def __init__(self, path, save_opp):
self.path = path
self.save_opp = save_opp
self.mcc = Measure(create_path(path, 'mcc.csv'))
self.acc = Measure(create_path(path, 'accuracy.csv'))
if save_opp:
self.opp_mcc = Measure(create_path(path, 'opp_mcc.csv'))
def save_data(self, final=False):
"""
Saves updates the JSON file being generated at the of the data extraction or updates the current file
:param: bool (optional), if selected it saves the final under and new name and moves it to another folder
:return: null
"""
city = self.city.replace(' ', '_').lower()
company = self.company.replace(' ', '_').lower()
old_filepath = self.last_saved_file
filename = f'{unidecode(city)}_{unidecode(company)}_page_{self.page}.json'
new_filepath = create_path(filename=filename, folder='../data_in_progress')
data = self.all_results
if not len(old_filepath):
write_file(data, new_filepath)
self.logger.info(f"First file created - page{self.page}")
elif final:
final_pathname = create_path(filename=filename, folder='../data_raw', final=True)
rename_file(data, old_filepath, final_pathname)
else:
rename_file(data, old_filepath, new_filepath)
self.logger.info(f"File updated - {self.page}")
self.last_saved_file = new_filepath
def load_params():
print os.path.dirname(__file__)
params = parse_params("params.cfg", os.path.join(os.path.dirname(__file__), "paramspec_pop.cfg"))
create_path(params["prefix"])
create_path(os.path.join(params["prefix"], "thumbnails"))
print params
return params
def all_normalised_maru_histogram(path, activation_data):
normalised_maru_data_list = []
for seed in activation_data:
data = activation_data[seed]
mean_data = np.mean(data, axis=0)
std_data = np.std(data, axis=0)
maru_data = np.divide(mean_data,
std_data,
out=np.zeros_like(mean_data),
where=std_data != 0)
normalised_maru = minmax_scale(maru_data, feature_range=(0, 1))
normalised_maru_data_list.append(normalised_maru)
n, bins, patches = plt.hist(normalised_maru_data_list,
20,
range=(0.0, 1.0),
label=activation_data.keys(),
rwidth=1.0,
linewidth=0)
plt.ylabel('number of neurons')
plt.xlabel('maru value')
plt.title('normalised maru value for all seeds')
# plt.legend(loc='upper right')
# plt.ylim(0, 60)
# plt.show()
path = create_path(path, "activations")
plt.savefig(create_path(path, "all_normal_maru.png"), dpi=__PNG_DPI__)
plt.clf()
def output_single(sim):
print "Processing data and generating output plots..."
fig_prefix = sim.params['prefix']
create_path(fig_prefix)
output_hh_life_cycle(
sim, os.path.join(fig_prefix, 'hh_life_cycle.%s' % sim.params['ext']))
output_comp_by_hh_size(
sim, os.path.join(fig_prefix, 'age_by_hh_size.%s' % sim.params['ext']))
output_comp_by_hh_age(
sim, os.path.join(fig_prefix, 'age_by_hh_age.%s' % sim.params['ext']))
output_hh_size_distribution(
sim, 10, os.path.join(fig_prefix,
'hh_size_dist.%s' % sim.params['ext']))
output_age_distribution(
sim, os.path.join(fig_prefix, 'age_dist.%s' % sim.params['ext']))
output_household_type(
sim, os.path.join(fig_prefix, 'hh_type.%s' % sim.params['ext']))
output_household_composition(
sim, os.path.join(fig_prefix, 'hh_comp.%s' % sim.params['ext']))
output_hh_size_time(sim,
os.path.join(fig_prefix,
'hh_size_time.%s' % sim.params['ext']),
comp=False)
output_fam_type_time(sim,
os.path.join(fig_prefix,
'fam_type_time.%s' % sim.params['ext']),
comp=False)
print 'Output written to ', sim.params['prefix']
def load_params():
print os.path.dirname(__file__)
params = parse_params(
'params.cfg',
os.path.join(os.path.dirname(__file__), 'paramspec_pop.cfg'))
create_path(params['prefix'])
create_path(os.path.join(params['prefix'], 'thumbnails'))
print params
return params
def __init__(self, crawler_worker_loop, path, start=0, max_fetch_cnt=50):
super().__init__()
self._max_fetch_cnt = max_fetch_cnt
self._start = start
self.crawler_worker_loop = crawler_worker_loop
self.base_url = 'https://api-prod.wallstreetcn.com/apiv1/content/articles?category=global&limit=100'
self.summary_urls = [self.base_url]
self._path = path
self.summary_saved_file = self._path + '/summary_{}.txt'
self.detail_saved_file = self._path + '/detail_{}.txt'
self._source = 'WallStreet'
create_path(self._path)
def main(train):
struct = np.array([[10,10,10,10]])
eql = EQL(2, struct, 1)
eql = eql.to(device)
dataset = TestDatasetV1()
model_path = "../saved_model/test_eql_v1"
create_path(model_path)
model_path = os.path.join(model_path, "state_dict_model.pt")
if train:
eql.train_model(DataLoader(dataset, batch_size=20, shuffle=True), model_path=model_path, max_epochs=100, lrate=0.001)
else:
test_dataset = TestEQLDataset()
eql.test_model(test_dataset, model_path)
def activation_mean_histogram(path, activation_data):
path = create_path(path, "activations")
for seed in activation_data:
data = activation_data[seed]
mean_data = np.mean(data, axis=0)
n, bins, patches = plt.hist(mean_data, 20, range=(0.0, 1.0))
plt.ylabel('number of neurons')
plt.xlabel('mean activation')
plt.title('mean activation for seed {}'.format(seed))
plt.ylim(0, 60)
# plt.show()
plt.savefig(create_path(path, "seed_{}_mean_act.png".format(seed)),
dpi=__PNG_DPI__)
plt.clf()
def __init__(self,
experiment_name,
run_num,
hyperparameters=None,
save_opp=False):
self.hyperparameters = hyperparameters
self.path = create_path('results_uni', experiment_name,
"run_{}".format(str(run_num)))
self.single_data = None
self.apoz_data = None
self.activation_data = {}
self.target = NetworkType(create_path(self.path, 'target'), save_opp)
self.naive = NetworkType(create_path(self.path, 'naive'),
save_opp=False)
self.source = NetworkType(create_path(self.path, 'source'), save_opp)
def _alt_sal_map(model, layer_idx, img, run, model_type, seed):
import keras.backend as K
# select class of interest
class_idx = 0
# define derivative d loss / d layer_input
layer_input = model.input
# This model must already use linear activation for the final layer
loss = model.layers[layer_idx].output[..., class_idx]
grad_tensor = K.gradients(loss, layer_input)[0]
# create function that evaluate the gradient for a given input
# This function accept numpy array
derivative_fn = K.function([layer_input], [grad_tensor])
# evaluate the derivative_fn
grad_eval_by_hand = derivative_fn([img[np.newaxis, ...]])[0]
print(grad_eval_by_hand.shape)
grad_eval_by_hand = np.abs(grad_eval_by_hand).max(axis=(0, 3))
# normalize to range between 0 and 1
arr_min, arr_max = np.min(grad_eval_by_hand), np.max(grad_eval_by_hand)
grad_eval_by_hand = (grad_eval_by_hand - arr_min) / (arr_max - arr_min + K.epsilon())
plt.imsave(create_path(run.path, model_type, "images", "sal_image_{}_alt.png".format(seed)), grad_eval_by_hand)
def create_sales(name):
with open("./users.db") as us:
users = json.load(us)
while True:
venta = input("\nNombre su venta: ")
if venta == "":
print("Nombre no valido, intente con uno diferente")
continue
elif name not in users:
users[name] = {venta: 1}
elif venta in users[name]:
print("Nombre existente, intente con uno diferente")
continue
else:
users[name][venta] = 1
path = create_path(name, venta)
with open(f"{path}.db", "w") as db:
json.dump({}, db)
with open(f"{path}_names.db", "w") as names:
json.dump({}, names)
with open("./users.db", "w") as user:
json.dump(users, user)
print("Venta creada exitosamente. \n")
break
def to_csv(self, path):
d = {
"source_max_epochs": [self.source_max_epochs],
"target_max_epochs": [self.target_max_epochs],
"num_starting_units": [self.num_starting_units],
"upper_threshold": [self.upper_threshold],
"lower_threshold": [self.lower_threshold],
"source_lr": [self.source_lr],
"target_lr": [self.target_lr],
"batch_size": [self.batch_size],
"conv_activation": [self.conv_activation],
"loss_function": [self.loss_function],
"pruning_method": [self.pruning_method],
"source_animal": [self.source_animal],
"target_animal": [self.target_animal],
"pruning_dataset": [self.pruning_dataset],
"save_opp": [self.save_opp],
"labels_per_category": [self.labels_per_category],
'reinit_weights': [self.reinit_weights]
}
hyperparams = pd.DataFrame(data=d)
file_path = create_path(path, 'params.csv')
hyperparams.to_csv(file_path, index=None)
def training(self):
vocab_to_id = get_vocab_to_id(self.train_data_path, self.vocab_file,
False)
logdir = os.path.join(
self.summary_path,
datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + "/")
self.vocab_size = len(vocab_to_id)
create_path(self.log_path)
logger = get_logger(self.logfile_path)
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(logdir, graph=sess.graph)
summary_writer.flush()
rnn_model = RNNModel(self.rnn_size, self.embedding_size,
self.class_num, self.vocab_size,
self.learning_rate, self.model_path)
test_data_generator = SentenceGenerator(self.test_data_path)
testBatchManage = BatchManager(test_data_generator, 0, vocab_to_id)
test_data = testBatchManage.get_all_data_to_batch()
sess.run(tf.global_variables_initializer())
current_step = 0
for e in range(self.epoch_num):
logger.info("Epoch num: " + str(e + 1) + "\n")
print("Epoch num: " + str(e + 1) + "\n")
train_data_generator = SentenceGenerator(self.train_data_path)
trainBatchManage = BatchManager(train_data_generator,
self.batch_size, vocab_to_id)
for batchs in trainBatchManage.getBatches():
current_step += 1
loss, accuracy, summary_op = rnn_model.train(
sess, batchs, self.dropout)
if current_step % self.epoch_step == 0:
loss_test, accuracy_test, _ = rnn_model.train_test(
sess, test_data, 1.0)
logger.info("loss:" + str(loss_test) + " accuracy:" +
str(accuracy_test) + "\n")
print("loss:" + str(loss_test) + " accuracy:" +
str(accuracy_test) + "\n")
summary_writer.add_summary(summary_op, current_step)
rnn_model.saver.save(sess,
self.model_path,
global_step=current_step)
def output_single(sim):
print "Processing data and generating output plots..."
fig_prefix = sim.params["prefix"]
create_path(fig_prefix)
output_hh_life_cycle(sim, os.path.join(fig_prefix, "hh_life_cycle.%s" % sim.params["ext"]))
output_comp_by_hh_size(sim, os.path.join(fig_prefix, "age_by_hh_size.%s" % sim.params["ext"]))
output_comp_by_hh_age(sim, os.path.join(fig_prefix, "age_by_hh_age.%s" % sim.params["ext"]))
output_hh_size_distribution(sim, 10, os.path.join(fig_prefix, "hh_size_dist.%s" % sim.params["ext"]))
output_age_distribution(sim, os.path.join(fig_prefix, "age_dist.%s" % sim.params["ext"]))
output_household_type(sim, os.path.join(fig_prefix, "hh_type.%s" % sim.params["ext"]))
output_household_composition(sim, os.path.join(fig_prefix, "hh_comp.%s" % sim.params["ext"]))
output_hh_size_time(sim, os.path.join(fig_prefix, "hh_size_time.%s" % sim.params["ext"]), comp=False)
output_fam_type_time(sim, os.path.join(fig_prefix, "fam_type_time.%s" % sim.params["ext"]), comp=False)
print "Output written to ", sim.params["prefix"]
def scatter_std_mean_activation(path, activation_data):
path = create_path(path, "activations")
for seed in activation_data:
data = activation_data[seed]
mean_data = np.mean(data, axis=0)
std_data = np.std(data, axis=0)
plt.scatter(mean_data, std_data)
plt.xlabel('mean activation')
plt.ylabel('std of activation')
plt.title('mean vs std for seed {}'.format(seed))
# plt.ylim(0.0, 4.0)
# plt.xlim(0.0, 3.0)
# plt.show()
plt.savefig(create_path(path, "seed_{}_mean_std_act.png".format(seed)),
dpi=__PNG_DPI__)
plt.clf()
def single_network_performance(title, measure, path, data):
plt.xlabel('epochs')
plt.ylabel(measure)
plt.title(title)
plt.ylim(0.0, 1.0)
plt.plot(data)
# plt.show()
plt.savefig(create_path(path, "{}.png".format(measure)), dpi=__PNG_DPI__)
plt.clf()
def all_scatter_std_mean_activation(path, activation_data):
for seed in activation_data:
data = activation_data[seed]
mean_data = np.mean(data, axis=0)
std_data = np.std(data, axis=0)
plt.scatter(mean_data, std_data, label=seed)
plt.xlabel('mean activation')
plt.ylabel('std of activation')
plt.title('mean vs std for all seeds')
# plt.ylim(0.0, 4.0)
# plt.xlim(0.0, 8.0)
# plt.legend(loc='upper right')
# plt.show()
path = create_path(path, "activations")
plt.savefig(create_path(path, "all_seed_mean_std_act.png"),
dpi=__PNG_DPI__)
plt.clf()
def network(seed, run, hp, num_seeded_units):
dog_train_labels, dog_train_images, dog_test_labels, dog_test_images = data.get_training_and_val_data(
hp.target_animal, labels_per_category=hp.labels_per_category)
dog_val_labels, dog_val_images = data.get_test_data(hp.target_animal)
if hp.reinit_weights:
num_starting_units = hp.num_starting_units
else:
num_starting_units = num_seeded_units
# Model
model = Sequential()
weight_init = glorot_uniform(seed)
model.add(Conv2D(32, (3, 3), padding='same',
input_shape=dog_train_images.shape[1:], kernel_initializer=weight_init))
model.add(Activation(hp.conv_activation))
model.add(Conv2D(32, (3, 3), kernel_initializer=weight_init))
model.add(Activation(hp.conv_activation))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same', kernel_initializer=weight_init))
model.add(Activation(hp.conv_activation))
model.add(Conv2D(64, (3, 3), kernel_initializer=weight_init))
model.add(Activation(hp.conv_activation))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(num_starting_units, kernel_initializer=weight_init))
model.add(Activation('relu'))
model.add(Dense(1, kernel_initializer=weight_init))
model.add(Activation('sigmoid'))
# Adam learning optimizer
opt = keras.optimizers.adam(lr=hp.target_lr)
# train the model using Adam
model.compile(loss=hp.loss_function, optimizer=opt, metrics=[binary_accuracy])
# Callbacks:
all_predictions = library_extensions.PredictionHistory(model, dog_train_images, dog_train_labels, dog_val_images,
dog_val_labels, dog_test_images, dog_test_labels)
# Training naive network
model.fit(dog_train_images, dog_train_labels, batch_size=hp.batch_size, epochs=hp.target_max_epochs,
validation_data=(dog_val_images, dog_val_labels), shuffle=True,
callbacks=[all_predictions])
# Save trained model
model.save(utils.create_path(run.path, "naive", "saved_models", "naive_model_{}.h5".format(seed)))
print("naive model saved")
# Generate results history
run.naive.update(seed, all_predictions)
def __init__(self, crawler_worker_loop, path, start=0, max_fetch_cnt=50):
super().__init__()
self._max_fetch_cnt = max_fetch_cnt
self._start = start
self.crawler_worker_loop = crawler_worker_loop
self.channels = {"economy": 129, "finance": 125, "companies": 130, "china": 131, "science": 179,
"international": 132, "culture": 134}
self.base_url = 'http://tag.caixin.com/news/homeInterface.jsp?' \
'channel={}&start={}&count={}&picdim=_145_97' \
'&callback=jQuery17209677058548357216_1530938601322&_=1530938933631'
self.comment_url = 'http://file.c.caixin.com/comment-sync/js/100/{}/{}.js'
self.summary_urls = [self.base_url.format(str(channel), self._start, self._max_fetch_cnt)
for _, channel in self.channels.items()]
self._path = path
self.summary_saved_file = self._path + '/summary_{}.txt'
self.detail_saved_file = self._path + '/detail_{}.txt'
self._source = 'Caixin'
create_path(self._path)
def simulate_background(input_yaml, jobs_yaml, count):
config_in = yaml.safe_load(open(input_yaml))
jobs_config = yaml.safe_load(open(jobs_yaml))
try:
ctools_pipe_path = create_path(jobs_config['exe']['software_path'])
except KeyError:
ctools_pipe_path = "."
# find proper IRF name
irf = IRFPicker(config_in, ctools_pipe_path)
name_irf = irf.irf_pick()
if irf.prod_number == "3b" and irf.prod_version == 0:
caldb = "prod3b"
else:
caldb = f'prod{irf.prod_number}-v{irf.prod_version}'
out_path = create_path(
f"{jobs_config['exe']['path']}/back_sim/{irf.prod_number}_{irf.prod_version}_{name_irf}"
)
# simulation details
sim_details = config_in['sim']
seed = int(count) * 10
# do the simulation
sim = ctools.ctobssim()
sim['inmodel'] = f"{ctools_pipe_path}/models/bkg_only_model.xml"
sim['caldb'] = caldb
sim['irf'] = name_irf
sim['ra'] = 0
sim['dec'] = 0
sim['rad'] = sim_details['radius']
sim['tmin'] = u.Quantity(sim_details['time']['t_min']).to_value(u.s)
sim['tmax'] = u.Quantity(sim_details['time']['t_max']).to_value(u.s)
sim['emin'] = u.Quantity(sim_details['energy']['e_min']).to_value(u.TeV)
sim['emax'] = u.Quantity(sim_details['energy']['e_max']).to_value(u.TeV)
sim['outevents'] = f"{out_path}/background_z-{irf.zenith}_site-{irf.irf_site}_{str(count).zfill(2)}_seed{seed}.fits"
sim['seed'] = seed
sim.execute()
def random_explain(dataset_path: Path, output_path: Path):
create_path(output_path)
nx_graphs, labels = read_graphs(dataset_path)
def explain(graph_num):
g = nx_graphs[graph_num]
random_importance = list(range(len(g.edges())))
random.shuffle(random_importance)
N = g.number_of_nodes()
masked_adj = np.zeros((N, N))
for (u, v), importance in zip(g.edges(), random_importance):
u = int(u)
v = int(v)
masked_adj[u, v] = masked_adj[v, u] = importance
return masked_adj
for gid in tq(nx_graphs):
masked_adj = explain(gid)
np.save(output_path / ('%s.npy' % gid), masked_adj)
def all_activation_mean_histogram(path, activation_data):
activation_data_list = []
for seed in activation_data:
data = activation_data[seed]
mean_data = np.mean(data, axis=0)
activation_data_list.append(mean_data)
n, bins, patches = plt.hist(activation_data_list,
20,
range=(0.0, 1.0),
label=activation_data.keys(),
rwidth=1.0,
linewidth=0)
plt.ylabel('number of neurons')
plt.xlabel('mean activation')
plt.title('mean activation for all seeds')
# plt.legend(loc='upper right')
# plt.ylim(0, 40)
# plt.show()
path = create_path(path, "activations")
plt.savefig(create_path(path, "all_seeds_mean_act.png"), dpi=__PNG_DPI__)
plt.clf()
def all_averaged_dataset_performance(network_name, measure, path, data,
source_animal, target_animal):
plt.plot(data["train"][measure], label='train')
plt.plot(data["val"][measure], label='val')
plt.plot(data["test"][measure], label='test')
plt.xlabel('epochs')
plt.ylabel(measure)
plt.title(network_name + " averaged results " + source_animal + " to " +
target_animal)
plt.legend(loc='lower right')
plt.ylim(0.0, 1.0)
# plt.show()
plt.savefig(create_path(path, "all_datasets_{}.png".format(measure)),
dpi=__PNG_DPI__)
plt.clf()
def _create_directory_structure(self):
if not self.created_dirs:
logger.info("creating output directory structure")
now = datetime.datetime.now().time().isoformat().replace(
":", "").replace(".", "")
output_dir_name = "pylicense_{}".format(now)
license_files = os.path.join(output_dir_name, "license_files")
output_path = os.path.join(self.output_path, license_files)
create_dirs = utils.create_path(output_path)
if create_dirs:
logger.info("Successfully created output directory structure")
self.created_dirs = output_path
return output_path
else:
return False
else:
return self.created_dirs
def main(args):
"""
Function for handling the arguments and starting the experiment.
Inputs:
args - Namespace object from the argument parser
"""
# set the seed
torch.manual_seed(args.seed)
# check if GPU is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# print the model parameters
print('-----TRAINING PARAMETERS-----')
print('Model version: {}'.format(args.model_version))
print('Labels: {}'.format(args.labels))
print('Setting: {}'.format(args.setting))
print('Test scenario: {}'.format(args.test_scenario))
print('Auxilary tasks: {}'.format(args.aux_tasks))
print('Auxilary task probing: {}'.format(args.aux_probing))
print('PyTorch device: {}'.format(device))
print('Max epochs: {}'.format(args.max_epochs))
print('Patience: {}'.format(args.patience))
print('Learning rates: {}'.format(args.lrs))
print('Batch size: {}'.format(args.batch_size))
print('Results directory: {}'.format(args.results_dir))
print('Progress bar: {}'.format(args.progress_bar))
print('Advanced metrics: {}'.format(args.advanced_metrics))
print('Pretrain: {}'.format(args.pretrain))
print('-----------------------------')
# generate the path to use for the results
path = create_path(args)
if not os.path.exists(path):
os.makedirs(path)
# check which setting is selected
if args.setting == 'matched':
handle_matched(args, device, path)
else:
handle_unmatched(args, device, path)
def compare_network_performance(dataset, measure, path, first_data,
second_data, source_animal, target_animal):
plt.figure(1)
plt.subplot(211)
plt.plot(first_data, label='target')
plt.xlabel('epochs')
plt.ylabel(measure)
plt.title("target " + dataset + " " + source_animal + " to " +
target_animal)
plt.ylim(0.0, 1.0)
plt.subplot(212)
plt.plot(second_data, label='naive')
plt.xlabel('epochs')
plt.ylabel(measure)
plt.title("naive " + dataset + " " + target_animal)
plt.ylim(0.0, 1.0)
# plt.show()
plt.savefig(create_path(path, "compare_{}_{}.png".format(dataset,
measure)),
dpi=__PNG_DPI__)
plt.clf()
def compare_average_network_performance(dataset, measure, path, first_data,
second_data, first_label, second_label,
source_animal, target_animal):
if first_label == "target":
plt.plot(first_data, label=first_label, color='C0')
plt.plot(second_data, label=second_label, color='C1')
else:
plt.plot(first_data, label=first_label, color='C1')
plt.plot(second_data, label=second_label, color='C0')
plt.xlabel('epochs')
plt.ylabel(measure)
plt.title(dataset + ' ' + measure + " averaged" + " " + source_animal +
" to " + target_animal)
plt.legend(loc='lower right')
plt.ylim(0.0, 1.0)
# plt.show()
plt.savefig(create_path(
path, "compare_average_{}_{}_{}.png".format(dataset, measure,
second_label)),
dpi=__PNG_DPI__)
plt.clf()
def saliency_map(run, model_type, seed, dataset_type, attempt, category=None, positive=True):
if not category:
category = run.hyperparameters.target_animal
img = _get_image(category, dataset_type, run, positive=positive)
plt.imsave(create_path(run.path, model_type, "images", category, "{}_original_image.png".format(attempt)), img)
img = img[np.newaxis, ...]
model = load_model(create_path(run.path, model_type, "saved_models", "{}_model_{}.h5".format(model_type, seed)))
print(model_type + " network predicts:")
print(model.predict(img))
layer_idx = 15
model.layers[layer_idx].activation = activations.linear
model = utils.apply_modifications(model)
grads = visualize_saliency(model, layer_idx, filter_indices=0, seed_input=img)
plt.imsave(
create_path(run.path, model_type, "images", category, "sal", "{}_sal_image_seed{}.png".format(attempt, seed)),
grads)
grads_guided = visualize_saliency(model, layer_idx, filter_indices=0, seed_input=img,
backprop_modifier="guided")
plt.imsave(create_path(run.path, model_type, "images", category, "sal",
"{}_sal_guided_image_seed{}.png".format(attempt, seed)), grads_guided)
acti = visualize_activation(model, layer_idx, filter_indices=0, seed_input=img)
plt.imsave(
create_path(run.path, model_type, "images", category, "acti", "{}_acti_image_seed{}.png".format(attempt, seed)),
acti)
cam = visualize_cam(model, layer_idx, filter_indices=0, seed_input=img)
plt.imsave(
create_path(run.path, model_type, "images", category, "cam", "{}_cam_image_seed{}.png".format(attempt, seed)),
cam)
cam_guided = visualize_cam(model, layer_idx, filter_indices=0, seed_input=img,
backprop_modifier="guided")
plt.imsave(create_path(run.path, model_type, "images", category, "cam",
"{}_cam_guided_image_seed{}.png".format(attempt, seed)), cam_guided)
def from_csv(file_path):
m = Hyperparameters()
hp_data = pd.read_csv(create_path(file_path, 'params.csv'))
dict_hp_data = hp_data.to_dict(orient='rows')[0]
m.source_max_epochs = dict_hp_data["source_max_epochs"]
m.target_max_epochs = dict_hp_data["target_max_epochs"]
m.num_starting_units = dict_hp_data["num_starting_units"]
m.upper_threshold = dict_hp_data["upper_threshold"]
m.lower_threshold = dict_hp_data["lower_threshold"]
m.source_lr = dict_hp_data["source_lr"]
m.target_lr = dict_hp_data["target_lr"]
m.batch_size = dict_hp_data["batch_size"]
m.conv_activation = dict_hp_data["conv_activation"]
m.loss_function = dict_hp_data["loss_function"]
m.pruning_method = dict_hp_data["pruning_method"]
m.source_animal = dict_hp_data["source_animal"]
m.target_animal = dict_hp_data["target_animal"]
m.pruning_dataset = dict_hp_data["pruning_dataset"]
m.save_opp = dict_hp_data["save_opp"]
m.labels_per_category = dict_hp_data["labels_per_category"]
m.reinit_weights = dict_hp_data["reinit_weights"]
return m
print(f"process {counter} started")
p = subprocess.Popen([
'python', 'background_sim.py', infile, in_jobs,
str(counter + 1)
],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
# if everything goes well, the output is None
# check this just for the first job
if counter == 0:
(result, error) = p.communicate()
print(result, error)
elif execution['mode'] == "bsub":
details = execution['details']
env_path = create_path(execution['env_path'])
# create string for jobs submission
exec_string = f"{execution['mode']} "
if details['queue']['name'] != "N/A":
exec_string += f"-q {details['queue']['name']} "
if details['queue']['flags'] != "N/A":
exec_string += f"{details['queue']['flags']} "
if details['mail'] != "N/A":
exec_string += f"-u {details['mail']} "
if execution['others'] != "N/A":
exec_string += f"{execution['others']} "
print(exec_string)
for counter in range(realizations):
def train(self, num_of_iters=1, data=None, hidden=None):
self.loss = 0.0
s = time.time()
# zero the parameter gradients
#self.optimizer.zero_grad()
for i in range(num_of_iters):
self.adjust_learning_rate(self.train_epoch, self.optimizer)
if self.train_iter % self.num_batches_per_epoch == 0 and self.train_iter > 0:
self.train_epoch += 1
logger.info('train iter: %d, num_batches_per_epoch: %d', self.train_iter, self.num_batches_per_epoch)
logger.info('Epoch %d, avg train acc: %f, lr: %f, avg loss: %f' % (self.train_iter//self.num_batches_per_epoch, np.mean(self.train_acc_top1), self.lr, self.avg_loss_per_epoch/self.num_batches_per_epoch))
if self.rank == 0 and self.writer is not None:
self.writer.add_scalar('cross_entropy', self.avg_loss_per_epoch/self.num_batches_per_epoch, self.train_epoch)
self.writer.add_scalar('top-1_acc', np.mean(self.train_acc_top1), self.train_epoch)
if self.rank == 0:
self.test(self.train_epoch)
self.sparsities = []
self.compression_ratios = []
self.communication_sizes = []
self.train_acc_top1 = []
self.epochs_info.append(self.avg_loss_per_epoch/self.num_batches_per_epoch)
self.avg_loss_per_epoch = 0.0
# Save checkpoint
if self.train_iter > 0 and self.rank == 0:
state = {'iter': self.train_iter, 'epoch': self.train_epoch, 'state': self.get_model_state()}
if self.prefix:
relative_path = './weights/%s/%s-n%d-bs%d-lr%.4f' % (self.prefix, self.dnn, self.nworkers, self.batch_size, self.base_lr)
else:
relative_path = './weights/%s-n%d-bs%d-lr%.4f' % (self.dnn, self.nworkers, self.batch_size, self.base_lr)
utils.create_path(relative_path)
filename = '%s-rank%d-epoch%d.pth'%(self.dnn, self.rank, self.train_epoch)
fn = os.path.join(relative_path, filename)
if self.train_epoch % 2== 0:
self.save_checkpoint(state, fn)
self.remove_dict(state)
if self.train_sampler and (self.nworkers > 1):
self.train_sampler.set_epoch(self.train_epoch)
ss = time.time()
if data is None:
data = self.data_iter()
if self.dataset == 'an4':
inputs, labels_cpu, input_percentages, target_sizes = data
input_sizes = input_percentages.mul_(int(inputs.size(3))).int()
else:
inputs, labels_cpu = data
if self.is_cuda:
if self.dnn == 'lstm' :
inputs = Variable(inputs.transpose(0, 1).contiguous()).cuda()
labels = Variable(labels_cpu.transpose(0, 1).contiguous()).cuda()
else:
inputs, labels = inputs.cuda(non_blocking=True), labels_cpu.cuda(non_blocking=True)
else:
labels = labels_cpu
self.iotime += (time.time() - ss)
sforward = time.time()
if self.dnn == 'lstman4':
out, output_sizes = self.net(inputs, input_sizes)
out = out.transpose(0, 1) # TxNxH
loss = self.criterion(out, labels_cpu, output_sizes, target_sizes)
#torch.cuda.synchronize()
self.forwardtime += (time.time() - sforward)
loss = loss / inputs.size(0) # average the loss by minibatch
elif self.dnn == 'lstm' :
hidden = lstmpy.repackage_hidden(hidden)
outputs, hidden = self.net(inputs, hidden)
tt = torch.squeeze(labels.view(-1, self.net.batch_size * self.net.num_steps))
loss = self.criterion(outputs.view(-1, self.net.vocab_size), tt)
#torch.cuda.synchronize()
self.forwardtime += (time.time() - sforward)
else:
# forward + backward + optimize
outputs = self.net(inputs)
loss = self.criterion(outputs, labels)
#torch.cuda.synchronize()
self.forwardtime += (time.time() - sforward)
sbackward = time.time()
if self.amp_handle is not None:
with apex.amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
loss = scaled_loss
else:
loss.backward()
loss_value = loss.item()
#torch.cuda.synchronize()
self.backwardtime += (time.time() - sbackward)
self.loss += loss_value
self.avg_loss_per_epoch += loss_value
if self.dnn not in ['lstm', 'lstman4']:
acc1, = self.cal_accuracy(outputs, labels, topk=(1,))
self.train_acc_top1.append(float(acc1))
self.train_iter += 1
self.num_of_updates_during_comm += 1
self.loss /= num_of_iters
self.timer += time.time() - s
display = 40
if self.train_iter % display == 0:
logger.warn('[%3d][%5d/%5d][rank:%d] loss: %.3f, average forward (%f) and backward (%f) time: %f, iotime: %f ' %
(self.train_epoch, self.train_iter, self.num_batches_per_epoch, self.rank, self.loss, self.forwardtime/display, self.backwardtime/display, self.timer/display, self.iotime/display))
self.timer = 0.0
self.iotime = 0.0
self.forwardtime = 0.0
self.backwardtime = 0.0
if self.dnn == 'lstm':
return num_of_iters, hidden
return num_of_iters
def create_module(module): src_path = get_src_file_path(module) inc_path = get_inc_file_path(module) utils.create_path(src_path) utils.create_path(inc_path) add_to_building_list(module)