def extract(model_path, gpu, out, split, in_episodes, out_episodes,
hidden_channels):
"""Extract hidden features for each data in dataset"""
import chainer
import chainervr
import os
import numpy as np
import pandas as pd
assert in_episodes == out_episodes
model = chainervr.models.DeepEpisodicMemory(
hidden_channels=hidden_channels, num_episodes=in_episodes)
model.reset_state()
if gpu >= 0:
P.info("Using GPU %d" % gpu)
chainer.cuda.get_device_from_id(gpu).use()
model.to_gpu()
else:
P.info("Using CPU")
chainer.serializers.load_npz(model_path, model)
P.info("Loading dataset")
dataset = chainervr.datasets.MovingMnistDataset(split=split,
channels_num=3)
os.makedirs(out, exist_ok=True)
chainer.config.train = False
P.info("Forwarding")
xp = model.xp
features = []
with click.progressbar(dataset, label="Extracting") as it:
for data in it:
in_data, next_data = data[:in_episodes], data[
in_episodes:in_episodes + out_episodes]
in_data, next_data = in_data[np.newaxis, :], next_data[
np.newaxis, :]
in_data = chainer.Variable(in_data)
if gpu >= 0:
with chainer.cuda.get_device_from_id(gpu):
in_data.to_gpu()
_, _, hidden = model(in_data)
if gpu >= 0:
hidden.to_cpu()
feature = hidden.array.reshape(-1)
features.append(feature)
features = np.asarray(features, dtype=features[0].dtype)
save_fn = os.path.join(out, "features.npz")
np.savez(save_fn, features=features)
P.info("Extracted features into %s" % save_fn)
def predict(model_path, gpu, out, split, hidden_channels, in_episodes,
out_episodes, start_from, images_num):
"""Generate image for visualization using trained model"""
assert in_episodes == out_episodes
P.info("Loading model from %s" % model_path)
model = chainervr.models.DeepEpisodicMemory(
hidden_channels=hidden_channels, num_episodes=in_episodes)
model.reset_state()
P.info("Loading dataset")
dataset = chainervr.datasets.MovingMnistDataset(split=split,
channels_num=3)
P.predict(model=model,
model_path=model_path,
dataset=dataset,
gpu=gpu,
out=out,
in_episodes=in_episodes,
out_episodes=out_episodes,
start_from=start_from,
images_num=images_num)
def predict(model_path, gpu, out, split, disable_predict,
in_episodes, out_episodes, start_from, images_num):
P.info("Loading model from %s" % model_path)
model = chainervr.models.ConvLSTM(
n_channels=1, patch_size=(64, 64),
predict=not disable_predict,
in_episodes=in_episodes, out_episodes=out_episodes)
model.reset_state()
P.info("Loading dataset")
dataset = chainervr.datasets.MovingMnistDataset(
split=split, channels_num=1)
P.predict(
model=model,
model_path=model_path,
dataset=dataset,
gpu=gpu, out=out,
in_episodes=in_episodes,
out_episodes=out_episodes,
start_from=start_from,
images_num=images_num)
def summary(model_dir, gpu, out, split, disable_predict, layers_num,
in_episodes, out_episodes, image_num, skip_num):
model = chainervr.models.RPLSTM(n_channels=1,
patch_size=(64, 64),
n_layers=layers_num,
predict=not disable_predict,
in_episodes=in_episodes,
out_episodes=out_episodes)
model.reset_state()
P.info("Loading dataset")
dataset = chainervr.datasets.MovingMnistDataset(split=split,
channels_num=1)
P.predict_summary(model=model,
model_dir=model_dir,
dataset=dataset,
gpu=gpu,
in_episodes=in_episodes,
out_episodes=out_episodes,
out=out,
image_num=image_num,
skip_num=skip_num)
def summary(model_dir, gpu, out, split, hidden_channels, in_episodes,
out_episodes, image_num):
"""Generate images for evaluation using each model in directory"""
assert in_episodes == out_episodes
model = chainervr.models.DeepEpisodicMemory(
hidden_channels=hidden_channels, num_episodes=in_episodes)
model.reset_state()
P.info("Loading dataset")
dataset = chainervr.datasets.MovingMnistDataset(split=split,
channels_num=3)
P.predict_summary(model=model,
model_dir=model_dir,
dataset=dataset,
gpu=gpu,
in_episodes=in_episodes,
out_episodes=out_episodes,
out=out,
image_num=image_num)
def detect(model_path, features_path, input_num, top_n, gpu, out, split,
in_episodes, out_episodes, hidden_channels):
"""Extract hidden features for each data in dataset"""
import chainer
import chainervr
import os
import numpy as np
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
import matplotlib.pyplot as plt
assert in_episodes == out_episodes
model = chainervr.models.DeepEpisodicMemory(
hidden_channels=hidden_channels, num_episodes=in_episodes)
model.reset_state()
if gpu >= 0:
P.info("Using GPU %d" % gpu)
chainer.cuda.get_device_from_id(gpu).use()
model.to_gpu()
else:
P.info("Using CPU")
chainer.serializers.load_npz(model_path, model)
P.info("Loading dataset")
dataset = chainervr.datasets.MovingMnistDataset(split=split,
channels_num=3)
os.makedirs(out, exist_ok=True)
chainer.config.train = False
P.info("Loading features")
features = np.load(features_path)["features"]
P.info("Forwarding: %d" % input_num)
in_data = dataset[input_num][:in_episodes][np.newaxis, :]
in_data = chainer.Variable(in_data)
if gpu >= 0:
with chainer.cuda.get_device_from_id(gpu):
in_data.to_gpu()
# forwarding
_, _, hidden = model(in_data)
if gpu >= 0:
hidden.to_cpu()
feature = hidden.array.reshape(-1)
# compute similarities
similarities = cosine_similarity(
features, np.broadcast_to(feature, (len(dataset), feature.shape[0])))
similarities = np.diag(similarities)
ranking = (-similarities).argsort()
rows = top_n + 1
fig = plt.figure(figsize=(3, rows))
ax = fig.add_subplot(rows, 1, 1)
ax = vis_episode(in_data, ax=ax, title="Input")
for n in range(top_n):
index = ranking[n]
similarity = similarities[index]
comp_data = dataset[index][:in_episodes][np.newaxis, :]
ax = fig.add_subplot(rows, 1, n + 2)
ax = vis_episode(comp_data,
ax=ax,
title="Top %d (index: %d, similarity: %f)" %
(n + 1, index, similarity))
os.makedirs(out, exist_ok=True)
out_path = os.path.join(out, "rank.png")
plt.savefig(out_path, bbox_inches="tight", dpi=300)
P.info("saved to %s" % out_path)
plt.close(fig)
