def print_components_pairwise(tellers,
drawers,
teller_splits='ab',
drawer_splits='ab',
limit=None,
split='dev'):
print(f"Component evaluations [{split}]")
print(
"Teller \t Drawer \t Dir \t Expr(human)\t Pose(human)\t Depth \t xy (sq.)\t x-only \t y-only"
)
for splits_group in [('ab', 'ba'), ('aa', 'bb')]:
for teller_name, teller_pair in tellers:
for drawer_name, drawer_pair in drawers:
for splits in splits_group:
if splits[0] not in teller_splits or splits[
1] not in drawer_splits:
continue
components = component_evaluator.eval_fns(make_fns(
splits, teller_pair, drawer_pair),
limit=limit,
split=split)
teller_caption = f"{teller_name}_{splits[0]}"
drawer_caption = f"{drawer_name}_{splits[1]}"
print(f"{teller_caption:17s}\t {drawer_caption:17s}\t",
"\t".join(f"{num: .6f}" for num in components))
print()
def print_script(drawers, drawer_splits='ab', limit=None, split='dev'):
print("Drawer evaluations against script")
print(f"Drawer \t Scene similarity [{split}]")
for drawer_name, drawer_pair in drawers:
for drawer_split in drawer_splits:
sims = eval_fns(make_fns(drawer_split, model.scripted_tell,
drawer_pair),
limit=limit,
split=split)
drawer_caption = f"{drawer_name}_{drawer_split}"
print(f"{drawer_caption:17s}\t {sims.mean():.2f}")
def print_components_script(drawers,
drawer_splits='ab',
limit=None,
split='dev'):
print(f"Drawer evaluations against script [{split}]")
print(
"Drawer \t Dir \t Expr(human)\t Pose(human)\t Depth \t xy (sq.)\t x-only \t y-only"
)
for drawer_name, drawer_pair in drawers:
for drawer_split in drawer_splits:
components = component_evaluator.eval_fns(make_fns(
drawer_split, model.scripted_tell, drawer_pair),
limit=limit,
split=split)
drawer_caption = f"{drawer_name}_{drawer_split}"
print(f"{drawer_caption:17s}\t",
"\t".join(f"{num: .6f}" for num in components))
def train_teller(split, teller_pair, num_epochs=50, limit=100):
splits_pair = split + 'a', split + 'b'
if split == 'a':
teller = teller_pair[0]
elif split == 'b':
teller = teller_pair[1]
else:
assert False
optimizer = torch.optim.Adam(teller.parameters())
print('perplexity-dev', model.calc_perplexity(teller))
print('perplexity-a', model.calc_perplexity(teller, 'a'))
print('avg-loss-dev', teller.calc_split_loss())
print('avg-loss-a', teller.calc_split_loss('a'))
for epoch in range(num_epochs):
teller.train()
for num, ex in enumerate(teller.datagen.get_examples_batch()):
optimizer.zero_grad()
loss = teller(ex)
loss.backward()
optimizer.step()
print(f'Done epoch {epoch} loss {float(loss)}')
if epoch % 5 == 0:
del ex, loss # clean up memory
print('perplexity-dev', model.calc_perplexity(teller))
print('perplexity-a', model.calc_perplexity(teller, 'a'))
print('avg-loss-dev', teller.calc_split_loss())
print('avg-loss-a', teller.calc_split_loss('a'))
for splits in splits_pair:
sims = eval_fns(
make_fns(splits, teller_pair,
(drawer_lstmaddonly_a, drawer_lstmaddonly_b)),
limit=limit)
print(splits, sims.mean())
def print_pairwise(tellers,
drawers,
teller_splits='ab',
drawer_splits='ab',
limit=None,
split='dev'):
print(
f"Teller \t Drawer \t Scene similarity [{split}]")
for splits_group in [('ab', 'ba'), ('aa', 'bb')]:
for teller_name, teller_pair in tellers:
for drawer_name, drawer_pair in drawers:
for splits in splits_group:
if splits[0] not in teller_splits or splits[
1] not in drawer_splits:
continue
sims = eval_fns(make_fns(splits, teller_pair, drawer_pair),
limit=limit,
split=split)
teller_caption = f"{teller_name}_{splits[0]}"
drawer_caption = f"{drawer_name}_{splits[1]}"
print(
f"{teller_caption:17s}\t {drawer_caption:17s}\t {sims.mean():.2f}"
)
print()
print(
f"Human scene similarity: mean={human_sims.mean():.6f} std={human_sims.std():.6f} median={np.median(human_sims):.6f}"
)
# %%
print()
print()
# %%
limit = None
print("Teller \t Drawer \t Scene similarity")
for splits_group in [('ab', 'ba'), ('aa', 'bb')]:
for teller_name, teller_pair in tellers:
for drawer_name, drawer_pair in drawers:
for splits in splits_group:
sims = eval_fns(make_fns(splits, teller_pair, drawer_pair),
limit=limit)
teller_caption = f"{teller_name}_{splits[0]}"
drawer_caption = f"{drawer_name}_{splits[1]}"
print(f"{teller_caption:17s}\t {drawer_caption:17s}\t",
sims.mean())
print()
# %%
print()
print()
# %%
limit = None
print("Drawer evaluations against script")
print("Drawer \t Scene similarity")
def validate():
for inference_method in ['greedy', 'sample']:
teller.inference_method = inference_method
for splits in splits_pair:
sims = eval_fns(make_fns(splits, teller_pair, drawer_pair), limit=limit)
print(splits, f'[{inference_method}]', sims.mean())
def train_teller(split, teller_pair, scenes,
utterance_penalty=0.1,
gamma=0.999,
uninformative_penalty=0.3,
batch_size=16,
num_batches=12500,
eval_every=2000,
lr=0.00007,
limit=100,
base_name="scene2seq_rl",
):
print("Training hyperparameters:")
for param in ['utterance_penalty',
'gamma',
'uninformative_penalty',
'batch_size',
'num_batches',
'lr',
'limit',
]:
print(param, '=', locals()[param])
drawer_pair = drawer_lstmaddonly_a, drawer_lstmaddonly_b
splits_pair = split + 'a', split + 'b'
if split == 'a':
teller = teller_pair[0]
elif split == 'b':
teller = teller_pair[1]
else:
assert False
teller.disable_dropout()
fns = make_fns(split + split, teller_pair, drawer_pair)
optimizer = torch.optim.Adam(teller.parameters(), lr=lr)
def validate():
for inference_method in ['greedy', 'sample']:
teller.inference_method = inference_method
for splits in splits_pair:
sims = eval_fns(make_fns(splits, teller_pair, drawer_pair), limit=limit)
print(splits, f'[{inference_method}]', sims.mean())
validate()
teller.inference_method = 'sample'
for batch_num in range(num_batches):
optimizer.zero_grad()
teller.eval()
episodes, ex = collect_episodes(
fns,
teller.datagen,
scenes=scenes,
batch_size=batch_size,
utterance_penalty=utterance_penalty,
gamma=gamma,
uninformative_penalty=uninformative_penalty,
)
teller.train()
loss = teller.calc_rl_loss(ex)
loss.backward()
# grad_norm = nn.utils.clip_grad_norm_(teller.parameters(), float('inf'))
# XXX(nikita): clip gradients in an attempt to stabilize. Need to see if
# there's an underlying bug, though.
grad_norm = nn.utils.clip_grad_norm_(teller.parameters(), 1.5)
optimizer.step()
mean_reward = float(ex['brw_rewards'].sum().item() / ex['b_scene_mask'].shape[0])
mean_len = np.mean([
len([event for event in episode if isinstance(event, codraw_data.TellGroup)])
for episode in episodes])
sims = np.array([episode.scene_similarity() for episode in episodes])
mean_sim = sims.mean()
std_sim = sims.std()
print(f'batch {batch_num} mean-reward {mean_reward} loss {float(loss)} grad {float(grad_norm)} mean-len {mean_len} mean-sim {mean_sim} std-sim {std_sim}')
if batch_num % 5 == 0:
for event in episodes[-1]:
if isinstance(event, codraw_data.TellGroup):
print(' >', event.msg)
if batch_num % 50 == 0:
del episodes, ex, loss # clean up memory
validate()
if batch_num > 0 and batch_num % eval_every == 0:
teller.eval()
print("Printing representative sampled dialogs")
teller.inference_method = 'sample'
episodes, ex = collect_episodes(fns, teller.datagen, scenes=scenes[:1], batch_size=5)
for episode in episodes:
for event in episode:
if isinstance(event, codraw_data.TellGroup):
print(' >', event.msg)
print('similarity', episode.scene_similarity())
print('-----')
print("Evaluating on the full dev set")
for inference_method in ['greedy', 'sample']:
teller.inference_method = inference_method
for splits in splits_pair:
sims = eval_fns(make_fns(splits, (teller_rl_a, teller_rl_b), (drawer_lstmaddonly_a, drawer_lstmaddonly_b)), limit=None)
print(splits, f'[{inference_method}]', sims.mean())
if base_name is not None:
print("Serializing teller to disk")
torch.save(teller.spec, Path(f'rl_models/{base_name}_{split}_{batch_num}.pt'))
# %%
# Episode.run(codraw_data.get_scenes('dev')[0], make_fns('aa', (teller_scenenn_a, teller_scenenn_b), (drawer_lstmaddonly_a, drawer_lstmaddonly_b))).display()
# %%
# %%
# %%
print()
print()
print("Final evaluation on full dev set")
# %%
for splits in ('aa', 'ab', 'ba', 'bb'):
sims = eval_fns(make_fns(splits, (teller_scenenn_a, teller_scenenn_b),
(drawer_lstmaddonly_a, drawer_lstmaddonly_b)),
limit=None)
print(splits, sims.mean())
# aa 1.3095491909624886
# ab 1.3115692170881366
# nohier aa 2.229799264350204
# nohier ab 2.255167911899865
# %%
for splits in ('ba', 'bb'):
sims = eval_fns(make_fns(splits, (teller_scenenn_a, teller_scenenn_b),
(drawer_lstmaddonly_a, drawer_lstmaddonly_b)),
limit=None)
print(splits, sims.mean())
#%%
for epoch in range(500):
drawer_sim_a.train()
for num, ex in enumerate(drawer_sim_a.datagen.get_examples_batch()):
optimizer_sim_a.zero_grad()
loss = drawer_sim_a.forward(ex)
loss.backward()
optimizer_sim_a.step()
print(f'Done epoch {epoch} loss {float(loss)}')
if epoch % 25 == 0:
drawer_sim_a.prepare_for_inference()
for splits in ('aa', 'ba'):
sims = eval_fns(make_fns(splits, (teller_nn_a, teller_nn_b),
(drawer_sim_a, drawer_sim_b)),
limit=100)
print(splits, sims.mean())
drawer_sim_a.prepare_for_inference()
# %%
for epoch in range(500):
drawer_sim_b.train()
for num, ex in enumerate(drawer_sim_b.datagen.get_examples_batch()):
optimizer_sim_b.zero_grad()
loss = drawer_sim_b.forward(ex)
loss.backward()
optimizer_sim_b.step()
print(f'Done epoch {epoch} loss {float(loss)}')
drawers = [
('drawer_nn', (models['drawer_nn_a'], models['drawer_nn_b'])),
('drawer_sim', (models['drawer_sim_a'], models['drawer_sim_b'])),
('drawer_bow2c', (models['drawer_bow2c_a'], models['drawer_bow2c_b'])),
('drawer_bow2bce', (models['drawer_bow2bce_a'], models['drawer_bow2bce_b'])),
('drawer_bowcanvas2bce', (models['drawer_bowcanvas2bce_a'], models['drawer_bowcanvas2bce_b'])),
]
# %%
limit = None
print("Drawer evaluations against script")
for drawer_name, drawer_pair in drawers:
for split in ('a', 'b'):
sims = eval_fns(make_fns(split, scripted_tell, drawer_pair), limit=limit)
print(f"{drawer_name}_{split}", sims.mean())
# %%
limit = None
print("Drawer evaluations against script before peek")
for drawer_name, drawer_pair in drawers:
for split in ('a', 'b'):
sims = eval_fns(make_fns(split, scripted_tell_before_peek, drawer_pair), limit=limit)
print(f"{drawer_name}_{split}", sims.mean())
# %%
limit = None
print("Drawer evaluations against script after peek")
)
# %%
print()
print()
print("Saving models")
torch.save(scene2seq_specs, Path('models/scene2seq.pt'))
# %%
print()
print("Final evaluation on full dev set (scene2seq)")
for splits in ('aa', 'ab', 'ba', 'bb'):
sims = eval_fns(make_fns(splits, (teller_scene2seq_a, teller_scene2seq_b),
(drawer_lstmaddonly_a, drawer_lstmaddonly_b)),
limit=None)
print(splits, sims.mean())
print("Final evaluation on full dev set (scene2seq_aux)")
for splits in ('aa', 'ab', 'ba', 'bb'):
sims = eval_fns(make_fns(splits,
(teller_scene2seq_aux_a, teller_scene2seq_aux_b),
(drawer_lstmaddonly_a, drawer_lstmaddonly_b)),
limit=None)
print(splits, sims.mean())
print("Final evaluation on full dev set (scene2seq_aux2)")
for splits in ('aa', 'ab', 'ba', 'bb'):
sims = eval_fns(
make_fns(splits, (teller_scene2seq_aux2_a, teller_scene2seq_aux2_b),
optimizer_lstmaddonly_b = torch.optim.Adam(drawer_lstmaddonly_b.parameters())
#%%
for epoch in range(15):
drawer_lstmaddonly_a.train()
for num, ex in enumerate(drawer_lstmaddonly_a.datagen.get_examples_batch()):
optimizer_lstmaddonly_a.zero_grad()
loss = drawer_lstmaddonly_a.forward(ex)
loss.backward()
optimizer_lstmaddonly_a.step()
print(f'Done epoch {epoch} loss {float(loss)}')
if epoch % 1 == 0:
for split in ('a',):
sims = eval_fns(make_fns(split, scripted_tell, (drawer_lstmaddonly_a, drawer_lstmaddonly_b)), limit=100)
print(split, sims.mean())
sims = eval_fns(make_fns(split, scripted_tell_before_peek, (drawer_lstmaddonly_a, drawer_lstmaddonly_b)), limit=100)
print(split, 'before', sims.mean())
sims = eval_fns(make_fns(split, scripted_tell_after_peek, (drawer_lstmaddonly_a, drawer_lstmaddonly_b)), limit=100)
print(split, 'after', sims.mean())
#%%
for epoch in range(15):
drawer_lstmaddonly_b.train()
for num, ex in enumerate(drawer_lstmaddonly_b.datagen.get_examples_batch()):
optimizer_lstmaddonly_b.zero_grad()
loss = drawer_lstmaddonly_b.forward(ex)
loss.backward()