def main():
args = parse_arguments()
if args.getmousepos:
utils.getjoinposn()
elif args.updatepass is not None:
utils.update_pass(args.updatepass)
elif args.append is not None:
utils.append(args.append)
elif args.changepass is not None:
utils.change_pass(args.changepass)
else:
config = configparser.ConfigParser()
config.read("data.ini")
joinposn = config["VALUES"]["join"].split(", ")
try:
joinposn = [int(x) for x in joinposn]
except ValueError:
# fmt: off
print("Oops, it looks like you haven't"
"set the positon of join button.")
# fmt: on
subject = utils.get_subject() if args.subject is None else args.subject
zoom_id, zoom_pass = utils.get_credentials(subject)
utils.auto_type(zoom_id, zoom_pass, joinposn)
def vote():
if request.method == "POST" :
vote = request.form["vote"]
url = "http://gdata.youtube.com/feeds/api/videos?q=%s&max-results=1&v=2&alt=jsonc" % urllib.quote_plus(vote)
result = simplejson.load(urllib.urlopen(url))
video_id = result['data']['items'][0]['id']
utils.append(utils.get_path(RADIO_ROOT, 'to_process_votes'), video_id)
return render_template("vote.html")
def update_response(
input_features: Optional[np.ndarray],
normals: Optional[np.ndarray],
preferences: Optional[np.ndarray],
phi_A: np.ndarray,
phi_B: np.ndarray,
preference: int,
outdir: Path,
):
input_features = append(input_features, np.stack([phi_A, phi_B]))
normals = append(normals, phi_A - phi_B)
preferences = append(preferences, preference)
np.save(outdir / "input_features.npy", input_features)
np.save(outdir / "normals.npy", normals)
np.save(outdir / "preferences.npy", preferences)
return input_features, normals, preferences
def create_posting_files(self, posting_dict, letter_word_mapping):
"""
The function will get the mapping of every word to its first char (a: atom, assertive..)
and will create the posting file for all the words of a certain character.
param posting_dict: Dictionary of all the terms and their info to save in the posting files.
param letter_word_mapping: Dictionary that map each word to its first char. For example:
{'a':['atom','arg'], 'b':['bar']}
"""
for char in letter_word_mapping:
word_data_dict = {}
if self.postings_data.get(char) is None:
name = "SPECIALS"
char_path = "SPECIALS"
else:
name = self.postings_data[char]['name']
char_path = char
for word in letter_word_mapping[char]:
word_data_dict[word] = posting_dict[word]
if word_data_dict:
utils.append(word_data_dict, f"{self.postings_data[char_path]['path']}\\{name}")
def forward(self, x, backward_on_y=False):
out = F.relu(self.conv1(x))
out = F.relu(self.conv2(out))
out = F.relu(self.conv3(out))
out = F.relu(self.conv4(out))
out = out.reshape(-1, 1024)
out = self.fc(out)
ys_logits, ys_idx, ys, zs_mean, zs_logvar, zs = [], [], [], [], [], []
ys_logits2 = []
out2 = out
for switch in self.fc_switched:
out, y_logits, y_idx, y, z_mean, z_logvar, z = switch(out, backward_on_y=backward_on_y)
U.append((ys_logits, ys_idx, ys, zs_mean, zs_logvar, zs),
(y_logits, y_idx, y, z_mean, z_logvar, z))
if self.training:
out2, y_logits2, _, _, _, _, _ = switch(out2, backward_on_y=backward_on_y)
ys_logits2.append(y_logits2)
out = F.relu(out)
z2_mean = self.fc_mean(out)
z2_logvar = self.fc_logvar(out)
return z2_mean, z2_logvar, ys_logits, ys_logits2, ys_idx, ys, zs_mean, zs_logvar, zs
def forward(self, y, large_z, context): # train time
y, lengths = append(truncate(y, 'eos'), 'sos')
if self.word_drop > 0.:
y = word_drop(y, self.word_drop)
embedded = self.embedding(y) # (B, l, 300)
embedded = torch.cat(
[embedded, context.repeat(1, embedded.size(1), 1)], dim=-1)
packed = pack_padded_sequence(embedded, lengths, batch_first=True)
init_hidden = self._transform_hidden(large_z)
packed_output, _ = self.lstm(packed, init_hidden)
total_length = embedded.size(1)
output, _ = pad_packed_sequence(packed_output,
batch_first=True,
total_length=total_length)
recon_logits = self.out(output)
return recon_logits # (B, L, vocab_size)
def forward(self, orig, para, z): # train time
orig, orig_lengths = orig # (B, l), (B,)
orig = self.embedding(orig) # (B, l, 300)
orig_packed = pack_padded_sequence(orig,
orig_lengths,
batch_first=True)
_, orig_hidden = self.lstm_orig(orig_packed)
para, _ = append(truncate(para, 'eos'), 'sos')
if self.word_drop > 0.:
para = word_drop(para, self.word_drop) # from Bowman's paper
para = self.embedding(para)
L = para.size(1)
para_z = torch.cat([para, z.repeat(1, L, 1)],
dim=-1) # (B, L, 1100+300)
para_output, _ = self.lstm_para(para_z, orig_hidden) # no packing
logits = self.linear(para_output)
return logits # (B, L, vocab_size)
def forward(self, z, l, x=None):
"""
z: (B, 500)
l: (B,)
x: tuple of (B, L+1), (B,)
"""
B = l.size(0)
l_embed = self.attr_emb(l) # (B, 200)
hidden = torch.cat([z, l_embed], dim=-1).unsqueeze(0) # (1, B, 700)
if x is not None: # loss computation with teacher forcing
x, lengths = append(truncate(x, 'eos'), 'sos')
x_embed = self.emb(x) # (B, L+1, 300)
packed_in = pack_padded_sequence(x_embed, lengths, batch_first=True)
packed_out, _ = self.gru(packed_in, hidden)
total_length = x.size(1)
# (B, L, 700)
hx, lengths = pad_packed_sequence(packed_out, batch_first=True,
total_length=total_length)
output = self.out(hx)
return (hx, lengths), (output, lengths) # (B, L+1, 700), (B,)
# (B, L+1, vocab), (B,)
else: # sample y
y = []
hy = []
input_ = l.new_full((B, 1), SOS_IDX)
for t in range(MAXLEN):
input_ = self.emb(input_) # (B, 1, 300)
# output (B, 1, 700), hidden (1, B, 700)
output, hidden = self.gru(input_, hidden)
input_ = self._hard_sampling(self.out(output))
hy.append(output)
y.append(input_)
input_ = l.new_full((B,1), EOS_IDX) # feed <eos> as last input,
output, _ = self.gru(self.emb(input_), hidden)
hy.append(output)
y.append(input_) # append <eos> as last token
hy = torch.cat(hy, dim=1)
y = torch.cat(y, dim=1)
hy, y, lengths = self._tighten(hy, y)
#lengths = y.new_full((B,), MAXLEN+1)
return (hy, lengths), (y, lengths) # (B, MAXLEN+1, 700), (B, MAXLEN+1), (B, )
def forward_all_cells(self):
""" move all agents in map one time step forward """
agents_durations = self.durations[cp.arange(0, self.durations.shape[0]), self.current_state_ids].flatten()
print(f'DEBUG: agents_durations.shape: {agents_durations.shape}, self.durations.shape: {self.durations.shape}, self.current_state_ids.shape: {self.current_state_ids.shape}')
to_transit = (self.current_state_durations == agents_durations)
self.current_state_durations += 1
to_transit = self.agent_ids[to_transit]
self.transit_states(to_transit)
# Contamination at home by end of the period
self.contaminate(self.agent_ids, self.home_cell_ids)
# Update r and associated variables
r = self.n_infected_period / self.n_diseased_period if self.n_diseased_period > 0 else 0
r = cp.array([r])
if self.verbose > 1:
print(f'period {self.current_period}: r={r}')
self.r_factors = append(self.r_factors, r)
self.n_diseased_period = self.get_n_diseased()
self.n_infected_period = 0
#Move one period forward
self.current_period += 1
def contaminate(self, selected_agents, selected_cells):
""" both arguments have same length. If an agent with sensitivity > 0 is in the same cell
than an agent with contagiousity > 0: possibility of contagion """
t_start = time()
i = 0
t0 = time()
selected_unsafeties = self.unsafeties[selected_cells]
selected_agents = selected_agents.astype(cp.uint32)
selected_states = self.current_state_ids[selected_agents]
selected_contagiousities = self.unique_contagiousities[selected_states]
selected_sensitivities = self.unique_sensitivities[selected_states]
print(f'ttt first part contaminate: {time() - t0}')
# Find cells where max contagiousity == 0 (no contagiousity can happen there)
t0 = time()
cont_sens = cp.multiply(selected_contagiousities, selected_sensitivities)
print(f'ttt group max sensitivities: {time() - t0}')
# Combine them
if cp.max(cont_sens) == 0:
return
t0 = time()
mask_zero = (cont_sens > 0)
selected_agents = selected_agents[mask_zero]
selected_contagiousities = selected_contagiousities[mask_zero]
selected_sensitivities = selected_sensitivities[mask_zero]
selected_cells = selected_cells[mask_zero]
selected_unsafeties = selected_unsafeties[mask_zero]
print(f'ttt mask zero all: {time() - t0}')
# Compute proportion (contagious agent) / (non contagious agent) by cell
t0 = time()
_, n_contagious_by_cell = cp.unique(selected_cells[selected_contagiousities > 0], return_counts=True)
_, n_non_contagious_by_cell = cp.unique(selected_cells[selected_contagiousities == 0], return_counts=True)
print(f'ttt non contagious: {time() - t0}')
i += 1
t0 = time()
p_contagious = cp.divide(n_contagious_by_cell, n_non_contagious_by_cell)
n_selected_agents = selected_agents.shape[0]
print(f'ttt p_contagious: {time() - t0}')
if self.verbose > 1:
print(f'{n_selected_agents} selected agents after removing cells with max sensitivity or max contagiousity==0')
if n_selected_agents == 0:
return
# Find for each cell which agent has the max contagiousity inside (it will be the contaminating agent)
t0 = time()
max_contagiousities, mask_max_contagiousities = group_max(data=selected_contagiousities, groups=selected_cells)
print(f'ttt max contagious: {time() - t0}')
t0 = time()
infecting_agents = selected_agents[mask_max_contagiousities]
selected_contagiousities = selected_contagiousities[mask_max_contagiousities]
print(f'ttt mask max contagious: {time() - t0}')
# Select agents that can be potentially infected ("pinfected") and corresponding variables
t0 = time()
pinfected_mask = (selected_sensitivities > 0)
pinfected_agents = selected_agents[pinfected_mask]
selected_sensitivities = selected_sensitivities[pinfected_mask]
selected_unsafeties = selected_unsafeties[pinfected_mask]
selected_cells = selected_cells[pinfected_mask]
print(f'ttt p_infected_mask: {time() - t0}')
# Group `selected_cells` and expand `infecting_agents` and `selected_contagiousities` accordingly
# There is one and only one infecting agent by pinselected_agentsfected_cell so #`counts` == #`infecting_agents`
t0 = time()
_, inverse = cp.unique(selected_cells, return_inverse=True)
print(f'ttt inverse select cell: {time() - t0}')
# TODO: ACHTUNG: count repeat replace by inverse here
t0 = time()
infecting_agents = infecting_agents[inverse]
selected_contagiousities = selected_contagiousities[inverse]
p_contagious = p_contagious[inverse]
print(f'ttt p_contagious inverse: {time() - t0}')
# Compute contagions
t0 = time()
res = cp.multiply(selected_contagiousities, selected_sensitivities)
res = cp.multiply(res, selected_unsafeties)
print(f'ttt cp.multiply: {time() - t0}')
# Modifiy probas contamination according to `p_contagious`
t0 = time()
mask_p = (p_contagious < 1)
res[mask_p] = cp.multiply(res[mask_p], p_contagious[mask_p])
res[~mask_p] = 1 - cp.divide(1 - res[~mask_p], p_contagious[~mask_p])
print(f'ttt res mask p: {time() - t0}')
t0 = time()
draw = cp.random.uniform(size=infecting_agents.shape[0])
draw = (draw < res)
infecting_agents = infecting_agents[draw]
infected_agents = pinfected_agents[draw]
n_infected_agents = infected_agents.shape[0]
print(f'ttt n_infected draw: {time() - t0}')
if self.verbose > 1:
print(f'Infecting and infected agents should be all different, are they? {((infecting_agents == infected_agents).sum() == 0)}')
print(f'Number of infected agents: {n_infected_agents}')
t0 = time()
self.current_state_ids[infected_agents] = self.least_state_ids[infected_agents]
self.current_state_durations[infected_agents] = 0
self.n_infected_period += n_infected_agents
self.infecting_agents = append(self.infecting_agents, infecting_agents)
self.infected_agents = append(self.infected_agents, infected_agents)
self.infected_periods = append(self.infected_periods, cp.multiply(cp.ones(n_infected_agents), self.current_period))
print(f'ttt final: {time() - t0}')
print(f'contaminate computed in {time() - t_start}')
continue
current.val_loss_delta = current.val_loss - parent.val_loss
if current.val_loss_delta > 0:
increases.append(current)
if current.val_loss > node_worst.val_loss: node_worst = current
if current.val_loss < node_best.val_loss: node_best = current
total += 1
if total == 0: fail('No matching Nodes found!')
fraction = 100.0 * len(increases) / total
print('increases/total = %i / %i (%02.f%%)' %
(len(increases), total, fraction))
file_increases = "increases-%s.data" % args.token
append(file_increases, "%i %5.1f" % (args.stage, fraction))
print('worst val_loss: ' + str(node_worst))
print('best val_loss: ' + str(node_best))
print('DELTAS:')
increases.sort(key=Node.get_val_loss_delta)
stopped_early = 0
for i in increases:
# print('%f %-14s %r' % (i.val_loss_delta, i.id, i.stopped_early))
if i.stopped_early: stopped_early += 1
def print_delta(prefix, node):
print(prefix, str(node), 'delta: %f' % node.val_loss_delta)
def append_validator_if_set(validators: List[callable], is_set: bool,
create_val: callable, compare: callable, limit,
template: str) -> List[callable]:
return append(validators, create_val(compare, limit, template %
str(limit))) if is_set else validators
def simulated(
outdir: Path,
criterion: Literal["information", "volume", "random"],
termination_threshold: float,
n_reward_samples: int,
query_type: Literal["strict", "weak"] = "strict",
equiv_size: Optional[float] = None,
true_reward_path: Optional[Path] = None,
continuous: bool = False,
overwrite: bool = False,
replicaitons: Optional[str] = None,
):
""" Generates a test by eliciting from a human simulated by a ground truth reward. """
if replicaitons is not None:
replication_indices = parse_replications(replicaitons)
if true_reward_path is not None:
reward_dir, reward_name = make_reward_path(true_reward_path)
Parallel(n_jobs=-2)(
delayed(simulated)(
outdir=Path(outdir) / str(i),
criterion=criterion,
termination_threshold=termination_threshold,
n_reward_smaples=n_reward_samples,
query_type=query_type,
equiv_size=equiv_size,
true_reward_path=reward_dir / str(i) / reward_name,
continuous=continuous,
overwrite=overwrite,
)
for i in replication_indices
)
else:
Parallel(n_jobs=-2)(
delayed(simulated)(
outdir=Path(outdir) / str(i),
criterion=criterion,
termination_threshold=termination_threshold,
n_reward_smaples=n_reward_samples,
query_type=query_type,
equiv_size=equiv_size,
continuous=continuous,
overwrite=overwrite,
)
for i in replication_indices
)
exit()
criterion, query_type, outdir = setup(criterion, query_type, outdir, delta=equiv_size)
env = Driver()
d = env.num_of_features
if true_reward_path is not None:
logging.info(f"Loading true reward from {true_reward_path}")
true_reward = np.load(true_reward_path)
else:
logging.info("Randomly generating true reward")
true_reward = np.random.normal(size=(4,))
true_reward = true_reward / np.linalg.norm(true_reward)
np.save(outdir / "true_reward.npy", true_reward)
pickle.dump(
{
"criterion": criterion,
"reward_iterations": n_reward_samples,
"stop_thresh": termination_threshold,
"query_type": query_type,
"equiv_size": equiv_size,
"continuous": continuous,
},
open(outdir / "flags.pkl", "wb"),
)
normals = load(outdir / "normals.npy", overwrite=overwrite)
preferences = load(outdir / "preferences.npy", overwrite=overwrite)
inputs = load(outdir / "inputs.npy", overwrite=overwrite)
input_features = load(outdir / "input_features.npy", overwrite=overwrite)
# If there is already data, feed it to the w_sampler to get the right posterior.
w_sampler = Sampler(d)
if inputs is not None and input_features is not None and preferences is not None:
for (a_phi, b_phi), preference in zip(input_features, preferences):
w_sampler.feed(a_phi, b_phi, [preference])
score = np.inf
try:
while score >= termination_threshold:
w_samples, delta_samples = w_sampler.sample_given_delta(
sample_count=n_reward_samples, query_type=query_type, delta=equiv_size
)
input_A, input_B, score = run_algo(criterion, env, w_samples, delta_samples, continuous)
logging.info(f"Score={score}")
if score > termination_threshold:
inputs = update_inputs(
a_inputs=input_A, b_inputs=input_B, inputs=inputs, outdir=outdir
)
phi_A, phi_B, preference = get_simulated_feedback(
simulation=env,
input_A=input_A,
input_B=input_B,
query_type=query_type,
true_reward=true_reward,
delta=equiv_size,
)
input_features = append(input_features, np.stack([phi_A, phi_B]))
normals = append(normals, phi_A - phi_B)
preferences = append(preferences, preference)
np.save(outdir / "input_features.npy", input_features)
np.save(outdir / "normals.npy", normals)
np.save(outdir / "preferences.npy", preferences)
w_sampler.feed(phi_A, phi_B, [preference])
except KeyboardInterrupt:
# Pass through to finally
logging.warning("\nSaving results, please do not exit again.")
finally:
save_reward(query_type, w_sampler, n_reward_samples, outdir, true_delta=equiv_size)
def human(
criterion: str,
query_type: str,
epsilon: float,
n_reward_samples: int,
equiv_size: float,
outdir: Path = Path("questions"),
continuous: bool = False,
overwrite: bool = False,
):
""" Generates a test by eliciting preferences from a human. """
criterion, query_type, outdir = setup(criterion, query_type, outdir, delta=equiv_size)
simulation_object = Driver()
d = simulation_object.num_of_features
pickle.dump(
{
"criterion": criterion,
"query_type": query_type,
"epsilon": epsilon,
"reward_iterations": n_reward_samples,
"delta": equiv_size,
"continuous": continuous,
},
open(outdir / "flags.pkl", "wb"),
)
normals = load(outdir / "normals.npy", overwrite=overwrite)
preferences = load(outdir / "preferences.npy", overwrite=overwrite)
inputs = load(outdir / "inputs.npy", overwrite=overwrite)
input_features = load(outdir / "input_features.npy", overwrite=overwrite)
w_sampler = Sampler(d)
if inputs is not None and input_features is not None and preferences is not None:
for (a_phi, b_phi), preference in zip(input_features, preferences):
w_sampler.feed(a_phi, b_phi, [preference])
score = np.inf
try:
while score >= epsilon:
w_samples, delta_samples = w_sampler.sample_given_delta(
n_reward_samples, query_type, equiv_size
)
input_A, input_B, score = run_algo(
criterion, simulation_object, w_samples, delta_samples, continuous
)
if score > epsilon:
inputs = update_inputs(
a_inputs=input_A, b_inputs=input_B, inputs=inputs, outdir=outdir
)
phi_A, phi_B, preference = get_feedback(
simulation_object, input_A, input_B, query_type
)
input_features = append(input_features, np.stack([phi_A, phi_B]))
normals = append(normals, phi_A - phi_B)
preferences = append(preferences, preference)
np.save(outdir / "input_features.npy", input_features)
np.save(outdir / "normals.npy", normals)
np.save(outdir / "preferences.npy", preferences)
w_sampler.feed(phi_A, phi_B, [preference])
except KeyboardInterrupt:
# Pass through to finally
logging.warning("\nSaving results, please do not exit again.")
finally:
save_reward(query_type, w_sampler, n_reward_samples, outdir, true_delta=equiv_size)
def main(cfg):
# setting up output directories, and writing to stdout
make_dirs(cfg.stdout_dir, replace=False)
if cfg.train:
run_type = 'train'
else:
if 'weight' in cfg.prune_type.lower():
run_type = 'weight-prune'
else:
run_type = 'unit-prune'
sys.stdout = open(
'{}/stdout_{}_{}.txt'.format(cfg.stdout_dir, cfg.model_name, run_type),
'w')
print(cfg)
print('\n')
sys.stdout.flush()
# if train mode, replace the previous plot and ckpt directories; if in prune mode, use existing directories
if cfg.plot:
make_dirs(os.path.join(cfg.plot_dir, cfg.model_name),
replace=cfg.train)
if cfg.save_model:
make_dirs(os.path.join(cfg.model_dir, cfg.model_name),
replace=cfg.train)
# set random seed
if cfg.random_seed != 0:
random_seed = cfg.random_seed
else:
random_seed = random.randint(1, 100000)
random.seed(random_seed)
np.random.seed(random_seed)
torch.manual_seed(random_seed)
# set device as cuda or cpu
if cfg.use_gpu and torch.cuda.is_available():
# reproducibility using cuda
torch.cuda.manual_seed(random_seed)
cudnn.deterministic = True
cudnn.benchmark = False
device = torch.device('cuda')
else:
device = torch.device('cpu')
if cfg.use_gpu:
print('gpu option was to <True>, but no cuda device was found')
print('\n')
# datasets and dataloaders
# normalizing training and validation images to [0, 1] suffices for the purposes of our research objective
# in training, <drop_last> minibatch in an epoch set to <True> for simplicity in tracking training performance
dataset_train = MNIST(root='./data/mnist',
train=True,
download=True,
transform=transforms.Compose([transforms.ToTensor()
]),
target_transform=None)
dataloader_train = DataLoader(dataset=dataset_train,
batch_size=cfg.batch_size,
shuffle=cfg.shuffle,
num_workers=cfg.num_workers,
pin_memory=True,
drop_last=True)
dataset_val = MNIST(root='./data/mnist',
train=False,
download=True,
transform=transforms.Compose([transforms.ToTensor()]),
target_transform=None)
dataloader_val = DataLoader(dataset=dataset_val,
batch_size=100,
shuffle=False,
num_workers=cfg.num_workers,
pin_memory=True,
drop_last=False)
# automatically compute number of classes
targets = np.asarray(dataset_train.targets)
c = np.unique(targets).shape[0]
# define model
# weights initialized using Kaiming uniform (He initialization)
# number of units per hidden layer is passed in as an argument
net = Net(np.product(cfg.img_size), c, cfg.units).to(device)
criterion = nn.CrossEntropyLoss()
if cfg.train:
# training mode
if cfg.use_sgd:
optimizer = optim.SGD(params=net.parameters(),
lr=cfg.lr,
momentum=cfg.momentum,
nesterov=cfg.use_nesterov)
else:
optimizer = optim.Adam(params=net.parameters(),
lr=cfg.lr,
betas=(cfg.beta1, cfg.beta2))
# tracking training and validation stats over epochs
epochs = []
train_loss_epochs, val_loss_epochs = [], []
train_acc_epochs, val_acc_epochs = [], []
# best model is defined as model with best performing validation loss
best_loss = float('inf')
for epoch in range(cfg.epochs):
# tracking training and validation stats over a given epoch
train_loss_epoch, val_loss_epoch = [], []
train_acc_epoch, val_acc_epoch = [], []
# training set
for i, (x, y) in enumerate(dataloader_train):
x, y = x.to(device), y.to(device)
optimizer.zero_grad()
logits = net(x)
loss = criterion(logits, y)
loss.backward()
optimizer.step()
acc = calculate_acc(logits, y)
append((train_loss_epoch, loss.item()),
(train_acc_epoch, acc.item()))
# validation set
with torch.no_grad():
for i, (x, y) in enumerate(dataloader_val):
x, y = x.to(device), y.to(device)
logits = net(x)
loss = criterion(logits, y)
acc = calculate_acc(logits, y)
append((val_loss_epoch, loss.item()),
(val_acc_epoch, acc.item()))
train_loss_epoch, val_loss_epoch = get_average(
train_loss_epoch), get_average(val_loss_epoch)
train_acc_epoch, val_acc_epoch = get_average(
train_acc_epoch), get_average(val_acc_epoch)
print('train_epoch{:0=3d}_loss{:.4f}_acc{:.4f}'.format(
epoch + 1, train_loss_epoch, train_acc_epoch))
print('valid_epoch{:0=3d}_loss{:.4f}_acc{:.4f}'.format(
epoch + 1, val_loss_epoch, val_acc_epoch))
print('\n')
sys.stdout.flush()
if cfg.plot:
append((epochs, epoch + 1),
(train_loss_epochs, train_loss_epoch),
(val_loss_epochs, val_loss_epoch),
(train_acc_epochs, train_acc_epoch),
(val_acc_epochs, val_acc_epoch))
plot_line(epochs, train_loss_epochs, val_loss_epochs,
'Epoch Number', 'Loss', cfg)
plot_line(epochs, train_acc_epochs, val_acc_epochs,
'Epoch Number', 'Accuracy', cfg)
if val_loss_epoch < best_loss:
best_loss = val_loss_epoch
print('New best model at epoch {:0=3d} with val_loss {:.4f}'.
format(epoch + 1, best_loss))
print('\n')
if cfg.save_model:
# save model when validation loss improves
save_name = '{}_net_epoch{:0=3d}_val_loss{:.4f}'.format(
cfg.model_name, epoch + 1, best_loss)
torch.save(
net.state_dict(),
os.path.join(cfg.model_dir, cfg.model_name,
'{}.pth'.format(save_name)))
with open(
os.path.join(cfg.model_dir, cfg.model_name,
'{}.txt'.format(cfg.model_name)),
'w') as file:
file.write('{}.pth'.format(save_name))
else:
# pruning mode
# checks on arguments passed in
for k in cfg.sparsity:
assert 0 <= k <= 1
if cfg.use_sparse_mul:
assert cfg.to_sparse
# load model
with open(
os.path.join(cfg.model_dir, cfg.model_name,
'{}.txt'.format(cfg.model_name)), 'r') as file:
load_name = file.readline()
net.load_state_dict(
torch.load(
os.path.join(cfg.model_dir, cfg.model_name,
'{}'.format(load_name))))
net.eval()
# select pruning approach to use
if 'weight' in cfg.prune_type.lower():
prune = weight_prune
else:
prune = unit_prune
sparsities = []
val_loss_sparse, val_acc_sparse = [], []
time_sparsities = []
for k in cfg.sparsity:
val_loss_k, val_acc_k = [], []
time_k = []
# copy network so that the sparsity changes are not additive for each k
net_sparse = copy.deepcopy(net)
pruned_weights = []
# prune model, except for the last layer
for (i, p) in enumerate(net_sparse.parameters()):
if i < len(cfg.units):
original_weights = copy.deepcopy(p.data)
if cfg.plot:
# plot magnitude of original weights (for comparison to post-pruned weights)
plot_hist([
torch.abs(
original_weights.flatten()).cpu().numpy()
], ['b'], cfg.prune_type, i + 1, k,
'Non-Pruned Weight Magnitudes', 'Counts',
cfg)
prune(p.data, k)
if cfg.plot:
# plot original magnitudes of pruned weights, and magnitudes of remaining weights, separately
pruned_weights_non_zero = torch.abs(
original_weights.flatten()[p.data.flatten() != 0])
pruned_weights_zeroed = torch.abs(
original_weights.flatten()[p.data.flatten() == 0])
plot_hist([
pruned_weights_non_zero.cpu().numpy(),
pruned_weights_zeroed.cpu().numpy()
], ['g', 'r'], cfg.prune_type, i + 1, k,
'Weight Magnitudes', 'Counts', cfg)
plot_hist([pruned_weights_non_zero.cpu().numpy()],
['k'], cfg.prune_type, i + 1, k,
'Surviving Weight Magnitudes', 'Counts', cfg)
if cfg.to_sparse and i < len(cfg.units):
pruned_weights.append(p.data.to_sparse())
else:
pruned_weights.append(p.data)
with torch.no_grad():
for i, (x, y) in enumerate(dataloader_val):
x, y = x.to(device), y.to(device)
start = time.time()
logits = forward(x, pruned_weights, cfg.use_sparse_mul)
end = time.time()
loss = criterion(logits, y)
acc = calculate_acc(logits, y)
append((val_loss_k, loss.item()), (val_acc_k, acc.item()),
(time_k, end - start))
val_loss_k, val_acc_k, time_k = get_average(
val_loss_k), get_average(val_acc_k), get_average(time_k)
print('valid_{}_k{:.2f}_loss{:.4f}_acc{:.4f}'.format(
run_type, k, val_loss_k, val_acc_k))
print('valid_{}_k{:.2f}_time/minibatch{:.6f}'.format(
run_type, k, time_k))
print('\n')
sys.stdout.flush()
if cfg.plot:
append((sparsities, k), (val_loss_sparse, val_loss_k),
(val_acc_sparse, val_acc_k), (time_sparsities, time_k))
plot_line(sparsities, [], val_loss_sparse,
'Sparsity {} Prune'.format(cfg.prune_type), 'Loss',
cfg)
plot_line(sparsities, [], val_acc_sparse,
'Sparsity {} Prune'.format(cfg.prune_type),
'Accuracy', cfg)
plot_line(sparsities, [], time_sparsities,
'Sparsity {} Prune'.format(cfg.prune_type), 'Time',
cfg)
if cfg.save_model:
torch.save(
net_sparse.state_dict(),
os.path.join(
cfg.model_dir, cfg.model_name,
'{}_sparse_net_{}_val_loss{:.4f}.pth'.format(
cfg.model_name, run_type, val_loss_k)))
def collect(
outdir: Path,
n_rewards: int,
test_reward_path: Optional[Path] = None,
std: Optional[float] = None,
mean_reward_path: Optional[Path] = None,
normals_paths: Optional[List[Path]] = None,
preferences_paths: Optional[List[Path]] = None,
use_random: bool = False,
use_plausible: bool = False,
skip_human: bool = False,
overwrite: bool = False,
) -> None:
"""Collects ground truth labels for the optimal trajectories of some reward functions.
Args:
outdir (Path): Directory to write output to
n_rewards (int): Number of rewards to generate or process
test_reward_path (Optional[Path], optional): Path to nupmy array of reward weights to test. Defaults to None.
std (Optional[float], optional): Standard deviation of normal distribution to draw test reward weigths from. Defaults to None.
mean_reward_path (Optional[Path], optional): Path to numpy array specifying mean reward weights to sample around. Defaults to None.
overwrite (bool, optional): Overwrite output? Defaults to False.
Raises:
ValueError: Raised if neither test_reward_path or both std and mean_reward_path are specified. The test rewards need to come from somewhere.
"""
outdir = Path(outdir)
outdir.mkdir(parents=True, exist_ok=True)
out_rewards = load(outdir, "test_rewards.npy", overwrite=overwrite)
new_rewards_index = out_rewards.shape[0] if out_rewards is not None else 0
num_new_rewards = n_rewards - new_rewards_index
env = Driver()
if num_new_rewards > 0:
if test_reward_path is not None:
rewards = np.load(
test_reward_path)[new_rewards_index:num_new_rewards]
elif mean_reward_path is not None and std is not None:
mean_reward = np.load(mean_reward_path)
rewards = default_rng().normal(loc=mean_reward,
scale=std,
size=(num_new_rewards,
*mean_reward.shape))
elif normals_paths is not None and preferences_paths is not None and std is not None:
# NOTE(joschnei): This turned out not to work, because the random baseline is poisoning the well
normals = None
for normals_path, preferences_path in zip(normals_paths,
preferences_paths):
single_normals = np.load(normals_path)
single_preferences = np.load(preferences_path)
single_normals = (single_normals.T * single_preferences).T
normals = append(normals, single_normals, flat=True)
# TODO(joschnei): These can all be loaded in from flags.pkl, but I'm too lazy for that.
mean_reward = make_mode_reward(
query_type="strict",
true_delta=1.1,
w_sampler=Sampler(env.num_of_features),
n_reward_samples=100,
)
assert np.all(np.isfinite(mean_reward))
rewards = default_rng().normal(loc=mean_reward,
scale=std,
size=(num_new_rewards,
*mean_reward.shape))
assert np.all(np.isfinite(rewards))
elif use_random:
rewards = default_rng().normal(loc=0,
scale=1,
size=(num_new_rewards,
env.num_of_features))
rewards = rewards / np.linalg.norm(rewards)
elif use_plausible:
# Generate uniform rewards with plausible weights i.e. ones with the right sign
rewards = default_rng().normal(loc=0,
scale=1,
size=(num_new_rewards,
env.num_of_features))
rewards = rewards / np.linalg.norm(rewards)
# See models.py for reward feature details.
rewards[:, 0] = np.abs(rewards[:, 0])
rewards[:, 1] = -np.abs(rewards[:, 1])
rewards[:, 2] = np.abs(rewards[:, 2])
rewards[:, 3] = -np.abs(rewards[:, 3])
else:
raise ValueError(
"You must either supply a path to the test rewards, or a mean reward and "
"std from which to sample the test rewards.")
out_rewards = append(out_rewards, rewards, flat=True)
else:
assert out_rewards is not None
assert np.all(np.isfinite(out_rewards))
np.save(open(outdir / "test_rewards.npy", "wb"), out_rewards)
paths = load(outdir, "optimal_paths.npy", overwrite=overwrite)
new_paths_index = paths.shape[0] if paths is not None else 0
num_new_paths = n_rewards - new_paths_index
if num_new_paths > 0:
new_paths = np.array(
Parallel(n_jobs=-2)(delayed(make_opt_traj)(reward)
for reward in out_rewards[new_paths_index:]))
paths = append(paths, new_paths, flat=True)
else:
assert paths is not None
np.save(open(outdir / "optimal_paths.npy", "wb"), np.array(paths))
gt_alignment = load(outdir, "alignment.npy", overwrite=overwrite)
new_gt_index = gt_alignment.size if gt_alignment is not None else 0
if skip_human:
exit()
for path in paths[new_gt_index:]:
env.set_ctrl(path)
env.watch(1)
alignment = input("Aligned (y/n):").lower()
while alignment not in ["y", "n"]:
alignment = input("Aligned (y/n):").lower()
gt_alignment = append(gt_alignment, alignment == "y")
np.save(open(outdir / "alignment.npy", "wb"), gt_alignment)
