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Update GeMOSA v4

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D-X-Y
2021-05-27 19:27:29 +08:00
parent 16861f0f3d
commit 08337138f1
3 changed files with 130 additions and 39 deletions
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51
exps/GeMOSA/main.py
View File

@@ -5,6 +5,7 @@
# python exps/GeMOSA/main.py --env_version v1 --lr 0.002 --hidden_dim 16 --meta_batch 256 --device cuda
# python exps/GeMOSA/main.py --env_version v2 --lr 0.002 --hidden_dim 16 --meta_batch 256 --device cuda
# python exps/GeMOSA/main.py --env_version v3 --lr 0.002 --hidden_dim 32 --time_dim 32 --meta_batch 256 --device cuda
# python exps/GeMOSA/main.py --env_version v4 --lr 0.002 --hidden_dim 32 --time_dim 32 --meta_batch 256 --device cuda
#####################################################
import sys, time, copy, torch, random, argparse
from tqdm import tqdm
@@ -32,15 +33,24 @@ from xautodl.procedures.advanced_main import basic_train_fn, basic_eval_fn
from xautodl.procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
from xautodl.datasets.synthetic_core import get_synthetic_env
from xautodl.models.xcore import get_model
from xautodl.xlayers import super_core, trunc_normal_
from xautodl.procedures.metric_utils import MSEMetric, Top1AccMetric
from meta_model import MetaModelV1
def online_evaluate(
env, meta_model, base_model, criterion, args, logger, save=False, easy_adapt=False
env,
meta_model,
base_model,
criterion,
metric,
args,
logger,
save=False,
easy_adapt=False,
):
logger.log("Online evaluate: {:}".format(env))
metric.reset()
loss_meter = AverageMeter()
w_containers = dict()
for idx, (future_time, (future_x, future_y)) in enumerate(env):
@@ -57,6 +67,8 @@ def online_evaluate(
future_y_hat = base_model.forward_with_container(future_x, future_container)
future_loss = criterion(future_y_hat, future_y)
loss_meter.update(future_loss.item())
# accumulate the metric scores
metric(future_y_hat, future_y)
if easy_adapt:
meta_model.easy_adapt(future_time.item(), future_time_embed)
refine, post_refine_loss = False, -1
@@ -79,7 +91,7 @@ def online_evaluate(
)
meta_model.clear_fixed()
meta_model.clear_learnt()
return w_containers, loss_meter
return w_containers, loss_meter.avg, metric.get_info()["score"]
def meta_train_procedure(base_model, meta_model, criterion, xenv, args, logger):
@@ -203,7 +215,16 @@ def main(args):
base_model = get_model(**model_kwargs)
base_model = base_model.to(args.device)
criterion = torch.nn.MSELoss()
if all_env.meta_info["task"] == "regression":
criterion = torch.nn.MSELoss()
metric = MSEMetric(True)
elif all_env.meta_info["task"] == "classification":
criterion = torch.nn.CrossEntropyLoss()
metric = Top1AccMetric(True)
else:
raise ValueError(
"This task ({:}) is not supported.".format(all_env.meta_info["task"])
)
shape_container = base_model.get_w_container().to_shape_container()
@@ -235,27 +256,29 @@ def main(args):
)
logger.log("In this enviornment, the total loss-meter is {:}".format(loss_meter))
"""
_, test_loss_meter_adapt_v1 = online_evaluate(
valid_env, meta_model, base_model, criterion, args, logger, False, False
_, loss_adapt_v1, metric_adapt_v1 = online_evaluate(
valid_env, meta_model, base_model, criterion, metric, args, logger, False, False
)
_, test_loss_meter_adapt_v2 = online_evaluate(
valid_env, meta_model, base_model, criterion, args, logger, False, True
_, loss_adapt_v2, metric_adapt_v2 = online_evaluate(
valid_env, meta_model, base_model, criterion, metric, args, logger, False, True
)
logger.log(
"In the online test enviornment, the total loss for refine-adapt is {:}".format(
test_loss_meter_adapt_v1
"[Refine-Adapt] loss = {:.6f}, metric = {:.6f}".format(
loss_adapt_v1, metric_adapt_v1
)
)
logger.log(
"In the online test enviornment, the total loss for easy-adapt is {:}".format(
test_loss_meter_adapt_v2
"[Easy-Adapt] loss = {:.6f}, metric = {:.6f}".format(
loss_adapt_v2, metric_adapt_v2
)
)
save_checkpoint(
{
"test_loss_adapt_v1": test_loss_meter_adapt_v1.avg,
"test_loss_adapt_v2": test_loss_meter_adapt_v2.avg,
"test_loss_adapt_v1": loss_adapt_v1,
"test_loss_adapt_v2": loss_adapt_v2,
"test_metric_adapt_v1": metric_adapt_v1,
"test_metric_adapt_v2": metric_adapt_v2,
},
logger.path(None) / "final-ckp-{:}.pth".format(args.rand_seed),
logger,

76
exps/GeMOSA/vis-synthetic.py
View File

@@ -33,7 +33,9 @@ from xautodl.procedures.metric_utils import MSEMetric
def plot_scatter(cur_ax, xs, ys, color, alpha, linewidths, label=None):
cur_ax.scatter([-100], [-100], color=color, linewidths=linewidths[0], label=label)
cur_ax.scatter(xs, ys, color=color, alpha=alpha, linewidths=linewidths[1], label=None)
cur_ax.scatter(
xs, ys, color=color, alpha=alpha, linewidths=linewidths[1], label=None
)
def draw_multi_fig(save_dir, timestamp, scatter_list, wh, fig_title=None):
@@ -193,16 +195,28 @@ def visualize_env(save_dir, version):
for idx, (timestamp, (allx, ally)) in enumerate(tqdm(dynamic_env, ncols=50)):
allxs.append(allx)
allys.append(ally)
if dynamic_env.meta_info['task'] == 'regression':
if dynamic_env.meta_info["task"] == "regression":
allxs, allys = torch.cat(allxs).view(-1), torch.cat(allys).view(-1)
print("x - min={:.3f}, max={:.3f}".format(allxs.min().item(), allxs.max().item()))
print("y - min={:.3f}, max={:.3f}".format(allys.min().item(), allys.max().item()))
elif dynamic_env.meta_info['task'] == 'classification':
print(
"x - min={:.3f}, max={:.3f}".format(allxs.min().item(), allxs.max().item())
)
print(
"y - min={:.3f}, max={:.3f}".format(allys.min().item(), allys.max().item())
)
elif dynamic_env.meta_info["task"] == "classification":
allxs = torch.cat(allxs)
print("x[0] - min={:.3f}, max={:.3f}".format(allxs[:,0].min().item(), allxs[:,0].max().item()))
print("x[1] - min={:.3f}, max={:.3f}".format(allxs[:,1].min().item(), allxs[:,1].max().item()))
print(
"x[0] - min={:.3f}, max={:.3f}".format(
allxs[:, 0].min().item(), allxs[:, 0].max().item()
)
)
print(
"x[1] - min={:.3f}, max={:.3f}".format(
allxs[:, 1].min().item(), allxs[:, 1].max().item()
)
)
else:
raise ValueError("Unknown task".format(dynamic_env.meta_info['task']))
raise ValueError("Unknown task".format(dynamic_env.meta_info["task"]))
for idx, (timestamp, (allx, ally)) in enumerate(tqdm(dynamic_env, ncols=50)):
dpi, width, height = 30, 1800, 1400
@@ -211,29 +225,51 @@ def visualize_env(save_dir, version):
fig = plt.figure(figsize=figsize)
cur_ax = fig.add_subplot(1, 1, 1)
if dynamic_env.meta_info['task'] == 'regression':
if dynamic_env.meta_info["task"] == "regression":
allx, ally = allx[:, 0].numpy(), ally[:, 0].numpy()
plot_scatter(cur_ax, allx, ally, "k", 0.99, (15, 1.5), "timestamp={:05d}".format(idx))
plot_scatter(
cur_ax, allx, ally, "k", 0.99, (15, 1.5), "timestamp={:05d}".format(idx)
)
cur_ax.set_xlim(round(allxs.min().item(), 1), round(allxs.max().item(), 1))
cur_ax.set_ylim(round(allys.min().item(), 1), round(allys.max().item(), 1))
elif dynamic_env.meta_info['task'] == 'classification':
elif dynamic_env.meta_info["task"] == "classification":
positive, negative = ally == 1, ally == 0
# plot_scatter(cur_ax, [1], [1], "k", 0.1, 1, "timestamp={:05d}".format(idx))
plot_scatter(cur_ax, allx[positive,0], allx[positive,1], "r", 0.99, (20, 10), "positive")
plot_scatter(cur_ax, allx[negative,0], allx[negative,1], "g", 0.99, (20, 10), "negative")
cur_ax.set_xlim(round(allxs[:,0].min().item(), 1), round(allxs[:,0].max().item(), 1))
cur_ax.set_ylim(round(allxs[:,1].min().item(), 1), round(allxs[:,1].max().item(), 1))
plot_scatter(
cur_ax,
allx[positive, 0],
allx[positive, 1],
"r",
0.99,
(20, 10),
"positive",
)
plot_scatter(
cur_ax,
allx[negative, 0],
allx[negative, 1],
"g",
0.99,
(20, 10),
"negative",
)
cur_ax.set_xlim(
round(allxs[:, 0].min().item(), 1), round(allxs[:, 0].max().item(), 1)
)
cur_ax.set_ylim(
round(allxs[:, 1].min().item(), 1), round(allxs[:, 1].max().item(), 1)
)
else:
raise ValueError("Unknown task".format(dynamic_env.meta_info['task']))
raise ValueError("Unknown task".format(dynamic_env.meta_info["task"]))
cur_ax.set_xlabel("X", fontsize=LabelSize)
cur_ax.set_ylabel("Y", rotation=0, fontsize=LabelSize)
for tick in cur_ax.xaxis.get_major_ticks():
tick.label.set_fontsize(LabelSize - font_gap)
tick.label.set_rotation(10)
tick.label.set_fontsize(LabelSize - font_gap)
tick.label.set_rotation(10)
for tick in cur_ax.yaxis.get_major_ticks():
tick.label.set_fontsize(LabelSize - font_gap)
cur_ax.legend(loc=1, fontsize=LegendFontsize)
tick.label.set_fontsize(LabelSize - font_gap)
cur_ax.legend(loc=1, fontsize=LegendFontsize)
pdf_save_path = (
save_dir
/ "pdf-{:}".format(version)

42
xautodl/procedures/metric_utils.py
View File

@@ -98,21 +98,53 @@ class ComposeMetric(Metric):
class MSEMetric(Metric):
"""The metric for mse."""
def __init__(self, ignore_batch):
super(MSEMetric, self).__init__()
self._ignore_batch = ignore_batch
def reset(self):
self._mse = AverageMeter()
def __call__(self, predictions, targets):
if isinstance(predictions, torch.Tensor) and isinstance(targets, torch.Tensor):
batch = predictions.shape[0]
loss = torch.nn.functional.mse_loss(predictions.data, targets.data)
loss = loss.item()
self._mse.update(loss, batch)
loss = torch.nn.functional.mse_loss(predictions.data, targets.data).item()
if self._ignore_batch:
self._mse.update(loss, 1)
else:
self._mse.update(loss, predictions.shape[0])
return loss
else:
raise NotImplementedError
def get_info(self):
return {"mse": self._mse.avg}
return {"mse": self._mse.avg, "score": self._mse.avg}
class Top1AccMetric(Metric):
"""The metric for the top-1 accuracy."""
def __init__(self, ignore_batch):
super(Top1AccMetric, self).__init__()
self._ignore_batch = ignore_batch
def reset(self):
self._accuracy = AverageMeter()
def __call__(self, predictions, targets):
if isinstance(predictions, torch.Tensor) and isinstance(targets, torch.Tensor):
max_prob_indexes = torch.argmax(predictions, dim=-1)
corrects = torch.eq(max_prob_indexes, targets)
accuracy = corrects.float().mean().float()
if self._ignore_batch:
self._accuracy.update(accuracy, 1)
else: # [TODO] for 3-d tensor
self._accuracy.update(accuracy, predictions.shape[0])
return accuracy
else:
raise NotImplementedError
def get_info(self):
return {"accuracy": self._accuracy.avg, "score": self._accuracy.avg * 100}
class SaveMetric(Metric):