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utils.py
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utils.py
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import os
import yaml
import multiprocessing
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow as tf
import matplotlib.pyplot as plt
from keras_custom.generators import gen, gen_w_heldout
"""
Shared utility functionalities.
`load_config`: load configuration file
`produce_orig_reprs`:
1. Given the original stimuli (or the backgrounded stimuli for task6),
2. Load a pretrained model, compute layer (user given) representations.
`data_loader`:
1. Take the layer reprs, stack them into X, Y (giant matrices)
2. Save them and load back in later for training.
3. The stacking/saving thing only needs to do once.
"""
def load_config(config_version):
with open(os.path.join('configs', f'{config_version}.yaml')) as f:
config = yaml.safe_load(f)
print(f'[Check] Loading [{config_version}]')
return config
def produce_orig_reprs(model, model_name, layer, preprocess_func, stimulus_set, return_images=False):
"""
Purpose:
--------
Produce given layer's representations of 8 or 16
stimuli with no data augmentation only preprocessed.
inputs:
-------
model: A specified model capped at some layer
model_name: vgg16 / vgg19 / vit_b16
layer: the layer to intercept representations from
preprocess_func: Model-specific preprocessing routine
stimulus_set: ..
return_images: default False (return model predictions)
If True, return the preprocessed original
images.
returns:
--------
reprs: layer activations for all images.
reprs will have shape (N, D)
dataset: A tf Dataset which produces original images later
used for visual examination.
"""
data_dir = f'stimuli/original/task{stimulus_set}'
print(f'[Check]: using data from {data_dir}')
batch_size = len(os.listdir(data_dir))
print(f'[Check] batch_size={batch_size}')
# this loads original images
dataset = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
shuffle=False,
image_size=(224, 224),
batch_size=batch_size)
# this loads model-specific processed images.
if model_name == 'vit_b16':
from utils import ViT_ImageDataGenerator
datagen = ViT_ImageDataGenerator(
preprocessing_function=preprocess_func)
else:
datagen = tf.keras.preprocessing.image.ImageDataGenerator(
preprocessing_function=preprocess_func)
generator = datagen.flow_from_directory(
data_dir,
target_size=(224, 224),
batch_size=batch_size,
class_mode='sparse',
shuffle=False)
if return_images is False:
if model_name == 'vit_b16':
# `layer_x_..`
layer_index = int(layer[6:7])
# Iterate over the generator to
# collect the representations.
reprs = []
for i in range(len(generator)):
x, y = next(generator) # x -> (8, 3, 224, 224)
if 'msa' in layer:
# Grabs the MSA outputs
# \in (bs, seq_len, num_heads, head_dim)
# e.g. (8, 197, 12, 64)
layer_reprs = model(
x, training=False,
output_msa_states=True
).attentions[layer_index].numpy()
else:
layer_reprs = model(
x, training=False,
output_hidden_states=True
).hidden_states[layer_index].numpy()
# Need to flatten the non-batch dimensions as
# we defer layer output in data pipeline instead
# of in models.py
layer_reprs = layer_reprs.reshape(x.shape[0], -1)
reprs.append(layer_reprs)
# vstack to reduce the generator step dimension.
reprs = np.vstack(reprs)
else:
reprs = model.predict(generator)
else:
reprs, _ = next(generator)
### TEST gen match real image. ###
# fig, ax = plt.subplots(1, reprs.shape[0])
# for i in range(reprs.shape[0]):
# ax[i].imshow(reprs[i]/255.)
# ax[i].set_title(f'type[{i}]')
# plt.savefig('testGenmatch.pdf')
# exit()
### ###
return reprs, dataset
def data_loader(config, input_shape, seed=42):
"""
Purpose:
--------
- Load train/val datasets.
- Also we have the option to load dataset for heldout training,
in other words, a class will be held out during training.
- This data_loader is compatible for task1-6.
Impl:
-----
Load and stack all data-points as a giant matrix to be
shuffled and splitted later for training/validation.
The entire matrix will be saved so next time we do not
have to stack one data-point at a time but loading in
the entire matrix at once for training/validation.
inputs:
-------
config: ..
input_shape: this is used to set the empty array to enable concat.
and should be the fc1 output size.
seed: control randomness in train/val split
"""
XY_dir = config['XY_dir']
stimulus_set = config['stimulus_set']
split_ratio = config['split_ratio']
model_name = config['model_name']
layer = config['layer']
# we only stack the data once, once saved we can load off the disk.
if os.path.exists(f'resources/{XY_dir}/{model_name}/{layer}/task{stimulus_set}/X.npy'):
print(f'[Check] Loading pre-saved X and Y from {XY_dir}/{model_name}/{layer}/task{stimulus_set}/')
X = np.load(f'resources/{XY_dir}/{model_name}/{layer}/task{stimulus_set}/X.npy')
Y = np.load(f'resources/{XY_dir}/{model_name}/{layer}/task{stimulus_set}/Y.npy')
print(f'[Check] X, Y shape = {X.shape}, {Y.shape}')
# first time, we stack and save dataset into disk.
else:
# if not the Current Biology set, we have 3 features.
if stimulus_set not in [6, '6']:
orig2binary = {
'000': [0, 0, 0],
'001': [0, 0, 1],
'010': [0, 1, 0],
'011': [0, 1, 1],
'100': [1, 0, 0],
'101': [1, 0, 1],
'110': [1, 1, 0],
'111': [1, 1, 1]}
# 4 features.
else:
orig2binary = {'0000': [0,0,0,0],
'0001': [0,0,0,1],
'0010': [0,0,1,0],
'0011': [0,0,1,1],
'0100': [0,1,0,0],
'0101': [0,1,0,1],
'0110': [0,1,1,0],
'0111': [0,1,1,1],
'1000': [1,0,0,0],
'1001': [1,0,0,1],
'1010': [1,0,1,0],
'1011': [1,0,1,1],
'1100': [1,1,0,0],
'1101': [1,1,0,1],
'1110': [1,1,1,0],
'1111': [1,1,1,1]}
X = np.empty(input_shape)
if config['stimulus_set'] not in ['6', 6]:
Y = np.empty(3)
else:
Y = np.empty(4)
mapping = orig2binary
preprocessed_dir = config['preprocessed_dir']
data_dir = f'stimuli/{preprocessed_dir}/{model_name}/{layer}_reprs/task{stimulus_set}/'
print(f'[Check] Stacking reprs from {data_dir}')
for stimulus_type in sorted(os.listdir(data_dir)):
print(f'[Check] Stacking stimulus [{stimulus_type}]')
y = mapping[stimulus_type]
for fname in os.listdir(os.path.join(data_dir, stimulus_type)):
fpath = os.path.join(data_dir, stimulus_type, fname)
x = np.load(fpath)
X = np.vstack((X, x))
Y = np.vstack((Y, y))
X = X[1:, :]
Y = Y[1:, :]
print(f'[Check] X.shape={X.shape}')
print(f'[Check] Y.shape={Y.shape}')
# save the stacked dataset
if not os.path.exists(f'resources/{XY_dir}/{model_name}/{layer}/task{stimulus_set}'):
os.makedirs(f'resources/{XY_dir}/{model_name}/{layer}/task{stimulus_set}')
np.save(f'resources/{XY_dir}/{model_name}/{layer}/task{stimulus_set}/X.npy', X)
np.save(f'resources/{XY_dir}/{model_name}/{layer}/task{stimulus_set}/Y.npy', Y)
print('[Check] saved X, Y.')
# if heldout, we slice a subset of the X and Y
# based on the stimulus type we want to hold out.
heldout_class = config['heldout']
if config['heldout'] is not None:
if stimulus_set not in [6, '6']:
num_sample_per_class = 1024
if heldout_class == '000':
a = num_sample_per_class * 0
if heldout_class == '001':
a = num_sample_per_class * 1
if heldout_class == '010':
a = num_sample_per_class * 2
if heldout_class == '011':
a = num_sample_per_class * 3
if heldout_class == '100':
a = num_sample_per_class * 4
if heldout_class == '101':
a = num_sample_per_class * 5
if heldout_class == '110':
a = num_sample_per_class * 6
if heldout_class == '111':
a = num_sample_per_class * 7
heldout_indices = np.arange(a, a+num_sample_per_class)
X = np.delete(X, heldout_indices, axis=0)
Y = np.delete(Y, heldout_indices, axis=0)
print(f'[Check] holding out [{heldout_class}]')
else:
# because task6 has different number of samples
# the slices need to set up differently
num_sample_per_class = 400
if heldout_class == '0000':
a = num_sample_per_class * 0
if heldout_class == '0001':
a = num_sample_per_class * 1
if heldout_class == '0010':
a = num_sample_per_class * 2
if heldout_class == '0011':
a = num_sample_per_class * 3
if heldout_class == '0100':
a = num_sample_per_class * 4
if heldout_class == '0101':
a = num_sample_per_class * 5
if heldout_class == '0110':
a = num_sample_per_class * 6
if heldout_class == '0111':
a = num_sample_per_class * 7
if heldout_class == '1000':
a = num_sample_per_class * 8
if heldout_class == '1001':
a = num_sample_per_class * 9
if heldout_class == '1010':
a = num_sample_per_class * 10
if heldout_class == '1011':
a = num_sample_per_class * 11
if heldout_class == '1100':
a = num_sample_per_class * 12
if heldout_class == '1101':
a = num_sample_per_class * 13
if heldout_class == '1110':
a = num_sample_per_class * 14
if heldout_class == '1111':
a = num_sample_per_class * 15
heldout_indices = np.arange(a, a+num_sample_per_class)
X = np.delete(X, heldout_indices, axis=0)
Y = np.delete(Y, heldout_indices, axis=0)
print(f'[Check] holding out [{heldout_class}]')
X_train, X_val, \
Y_train, Y_val = train_test_split(
X, Y,
test_size=split_ratio,
random_state=seed)
print(f'[Check] Training data: {X_train.shape}')
print(f'[Check] Validation data: {X_val.shape}')
return (X_train, Y_train), (X_val, Y_val)
def data_loader_gen(config, preprocess_func, shuffle, seed=42):
"""Use generator as data loader for training"""
preprocessed_dir = config['preprocessed_dir']
model_name = config['model_name']
stimulus_set = config['stimulus_set']
directory = f'stimuli/{preprocessed_dir}/{model_name}/processed_imgs/task{stimulus_set}'
# TODO. Not ideal but does the trick of loading .npy images
# from `gen.py`
if stimulus_set not in ['6', 6]:
class_mode = 'binary_feat3'
preprocess_func = None
else:
class_mode = 'binary_feat4'
print(f'[Check] Generator loading data from {directory}')
train_data = gen.DirectoryIterator(
directory=directory,
class_mode=class_mode,
batch_size=config['batch_size'],
shuffle=shuffle,
seed=seed,
validation_split=config['split_ratio'],
subset='training',
preprocessing_function=preprocess_func)
train_data = label_converter(train_data, stimulus_set)
train_steps = train_data.compute_step_size()
val_data = gen.DirectoryIterator(
directory=directory,
class_mode=class_mode,
batch_size=config['batch_size'],
shuffle=shuffle,
seed=seed,
validation_split=config['split_ratio'],
subset='validation',
preprocessing_function=preprocess_func)
val_data = label_converter(val_data, stimulus_set)
val_steps = val_data.compute_step_size()
print(f'[Check] train/val steps={train_steps},{val_steps}')
return train_data, train_steps, val_data, val_steps
def data_loader_gen_v2(config, preprocess_func, shuffle, seed=42):
"""
v2: supports heldout training
Use generator as data loader for training
"""
preprocessed_dir = config['preprocessed_dir']
model_name = config['model_name']
stimulus_set = config['stimulus_set']
directory = f'stimuli/{preprocessed_dir}/{model_name}/processed_imgs/task{stimulus_set}'
heldout_class = config['heldout']
if stimulus_set not in ['6', 6]:
class_mode = 'binary_feat3'
preprocess_func = None
all_classes = ['000', '001', '010', '011',
'100', '101', '110', '111']
else:
class_mode = 'binary_feat4'
NotImplementedError()
all_classes_indices = dict(zip(all_classes, range(len(all_classes))))
# NOTE(ken), this is a hacky bit where we maually construct the dict
# such that heldout can be done.
if heldout_class is None:
classes = all_classes
class_indices = all_classes_indices
else:
classes = [c for c in all_classes if c!= heldout_class]
class_indices = {}
for c in classes:
class_indices[c] = all_classes_indices[c]
print(f'[Check] class_indices = {class_indices}')
print(f'[Check] Generator loading data from {directory}')
train_data = gen_w_heldout.DirectoryIterator(
directory=directory,
class_mode=class_mode,
batch_size=config['batch_size'],
shuffle=shuffle,
seed=seed,
validation_split=config['split_ratio'],
subset='training',
preprocessing_function=preprocess_func,
classes=classes,
class_indices=class_indices)
train_data = label_converter(train_data, stimulus_set)
train_steps = train_data.compute_step_size()
val_data = gen_w_heldout.DirectoryIterator(
directory=directory,
class_mode=class_mode,
batch_size=config['batch_size'],
shuffle=shuffle,
seed=seed,
validation_split=config['split_ratio'],
subset='validation',
preprocessing_function=preprocess_func,
classes=classes,
class_indices=class_indices)
val_data = label_converter(val_data, stimulus_set)
val_steps = val_data.compute_step_size()
print(f'[Check] train/val steps={train_steps},{val_steps}')
return train_data, train_steps, val_data, val_steps
def label_converter(generator, stimulus_set):
"""
Purpose:
--------
Only used when
train == 'fulltrain' & task == 'binary'
Or train == 'funtune' & task == 'binary' & stimulus_set = 6
This is because the default generator produces labels
as `sparse` ints whereas for binary prediction we want
to predict 0/1.
Impl:
-----
We have to intercept the default generators and manually
substitute the y labels using a mapping.
"""
if stimulus_set not in ['6', 6]:
class2binary = {0: [0, 0, 0],
1: [0, 0, 1],
2: [0, 1, 0],
3: [0, 1, 1],
4: [1, 0, 0],
5: [1, 0, 1],
6: [1, 1, 0],
7: [1, 1, 1]}
# This is when task=6, we have 4 features as targets.
else:
class2binary = {0: [0,0,0,0],
1: [0,0,0,1],
2: [0,0,1,0],
3: [0,0,1,1],
4: [0,1,0,0],
5: [0,1,0,1],
6: [0,1,1,0],
7: [0,1,1,1],
8: [1,0,0,0],
9: [1,0,0,1],
10: [1,0,1,0],
11: [1,0,1,1],
12: [1,1,0,0],
13: [1,1,0,1],
14: [1,1,1,0],
15: [1,1,1,1]}
mapped_classes = []
for i, label in enumerate(generator.classes):
temp = class2binary[label]
mapped_classes.append(temp)
generator.classes = mapped_classes
return generator
def cuda_manager(target, args_list, cuda_id_list, n_concurrent=None):
"""Create CUDA manager.
Arguments:
target: A target function to be evaluated.
args_list: A list of dictionaries, where each dictionary
contains the arguments necessary for the target function.
cuda_id_list: A list of eligable CUDA IDs.
n_concurrent (optional): The number of concurrent CUDA
processes allowed. By default this is equal to the length
of `cuda_id_list`.
Raises:
Exception
"""
if n_concurrent is None:
n_concurrent = len(cuda_id_list)
else:
n_concurrent = min([n_concurrent, len(cuda_id_list)])
shared_exception = multiprocessing.Queue()
n_task = len(args_list)
args_queue = multiprocessing.Queue()
for args in args_list:
args_queue.put(args)
# Use a semaphore to make one child process per CUDA ID.
# NOTE: Using a pool of workers may not work with TF because it
# re-uses existing processes, which may not release the GPU's memory.
sema = multiprocessing.BoundedSemaphore(n_concurrent)
# Use manager to share list of available CUDA IDs among child processes.
with multiprocessing.Manager() as manager:
available_cuda = manager.list(cuda_id_list)
process_list = []
for _ in range(n_task):
process_list.append(
multiprocessing.Process(
target=cuda_child,
args=(
target, args_queue, available_cuda, shared_exception,
sema
)
)
)
for p in process_list:
p.start()
for p in process_list:
p.join()
# Check for raised exceptions.
e_list = [shared_exception.get() for _ in process_list]
for e in e_list:
if e is not None:
raise e
def cuda_child(target, args_queue, available_cuda, shared_exception, sema):
"""Create child process of the CUDA manager.
Arguments:
target: The function to evaluate.
args_queue: A multiprocessing.Queue that yields a dictionary
for consumption by `target`.
available_cuda: A multiprocessing.Manager.list object for
tracking CUDA device availablility.
shared_exception: A multiprocessing.Queue for exception
handling.
sema: A multiprocessing.BoundedSemaphore object ensuring there
are never more processes than eligable CUDA devices.
"""
try:
sema.acquire()
args = args_queue.get()
cuda_id = available_cuda.pop()
os.environ["CUDA_VISIBLE_DEVICES"] = "{0}".format(cuda_id)
target(**args)
shared_exception.put(None)
available_cuda.append(cuda_id)
sema.release()
except Exception as e:
shared_exception.put(e)
class ViT_ImageDataGenerator(tf.keras.preprocessing.image.ImageDataGenerator):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.data_format = 'channels_first'
def standardize(self, x):
"""Applies the normalization configuration in-place to a batch of inputs.
`x` is changed in-place since the function is mainly used internally
to standardize images and feed them to your network. If a copy of `x`
would be created instead it would have a significant performance cost.
If you want to apply this method without changing the input in-place
you can call the method creating a copy before:
standardize(np.copy(x))
# Arguments
x: Batch of inputs to be normalized.
# Returns
The inputs, normalized.
"""
if self.preprocessing_function:
x = self.preprocessing_function(x, return_tensors="tf")['pixel_values']
if self.rescale:
x *= self.rescale
if self.samplewise_center:
x -= np.mean(x, keepdims=True)
if self.samplewise_std_normalization:
x /= (np.std(x, keepdims=True) + 1e-6)
if self.featurewise_center:
if self.mean is not None:
x -= self.mean
else:
warnings.warn('This ImageDataGenerator specifies '
'`featurewise_center`, but it hasn\'t '
'been fit on any training data. Fit it '
'first by calling `.fit(numpy_data)`.')
if self.featurewise_std_normalization:
if self.std is not None:
x /= (self.std + 1e-6)
else:
warnings.warn('This ImageDataGenerator specifies '
'`featurewise_std_normalization`, '
'but it hasn\'t '
'been fit on any training data. Fit it '
'first by calling `.fit(numpy_data)`.')
if self.zca_whitening:
if self.principal_components is not None:
flatx = np.reshape(x, (-1, np.prod(x.shape[-3:])))
whitex = np.dot(flatx, self.principal_components)
x = np.reshape(whitex, x.shape)
else:
warnings.warn('This ImageDataGenerator specifies '
'`zca_whitening`, but it hasn\'t '
'been fit on any training data. Fit it '
'first by calling `.fit(numpy_data)`.')
return x
if __name__ == '__main__':
pass