def info_tutorial():
nn = NeuralNet()
x_shape = dat.shape()
test_gen, _ = dat.testset(batch_size=cfg.BATCH_SIZE,
max_samples=cfg.TEST_SET_SIZE)
nn.test(test_gen, print_it=True)
nn.net.initialize_spatial_layers(x_shape, cfg.BATCH_SIZE, PATCH_SIZE)
nn.summary(x_shape, print_it=True)
nn.print_weights()
print(nn.output_size(x_shape))
# Spatial Operations, defined one the net itself. Remember that after enabling a layer, ops are affected
assert nn.net.num_spatial_layers() != 0
nn.net.print_spatial_status()
# nn.train(epochs=1, set_size=5000, lr=0.1, batch_size=cfg.BATCH_SIZE) # Train to see fully disabled performance
nn.net.print_ops_summary()
nn.net.print_ops_summary(
use_conv=True) # Count convlution operations instead of MAC
print(nn.net.num_ops()) # (ops_saved, total_ops)
# Given x, we generate all spatial layer requirement sizes:
spat_sizes = nn.net.generate_spatial_sizes(x_shape)
print(spat_sizes)
p_spat_sizes = nn.net.generate_padded_spatial_sizes(x_shape, PATCH_SIZE)
print(p_spat_sizes)
# Generate a constant 1 value mask over all spatial nets
print(nn.net.enabled_layers())
nn.net.fill_masks_to_val(1)
print(nn.net.enabled_layers())
print(nn.net.disabled_layers())
nn.net.print_spatial_status(
) # Now all are enabled, seeing the mask was set
nn.train(epochs=1, set_size=5000, lr=0.1, batch_size=cfg.BATCH_SIZE
) # Train to see all layers enabled performance
nn.net.print_ops_summary()
nn.net.print_ops_summary(
use_conv=True) # Count convlution operations instead of MAC
nn.net.reset_spatial() # Disables layers as well
nn.net.print_ops_summary()
nn.net.print_ops_summary(use_conv=True)
# Turns on 3 ids and turns off all others
chosen_victims = random.sample(range(nn.net.num_spatial_layers()), 4)
nn.net.strict_mask_update(update_ids=chosen_victims[0:3],
masks=[
torch.zeros(p_spat_sizes[chosen_victims[0]]),
torch.zeros(p_spat_sizes[chosen_victims[1]]),
torch.zeros(p_spat_sizes[chosen_victims[2]])
])
# Turns on one additional id and *does not* turn off all others
nn.net.lazy_mask_update(
update_ids=[chosen_victims[3]],
masks=[torch.zeros(p_spat_sizes[chosen_victims[3]])])
nn.net.print_spatial_status() #
print(nn.net.enabled_layers())
nn.train(epochs=1, set_size=5000, lr=0.1,
batch_size=cfg.BATCH_SIZE) # Run with 4 layers on
nn.net.print_ops_summary()
nn.net.print_ops_summary(use_conv=True)
def training():
# dat.data_summary(show_sample=False)
nn = NeuralNet(resume=True) # Spatial layers are by default, disabled
nn.summary(dat.shape())
nn.train(epochs=50, lr=0.01)
test_gen, _ = dat.testset(batch_size=cfg.BATCH_SIZE,
max_samples=cfg.TEST_SET_SIZE)
test_loss, test_acc, count = nn.test(test_gen)
print(
f'==> Final testing results: test acc: {test_acc:.3f} with {count}, test loss: {test_loss:.3f}'
)
def __init__(self,
patch_size,
ones_range,
gran_thresh,
max_acc_loss,
init_acc=None,
test_size=cfg.TEST_SET_SIZE,
patterns_idx=None):
self.ps = patch_size
self.max_acc_loss = max_acc_loss
self.gran_thresh = gran_thresh
if patterns_idx is None:
self.ones_range = ones_range
self.input_patterns = None
else:
patterns_rec = load_from_file(
f'all_patterns_ps{self.ps}_cluster{patterns_idx}.pkl',
path=cfg.RESULTS_DIR)
self.ones_range = (patterns_rec[1], patterns_rec[1] + 1)
self.input_patterns = patterns_rec[2]
self.full_net_run_time = None
self.total_ops = None
self.nn = NeuralNet()
self.nn.net.initialize_spatial_layers(dat.shape(), cfg.BATCH_SIZE,
self.ps)
self.test_gen, _ = dat.testset(batch_size=cfg.BATCH_SIZE,
max_samples=cfg.TEST_SET_SIZE)
self.test_set_size = cfg.TEST_SET_SIZE
if INNAS_COMP:
init_acc = DEBUG_INIT_ACC
if init_acc is None:
_, test_acc, correct = self.nn.test(self.test_gen)
print(f'==> Asserted test-acc of: {test_acc} [{correct}]\n ')
self.init_acc = test_acc # TODO - Fix initialize bug
else:
self.init_acc = init_acc
self.record_finder = RecordFinder(cfg.NET.__name__, dat.name(),
patch_size, ones_range, gran_thresh,
max_acc_loss, self.init_acc)
def test():
nn = NeuralNet(resume=True)
test_gen, _ = dat.testset(batch_size=cfg.BATCH_SIZE,
max_samples=cfg.TEST_SET_SIZE)
test_loss, test_acc, count = nn.test(test_gen, print_it=True)