def save(self, fname, x):
path = os.path.join(self.path, fname + self.ext)
if os.path.exists(path) and 'grad' in fname:
x = cp.asnumpy(x)
old = np.load(path, mmap_mode='c')
np.save(path, old + x)
cp.save(path, x)
def save_mean_and_variance(self):
subset_size = self.num_observations_per_file
new_total_size = self.total_images + subset_size
co1 = self.total_images / new_total_size
co2 = subset_size / new_total_size
images = cp.asarray(self.images)
subset_mean = cp.mean(images, axis=(0, 1))
subset_var = cp.var(images, axis=(0, 1))
new_dataset_mean = subset_mean if self.dataset_mean is None else co1 * self.dataset_mean + co2 * subset_mean
new_dataset_var = subset_var if self.dataset_var is None else co1 * (
self.dataset_var + self.dataset_mean**2) + co2 * (
subset_var + subset_mean**2) - new_dataset_mean**2
# avoid negative value
new_dataset_var[new_dataset_var < 0] = 0
self.dataset_var = new_dataset_var
self.dataset_mean = new_dataset_mean
self.dataset_std = cp.sqrt(self.dataset_var)
cp.save(os.path.join(self.path, "mean.npy"), self.dataset_mean)
cp.save(os.path.join(self.path, "std.npy"), self.dataset_std)
def main():
count = 0
temp = torch.zeros((384, 256, 3), dtype=torch.float64)
temp.cuda()
for id in idlist:
img_np = torch.tensor(np.asarray(sampler.id2pix(str(id))),
dtype=torch.float64)
img_np.cuda()
"""
for x in range(384):
for y in range(256):
for c in range(3):
temp[x][y][c] += img_np[x][y][c]/255
"""
try:
temp += img_np / 255
except:
print("ERROR")
count += 1
if count % 10000 == 0: print(count)
temp = temp.numpy()
cp.save('pix_mean', temp)
plt.imshow(temp)
plt.show()
def mlp(path):
X,y,Xt,yt,cols = get_data(fillna=0, norm=True, dropids=True)
X = cp.asarray(X)
y = cp.asarray(y)
Xt = cp.asarray(Xt)
yt = cp.asarray(yt)
params = {
'learning_rate_init':0.005,
'verbose': True,
'hidden_layer_sizes': (64,64,64),
'max_iter':100,
'batch_size':4096,
'shuffle':True,
'alpha':0.000,
'model_path':f'{path}/cache/mlp.pth',
}
clf = MLPClassifier(**params)
clf.fit(X, y, Xt, yt)
yp = clf.predict_proba(Xt)[:,1]
score = roc_auc_score(yt, yp)
print('AUC: %.4f'%score)
cp.save('mlp.npy', yp)
#return yp
print(cols)
return score
def save(self):
path_gamma = self.prefix + self.name + '_gamma.npy'
path_beta = self.prefix + self.name + '_beta.npy'
with open(path_gamma, 'wb') as f:
cp.save(f, self.gamma)
with open(path_beta, 'wb') as f:
cp.save(f, self.beta)
def save(self):
path_w = self.prefix + self.name + '_w.npy'
path_b = self.prefix + self.name + '_b.npy'
with open(path_w, 'wb') as f:
cp.save(f, self.weights)
with open(path_b, 'wb') as f:
cp.save(f, self.b)
def np_to_cp(self):
for name, param in self.state_dict.items():
if "weight" in name:
param_np = param.cpu().numpy()
param_cp = cp.asarray(param_np)
cp.save("./save_cp/" + str(name) + ".npy", param_cp)
print("Converted weights to cupy")
def xgb(path):
X, y, Xt, yt, cols = get_data()
params = {
'n_estimators': 100,
'eta': 0.1,
'early_stopping_rounds': 10,
'max_depth': 7,
'colsample_bytree': 1.0,
'subsample': 0.5,
'verbosity': 1,
'objective': 'binary:logistic',
'eval_metric': 'auc',
'validation_fraction': 0,
}
clf = XGBClassifier(**params)
clf.fit(X, y, Xt, yt)
clf.clf.save_model(f'{path}/cache/xgb.json')
yp = clf.predict_proba(Xt)
#return yp
print(cols)
cp.save('xgb_va.npy', cp.asarray(yp))
yx = clf.predict_proba(X)
cp.save('xgb_tr.npy', cp.asarray(yx))
return roc_auc_score(yt, yp)
def build_cbow_model(self):
#Iterate over epochs
for k in range(self.epochs):
print("We are at epoch : ", k + 1)
#For each training example
for i in range(len(self.X_train)):
#Forward propagation of the CBOW network-----
#Take average
x = np.zeros((self.vocab_size, 1))
for word in self.X_train[i]:
x += self.one_hot(self.words_to_int[word])
x /= len(self.X_train[i])
h = np.dot(self.w_hidden.T, x)
'''
h = np.zeros((self.dim, 1))
for word in self.X_train[i]:
h += np.dot(self.w_hidden.T, self.one_hot(self.words_to_int[word]))
h/=len(self.X_train[i])
print ("Forward propagation done...", i, k)
'''
print("-----------")
print("Forward propagation done CBOW...: ", i, "Epoch: ",
k + 1)
#h = np.dot(self.w_hidden.T , onehot(X_train[i])
u = np.dot(self.w_output.T, h)
pred = self.softmax(u)
#Backward propagation------
#err_sum = np.zeros((self.vocab_size,1))
err = pred - self.one_hot(self.words_to_int[self.Y_train[i]])
print("Calculated error..", i, k + 1)
#Calculate dL/dW
dw_hidden = np.outer(x, np.dot(self.w_output, err))
#Calculate dL/dW'
dw_output = np.outer(h, err)
#Gradient descent
self.w_hidden += -self.lr * dw_hidden
self.w_output += -self.lr * dw_output
print("Gradient descent done..", i, k + 1)
#Update model after each epoch
print("Saving model...")
for key, value in self.words_to_int.items():
self.model[key] = self.w_hidden[value].reshape(
1, self.w_hidden.shape[1])
#Store model after every epoch
print("Model to npy file...")
np.save('./utils/cbow_new_' + str(k), self.model)
def build_skipgram_model(self):
#Iterate over epochs
print("No. of training samples are: ", len(self.X_train))
for k in range(self.epochs):
print("We are at epoch : ", k)
print()
print("No. of training samples: ", len(self.X_train))
#For each training example
for i in range(len(self.X_train)):
#Forward propagation of the SkipGram network-----
#Here X_train[i] is a Vx1 vector.
#print "self.X_train[i] is ", self.X_train[i]
#print "self.words_to_int[i] is ", self.words_to_int[self.X_train[i]]
h = np.dot(self.w_hidden.T,
self.one_hot(self.words_to_int[self.X_train[i]]))
output = np.dot(self.w_output.T, h)
pred = self.softmax(output)
print("---------------")
print("Forward propagation done SKIPGRAM...",
str(i) + "/" + str(len(self.X_train)), " Epoch: ",
str(k + 1) + "/" + str(self.epochs))
#Backward propagation------
err_sum = np.zeros((self.vocab_size, 1))
for word in self.Y_train[i]:
err_sum += (pred - self.one_hot(self.words_to_int[word]))
#err_sum/= self.vocab_size
print("Calculated error..", i, k + 1)
#Calculate dL/dW
dw_hidden = np.outer(
self.one_hot(self.words_to_int[self.X_train[i]]),
np.dot(self.w_output, err_sum))
#Calculate dL/dW'
dw_output = np.outer(h, err_sum)
#Gradient descent
self.w_hidden += -self.lr * dw_hidden
self.w_output += -self.lr * dw_output
print("Gradient descent done..", i, k + 1)
#Update model after each epoch
print("Saving model...")
for key, value in self.int_to_words.items():
self.model[value] = self.w_hidden[key].reshape(
1, self.w_hidden.shape[1])
#Store model after every epoch
#if (k!k%2==0):
print("Model to npy file...")
np.save('./utils/skipgram_' + str(k), self.model)
def save_map(state_dict, save_map_path, device):
save_weights = collections.OrderedDict({})
save_binary = collections.OrderedDict({})
# Save mapped float weights
for name, param in tqdm(state_dict.items(), desc="Saving weight maps: "):
if "weight" in name:
weights = param.view(-1)
map_cases = wmp.mapallweights2(weights)
map_cases_np = map_cases.cpu().numpy()
map_cases_cp = cp.asarray(map_cases_np)
cp.save(save_map_path + str(name) + ".npy", map_cases_cp)
def test_save_pickle(self):
data = object()
sio = io.BytesIO()
with self.assertRaises(ValueError):
cupy.save(sio, data, allow_pickle=False)
sio.close()
sio = io.BytesIO()
cupy.save(sio, data, allow_pickle=True)
sio.close()
def test_save_load(self, dtype):
a = testing.shaped_arange((2, 3, 4), dtype=dtype)
sio = io.BytesIO()
cupy.save(sio, a)
s = sio.getvalue()
sio.close()
sio = io.BytesIO(s)
b = cupy.load(sio)
sio.close()
testing.assert_array_equal(a, b)
def test_save_load(self, dtype):
a = testing.shaped_arange((2, 3, 4), dtype=dtype)
sio = six.BytesIO()
cupy.save(sio, a)
s = sio.getvalue()
sio.close()
sio = six.BytesIO(s)
b = cupy.load(sio)
sio.close()
testing.assert_array_equal(a, b)
def save(self, savedir):
""" persist map in `savedir` """
if not os.path.isdir(savedir):
os.makedirs(savedir)
# Persist arrays
dsave = {}
dsave['unique_state_ids'] = self.unique_state_ids,
dsave['unique_contagiousities'] = self.unique_contagiousities,
dsave['unique_sensitivities'] = self.unique_sensitivities,
dsave['unique_severities'] = self.unique_severities,
dsave['cell_ids'] = self.cell_ids,
dsave['unsafeties'] = self.unsafeties,
dsave['square_sampling_probas'] = self.square_sampling_probas,
dsave['eligible_cells'] = self.eligible_cells,
dsave['coords_squares'] = self.coords_squares,
dsave['square_ids_cells'] = self.square_ids_cells,
dsave['cell_sampling_probas'] = self.cell_sampling_probas,
dsave['cell_index_shift'] = self.cell_index_shift,
dsave['agent_ids'] = self.agent_ids,
dsave['p_moves'] = self.p_moves,
dsave['least_state_ids'] = self.least_state_ids,
dsave['unique_state_ids'] = self.unique_state_ids,
dsave['home_cell_ids'] = self.home_cell_ids,
dsave['current_state_ids'] = self.current_state_ids,
dsave['current_state_durations'] = self.current_state_durations,
dsave['agent_squares'] = self.agent_squares
dsave['transitions'] = self.transitions,
dsave['transitions_ids'] = self.transitions_ids,
dsave['durations'] = self.durations,
dsave['r_factors'] = self.r_factors,
dsave['infecting_agents'] = self.infecting_agents,
dsave['infected_agents'] = self.infected_agents,
dsave['infected_periods'] = self.infected_periods
for fname, arr in dsave.items():
filepath = os.path.join(savedir, f'{fname}.npy')
cp.save(filepath, arr)
# Persist scalars and other parameters
sdict = {}
sdict['current_period'] = self.current_period
sdict['verbose'] = self.verbose
sdict['dcale'] = self.dscale
sdict['n_infected_period'] = self.n_infected_period
sdict['n_diseased_period'] = self.n_diseased_period
sdict_path = os.path.join(savedir, 'params.pkl')
with open(sdict_path, 'wb') as f:
pickle.dump(sdict, f, protocol=pickle.HIGHEST_PROTOCOL)
if self.verbose > 0:
print(f'Map persisted under folder: {savedir}')
def multiply(self, factor, new_files):
loc = 0
norm = np.zeros(self.num_rows)
for i in range(len(self.files)):
with cp.cuda.Device(self.gpu_list[i % self.num_gpus]):
coef = cp.asarray(factor)
matrix_part = cp.load(self.files[i])
n = matrix_part.shape[0]
matrix_part = matrix_part * coef
cp.save(new_files[i], matrix_part)
norm_part = cp.sum(matrix_part, axis=1)
norm[loc:loc + n] = norm_part.get()
loc = loc + n
return norm
def main(config_file):
# 0. Load config and mkdir
with open(config_file) as fin:
config = json.load(fin, object_hook=lambda d: SimpleNamespace(**d))
data = Data(config.train_file_path)
mat = data.load_file()
t = time.process_time()
apsp = MODEL_MAP[config.model_type](mat, config)
mr = apsp.apsp(g_diameter=config.diameter)
te = time.process_time()
print('time:', (te - t))
print(mr.shape)
if config.device == 'cpu':
numpy.save(mr, config.experiment_name + '.npz')
else:
cupy.save(mr, config.experiment_name + '.npz')
def __get_D_word_embeddings(self, path):
if exists(f'{path}.{EMBEDDINGS_LIMIT}.npy'):
with open(f'{path}.{EMBEDDINGS_LIMIT}.pkl', mode='rb') as f:
DFreqs, DFreqsToWord = pickle.load(f)
with open(f'{path}.{EMBEDDINGS_LIMIT}.npy', mode='rb') as f:
a = np.load(f)
return DFreqs, DFreqsToWord, a
DWords = {} # Note this isn't stored in `self`
DFreqs = {}
DFreqsToWord = {}
with open(path, 'r', encoding='utf-8') as f:
for x, line in enumerate(f):
values = line.rstrip().rsplit(' ')
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
DWords[word] = coefs
DFreqs[word] = x # More common words might be higher up(?)
DFreqsToWord[x] = word
if x % 1000 == 0:
print(x)
if x > EMBEDDINGS_LIMIT:
# A lot of the time it might be better to
# clip results - millions of results might actually reduce
# the quality as frequencies get lower!
break
#print(word)
a = np.ndarray(shape=(len(DWords), len(DWords[word])), dtype='float32')
for word, vec in DWords.items():
# NOTE: the index is also the frequency of the word
# (with 0 being the most common), as the files
# are sorted in order of most to least common
a[DFreqs[word], :] = vec
with open(f'{path}.{EMBEDDINGS_LIMIT}.pkl', mode='wb') as f:
pickle.dump((DFreqs, DFreqsToWord), f)
with open(f'{path}.{EMBEDDINGS_LIMIT}.npy', mode='wb') as f:
np.save(f, a)
return DFreqs, DFreqsToWord, a
def get_resized_data(subset: str,
PATH: str,
resize: tuple,
fn_type='.txt',
sep=' ',
header=None):
try:
print('Loading the resized', subset, 'images ...', end=' ')
X = np.load(PATH + 'X_' + subset[:2] + '_' + str(resize[0]) + '.npy',
allow_pickle=True)
y = np.load(PATH + 'y_' + subset[:2] + '.npy', allow_pickle=True)
except:
print('Failed! QAQ')
print('Loading the original', subset, 'images ...', end=' ')
img_list, fn_list, y = load_images(subset, PATH, fn_type, sep, header)
X = img_resize(img_list, resize)
np.save(PATH + 'X_' + subset[:2] + '_' + str(resize[0]) + '.npy', X)
np.save(PATH + 'y_' + subset[:2] + '.npy', y)
print('Done!')
return X, y
def read_to_dat():
# Load raw images from ubytes
def read_train_data():
x, y = loadlocal_mnist(
images_path = train_images_raw,
labels_path = train_labels_raw)
return x, y
def read_test_data():
x, y = loadlocal_mnist(
images_path = test_images_raw,
labels_path = test_labels_raw)
return x, y
def convert_targets_train(train_array):
target_array = np.zeros((SAMPLES, NEURONS), dtype="float32")
for t in range(SAMPLES):
target_array[t][int(train_array[t])] = 1
return target_array
def convert_targets_test(test_array):
target_array = np.zeros((SAMPLES_T, NEURONS), dtype='float32')
for t in range(SAMPLES_T):
target_array[t][int(test_array[t])] = 1
return target_array
train_images, train_labels = read_train_data()
train_labels = convert_targets_train(train_labels)
# Normalize
train_images = train_images / 255
# Create memmap pointer on disk and read into .dats
np.save(train_images_dat, train_images, True)
# fp0 = np.memmap(train_images_dat, dtype='float64',
# mode='w+', shape=(SAMPLES,INPUTS))
# fp0 = np.memmap(train_images_dat, mode='w+', shape=(SAMPLES,INPUTS))
# Copy into file pointer
# fp0[:] = train_images[:]
# del fp0
np.save(train_labels_dat, train_labels, True)
# fp1 = np.memmap(train_labels_dat, dtype='float64',
# mode='w+', shape=(SAMPLES,NEURONS))
# fp1[:] = train_labels[:]
# del fp1
# Same as above but with test samples
test_images, test_labels = read_test_data()
test_labels = convert_targets_test(test_labels)
test_images = test_images / 255
np.save(test_images_dat, test_images, True)
# fp2 = np.memmap(test_images_dat, dtype='float64',
# mode='w+', shape=(SAMPLES_T,INPUTS))
# fp2[:] = test_images[:]
# del fp2
np.save(test_labels_dat, test_labels, True)
def main(id):
with chainer.using_config("train", False):
with chainer.using_config("enable_backprop", False):
model_path = "/efs/fMRI_AE/SimpleFCAE_E32D32/model/model_iter_108858"
gpu = 0
get_device_from_id(gpu).use()
"""NibDataset
def __init__(self, directory: str, crop: list):
"""
crop = [[9, 81], [11, 99], [0, 80]]
test_dataset = NibDataset("/data/test", crop=crop)
"""
def __init__(self, dataset, batch_size, repeat=True, shuffle=True):
"""
mask = load_mask_nib("/data/mask/average_optthr.nii", crop)
model = Model(mask, 2, "mask", "mask")
load_npz(model_path, model)
model.to_gpu()
for i in range(len(test_dataset)):
if i % 8 != id:
continue
inp = to_gpu(test_dataset.get_example(i))
inp = xp.expand_dims(inp, 0)
subject = test_dataset.get_subject(i)
frame = test_dataset.get_frame(i)
sys.stdout.write("\rsubject{:03d} frame{:03d}".format(
subject, frame))
sys.stdout.flush()
out = model.reconstruct(inp).array
out = xp.squeeze(out)
xp.save(
"/efs/fMRI_AE/SimpleFCAE_E32D32/reconstruct/reconstruction_subject{:03d}_frame{:03d}.npy"
.format(subject, frame), out)
def save_arrays(array_dir, **arrays):
for name, array in arrays.items():
cp.save("%s/%s" % (array_dir, name), array)
def saveCupy(self, saveFileName):
cupy.save(saveFileName, self.CUPYcorpus)
def save(filename, arr):
cp.save(filename, arr)
def save(self, tsr, filename):
with open(filename, 'w+b') as file:
cp.save(file, tsr.unwrap(), allow_pickle=False)
def save_layers(self, path, i):
"""Save weights and biases to file. """
np.save("{:}/w_layer{:}.npy".format(path, i), self.W)
np.save("{:}/b_layer{:}.npy".format(path, i), self.b)
bytes_length = sys.getsizeof(10**end)
for i in range(start, end + 1, step):
print(f'Preparing for length {i}')
# Generate random array
tensor = cp.random.randint(0, R, size=(ns.pairs * 2, i // ns.segment))
# Iterate through all arrays and convert into integers
integers = set()
for j in range(tensor.shape[0]):
x = int(''.join([
n.zfill(len(n) + (-len(n) % ns.segment))
for n in (str(m) for m in tensor[j])
]))
if random.choice([False, True]):
tensor[j] *= -1
x *= -1
integers.add(x)
# Save tensor to file
cp.save(f'{path_tensors}/{i}', tensor)
# Save integers to file
with open(f'{path_integers}/{i}.lsi', 'wb') as f:
for x in integers:
x = x.to_bytes(bytes_length, byteorder='big', signed=True)
f.write(x + b'[END_OF_INTEGER]')
print("Running FMAEE:")
start = time.time()
# Here we process (train/execute) each individual observation.
# In this way, X is essentially a stream, and each observation is discarded after performing process() method.
for j in range(1, epoch * 2 + 1):
for i in range(X.shape[0]):
K.process(
X[i, ]
) #will train during the grace periods, then execute on all the rest.
if i % X.shape[0] == 0:
print(str(j) + " epoch")
K.process(X[0, ]) #will trigger saving the models
stop = time.time()
print("Training completed in: " + str(round(stop - start)) + " seconds")
RMSEs = np.zeros(X.shape[0]) # a place to save the scores
for i in range(X.shape[0]):
RMSEs[i] = K.execute(X[i, ])
if settings["sens"] == "low":
threshold = np.mean(RMSEs) + np.std(RMSEs)
level = 1
if settings["sens"] == "med":
threshold = np.mean(RMSEs) + 2 * np.std(RMSEs)
level = 2
if settings["sens"] == "high":
threshold = np.mean(RMSEs) + 3 * np.std(RMSEs)
level = 3
params = [threshold, level, np.mean(RMSEs), np.std(RMSEs)]
np.save("./models/threshold.npy", params)
def _save(name, arr, dtype=None):
cp.save(join(savePath, name + '.npy'), arr.astype(dtype or arr.dtype))
def saveCupy(self, saveFileName):
cupy.save(saveFileName, self.CUPYmemmap)
def main(id):
model_path = "/efs/fMRI_AE/SimpleFCAE_E32D32/model/model_iter_108858"
gpu = 0
get_device_from_id(gpu).use()
"""NibDataset
def __init__(self, directory: str, crop: list):
"""
crop = [[9, 81], [11, 99], [0, 80]]
test_dataset = NibDataset("/data/test", crop=crop)
mask = load_mask_nib("/data/mask/average_optthr.nii", crop)
"""SimpleFCAE_E32D32
def __init__(self, mask, r: int, in_mask: str, out_mask: str):
"""
model = Model(mask, 2, "mask", "mask")
load_npz(model_path, model)
model.to_gpu()
# feature_idx = 0
# feature_idx = (0, 4, 5, 5) # == [0, 9/2, 11/2, 10/2]
# feature_idx = (0, 1, 1, 1)
feature_idx = (0, 2, 7, 4)
resample_size = 100
batch_size = 10
noise_level = 0.2
for i in range(len(test_dataset)):
if i % 8 != id:
continue
print("{:4}/{:4}".format(i, len(test_dataset)))
subject = test_dataset.get_subject(i)
frame = test_dataset.get_frame(i)
test_img = xp.asarray(test_dataset[i])
resample_remain = resample_size
resample_processed = 0
ret = xp.zeros(test_img.shape)
while resample_remain > 0:
batch_size_this_loop = min(batch_size, resample_remain)
resample_remain -= batch_size_this_loop
batch = xp.broadcast_to(
test_img, chain((batch_size_this_loop, ), test_img.shape))
sigma = noise_level / (xp.max(test_img) - xp.min(test_img))
batch += sigma * xp.random.randn(*batch.shape)
x = Variable(batch)
feature = model.extract(x)
assert feature.shape == (batch_size, 1, 9, 11, 10)
feature = F.sum(feature, axis=0)
assert feature.shape == (1, 9, 11, 10)
feature = F.get_item(feature, feature_idx)
feature.backward()
grad = xp.mean(x.grad, axis=0)
ret = (ret * resample_processed + grad * batch_size_this_loop) / (
resample_processed + batch_size_this_loop)
model.cleargrads()
xp.save(
"/efs/fMRI_AE/SimpleFCAE_E32D32/grad/sensitivity_map_feature_{}_{}_{}_subject{:03d}_frame{:03d}"
.format(feature_idx[1], feature_idx[2], feature_idx[3], subject,
frame), ret)
