 'bandwidth_weight_summary':
continue
ki += 4
writer.add_scalar('Val/{}_{}'.format(str(ki).zfill(2), k),
ret_dict[k], val_global_iter)
val_global_iter += 1
tracking_loss /= len(val_dataset_loader)
if dist_train:
try:
before_merge_evaluator = common_utils.merge_evaluator(
before_merge_evaluator, tmp_dir, prefix='before_')
dist.barrier()
after_merge_evaluator = common_utils.merge_evaluator(
after_merge_evaluator, tmp_dir, prefix='after_')
except:
print("Someting went wrong when merging evaluator in rank {}".
format(rank))
if rank == 0:
vbar.close()
if rank == 0:
## print results
logger.info("Before Merge Semantic Scores")
before_merge_results = printResults(before_merge_evaluator,
logger=logger,
sem_only=True)
logger.info("After Merge Panoptic Scores")
after_merge_results = printResults(after_merge_evaluator,
logger=logger)
## save ckpt
other_state = {
'best_before_iou': best_before_iou,
'best_pq': best_pq,
'best_after_iou': best_after_iou,
'global_iter': global_iter,
'val_global_iter': val_global_iter,
'best_tracking_loss': best_tracking_loss,
}
saved_flag = False
if best_tracking_loss > tracking_loss and cfg.MODEL.NAME.startswith(
'PolarOffsetSpconvPytorchMeanshiftTracking'
) or cfg.MODEL.NAME.startswith('PolarOffsetSpconvTracking'):
best_tracking_loss = tracking_loss
if not saved_flag:
states = train_utils.checkpoint_state(
model, optimizer, epoch, other_state)
train_utils.save_checkpoint(
states,
os.path.join(
ckpt_dir, 'checkpoint_epoch_{}_{}.pth'.format(
epoch,
str(tracking_loss)[:5])))
saved_flag = True
if best_before_iou < before_merge_results['iou_mean']:
best_before_iou = before_merge_results['iou_mean']
if not saved_flag:
states = train_utils.checkpoint_state(
model, optimizer, epoch, other_state)
train_utils.save_checkpoint(
states,
os.path.join(
ckpt_dir,
'checkpoint_epoch_{}_{}_{}_{}.pth'.format(
epoch,
str(best_before_iou)[:5],
str(best_pq)[:5],
str(best_after_iou)[:5])))
saved_flag = True
if best_pq < after_merge_results['pq_mean']:
best_pq = after_merge_results['pq_mean']
if not saved_flag:
states = train_utils.checkpoint_state(
model, optimizer, epoch, other_state)
train_utils.save_checkpoint(
states,
os.path.join(
ckpt_dir,
'checkpoint_epoch_{}_{}_{}_{}.pth'.format(
epoch,
str(best_before_iou)[:5],
str(best_pq)[:5],
str(best_after_iou)[:5])))
saved_flag = True
if best_after_iou < after_merge_results['iou_mean']:
best_after_iou = after_merge_results['iou_mean']
if not saved_flag:
states = train_utils.checkpoint_state(
model, optimizer, epoch, other_state)
train_utils.save_checkpoint(
states,
os.path.join(
ckpt_dir,
'checkpoint_epoch_{}_{}_{}_{}.pth'.format(
epoch,
str(best_before_iou)[:5],
str(best_pq)[:5],
str(best_after_iou)[:5])))
saved_flag = True
logger.info("Current best before IoU: {}".format(best_before_iou))
logger.info("Current best after IoU: {}".format(best_after_iou))
logger.info("Current best after PQ: {}".format(best_pq))
logger.info(
"Current best tracking loss: {}".format(best_tracking_loss))
if lr_scheduler != None:
lr_scheduler.step(epoch) # new feature