def get_test(self, classes, encoder, batch_size):
dataset = MNISTWrapper(encoder,
subset_idx=range(self.n_eval),
classes=classes,
label_shape=self.label_shape,
label_intensity=0.,
assignments=self.assignments,
root=P.DATA_FOLDER,
train=False,
download=True,
transform=self.T)
return DataLoader(dataset,
batch_size=batch_size,
shuffle=False,
num_workers=P.N_WORKERS,
pin_memory=P.GPU)
def get_val(self, classes, encoder, batch_size):
if self.validate_on_tst_set:
return self.get_test(classes, encoder, batch_size)
dataset = MNISTWrapper(encoder,
subset_idx=range(P.TRN_SET_SIZE - self.n_eval,
P.TRN_SET_SIZE),
classes=classes,
label_shape=self.label_shape,
label_intensity=0.,
assignments=self.assignments,
root=P.DATA_FOLDER,
train=True,
download=True,
transform=self.T)
return DataLoader(dataset,
batch_size=batch_size,
shuffle=False,
num_workers=P.N_WORKERS,
pin_memory=P.GPU)
def train(self) -> None:
# language=rst
"""
Training loop that runs for the set number of epochs and creates a new
``DataLoader`` at each epoch.
"""
for epoch in range(self.num_epochs):
train_dataloader = DataLoader(
self.train_ds,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=self.pin_memory,
shuffle=self.shuffle,
)
for step, batch in enumerate(
tqdm(
train_dataloader,
desc="Epoch %d/%d" % (epoch + 1, self.num_epochs),
total=len(self.train_ds) // self.batch_size,
)):
self.step(batch)
def main(args):
if args.update_steps is None:
args.update_steps = max(
250 // args.batch_size, 1
) #Its value is 16 # why is it always multiplied with step? #update_steps is how many batch to classify before updating the graphs
update_interval = args.update_steps * args.batch_size # Value is 240 #update_interval is how many pictures to classify before updating the graphs
# Sets up GPU use
torch.backends.cudnn.benchmark = False
if args.gpu and torch.cuda.is_available():
torch.cuda.manual_seed_all(
args.seed
) #to enable reproducability of the code to get the same result
else:
torch.manual_seed(args.seed)
# Determines number of workers to use
if args.n_workers == -1:
args.n_workers = args.gpu * 4 * torch.cuda.device_count()
n_sqrt = int(np.ceil(np.sqrt(args.n_neurons)))
if args.reduction == "sum": #could have used switch to improve performance
reduction = torch.sum #weight updates for the batch
elif args.reduction == "mean":
reduction = torch.mean
elif args.reduction == "max":
reduction = max_without_indices
else:
raise NotImplementedError
# Build network.
network = DiehlAndCook2015v2( #Changed here
n_inpt=784, # input dimensions are 28x28=784
n_neurons=args.n_neurons,
inh=args.inh,
dt=args.dt,
norm=78.4,
nu=(1e-4, 1e-2),
reduction=reduction,
theta_plus=args.theta_plus,
inpt_shape=(1, 28, 28),
)
# Directs network to GPU
if args.gpu:
network.to("cuda")
# Load MNIST data.
dataset = MNIST(
PoissonEncoder(time=args.time, dt=args.dt),
None,
root=os.path.join(ROOT_DIR, "data", "MNIST"),
download=True,
train=True,
transform=transforms.Compose( #Composes several transforms together
[
transforms.ToTensor(),
transforms.Lambda(lambda x: x * args.intensity)
]),
)
test_dataset = MNIST(
PoissonEncoder(time=args.time, dt=args.dt),
None,
root=os.path.join(ROOT_DIR, "data", "MNIST"),
download=True,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: x * args.intensity)
]),
)
# Neuron assignments and spike proportions.
n_classes = 10 #changed
assignments = -torch.ones(args.n_neurons) #assignments is set to -1
proportions = torch.zeros(args.n_neurons,
n_classes) #matrix of 100x10 filled with zeros
rates = torch.zeros(args.n_neurons,
n_classes) #matrix of 100x10 filled with zeros
# Set up monitors for spikes and voltages
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(
network.layers[layer], state_vars=["s"], time=args.time
) # Monitors: Records state variables of interest. obj:An object to record state variables from during network simulation.
network.add_monitor(
spikes[layer], name="%s_spikes" % layer
) #state_vars: Iterable of strings indicating names of state variables to record.
#param time: If not ``None``, pre-allocate memory for state variable recording.
weights_im = None
spike_ims, spike_axes = None, None
# Record spikes for length of update interval.
spike_record = torch.zeros(update_interval, args.time, args.n_neurons)
if os.path.isdir(
args.log_dir): #checks if the path is a existing directory
shutil.rmtree(
args.log_dir) # is used to delete an entire directory tree
# Summary writer.
writer = SummaryWriter(
log_dir=args.log_dir, flush_secs=60
) #SummaryWriter: these utilities let you log PyTorch models and metrics into a directory for visualization
#flush_secs: in seconds, to flush the pending events and summaries to disk.
for epoch in range(args.n_epochs): #default is 1
print("\nEpoch: {epoch}\n")
labels = []
# Create a dataloader to iterate and batch data
dataloader = DataLoader( #It represents a Python iterable over a dataset
dataset,
batch_size=args.batch_size, #how many samples per batch to load
shuffle=
True, #set to True to have the data reshuffled at every epoch
num_workers=args.n_workers,
pin_memory=args.
gpu, #If True, the data loader will copy Tensors into CUDA pinned memory before returning them.
)
for step, batch in enumerate(
dataloader
): #Enumerate() method adds a counter to an iterable and returns it in a form of enumerate object
print("Step:", step)
global_step = 60000 * epoch + args.batch_size * step
if step % args.update_steps == 0 and step > 0:
# Convert the array of labels into a tensor
label_tensor = torch.tensor(labels)
# Get network predictions.
all_activity_pred = all_activity(spikes=spike_record,
assignments=assignments,
n_labels=n_classes)
proportion_pred = proportion_weighting(
spikes=spike_record,
assignments=assignments,
proportions=proportions,
n_labels=n_classes,
)
writer.add_scalar(
tag="accuracy/all vote",
scalar_value=torch.mean(
(label_tensor.long() == all_activity_pred).float()),
global_step=global_step,
)
#Vennila: Records the accuracies in each step
value = torch.mean(
(label_tensor.long() == all_activity_pred).float())
value = value.item()
accuracy.append(value)
print("ACCURACY:", value)
writer.add_scalar(
tag="accuracy/proportion weighting",
scalar_value=torch.mean(
(label_tensor.long() == proportion_pred).float()),
global_step=global_step,
)
writer.add_scalar(
tag="spikes/mean",
scalar_value=torch.mean(torch.sum(spike_record, dim=1)),
global_step=global_step,
)
square_weights = get_square_weights(
network.connections["X", "Y"].w.view(784, args.n_neurons),
n_sqrt,
28,
)
img_tensor = colorize(square_weights, cmap="hot_r")
writer.add_image(
tag="weights",
img_tensor=img_tensor,
global_step=global_step,
dataformats="HWC",
)
# Assign labels to excitatory layer neurons.
assignments, proportions, rates = assign_labels(
spikes=spike_record,
labels=label_tensor,
n_labels=n_classes,
rates=rates,
)
labels = []
labels.extend(
batch["label"].tolist()
) #for each batch or 16 pictures the labels of it is added to this list
# Prep next input batch.
inpts = {"X": batch["encoded_image"]}
if args.gpu:
inpts = {
k: v.cuda()
for k, v in inpts.items()
} #.cuda() is used to set up and run CUDA operations in the selected GPU
# Run the network on the input.
t0 = time()
network.run(inputs=inpts, time=args.time, one_step=args.one_step
) # Simulate network for given inputs and time.
t1 = time() - t0
# Add to spikes recording.
s = spikes["Y"].get("s").permute((1, 0, 2))
spike_record[(step * args.batch_size) %
update_interval:(step * args.batch_size %
update_interval) + s.size(0)] = s
writer.add_scalar(tag="time/simulation",
scalar_value=t1,
global_step=global_step)
# if(step==1):
# input_exc_weights = network.connections["X", "Y"].w
# an_array = input_exc_weights.detach().cpu().clone().numpy()
# #print(np.shape(an_array))
# data = asarray(an_array)
# savetxt('data.csv',data)
# print("Beginning weights saved")
# if(step==3749):
# input_exc_weights = network.connections["X", "Y"].w
# an_array = input_exc_weights.detach().cpu().clone().numpy()
# #print(np.shape(an_array))
# data2 = asarray(an_array)
# savetxt('data2.csv',data2)
# print("Ending weights saved")
# Plot simulation data.
if args.plot:
input_exc_weights = network.connections["X", "Y"].w
# print("Weights:",input_exc_weights)
square_weights = get_square_weights(
input_exc_weights.view(784, args.n_neurons), n_sqrt, 28)
spikes_ = {
layer: spikes[layer].get("s")[:, 0]
for layer in spikes
}
spike_ims, spike_axes = plot_spikes(spikes_,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_weights(square_weights, im=weights_im)
plt.pause(1e-8)
# Reset state variables.
network.reset_state_variables()
print(end_accuracy()) #Vennila
# Train the network.
print("Begin training.\n")
start = t()
weights_im = None
for epoch in range(n_epochs):
if epoch % progress_interval == 0:
print("Progress: %d / %d (%.4f seconds)" %
(epoch, n_epochs, t() - start))
start = t()
train_dataloader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4,
pin_memory=gpu,
)
for step, batch in enumerate(tqdm(train_dataloader)):
# Get next input sample.
inpts = {"X": batch["encoded_image"]}
if gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
label = batch["label"]
# Run the network on the input.
network.run(inpts=inpts, time=time, input_time_dim=1)
def main(args):
if args.gpu:
torch.cuda.manual_seed_all(args.seed)
else:
torch.manual_seed(args.seed)
conv_size = int(
(28 - args.kernel_size + 2 * args.padding) / args.stride) + 1
# Build network.
network = Network()
input_layer = Input(n=784, shape=(1, 28, 28), traces=True)
conv_layer = DiehlAndCookNodes(
n=args.n_filters * conv_size * conv_size,
shape=(args.n_filters, conv_size, conv_size),
traces=True,
)
conv_conn = Conv2dConnection(
input_layer,
conv_layer,
kernel_size=args.kernel_size,
stride=args.stride,
update_rule=PostPre,
norm=0.4 * args.kernel_size**2,
nu=[0, args.lr],
reduction=max_without_indices,
wmax=1.0,
)
w = torch.zeros(args.n_filters, conv_size, conv_size, args.n_filters,
conv_size, conv_size)
for fltr1 in range(args.n_filters):
for fltr2 in range(args.n_filters):
if fltr1 != fltr2:
for i in range(conv_size):
for j in range(conv_size):
w[fltr1, i, j, fltr2, i, j] = -100.0
w = w.view(args.n_filters * conv_size * conv_size,
args.n_filters * conv_size * conv_size)
recurrent_conn = Connection(conv_layer, conv_layer, w=w)
network.add_layer(input_layer, name="X")
network.add_layer(conv_layer, name="Y")
network.add_connection(conv_conn, source="X", target="Y")
network.add_connection(recurrent_conn, source="Y", target="Y")
# Voltage recording for excitatory and inhibitory layers.
voltage_monitor = Monitor(network.layers["Y"], ["v"], time=args.time)
network.add_monitor(voltage_monitor, name="output_voltage")
if args.gpu:
network.to("cuda")
# Load MNIST data.
train_dataset = MNIST(
PoissonEncoder(time=args.time, dt=args.dt),
None,
os.path.join(ROOT_DIR, "data", "MNIST"),
download=True,
train=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: x * args.intensity)
]),
)
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=["s"],
time=args.time)
network.add_monitor(spikes[layer], name="%s_spikes" % layer)
voltages = {}
for layer in set(network.layers) - {"X"}:
voltages[layer] = Monitor(network.layers[layer],
state_vars=["v"],
time=args.time)
network.add_monitor(voltages[layer], name="%s_voltages" % layer)
# Train the network.
print("Begin training.\n")
start = time()
weights_im = None
for epoch in range(args.n_epochs):
if epoch % args.progress_interval == 0:
print("Progress: %d / %d (%.4f seconds)" %
(epoch, args.n_epochs, time() - start))
start = time()
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=args.gpu,
)
for step, batch in enumerate(tqdm(train_dataloader)):
# Get next input sample.
inpts = {"X": batch["encoded_image"]}
if args.gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
# Run the network on the input.
network.run(inpts=inpts, time=args.time, input_time_dim=0)
# Decay learning rate.
network.connections["X", "Y"].nu[1] *= 0.99
# Optionally plot various simulation information.
if args.plot:
weights = conv_conn.w
weights_im = plot_conv2d_weights(weights, im=weights_im)
plt.pause(1e-8)
network.reset_() # Reset state variables.
print("Progress: %d / %d (%.4f seconds)\n" %
(args.n_epochs, args.n_epochs, time() - start))
print("Training complete.\n")
def main(args):
update_interval = args.update_steps * args.batch_size
# Sets up GPU use
torch.backends.cudnn.benchmark = False
if args.gpu and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
else:
torch.manual_seed(args.seed)
# Determines number of workers to use
if args.n_workers == -1:
args.n_workers = args.gpu * 4 * torch.cuda.device_count()
n_sqrt = int(np.ceil(np.sqrt(args.n_neurons)))
if args.reduction == "sum":
reduction = torch.sum
elif args.reduction == "mean":
reduction = torch.mean
elif args.reduction == "max":
reduction = max_without_indices
else:
raise NotImplementedError
# Build network.
network = DiehlAndCook2015v2(
n_inpt=784,
n_neurons=args.n_neurons,
inh=args.inh,
dt=args.dt,
norm=78.4,
nu=(0.0, 1e-2),
reduction=reduction,
theta_plus=args.theta_plus,
inpt_shape=(1, 28, 28),
)
# Directs network to GPU.
if args.gpu:
network.to("cuda")
# Load MNIST data.
dataset = MNIST(
PoissonEncoder(time=args.time, dt=args.dt),
None,
root=os.path.join(ROOT_DIR, "data", "MNIST"),
download=True,
train=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: x * args.intensity)
]),
)
dataset, valid_dataset = torch.utils.data.random_split(
dataset, [59000, 1000])
test_dataset = MNIST(
PoissonEncoder(time=args.time, dt=args.dt),
None,
root=os.path.join(ROOT_DIR, "data", "MNIST"),
download=True,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: x * args.intensity)
]),
)
# Neuron assignments and spike proportions.
n_classes = 10
assignments = -torch.ones(args.n_neurons)
proportions = torch.zeros(args.n_neurons, n_classes)
rates = torch.zeros(args.n_neurons, n_classes)
# Set up monitors for spikes and voltages
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=["s"],
time=args.time)
network.add_monitor(spikes[layer], name="%s_spikes" % layer)
weights_im = None
spike_ims, spike_axes = None, None
# Record spikes for length of update interval.
spike_record = torch.zeros(update_interval, args.time, args.n_neurons)
if os.path.isdir(args.log_dir):
shutil.rmtree(args.log_dir)
# Summary writer.
writer = SummaryWriter(log_dir=args.log_dir, flush_secs=60)
for epoch in range(args.n_epochs):
print(f"\nEpoch: {epoch}\n")
labels = []
# Get training data loader.
dataloader = DataLoader(
dataset=dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.n_workers,
pin_memory=args.gpu,
)
for step, batch in enumerate(dataloader):
print(f"Step: {step} / {len(dataloader)}")
global_step = 60000 * epoch + args.batch_size * step
if step % args.update_steps == 0 and step > 0:
# Disable learning.
network.train(False)
# Get test data loader.
valid_dataloader = DataLoader(
dataset=valid_dataset,
batch_size=args.test_batch_size,
shuffle=True,
num_workers=args.n_workers,
pin_memory=args.gpu,
)
test_labels = []
test_spike_record = torch.zeros(len(valid_dataset), args.time,
args.n_neurons)
t0 = time()
for test_step, test_batch in enumerate(valid_dataloader):
# Prep next input batch.
inpts = {"X": test_batch["encoded_image"]}
if args.gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
# Run the network on the input (inference mode).
network.run(inpts=inpts,
time=args.time,
one_step=args.one_step)
# Add to spikes recording.
s = spikes["Y"].get("s").permute((1, 0, 2))
test_spike_record[(test_step * args.test_batch_size
):(test_step * args.test_batch_size) +
s.size(0)] = s
# Plot simulation data.
if args.valid_plot:
input_exc_weights = network.connections["X", "Y"].w
square_weights = get_square_weights(
input_exc_weights.view(784, args.n_neurons),
n_sqrt, 28)
spikes_ = {
layer: spikes[layer].get("s")[:, 0]
for layer in spikes
}
spike_ims, spike_axes = plot_spikes(spikes_,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_weights(square_weights,
im=weights_im)
plt.pause(1e-8)
# Reset state variables.
network.reset_()
test_labels.extend(test_batch["label"].tolist())
t1 = time() - t0
writer.add_scalar(tag="time/test",
scalar_value=t1,
global_step=global_step)
# Convert the list of labels into a tensor.
test_label_tensor = torch.tensor(test_labels)
# Get network predictions.
all_activity_pred = all_activity(
spikes=test_spike_record,
assignments=assignments,
n_labels=n_classes,
)
proportion_pred = proportion_weighting(
spikes=test_spike_record,
assignments=assignments,
proportions=proportions,
n_labels=n_classes,
)
writer.add_scalar(
tag="accuracy/valid/all vote",
scalar_value=100 * torch.mean(
(test_label_tensor.long()
== all_activity_pred).float()),
global_step=global_step,
)
writer.add_scalar(
tag="accuracy/valid/proportion weighting",
scalar_value=100 * torch.mean(
(test_label_tensor.long() == proportion_pred).float()),
global_step=global_step,
)
square_weights = get_square_weights(
network.connections["X", "Y"].w.view(784, args.n_neurons),
n_sqrt,
28,
)
img_tensor = colorize(square_weights, cmap="hot_r")
writer.add_image(
tag="weights",
img_tensor=img_tensor,
global_step=global_step,
dataformats="HWC",
)
# Convert the array of labels into a tensor
label_tensor = torch.tensor(labels)
# Get network predictions.
all_activity_pred = all_activity(spikes=spike_record,
assignments=assignments,
n_labels=n_classes)
proportion_pred = proportion_weighting(
spikes=spike_record,
assignments=assignments,
proportions=proportions,
n_labels=n_classes,
)
writer.add_scalar(
tag="accuracy/train/all vote",
scalar_value=100 * torch.mean(
(label_tensor.long() == all_activity_pred).float()),
global_step=global_step,
)
writer.add_scalar(
tag="accuracy/train/proportion weighting",
scalar_value=100 * torch.mean(
(label_tensor.long() == proportion_pred).float()),
global_step=global_step,
)
# Assign labels to excitatory layer neurons.
assignments, proportions, rates = assign_labels(
spikes=spike_record,
labels=label_tensor,
n_labels=n_classes,
rates=rates,
)
# Re-enable learning.
network.train(True)
labels = []
labels.extend(batch["label"].tolist())
# Prep next input batch.
inpts = {"X": batch["encoded_image"]}
if args.gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
# Run the network on the input (training mode).
t0 = time()
network.run(inpts=inpts, time=args.time, one_step=args.one_step)
t1 = time() - t0
writer.add_scalar(tag="time/train/step",
scalar_value=t1,
global_step=global_step)
# Add to spikes recording.
s = spikes["Y"].get("s").permute((1, 0, 2))
spike_record[(step * args.batch_size) %
update_interval:(step * args.batch_size %
update_interval) + s.size(0)] = s
# Plot simulation data.
if args.plot:
input_exc_weights = network.connections["X", "Y"].w
square_weights = get_square_weights(
input_exc_weights.view(784, args.n_neurons), n_sqrt, 28)
spikes_ = {
layer: spikes[layer].get("s")[:, 0]
for layer in spikes
}
spike_ims, spike_axes = plot_spikes(spikes_,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_weights(square_weights, im=weights_im)
plt.pause(1e-8)
# Reset state variables.
network.reset_()
def main(args):
# Random seeding.
torch.manual_seed(args.seed)
# Device.
device = torch.device("cuda" if args.gpu else "cpu")
# No. workers.
if args.n_workers == -1:
args.n_workers = args.gpu * 4 * torch.cuda.device_count()
# Build network.
network = Network(batch_size=args.batch_size)
network.add_layer(Input(shape=(1, 28, 28), traces=True), name="I")
network.add_layer(LIFNodes(n=10,
traces=True,
rest=0,
reset=0,
thresh=1,
refrac=0),
name="O")
network.add_connection(
Connection(
source=network.layers["I"],
target=network.layers["O"],
nu=(0.0, 0.01),
update_rule=Hebbian,
wmin=0.0,
wmax=1.0,
norm=100.0,
reduction=torch.sum,
),
source="I",
target="O",
)
if args.plot:
for l in network.layers:
network.add_monitor(Monitor(network.layers[l],
state_vars=("s", ),
time=args.time),
name=l)
network.to(device)
# Load dataset.
dataset = MNIST(
image_encoder=PoissonEncoder(time=args.time, dt=1.0),
label_encoder=None,
root=os.path.join(ROOT_DIR, "data", "MNIST"),
download=True,
train=False,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Lambda(lambda x: x * 250)]),
)
# Create a dataloader to iterate and batch data
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.n_workers,
pin_memory=args.gpu,
)
spike_ims = None
spike_axes = None
weights_im = None
t0 = time()
for step, batch in enumerate(tqdm(dataloader)):
# Prep next input batch.
inputs = batch["encoded_image"]
inpts = {"I": inputs}
if args.gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
clamp = torch.nn.functional.one_hot(batch["label"],
num_classes=10).byte()
unclamp = ~clamp
clamp = {"O": clamp}
unclamp = {"O": unclamp}
# Run the network on the input.
network.run(
inpts=inpts,
time=args.time,
one_step=args.one_step,
clamp=clamp,
unclamp=unclamp,
)
if args.plot:
# Plot output spikes.
spikes = {
l: network.monitors[l].get("s")[:, 0]
for l in network.monitors
}
spike_ims, spike_axes = plot_spikes(spikes=spikes,
ims=spike_ims,
axes=spike_axes)
# Plot connection weights.
weights = network.connections["I", "O"].w
weights = get_square_weights(weights, n_sqrt=4, side=28)
weights_im = plot_weights(weights,
wmax=network.connections["I", "O"].wmax,
im=weights_im)
plt.pause(1e-2)
# Reset state variables.
network.reset_()
network.learning = False
for step, batch in enumerate(tqdm(dataloader)):
# Prep next input batch.
inputs = batch["encoded_image"]
inpts = {"I": inputs}
if args.gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
# Run the network on the input.
network.run(inpts=inpts, time=args.time, one_step=args.one_step)
if args.plot:
# Plot output spikes.
spikes = {
l: network.monitors[l].get("s")[:, 0]
for l in network.monitors
}
spike_ims, spike_axes = plot_spikes(spikes=spikes,
ims=spike_ims,
axes=spike_axes)
# Plot connection weights.
weights = network.connections["I", "O"].w
weights = get_square_weights(weights, n_sqrt=4, side=28)
weights_im = plot_weights(weights,
wmax=network.connections["I", "O"].wmax,
im=weights_im)
plt.pause(1e-2)
t1 = time() - t0
print(f"Time: {t1}")
def main(args):
if args.gpu and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_workers == -1:
args.n_workers = args.gpu * 4 * torch.cuda.device_count()
device = torch.device("cuda" if args.gpu else "cpu")
# Load trained MLP from disk.
ann = MLP().to(device)
f = os.path.join(args.job_dir, "ann.pt")
ann.load_state_dict(state_dict=torch.load(f=f))
# Load dataset.
dataset = MNIST(
image_encoder=RepeatEncoder(time=args.time, dt=1.0),
label_encoder=None,
root=os.path.join(ROOT_DIR, "data", "MNIST"),
download=True,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, )),
transforms.Lambda(lambda x: x.view(-1)),
]),
)
# Do ANN to SNN conversion.
data = dataset.data.float()
data /= data.max()
data = data.view(-1, 784)
snn = ann_to_snn(ann, input_shape=(784, ), data=data.to(device))
snn = snn.to(device)
print(snn)
# Create a dataloader to iterate and batch data
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.n_workers,
pin_memory=args.gpu,
)
correct = 0
t0 = time()
for step, batch in enumerate(tqdm(dataloader)):
# Prep next input batch.
inputs = batch["encoded_image"]
labels = batch["label"]
inpts = {"Input": inputs}
if args.gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
# Run the network on the input.
snn.run(inpts=inpts, time=args.time, one_step=args.one_step)
output_voltages = snn.layers["5"].summed
prediction = torch.softmax(output_voltages, dim=1).argmax(dim=1)
correct += (prediction.cpu() == labels).sum().item()
# Reset state variables.
snn.reset_()
t1 = time() - t0
accuracy = 100 * correct / len(dataloader.dataset)
print(f"SNN accuracy: {accuracy:.2f}")
path = os.path.join(ROOT_DIR, "results", args.results_file)
os.makedirs(os.path.dirname(path), exist_ok=True)
if not os.path.isfile(path):
with open(os.path.join(path), "w") as f:
f.write(
"seed,simulation time,batch size,inference time,accuracy\n")
to_write = [args.seed, args.time, args.batch_size, t1, accuracy]
to_write = ",".join(map(str, to_write)) + "\n"
with open(os.path.join(path), "a") as f:
f.write(to_write)
return t1
def main(args):
# Random seed.
if args.gpu and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
else:
torch.manual_seed(args.seed)
# Determines number of workers.
if args.n_workers == -1:
args.n_workers = args.gpu * 4 * torch.cuda.device_count()
# Build network.
network = bindsnet.network.Network(dt=args.dt, batch_size=args.batch_size)
# Layers.
input_layer = Input(shape=(1, 28, 28), traces=True)
conv1_layer = LIFNodes(shape=(20, 24, 24), traces=True)
pool1_layer = PassThroughNodes(shape=(20, 12, 12), traces=True)
conv2_layer = LIFNodes(shape=(50, 8, 8), traces=True)
pool2_layer = PassThroughNodes(shape=(50, 4, 4), traces=True)
dense_layer = LIFNodes(shape=(200, ), traces=True)
output_layer = LIFNodes(shape=(10, ), traces=True)
network.add_layer(input_layer, name="I")
network.add_layer(conv1_layer, name="C1")
network.add_layer(pool1_layer, name="P1")
network.add_layer(conv2_layer, name="C2")
network.add_layer(pool2_layer, name="P2")
network.add_layer(dense_layer, name="D")
network.add_layer(output_layer, name="O")
# Connections.
conv1_connection = Conv2dConnection(
source=input_layer,
target=conv1_layer,
update_rule=WeightDependentPost,
nu=(0.0, args.nu),
kernel_size=5,
stride=1,
wmin=-1.0,
wmax=1.0,
)
pool1_connection = SpatialPooling2dConnection(source=conv1_layer,
target=pool1_layer,
kernel_size=2,
stride=2)
conv2_connection = Conv2dConnection(
source=pool1_layer,
target=conv2_layer,
update_rule=WeightDependentPost,
nu=(0.0, args.nu),
kernel_size=5,
stride=1,
wmin=-1.0,
wmax=1.0,
)
pool2_connection = SpatialPooling2dConnection(source=conv2_layer,
target=pool2_layer,
kernel_size=2,
stride=2)
dense_connection = Connection(
source=pool2_layer,
target=dense_layer,
update_rule=WeightDependentPost,
nu=(0.0, args.nu),
wmin=-1.0,
wmax=1.0,
)
output_connection = Connection(
source=dense_layer,
target=output_layer,
update_rule=WeightDependentPost,
nu=(0.0, args.nu),
wmin=-1.0,
wmax=1.0,
)
network.add_connection(connection=conv1_connection,
source="I",
target="C1")
network.add_connection(connection=pool1_connection,
source="C1",
target="P1")
network.add_connection(connection=conv2_connection,
source="P1",
target="C2")
network.add_connection(connection=pool2_connection,
source="C2",
target="P2")
network.add_connection(connection=dense_connection,
source="P2",
target="D")
network.add_connection(connection=output_connection,
source="D",
target="O")
# Monitors.
for name, layer in network.layers.items():
monitor = Monitor(obj=layer, state_vars=("s", ), time=args.time)
network.add_monitor(monitor=monitor, name=name)
# Directs network to GPU.
if args.gpu:
network.to("cuda")
# Load MNIST data.
dataset = MNIST(
PoissonEncoder(time=args.time, dt=args.dt),
None,
root=os.path.join(bindsnet.ROOT_DIR, "data", "MNIST"),
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: x * args.intensity)
]),
)
spike_ims = None
spike_axes = None
conv1_weights_im = None
conv2_weights_im = None
dense_weights_im = None
output_weights_im = None
for epoch in range(args.n_epochs):
# Create a dataloader to iterate over dataset.
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.n_workers,
pin_memory=args.gpu,
)
for step, batch in enumerate(tqdm(dataloader)):
# Prep next input batch.
inpts = {"I": batch["encoded_image"]}
if args.gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
# Run the network on the input.
network.run(inpts=inpts, time=args.time)
# Plot simulation data.
if args.plot:
spikes = {}
for name, monitor in network.monitors.items():
spikes[name] = monitor.get("s")[:, 0].view(args.time, -1)
spike_ims, spike_axes = plot_spikes(spikes,
ims=spike_ims,
axes=spike_axes)
conv1_weights_im = plot_conv2d_weights(conv1_connection.w,
im=conv1_weights_im,
wmin=-1.0,
wmax=1.0)
conv2_weights_im = plot_conv2d_weights(conv2_connection.w,
im=conv2_weights_im,
wmin=-1.0,
wmax=1.0)
dense_weights_im = plot_weights(dense_connection.w,
im=dense_weights_im,
wmin=-1.0,
wmax=1.0)
output_weights_im = plot_weights(output_connection.w,
im=output_weights_im,
wmin=-1.0,
wmax=1.0)
plt.pause(1e-8)
# Reset state variables.
network.reset_()
def main(args):
if args.gpu and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_workers == -1:
args.n_workers = args.gpu * 4 * torch.cuda.device_count()
device = torch.device("cuda" if args.gpu else "cpu")
# Load trained ANN from disk.
if args.arch == 'vgg15ab':
ann = vgg_15_avg_before_relu(dataset=args.dataset)
# add other architectures here#
else:
raise ValueError('Unknown architecture')
ann.features = torch.nn.DataParallel(ann.features)
ann.cuda()
if not os.path.isdir(args.job_dir):
os.mkdir(args.job_dir)
f = os.path.join('.', args.model)
try:
dictionary = torch.load(f=f)['state_dict']
except KeyError:
dictionary = torch.load(f=f)
ann.load_state_dict(state_dict=dictionary, strict=True)
if args.dataset == 'imagenet':
input_shape = (3, 224, 224)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# the actual data to be evaluated
val_loader = ImageNet(image_encoder=RepeatEncoder(time=args.time,
dt=1.0),
label_encoder=None,
root=args.data,
download=False,
transform=transforms.Compose([
transforms.Resize((256, 256)),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]),
split='val')
# a wrapper class
dataloader = DataLoader(
val_loader,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=args.gpu,
)
# A loader of samples for normalization of the SNN from the training set
norm_loader = ImageNet(
image_encoder=RepeatEncoder(time=args.time, dt=1.0),
label_encoder=None,
root=args.data,
download=False,
split='train',
transform=transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]),
)
elif args.dataset == 'cifar100':
input_shape = (3, 32, 32)
print('==> Using Pytorch CIFAR-100 Dataset')
normalize = transforms.Normalize(mean=[0.507, 0.487, 0.441],
std=[0.267, 0.256, 0.276])
val_loader = CIFAR100(image_encoder=RepeatEncoder(time=args.time,
dt=1.0),
label_encoder=None,
root=args.data,
download=True,
train=False,
transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(0.5),
transforms.ToTensor(),
normalize,
]))
dataloader = DataLoader(
val_loader,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
pin_memory=args.gpu,
)
norm_loader = CIFAR100(image_encoder=RepeatEncoder(time=args.time,
dt=1.0),
label_encoder=None,
root=args.data,
download=True,
train=True,
transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(0.5),
transforms.ToTensor(),
normalize,
]))
else:
raise ValueError('Unsupported dataset.')
if args.eval_size == -1:
args.eval_size = len(val_loader)
for step, batch in enumerate(
torch.utils.data.DataLoader(norm_loader, batch_size=args.norm)):
data = batch['image']
break
snn = ann_to_snn(ann,
input_shape=input_shape,
data=data,
percentile=args.percentile)
torch.cuda.empty_cache()
snn = snn.to(device)
correct = 0
t0 = time()
accuracies = np.zeros((args.time, (args.eval_size // args.batch_size) + 1),
dtype=np.float32)
for step, batch in enumerate(tqdm(dataloader)):
if (step + 1) * args.batch_size > args.eval_size:
break
# Prep next input batch.
inputs = batch["encoded_image"]
labels = batch["label"]
inpts = {"Input": inputs}
if args.gpu:
inpts = {k: v.cuda() for k, v in inpts.items()}
snn.run(inpts=inpts,
time=args.time,
step=step,
acc=accuracies,
labels=labels,
one_step=args.one_step)
last_layer = list(snn.layers.keys())[-1]
output_voltages = snn.layers[last_layer].summed
prediction = torch.softmax(output_voltages, dim=1).argmax(dim=1)
correct += (prediction.cpu() == labels).sum().item()
snn.reset_()
t1 = time() - t0
final = accuracies.sum(axis=1) / args.eval_size
plt.plot(final)
plt.suptitle('{} {} ANN-SNN@{} percentile'.format(args.dataset, args.arch,
args.percentile),
fontsize=20)
plt.xlabel('Timestep', fontsize=19)
plt.ylabel('Accuracy', fontsize=19)
plt.grid()
plt.show()
plt.savefig('{}/{}_{}.png'.format(args.job_dir, args.arch,
args.percentile))
np.save(
'{}/voltage_accuracy_{}_{}.npy'.format(args.job_dir, args.arch,
args.percentile), final)
accuracy = 100 * correct / args.eval_size
print(f"SNN accuracy: {accuracy:.2f}")
print(f"Clock time used: {t1:.4f} ms.")
path = os.path.join(args.job_dir, "results", args.results_file)
os.makedirs(os.path.dirname(path), exist_ok=True)
if not os.path.isfile(path):
with open(path, "w") as f:
f.write(
"seed,simulation time,batch size,inference time,accuracy\n")
to_write = [args.seed, args.time, args.batch_size, t1, accuracy]
to_write = ",".join(map(str, to_write)) + "\n"
with open(path, "a") as f:
f.write(to_write)
return t1
